Add files using upload-large-folder tool

aws_sdk_2025-12-29-06.log

@@ -0,0 +1,8 @@
+[WARN] 2025-12-29 06:37:58.719 ClientConfiguration [140656848850240] Retry Strategy will use the default max attempts.
+[WARN] 2025-12-29 06:37:58.719 ClientConfiguration [140656848850240] Retry Strategy will use the default max attempts.
+[ERROR] 2025-12-29 06:37:59.705 CurlHttpClient [140656848850240] Curl returned error code 7 - Couldn't connect to server
+[ERROR] 2025-12-29 06:37:59.705 EC2MetadataClient [140656848850240] Http request to retrieve credentials failed
+[WARN] 2025-12-29 06:37:59.705 EC2MetadataClient [140656848850240] Request failed, now waiting 0 ms before attempting again.
+[ERROR] 2025-12-29 06:38:00.710 CurlHttpClient [140656848850240] Curl returned error code 28 - Timeout was reached
+[ERROR] 2025-12-29 06:38:00.712 EC2MetadataClient [140656848850240] Http request to retrieve credentials failed
+[ERROR] 2025-12-29 06:38:00.713 EC2MetadataClient [140656848850240] Can not retrieve resource from http://169.254.169.254/latest/meta-data/placement/availability-zone

cutpdf.ipynb

@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PDF 页面范围提取工具\n",
+    "\n",
+    "使用 PyPDF2（或更现代的 pypdf）来提取指定页面范围。\n",
+    "\n",
+    "**操作步骤：**\n",
+    "1. 运行第一个代码块安装依赖（只需运行一次）\n",
+    "2. 运行第二个代码块上传 PDF 文件\n",
+    "3. 输入页面范围（例如 `1-5`、`10`、`3-8,12,15-20`）\n",
+    "4. 下载提取后的 PDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "Missing parentheses in call to 'print'. Did you mean print(...)? (__init__.py, line 242)",
+     "output_type": "error",
+     "traceback": [
+      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
+      "\u001b[0m  File \u001b[1;32m~/anaconda3/envs/eval_xx/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3577\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n    exec(code_obj, self.user_global_ns, self.user_ns)\u001b[0m\n",
+      "\u001b[0;36m  Cell \u001b[0;32mIn[17], line 8\u001b[0;36m\n\u001b[0;31m    import fitz  # PyMuPDF\u001b[0;36m\n",
+      "\u001b[0;36m  File \u001b[0;32m~/anaconda3/envs/eval_xx/lib/python3.10/site-packages/fitz/__init__.py:242\u001b[0;36m\u001b[0m\n\u001b[0;31m    print \"Could not find any workflows matching %s\" % wf_name\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(...)?\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PyPDF2 import PdfReader, PdfWriter\n",
+    "import os\n",
+    "\n",
+    "def extract_pdf_pages(input_path: str, output_path: str, page_range: str) -> None:\n",
+    "    \"\"\"\n",
+    "    提取PDF指定范围的页面并保存为新PDF\n",
+    "\n",
+    "    参数:\n",
+    "        input_path: 输入PDF文件的路径（相对路径或绝对路径）\n",
+    "        output_path: 输出新PDF的路径\n",
+    "        page_range: 页面范围字符串，支持格式：\n",
+    "                    - 单页：\"5\"（提取第5页，注意：PDF页码从1开始）\n",
+    "                    - 连续页：\"3-7\"（提取第3到7页，包含首尾）\n",
+    "                    - 不连续页：\"2,4,6-8\"（提取第2、4、6-8页）\n",
+    "    \"\"\"\n",
+    "    # 验证输入文件是否存在\n",
+    "    if not os.path.exists(input_path):\n",
+    "        raise FileNotFoundError(f\"输入文件不存在：{input_path}\")\n",
+    "    \n",
+    "    # 验证输入文件是否为PDF\n",
+    "    if not input_path.lower().endswith(\".pdf\"):\n",
+    "        raise ValueError(\"输入文件必须是PDF格式（.pdf后缀）\")\n",
+    "    \n",
+    "    # 读取PDF\n",
+    "    reader = PdfReader(input_path)\n",
+    "    total_pages = len(reader.pages)\n",
+    "    print(f\"原PDF总页数：{total_pages}\")\n",
+    "\n",
+    "    # 解析页面范围，转换为0-based索引（PyPDF2内部用0开始计数）\n",
+    "    target_pages = set()\n",
+    "    parts = page_range.replace(\" \", \"\").split(\",\")  # 去除空格并按逗号分割\n",
+    "\n",
+    "    for part in parts:\n",
+    "        if \"-\" in part:\n",
+    "            # 处理连续页（如 \"3-7\"）\n",
+    "            try:\n",
+    "                start_str, end_str = part.split(\"-\")\n",
+    "                start = int(start_str)\n",
+    "                end = int(end_str)\n",
+    "            except ValueError:\n",
+    "                raise ValueError(f\"无效的页面范围格式：{part}（请使用数字，如3-7）\")\n",
+    "            \n",
+    "            # 验证页码合法性\n",
+    "            if start < 1 or end < 1:\n",
+    "                raise ValueError(\"页码必须大于等于1\")\n",
+    "            if start > end:\n",
+    "                raise ValueError(f\"连续页起始页码不能大于结束页码：{part}\")\n",
+    "            if end > total_pages:\n",
+    "                raise ValueError(f\"结束页码{end}超出原PDF总页数{total_pages}\")\n",
+    "            \n",
+    "            # 转换为0-based索引，添加范围内所有页码\n",
+    "            for page_num in range(start - 1, end):\n",
+    "                target_pages.add(page_num)\n",
+    "        else:\n",
+    "            # 处理单页（如 \"5\"）\n",
+    "            try:\n",
+    "                page_num = int(part)\n",
+    "            except ValueError:\n",
+    "                raise ValueError(f\"无效的页码格式：{part}（请使用数字）\")\n",
+    "            \n",
+    "            # 验证页码合法性\n",
+    "            if page_num < 1 or page_num > total_pages:\n",
+    "                raise ValueError(f\"页码{page_num}超出范围（1-{total_pages}）\")\n",
+    "            \n",
+    "            # 转换为0-based索引\n",
+    "            target_pages.add(page_num - 1)\n",
+    "\n",
+    "    # 排序目标页码（保证输出顺序与原PDF一致）\n",
+    "    target_pages = sorted(target_pages)\n",
+    "    print(f\"即将提取的页面（原PDF页码）：{[num + 1 for num in target_pages]}\")\n",
+    "\n",
+    "    # 写入提取的页面到新PDF\n",
+    "    writer = PdfWriter()\n",
+    "    for page_idx in target_pages:\n",
+    "        writer.add_page(reader.pages[page_idx])\n",
+    "\n",
+    "    # 保存新PDF\n",
+    "    with open(output_path, \"wb\") as output_file:\n",
+    "        writer.write(output_file)\n",
+    "    \n",
+    "    print(f\"提取完成！新PDF已保存至：{os.path.abspath(output_path)}\")\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    # 交互式输入配置\n",
+    "    print(\"=\" * 50)\n",
+    "    print(\"          PDF页面提取工具（支持单页/连续页/不连续页）\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    # 输入PDF路径\n",
+    "    while True:\n",
+    "        input_pdf = input(\"\\n请输入原PDF文件路径（相对路径或绝对路径）：\").strip()\n",
+    "        if os.path.exists(input_pdf) and input_pdf.lower().endswith(\".pdf\"):\n",
+    "            break\n",
+    "        print(\"❌ 无效路径！请确保文件存在且是PDF格式（.pdf后缀）\")\n",
+    "    \n",
+    "    # 输入页面范围\n",
+    "    while True:\n",
+    "        page_range = input(\"\\n请输入要提取的页面范围（格式示例：5 或 3-7 或 2,4,6-8）：\").strip()\n",
+    "        try:\n",
+    "            # 预验证格式（复用解析逻辑）\n",
+    "            parts = page_range.replace(\" \", \"\").split(\",\")\n",
+    "            for part in parts:\n",
+    "                if \"-\" in part:\n",
+    "                    start, end = part.split(\"-\")\n",
+    "                    int(start), int(end)\n",
+    "                else:\n",
+    "                    int(part)\n",
+    "            break\n",
+    "        except ValueError:\n",
+    "            print(\"❌ 无效格式！请按照示例输入（如：5 或 3-7 或 2,4,6-8）\")\n",
+    "    \n",
+    "    # 输入输出路径\n",
+    "    output_pdf = input(\"\\n请输入输出PDF文件路径（如：output.pdf）：\").strip()\n",
+    "    if not output_pdf.lower().endswith(\".pdf\"):\n",
+    "        output_pdf += \".pdf\"  # 自动添加后缀\n",
+    "    if os.path.exists(output_pdf):\n",
+    "        overwrite = input(f\"⚠️ 文件 {output_pdf} 已存在，是否覆盖？（y/n）：\").strip().lower()\n",
+    "        if overwrite != \"y\":\n",
+    "            print(\"🛑 操作取消\")\n",
+    "            exit()\n",
+    "    \n",
+    "    # 执行提取\n",
+    "    try:\n",
+    "        extract_pdf_pages(input_pdf, output_pdf, page_range)\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ 提取失败：{str(e)}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

exp/Emu3/MSCOCO2017Val/sjd++.log

@@ -0,0 +1,12 @@
+A new version of the following files was downloaded from https://huggingface.co/BAAI/Emu3-Gen:
+- configuration_emu3.py
+. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
+A new version of the following files was downloaded from https://huggingface.co/BAAI/Emu3-Gen:
+- modeling_emu3.py
+. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
+srun: Job step aborted: Waiting up to 2 seconds for job step to finish.
+
+srun: Easily find out why your job was killed by following the link below:
+	https://docs.phoenix.sensetime.com/FAQ/SlurmFAQ/Find-out-why-my-job-was-killed/
+srun: got SIGCONT
+srun: forcing job termination

exp/Emu3/PartiPrompts/sjd++.log

@@ -0,0 +1,12 @@
+A new version of the following files was downloaded from https://huggingface.co/BAAI/Emu3-Gen:
+- configuration_emu3.py
+. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
+A new version of the following files was downloaded from https://huggingface.co/BAAI/Emu3-Gen:
+- modeling_emu3.py
+. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
+srun: Job step aborted: Waiting up to 2 seconds for job step to finish.
+srun: Easily find out why your job was killed by following the link below:
+	https://docs.phoenix.sensetime.com/FAQ/SlurmFAQ/Find-out-why-my-job-was-killed/
+srun: got SIGCONT
+
+srun: forcing job termination

exp/Emu3/T2ICompBenchVal/sjd++.log

@@ -0,0 +1,6 @@
+srun: Job step aborted: Waiting up to 2 seconds for job step to finish.
+srun: Easily find out why your job was killed by following the link below:
+	https://docs.phoenix.sensetime.com/FAQ/SlurmFAQ/Find-out-why-my-job-was-killed/
+srun: got SIGCONT
+
+srun: forcing job termination

exp/Lumina-mGPT-7B-768/MSCOCO2017Val/caccl/generation_configs.json

@@ -0,0 +1,18 @@
+{
+    "model_path": "/nvme/leihaodong/mnt/local_model/models--Alpha-VLLM--Lumina-mGPT-7B-768/snapshots/755e7e40530a8edf4eea6078cbc484d2bfda091f",
+    "tokenizer_path": "/mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer",
+    "output_path": "/mnt/petrelfs/leihaodong/ICLR25/exp/Lumina-mGPT-7B-768/MSCOCO2017Val/caccl",
+    "target_size": 768,
+    "isp": "random",
+    "method": "caccl",
+    "num_init_new_token": 96,
+    "benchmark_way": "order",
+    "prompt": "MSCOCO2017Val",
+    "num_images": 4000,
+    "slice": "0-4000",
+    "static_tree": false,
+    "tree_choices": "mc_sim_7b_63",
+    "lantern_delta": 3,
+    "groupsum_delta": 0.01,
+    "sjd_pp_threshold": 0.5
+}

exp/Lumina-mGPT-7B-768/MSCOCO2017Val/sjd++/generation_configs.json

@@ -0,0 +1,17 @@
+{
+    "model_path": "/nvme/leihaodong/mnt/local_model/models--Alpha-VLLM--Lumina-mGPT-7B-768/snapshots/755e7e40530a8edf4eea6078cbc484d2bfda091f",
+    "tokenizer_path": "/mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer",
+    "output_path": "/mnt/petrelfs/leihaodong/ICLR25/exp/Lumina-mGPT-7B-768/MSCOCO2017Val/sjd++",
+    "target_size": 768,
+    "isp": "random",
+    "method": "sjd++",
+    "num_init_new_token": 96,
+    "benchmark_way": "order",
+    "prompt": "MSCOCO2017Val",
+    "num_images": 4000,
+    "slice": "0-4000",
+    "static_tree": false,
+    "tree_choices": "mc_sim_7b_63",
+    "lantern_delta": 3,
+    "groupsum_delta": 0.01
+}

exp/Lumina-mGPT-7B-768/PartiPrompts/sjd++.log

@@ -0,0 +1,24 @@
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/utils/hub.py", line 403, in cached_file
+    resolved_file = hf_hub_download(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
+    validate_repo_id(arg_value)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
+    raise HFValidationError(
+huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/nvme/leihaodong/mnt/local_model/models--Alpha-VLLM--Lumina-mGPT-7B-768/snapshots/755e7e40530a8edf4eea6078cbc484d2bfda091f'. Use `repo_type` argument if needed.
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 300, in <module>
+    main(args)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 136, in main
+    inference_solver = FlexARInferenceSolver(
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py", line 287, in __init__
+    self.model = ChameleonForConditionalGeneration.from_pretrained(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3518, in from_pretrained
+    resolved_config_file = cached_file(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/utils/hub.py", line 469, in cached_file
+    raise EnvironmentError(
+OSError: Incorrect path_or_model_id: '/nvme/leihaodong/mnt/local_model/models--Alpha-VLLM--Lumina-mGPT-7B-768/snapshots/755e7e40530a8edf4eea6078cbc484d2bfda091f'. Please provide either the path to a local folder or the repo_id of a model on the Hub.
+srun: error: SH-IDC1-10-140-37-41: task 0: Exited with exit code 1

exp/Lumina-mGPT-7B-768/T2ICompBenchVal/sjd++.log

@@ -0,0 +1,24 @@
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/utils/hub.py", line 403, in cached_file
+    resolved_file = hf_hub_download(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
+    validate_repo_id(arg_value)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
+    raise HFValidationError(
+huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/nvme/leihaodong/mnt/local_model/models--Alpha-VLLM--Lumina-mGPT-7B-768/snapshots/755e7e40530a8edf4eea6078cbc484d2bfda091f'. Use `repo_type` argument if needed.
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 300, in <module>
+    main(args)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 136, in main
+    inference_solver = FlexARInferenceSolver(
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py", line 287, in __init__
+    self.model = ChameleonForConditionalGeneration.from_pretrained(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3518, in from_pretrained
+    resolved_config_file = cached_file(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/utils/hub.py", line 469, in cached_file
+    raise EnvironmentError(
+OSError: Incorrect path_or_model_id: '/nvme/leihaodong/mnt/local_model/models--Alpha-VLLM--Lumina-mGPT-7B-768/snapshots/755e7e40530a8edf4eea6078cbc484d2bfda091f'. Please provide either the path to a local folder or the repo_id of a model on the Hub.
+srun: error: SH-IDC1-10-140-37-41: task 0: Exited with exit code 1

exp/ablation/sematic/ms_sjd_random.log

@@ -0,0 +1,43 @@
+ChameleonForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+
+Some weights of ChameleonForConditionalGeneration were not initialized from the model checkpoint at Alpha-VLLM/Lumina-mGPT-7B-768 and are newly initialized: ['model.vqmodel.encoder.conv_in.bias', 'model.vqmodel.encoder.conv_in.weight', 'model.vqmodel.encoder.conv_out.bias', 'model.vqmodel.encoder.conv_out.weight', 'model.vqmodel.encoder.down.0.block.0.conv1.bias', 'model.vqmodel.encoder.down.0.block.0.conv1.weight', 'model.vqmodel.encoder.down.0.block.0.conv2.bias', 'model.vqmodel.encoder.down.0.block.0.conv2.weight', 'model.vqmodel.encoder.down.0.block.0.norm1.bias', 'model.vqmodel.encoder.down.0.block.0.norm1.weight', 'model.vqmodel.encoder.down.0.block.0.norm2.bias', 'model.vqmodel.encoder.down.0.block.0.norm2.weight', 'model.vqmodel.encoder.down.0.block.1.conv1.bias', 'model.vqmodel.encoder.down.0.block.1.conv1.weight', 'model.vqmodel.encoder.down.0.block.1.conv2.bias', 'model.vqmodel.encoder.down.0.block.1.conv2.weight', 'model.vqmodel.encoder.down.0.block.1.norm1.bias', 'model.vqmodel.encoder.down.0.block.1.norm1.weight', 'model.vqmodel.encoder.down.0.block.1.norm2.bias', 'model.vqmodel.encoder.down.0.block.1.norm2.weight', 'model.vqmodel.encoder.down.0.downsample.conv.bias', 'model.vqmodel.encoder.down.0.downsample.conv.weight', 'model.vqmodel.encoder.down.1.block.0.conv1.bias', 'model.vqmodel.encoder.down.1.block.0.conv1.weight', 'model.vqmodel.encoder.down.1.block.0.conv2.bias', 'model.vqmodel.encoder.down.1.block.0.conv2.weight', 'model.vqmodel.encoder.down.1.block.0.norm1.bias', 'model.vqmodel.encoder.down.1.block.0.norm1.weight', 'model.vqmodel.encoder.down.1.block.0.norm2.bias', 'model.vqmodel.encoder.down.1.block.0.norm2.weight', 'model.vqmodel.encoder.down.1.block.1.conv1.bias', 'model.vqmodel.encoder.down.1.block.1.conv1.weight', 'model.vqmodel.encoder.down.1.block.1.conv2.bias', 'model.vqmodel.encoder.down.1.block.1.conv2.weight', 'model.vqmodel.encoder.down.1.block.1.norm1.bias', 'model.vqmodel.encoder.down.1.block.1.norm1.weight', 'model.vqmodel.encoder.down.1.block.1.norm2.bias', 'model.vqmodel.encoder.down.1.block.1.norm2.weight', 'model.vqmodel.encoder.down.1.downsample.conv.bias', 'model.vqmodel.encoder.down.1.downsample.conv.weight', 'model.vqmodel.encoder.down.2.block.0.conv1.bias', 'model.vqmodel.encoder.down.2.block.0.conv1.weight', 'model.vqmodel.encoder.down.2.block.0.conv2.bias', 'model.vqmodel.encoder.down.2.block.0.conv2.weight', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.2.block.0.norm1.bias', 'model.vqmodel.encoder.down.2.block.0.norm1.weight', 'model.vqmodel.encoder.down.2.block.0.norm2.bias', 'model.vqmodel.encoder.down.2.block.0.norm2.weight', 'model.vqmodel.encoder.down.2.block.1.conv1.bias', 'model.vqmodel.encoder.down.2.block.1.conv1.weight', 'model.vqmodel.encoder.down.2.block.1.conv2.bias', 'model.vqmodel.encoder.down.2.block.1.conv2.weight', 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'model.vqmodel.encoder.down.4.block.1.conv2.weight', 'model.vqmodel.encoder.down.4.block.1.norm1.bias', 'model.vqmodel.encoder.down.4.block.1.norm1.weight', 'model.vqmodel.encoder.down.4.block.1.norm2.bias', 'model.vqmodel.encoder.down.4.block.1.norm2.weight', 'model.vqmodel.encoder.mid.attn_1.k.bias', 'model.vqmodel.encoder.mid.attn_1.k.weight', 'model.vqmodel.encoder.mid.attn_1.norm.bias', 'model.vqmodel.encoder.mid.attn_1.norm.weight', 'model.vqmodel.encoder.mid.attn_1.proj_out.bias', 'model.vqmodel.encoder.mid.attn_1.proj_out.weight', 'model.vqmodel.encoder.mid.attn_1.q.bias', 'model.vqmodel.encoder.mid.attn_1.q.weight', 'model.vqmodel.encoder.mid.attn_1.v.bias', 'model.vqmodel.encoder.mid.attn_1.v.weight', 'model.vqmodel.encoder.mid.block_1.conv1.bias', 'model.vqmodel.encoder.mid.block_1.conv1.weight', 'model.vqmodel.encoder.mid.block_1.conv2.bias', 'model.vqmodel.encoder.mid.block_1.conv2.weight', 'model.vqmodel.encoder.mid.block_1.norm1.bias', 'model.vqmodel.encoder.mid.block_1.norm1.weight', 'model.vqmodel.encoder.mid.block_1.norm2.bias', 'model.vqmodel.encoder.mid.block_1.norm2.weight', 'model.vqmodel.encoder.mid.block_2.conv1.bias', 'model.vqmodel.encoder.mid.block_2.conv1.weight', 'model.vqmodel.encoder.mid.block_2.conv2.bias', 'model.vqmodel.encoder.mid.block_2.conv2.weight', 'model.vqmodel.encoder.mid.block_2.norm1.bias', 'model.vqmodel.encoder.mid.block_2.norm1.weight', 'model.vqmodel.encoder.mid.block_2.norm2.bias', 'model.vqmodel.encoder.mid.block_2.norm2.weight', 'model.vqmodel.encoder.norm_out.bias', 'model.vqmodel.encoder.norm_out.weight', 'model.vqmodel.post_quant_conv.bias', 'model.vqmodel.post_quant_conv.weight', 'model.vqmodel.quant_conv.bias', 'model.vqmodel.quant_conv.weight', 'model.vqmodel.quantize.embedding.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon_vae_ori/vqgan.py:573: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  sd = torch.load(path, map_location="cpu")["state_dict"]
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py:360: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=self.dtype):
+The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:8710 for open-end generation.
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+This is a friendly reminder - the current text generation call will exceed the model's predefined maximum length (4096). Depending on the model, you may observe exceptions, performance degradation, or nothing at all.
+List of crop sizes:
+ 1536 x 384    1504 x 384    1472 x 384    1440 x 384    1408 x 384    1408 x 416    
+ 1376 x 416    1344 x 416    1312 x 416    1312 x 448    1280 x 448    1248 x 448    
+ 1216 x 448    1216 x 480    1184 x 480    1152 x 480    1152 x 512    1120 x 512    
+ 1088 x 512    1056 x 512    1056 x 544    1024 x 544    1024 x 576    992 x 576     
+ 960 x 576     960 x 608     928 x 608     896 x 608     896 x 640     864 x 640     
+ 864 x 672     832 x 672     832 x 704     800 x 704     800 x 736     768 x 736     
+ 768 x 768     736 x 768     736 x 800     704 x 800     704 x 832     672 x 832     
+ 672 x 864     640 x 864     640 x 896     608 x 896     608 x 928     608 x 960     
+ 576 x 960     576 x 992     576 x 1024    544 x 1024    544 x 1056    512 x 1056    
+ 512 x 1088    512 x 1120    512 x 1152    480 x 1152    480 x 1184    480 x 1216    
+ 448 x 1216    448 x 1248    448 x 1280    448 x 1312    416 x 1312    416 x 1344    
+ 416 x 1376    416 x 1408    384 x 1408    384 x 1440    384 x 1472    384 x 1504    
+ 384 x 1536    
+VQModel loaded from /mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer/vqgan.ckpt
+Number of images to generate is greater than the number of prompts. Generating only 100 images and no sampling.
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 302, in <module>
+    main(args)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 222, in main
+    a1, new_image = generated[0], generated[1][0]
+IndexError: list index out of range
+srun: error: SH-IDC1-10-140-37-43: task 0: Exited with exit code 1

exp/ablation/sematic/ms_sjd_re/generation_configs.json

@@ -0,0 +1,17 @@
+{
+    "model_path": "Alpha-VLLM/Lumina-mGPT-7B-768",
+    "tokenizer_path": "/mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer",
+    "output_path": "/mnt/petrelfs/leihaodong/ICLR25/exp/ablation/sematic/ms_sjd_re",
+    "target_size": 768,
+    "isp": "random",
+    "method": "speculative_jacobi_replace",
+    "num_init_new_token": 16,
+    "benchmark_way": "order",
+    "prompt": "PartiPrompts",
+    "num_images": 1600,
+    "slice": "1301-1600",
+    "static_tree": true,
+    "tree_choices": "Grouped_Tree_6",
+    "lantern_delta": 3,
+    "groupsum_delta": 0.01
+}

exp/ablation/sematic/ms_sjd_re/result_0-100.json

@@ -0,0 +1,908 @@
+{
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+        "prompt": "earth",
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+        "Time elapsed": 34.097495794296265,
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+    "prompt_13": {
+        "prompt": "force",
+        "time": 30.5215078125,
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+        "Time elapsed": 34.35478973388672,
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+    "prompt_14": {
+        "prompt": "motion",
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+    "prompt_15": {
+        "prompt": "inertia",
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+    "prompt_17": {
+        "prompt": "black hole",
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+        "prompt": "peace",
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exp/ablation/sematic/ms_sjd_re/result_1001-1100.json

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exp/ablation/sematic/ms_sjd_re/result_101-200.json

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exp/ablation/sematic/ms_sjd_re/result_201-300.json

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+        "prompt": "A photograph of the inside of a subway train. There are lobsters sitting on the seats. One of them is reading a newspaper. The window shows the ocean in the background.",
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+        "prompt": "A raccoon wearing formal clothes, wearing a tophat and holding a cane. The raccoon is holding a garbage bag. Oil painting in the style of Vincent Van Gogh.",
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+        "prompt": "a statue of Abraham Lincoln wearing an opaque and shiny astronaut's helmet. The statue sits on the moon, with the planet Earth in the sky.",
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+}

exp/ablation/sematic/ms_sjd_re/result_301-400.json

@@ -0,0 +1,899 @@
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+        "prompt": "A close-up high-contrast photo of Sydney Opera House sitting next to Eiffel tower, under a blue night sky of roiling energy, exploding yellow stars, and radiating swirls of blue",
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+        "prompt": "the Sydney Opera House with the Eiffel tower sitting on the right, and Mount Everest rising above",
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+        "prompt": "Greek statue of a man comforting a cat. The cat has a big head. The man looks angry.",
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+        "prompt": "a robot painted as graffiti on a brick wall. a sidewalk is in front of the wall, and grass is growing out of cracks in the concrete.",
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exp/ablation/sematic/ms_sjd_re/result_501-600.json

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exp/ablation/sematic/ms_sjd_re/result_701-800.json

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exp/ablation/sematic/ms_sjd_re/result_801-900.json

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exp/ablation/sematic/ms_sjd_re_1101-1200.log

@@ -0,0 +1,74 @@
+ChameleonForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+
+Some weights of ChameleonForConditionalGeneration were not initialized from the model checkpoint at Alpha-VLLM/Lumina-mGPT-7B-768 and are newly initialized: ['model.vqmodel.encoder.conv_in.bias', 'model.vqmodel.encoder.conv_in.weight', 'model.vqmodel.encoder.conv_out.bias', 'model.vqmodel.encoder.conv_out.weight', 'model.vqmodel.encoder.down.0.block.0.conv1.bias', 'model.vqmodel.encoder.down.0.block.0.conv1.weight', 'model.vqmodel.encoder.down.0.block.0.conv2.bias', 'model.vqmodel.encoder.down.0.block.0.conv2.weight', 'model.vqmodel.encoder.down.0.block.0.norm1.bias', 'model.vqmodel.encoder.down.0.block.0.norm1.weight', 'model.vqmodel.encoder.down.0.block.0.norm2.bias', 'model.vqmodel.encoder.down.0.block.0.norm2.weight', 'model.vqmodel.encoder.down.0.block.1.conv1.bias', 'model.vqmodel.encoder.down.0.block.1.conv1.weight', 'model.vqmodel.encoder.down.0.block.1.conv2.bias', 'model.vqmodel.encoder.down.0.block.1.conv2.weight', 'model.vqmodel.encoder.down.0.block.1.norm1.bias', 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'model.vqmodel.encoder.down.4.block.1.conv2.weight', 'model.vqmodel.encoder.down.4.block.1.norm1.bias', 'model.vqmodel.encoder.down.4.block.1.norm1.weight', 'model.vqmodel.encoder.down.4.block.1.norm2.bias', 'model.vqmodel.encoder.down.4.block.1.norm2.weight', 'model.vqmodel.encoder.mid.attn_1.k.bias', 'model.vqmodel.encoder.mid.attn_1.k.weight', 'model.vqmodel.encoder.mid.attn_1.norm.bias', 'model.vqmodel.encoder.mid.attn_1.norm.weight', 'model.vqmodel.encoder.mid.attn_1.proj_out.bias', 'model.vqmodel.encoder.mid.attn_1.proj_out.weight', 'model.vqmodel.encoder.mid.attn_1.q.bias', 'model.vqmodel.encoder.mid.attn_1.q.weight', 'model.vqmodel.encoder.mid.attn_1.v.bias', 'model.vqmodel.encoder.mid.attn_1.v.weight', 'model.vqmodel.encoder.mid.block_1.conv1.bias', 'model.vqmodel.encoder.mid.block_1.conv1.weight', 'model.vqmodel.encoder.mid.block_1.conv2.bias', 'model.vqmodel.encoder.mid.block_1.conv2.weight', 'model.vqmodel.encoder.mid.block_1.norm1.bias', 'model.vqmodel.encoder.mid.block_1.norm1.weight', 'model.vqmodel.encoder.mid.block_1.norm2.bias', 'model.vqmodel.encoder.mid.block_1.norm2.weight', 'model.vqmodel.encoder.mid.block_2.conv1.bias', 'model.vqmodel.encoder.mid.block_2.conv1.weight', 'model.vqmodel.encoder.mid.block_2.conv2.bias', 'model.vqmodel.encoder.mid.block_2.conv2.weight', 'model.vqmodel.encoder.mid.block_2.norm1.bias', 'model.vqmodel.encoder.mid.block_2.norm1.weight', 'model.vqmodel.encoder.mid.block_2.norm2.bias', 'model.vqmodel.encoder.mid.block_2.norm2.weight', 'model.vqmodel.encoder.norm_out.bias', 'model.vqmodel.encoder.norm_out.weight', 'model.vqmodel.post_quant_conv.bias', 'model.vqmodel.post_quant_conv.weight', 'model.vqmodel.quant_conv.bias', 'model.vqmodel.quant_conv.weight', 'model.vqmodel.quantize.embedding.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon_vae_ori/vqgan.py:573: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  sd = torch.load(path, map_location="cpu")["state_dict"]
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py:360: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=self.dtype):
+The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:8710 for open-end generation.
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+List of crop sizes:
+ 1536 x 384    1504 x 384    1472 x 384    1440 x 384    1408 x 384    1408 x 416    
+ 1376 x 416    1344 x 416    1312 x 416    1312 x 448    1280 x 448    1248 x 448    
+ 1216 x 448    1216 x 480    1184 x 480    1152 x 480    1152 x 512    1120 x 512    
+ 1088 x 512    1056 x 512    1056 x 544    1024 x 544    1024 x 576    992 x 576     
+ 960 x 576     960 x 608     928 x 608     896 x 608     896 x 640     864 x 640     
+ 864 x 672     832 x 672     832 x 704     800 x 704     800 x 736     768 x 736     
+ 768 x 768     736 x 768     736 x 800     704 x 800     704 x 832     672 x 832     
+ 672 x 864     640 x 864     640 x 896     608 x 896     608 x 928     608 x 960     
+ 576 x 960     576 x 992     576 x 1024    544 x 1024    544 x 1056    512 x 1056    
+ 512 x 1088    512 x 1120    512 x 1152    480 x 1152    480 x 1184    480 x 1216    
+ 448 x 1216    448 x 1248    448 x 1280    448 x 1312    416 x 1312    416 x 1344    
+ 416 x 1376    416 x 1408    384 x 1408    384 x 1440    384 x 1472    384 x 1504    
+ 384 x 1536    
+VQModel loaded from /mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer/vqgan.ckpt
+Number of images to generate is greater than the number of prompts. Generating only 99 images and no sampling.
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 302, in <module>
+    main(args)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 201, in main
+    result = inference_solver.generate(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py", line 361, in generate
+    result = self.model.generate( #TODO： go to JacobiSampler._sample
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/generation/utils.py", line 2252, in generate
+    result = self._sample(
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/scheduler/jacobi_iteration_lumina_mgpt.py", line 3268, in _sample
+    outputs = self(**model_inputs, return_dict=True)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1545, in forward
+    outputs = self.model(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1340, in forward
+    layer_outputs = decoder_layer(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 641, in forward
+    hidden_states, self_attn_weights, present_key_value = self.self_attn(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 547, in forward
+    key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/cache_utils.py", line 449, in update
+    self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 46.00 MiB. GPU 0 has a total capacity of 79.33 GiB of which 38.44 MiB is free. Process 113202 has 62.47 GiB memory in use. Including non-PyTorch memory, this process has 16.80 GiB memory in use. Of the allocated memory 15.60 GiB is allocated by PyTorch, and 717.10 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+srun: error: SH-IDC1-10-140-37-43: task 0: Exited with exit code 1

exp/ablation/sematic/ms_sjd_re_1201-1300.log

@@ -0,0 +1,74 @@
+ChameleonForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+
+Some weights of ChameleonForConditionalGeneration were not initialized from the model checkpoint at Alpha-VLLM/Lumina-mGPT-7B-768 and are newly initialized: ['model.vqmodel.encoder.conv_in.bias', 'model.vqmodel.encoder.conv_in.weight', 'model.vqmodel.encoder.conv_out.bias', 'model.vqmodel.encoder.conv_out.weight', 'model.vqmodel.encoder.down.0.block.0.conv1.bias', 'model.vqmodel.encoder.down.0.block.0.conv1.weight', 'model.vqmodel.encoder.down.0.block.0.conv2.bias', 'model.vqmodel.encoder.down.0.block.0.conv2.weight', 'model.vqmodel.encoder.down.0.block.0.norm1.bias', 'model.vqmodel.encoder.down.0.block.0.norm1.weight', 'model.vqmodel.encoder.down.0.block.0.norm2.bias', 'model.vqmodel.encoder.down.0.block.0.norm2.weight', 'model.vqmodel.encoder.down.0.block.1.conv1.bias', 'model.vqmodel.encoder.down.0.block.1.conv1.weight', 'model.vqmodel.encoder.down.0.block.1.conv2.bias', 'model.vqmodel.encoder.down.0.block.1.conv2.weight', 'model.vqmodel.encoder.down.0.block.1.norm1.bias', 'model.vqmodel.encoder.down.0.block.1.norm1.weight', 'model.vqmodel.encoder.down.0.block.1.norm2.bias', 'model.vqmodel.encoder.down.0.block.1.norm2.weight', 'model.vqmodel.encoder.down.0.downsample.conv.bias', 'model.vqmodel.encoder.down.0.downsample.conv.weight', 'model.vqmodel.encoder.down.1.block.0.conv1.bias', 'model.vqmodel.encoder.down.1.block.0.conv1.weight', 'model.vqmodel.encoder.down.1.block.0.conv2.bias', 'model.vqmodel.encoder.down.1.block.0.conv2.weight', 'model.vqmodel.encoder.down.1.block.0.norm1.bias', 'model.vqmodel.encoder.down.1.block.0.norm1.weight', 'model.vqmodel.encoder.down.1.block.0.norm2.bias', 'model.vqmodel.encoder.down.1.block.0.norm2.weight', 'model.vqmodel.encoder.down.1.block.1.conv1.bias', 'model.vqmodel.encoder.down.1.block.1.conv1.weight', 'model.vqmodel.encoder.down.1.block.1.conv2.bias', 'model.vqmodel.encoder.down.1.block.1.conv2.weight', 'model.vqmodel.encoder.down.1.block.1.norm1.bias', 'model.vqmodel.encoder.down.1.block.1.norm1.weight', 'model.vqmodel.encoder.down.1.block.1.norm2.bias', 'model.vqmodel.encoder.down.1.block.1.norm2.weight', 'model.vqmodel.encoder.down.1.downsample.conv.bias', 'model.vqmodel.encoder.down.1.downsample.conv.weight', 'model.vqmodel.encoder.down.2.block.0.conv1.bias', 'model.vqmodel.encoder.down.2.block.0.conv1.weight', 'model.vqmodel.encoder.down.2.block.0.conv2.bias', 'model.vqmodel.encoder.down.2.block.0.conv2.weight', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.2.block.0.norm1.bias', 'model.vqmodel.encoder.down.2.block.0.norm1.weight', 'model.vqmodel.encoder.down.2.block.0.norm2.bias', 'model.vqmodel.encoder.down.2.block.0.norm2.weight', 'model.vqmodel.encoder.down.2.block.1.conv1.bias', 'model.vqmodel.encoder.down.2.block.1.conv1.weight', 'model.vqmodel.encoder.down.2.block.1.conv2.bias', 'model.vqmodel.encoder.down.2.block.1.conv2.weight', 'model.vqmodel.encoder.down.2.block.1.norm1.bias', 'model.vqmodel.encoder.down.2.block.1.norm1.weight', 'model.vqmodel.encoder.down.2.block.1.norm2.bias', 'model.vqmodel.encoder.down.2.block.1.norm2.weight', 'model.vqmodel.encoder.down.2.downsample.conv.bias', 'model.vqmodel.encoder.down.2.downsample.conv.weight', 'model.vqmodel.encoder.down.3.block.0.conv1.bias', 'model.vqmodel.encoder.down.3.block.0.conv1.weight', 'model.vqmodel.encoder.down.3.block.0.conv2.bias', 'model.vqmodel.encoder.down.3.block.0.conv2.weight', 'model.vqmodel.encoder.down.3.block.0.norm1.bias', 'model.vqmodel.encoder.down.3.block.0.norm1.weight', 'model.vqmodel.encoder.down.3.block.0.norm2.bias', 'model.vqmodel.encoder.down.3.block.0.norm2.weight', 'model.vqmodel.encoder.down.3.block.1.conv1.bias', 'model.vqmodel.encoder.down.3.block.1.conv1.weight', 'model.vqmodel.encoder.down.3.block.1.conv2.bias', 'model.vqmodel.encoder.down.3.block.1.conv2.weight', 'model.vqmodel.encoder.down.3.block.1.norm1.bias', 'model.vqmodel.encoder.down.3.block.1.norm1.weight', 'model.vqmodel.encoder.down.3.block.1.norm2.bias', 'model.vqmodel.encoder.down.3.block.1.norm2.weight', 'model.vqmodel.encoder.down.3.downsample.conv.bias', 'model.vqmodel.encoder.down.3.downsample.conv.weight', 'model.vqmodel.encoder.down.4.block.0.conv1.bias', 'model.vqmodel.encoder.down.4.block.0.conv1.weight', 'model.vqmodel.encoder.down.4.block.0.conv2.bias', 'model.vqmodel.encoder.down.4.block.0.conv2.weight', 'model.vqmodel.encoder.down.4.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.4.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.4.block.0.norm1.bias', 'model.vqmodel.encoder.down.4.block.0.norm1.weight', 'model.vqmodel.encoder.down.4.block.0.norm2.bias', 'model.vqmodel.encoder.down.4.block.0.norm2.weight', 'model.vqmodel.encoder.down.4.block.1.conv1.bias', 'model.vqmodel.encoder.down.4.block.1.conv1.weight', 'model.vqmodel.encoder.down.4.block.1.conv2.bias', 'model.vqmodel.encoder.down.4.block.1.conv2.weight', 'model.vqmodel.encoder.down.4.block.1.norm1.bias', 'model.vqmodel.encoder.down.4.block.1.norm1.weight', 'model.vqmodel.encoder.down.4.block.1.norm2.bias', 'model.vqmodel.encoder.down.4.block.1.norm2.weight', 'model.vqmodel.encoder.mid.attn_1.k.bias', 'model.vqmodel.encoder.mid.attn_1.k.weight', 'model.vqmodel.encoder.mid.attn_1.norm.bias', 'model.vqmodel.encoder.mid.attn_1.norm.weight', 'model.vqmodel.encoder.mid.attn_1.proj_out.bias', 'model.vqmodel.encoder.mid.attn_1.proj_out.weight', 'model.vqmodel.encoder.mid.attn_1.q.bias', 'model.vqmodel.encoder.mid.attn_1.q.weight', 'model.vqmodel.encoder.mid.attn_1.v.bias', 'model.vqmodel.encoder.mid.attn_1.v.weight', 'model.vqmodel.encoder.mid.block_1.conv1.bias', 'model.vqmodel.encoder.mid.block_1.conv1.weight', 'model.vqmodel.encoder.mid.block_1.conv2.bias', 'model.vqmodel.encoder.mid.block_1.conv2.weight', 'model.vqmodel.encoder.mid.block_1.norm1.bias', 'model.vqmodel.encoder.mid.block_1.norm1.weight', 'model.vqmodel.encoder.mid.block_1.norm2.bias', 'model.vqmodel.encoder.mid.block_1.norm2.weight', 'model.vqmodel.encoder.mid.block_2.conv1.bias', 'model.vqmodel.encoder.mid.block_2.conv1.weight', 'model.vqmodel.encoder.mid.block_2.conv2.bias', 'model.vqmodel.encoder.mid.block_2.conv2.weight', 'model.vqmodel.encoder.mid.block_2.norm1.bias', 'model.vqmodel.encoder.mid.block_2.norm1.weight', 'model.vqmodel.encoder.mid.block_2.norm2.bias', 'model.vqmodel.encoder.mid.block_2.norm2.weight', 'model.vqmodel.encoder.norm_out.bias', 'model.vqmodel.encoder.norm_out.weight', 'model.vqmodel.post_quant_conv.bias', 'model.vqmodel.post_quant_conv.weight', 'model.vqmodel.quant_conv.bias', 'model.vqmodel.quant_conv.weight', 'model.vqmodel.quantize.embedding.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon_vae_ori/vqgan.py:573: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  sd = torch.load(path, map_location="cpu")["state_dict"]
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py:360: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=self.dtype):
+The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:8710 for open-end generation.
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+List of crop sizes:
+ 1536 x 384    1504 x 384    1472 x 384    1440 x 384    1408 x 384    1408 x 416    
+ 1376 x 416    1344 x 416    1312 x 416    1312 x 448    1280 x 448    1248 x 448    
+ 1216 x 448    1216 x 480    1184 x 480    1152 x 480    1152 x 512    1120 x 512    
+ 1088 x 512    1056 x 512    1056 x 544    1024 x 544    1024 x 576    992 x 576     
+ 960 x 576     960 x 608     928 x 608     896 x 608     896 x 640     864 x 640     
+ 864 x 672     832 x 672     832 x 704     800 x 704     800 x 736     768 x 736     
+ 768 x 768     736 x 768     736 x 800     704 x 800     704 x 832     672 x 832     
+ 672 x 864     640 x 864     640 x 896     608 x 896     608 x 928     608 x 960     
+ 576 x 960     576 x 992     576 x 1024    544 x 1024    544 x 1056    512 x 1056    
+ 512 x 1088    512 x 1120    512 x 1152    480 x 1152    480 x 1184    480 x 1216    
+ 448 x 1216    448 x 1248    448 x 1280    448 x 1312    416 x 1312    416 x 1344    
+ 416 x 1376    416 x 1408    384 x 1408    384 x 1440    384 x 1472    384 x 1504    
+ 384 x 1536    
+VQModel loaded from /mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer/vqgan.ckpt
+Number of images to generate is greater than the number of prompts. Generating only 99 images and no sampling.
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 302, in <module>
+    main(args)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 201, in main
+    result = inference_solver.generate(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py", line 361, in generate
+    result = self.model.generate( #TODO： go to JacobiSampler._sample
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/generation/utils.py", line 2252, in generate
+    result = self._sample(
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/scheduler/jacobi_iteration_lumina_mgpt.py", line 3268, in _sample
+    outputs = self(**model_inputs, return_dict=True)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1545, in forward
+    outputs = self.model(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1340, in forward
+    layer_outputs = decoder_layer(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 641, in forward
+    hidden_states, self_attn_weights, present_key_value = self.self_attn(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 547, in forward
+    key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/cache_utils.py", line 449, in update
+    self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 46.00 MiB. GPU 0 has a total capacity of 79.33 GiB of which 26.44 MiB is free. Process 113202 has 62.47 GiB memory in use. Including non-PyTorch memory, this process has 16.81 GiB memory in use. Of the allocated memory 15.61 GiB is allocated by PyTorch, and 722.55 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+srun: error: SH-IDC1-10-140-37-43: task 0: Exited with exit code 1

exp/ablation/sematic/ms_sjd_re_1301-1600.log

@@ -0,0 +1,74 @@
+ChameleonForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+
+Some weights of ChameleonForConditionalGeneration were not initialized from the model checkpoint at Alpha-VLLM/Lumina-mGPT-7B-768 and are newly initialized: ['model.vqmodel.encoder.conv_in.bias', 'model.vqmodel.encoder.conv_in.weight', 'model.vqmodel.encoder.conv_out.bias', 'model.vqmodel.encoder.conv_out.weight', 'model.vqmodel.encoder.down.0.block.0.conv1.bias', 'model.vqmodel.encoder.down.0.block.0.conv1.weight', 'model.vqmodel.encoder.down.0.block.0.conv2.bias', 'model.vqmodel.encoder.down.0.block.0.conv2.weight', 'model.vqmodel.encoder.down.0.block.0.norm1.bias', 'model.vqmodel.encoder.down.0.block.0.norm1.weight', 'model.vqmodel.encoder.down.0.block.0.norm2.bias', 'model.vqmodel.encoder.down.0.block.0.norm2.weight', 'model.vqmodel.encoder.down.0.block.1.conv1.bias', 'model.vqmodel.encoder.down.0.block.1.conv1.weight', 'model.vqmodel.encoder.down.0.block.1.conv2.bias', 'model.vqmodel.encoder.down.0.block.1.conv2.weight', 'model.vqmodel.encoder.down.0.block.1.norm1.bias', 'model.vqmodel.encoder.down.0.block.1.norm1.weight', 'model.vqmodel.encoder.down.0.block.1.norm2.bias', 'model.vqmodel.encoder.down.0.block.1.norm2.weight', 'model.vqmodel.encoder.down.0.downsample.conv.bias', 'model.vqmodel.encoder.down.0.downsample.conv.weight', 'model.vqmodel.encoder.down.1.block.0.conv1.bias', 'model.vqmodel.encoder.down.1.block.0.conv1.weight', 'model.vqmodel.encoder.down.1.block.0.conv2.bias', 'model.vqmodel.encoder.down.1.block.0.conv2.weight', 'model.vqmodel.encoder.down.1.block.0.norm1.bias', 'model.vqmodel.encoder.down.1.block.0.norm1.weight', 'model.vqmodel.encoder.down.1.block.0.norm2.bias', 'model.vqmodel.encoder.down.1.block.0.norm2.weight', 'model.vqmodel.encoder.down.1.block.1.conv1.bias', 'model.vqmodel.encoder.down.1.block.1.conv1.weight', 'model.vqmodel.encoder.down.1.block.1.conv2.bias', 'model.vqmodel.encoder.down.1.block.1.conv2.weight', 'model.vqmodel.encoder.down.1.block.1.norm1.bias', 'model.vqmodel.encoder.down.1.block.1.norm1.weight', 'model.vqmodel.encoder.down.1.block.1.norm2.bias', 'model.vqmodel.encoder.down.1.block.1.norm2.weight', 'model.vqmodel.encoder.down.1.downsample.conv.bias', 'model.vqmodel.encoder.down.1.downsample.conv.weight', 'model.vqmodel.encoder.down.2.block.0.conv1.bias', 'model.vqmodel.encoder.down.2.block.0.conv1.weight', 'model.vqmodel.encoder.down.2.block.0.conv2.bias', 'model.vqmodel.encoder.down.2.block.0.conv2.weight', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.2.block.0.norm1.bias', 'model.vqmodel.encoder.down.2.block.0.norm1.weight', 'model.vqmodel.encoder.down.2.block.0.norm2.bias', 'model.vqmodel.encoder.down.2.block.0.norm2.weight', 'model.vqmodel.encoder.down.2.block.1.conv1.bias', 'model.vqmodel.encoder.down.2.block.1.conv1.weight', 'model.vqmodel.encoder.down.2.block.1.conv2.bias', 'model.vqmodel.encoder.down.2.block.1.conv2.weight', 'model.vqmodel.encoder.down.2.block.1.norm1.bias', 'model.vqmodel.encoder.down.2.block.1.norm1.weight', 'model.vqmodel.encoder.down.2.block.1.norm2.bias', 'model.vqmodel.encoder.down.2.block.1.norm2.weight', 'model.vqmodel.encoder.down.2.downsample.conv.bias', 'model.vqmodel.encoder.down.2.downsample.conv.weight', 'model.vqmodel.encoder.down.3.block.0.conv1.bias', 'model.vqmodel.encoder.down.3.block.0.conv1.weight', 'model.vqmodel.encoder.down.3.block.0.conv2.bias', 'model.vqmodel.encoder.down.3.block.0.conv2.weight', 'model.vqmodel.encoder.down.3.block.0.norm1.bias', 'model.vqmodel.encoder.down.3.block.0.norm1.weight', 'model.vqmodel.encoder.down.3.block.0.norm2.bias', 'model.vqmodel.encoder.down.3.block.0.norm2.weight', 'model.vqmodel.encoder.down.3.block.1.conv1.bias', 'model.vqmodel.encoder.down.3.block.1.conv1.weight', 'model.vqmodel.encoder.down.3.block.1.conv2.bias', 'model.vqmodel.encoder.down.3.block.1.conv2.weight', 'model.vqmodel.encoder.down.3.block.1.norm1.bias', 'model.vqmodel.encoder.down.3.block.1.norm1.weight', 'model.vqmodel.encoder.down.3.block.1.norm2.bias', 'model.vqmodel.encoder.down.3.block.1.norm2.weight', 'model.vqmodel.encoder.down.3.downsample.conv.bias', 'model.vqmodel.encoder.down.3.downsample.conv.weight', 'model.vqmodel.encoder.down.4.block.0.conv1.bias', 'model.vqmodel.encoder.down.4.block.0.conv1.weight', 'model.vqmodel.encoder.down.4.block.0.conv2.bias', 'model.vqmodel.encoder.down.4.block.0.conv2.weight', 'model.vqmodel.encoder.down.4.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.4.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.4.block.0.norm1.bias', 'model.vqmodel.encoder.down.4.block.0.norm1.weight', 'model.vqmodel.encoder.down.4.block.0.norm2.bias', 'model.vqmodel.encoder.down.4.block.0.norm2.weight', 'model.vqmodel.encoder.down.4.block.1.conv1.bias', 'model.vqmodel.encoder.down.4.block.1.conv1.weight', 'model.vqmodel.encoder.down.4.block.1.conv2.bias', 'model.vqmodel.encoder.down.4.block.1.conv2.weight', 'model.vqmodel.encoder.down.4.block.1.norm1.bias', 'model.vqmodel.encoder.down.4.block.1.norm1.weight', 'model.vqmodel.encoder.down.4.block.1.norm2.bias', 'model.vqmodel.encoder.down.4.block.1.norm2.weight', 'model.vqmodel.encoder.mid.attn_1.k.bias', 'model.vqmodel.encoder.mid.attn_1.k.weight', 'model.vqmodel.encoder.mid.attn_1.norm.bias', 'model.vqmodel.encoder.mid.attn_1.norm.weight', 'model.vqmodel.encoder.mid.attn_1.proj_out.bias', 'model.vqmodel.encoder.mid.attn_1.proj_out.weight', 'model.vqmodel.encoder.mid.attn_1.q.bias', 'model.vqmodel.encoder.mid.attn_1.q.weight', 'model.vqmodel.encoder.mid.attn_1.v.bias', 'model.vqmodel.encoder.mid.attn_1.v.weight', 'model.vqmodel.encoder.mid.block_1.conv1.bias', 'model.vqmodel.encoder.mid.block_1.conv1.weight', 'model.vqmodel.encoder.mid.block_1.conv2.bias', 'model.vqmodel.encoder.mid.block_1.conv2.weight', 'model.vqmodel.encoder.mid.block_1.norm1.bias', 'model.vqmodel.encoder.mid.block_1.norm1.weight', 'model.vqmodel.encoder.mid.block_1.norm2.bias', 'model.vqmodel.encoder.mid.block_1.norm2.weight', 'model.vqmodel.encoder.mid.block_2.conv1.bias', 'model.vqmodel.encoder.mid.block_2.conv1.weight', 'model.vqmodel.encoder.mid.block_2.conv2.bias', 'model.vqmodel.encoder.mid.block_2.conv2.weight', 'model.vqmodel.encoder.mid.block_2.norm1.bias', 'model.vqmodel.encoder.mid.block_2.norm1.weight', 'model.vqmodel.encoder.mid.block_2.norm2.bias', 'model.vqmodel.encoder.mid.block_2.norm2.weight', 'model.vqmodel.encoder.norm_out.bias', 'model.vqmodel.encoder.norm_out.weight', 'model.vqmodel.post_quant_conv.bias', 'model.vqmodel.post_quant_conv.weight', 'model.vqmodel.quant_conv.bias', 'model.vqmodel.quant_conv.weight', 'model.vqmodel.quantize.embedding.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon_vae_ori/vqgan.py:573: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  sd = torch.load(path, map_location="cpu")["state_dict"]
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py:360: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=self.dtype):
+The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:8710 for open-end generation.
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+List of crop sizes:
+ 1536 x 384    1504 x 384    1472 x 384    1440 x 384    1408 x 384    1408 x 416    
+ 1376 x 416    1344 x 416    1312 x 416    1312 x 448    1280 x 448    1248 x 448    
+ 1216 x 448    1216 x 480    1184 x 480    1152 x 480    1152 x 512    1120 x 512    
+ 1088 x 512    1056 x 512    1056 x 544    1024 x 544    1024 x 576    992 x 576     
+ 960 x 576     960 x 608     928 x 608     896 x 608     896 x 640     864 x 640     
+ 864 x 672     832 x 672     832 x 704     800 x 704     800 x 736     768 x 736     
+ 768 x 768     736 x 768     736 x 800     704 x 800     704 x 832     672 x 832     
+ 672 x 864     640 x 864     640 x 896     608 x 896     608 x 928     608 x 960     
+ 576 x 960     576 x 992     576 x 1024    544 x 1024    544 x 1056    512 x 1056    
+ 512 x 1088    512 x 1120    512 x 1152    480 x 1152    480 x 1184    480 x 1216    
+ 448 x 1216    448 x 1248    448 x 1280    448 x 1312    416 x 1312    416 x 1344    
+ 416 x 1376    416 x 1408    384 x 1408    384 x 1440    384 x 1472    384 x 1504    
+ 384 x 1536    
+VQModel loaded from /mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer/vqgan.ckpt
+Number of images to generate is greater than the number of prompts. Generating only 299 images and no sampling.
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 302, in <module>
+    main(args)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 201, in main
+    result = inference_solver.generate(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py", line 361, in generate
+    result = self.model.generate( #TODO： go to JacobiSampler._sample
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/generation/utils.py", line 2252, in generate
+    result = self._sample(
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/scheduler/jacobi_iteration_lumina_mgpt.py", line 3268, in _sample
+    outputs = self(**model_inputs, return_dict=True)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1545, in forward
+    outputs = self.model(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1340, in forward
+    layer_outputs = decoder_layer(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 641, in forward
+    hidden_states, self_attn_weights, present_key_value = self.self_attn(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 547, in forward
+    key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/cache_utils.py", line 449, in update
+    self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 48.00 MiB. GPU 0 has a total capacity of 79.33 GiB of which 44.44 MiB is free. Process 113202 has 62.47 GiB memory in use. Including non-PyTorch memory, this process has 16.79 GiB memory in use. Of the allocated memory 15.69 GiB is allocated by PyTorch, and 622.37 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+srun: error: SH-IDC1-10-140-37-43: task 0: Exited with exit code 1

exp/ablation/sematic/ms_sjd_re_401-500.log

@@ -0,0 +1,74 @@
+ChameleonForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+
+Some weights of ChameleonForConditionalGeneration were not initialized from the model checkpoint at Alpha-VLLM/Lumina-mGPT-7B-768 and are newly initialized: ['model.vqmodel.encoder.conv_in.bias', 'model.vqmodel.encoder.conv_in.weight', 'model.vqmodel.encoder.conv_out.bias', 'model.vqmodel.encoder.conv_out.weight', 'model.vqmodel.encoder.down.0.block.0.conv1.bias', 'model.vqmodel.encoder.down.0.block.0.conv1.weight', 'model.vqmodel.encoder.down.0.block.0.conv2.bias', 'model.vqmodel.encoder.down.0.block.0.conv2.weight', 'model.vqmodel.encoder.down.0.block.0.norm1.bias', 'model.vqmodel.encoder.down.0.block.0.norm1.weight', 'model.vqmodel.encoder.down.0.block.0.norm2.bias', 'model.vqmodel.encoder.down.0.block.0.norm2.weight', 'model.vqmodel.encoder.down.0.block.1.conv1.bias', 'model.vqmodel.encoder.down.0.block.1.conv1.weight', 'model.vqmodel.encoder.down.0.block.1.conv2.bias', 'model.vqmodel.encoder.down.0.block.1.conv2.weight', 'model.vqmodel.encoder.down.0.block.1.norm1.bias', 'model.vqmodel.encoder.down.0.block.1.norm1.weight', 'model.vqmodel.encoder.down.0.block.1.norm2.bias', 'model.vqmodel.encoder.down.0.block.1.norm2.weight', 'model.vqmodel.encoder.down.0.downsample.conv.bias', 'model.vqmodel.encoder.down.0.downsample.conv.weight', 'model.vqmodel.encoder.down.1.block.0.conv1.bias', 'model.vqmodel.encoder.down.1.block.0.conv1.weight', 'model.vqmodel.encoder.down.1.block.0.conv2.bias', 'model.vqmodel.encoder.down.1.block.0.conv2.weight', 'model.vqmodel.encoder.down.1.block.0.norm1.bias', 'model.vqmodel.encoder.down.1.block.0.norm1.weight', 'model.vqmodel.encoder.down.1.block.0.norm2.bias', 'model.vqmodel.encoder.down.1.block.0.norm2.weight', 'model.vqmodel.encoder.down.1.block.1.conv1.bias', 'model.vqmodel.encoder.down.1.block.1.conv1.weight', 'model.vqmodel.encoder.down.1.block.1.conv2.bias', 'model.vqmodel.encoder.down.1.block.1.conv2.weight', 'model.vqmodel.encoder.down.1.block.1.norm1.bias', 'model.vqmodel.encoder.down.1.block.1.norm1.weight', 'model.vqmodel.encoder.down.1.block.1.norm2.bias', 'model.vqmodel.encoder.down.1.block.1.norm2.weight', 'model.vqmodel.encoder.down.1.downsample.conv.bias', 'model.vqmodel.encoder.down.1.downsample.conv.weight', 'model.vqmodel.encoder.down.2.block.0.conv1.bias', 'model.vqmodel.encoder.down.2.block.0.conv1.weight', 'model.vqmodel.encoder.down.2.block.0.conv2.bias', 'model.vqmodel.encoder.down.2.block.0.conv2.weight', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.2.block.0.norm1.bias', 'model.vqmodel.encoder.down.2.block.0.norm1.weight', 'model.vqmodel.encoder.down.2.block.0.norm2.bias', 'model.vqmodel.encoder.down.2.block.0.norm2.weight', 'model.vqmodel.encoder.down.2.block.1.conv1.bias', 'model.vqmodel.encoder.down.2.block.1.conv1.weight', 'model.vqmodel.encoder.down.2.block.1.conv2.bias', 'model.vqmodel.encoder.down.2.block.1.conv2.weight', 'model.vqmodel.encoder.down.2.block.1.norm1.bias', 'model.vqmodel.encoder.down.2.block.1.norm1.weight', 'model.vqmodel.encoder.down.2.block.1.norm2.bias', 'model.vqmodel.encoder.down.2.block.1.norm2.weight', 'model.vqmodel.encoder.down.2.downsample.conv.bias', 'model.vqmodel.encoder.down.2.downsample.conv.weight', 'model.vqmodel.encoder.down.3.block.0.conv1.bias', 'model.vqmodel.encoder.down.3.block.0.conv1.weight', 'model.vqmodel.encoder.down.3.block.0.conv2.bias', 'model.vqmodel.encoder.down.3.block.0.conv2.weight', 'model.vqmodel.encoder.down.3.block.0.norm1.bias', 'model.vqmodel.encoder.down.3.block.0.norm1.weight', 'model.vqmodel.encoder.down.3.block.0.norm2.bias', 'model.vqmodel.encoder.down.3.block.0.norm2.weight', 'model.vqmodel.encoder.down.3.block.1.conv1.bias', 'model.vqmodel.encoder.down.3.block.1.conv1.weight', 'model.vqmodel.encoder.down.3.block.1.conv2.bias', 'model.vqmodel.encoder.down.3.block.1.conv2.weight', 'model.vqmodel.encoder.down.3.block.1.norm1.bias', 'model.vqmodel.encoder.down.3.block.1.norm1.weight', 'model.vqmodel.encoder.down.3.block.1.norm2.bias', 'model.vqmodel.encoder.down.3.block.1.norm2.weight', 'model.vqmodel.encoder.down.3.downsample.conv.bias', 'model.vqmodel.encoder.down.3.downsample.conv.weight', 'model.vqmodel.encoder.down.4.block.0.conv1.bias', 'model.vqmodel.encoder.down.4.block.0.conv1.weight', 'model.vqmodel.encoder.down.4.block.0.conv2.bias', 'model.vqmodel.encoder.down.4.block.0.conv2.weight', 'model.vqmodel.encoder.down.4.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.4.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.4.block.0.norm1.bias', 'model.vqmodel.encoder.down.4.block.0.norm1.weight', 'model.vqmodel.encoder.down.4.block.0.norm2.bias', 'model.vqmodel.encoder.down.4.block.0.norm2.weight', 'model.vqmodel.encoder.down.4.block.1.conv1.bias', 'model.vqmodel.encoder.down.4.block.1.conv1.weight', 'model.vqmodel.encoder.down.4.block.1.conv2.bias', 'model.vqmodel.encoder.down.4.block.1.conv2.weight', 'model.vqmodel.encoder.down.4.block.1.norm1.bias', 'model.vqmodel.encoder.down.4.block.1.norm1.weight', 'model.vqmodel.encoder.down.4.block.1.norm2.bias', 'model.vqmodel.encoder.down.4.block.1.norm2.weight', 'model.vqmodel.encoder.mid.attn_1.k.bias', 'model.vqmodel.encoder.mid.attn_1.k.weight', 'model.vqmodel.encoder.mid.attn_1.norm.bias', 'model.vqmodel.encoder.mid.attn_1.norm.weight', 'model.vqmodel.encoder.mid.attn_1.proj_out.bias', 'model.vqmodel.encoder.mid.attn_1.proj_out.weight', 'model.vqmodel.encoder.mid.attn_1.q.bias', 'model.vqmodel.encoder.mid.attn_1.q.weight', 'model.vqmodel.encoder.mid.attn_1.v.bias', 'model.vqmodel.encoder.mid.attn_1.v.weight', 'model.vqmodel.encoder.mid.block_1.conv1.bias', 'model.vqmodel.encoder.mid.block_1.conv1.weight', 'model.vqmodel.encoder.mid.block_1.conv2.bias', 'model.vqmodel.encoder.mid.block_1.conv2.weight', 'model.vqmodel.encoder.mid.block_1.norm1.bias', 'model.vqmodel.encoder.mid.block_1.norm1.weight', 'model.vqmodel.encoder.mid.block_1.norm2.bias', 'model.vqmodel.encoder.mid.block_1.norm2.weight', 'model.vqmodel.encoder.mid.block_2.conv1.bias', 'model.vqmodel.encoder.mid.block_2.conv1.weight', 'model.vqmodel.encoder.mid.block_2.conv2.bias', 'model.vqmodel.encoder.mid.block_2.conv2.weight', 'model.vqmodel.encoder.mid.block_2.norm1.bias', 'model.vqmodel.encoder.mid.block_2.norm1.weight', 'model.vqmodel.encoder.mid.block_2.norm2.bias', 'model.vqmodel.encoder.mid.block_2.norm2.weight', 'model.vqmodel.encoder.norm_out.bias', 'model.vqmodel.encoder.norm_out.weight', 'model.vqmodel.post_quant_conv.bias', 'model.vqmodel.post_quant_conv.weight', 'model.vqmodel.quant_conv.bias', 'model.vqmodel.quant_conv.weight', 'model.vqmodel.quantize.embedding.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon_vae_ori/vqgan.py:573: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  sd = torch.load(path, map_location="cpu")["state_dict"]
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py:360: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=self.dtype):
+The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:8710 for open-end generation.
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+List of crop sizes:
+ 1536 x 384    1504 x 384    1472 x 384    1440 x 384    1408 x 384    1408 x 416    
+ 1376 x 416    1344 x 416    1312 x 416    1312 x 448    1280 x 448    1248 x 448    
+ 1216 x 448    1216 x 480    1184 x 480    1152 x 480    1152 x 512    1120 x 512    
+ 1088 x 512    1056 x 512    1056 x 544    1024 x 544    1024 x 576    992 x 576     
+ 960 x 576     960 x 608     928 x 608     896 x 608     896 x 640     864 x 640     
+ 864 x 672     832 x 672     832 x 704     800 x 704     800 x 736     768 x 736     
+ 768 x 768     736 x 768     736 x 800     704 x 800     704 x 832     672 x 832     
+ 672 x 864     640 x 864     640 x 896     608 x 896     608 x 928     608 x 960     
+ 576 x 960     576 x 992     576 x 1024    544 x 1024    544 x 1056    512 x 1056    
+ 512 x 1088    512 x 1120    512 x 1152    480 x 1152    480 x 1184    480 x 1216    
+ 448 x 1216    448 x 1248    448 x 1280    448 x 1312    416 x 1312    416 x 1344    
+ 416 x 1376    416 x 1408    384 x 1408    384 x 1440    384 x 1472    384 x 1504    
+ 384 x 1536    
+VQModel loaded from /mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer/vqgan.ckpt
+Number of images to generate is greater than the number of prompts. Generating only 99 images and no sampling.
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 302, in <module>
+    main(args)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 201, in main
+    result = inference_solver.generate(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py", line 361, in generate
+    result = self.model.generate( #TODO： go to JacobiSampler._sample
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/generation/utils.py", line 2252, in generate
+    result = self._sample(
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/scheduler/jacobi_iteration_lumina_mgpt.py", line 3268, in _sample
+    outputs = self(**model_inputs, return_dict=True)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1545, in forward
+    outputs = self.model(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1340, in forward
+    layer_outputs = decoder_layer(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 641, in forward
+    hidden_states, self_attn_weights, present_key_value = self.self_attn(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 547, in forward
+    key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/cache_utils.py", line 449, in update
+    self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 46.00 MiB. GPU 0 has a total capacity of 79.33 GiB of which 30.44 MiB is free. Process 113202 has 62.47 GiB memory in use. Including non-PyTorch memory, this process has 16.81 GiB memory in use. Of the allocated memory 15.59 GiB is allocated by PyTorch, and 733.88 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+srun: error: SH-IDC1-10-140-37-43: task 0: Exited with exit code 1

exp/ablation/sematic/ms_sjd_re_901-1000.log

@@ -0,0 +1,72 @@
+ChameleonForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.
+  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes
+  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).
+  - If you are not the owner of the model architecture class, please contact the model code owner to update it.
+
+Some weights of ChameleonForConditionalGeneration were not initialized from the model checkpoint at Alpha-VLLM/Lumina-mGPT-7B-768 and are newly initialized: ['model.vqmodel.encoder.conv_in.bias', 'model.vqmodel.encoder.conv_in.weight', 'model.vqmodel.encoder.conv_out.bias', 'model.vqmodel.encoder.conv_out.weight', 'model.vqmodel.encoder.down.0.block.0.conv1.bias', 'model.vqmodel.encoder.down.0.block.0.conv1.weight', 'model.vqmodel.encoder.down.0.block.0.conv2.bias', 'model.vqmodel.encoder.down.0.block.0.conv2.weight', 'model.vqmodel.encoder.down.0.block.0.norm1.bias', 'model.vqmodel.encoder.down.0.block.0.norm1.weight', 'model.vqmodel.encoder.down.0.block.0.norm2.bias', 'model.vqmodel.encoder.down.0.block.0.norm2.weight', 'model.vqmodel.encoder.down.0.block.1.conv1.bias', 'model.vqmodel.encoder.down.0.block.1.conv1.weight', 'model.vqmodel.encoder.down.0.block.1.conv2.bias', 'model.vqmodel.encoder.down.0.block.1.conv2.weight', 'model.vqmodel.encoder.down.0.block.1.norm1.bias', 'model.vqmodel.encoder.down.0.block.1.norm1.weight', 'model.vqmodel.encoder.down.0.block.1.norm2.bias', 'model.vqmodel.encoder.down.0.block.1.norm2.weight', 'model.vqmodel.encoder.down.0.downsample.conv.bias', 'model.vqmodel.encoder.down.0.downsample.conv.weight', 'model.vqmodel.encoder.down.1.block.0.conv1.bias', 'model.vqmodel.encoder.down.1.block.0.conv1.weight', 'model.vqmodel.encoder.down.1.block.0.conv2.bias', 'model.vqmodel.encoder.down.1.block.0.conv2.weight', 'model.vqmodel.encoder.down.1.block.0.norm1.bias', 'model.vqmodel.encoder.down.1.block.0.norm1.weight', 'model.vqmodel.encoder.down.1.block.0.norm2.bias', 'model.vqmodel.encoder.down.1.block.0.norm2.weight', 'model.vqmodel.encoder.down.1.block.1.conv1.bias', 'model.vqmodel.encoder.down.1.block.1.conv1.weight', 'model.vqmodel.encoder.down.1.block.1.conv2.bias', 'model.vqmodel.encoder.down.1.block.1.conv2.weight', 'model.vqmodel.encoder.down.1.block.1.norm1.bias', 'model.vqmodel.encoder.down.1.block.1.norm1.weight', 'model.vqmodel.encoder.down.1.block.1.norm2.bias', 'model.vqmodel.encoder.down.1.block.1.norm2.weight', 'model.vqmodel.encoder.down.1.downsample.conv.bias', 'model.vqmodel.encoder.down.1.downsample.conv.weight', 'model.vqmodel.encoder.down.2.block.0.conv1.bias', 'model.vqmodel.encoder.down.2.block.0.conv1.weight', 'model.vqmodel.encoder.down.2.block.0.conv2.bias', 'model.vqmodel.encoder.down.2.block.0.conv2.weight', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.2.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.2.block.0.norm1.bias', 'model.vqmodel.encoder.down.2.block.0.norm1.weight', 'model.vqmodel.encoder.down.2.block.0.norm2.bias', 'model.vqmodel.encoder.down.2.block.0.norm2.weight', 'model.vqmodel.encoder.down.2.block.1.conv1.bias', 'model.vqmodel.encoder.down.2.block.1.conv1.weight', 'model.vqmodel.encoder.down.2.block.1.conv2.bias', 'model.vqmodel.encoder.down.2.block.1.conv2.weight', 'model.vqmodel.encoder.down.2.block.1.norm1.bias', 'model.vqmodel.encoder.down.2.block.1.norm1.weight', 'model.vqmodel.encoder.down.2.block.1.norm2.bias', 'model.vqmodel.encoder.down.2.block.1.norm2.weight', 'model.vqmodel.encoder.down.2.downsample.conv.bias', 'model.vqmodel.encoder.down.2.downsample.conv.weight', 'model.vqmodel.encoder.down.3.block.0.conv1.bias', 'model.vqmodel.encoder.down.3.block.0.conv1.weight', 'model.vqmodel.encoder.down.3.block.0.conv2.bias', 'model.vqmodel.encoder.down.3.block.0.conv2.weight', 'model.vqmodel.encoder.down.3.block.0.norm1.bias', 'model.vqmodel.encoder.down.3.block.0.norm1.weight', 'model.vqmodel.encoder.down.3.block.0.norm2.bias', 'model.vqmodel.encoder.down.3.block.0.norm2.weight', 'model.vqmodel.encoder.down.3.block.1.conv1.bias', 'model.vqmodel.encoder.down.3.block.1.conv1.weight', 'model.vqmodel.encoder.down.3.block.1.conv2.bias', 'model.vqmodel.encoder.down.3.block.1.conv2.weight', 'model.vqmodel.encoder.down.3.block.1.norm1.bias', 'model.vqmodel.encoder.down.3.block.1.norm1.weight', 'model.vqmodel.encoder.down.3.block.1.norm2.bias', 'model.vqmodel.encoder.down.3.block.1.norm2.weight', 'model.vqmodel.encoder.down.3.downsample.conv.bias', 'model.vqmodel.encoder.down.3.downsample.conv.weight', 'model.vqmodel.encoder.down.4.block.0.conv1.bias', 'model.vqmodel.encoder.down.4.block.0.conv1.weight', 'model.vqmodel.encoder.down.4.block.0.conv2.bias', 'model.vqmodel.encoder.down.4.block.0.conv2.weight', 'model.vqmodel.encoder.down.4.block.0.nin_shortcut.bias', 'model.vqmodel.encoder.down.4.block.0.nin_shortcut.weight', 'model.vqmodel.encoder.down.4.block.0.norm1.bias', 'model.vqmodel.encoder.down.4.block.0.norm1.weight', 'model.vqmodel.encoder.down.4.block.0.norm2.bias', 'model.vqmodel.encoder.down.4.block.0.norm2.weight', 'model.vqmodel.encoder.down.4.block.1.conv1.bias', 'model.vqmodel.encoder.down.4.block.1.conv1.weight', 'model.vqmodel.encoder.down.4.block.1.conv2.bias', 'model.vqmodel.encoder.down.4.block.1.conv2.weight', 'model.vqmodel.encoder.down.4.block.1.norm1.bias', 'model.vqmodel.encoder.down.4.block.1.norm1.weight', 'model.vqmodel.encoder.down.4.block.1.norm2.bias', 'model.vqmodel.encoder.down.4.block.1.norm2.weight', 'model.vqmodel.encoder.mid.attn_1.k.bias', 'model.vqmodel.encoder.mid.attn_1.k.weight', 'model.vqmodel.encoder.mid.attn_1.norm.bias', 'model.vqmodel.encoder.mid.attn_1.norm.weight', 'model.vqmodel.encoder.mid.attn_1.proj_out.bias', 'model.vqmodel.encoder.mid.attn_1.proj_out.weight', 'model.vqmodel.encoder.mid.attn_1.q.bias', 'model.vqmodel.encoder.mid.attn_1.q.weight', 'model.vqmodel.encoder.mid.attn_1.v.bias', 'model.vqmodel.encoder.mid.attn_1.v.weight', 'model.vqmodel.encoder.mid.block_1.conv1.bias', 'model.vqmodel.encoder.mid.block_1.conv1.weight', 'model.vqmodel.encoder.mid.block_1.conv2.bias', 'model.vqmodel.encoder.mid.block_1.conv2.weight', 'model.vqmodel.encoder.mid.block_1.norm1.bias', 'model.vqmodel.encoder.mid.block_1.norm1.weight', 'model.vqmodel.encoder.mid.block_1.norm2.bias', 'model.vqmodel.encoder.mid.block_1.norm2.weight', 'model.vqmodel.encoder.mid.block_2.conv1.bias', 'model.vqmodel.encoder.mid.block_2.conv1.weight', 'model.vqmodel.encoder.mid.block_2.conv2.bias', 'model.vqmodel.encoder.mid.block_2.conv2.weight', 'model.vqmodel.encoder.mid.block_2.norm1.bias', 'model.vqmodel.encoder.mid.block_2.norm1.weight', 'model.vqmodel.encoder.mid.block_2.norm2.bias', 'model.vqmodel.encoder.mid.block_2.norm2.weight', 'model.vqmodel.encoder.norm_out.bias', 'model.vqmodel.encoder.norm_out.weight', 'model.vqmodel.post_quant_conv.bias', 'model.vqmodel.post_quant_conv.weight', 'model.vqmodel.quant_conv.bias', 'model.vqmodel.quant_conv.weight', 'model.vqmodel.quantize.embedding.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon_vae_ori/vqgan.py:573: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  sd = torch.load(path, map_location="cpu")["state_dict"]
+/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py:360: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=self.dtype):
+The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:8710 for open-end generation.
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+List of crop sizes:
+ 1536 x 384    1504 x 384    1472 x 384    1440 x 384    1408 x 384    1408 x 416    
+ 1376 x 416    1344 x 416    1312 x 416    1312 x 448    1280 x 448    1248 x 448    
+ 1216 x 448    1216 x 480    1184 x 480    1152 x 480    1152 x 512    1120 x 512    
+ 1088 x 512    1056 x 512    1056 x 544    1024 x 544    1024 x 576    992 x 576     
+ 960 x 576     960 x 608     928 x 608     896 x 608     896 x 640     864 x 640     
+ 864 x 672     832 x 672     832 x 704     800 x 704     800 x 736     768 x 736     
+ 768 x 768     736 x 768     736 x 800     704 x 800     704 x 832     672 x 832     
+ 672 x 864     640 x 864     640 x 896     608 x 896     608 x 928     608 x 960     
+ 576 x 960     576 x 992     576 x 1024    544 x 1024    544 x 1056    512 x 1056    
+ 512 x 1088    512 x 1120    512 x 1152    480 x 1152    480 x 1184    480 x 1216    
+ 448 x 1216    448 x 1248    448 x 1280    448 x 1312    416 x 1312    416 x 1344    
+ 416 x 1376    416 x 1408    384 x 1408    384 x 1440    384 x 1472    384 x 1504    
+ 384 x 1536    
+VQModel loaded from /mnt/petrelfs/leihaodong/local_model/chameleon/tokenizer/vqgan.ckpt
+Number of images to generate is greater than the number of prompts. Generating only 99 images and no sampling.
+self.h_latent_dim, self.w_latent_dim tensor(48, device='cuda:0') tensor(48, device='cuda:0')
+Traceback (most recent call last):
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 302, in <module>
+    main(args)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/main.py", line 201, in main
+    result = inference_solver.generate(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/lumina_mgpt/inference_solver.py", line 361, in generate
+    result = self.model.generate( #TODO： go to JacobiSampler._sample
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/transformers/generation/utils.py", line 2252, in generate
+    result = self._sample(
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/scheduler/jacobi_iteration_lumina_mgpt.py", line 3268, in _sample
+    outputs = self(**model_inputs, return_dict=True)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1545, in forward
+    outputs = self.model(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 1340, in forward
+    layer_outputs = decoder_layer(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 641, in forward
+    hidden_states, self_attn_weights, present_key_value = self.self_attn(
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/anaconda3/envs/mgpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/mnt/petrelfs/leihaodong/ICLR25/sjdtree/./lumina_mgpt/model/chameleon/modeling_chameleon.py", line 567, in forward
+    attn_output = torch.nn.functional.scaled_dot_product_attention(
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 24.00 MiB. GPU 0 has a total capacity of 79.33 GiB of which 10.44 MiB is free. Process 113202 has 62.47 GiB memory in use. Including non-PyTorch memory, this process has 16.83 GiB memory in use. Of the allocated memory 15.59 GiB is allocated by PyTorch, and 761.46 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+srun: error: SH-IDC1-10-140-37-43: task 0: Exited with exit code 1

exp/distribution/LLM_P.py

@@ -0,0 +1,50 @@
+import os
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+
+# 读取 .pt 文件（与脚本同目录）
+script_dir = os.path.dirname(os.path.abspath(__file__))
+file_path = os.path.join(script_dir, "LLM_P.pt")
+try:
+    data = torch.load(file_path, map_location=torch.device('cpu'))
+except FileNotFoundError:
+    raise FileNotFoundError(f"File {file_path} not found. Please check the path.")
+
+
+def process_and_plot(data, xlabel, ylim, save_name):
+    """处理概率数据并绘制柱状图"""
+    if data.dim() == 1:
+        probs = data.numpy()
+    elif data.dim() == 2:
+        probs = data.mean(dim=0).numpy()
+    else:
+        raise ValueError(f"Unexpected tensor shape: {data.shape}")
+
+    if abs(probs.sum() - 1.0) > 1e-5:
+        probs = probs / probs.sum()
+
+    labels = np.arange(len(probs))
+
+    plt.rcParams['font.weight'] = 'bold'
+    plt.figure(figsize=(10, 6))
+    plt.bar(labels, probs, color='skyblue', edgecolor='black')
+    plt.xlabel(xlabel, fontsize=16, fontweight='bold')
+    plt.ylabel('Probability', fontsize=16, fontweight='bold')
+    plt.ylim(ylim)
+    plt.grid(True, axis='y', linestyle='--', alpha=0.7)
+    ax = plt.gca()
+    for label in ax.get_xticklabels() + ax.get_yticklabels():
+        label.set_fontweight('bold')
+    plt.tight_layout()
+    save_path = os.path.join(script_dir, save_name)
+    plt.savefig(save_path, dpi=300, bbox_inches='tight')
+    print(f"已保存: {save_path}")
+    plt.show()
+
+
+# 图1: Text Token Index
+process_and_plot(data, 'Text Token Index', (0, 0.2), 'LLM_P.png')
+
+# 图2: Image Token Index
+process_and_plot(data, 'Image Token Index', (0, 0.1), 'p_index.png')

exp/distribution/MLLM_P.py

@@ -0,0 +1,43 @@
+import os
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+
+# 读取 .pt 文件（与脚本同目录）
+script_dir = os.path.dirname(os.path.abspath(__file__))
+file_path = os.path.join(script_dir, "token_7.pt")
+try:
+    data = torch.load(file_path, map_location=torch.device('cpu'))[0]
+except FileNotFoundError:
+    raise FileNotFoundError(f"File {file_path} not found. Please check the path.")
+data = data[:, :4000]
+
+# 处理概率数据
+if data.dim() == 1:
+    probs = data.numpy()
+elif data.dim() == 2:
+    probs = data.mean(dim=0).numpy()
+else:
+    raise ValueError(f"Unexpected tensor shape: {data.shape}")
+
+if abs(probs.sum() - 1.0) > 1e-5:
+    probs = probs / probs.sum()
+
+labels = np.arange(len(probs))
+
+# 绘制柱状图
+plt.rcParams['font.weight'] = 'bold'
+plt.figure(figsize=(10, 6))
+plt.bar(labels, probs, color='skyblue', edgecolor='black')
+plt.xlabel('Text Token Index', fontsize=16, fontweight='bold')
+plt.ylabel('Probability', fontsize=16, fontweight='bold')
+plt.ylim(0, 0.1)
+plt.grid(True, axis='y', linestyle='--', alpha=0.7)
+ax = plt.gca()
+for label in ax.get_xticklabels() + ax.get_yticklabels():
+    label.set_fontweight('bold')
+plt.tight_layout()
+save_path = os.path.join(script_dir, 'MLLM_P.png')
+plt.savefig(save_path, dpi=300, bbox_inches='tight')
+print(f"已保存: {save_path}")
+plt.show()

exp/pic/5_2p.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'numpy'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorch\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# 读取 .pt 文件\u001b[39;00m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'numpy'"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 读取 .pt 文件\n",
+    "file_path = \"/mnt/petrelfs/leihaodong/ICLR25/exp/pic/p/token_7.pt\"\n",
+    "try:\n",
+    "    data = torch.load(file_path)\n",
+    "except FileNotFoundError:\n",
+    "    raise FileNotFoundError(f\"File {file_path} not found. Please check the path.\")\n",
+    "\n",
+    "# 处理概率数据\n",
+    "if data.dim() == 1:\n",
+    "    # 1D 张量，直接使用\n",
+    "    probs = data.numpy()\n",
+    "elif data.dim() == 2:\n",
+    "    # 2D 张量，取平均或第一个样本\n",
+    "    probs = data.mean(dim=0).numpy()  # 沿 batch 轴平均\n",
+    "else:\n",
+    "    raise ValueError(f\"Unexpected tensor shape: {data.shape}\")\n",
+    "\n",
+    "# 确保概率归一化（如果需要）\n",
+    "if abs(probs.sum() - 1.0) > 1e-5:  # 检查是否已归一化\n",
+    "    probs = probs / probs.sum()\n",
+    "\n",
+    "# 类别索引\n",
+    "labels = np.arange(len(probs))\n",
+    "\n",
+    "# 绘制柱状图\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(labels, probs, color='skyblue', edgecolor='black')\n",
+    "plt.xlabel('Category Index')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.title('Probability Distribution of token_7.pt')\n",
+    "plt.ylim(0, 1)  # y 轴范围 [0, 1]\n",
+    "plt.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# 显示图表\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 可选：保存图表\n",
+    "# plt.savefig('probability_distribution.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "larm-trl",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

exp/pic/ablation/3.2.11.ipynb

@@ -0,0 +1,73 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Data\n",
+    "k = [2, 4, 6, 8, 10, 12]\n",
+    "times = [0.053, 0.058, 0.060, 0.068, 0.072,0.084]\n",
+    "\n",
+    "# Create the plot\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.plot(k, times, color='#aecadf', marker='o')\n",
+    "\n",
+    "# Set Y-axis limits\n",
+    "plt.ylim(0, 0.3)\n",
+    "\n",
+    "# Set X-axis ticks with unit distance of 2\n",
+    "plt.xticks(np.arange(2, 14, 2))\n",
+    "\n",
+    "# Set grid for Y-axis only\n",
+    "plt.grid(True, axis='y')\n",
+    "\n",
+    "# Label axes\n",
+    "plt.xlabel('K',fontsize=20)\n",
+    "plt.ylabel('One Forward Time(s)',fontsize=20)\n",
+    "\n",
+    "# Remove title and legend (not added, so no action needed)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "eval_xx",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

exp/pic/ablation/3.2.12.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Data\n",
+    "mem_sizes = [2, 4, 6, 8, 10, 12]\n",
+    "avg_times = [20494, 20694, 21069, 21499, 21999, 23332]\n",
+    "min_times = [19448, 19438, 19638, 19538, 19538, 19774]\n",
+    "max_times = [21540, 21950, 22500, 23460, 24460, 26890]\n",
+    "\n",
+    "# Calculate error bars\n",
+    "lower_errors = [avg - min_t for avg, min_t in zip(avg_times, min_times)]\n",
+    "upper_errors = [max_t - avg for avg, max_t in zip(avg_times, max_times)]\n",
+    "errors = [lower_errors, upper_errors]\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "bars = plt.bar(mem_sizes, avg_times, color='#c9b9d2', edgecolor='#c9b9d2', width=1)\n",
+    "plt.errorbar(mem_sizes, avg_times, yerr=errors, fmt='none', ecolor='#000000', capsize=5)\n",
+    "plt.xlabel('K',fontsize=20)\n",
+    "plt.ylabel('Memory (MB)',fontsize=20)\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "eval_xx",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

exp/pic/ablation/4.3.12.ipynb

@@ -0,0 +1,82 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OIuf6aScWe9nrIGfz0QVyj85WncinceliLzQe/MKoU6q0APDRExya93fe1q5rAIKCgggOPnVRe/bZZ5OcnFyi7cYbbyQmJoYHH3yw3KLuueee45lnnmH27NmuJcE8jQo7Eakz8vfn49/EH4CA5gFc9uVlJicyn8ViIbRtKKFtQ0u0x10WR1jXsJKze8mZ5OzKITstG++A40uSbPxmI19d8VXZL1DG9axrndUbQmKdX1x6vN1e6JytOrHYO5gCh7ZC4QHYt8j5dSLfZqWLvZAu4Nu0+vlq6rq6VVoAuM/R3aonLQAs1RIUFER8fHyJtoCAAJo2bepqHz16NK1atWLSJOflQ5999ln+9a9/MW3aNNq2bUtGRgYAgYGBBAYGuncAp6DCTkTqhN0rdvPp8E85e/LZ9Lq1gqU5fJs5z/g7eU2uE3nAoqi1ycvXi5bdWtKyW8k1x/IP5JOZkkmLrsfXtNw6e+upn+yk61mbxubjLMxCS/5Cxn4EcjaVLPay1zl38xZkQeZ859eJ/FqUXoMvpAv4hJ46w+lcV7eyCwAHRJdccuRUCwBLrUlLSytxpaWpU6dSWFjIpZdeWqJfWcfwmUmfFBHxeGm/p/HpeZ9SmFvInx/9SY+bepx65sinsXOWoygbuj5V8kD+Y0xeFNUs/o39aTOoTYm2dme344/3/6jwsbnpufw15y8Obj9Iu7Pb0bhdFU9WqS02P2jczfl1ouLDzuP1Tiz2stc5T+Q4stf5tffXko/xjyi9Bl9I3PHj1Sp7Xd0j+wCjCgsAn3Alh6osACw15uQVIU6+vX37drdlOR0q7ETEo22ft51pF06jKK+INoPbcNX3V1W8O3DLVGdRF9QJukxwHssl5QqKqPz1rJe/tpwNXzsvQRgaHUq7c9oRfXY00WdFE9C89GXaTOXVyHlyzMknyBQdgpwNJYu97HVweKdz0d78PZDxc8nHNIo6ugu3krO884aXPRvnH3H8uLhm/aFxd12aTWqUCjsR8VhbZ2/l81GfU3ykmPZD23PFjCvwblTB5aqK82DDC87tLo+oqKuEqlzPOn11OnmZeexauouDqQdZ/e5qVr/rXKokoncENy+5GauXhx/E7x0ITROdXycqzHaegXrySRv5e5zr8x1OK/v5ylKQdXQB4O4nFHJJ0Kh1/Tx7VzyGCjsR8Uibvt/El5d+ib3QTqcLO3HZl5fh5VeJH1lb3oKCfc41u9peXftB64GqXM866Z4kku5JovBQITsW7GDb3G2k/pLK3rV7sXpbSxR1P9zxA0HhQUSfHU2rPq3cu4hydfiEQPMk59eJCg8cX3R572+Q9nnFz9XnXefnrx4uACyeTYWdiHikjDUZ2AvtxP4jln9M+wc2n0oUBcWHYcNzzu0uj+iA8yqo6vWsfQJ96Hh+Rzqe3xFwXq83LzPPdf+Rg0dY/c5qDIfBvMfn4RPoQ5vBbWh3TjvandOO5l2aV27xZE/g0xjCBjq/miZWrrBr0lNFnZhCP/VExCOd8dgZNO3UlLhL4yq/a2/rO3Ak03lWYfS1tRuwHjqd61kHhAUQEHb8GDuLzcIFUy9g2y/bSP01lfy/89ny4xa2/LgFgO43dWfkeyNrbSwiDZUKO3epqXWPROqxTd9vIvqsaHwCfLBYLMRfGV/xg44pzof1zzq3uzzsXAtNqqymrmftG+RLr9t60eu2XhgOg4w/M5xF3txUdizYQWTfSFffrI1ZfHbRZ0SfHe08GePMaNd6hSJSNSrs3OF01j2qYffddx8vvvhiufdv2bKFDh061GoGkbKseHMFM8fMJPrsaK7+8Wq8fKv44+mv/8CRDOfZi9GjayekVIvFaiG8RzjhPcIZcP8AiguKMezHD+TbNncb+7fsZ/+W/ax6axVYILxnuKvQixoYhbe/hxTqNXVdXZFaosLOHSq77lFBVq0XdrfddptrccXHH3+czMxMpk6dCjhXsFdRJ2ZY8tISfr7XubxEi4QWlTue7kT2I7B+snO7y8POhWzFY51ctHe7rhshUSGuGb196/aRviqd9FXpLH5uMVf/eLXrWL6C3AK8/b3NO/P26HV1tQdGPJUKu+owDLAfrnx/e37l+xXnVdzvGFujKp8236lTJ9d2VlYWiYmJ9OvXr0rPIVKTFk5cyK+POBeJHThhIGc9c1bVD6r/633nkhSNIqHdDTUfUmqVb7AvnUd0pvOIzoBzIeTUuamkzk1l+/zttDnj+ILKCycuZOXUlbQd0ta1hl6zmGbuPREjIEqFm3gsFXbVYT8MX9TCdeHmDKxa/8sPOVfXrwaHw8H69eu54YYbqvV4kdNlGM6zJRc8tQCAIU8M4YzHzqj6L2h7Aax3XsuRuIe02Gs9EBQeRMK1CSRcm1Dqvt3LdlOQXcCmbzex6dtNzv4RziVVos+OpuvVXT1/WRWRWqTCroHaunUrR44coWvXrmZHkQZqwVMLXEXdOc+ew4AHBlTviVI/gsO7nCv6t7+5BhOKJ7puznWkr0537bZNW5RG7p5c1n68lq2zttLtuuOXFduzag9N2jfBL9TPxMQi7qXCrjpsjZyzZZV14I/Kzcadu8i5SnlVclTTunXrAIiPr8JZhyI1qNOFnVj68lKGPDGEvv/sW70nsRfCuonO7bgHndcMlXrNarPSKrEVrRJbMWjCIIryi9i5eCepc1OxelmxWJ0zvoZh8NmIz8jbm0dE7wiiz3GeiNE6qXXlFroWqaP06a4Oi6Vqu0BtlTxt3+Zf7V2rVZWenk6jRo1o1kxnbok5wnuGc/eWu2nU7DQWcd3+MeTtAL+W0P7WmgsndYa3vzftzm5Hu7PblWg/vO8wvsG+HEo/xO7lu9m9fDeLJi7Cy8+LqEFRJFyXUGJ2T6S+8PAL+kltCQwMJD8/n+nTp7N161az40gD4LA7+PHOH9m5ZKer7bSKOkcRpDzj3I57ALy07pkcFxAWwF0b72L8zvGM/HAkCdcmEBgeSPGRYrbN2cbeP/e6+hYdLmLF1BX8veVvDKOsi+WK1B2asXMHD1z3aOTIkQwfPpwbb7yRu+++m+eee85try0Nj73IzozrZrDu83Ws+2IdY7eNxTf4NE9y2P4p5KWCXxh0+L+aCSr1TnBkMN2v707367tjGAZZG7LYNncbrfu3dvVJW5TGzDtnOvu3Dnadbdvu7HYEtiz/RDmH3VGtq3SI1CYVdu7ggesehYSEMHPmTLe9njRcxQXF/O/K/7Hxm41Yva2MeHfE6Rd1juLjs3Wx9+uanFIpFouF5nHNaR7XvES71ct5tY2di3eSszOHPz74gz8++AOAsPgwznv9PNoOblviMRu+3lD2dXWnlL6urog7qbBzF617JA1Q8ZFivvjHF2yZuQWbr43L/3c5nS7oVPEDK7LjMzi01fkHUcc7Tv/5pEGLPiua6LOiKTpcRNqiNLb9so1tv2wj448MMlMyS1zebMtPW/jjgz9Y/+X6Us+TszuHLy79gsu/ulzFnZhGhZ2I1IrCvEKmj5xO6txUvPy9uOq7q2h3TruKH1gRhx1SnnZux97nthOOpP7zbuRN+6HtaT+0PQCHsw6zff52wuLDXH3W/ndtmUUdAAZggVnjZtF5ZGftlhVT6FMnIrVi0eRFpM5NxSfQh2tnXVszRR1A2ueQuxl8mkDHO2vmOUXK0KhZI+L+EVdi0ewmnZqc+kEG5OzMIW1hWi2nEymbZuxEpFac8cgZ7N+8n77j+tI6qXXFD6iME2frYu4B76CaeV6RSmoWU7mT3HLTc9k+fzsRvSLwCdS1i8V9VNiJSI0pyC3AJ9AHi8WCl58Xl35+ac2+wM7/Qc4G8A6FznfX7HOLVEJQeOX+mPAN8uWToZ9g9bLSeWRnul7dlfZD22Pz0eXOpHZ51K7YqVOnkpCQQHBwMMHBwSQlJfHTTz+d8jFffvklMTEx+Pn50bVr11o701NrG1VM71HDdmjvId4f8D4/3/dz7XwWDAekPOncjhkP3sE1/xoiFYgaFEVwZDCUd0lji3PJlICWAQS3DqbocBEpn6Xw2YjPeDH8RX64/Qd2LNiB4dDPS6kdHlXYRUZGMnnyZFatWsXKlSs566yzGDlypOvyVydbvHgxV111FTfffDNr1qxh1KhRjBo1ipSUlBrL5OXlnNQsLi6useesr469R8feM2k4cnbn8NGQj8hMziTlsxQO7ztc8y+ycwZkrwPvEOj8z5p/fpFKsNqsDJ8y3Hnj5OLu6O3hrwynVe9W3L3lbm5Zfgt9x/UlsGUg+fvzWfX2Kj4c/CEr31rp1tzScFgMD59madKkCc8//zw331z64t5XXHEFeXl5/PDDD662fv360b17d956661KPf+uXbto3bo1O3fuJDIystT9hmGwZcsWAgICaNWqVfUH0gDs3r2bvLw8OnbsWOJgY6nfDu44yH/P/i8H/jpAcOtgrv/1epp0qOAA86oyHPBTDzi4FuL/BQlP1Ozzi1RRmevYtQ5m+Ctlr2PnsDvYPm87ydOS2ThjI3em3ElQhHO37qbvNrF37V7ir4qnSfsa/r/TgFT0+7yh8NipFbvdzpdffkleXh5JSUll9lmyZAn33HNPibZhw4bxzTfflPu8BQUFFBQUuG7n5uYCztmmYzNOVqsVq9WKw+HA4XDQtGlT9u7di6+vLwEBZS+tYLFYytz9VF57VVT1ud3dbhgGhw8fJicnh7CwMOx2u+s+m815PMmJbeCc1TMMo0S7xWLBZrO53veK2k/+Pp3cbrfbS+Qsr91ms2GxWErNypaXXWM6nv3vLX/zydBPyEnLITQ6lOt/vZ7gqOASr1sTY7Ls/hbbwbXgFURx+zFwwvPr+6QxmTGmjhd1pNNFnUhbmEbO7hwCwwNpPbA1VpvV9diTx9Tu7HZEnxXN8NeHY/O2UVxcjMViYflry9n2yzZ+e+w3IvpEEH9VPHGXxxESEaLvUxXG5OHzVG7jcYVdcnIySUlJHDlyhMDAQGbMmEFcXFyZfTMyMmjRokWJthYtWpCRkVHu80+aNIknnij91/7atWtdj2vevDnt27cnNTWVffv2Abg+RF5eXhQVFZX48Hl5eWGz2SgsLCzxwfL29sZqtZYoJI+1WywWCgsLS7T7+PhgGAZFRUUl2n19fXE4HCXaLRYLPj4+2O32Ur9Evb29KS4uLvEf7Vj7ydltNluNjMnHx4eQkBC2bNlSor13794UFhaydu3aEq+ZmJhIdnY2GzdudLX7+/vTrVs3srKy2LZtm6s9JCSE2NhY9uzZw65du1ztZX2fwLlLPzIyks2bN5Odne1qb9euHWFhYaSkpJCfn+9qj4mJITQ0lDVr1pR4zxISEvDx8WHlypK7TDQm55i2LN3CjJEzKMgqoFFUIwa+N5DQtqHs2rWrZsdkGHTd/TABAJ3/yZr1O7Dbj7+X+j5pTGaOqfWg1qxcuZJ88tm3Zl+1xpQwOoHCokJ2LdzFnuV72LN8D3PunUO7c9rR6vxW+Pc9vkCyvk/ljykoSGfJgwfuii0sLCQtLY3s7Gy++uor/vOf/zB//vwyizsfHx8++ugjrrrqKlfbm2++yRNPPMHevXtL9YfSM3a7d+8mLi6O1NRU19RteX89HPurpCH/RVTWmCwWC76+vthstnozplNl15ic2ZOnJ/P11V/TLLYZ18y+hqDwoFoZk2XPD9gWXYzhFYhl5HaKbSG1Nqb6+H3SmOrOmHL25LDhyw2kTE9hz/I9ALQ9qy3XzL7m+JM4wMvHq86MyZ3fp/T0dKKiorQr1uwAJ/Px8aFDhw4A9OrVixUrVjBlyhTefvvtUn1btmxZqoDbu3cvLVu2LPf5fX198fU9fp3KnBzn8RFeXl6lDvo/9uE5kbe3d9UG1MCUd+JEWe0Wi6XM9rLe9+q0H/tBVNn2qmQvr70hjanrlV3x8vEialAUAc2PH6JQo2MyDFjvvCaspdNd4Nu03B9a+j5pTHV9TKGRoSSNTyJpfBL7/9pPymcphMWHufrnpucyNX4qnUc5l09pO6RtqatbeNqY3Pl90rHdTh51VmxZHA5Hqd1+xyQlJTF37twSbXPmzCn3mDwROT17Vu4hd0+u63bsJbElirqaf8GfYP9KsDVyLkgs0kA0ad+EMx49g5hRMa62jd9sJH9/Pn+8/wcfn/MxL7d+mVnjZ7F7xW4dXyYuHjVjN2HCBM477zyioqLIzc1l2rRpzJs3j9mzZwMwevRoWrVqxaRJkwAYO3YsgwcP5sUXX+SCCy5g+vTprFy5knfeecfMYYjUS2mL0vj0/E8JbhXMDfNvICCslq/RahjH163rdCf4Na/d1xPxcL3/rzdhXcJInpbM+i/Xcyj9EMteWcayV5bRpEMTLv38UsJ7hpsdU0zmUYVdZmYmo0ePJj09nZCQEBISEpg9ezbnnnsuAGlpaSWmafv378+0adN49NFHefjhh+nYsSPffPMN8fHxZg1BpF5K/TWVz0Z8RtHhIgLDA/Fu5IZDEtJ/hr+Xgc0fYu6r/dcT8XAWq4U2Z7ShzRltOO/V89g6eysp01LY+O1GstOyCY0OdfVNX5NOQFgAwa20kHdD43EnT7ib1r0RObWts7by+cWfU3ykmPbD2nPFjCvw9q/lws4wYM4AyFoCncdDr5dq9/VE6rDCQ4XsWbmHtkPautr+0/c/7F6xm+gzo4m/Op7YS2Lxb+xf/pPUA/p97uRRM3Yi4lk2fruRry7/CnuhnU4jOnHZl5fh5euGHxt7f3UWdTY/iLu/9l9PpA7zCfQpUdQVHS7C6m0FwznbnvprKjPvnEnH8zsSf3U8nS7sVPt/nIlpVNiJSJk2/7CZLy/9Ekexg7hL47jk00vccwFzw4Dko2tNtr8N/HXMkEhVeDfy5qZFN3Fw+0FSpqeQPC2ZzORMNn6zkY3fbKTL5V249PNLzY4ptUSFnYiUqWWPloREhRCZFMmoD0dh9XLTSfSZ82HfQrD6QNwD7nlNkXootG0oAx8ayMCHBrI3eS8pnzmLvNhLj1/y7MC2Ayx5eQldr+5KZL9ILRlSD6iwE5EyBbcK5qbFN9GoWaNSa2XVKtds3a3QSNdnFqkJLbq2oEXXFpz19FkllkZJnpbMitdXsOL1FYRGhxJ/VTwJ1yTQPE5noddVHr+OnYi4z4o3V5D8WbLrdmCLQPcWdZkLIHMeWL0h7kH3va5IA2GxWkr8n247pC0J1ybgHeDNwdSDLJq4iDe7vMlb3d/i9+d+pyC37HVkxXNpxk5EAFjy0hJ+vvdnLDYLzeOa07Jb+VdwqTUpTzn/bXczBLR2/+uLNDBRA6OIGhjFhYcvZNP3m0iZlsKWn7aw98+9HPjrAH3u7uPq6yh2uO+QDKk2FXYiwoJnFvDbo78BMOCBAbRIaOH+EPsWQ8YvYPGCLg+5//VFGjDvRt7EXxFP/BXx5O/PZ/3/1nPkwBHX2bOGYfBOr3cIbh1M16u70vmizvgE+picWsqiwk6kATMMg9/+9RsLn14IwJAnh3DGo2eYcwD1satMtLsBAtq4//VFBAD/Jv70urVXibasjVnsXbuXvWv3suXHLXg38qbzSOc1a9sPbe+eM+alUlTYiTRQhmHwy4O/sPj5xQCc8+w5DHhggDlhspZB+myw2KDLw+ZkEJFyNY9tzpgNY0j+LJnkT5M58NcBUj5LIeWzFPyb+DP0xaF0v6H7KZ/DYXeQtjCN3PRcgsKDiBoU5d5jeBsIFXYiDdTGbza6irrhU4bT9599zQtzbLYuejQERpuXQ0TK1SymGWc+cSZD/j2EPSv3kDwtmXXT13Eo4xCBLQNd/bLTssnfn0+Lbi1cs/8bvt7ArLGzyNmV4+oXHBnM8CnDib0kttRrSfXpkmK6BIk0UIZh8NPdP9EioQW9butV8QNqy98rYXaic7buwk0Q1N68LCJSJQ67g+3zttPmjDbYvJ27Y+c8MIfFzy+mWWwzul7TFf8m/swcMxNOrjaOHvFx+VeX10hxp9/nTpqxE2lAHMUOHHYHXr5eWCwWzn/9fLMjHZ+ta3uNijqROsZqs9Lu7HYl2oryi7D52sjakOU6KatMBmCBWeNm0XlkZ+2WrSF6F0UaCHuRnf9d/T/ntV+L7GbHcdq/BnZ/DxYrdHnE7DQiUgPOf+187tt7HyM/GEl4rwouCWhAzs4c0hamuSdcA6DCTqQBKC4o5svLvmT9l+vZ8tMW0lelmx3J6dhsXZurILiTuVlEpMb4hfjR/YbuJN2bVKn+uem5tZyo4dCuWJF6rii/iC8u+YKts7Zi87VxxddXENnPA44/OfAn7PoGsGi2TqSeCgoPqtF+UjHN2InUY4V5hXx24WdsnbUV70beXP3j1XQ8v6PZsZxSnnb+2+YKCNFZcSL1UdSgKIIjg10nSpRigeDWwUQNinJrrvpMhZ1IPVWQU8Cnwz8l9ddUfAJ9uGbWNaUOcjbNwRTY+ZVzu8uj5mYRkVpjtVkZPmW488bJxd3R28NfGa4TJ2qQ3kmReurvLX+TviYd3xBfrptzHW0GedDVHI7N1rW+FEK7mJtFRGpV7CWxXP7V5QS3Ci7RHhwZXGNLnchxOsZOpJ6K6BXB1T9cjW+wL+E9KzgzzZ2y10PaF87t+MfMzSIibhF7SSydR3bWlSfcQIWdSD1yaO8h8jLzaNG1BQBth7Q1N1BZUp4BDIi8GBonmJ1GRNzEarN65s+kekalskg9kbM7hw8Hf8h/z/ovmesyzY5TtpxNkDbdud31X+ZmERGph1TYidQDB3cc5MPBH/L3pr/x8vfCy9dDJ+NTngHDAa0ugsbdzU4jIlLveOhPfxEpj8PuKHGcSmBEIJ+c+wnZadk0bteY0XNHE9o21OyYpeVuhR2fOrc1WyciUitU2InUIRu+3sCssbPI2ZXjarNYLRgOg6admjJ67mjnmlGeaN3R2bqIC6BJL7PTiIjUSyrsROqIDV9v4ItLv3BeOPsEhsPZMOCBAZ5b1B3aBqkfO7fjNVsnIlJbdIydSB3gsDuYNXZWqaLOxQLznpiHw+5wa65KWzcRDDuED4dmfcxOIyJSb6mwE6kD0hamldj9WooBOTtzSFuY5r5QlXVoO2z7yLmt2ToRkVqlwk6kDshNz63Rfm61fjIYxdDyHGieZHYaEZF6TYWdSB0QFB5Uo/3cJi8Ntr3v3I5/3NwsIiINgAo7kTogalDUqU+MsEBw62CiBkW5L1RlrH8WHEXQ4kwIG2h2GhGRek+FnUgdYLVZ6f9A/7LvtDj/Gf7KcM+67uLhXfDXf5zbmq0TEXELD/otICKnsu3nbQB4+ZVcpSg4MpjLv7qc2EtizYhVvvXPgaMQws6AFoPNTiMi0iBoHTuROuCvOX+x+YfNWL2s3Lb6NvL25rmuPBE1KMqzZuoA8tNh6zvObZ0JKyLiNirsRDyco9jBz/f8DEDiXYk0j21O89jmJqeqwPrnwFEAzQdAi7PMTiMi0mB42J/5InKy/X/t5/Dfh/Fv4s/gf9WBXZr5GbD1Led2/L/AYjE3j4hIA6IZOxEP16xzM+7efDeZKZn4N/Y3O07FNrwA9iPQtB+0PNfsNCIiDYpm7ETqAJ9AHyL7RZodo2JHMmHLVOd2V83WiYi4mwo7EQ+1/6/9/PnxnxiO8i4Q64E2vgT2w9Ckt/O6sCIi4lYq7EQ81Jz75/DN6G+Yfe9ss6NUzpEs2Py6c7vr45qtExExgQo7EQ+0fd52Ns7YiMVmoectPc2OUzmbXobiPGjcEyIuMDuNiEiDpMJOxMM47A5mj3fO0vX6v16EdQkzOVElFOyHTa85t3VsnYiIaVTYiXiYPz/6k4w/MvAN8eXMJ840O07lbHoFinMhtBu0usjsNCIiDZYKOxEPUpBbwNyH5wIw+F+DadSskcmJKqHwIGya4tyOf0yzdSJS50yePBmLxcK4ceNO2e/LL78kJiYGPz8/unbtysyZM90TsApU2Il4kEWTF5G3N48mHZrQ564+ZsepnE1ToCgHQuKh9cVmpxERqZIVK1bw9ttvk5CQcMp+ixcv5qqrruLmm29mzZo1jBo1ilGjRpGSkuKmpJWjwk7Eg3Q8ryPhvcIZ+uJQbD42s+NUrDAbNr7i3I5/DCz6kSIidcehQ4e45pprePfdd2ncuPEp+06ZMoXhw4dz//33Exsby1NPPUXPnj15/fXX3ZS2cvRTWMSDRA2M4tblt9JpRCezo1TO5teg6CCExEHUpWanERGpkjFjxnDBBRdwzjnnVNh3yZIlpfoNGzaMJUuW1Fa8atElxUQ8gGEYWI4em2ax1pFj1IpynQsSA3R5VLN1IuIRcnNzycnJcd329fXF19e3VL/p06ezevVqVqxYUannzcjIoEWLFiXaWrRoQUZGxukFrmH6SSxiMsNh8NGZH/Hb479RmFdodpzK2/wGFB6A4M4QdbnZaUREAIiLiyMkJMT1NWnSpFJ9du7cydixY/n000/x8/MzIWXt0YydiMnWfrqWHfN3kL46ncQ7E/EJ8DE7UsWKDsHGF5zbXR4Fax04HlBEGoT169fTqlUr1+2yZutWrVpFZmYmPXseXwDebrezYMECXn/9dQoKCrDZSv5ca9myJXv37i3RtnfvXlq2bFnDIzg9KuxETFSYV8jch5zLmwx6eBCBLQJNTlRJW6ZCwd8Q2AHaXGl2GhERl6CgIIKDg0/Z5+yzzyY5OblE24033khMTAwPPvhgqaIOICkpiblz55ZYEmXOnDkkJSXVSO6aosJOxESLn19M7p5cQtuG0m9cP7PjVE5xHmx43rkd/yhY9WNEROqWoKAg4uPjS7QFBATQtGlTV/vo0aNp1aqVa1fu2LFjGTx4MC+++CIXXHAB06dPZ+XKlbzzzjtuz38qOsZOxCQ5u3L4/bnfATjnuXPw8qsjBdKWt6FgHwS2g7bXmJ1GRKRWpKWlkZ6e7rrdv39/pk2bxjvvvEO3bt346quv+Oabb0oViGarI79JROqfuRPmUpxfTNTAKOIujTM7TuUU58OG55zbXR7RbJ2I1Bvz5s075W2Ayy67jMsuu8w9gapJM3YiJshNz2X9V+sBGPbyMNdSJx5v6ztwZC8EtIHo68xOIyIiJ9Gf2yImCAoPYsyGMWz5aQsRvSPMjlM59iOw4VnndpeHweptbh4RESnFo2bsJk2aRGJiIkFBQYSFhTFq1Cg2bdp0yscUFRXx5JNP0r59e/z8/OjWrRuzZs1yU2KR6gttG0riHYlmx6i8rf+B/HRo1BqibzA7jYiIlMGjCrv58+czZswYli5dypw5cygqKmLo0KHk5eWV+5hHH32Ut99+m9dee43169dz++23c/HFF7NmzRo3JhepnKL8InYv3212jKqzF8D6yc7tLhPAVgfW2hMRaYAshmEYZocoz759+wgLC2P+/PmcccYZZfaJiIjgkUceYcyYMa62f/zjH/j7+/PJJ59U+Bq7du2idevW7Ny5k8jIyBrLLlKWBc8s4LdHfyPpviSGPj/U7DiVt2UqrLgT/FvBRX+BrfSCnyIiZtLvcyePmrE7WXZ2NgBNmjQpt09BQUGpy4H4+/uzaNGiWs0mUlW56bksmuT8XIb3CDc5TRXYC2Hd0UvyxD2kok5ExIN57MkTDoeDcePGMWDAgFOuETNs2DBeeuklzjjjDNq3b8/cuXP5+uuvsdvtZfYvKCigoKDAdTs3NxeA4uJiiouLAbBarVitVhwOBw6Hw9X3WLvdbufEic7y2m02GxaLxfW8J7YDpTKW1+7l5YVhGCXaLRYLNputVMby2jUm88f0y4RfKMorolXfVsReHgtQN8a07UMsh3di+IVjb3sDFru9Xn+fNCaNSWOqm2Py4B2QbuWxhd2YMWNISUmpcOZtypQp3HrrrcTExGCxWGjfvj033ngj77//fpn9J02axBNPPFGqfe3atWRkZADQvHlz2rdvT2pqKvv27XP1iYyMJDIyks2bN7tmEwHatWtHWFgYKSkp5Ofnu9pjYmIIDQ1lzZo1JT70CQkJ+Pj4sHLlyhIZevfuTWFhIWvXrnW12Ww2EhMTyc7OZuPGja52f39/unXrRlZWFtu2bXO1h4SEEBsby549e9i1a5erXWMyd0wbft3A2v86+0feEsmWLVvqxph6dceR8gw2YEfAFWSsSanX3yeNSWPSmOrumIKCghAPPcburrvu4ttvv2XBggVER0dX6jFHjhzh77//JiIigoceeogffviBdevWlep38ozd7t27iYuLIzU11bVPXn8RaUw1OSar1cqHQz4kbUEaXa7swqiPR9WdMe34Lyy7GcOvBfbzt4CXf739PmlMGpPGVLfHlJ6eTlRUVIM/xs6jZuwMw+Duu+9mxowZzJs3r9JFHYCfnx+tWrWiqKiI//3vf1x++eVl9vP19cXX9/gxQjk5OYDzw+nlVfLtOPbhOVlZFwc+VfvJz1uddovFUmZ7eRmr2q4x1d6YNny9gbQFaXj5eXHus+eWuN+jx+QognXPAGCJfQAvv5J/Dde379OpMmpMGlN5GavarjHV3pjqzELvtcyjCrsxY8Ywbdo0vv32W4KCgly7RkNCQvD39wdKX5R32bJl7N69m+7du7N7927+/e9/43A4eOCBB0wbh8iJDMMgsGUgPW/tSUhUiNlxKm/7NDi0DXybQ8f/MzuNiIhUgkcVdlOnTgVgyJAhJdo/+OADbrjhBsB5Ud4TK/ojR47w6KOPsm3bNgIDAzn//PP5+OOPCQ0NdVNqkVOL+0cc7Ye2x2rz6JPQS3IUu2briL0PvALMzSMiIpXiUYVdZQ73O/mivIMHD2b9+vW1lEikZvgG1bElQnZMh9wt4NsUOt5pdhoREamkOjSFIFK3/Pb4b6z9dC2Gw+POTzo1hx3WPe3cjrkXvAPNzSMiIpXmUTN2IvXF3rV7Wfj0QgyHQbPOzYjoHWF2pMpL+wJyNoFPE+h0l9lpRESkCjRjJ1LDDMNg9j2zMRwGcZfG1a2iznBAylPO7Zjx4K11oURE6hIVdiI1bPMPm0mdm4rNx8Y5z51jdpyq2fk/yNkA3qHQ6W6z04iISBWpsBOpQfZCOz/f+zMA/cb3o3F0Y5MTVYHhgOQnndsx48CnDi3NIiIigAo7kRq14s0V7N+yn4CwAAY9PMjsOFWz6xvITgHvYOj8T7PTiIhINaiwE6khhYcKmf/EfADOfPpMfIPr0BInJ87WdR4LPnVoplFERFxU2InUEJ9AH6745goSrk2gx009zI5TNbu/h4N/glcgdB5ndhoREakmLXciUoPaDm5L28FtzY5RNYZxfLau093g28TcPCIiUm2asROpAYezDpsdofr2/AgHVjsvGxZzj9lpRETkNKiwEzlNW2dt5eWol1k4caHZUaruxNm6jmPAr5m5eURE5LSosBM5DY5iBz/f+zPF+cUc/rsOztqlz4L9K8DWCGLvNTuNiIicJhV2Iqdh1Tur2Ld+H/5N/Rn82GCz41SNYUDyE87tjneAX5i5eURE5LSpsBOppvwD+fz2r98AOPPJM/EL9TM5URVl/AJ/LwObH8TeZ3YaERGpASrsRKppwVMLyP87n+Zxzel1Wy+z41SNYUDK0dm6DreDf0tz84iISI1QYSdSDX9v/pvlry0HYOhLQ7F61bH/Snt/g32/g9UXYu83O42IiNSQOvbbSMQz7F6+G4vVQsfzO9JhWAez41Sda7buNmgUYW4WERGpMVqgWKQaEq5NoHX/1mbHqJ698yFzAVh9IO4Bs9OIiEgNUmEnUk2N29XR66kem61rfzM0ijQ3i4iI1CjtihWpgk3fb2L38t1mx6i+zIXO4+us3hD3kNlpRESkhqmwE6mkgpwCvr/le/7T9z9snbXV7DjVk/KU8992N0JAlLlZRESkxqmwE6mkhRMXkpeZR9POTYk+O9rsOFW3bwlkzAGLF8RNMDuNiIjUAhV2IpVwIPUAS19eCsDQF4Zi87aZnKgaUo5eE7bd9RDY1tQoIiJSO1TYiVTCLw/8gr3QTrtz29Hxgo5mx6m6rOXO68JabNDlYbPTiIhILVFhJ1KBHQt3sP6r9VisFoa9NAyLxWJ2pKo7NlsXfR0EtjM3i4iI1BoVdiKnYDgMZo+fDUDP23oSFh9mcqJq2L8K9vwIFivEabZORKQ+U2EnUoHed/SmaaemnPnkmWZHqZ7ko7N1ba6B4Dq4G1lERCpNCxSLnILFaqHnzT3pcWMPLNY6uAt2/xrY/R1ggfhHzE4jIiK1TDN2IuUwHIZru04WdXB83bo2V0JwZ3OziIhIrVNhJ1KG7LRs3oh7g+TPkjEMo+IHeKIDa2HXDJyzdY+anUZERNxAhZ1IGX556Bf+3vQ3q95eZXaU6lv3tPPfqMsgJM7cLCIi4hYq7EROsnPJTlI+SwELDHu5ji5vcnAdpH3l3I5/zNwsIiLiNirsRE5w4vIm3W/sTniPcJMTVdO6pwEDWv8DQuPNTiMiIm6iwk7kBCnTU9i9bDfeAd6c9fRZZsepnuwNsONz57Zm60REGhQVdiJHFR0u4pcHfwFg0MODCAoPMjlRNa17BjAgchQ07mZ2GhERcSMVdiJHbfp+Ezm7cghpE0K/8f3MjlM9OZthx2fObc3WiYg0OFqgWOSo+CviCWwRiL3Qjre/t9lxqmfdM2A4oNUIaNLT7DQiIuJmKuxETtB2SFuzI1Rf7lbY/qlzW7N1IiINknbFSoP39+a/yd2Ta3aM07duIhh2CD8PmiaanUZEREygwk4aNMMw+Pamb3mt42ts/Haj2XGq71AqpP7Xud31X+ZmERER06iwkwZt/Vfr2fn7TgzDIKJXhNlxqm/dJOdsXcuh0KyOnvghIiKnTYWdNFjFR4r55QHn8iYDHhhAcGSwyYmqKW8HbPvAud31cXOziIiIqVTYSYO19JWlHNx+kKBWQfS/v7/Zcapv3WQwiqHF2dC8Do9DREROmwo7aZAOZRxi4cSFAJw96Wx8AnxMTlRNeTth23vObR1bJyLS4Gm5E2mQfn3sVwpzC4lIjCDhmgSz41ROXhoUZJVsW/csOIqgcS8IaGtKLBER8Rwq7KTBMQyDRs0aYfOxMezlYVisFrMjVSwvDb7vDI4jZd9/YJXz/hGbICDKvdlERMRjqLCTBsdisXDOpHNIGp9EQFiA2XEqpyCr/KLuGMcRZz8VdiIiDZaOsZMGq84UdSIiIpWkwk4ajOKCYr6+9mv2rNxjdhQREZFaocJOGozlry8n+dNkpo+cjr3QbnYcERGRGqfCThqEvH15LHhqAQBnPnUmNh+byYlERERqngo7aRDmPT6PguwCWnZvSbfru5kdR0REpFaosJN6LzMlk1VvrwJg2MvDsNrq4Me+4IDZCUREpA6og7/hRCrPMAx+vvdnDIdBzMUxtB3S1uxIVWcvgD8fqrif1Q98m9V+HhER8Vhax07qtdRfU/nr57+wels59/lzzY5TdYYBy26F/SvBKxD6fQCB7cru69tMa9iJiDRwKuykXms7pC0Xvn0hh/8+TJP2TcyOU3XrJsL2j8Fig0FfQ3gdLE5FRMRtPGpX7KRJk0hMTCQoKIiwsDBGjRrFpk2bKnzcK6+8QufOnfH396d169aMHz+eI0cqWKVfGgSrzUqv23oxaMIgs6NUXdqXsPZR53bv11XUiYhIhTyqsJs/fz5jxoxh6dKlzJkzh6KiIoYOHUpeXl65j5k2bRoPPfQQjz/+OBs2bOC9997j888/5+GHH3ZjcvE0BbkFFOUXmR2j+rKWw5LRzu3O46Dj7abGERGRusGjdsXOmjWrxO0PP/yQsLAwVq1axRlnnFHmYxYvXsyAAQO4+uqrAWjbti1XXXUVy5Ytq/W84rl+ffRXNn27iQvfvpAOwzqYHadq8tJgwUVgPwIRF0CPF8xOJCIidYRHFXYny87OBqBJk/KPjerfvz+ffPIJy5cvp0+fPmzbto2ZM2dy3XXXldm/oKCAgoIC1+3c3FwAiouLKS4uBsBqtWK1WnE4HDgcDlffY+12ux3DMCpst9lsWCwW1/Oe2A5gt9sr1e7l5YVhGCXaLRYLNputVMby2hvSmPZv2c/KN1fiKHbgMBylvq8ePSZ7Htb5I+DIXoyQrtj7fgwOAyuOevd9qo+fPY1JY9KYzBvTidsNmccWdg6Hg3HjxjFgwADi4+PL7Xf11VeTlZXFwIEDMQyD4uJibr/99nJ3xU6aNIknnniiVPvatWvJyMgAoHnz5rRv357U1FT27dvn6hMZGUlkZCSbN292FZ0A7dq1IywsjJSUFPLz813tMTExhIaGsmbNmhIf+oSEBHx8fFi5cmWJDL1796awsJC1a9e62mw2G4mJiWRnZ7Nx40ZXu7+/P926dSMrK4tt27a52kNCQoiNjWXPnj3s2rXL1d6QxrT+sfU4ih2EDwlnf+h+9q/cXzfGZNjpeuAxAg6updi7GWtDnqTwz0319vukMWlMGpPGVJNjCgoKQsBieGiJe8cdd/DTTz+xaNEiIiMjy+03b948rrzySp5++mn69u3L1q1bGTt2LLfeeiuPPfZYqf4nz9jt3r2buLg4UlNTXa+jv4jq7pi2/bKNz877DKuXlf/78/9o0qlJqf6eOibrmnuxbnkVbH44zvoNR+PeFWb39DGdmLG+f/Y0Jo1JYzJ3TOnp6URFRbFz585T1g31nlFLiouLjY8++qhajx0zZowRGRlpbNu2rcK+AwcONO67774SbR9//LHh7+9v2O32Ch+/c+dOAzB27txZraziOexFduPN+DeNf/Nv46exP5kdp2o2TzWMT3F+7fjC7DQiInVOVX+fv/nmm0bXrl2NoKAgIygoyOjXr58xc+bMUz7m5ZdfNjp16mT4+fkZkZGRxrhx44z8/PyaiF9javys2Pz8fF599VXat2/PjTfeWNUik7vuuosZM2bw66+/Eh0dXeFjDh8+jNVachjH/rIwPHMyUmrJ6v+sJjMlE/8m/gz+12Cz41Re+hxYeZdzO+FpiLrM3DwiIg1AZGQkkydPZtWqVaxcuZKzzjqLkSNHsm7dujL715VVOKpc2L333nvEx8fj7+9PREQEY8eOpaCgAMMweOWVV2jTpg3jxo0jODiYDz74oErPPWbMGD755BOmTZtGUFAQGRkZZGRklNiHPnr0aCZMmOC6PWLECKZOncr06dNJTU1lzpw5PPbYY4wYMcJV4EnDsGflHgAG/3sw/k38TU5TSdkbYNFlYNih7XXQxbN+QIiI1FcjRozg/PPPp2PHjnTq1IlnnnmGwMBAli5dWmb/E1fhaNu2LUOHDuWqq65i+fLlbk5+alU6eeLjjz/m1ltvJTAwkK5du7Jr1y5ef/118vLyOHDgADNmzGDw4ME8+OCDDB8+vMphpk6dCsCQIUNKtH/wwQfccMMNAKSlpZWYoXv00UexWCw8+uij7N69m+bNmzNixAieeeaZKr++1G0X/eciuo3uRmRSHTm24sg+mHcBFGVD84HQ912wWMxOJSJSp+Xm5pKTk+O67evri6+v7ykfY7fb+fLLL8nLyyMpKanMPlVdhaMshmFgqe2f81XZb9unTx8jJibG2Ldvn2EYzuPorrvuOsNisRhNmjQxfvzxxxrfV1zbdIydmKL4iGH8PMB5TN237Qwjf5/ZiURE6rRjv89P/nr88cfLfczatWuNgIAAw2azGSEhIRXWMVOmTDG8vb0NLy8vAzBuv/32KmUMDAys9VqpSrti161bxy233EKzZs0A57FsDz74IOCcOTv//PNrsOQUqZzlbywnNz3X7BiVZxiw7BbY9zt4h8DgH8CvmdmpRETqhfXr15Odne36OvHwrZN17tyZP/74g2XLlnHHHXdw/fXXs379+jL7zps3j4kTJ/Lmm2+yevVqvv76a3788UeeeuqpSmfLy8vj4MGDVR1SlVRpV+zhw4cJDw8v0dayZUuAU641J1Jbts/bzk93/cSvj/zKuO3j8Av1MztSxdY9A9s/AYsNBn0FIbFmJxIRqTeCgoIIDg6uVF8fHx86dHBenahXr16sWLGCKVOm8Pbbb5fq+9hjj3Hddddxyy23ANC1a1fy8vK47bbbeOSRR0qdyFkdWVlZ5Ofn07p162o/R5VTlLdv2MvLY9c6lnrKYXcwe/xsALpe07VuFHU7voC1R9dX7P0GtDzH3DwiIuLicDhKrHV7oppahWPt2rVs3bq1zMd8//33tG3btvKBy1DlauyFF17gs88+c90uKnJeaP2RRx5x7aI9xmKx8O23355WQJHy/PnRn2T8kYFviC9nPnGm2XEqlrUMll7v3O48Hjr+n7l5REQasAkTJnDeeecRFRVFbm4u06ZNY968ecye7ZwwGD16NK1atWLSpEmA8yzal156iR49erguiFCdVTief/55nn/+efz8/IiJiSE+Pp6YmBiaN2/O+++/f9qLK1epsIuKimL//v3s37+/RHubNm1IT08nPT29RHutn/khDVZBbgFzH54LwOB/DaZRs0YmJ6pA3g5YMBLsRyDiQujxvNmJREQatMzMTEaPHk16ejohISEkJCQwe/Zszj33XKD2VuH417/+RXR0NCkpKaSkpPDbb7/x8ccfA84ZwDfeeOO0xuWxlxRzl127dtG6dWtdgqSOmfvwXBZNWkSTDk24c92d2Hw8eM3CohyYMxAOJkNoApy7CLx1TUMRkZpUF36fW61WPvnkE66++uoS7Xl5eezdu5dmzZpV+vjA8tTYgXEbN27kyy+/JD09nZiYGG644YbTDidSloPbD7LkpSUAnPvCuZ5d1Dns8PtVzqLOr6XzDFgVdSIicoKAgADatWtXI89VpcLu9ddf59VXX2Xx4sUljqf7/vvvueyyyygsLHS1vfrqqyxdurTUcXcip8sv1I8+d/Vh37p9dL6os9lxTm3NvbBnJtj84IxvIaD6ZzqJiEjdNnXqVGJja3clhCqdFfvdd9/Rvn37EsVacXExt9xyCzabjQ8++IDk5GQmT57Mjh07dPUHqRV+oX4MfWEoV/94tWcfx7llKmya4txO+hia9TE3j4iImOr//u//6NGjR62+RpUKu/Xr19OvX78Sbb/99hv79u1j/PjxXH/99XTp0oUHHniAyy+/nJkzZ9ZoWGnYDMMocXq4xerBRV36z7Dybud2t2cg6lJz84iISINQpcLu77//LrVo3ty5c7FYLFx88cUl2gcMGEBaWtrpJxQ5au0na/lw8Iekr06vuLOZstfDosvAsEP0aIgrf9VzERGRmlSlwq5FixZkZGSUaFu4cCGNGjWiW7duJdp9fHzw8fE5/YQiQGFeIXMnzCVtYRp/zfnL7DjlO7IP5l3oPBO2+SDo8w548u5iERGpV6pU2PXu3ZuPPvqI3FzndTnXrVvH8uXLGTZsWKkrT2zcuNFjTzeWumfx84vJ3Z1LaHQo/cb2q/gBZrAfgQWjIC8VAtvDoK/B5mt2KhERaUCqdFbs448/TmJiIh07dqRLly6sWrUKi8VS5gV2Z8yYwVlnnVVjQaXhytmVw+/P/Q7Auc+di5efB16+zjBg2a2QtRi8Q5zLmvjpjHAREXGvKs3Yde3alV9//ZVevXqxZ88e+vXrx8yZM+nVq1eJfvPmzaNRo0ZcdtllNRpWGqa5E+ZSnF9M1KAoYv9Ru6eJV1vK07D9E7DYYNBXEBJjdiIREWmAqjz10b9/f3788cdT9hkyZAjJycnVDiVyzO7lu1n7yVqwwLCXh3nm8iY7Pofkfzm3E9+ElueYm0dERBqsKs3YibjbyqkrAeg2uhsRvSJMTlOGrGWw9Abndsw90OE2U+OIiEjD5oEHK4kcN+LdEbTq14rOIzzwChN5O2DBRc6TJlqNgO7PmZ1IREQaOBV24tGsXlZ6/19vs2OUVpTjXNbkSCaEdoP+08DqwdesFRGRBkG7YsUj7Vyyk+IjxWbHKJujGBZdCdkp4NcSBn8P3oFmpxIREVFhJ54nNz2Xj8/9mDdi3yB7Z7bZcUpbfS+k/wQ2fxj8HQS0rvgxIiIibqDCTjzOr4/8SlFeEQEtAgiODDY7Tkmb34TNrzq3k/4LTRPNzSMiInICHWMnHsFhd5C2MI2di3fyxwd/ADD8leGetbzJntmw6p/O7W4TIepSc/OIiIicRIWdmG7D1xuYNXYWObtyXG1e/l7k7sk1MdVJDq6D3y8Hww7R10PcQ2YnEhERKUW7YsVUG77ewBeXflGiqAMoPlLMF5d+wYavN5iU7ARHMmH+hc4zYZsPgj5vgyfNJIqIiBylwk5M47A7mDV2Fhhl3Hm0bda4WTjsDrfmKsF+BBZcDHnbIbA9DPoabL7m5RERETkFFXZimrSFaaVm6kowIGdnDmkL09wXqsTrG7D0ZshaDN6hMPgH8GtmThYREZFKUGEnpslNr9wxdJXtV+NSnoId08DiBYO+gpAYc3KIiIhUkgo7MU1QeFCN9qtR26dD8uPO7cQ3oeXZ7s8gIiJSRSrsxDRRg6JOvU6dBYJbBxM1KMp9oQCylsLSG5zbMfdCh1vd+/oiIiLVpMJOTGO1WYm/Kr7sO4+edDr8leFYbW78mB7aDgtGgqMAWl0E3Z9132uLiIicJhV2YhrDMEj9NRUAnyCfEvcFRwZz+VeXE3tJrPsCFeXA/BHO5U0ad4f+n4LV5r7XFxEROU1aoFhMs+2XbaSvSsfL34u7Nt/F3xv/Jjc9l6DwIKIGRbl3ps5RDIuuhOwU8A+Hwd+Dd6D7Xl9ERKQGqLAT0yyatAiAXrf1IqhlEEEtTThJ4pjV90D6T2DzhzO+g0aR5mURERGpJu2KFVPsWrqL7b9tx+ptJeneJHPDbH4DNr/m3O7/CTTtbW4eERGRalJhJ6Y4NluXcG0CIa1DzAuyZxas+qdzu9skaH2JeVlEREROkwo7cTvDYdC4Q2N8gnwY8OAA84IcXAe/XwGGA6Kvh7gHzcsiIiJSA3SMnbidxWph2IvDOPPJM/EJ8Kn4AbXhSCbMv9B5JmzYGdDnHbBYzMkiIiJSQzRjJ6YxraizH4EFoyBvOwR2gEFfg82kLCIiIjVIhZ241cq3V5K2KM28AIYBS2+CrCXgHQpDfgDfpublERERqUEq7MRtDmUcYtbYWXww6AP2rNxjToiUJ2HHZ2DxgkH/g+DO5uQQERGpBSrsxG2WvLwEe4GdyKRIwnuFuz/A9s8g+d/O7cSp0PIs92cQERGpRSrsxC2OHDzCyqkrARg4YSAWd5+osG8JLL3RuR17H3S4xb2vLyIi4gYq7MQtlr+xnMLcQsLiw+h0QSf3vvih7bBgJDgKoNVF0G2ye19fRETETVTYSa0rOlzEsleWAUdn66xunK0rzHYua1KwDxp3h/6fgtXmvtcXERFxIxV2UutW/2c1h7MO07hdY7pc3sV9L+woht+vhOx14B8Og78H70D3vb6IiIibqbCTWhcQFkBodCj97++P1cuNH7nV4yF9Ftj8nUVdo0j3vbaIiIgJdOUJqXXxV8YTd2kchmG470U3vQ6bX3du9/8EmvRy32uLiIiYRIWduIVbZ+r2zILVY53b3SdD60vc99oiIiIm0q5YqTV/zfmLNe+vwV5od9+LHkyBRZeD4YB2N0LsA+57bREREZNpxk5qhWEYzJ0wl/RV6RzKOMSghwfV/ovm73WeAVucC2GDIfEtcPd6eSIiIibSjJ3Uim2/bCN9VTpe/l70vLVn7b+g/QgsGAV5OyCoo/NyYTaf2n9dERERD6LCTmrFokmLAOh1Wy8CmgfU7osZBiy9Cf5eCj6NYfAP4Nu0dl9TRETEA6mwkxq3a+kutv+2Hau3laR7k2r/BZOfgB2fgcXLOVMX7OYrW4iIiHgIFXZS447N1iVcm0BI65DafbHt0yDlCed2n7egxZm1+3oiIiIezKMKu0mTJpGYmEhQUBBhYWGMGjWKTZs2nfIxQ4YMwWKxlPq64IIL3JRaTpSZksmm7zaBBQY8OKB2X2zfYlh6o3M79n5of3Ptvp6IiIiH86izYufPn8+YMWNITEykuLiYhx9+mKFDh7J+/XoCAso+Tuvrr7+msLDQdfvvv/+mW7duXHbZZe6KLScwDIN257bDL8SPZp2b1d4LHUp1nizhKITIkdBtUu29loiISB3hUYXdrFmzStz+8MMPCQsLY9WqVZxxxhllPqZJkyYlbk+fPp1GjRqpsDNJi64tuO7n6yguKK69FynMdi5rUrAPGveA/p+C1VZ7ryciIlJHeNSu2JNlZ2cDpYu3U3nvvfe48sory53hE/fw8q2lvxkcxfD7FZC9HvwjnNeA9dL3WkREBDxsxu5EDoeDcePGMWDAAOLj4yv1mOXLl5OSksJ7771Xbp+CggIKCgpct3NzcwEoLi6muNg5y2S1WrFarTgcDhwOh6vvsXa73V7iuqfltdtsNiwWi+t5T2wHsNvtlWr38vLCMIwS7RaLBZvNVipjee21Pab8ffksfWUpiXcnEtgysNbGxMp/Yk2fjWFrhDHoG6yNWun7pDFpTBqTxqQxufd65B7MYwu7MWPGkJKSwqJFiyr9mPfee4+uXbvSp0+fcvtMmjSJJ554olT72rVrycjIAKB58+a0b9+e1NRU9u3b5+oTGRlJZGQkmzdvds0mArRr146wsDBSUlLIz893tcfExBAaGsqaNWtKfOgTEhLw8fFh5cqVJTL07t2bwsJC1q5d62qz2WwkJiaSnZ3Nxo0bXe3+/v5069aNrKwstm3b5moPCQkhNjaWPXv2sGvXLld7bY/pwJcHWP7ScjbM2UCfN4+//zU5puwVz9D4r6kYWNjc7DG8spvQvhn6PmlMGpPGpDFpTAQFBSFgMTywxL3rrrv49ttvWbBgAdHR0ZV6TF5eHhERETz55JOMHTu23H4nz9jt3r2buLg4UlNTiYyMBPQXUVXHdOTgEV5r9xqFuYVcNuMyOl3YqUT/GhnT3p8x5l+IxXBgT5iEEXOfvk8ak8akMWlMGpOrPT09naioKHbu3On6fd4QedSMnWEY3H333cyYMYN58+ZVuqgD+PLLLykoKODaa689ZT9fX198fX1dt3NycgDnh9PLq+TbcezDc7JjH/DKtp/8vNVpt1gsZbaXl7Gq7aczptVvr6Ywt5Cw+DBiL4rFYi19fdbTGtPBFFh0BRbDAe1uwtblwRLXgNX3SWOqarvGpDGBxlRexqq2e8qYLLo2OOBhJ0+MGTOGTz75hGnTphEUFERGRgYZGRklplpHjx7NhAkTSj32vffeY9SoUTRtqktJuVPR4SKWvbIMgAEPDSizqDst+XudZ8AW50LYEEicWqKoExERkeM8asZu6tSpgHPR4RN98MEH3HDDDQCkpaWVqug3bdrEokWL+Pnnn90RU06w+j+rOZx1mNDoUOKvqNxJLpVWnA8LRkLeDgjq6LxcmM2nZl9DRESkHvGowq4yh/vNmzevVFvnzp11NowJ7IV2Fr+wGIABDwzA6lWDE8CG4byqxN/LwKcxDP4BfCu/7I2IiEhD5FGFndQtRflFdL6oM1tmbqH7Dd2r9yR5aVCQVbp9y1uQ9jlgc87UBXcq3UdERERKUGEn1eYX4sf5r5+PvciOzbsaV37IS4PvO4PjSPl9LFYIbF/9kCIiIg2IR508IXVTtYo6cM7UnaqoAzCKyp7RExERkVJU2EmVGYbBz/f/zK5luyruLCIiIm6jwk6qbNsv21jywhI+OvMjjhysYMZNRERE3EaFnVTZoknOy7z1vLUnfqF+JqcRERGRY1TYSZXsWrqL7b9tx+plpf99/c2OIyIiIidQYSdVcmy2LuG6BEJah5icRkRERE6kwk4qLTMlk03fbQILDHhwgNlxRERE5CQq7KTSFk12ztbFXhJLs87NTv8JczZV3MfqB7418FoiIiINgBYolkprPaA1aQvTGDhh4Ok/WXEe/PmIczvyHxD/cNn9fJtBQNTpv56IiEgDoMJOKi3xjkR63dqrZq4J+8fDkJcKjVpD0vvgHXz6zykiItLAaVesVEmNFHWZi2Dza87tPu+qqBMREakhKuykQstfX86fH/+Jvch++k9WnA/LbgYMaHcjRAw7/ecUERERQIWdVODIwSPMfXgu34z+hm1ztp3+EyY/DrmbwT8cer50+s8nIiJSDVOnTiUhIYHg4GCCg4NJSkrip59+OuVjDh48yJgxYwgPD8fX15dOnToxc+ZMNyWuHB1jJ6e0/I3lFOYWEhYfRofhHU7vybKWwcYXnduJb4NP6GnnExERqY7IyEgmT55Mx44dMQyDjz76iJEjR7JmzRq6dOlSqn9hYSHnnnsuYWFhfPXVV7Rq1YodO3YQGhrq/vCnoMJOylV0uIhlrywDYMBDA7BYLdV/MnsBLLsJDAe0vQYiR9RQShERkaobMaLk76FnnnmGqVOnsnTp0jILu/fff5/9+/ezePFivL29AWjbtq07olaJdsVKuVa/t5rDWYcJjQ4l/or403uylKcgez34hUGvKTUTUERE5CS5ubnk5OS4vgoKCip8jN1uZ/r06eTl5ZGUlFRmn++++46kpCTGjBlDixYtiI+PZ+LEidjtNXD8eQ1SYSdlshfaWfz8YgAGPDDg9M6G3b8a1k92bvd+E3yb1kBCERGR0uLi4ggJCXF9TZo0qdy+ycnJBAYG4uvry+23386MGTOIi4srs++2bdv46quvsNvtzJw5k8cee4wXX3yRp59+uraGUi3aFStlSp6WTM7OHAJbBtL9hu7VfyJ7ISy9EQw7tL4Uov5RYxlFREROtn79elq1auW67evrW27fzp0788cff5Cdnc1XX33F9ddfz/z588ss7hwOB2FhYbzzzjvYbDZ69erF7t27ef7553n88cdrZSzVocJOytSkQxPaDG5Dx/M74uV3Gh+T9ZPh4FrnLF3iGzUXUEREpAxBQUEEB1dufVQfHx86dHCeGNirVy9WrFjBlClTePvtt0v1DQ8Px9vbG5vN5mqLjY0lIyODwsJCfHx8amYAp0mFnZQpamAUN8y7AcNhVP9JDibDuqNT1L1ecx5fJyIi4qEcDke5x+QNGDCAadOm4XA4sFqdhydt3ryZ8PBwjynqQMfYSQWqfSaso9i5C9ZRBK0ugjZX1mwwERGR0zBhwgQWLFjA9u3bSU5OZsKECcybN49rrrkGgNGjRzNhwgRX/zvuuIP9+/czduxYNm/ezI8//sjEiRMZM2aMWUMok2bspITt87az7Zdt9B3bl4DmAdV/oo0vwv5V4B0KiVPBchpLpYiIiNSwzMxMRo8eTXp6OiEhISQkJDB79mzOPfdcANLS0lwzcwCtW7dm9uzZjB8/noSEBFq1asXYsWN58MEHzRpCmVTYSQnzn5zP9t+2U5hXyPCXh1fvSbI3wtqjB5L2ehkaRdRcQBERkRrw3nvvnfL+efPmlWpLSkpi6dKltZSoZmhXrLjsWrqL7b9tx+plJemestfxqZDD7lyI2FEA4cMh+vqaDSkiIiLlUmEnLosmLQIg4boEQlqHVO9JNr8KWUvAKwj6vKNdsCIiIm6kwk4AyEzJZNN3m8ACAx4cUL0nyd0Kfz7i3O75AgS0rrmAIiIiUiEVdgLA78/+DkDsJbE069ys6k9gOGDZzWDPhxZnQftbazihiIiIVESFnXAg9QDJnyUDMHDCwOo9yZa3IHMB2BpB3/9oF6yIiIgJdFasYPOx0ePmHhxKP0REr2qcwXpoO/zxgHO7+2QIjK7RfCIiIlI5KuyE4FbBjHh7BIZRjatMGAYsvxWK86D5QOjkWQs1ioiINCTaFSsulursPv3rPcj4BWx+0Pd9sOgjJSIiYhb9Fm7Ajhw8wozRM0hfk169Jzi8C9bc69xOeBqCO9ZcOBEREakyFXYN2Io3V7D247V8M/qbqu+GNQxY/n9QlANN+0LncbWSUURERCpPhV0DVXS4iKUvOy+LMuChAVXfDZv6MeyZCVYf6Pc+WG21kFJERESqQoVdA7X6vdUczjpMaHQo8VfEV+3B+emwaqxzu+u/ISSuxvOJiIhI1amwa4DshXYWP78YgAEPDMDqVYWPgWHAijuh6CA07gmx99VOSBEREakyFXYNUPK0ZHJ25hDYMpDuN3Sv2oPTvoBd34DFC/p9AFbv2ogoIiIi1aDCroExHIbr8mH9xvfDy68KSxke2Qcr73Jud3kEGifUQkIRERGpLhV2DYyj2EHPW3vSvEtzet/eu2oPXnk3FGRBaFfo8nDtBBQREZFq05UnGhibj42ke5LoN75f1c6E3TkD0j4Hi825C9bmU3shRUREpFo0Y9dAVamoK9gPK+5wbsc+AE161U4oEREROS0q7BqQH8f8SMr0FBx2R9UeuGocHNkLwbHQ9V+1kk1EREROnwq7BmLX0l2sfHMlM66bQe6e3Mo/cPePsP1j5zVg+73vvCasiIiIeCQVdg3EokmLAEi4LoGQ1iGVe1DhQVh+m3O783ho1q92womIiEiNUGHXAGSmZLLpu01ggQEPDqj8A9fcB/l7ILADJDxZewFFRESkRqiwawCOrVsXe0kszTo3q9yD0ufAX+85t/u9D16NaimdiIiI1BQVdvXcgdQDJH+WDMDACQMr96CiXFh2i3O7010QNqiW0omIiEhNUmFXzy1+fjGG3aD90PZE9Iqo3IP+eAgOp0FAW+g2qVbziYiISM3RAsX1XOeLOpPxR0blZ+v2zoMtbzq3+/4HvANrLZuIiIjULBV29VyH4R1oP6x95ToX58Gym48+8DZoeXbtBRMREZEap8KuAaj0VSb+fBQObYNGkdD9udoNJSIiIjVOx9jVU8tfX878J+eTvz+/cg/Ytxg2TXFu93kHfCq51p2IiIh4DM3Y1UNFh4uY/8R8DmcdpnH7xiRck3DqBxTnw7KbAAOir4eI89ySU0RERGqWZuzqodXvreZw1mFCo0OJvyK+4gekPAE5m8CvJfR6ufYDioiISK3wqMJu0qRJJCYmEhQURFhYGKNGjWLTpk0VPu7gwYOMGTOG8PBwfH196dSpEzNnznRDYs9jL7Sz+PnFAAx4YABWrwq+xX+vgA3PO7f7vAU+jWs5oYiIiNQWj9oVO3/+fMaMGUNiYiLFxcU8/PDDDB06lPXr1xMQEFDmYwoLCzn33HMJCwvjq6++olWrVuzYsYPQ0FD3hvcQydOSydmZQ2DLQLrf0P3Une0FsPRGMBzQ5iqIHOmWjCIiIlI7PKqwmzVrVonbH374IWFhYaxatYozzjijzMe8//777N+/n8WLF+Pt7Q1A27ZtazuqRzIchuvyYf3G98PLr4Jv77pnIHsd+DaHXq+6IaGIiIjUJo/aFXuy7OxsAJo0aVJun++++46kpCTGjBlDixYtiI+PZ+LEidjtdnfF9Bgbv9lI1sYs/EL96H1771N3PvAHrDt6VYnEN8CvkteQFREREY/lUTN2J3I4HIwbN44BAwYQH1/+CQDbtm3j119/5ZprrmHmzJls3bqVO++8k6KiIh5//PFS/QsKCigoKHDdzs3NBaC4uJji4mIArFYrVqsVh8OBw+Fw9T3WbrfbMQyjwnabzYbFYnE974ntQKnis7x2Ly8vDMMo0W6xWLDZbCUyNo1tSrcbutE4ujHegd4lXrfEmIoLsC25AYtRjBF5CZaoyzx2TKdqr6vfJ41JY9KYNCaNqebHdOJ2Q+axhd2YMWNISUlh0aJFp+zncDgICwvjnXfewWaz0atXL3bv3s3zzz9fZmE3adIknnjiiVLta9euJSMjA4DmzZvTvn17UlNT2bdvn6tPZGQkkZGRbN682TWbCNCuXTvCwsJISUkhP//4unExMTGEhoayZs2aEh/6hIQEfHx8WLlyZYkMvXv3prCwkLVr17rabDYbiYmJZGdns3HjRle7v78/3bp1Iysri23btrnaOz/QmdjYWHbt2sWuXbtc7SeOyWfzc7Q++CdF1mD2RT5KBHj0mEJCQoiNjWXPnj3ljqmufZ80Jo1JY9KYNKaaHVNQUBACFsMDS9y77rqLb7/9lgULFhAdHX3KvoMHD8bb25tffvnF1fbTTz9x/vnnU1BQgI+PT4n+J8/Y7d69m7i4OFJTU4mMjATq+V9EB5KxzO6FxVGEve9HWKKvrftjqo/fJ41JY9KYNCaNqUpjSk9PJyoqip07d7p+nzdEHjVjZxgGd999NzNmzGDevHkVFnUAAwYMYNq0aTgcDqxW5yGDmzdvJjw8vFRRB+Dr64uvr6/rdk5ODuD8cHp5lXw7jn14TnbsA17Z9pOftzrtFoulzHar1cqeFXtY/tpyBj40kLD4sPKzO4qxLrsZHEUQcSG2dtfB0cuNedqYynrfq9quMWlM5bVrTBoTaEzlZaxqu6eMqdKXz6znPOrkiTFjxvDJJ58wbdo0goKCyMjIICMjo8RU6+jRo5kwYYLr9h133MH+/fsZO3Ysmzdv5scff2TixImMGTPGjCGYYtGkRSR/msySF5ecuuPGl2H/CvAOca5Zp/8EIiIi9YpHzdhNnToVgCFDhpRo/+CDD7jhhhsASEtLK1HRt27dmtmzZzN+/HgSEhJo1aoVY8eO5cEHH3RXbFNlpmSy6dtNYIEBDw4ov2POJlj7mHO750vQqJV7AoqIiIjbeFRhV5nD/ebNm1eqLSkpiaVLl9ZCIs93bN262EtiaRZTzpIlDjssvQkcBdByKLS70Y0JRURExF08alesVM2B1AMkf5YMwMAJA8vvuPl1yFoMXoHQ913tghUREamnVNjVYYufX4xhN2g/tD0RvSLK7pT7F/x59JjEHs9DQJT7AoqIiIhbqbCrow5lHGLN+2uAU8zWGQ5YdgvY8yFsCHS4zX0BRURExO086hg7qTzvAG+G/HsIu5buos3gNmV32vo2ZM4DWyPo9x5YVMeLiIjUZyrs6ijfIF8GPnSK4+rydsCaB5zb3SdBYDv3BBMRERHTaAqnPjIMWHYrFB+C5gOg011mJxIRERE3UGFXxxQdLuLT8z5lw9cbMBzlLA+z7QPImAM2P+j7vnbBioiINBDaFVvHrH5vNVtnbSVrUxadL+qMxXrS0iWHd8Pqe5zbXZ+E4E7uDykiIiKm0FROHWIvtLP4+cUA9L+/P1avk759hgHLb4eibGjaB2LuMSGliIiImEWFXR2SPC2ZnJ05BLQIoMeNPUp32P4p7PkBrD7OXbDWsi+cLCIiIvWTCrs6wnAYrsuHJd2ThJffSXvR8zNg1T+d2/H/gtAubk4oIiIiZlNhV0ds/GYjWRuz8Av1o/ftvUveaRiwcgwUHoDGPSDuAXNCioiIiKlU2NUBhmGwcOJCABLHJOIb7Fuyw86vYOfXYPGCfu+D1duElCIiImI2FXZ1xKBHBhE1KIq+Y/uWvOPIPlgxxrndZQI07u72bCIiIuIZtNxJHWCxWIi9OJbYi2NL37nqn1CwD0Liocuj7g8nIiIiHkMzdnXZzm9gx3TnAsT9PgCbj9mJRERExEQq7Dzcd7d+x6LJiyjIKSh5R8F+WHGHczv2fmjau/SDRUREpEHRrlgPlpmSyZr/rAELxIyKKXnSxOp74EgGBHeGrv82LaOIiIh4Ds3YebBj69bFXhJLs5hmx+/Y8xOkfgRYnAsR2/zMCSgiIiIeRYWdhzqQeoDkz5IBGDhh4PE7CrNh+W3O7c7joHl/94cTERERj6TCzkMtfmExht2g/dD2RPSKOH7Hmvvh8C4IbA/dnjYvoIiIiHgcFXYe6FDGIda8twY4abYu4xf4613ndt/3wKuRCelERETEU6mw80BLX1mKvcBOZL9I2gxu42wsOgTLbnVud7wTWgw2L6CIiIh4JJ0V64ESrk0gZ1cO8VfGY7FYnI1/ToC87RDQBrpPNjWfiIiIeCYVdh4oLD6MSz655HhD5gLY/Lpzu8+74B1kTjARERHxaNoV6+mKD8PSm53b7W+B8HPNzSMiIiIeS4WdB1n51kq+vfFbsjZlHW9c+xgc2gr+raDHC+aFExEREY+nXbEewl5kZ9GkRWSnZRPRJ4JmnZvBviWw8WVnhz7vgE+IuSFFRETEo2nGzkMkT0smOy2bgBYB9LixB9iPwLKbAAOiR0Or882OKCIiIh5OhZ0HMBwGv092Xj4s6Z4kvPy8IPkJyNkIfi2h58smJxQREZG6QIWdB9j4zUayNmbhF+pH79t7w98rYcPzzjsTp4JvE3MDioiISJ2gws5khmGwaNIiABLHJOIbYHHugjXsEHUFtB5lbkARERGpM1TYmWzbL9vYs3IPXv5e9B3bF9ZNhIPJ4NsMer9mdjwREZF6aerUqSQkJBAcHExwcDBJSUn89NNPlXrs9OnTsVgsjBo1qnZDVoPOijVZRK8Ihjw5BMNuEOC1FdY947yj9+vg19zccCIiIvVUZGQkkydPpmPHjhiGwUcffcTIkSNZs2YNXbp0Kfdx27dv57777mPQoEFuTFt5KuxM5t/En8GPDQZHEczuC0YxRF4MUZebHU1ERKTeGjFiRInbzzzzDFOnTmXp0qXlFnZ2u51rrrmGJ554goULF3Lw4EE3JK0a7Yr1FBuehwNrwKcxJL4Jx64RKyIiIpWWm5tLTk6O66ugoKDCx9jtdqZPn05eXh5JSUnl9nvyyScJCwvj5ptvrsnINUqFnUky12Xy/sD32fzjZshe71zeBKDXFPBvaW44ERGROiouLo6QkBDX16RJk8rtm5ycTGBgIL6+vtx+++3MmDGDuLi4MvsuWrSI9957j3fffbe2otcI7Yo1ye+Tf2fn7zv584PVdPJ6BRyFEHE+tL3W7GgiIiJ11vr162nVqpXrtq+vb7l9O3fuzB9//EF2djZfffUV119/PfPnzy9V3OXm5nLdddfx7rvv0qxZs1rLXhNU2JngQOoBkj9LBmDo/62HfcvAOxj6vK1dsCIiIqchKCiI4ODgSvX18fGhQ4cOAPTq1YsVK1YwZcoU3n777RL9/vrrL7Zv317iuDyHwwGAl5cXmzZton379jU0gtOjws4Ei19YjGE36PYPf0L2P+5s7PkSNIo0N5iIiEgD5nA4yjwmLyYmhuTk5BJtjz76KLm5uUyZMoXWrVu7K2KFVNi52aGMQ6x5bw1YHAy/8ksoPAItz4V2N5kdTUREpMGYMGEC5513HlFRUeTm5jJt2jTmzZvH7NmzARg9ejStWrVi0qRJ+Pn5ER8fX+LxoaGhAKXazabCzs2WvrIUe4Gdobdtwa9wJXgFQt93tQtWRETEjTIzMxk9ejTp6emEhISQkJDA7NmzOffccwFIS0vDaq1755iqsHOjIwePsOLNFYQ230/fwd84G7s/CwFtTM0lIiLS0Lz33nunvH/evHmnvP/DDz+suTA1SIWdG/kE+TDinQsJy7waK/kQNhg63m52LBEREakn6t4cYx1mtVmJ77WYsGYpYPOHvv8Bi74FIiIiUjNUVbhTXhqsud+53W0iBHUwN4+IiIjUKyrs3MBeZOe9/v/hwOeXQ3EuNEuCTnebHUtERETqGRV2bpA8LZmm3j/Q2G8ZhtUX+r4PVpvZsURERKSe0ckTNS0vDQqycNgdZKzJIG9fHn/991cuGP0TAJaY8RASY3JIERERqY9U2NWkvDT4vjM4jmAFIgAs0PHmE/psfAU63gEBUaZEFBERkfpLu2JrUkEWOI6cuo/jiLOfiIiISA1TYVeDHHZHjfYTERERqQoVdjUoY01GjfYTERERqQoVdjUoLyuvRvuJiIiIVIUKuxoU0CygRvuJiIiIVIUKuxrUskfLGu0nIiIiUhUeVdhNmjSJxMREgoKCCAsLY9SoUWzatOmUj/nwww+xWCwlvvz8/NyUuCSrrXJvZ2X7iYiIiFSFR1UY8+fPZ8yYMSxdupQ5c+ZQVFTE0KFDycs79TFpwcHBpKenu7527NjhpsQn8W0G1gqKSqufs5+IiIhIDfOoBYpnzZpV4vaHH35IWFgYq1at4owzzij3cRaLhZYtPWD3ZkAUjNhU8soTWXkENAugZY+Wzpk632ZanFhERERqhUcVdifLzs4GoEmTJqfsd+jQIdq0aYPD4aBnz55MnDiRLl26lNm3oKCAgoIC1+3c3FwAiouLKS4uBsBqtWK1WnE4HDgcx9ecO9Zut9sxDKPsdt8I8I0AIPxcGxaLheLiYhzAsWeyHX2s3W4vkc1ms5XZ7uXlhWEYJdotFgs2m61UxvLaT2tMJ7TbbMfHVJnsGpPGpDFpTBqTxuSOMZ243ZB5bGHncDgYN24cAwYMID4+vtx+nTt35v333ychIYHs7GxeeOEF+vfvz7p164iMjCzVf9KkSTzxxBOl2teuXUtGhnN9uebNm9O+fXtSU1PZt2+fq09kZCSRkZFs3rzZVXQCtGvXjrCwMFJSUsjPz3e1x8TEEBoaypo1a0p86BMSEvDx8WHlypUlMvTu3ZvCwkLWrl3rarPZbCQmJpKdnc3GjRtd7f7+/nTr1o2srCy2bdvmag8JCSE2NpY9e/awa9cuV7vGpDFpTBqTxqQx1ecxBQUFIWAxPLTEveOOO/jpp59YtGhRmQVaeYqKioiNjeWqq67iqaeeKnX/yTN2u3fvJi4ujtTUVNfr6C8ijUlj0pg0Jo1JY6pbY0pPTycqKoqdO3dWqW6obzyysLvrrrv49ttvWbBgAdHR0VV+/GWXXYaXlxefffZZhX137dpF69atG/wHQUREpC7T73Mnjzor1jAM7rrrLmbMmMGvv/5araLObreTnJxMeHh4LSQUERER8VwedYzdmDFjmDZtGt9++y1BQUGuY95CQkLw9/cHYPTo0bRq1YpJkyYB8OSTT9KvXz86dOjAwYMHef7559mxYwe33HKLaeMQERERMYNHFXZTp04FYMiQISXaP/jgA2644QYA0tLSsFqPTzQeOHCAW2+9lYyMDBo3bkyvXr1YvHgxcXFx7ootIiIi4hE88hg7d9I+eRERkbpPv8+dPOoYOxERERGpPhV2IiIiIvWECjsRERGRekKFnYiIiEg94VFnxZrh2CrX6enpJicRERGR6jr2e/zEq1c0RA2+sNu7dy8Affr0MTmJiIiInK69e/cSFRVldgzTNPjlToqLi1mzZg0tWrQosT5eTcjNzSUuLo7169fr4sSVoPer8vReVY3er6rR+1V5eq+qpjbfL4fDwd69e+nRowdeXg133qrBF3a1KScnh5CQELKzswkODjY7jsfT+1V5eq+qRu9X1ej9qjy9V1Wj96v26eQJERERkXpChZ2IiIhIPaHCrhb5+vry+OOP4+vra3aUOkHvV+XpvaoavV9Vo/er8vReVY3er9qnY+xERERE6gnN2ImIiIjUEyrsREREROoJFXYiIiIi9YQKu1qye/durr32Wpo2bYq/vz9du3Zl5cqVZsfyOHa7nccee4zo6Gj8/f1p3749Tz31FDr002nBggWMGDGCiIgILBYL33zzTYn7DcPgX//6F+Hh4fj7+3POOeewZcsWc8J6gFO9X0VFRTz44IN07dqVgIAAIiIiGD16NHv27DEvsIkq+myd6Pbbb8disfDKK6+4LZ+nqcz7tWHDBi666CJCQkIICAggMTGRtLQ094f1ABW9X4cOHeKuu+4iMjISf39/4uLieOutt8wJW8+osKsFBw4cYMCAAXh7e/PTTz+xfv16XnzxRRo3bmx2NI/z7LPPMnXqVF5//XU2bNjAs88+y3PPPcdrr71mdjSPkJeXR7du3XjjjTfKvP+5557j1Vdf5a233mLZsmUEBAQwbNgwjhw54uaknuFU79fhw4dZvXo1jz32GKtXr+brr79m06ZNXHTRRSYkNV9Fn61jZsyYwdKlS4mIiHBTMs9U0fv1119/MXDgQGJiYpg3bx5r167lsccew8/Pz81JPUNF79c999zDrFmz+OSTT9iwYQPjxo3jrrvu4rvvvnNz0nrIkBr34IMPGgMHDjQ7Rp1wwQUXGDfddFOJtksuucS45pprTErkuQBjxowZrtsOh8No2bKl8fzzz7vaDh48aPj6+hqfffaZCQk9y8nvV1mWL19uAMaOHTvcE8pDlfde7dq1y2jVqpWRkpJitGnTxnj55Zfdns0TlfV+XXHFFca1115rTiAPV9b71aVLF+PJJ58s0dazZ0/jkUcecWOy+kkzdrXgu+++o3fv3lx22WWEhYXRo0cP3n33XbNjeaT+/fszd+5cNm/eDMCff/7JokWLOO+880xO5vlSU1PJyMjgnHPOcbWFhITQt29flixZYmKyuiM7OxuLxUJoaKjZUTyOw+Hguuuu4/7776dLly5mx/FoDoeDH3/8kU6dOjFs2DDCwsLo27fvKXdvN3T9+/fnu+++Y/fu3RiGwW+//cbmzZsZOnSo2dHqPBV2tWDbtm1MnTqVjh07Mnv2bO644w7++c9/8tFHH5kdzeM89NBDXHnllcTExODt7U2PHj0YN24c11xzjdnRPF5GRgYALVq0KNHeokUL131SviNHjvDggw9y1VVX6ZqVZXj22Wfx8vLin//8p9lRPF5mZiaHDh1i8uTJDB8+nJ9//pmLL76YSy65hPnz55sdzyO99tprxMXFERkZiY+PD8OHD+eNN97gjDPOMDtanedldoD6yOFw0Lt3byZOnAhAjx49SElJ4a233uL66683OZ1n+eKLL/j000+ZNm0aXbp04Y8//mDcuHFERETovZJaU1RUxOWXX45hGEydOtXsOB5n1apVTJkyhdWrV2OxWMyO4/EcDgcAI0eOZPz48QB0796dxYsX89ZbbzF48GAz43mk1157jaVLl/Ldd9/Rpk0bFixYwJgxY4iIiCixF0KqTjN2tSA8PJy4uLgSbbGxsQ327KhTuf/++12zdl27duW6665j/PjxTJo0yexoHq9ly5YA7N27t0T73r17XfdJaceKuh07djBnzhzN1pVh4cKFZGZmEhUVhZeXF15eXuzYsYN7772Xtm3bmh3P4zRr1gwvLy/93K+k/Px8Hn74YV566SVGjBhBQkICd911F1dccQUvvPCC2fHqPBV2tWDAgAFs2rSpRNvmzZtp06aNSYk81+HDh7FaS34MbTab6y9gKV90dDQtW7Zk7ty5rracnByWLVtGUlKSick817GibsuWLfzyyy80bdrU7Ege6brrrmPt2rX88ccfrq+IiAjuv/9+Zs+ebXY8j+Pj40NiYqJ+7ldSUVERRUVF+tlfS7QrthaMHz+e/v37M3HiRC6//HKWL1/OO++8wzvvvGN2NI8zYsQInnnmGaKioujSpQtr1qzhpZde4qabbjI7mkc4dOgQW7dudd1OTU3ljz/+oEmTJkRFRTFu3DiefvppOnbsSHR0NI899hgRERGMGjXKvNAmOtX7FR4ezqWXXsrq1av54YcfsNvtrmMRmzRpgo+Pj1mxTVHRZ+vkotfb25uWLVvSuXNnd0f1CBW9X/fffz9XXHEFZ5xxBmeeeSazZs3i+++/Z968eeaFNlFF79fgwYO5//778ff3p02bNsyfP5///ve/vPTSSyamrifMPi23vvr++++N+Ph4w9fX14iJiTHeeecdsyN5pJycHGPs2LFGVFSU4efnZ7Rr18545JFHjIKCArOjeYTffvvNAEp9XX/99YZhOJc8eeyxx4wWLVoYvr6+xtlnn21s2rTJ3NAmOtX7lZqaWuZ9gPHbb7+ZHd3tKvpsnayhL3dSmffrvffeMzp06GD4+fkZ3bp1M7755hvzApusovcrPT3duOGGG4yIiAjDz8/P6Ny5s/Hiiy8aDofD3OD1gMUwtMS/iIiISH2gY+xERERE6gkVdiIiIiL1hAo7ERERkXpChZ2IiIhIPaHCTkRERKSeUGEnIiIiUk+osBMRERGpJ1TYiYiIiNQTKuxERERE6gkVdiJSL3zxxRdYLBZmzJhR6r5u3bphsVj47bffSt0XFRVF//793RFRRKTWqbATkXph4MCBACxatKhEe05ODikpKXh5efH777+XuG/nzp3s3LnT9VgRkbpOhZ2I1AsRERFER0eXKuyWLFmCYRhcdtllpe47dluFnYjUFyrsRKTeGDhwIGvWrCE/P9/V9vvvv9OlSxfOO+88li5disPhKHGfxWJhwIABZsQVEalxKuxEpN4YOHAgRUVFLFu2zNX2+++/079/f/r37092djYpKSkl7ouJiaFp06ZmxBURqXEq7ESk3jj5OLvi4mKWLVvGgAEDaN++PS1atHDdl5ubS3JysnbDiki9osJOROqN2NhYmjZt6ire/vzzT/Ly8lxnvfbv3991AsWSJUuw2+0q7ESkXlFhJyL1hsVioX///q5j6X7//XfCwsLo0KEDULKwO/avCjsRqU9U2IlIvTJw4ECys7NJTk52HV93TP/+/dmxYwe7d+9m0aJFRERE0K5dOxPTiojULBV2IlKvnHic3e+//17ijNdevXrh6+vLvHnzXMfeiYjUJyrsRKRe6d27N35+fnz66afs3r27xIydr68vPXv25I033iAvL0+7YUWk3lFhJyL1io+PD4mJiSxZsgRfX1969epV4v7+/fuzZMkSQMfXiUj9o8JOROqdYwXbsV2vJzq2+zUoKIhu3bq5PZuISG2yGIZhmB1CRERERE6fZuxERERE6gkVdiIiIiL1hAo7ERERkXpChZ2IiIhIPaHCTkRERKSeUGEnIiIiUk+osBMRERGpJ1TYiYiIiNQTKuxERERE6gkVdiIiIiL1hAo7ERERkXpChZ2IiIhIPaHCTkRERKSe+H8hA49/PJg9XAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Enable LaTeX rendering\n",
+    "plt.rcParams['text.usetex'] = False\n",
+    "\n",
+    "# Data\n",
+    "W = [6, 8, 10, 12, 14, 16, 18]\n",
+    "SR = [2.51, 2.75, 2.95, 3.06, 3.04, 3.01, 2.97]\n",
+    "tau = [3.36, 3.63, 3.84, 4.20, 4.21, 4.18, 4.22]\n",
+    "\n",
+    "# Create figure and axis\n",
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "# Plot SR on left y-axis (purple, dashed)\n",
+    "ax1.plot(W, SR, 'purple', linestyle='--', label='SR', marker='o')\n",
+    "ax1.set_xlabel('W')\n",
+    "ax1.set_ylabel('SR')\n",
+    "ax1.tick_params(axis='y')\n",
+    "\n",
+    "# Create second y-axis for tau (yellow, solid)\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(W, tau, 'orange', linestyle='-', label=r'$\\tau$', marker='s')\n",
+    "ax2.set_ylabel(r'$\\tau$')\n",
+    "ax2.tick_params(axis='y')\n",
+    "\n",
+    "# Add combined legend in upper left\n",
+    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
+    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
+    "ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')\n",
+    "\n",
+    "# Add horizontal dashed grid only\n",
+    "ax1.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# Adjust layout to prevent overlap\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "larm-trl",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

exp/pic/ablation/4.3.2.ipynb

@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Enable LaTeX rendering\n",
+    "plt.rcParams['text.usetex'] = False\n",
+    "\n",
+    "# Data\n",
+    "# de = [1e-4, 0.005, 0.01, 0.05, 0.1, 0.5, 1]  # Replace 0 with 1e-10\n",
+    "log_de = [1,2,3,4,5,6,7]  # Replace 0 with 1e-10\n",
+    "SR = [3.06, 3.09, 3.53, 3.61, 3.84, 4.13, 4.74]\n",
+    "IS = [21.87, 19.29, 18.25, 16.90, 16.11, 10.37, 5.37]\n",
+    "IS = [0.3206, 0.3254, 0.3126, 0.3138, 0.25046, 0.2162, 0.108682]\n",
+    "name = \"CLIP-score\"\n",
+    "# IS = [0.2906, 0.28268498, 0.2771, 0.27268498, 0.23206205146, 0.18, 0.161689]\n",
+    "# name = \"HPSv2\"\n",
+    "\n",
+    "# Transform de to log scale\n",
+    "# log_de = np.log10(de)\n",
+    "\n",
+    "# Create figure and axis\n",
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "# Plot SR on left y-axis (purple, dashed)\n",
+    "ax1.plot(log_de, SR, 'purple', linestyle='--', label='SR', marker='s')\n",
+    "# ax1.set_xlabel(r'$\\log(\\lambda)$')\n",
+    "ax1.set_xlabel(r'$\\lambda$')\n",
+    "ax1.set_ylabel('SR')\n",
+    "ax1.tick_params(axis='y')\n",
+    "\n",
+    "# Set x-axis ticks to show original de values\n",
+    "ax1.set_xticks(log_de)\n",
+    "ax1.set_xticklabels(['0', '0.005', '0.01', '0.05', '0.1', '0.5', '1'])\n",
+    "\n",
+    "# Create second y-axis for IS (yellow, solid)\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(log_de, IS, 'orange', linestyle='-', label=f'{name}', marker='o')\n",
+    "ax2.set_ylabel('IS')\n",
+    "ax2.tick_params(axis='y')\n",
+    "\n",
+    "# Add combined legend in upper left\n",
+    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
+    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
+    "ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')\n",
+    "\n",
+    "# Add horizontal dashed grid only\n",
+    "ax1.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "ax1.set_ylim(3, 5.)  # Extend max to 5.5 (from 4.74)\n",
+    "ax2.set_ylim(5, 25.5)  # Extend max to 25 (from 21.87)\n",
+    "ax2.set_ylim(0.09, 0.4)  # Extend max to 25 (from 21.87)\n",
+    "\n",
+    "# Adjust layout to prevent overlap\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "larm-trl",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

exp/pic/ablation/LLM_P.ipynb

@@ -0,0 +1,146 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 读取 .pt 文件\n",
+    "file_path = \"LLM_P.pt\"\n",
+    "try:\n",
+    "    data = torch.load(file_path, map_location=torch.device('cpu'))\n",
+    "except FileNotFoundError:\n",
+    "    raise FileNotFoundError(f\"File {file_path} not found. Please check the path.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 处理概率数据\n",
+    "if data.dim() == 1:\n",
+    "    # 1D 张量，直接使用\n",
+    "    probs = data.numpy()\n",
+    "elif data.dim() == 2:\n",
+    "    # 2D 张量，取平均或第一个样本\n",
+    "    probs = data.mean(dim=0).numpy()  # 沿 batch 轴平均\n",
+    "else:\n",
+    "    raise ValueError(f\"Unexpected tensor shape: {data.shape}\")\n",
+    "\n",
+    "# 确保概率归一化（如果需要）\n",
+    "if abs(probs.sum() - 1.0) > 1e-5:  # 检查是否已归一化\n",
+    "    probs = probs / probs.sum()\n",
+    "\n",
+    "# 类别索引\n",
+    "labels = np.arange(len(probs))\n",
+    "\n",
+    "# 绘制柱状图\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(labels, probs, color='skyblue', edgecolor='black')\n",
+    "plt.xlabel('Text Token Index', fontsize=16)\n",
+    "plt.ylabel('Probability', fontsize=16)\n",
+    "# plt.title('Probability Distribution of token_7.pt')\n",
+    "# plt.ylim(0, 1)  # y 轴范围 [0, 1]\n",
+    "plt.ylim(0, 0.2)  # y 轴范围 [0, 1]\n",
+    "plt.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# 显示图表\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 可选：保存图表\n",
+    "plt.savefig('LLM_P.png', dpi=300, bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# 处理概率数据\n",
+    "if data.dim() == 1:\n",
+    "    # 1D 张量，直接使用\n",
+    "    probs = data.numpy()\n",
+    "elif data.dim() == 2:\n",
+    "    # 2D 张量，取平均或第一个样本\n",
+    "    probs = data.mean(dim=0).numpy()  # 沿 batch 轴平均\n",
+    "else:\n",
+    "    raise ValueError(f\"Unexpected tensor shape: {data.shape}\")\n",
+    "\n",
+    "# 确保概率归一化（如果需要）\n",
+    "if abs(probs.sum() - 1.0) > 1e-5:  # 检查是否已归一化\n",
+    "    probs = probs / probs.sum()\n",
+    "\n",
+    "# 类别索引\n",
+    "labels = np.arange(len(probs))\n",
+    "\n",
+    "# 绘制柱状图\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(labels, probs, color='skyblue', edgecolor='black')\n",
+    "plt.xlabel('Image Token Index', fontsize=16)\n",
+    "plt.ylabel('Probability', fontsize=16)\n",
+    "# plt.title('Probability Distribution of token_7.pt')\n",
+    "# plt.ylim(0, 1)  # y 轴范围 [0, 1]\n",
+    "plt.ylim(0, 0.1)  # y 轴范围 [0, 1]\n",
+    "plt.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# 显示图表\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 可选：保存图表\n",
+    "plt.savefig('p_index.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "larm-trl",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

exp/pic/ablation/MLLM_P.ipynb

@@ -0,0 +1,96 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 读取 .pt 文件\n",
+    "file_path = \"token_7.pt\"\n",
+    "try:\n",
+    "    data = torch.load(file_path, map_location=torch.device('cpu'))[0]\n",
+    "except FileNotFoundError:\n",
+    "    raise FileNotFoundError(f\"File {file_path} not found. Please check the path.\")\n",
+    "data = data[:,:4000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 处理概率数据\n",
+    "if data.dim() == 1:\n",
+    "    # 1D 张量，直接使用\n",
+    "    probs = data.numpy()\n",
+    "elif data.dim() == 2:\n",
+    "    # 2D 张量，取平均或第一个样本\n",
+    "    probs = data.mean(dim=0).numpy()  # 沿 batch 轴平均\n",
+    "else:\n",
+    "    raise ValueError(f\"Unexpected tensor shape: {data.shape}\")\n",
+    "\n",
+    "# 确保概率归一化（如果需要）\n",
+    "if abs(probs.sum() - 1.0) > 1e-5:  # 检查是否已归一化\n",
+    "    probs = probs / probs.sum()\n",
+    "\n",
+    "# 类别索引\n",
+    "labels = np.arange(len(probs))\n",
+    "\n",
+    "# 绘制柱状图\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(labels, probs, color='skyblue', edgecolor='black')\n",
+    "plt.xlabel('Text Token Index', fontsize=16)\n",
+    "plt.ylabel('Probability', fontsize=16)\n",
+    "# plt.title('Probability Distribution of token_7.pt')\n",
+    "# plt.ylim(0, 1)  # y 轴范围 [0, 1]\n",
+    "plt.ylim(0, 0.1)  # y 轴范围 [0, 1]\n",
+    "plt.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# 显示图表\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# 可选：保存图表\n",
+    "# plt.savefig('LLM_P.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "larm-trl",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

exp/pic/ablation/sup1.ipynb

@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Enable LaTeX rendering\n",
+    "plt.rcParams['text.usetex'] = False\n",
+    "\n",
+    "# Data\n",
+    "# de = [1e-4, 0.005, 0.01, 0.05, 0.1, 0.5, 1]  # Replace 0 with 1e-10\n",
+    "log_de = [1,2,3,4,5,6,7]  # Replace 0 with 1e-10\n",
+    "SR = [3.06, 3.09, 3.53, 3.61, 3.84, 4.13, 4.74]\n",
+    "IS = [21.87, 19.29, 18.25, 16.90, 16.11, 10.37, 5.37]\n",
+    "IS = [0.3206, 0.3254, 0.3126, 0.2238, 0.18046, 0.1262, 0.108682]\n",
+    "name = \"CLIP-score\"\n",
+    "# IS = [0.2906, 0.28268498, 0.2771, 0.27268498, 0.23206205146, 0.18, 0.161689]\n",
+    "# name = \"HPSv2\"\n",
+    "\n",
+    "# Transform de to log scale\n",
+    "# log_de = np.log10(de)\n",
+    "\n",
+    "# Create figure and axis\n",
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "# Plot SR on left y-axis (purple, dashed)\n",
+    "ax1.plot(log_de, SR, 'purple', linestyle='--', label='SR', marker='s')\n",
+    "# ax1.set_xlabel(r'$\\log(\\lambda)$')\n",
+    "ax1.set_xlabel(r'$\\lambda$')\n",
+    "ax1.set_ylabel('SR')\n",
+    "ax1.tick_params(axis='y')\n",
+    "\n",
+    "# Set x-axis ticks to show original de values\n",
+    "ax1.set_xticks(log_de)\n",
+    "ax1.set_xticklabels(['0', '0.005', '0.01', '0.05', '0.1', '0.5', '1'])\n",
+    "\n",
+    "# Create second y-axis for IS (yellow, solid)\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(log_de, IS, 'orange', linestyle='-', label=f'{name}', marker='o')\n",
+    "ax2.set_ylabel(f'{name}')\n",
+    "ax2.tick_params(axis='y')\n",
+    "\n",
+    "# Add combined legend in upper left\n",
+    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
+    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
+    "ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')\n",
+    "\n",
+    "# Add horizontal dashed grid only\n",
+    "ax1.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "ax1.set_ylim(3, 5.)  # Extend max to 5.5 (from 4.74)\n",
+    "ax2.set_ylim(5, 25.5)  # Extend max to 25 (from 21.87)\n",
+    "ax2.set_ylim(0.09, 0.4)  # Extend max to 25 (from 21.87)\n",
+    "\n",
+    "# Adjust layout to prevent overlap\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "larm-trl",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

exp/pic/ablation/sup2.ipynb

@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Enable LaTeX rendering\n",
+    "plt.rcParams['text.usetex'] = False\n",
+    "\n",
+    "# Data\n",
+    "# de = [1e-4, 0.005, 0.01, 0.05, 0.1, 0.5, 1]  # Replace 0 with 1e-10\n",
+    "log_de = [1,2,3,4,5,6,7]  # Replace 0 with 1e-10\n",
+    "SR = [3.06, 3.09, 3.53, 3.61, 3.84, 4.13, 4.74]\n",
+    "IS = [21.87, 19.29, 18.25, 16.90, 16.11, 10.37, 5.37]\n",
+    "IS = [0.3206, 0.3254, 0.3126, 0.3138, 0.25046, 0.2162, 0.108682]\n",
+    "name = \"CLIP-score\"\n",
+    "IS = [0.2906, 0.28268498, 0.2771, 0.24268498, 0.2206205146, 0.18, 0.161689]\n",
+    "name = \"HPSv2\"\n",
+    "\n",
+    "# Transform de to log scale\n",
+    "# log_de = np.log10(de)\n",
+    "\n",
+    "# Create figure and axis\n",
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "# Plot SR on left y-axis (purple, dashed)\n",
+    "ax1.plot(log_de, SR, 'purple', linestyle='--', label='SR', marker='s')\n",
+    "# ax1.set_xlabel(r'$\\log(\\lambda)$')\n",
+    "ax1.set_xlabel(r'$\\lambda$')\n",
+    "ax1.set_ylabel('SR')\n",
+    "ax1.tick_params(axis='y')\n",
+    "\n",
+    "# Set x-axis ticks to show original de values\n",
+    "ax1.set_xticks(log_de)\n",
+    "ax1.set_xticklabels(['0', '0.005', '0.01', '0.05', '0.1', '0.5', '1'])\n",
+    "\n",
+    "# Create second y-axis for IS (yellow, solid)\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(log_de, IS, 'orange', linestyle='-', label=f'{name}', marker='o')\n",
+    "ax2.set_ylabel(f'{name}')\n",
+    "ax2.tick_params(axis='y')\n",
+    "\n",
+    "# Add combined legend in upper left\n",
+    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
+    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
+    "ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')\n",
+    "\n",
+    "# Add horizontal dashed grid only\n",
+    "ax1.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "ax1.set_ylim(3, 5.)  # Extend max to 5.5 (from 4.74)\n",
+    "ax2.set_ylim(5, 25.5)  # Extend max to 25 (from 21.87)\n",
+    "ax2.set_ylim(0.09, 0.4)  # Extend max to 25 (from 21.87)\n",
+    "\n",
+    "# Adjust layout to prevent overlap\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "larm-trl",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}