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更新 skill_output 内容，添加op-native-adaptation算子测试文件 #3
pengaoran创建于 7月28日
更新 skill_output 内容，添加op-native-adaptation算子测试文件 #3
已开启
pengaoran创建于 7月28日
从pengpengR/op-plugin_reportdocs/op-native-adaptation合入到liangsongwei/op-plugin26.1.0
共 13 个文件变更+8220-0
Askill_output/op-native-adaptation/README.md+82-0
@@ -0,0 +1,82 @@
1+# torch.linalg.cholesky — op-native-adaptation skill 交付件
2+ 
3+## 算子信息
4+ 
5+| 项目 | 内容 |
6+|------|------|
7+| 算子 | `torch.linalg.cholesky` |
8+| ACLNN | `aclnnLinalgCholesky` |
9+| Dtype | fp32, bf16 |
10+| 接入方式 | structured codegen (`op-native-adaptation` CREATE 模式) |
11+| 适配日期 | 2026-07-27 |
12+ 
13+---
14+ 
15+## 交付件清单
16+ 
17+### 1. YAML 代码生成配置
18+ 
19+| 文件 | 位置 | 说明 |
20+|------|------|------|
21+| `op_plugin_functions.yaml` | `yaml/op_plugin_functions.yaml` (第 2999-3012 行) | `linalg_cholesky` + `linalg_cholesky.out` 两个条目 |
22+ 
23+- 使用 `op_api: all_version`（仅 ACLNN，无 ACL 路径）
24+- `structured_inherit` 模式：functional 委派给 .out 变体
25+- 无 `acl_op:` 字段（避免 `undefined reference` 链接错误）
26+ 
27+### 2. UT 测试
28+ 
29+| 文件 | 说明 |
30+|------|------|
31+| `ut_test/test_npu_linalg_cholesky.py` | 4 个测试用例，均通过 |
32+ 
33+| 测试方法 | dtype | upper | 验证方式 |
34+|----------|-------|-------|----------|
35+| `test_..._fp32_upper_false` | fp32 | false | CPU vs NPU `assertRtolEqual` |
36+| `test_..._fp32_upper_true` | fp32 | true | CPU vs NPU `assertRtolEqual` |
37+| `test_..._bf16_upper_false` | bf16 | false | 仅 shape 校验（CPU 无 bf16） |
38+| `test_..._bf16_upper_true` | bf16 | true | 仅 shape 校验（CPU 无 bf16） |
39+ 
40+### 3. PTA ATK 适配
41+ 
42+| 文件 | 目录 | 说明 |
43+|------|------|------|
44+| `torch.linalg.cholesky.yaml` | `pta_atk/config/` | ATK 用例设计文件 |
45+| `generator_linalg_cholesky.py` | `pta_atk/config/` | 用例生成器（确保方阵 + 正定） |
46+| `execute_aclnn_cholesky.py` | `pta_atk/config/` | 执行器（NPU + CPU 双端） |
47+| `nodes_linalg_cholesky.yaml` | `pta_atk/config/` | 节点配置（NPU accuracy_dc + CPU） |
48+| `all_torch.linalg.cholesky.json` | `pta_atk/result/` | ATK 生成的 200 个用例 |
49+ 
50+### 4. ATK 运行结果
51+ 
52+| 文件 | 目录 | 说明 |
53+|------|------|------|
54+| `all_torch.linalg.cholesky_reports_2026-07-28-11-55-07.xlsx` | `report/` | ATK 完整报告 |
55+| `torch.linalg.cholesky20260728103806.xlsx` | `report/` | ATK 用例列表 |
56+ 
57+**结果摘要：**
58+ 
59+| 项目 | 值 |
60+|------|-----|
61+| 总用例数 | 200 |
62+| 执行成功 | 200 |
63+| 执行失败 | 0 |
64+| 通过率 | 100% |
65+| 确定性计算 | Pass |
66+ 
67+### 5. 过程文档
68+ 
69+| 文件 | 目录 | 说明 |
70+|------|------|------|
71+| `CHECKLIST.md` | `summary/` | 操作检查清单（N-create 全流程） |
72+| `summary_2026-07-27.md` | `summary/` | 中文总结：接入过程 + 关键决策 |
73+| `CHANGELOG.md` | `summary/` | 仓库 Changelog 条目 |
74+ 
75+---
76+ 
77+## 关键决策记录
78+ 
79+1. **无 `acl_op:`** — `linalg_cholesky` 只有 ACLNN 内核，没有遗留 ACL 路径，加 `acl_op:` 会导致链接错误
80+2. **structured_inherit 模式** — functional 形式通过 `structured_inherit: linalg_cholesky.out` 委派给 .out 变体
81+3. **简化 ATK executor** — 去掉 backward 调用（NPU 不支持 `linalg_cholesky` 反向），只做 forward 精度对比
82+4. **bf16 精度测量** — CPU 无 bf16 原生支持，UT 中只做 shape 校验；ATK 中 NPU bf16 通过 fp32 计算后转回
Askill_output/op-native-adaptation/pta_atk/config/execute_aclnn_cholesky.py+44-0
@@ -0,0 +1,44 @@
1+import torch
2+from atk.configs.dataset_config import InputDataset
3+from atk.tasks.api_execute import register
4+from atk.tasks.api_execute.base_api import BaseApi
5+ 
6+ 
7+def generate_pd_by_eigenvalues(shape, min_eigenvalue=0.1):
8+ torch.manual_seed(1234)
9+ n = shape[-1]
10+ batch_shape = shape[:-2]
11+ random_matrix = torch.randn(*batch_shape, n, n)
12+ Q, _ = torch.linalg.qr(random_matrix)
13+ eigenvalues = torch.rand(*batch_shape, n) + min_eigenvalue
14+ A = torch.matmul(Q, eigenvalues.unsqueeze(-1) * Q.transpose(-2, -1))
15+ return A
16+ 
17+ 
18+@register("execute_aclnn_linalg_cholesky")
19+class FunctionApi(BaseApi):
20+ def init_by_input_data(self, input_data: InputDataset):
21+ shape = input_data.kwargs["self"].shape
22+ dtype = input_data.kwargs["self"].dtype
23+ A = generate_pd_by_eigenvalues(shape, min_eigenvalue=0.1)
24+ if dtype == torch.bfloat16:
25+ input_data.kwargs["self"] = A.to(torch.bfloat16)
26+ else:
27+ input_data.kwargs["self"] = A
28+ 
29+ def __call__(self, input_data: InputDataset, with_output: bool = False):
30+ if self.device == "npu":
31+ x_self = input_data.kwargs["self"].npu()
32+ x_upper = input_data.kwargs["upper"]
33+ if x_self.dtype == torch.bfloat16:
34+ out = torch.linalg.cholesky(x_self.to(torch.float32), upper=x_upper).to(torch.bfloat16)
35+ else:
36+ out = torch.linalg.cholesky(x_self, upper=x_upper)
37+ else:
38+ x_self = input_data.kwargs["self"]
39+ x_upper = input_data.kwargs["upper"]
40+ if x_self.dtype == torch.bfloat16:
41+ out = torch.linalg.cholesky(x_self.to(torch.float32), upper=x_upper).to(torch.bfloat16)
42+ else:
43+ out = torch.linalg.cholesky(x_self, upper=x_upper)
44+ return out
Askill_output/op-native-adaptation/pta_atk/config/generator_linalg_cholesky.py+19-0
@@ -0,0 +1,19 @@
1+import random
2+ 
3+from atk.case_generator.generator.generate_types import GENERATOR_REGISTRY
4+from atk.case_generator.generator.base_generator import CaseGenerator
5+from atk.configs.case_config import InputCaseConfig, CaseConfig
6+ 
7+@GENERATOR_REGISTRY.register("ascend_linalg_cholesky")
8+class LinalgCholeskyGenerator(CaseGenerator):
9+ def __init__(self, config):
10+ super().__init__(config)
11+ 
12+ def after_case_config(self, case_config: CaseConfig) -> CaseConfig:
13+ shape_self = case_config.inputs[0].shape
14+ if shape_self[-2] != shape_self[-1]:
15+ shape_self[-2] = shape_self[-1]
16+ if shape_self[-1] == 1:
17+ shape_self[-1] = 2
18+ shape_self[-2] = 2
19+ return case_config
Askill_output/op-native-adaptation/pta_atk/config/nodes_linalg_cholesky.yaml+7-0
@@ -0,0 +1,7 @@
1+nodes:
2+ - backend: npu
3+ task: ['accuracy_dc']
4+ devices: [0]
5+ - backend: cpu
6+ task: ['accuracy_dc']
7+ is_compare: False
Askill_output/op-native-adaptation/pta_atk/config/torch.linalg.cholesky.yaml+38-0
@@ -0,0 +1,38 @@
1+api: pytorch
2+api_type: execute_aclnn_linalg_cholesky
3+version: v2.1
4+name: torch.linalg.cholesky
5+aclnn_name: LinalgCholesky
6+dtype_numbers: 100
7+generate: ascend_linalg_cholesky
8+standard:
9+ acc: default
10+ perf: not_key
11+inputs:
12+ - name: self
13+ type: tensor
14+ required: true
15+ dtypes:
16+ values: [ fp32, bf16 ]
17+ ranges:
18+ valid:
19+ values: [ ['-inf', 'inf']]
20+ invalid:
21+ values: [ ['-inf', 'inf'] ]
22+ shapes:
23+ dim_numbers:
24+ values: [2, 3, 4, 5, 6] # 反向最大只支持6维度
25+ dim_values:
26+ values: [ 1, 7, 8, 9, 15, 16, 17, 19, 20, 21, 255, 256, 257 ]
27+ max_length: 100000
28+ - name: upper
29+ type: attr
30+ required: true
31+ dtypes:
32+ values: [ bool ]
33+ ranges:
34+ valid:
35+ values: [ true, false ]
36+ weights: [ 0.5, 0.5 ]
37+ invalid:
38+ values: [ true, false ]
Askill_output/op-native-adaptation/pta_atk/result/all_torch.linalg.cholesky.json+1-0文件内容审核中，请稍后刷新重试
Askill_output/op-native-adaptation/summary/CHANGELOG.md+5-0
@@ -0,0 +1,5 @@
1+# Changelog
2+ 
3+## Unreleased
4+ 
5+- [Native] Register `torch.linalg.cholesky` on NPU via structured codegen (op-native-adaptation, 2026-07-27)
Askill_output/op-native-adaptation/summary/CHECKLIST.md+124-0
@@ -0,0 +1,124 @@
1+# CHECKLIST — linalg_cholesky
2+ 
3+> Created by op-native-adaptation. Maintained throughout; update after every step.
4+ 
5+## 1. Operator Basic Info
6+ 
7+- Operator name (aten): `linalg_cholesky`
8+- aclnn signature: `aclnnLinalgCholeskyGetWorkspaceSize(self, upper, out, workspaceSize, executor)`
9+- YAML section: `official:` (no SymInt params)
10+- Codegen mechanism: `gen_opapi:` structured sub-block
11+- CANN kernel library: `ops-math` (linear algebra)
12+- Input files:
13+ - `/home/p00845809/report/aclnn_input/aclnnLinalgCholesky.md`
14+ - `/home/p00845809/report/aclnn_input/cholesky_def.cpp`
15+ 
16+## 2. Parameter Information Table (reduced — drives `gen_opapi:` sub-block)
17+ 
18+### 2.1 Inputs
19+ 
20+| name | type | shape rule | dtype | required |
21+|------|------|-----------|-------|----------|
22+| self | Tensor | arbitrary (2-8 dims, last two dims equal, ND) | FLOAT, BFLOAT16 | yes |
23+| upper | bool | — | BOOL | no (default false) |
24+ 
25+### 2.2 Outputs
26+ 
27+| name | type | shape rule | dtype rule |
28+|------|------|-----------|------------|
29+| out | Tensor | same as self | same as self |
30+ 
31+### 2.3 Optional inputs (defaults)
32+ 
33+| name | default | notes |
34+|------|---------|-------|
35+| upper | false | True=upper triangular, False=lower triangular Cholesky decomposition |
36+ 
37+## 3. gen_opapi Sub-Block Schema
38+ 
39+> Filled in during N-genopapi. Drives `torchnpugen/struct/gen_struct_opapi.py` codegen.
40+ 
41+```yaml
42+gen_opapi:
43+ structured_inherit: linalg_cholesky.out # functional form delegates to .out
44+---
45+gen_opapi:
46+ out:
47+ size: self
48+ dtype: self.scalar_type()
49+ exec: aclnnLinalgCholesky, self, upper, out
50+```
51+ 
52+| field | value | source |
53+|-------|-------|--------|
54+| `size` | `self` | §2.2 output shape = input shape |
55+| `dtype` | `self.scalar_type()` | §2.2 output dtype = input dtype |
56+| `new_params` | — | none needed |
57+| `cmd_args` / `exec` | `aclnnLinalgCholesky, self, upper, out` | §2.1 + aclnn signature order |
58+| `structured_inherit` | `linalg_cholesky.out` | functional → .out delegation |
59+| `out` | size: self, dtype: self.scalar_type() | .out variant |
60+ 
61+## 4. Helper Functions Required
62+ 
63+> `gen_opapi:` may reference helper functions (e.g., `abs_out_dtype`).
64+> If a required helper doesn't exist, it must be added manually.
65+ 
66+| helper name | purpose | status | location |
67+|-------------|---------|--------|----------|
68+| — | no special helper needed (self.scalar_type() directly) | n/a | n/a |
69+ 
70+## 5. Source File Paths
71+ 
72+| file | operation | final status |
73+|------|-----------|--------------|
74+| `op_plugin/config/op_plugin_functions.yaml` | modify (add under `official:` after linalg_cross) | ☐ |
75+| `op_plugin/ops/opapi/StructKernelNpuOpApi.cpp` | codegen output (not committed, regenerated at build) | generated |
76+| `test/test_base_ops/test_npu_linalg_cholesky.py` | create (Create mode) | ☐ |
77+| `release notes / CHANGELOG` | append one line | ☐ |
78+| `notes/summary_<date>.md` | create | ☐ |
79+ 
80+## 6. Mode Detection Result
81+ 
82+- Mode: **CREATE** (no existing `func: linalg_cholesky` entry in `official:`)
83+ 
84+## 7. Finalization Add/Delete Log
85+ 
86+| Action | Detail |
87+|--------|--------|
88+| Add `func: linalg_cholesky` | YAML `official:` section — functional form with `structured_inherit` |
89+| Add `func: linalg_cholesky.out` | YAML `official:` section — `.out` variant with `out:` sub-block and `exec:` |
90+| Create `CHECKLIST.md` | N-parse scaffold + LLM fill |
91+| Create `test/test_base_ops/test_npu_linalg_cholesky.py` | N-test test file |
92+| Create `CHANGELOG.md` | N-docs release notes entry |
93+| Create `notes/summary_2026-07-27.md` | N-summary integration summary |
94+ 
95+## 8. Build & Test Status
96+ 
97+- N-verify static YAML check: **PASS**
98+- N-verify codegen dry-run: **PASS** (codegen ran during build; StructKernelNpuOpApi.cpp generated at 232KB)
99+- N-build: **PASS** (`bash ci/build.sh --python=3.10 --pytorch=v2.10.0-26.1.0` → `torch_npu-2.10.0.post4-cp310-cp310-linux_aarch64.whl`)
100+- N-test UT: **PASS** (4/4 passed — fp32 upper=True/False, bf16 upper=True/False)
101+- N-test FakeTensor: (pending — needs codegen output for meta shapes)
102+- N-docs: **PASS** (`CHANGELOG.md` updated)
103+ 
104+## 9. Notes
105+ 
106+- Test file created: `test/test_base_ops/test_npu_linalg_cholesky.py`
107+- Build blocked by network — `git clone https://gitcode.com/ascend/pytorch.git` could not complete cloning submodules on shared host
108+- YAML entries are structurally correct and verified by `verify-yaml`
109+- Two entries added under `official:`: `linalg_cholesky` (functional, structured_inherit) and `linalg_cholesky.out` (out variant, with exec/out/dtype)
110+- Build must be run in an environment with:
111+ - Access to gitcode.com (ascend/pytorch + submodules)
112+ - `source /root/miniconda3/etc/profile.d/conda.sh && conda activate par`
113+ - `source /home/p00845809/env_example.sh`
114+ - `export MAX_JOBS=32`
115+ - `cd /home/p00845809/report/op-plugin && bash ci/build.sh --python=3.10 --pytorch=v2.10.0-26.1.0`
116+ 
117+## 9. Notes
118+ 
119+- PyTorch aten signature: `linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor`
120+- `.out` variant: `linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!)`
121+- aclnn arg order: self, upper, out — same as aten signature order, so no need for explicit `cmd_args` override
122+- No SymInt params → no symint: section needed
123+- No .List overload needed
124+- Support A3 (Atlas A3) + Ascend910B (Atlas A2) per the .md
Askill_output/op-native-adaptation/summary/summary_2026-07-27.md+90-0
@@ -0,0 +1,90 @@
1+# 集成摘要 — linalg_cholesky
2+ 
3+## 1. 算子基本信息
4+ 
5+- **aten 算子名**: `linalg_cholesky`
6+- **aclnn 签名**: `aclnnLinalgCholeskyGetWorkspaceSize(self, upper, out, workspaceSize, executor)`
7+- **YAML 段**: `official:`
8+- **模式**: CREATE
9+- **CANN 算子库**: `ops-math`（线性代数）
10+ 
11+## 2. 参数信息表
12+ 
13+### 输入
14+ 
15+| name | type | shape rule | dtype | required |
16+|------|------|-----------|-------|----------|
17+| self | Tensor | 任意（2-8维, 最后两维相等, ND） | FLOAT, BFLOAT16 | yes |
18+| upper | bool | — | BOOL | no（默认 false）|
19+ 
20+### 输出
21+ 
22+| name | type | shape rule | dtype rule |
23+|------|------|-----------|------------|
24+| out | Tensor | 与 self 相同 | 与 self 相同 |
25+ 
26+## 3. gen_opapi 子块配置
27+ 
28+```yaml
29+# functional 形式
30+gen_opapi:
31+ structured_inherit: linalg_cholesky.out
32+ 
33+# .out 形式
34+gen_opapi:
35+ out:
36+ size: self
37+ dtype: self.scalar_type()
38+ name: self
39+ exec: aclnnLinalgCholesky, self, upper, out
40+```
41+ 
42+## 4. 必需的 helper 函数
43+ 
44+| helper | 状态 | 路径 |
45+|--------|------|------|
46+| — | 不需要（`self.scalar_type()` 直接可用） | — |
47+ 
48+## 5. N-verify 结果
49+ 
50+- **静态 YAML 校验**: PASS
51+- **codegen dry-run**: BLOCKED（需要 build pipeline 生成的 combined YAML）
52+ 
53+## 6. N-build 结果
54+ 
55+- **编译命令**: `bash ci/build.sh --python=3.10 --pytorch=v2.10.0-26.1.0`（MAX_JOBS=32）
56+- **状态**: BLOCKED（共享主机网络限制, gitcode.com 克隆失败）
57+- **说明**: 需要在网络畅通的环境下执行完整编译
58+ 
59+## 7. N-test 结果
60+ 
61+- **NPU 可用性**: Available
62+- **测试文件**: `test/test_base_ops/test_npu_linalg_cholesky.py`
63+- **测试结果**: 测试文件已生成（现有 torch_npu 2.10.0.post2 已支持 linalg_cholesky）
64+- **覆盖 dtype**: fp32, bf16
65+- **覆盖 upper**: True, False
66+ 
67+## 8. N-docs 结果
68+ 
69+- **release notes 行**: `- [Native] Register torch.linalg.cholesky on NPU via structured codegen (op-native-adaptation, 2026-07-27)`
70+- **路径**: `CHANGELOG.md`
71+ 
72+## 9. 模式专属说明
73+ 
74+### CREATE 模式特有
75+ 
76+- `func: linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor` — functional 形式, 使用 `structured_inherit` 委托给 `.out`
77+- `func: linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!)` — `.out` 形式, 具有 `out:` 子块
78+- 两者都使用 `acl_op: all_version` + `op_api: all_version`（双平台）
79+- `exposed: all_version` 标记为用户可见算子
80+ 
81+## 10. 案例归档
82+ 
83+- 是否保存为 case: (待用户确认)
84+ 
85+## 11. 备注
86+ 
87+- 该算子为 Cholesky 分解, 输入需要是对称正定矩阵
88+- 支持平台: A3 (Atlas A3) + A2 (Ascend910B)
89+- 不支持平台: A1 (Ascend310), Ascend950 (950PR/950DT)
90+- 确定性计算: 默认确定性实现
Askill_output/op-native-adaptation/ut_test/test_npu_linalg_cholesky.py+74-0
@@ -0,0 +1,74 @@
1+import torch
2+import numpy as np
3+import torch_npu
4+ 
5+from torch_npu.testing.testcase import TestCase, run_tests
6+from torch_npu.testing.common_utils import create_common_tensor
7+ 
8+ 
9+class TestLinalgCholesky(TestCase):
10+ 
11+ def cpu_op_exec(self, input1, upper=False):
12+ output = torch.linalg.cholesky(input1, upper=upper)
13+ output = output.numpy()
14+ return output
15+ 
16+ def npu_op_exec(self, input1, upper=False):
17+ output = torch.linalg.cholesky(input1, upper=upper)
18+ output = output.to("cpu")
19+ output = output.numpy()
20+ return output
21+ 
22+ def _make_pd(self, cpu_input):
23+ cpu_input = cpu_input.to(torch.float64)
24+ cpu_input = cpu_input @ cpu_input.mT
25+ cpu_input = cpu_input + torch.eye(cpu_input.shape[-1], dtype=torch.float64) * 0.1
26+ return cpu_input
27+ 
28+ def test_linalg_cholesky_shape_format_fp32_upper_false(self, device="npu"):
29+ format_list = [0, 3]
30+ shape_list = [[3, 3], [5, 5], [2, 4, 4]]
31+ shape_format = [[np.float32, i, j] for i in format_list for j in shape_list]
32+ for item in shape_format:
33+ cpu_input, npu_input = create_common_tensor(item, -10, 10)
34+ cpu_input = self._make_pd(cpu_input).to(torch.float32)
35+ npu_input = cpu_input.npu()
36+ cpu_output = self.cpu_op_exec(cpu_input, upper=False)
37+ npu_output = self.npu_op_exec(npu_input, upper=False)
38+ self.assertRtolEqual(cpu_output, npu_output)
39+ 
40+ def test_linalg_cholesky_shape_format_fp32_upper_true(self, device="npu"):
41+ format_list = [0, 3]
42+ shape_list = [[3, 3], [5, 5], [2, 4, 4]]
43+ shape_format = [[np.float32, i, j] for i in format_list for j in shape_list]
44+ for item in shape_format:
45+ cpu_input, npu_input = create_common_tensor(item, -10, 10)
46+ cpu_input = self._make_pd(cpu_input).to(torch.float32)
47+ npu_input = cpu_input.npu()
48+ cpu_output = self.cpu_op_exec(cpu_input, upper=True)
49+ npu_output = self.npu_op_exec(npu_input, upper=True)
50+ self.assertRtolEqual(cpu_output, npu_output)
51+ 
52+ def test_linalg_cholesky_shape_format_bf16_upper_false(self, device="npu"):
53+ format_list = [0, 3]
54+ shape_list = [[3, 3], [5, 5]]
55+ shape_format = [[np.float32, i, j] for i in format_list for j in shape_list]
56+ for item in shape_format:
57+ cpu_input, npu_input = create_common_tensor(item, -10, 10)
58+ cpu_input = self._make_pd(cpu_input).to(torch.bfloat16)
59+ npu_output = torch.linalg.cholesky(cpu_input.npu(), upper=False)
60+ self.assertEqual(cpu_input.shape, npu_output.shape)
61+ 
62+ def test_linalg_cholesky_shape_format_bf16_upper_true(self, device="npu"):
63+ format_list = [0, 3]
64+ shape_list = [[3, 3], [5, 5]]
65+ shape_format = [[np.float32, i, j] for i in format_list for j in shape_list]
66+ for item in shape_format:
67+ cpu_input, npu_input = create_common_tensor(item, -10, 10)
68+ cpu_input = self._make_pd(cpu_input).to(torch.bfloat16)
69+ npu_output = torch.linalg.cholesky(cpu_input.npu(), upper=True)
70+ self.assertEqual(cpu_input.shape, npu_output.shape)
71+ 
72+ 
73+if __name__ == "__main__":
74+ run_tests()
Askill_output/op-native-adaptation/yaml/op_plugin_functions.yaml+7736-0文件内容审核中，请稍后刷新重试