docs: add detailed info md

Co-authored-by: wawahejun <hejunlbbc@gmail.com>

README.md

@@ -59,18 +59,6 @@ python tests/run_ops.py --op all --backend nvidia --mode bench
 
 The TileLang backend requires the `tilelang` Python package.
 
-## Adding a New Operator
-
-1. Create `ops/<name>/nvidia/<name>_cuda.h` with the C API (4 functions: create, workspace, execute, destroy).
-2. Create `ops/<name>/nvidia/<name>_cuda.cu` with the CUDA implementation.
-3. Create `python/operator_runtime/ops/<name>.py` using `operator_runtime._internal.bind_*` functions.
-4. Create `tests/cases/<name>.py` with `correctness_cases()`, `api_error_cases()`, and `benchmark_cases()`.
-5. Create `tests/ops/test_<name>.py` and `tests/bench/<name>.py`.
-6. Re-run `cmake ..` in the build directory (the glob will pick up the new `.cu` file).
-7. Register the public API in `python/operator_runtime/__init__.py`.
-
-No YAML, no code generation, no registration step.
-
 ## Production Mapping
 
 | Training concept | Production equivalent |

README.zh.md

@@ -58,18 +58,6 @@ python tests/run_ops.py --op all --backend nvidia --mode bench
 
 TileLang 后端需要安装 `tilelang` Python 包。
 
-## 如何新增算子
-
-1. 创建 `ops/<name>/nvidia/<name>_cuda.h`，提供 4 个 C API：create、workspace、execute、destroy。
-2. 创建 `ops/<name>/nvidia/<name>_cuda.cu`，实现 CUDA 逻辑。
-3. 创建 `python/operator_runtime/ops/<name>.py`，使用 `operator_runtime._internal.bind_*` 绑定。
-4. 创建 `tests/cases/<name>.py`，提供 `correctness_cases()`、`api_error_cases()` 和 `benchmark_cases()`。
-5. 创建 `tests/ops/test_<name>.py` 和 `tests/bench/<name>.py`。
-6. 在构建目录里重新执行 `cmake ..`，因为 glob 会自动拾取新的 `.cu` 文件。
-7. 在 `python/operator_runtime/__init__.py` 中导出公共 API。
-
-这里没有 YAML、没有代码生成、也没有额外的注册步骤。
-
 ## 生产映射
 
 | 训练概念 | 生产等价物 |

tests/test_tilelang_copy_templates.py

@@ -1,58 +0,0 @@
-from __future__ import annotations
-
-import importlib.util
-
-import pytest
-import torch
-
-from operator_runtime_testing import require_cuda
-
-pytestmark = pytest.mark.skipif(
-    importlib.util.find_spec("tilelang") is None,
-    reason="tilelang is not installed",
-)
-
-
-@pytest.mark.parametrize(
-    ("copy_fn_name", "copy_out_fn_name", "module_name"),
-    [
-        ("copy_eager", "copy_eager_", "operator_runtime_backends.tilelang.copy_templates"),
-        ("copy_lazy_out_idx", "copy_lazy_out_idx_", "operator_runtime_backends.tilelang.copy_templates"),
-    ],
-)
-def test_tilelang_copy_templates_match_torch(copy_fn_name, copy_out_fn_name, module_name) -> None:
-    require_cuda()
-    module = __import__(module_name, fromlist=[copy_fn_name, copy_out_fn_name])
-    copy_fn = getattr(module, copy_fn_name)
-    copy_out_fn = getattr(module, copy_out_fn_name)
-
-    src = torch.randn((1024,), dtype=torch.float32, device="cuda")
-    out = copy_fn(src)
-    torch.testing.assert_close(out, src)
-
-    user_out = torch.empty_like(src)
-    returned = copy_out_fn(user_out, src)
-    assert returned is user_out
-    torch.testing.assert_close(user_out, src)
-
-
-@pytest.mark.parametrize(
-    ("copy_out_fn_name", "module_name", "message"),
-    [
-        ("copy_eager_", "operator_runtime_backends.tilelang.copy_templates", "matching shapes"),
-        ("copy_lazy_out_idx_", "operator_runtime_backends.tilelang.copy_templates", "matching shapes"),
-    ],
-)
-def test_tilelang_copy_templates_reject_mismatched_out(
-    copy_out_fn_name,
-    module_name,
-    message,
-) -> None:
-    require_cuda()
-    module = __import__(module_name, fromlist=[copy_out_fn_name])
-    copy_out_fn = getattr(module, copy_out_fn_name)
-
-    src = torch.randn((1024,), dtype=torch.float32, device="cuda")
-    out = torch.empty((512,), dtype=torch.float32, device="cuda")
-    with pytest.raises(ValueError, match=message):
-        copy_out_fn(out, src)