更新文档

doc/.gitignore

@@ -22,3 +22,4 @@ temp/
 *.json
 .temp/
 api/
+.temp/

doc/chaos/vta/index.md

@@ -1,4 +1,4 @@
-# VTA 教程
+# VTA 教程(chaos)
 
 官方教程见：[VTA](https://xinetzone.github.io/tvm/docs/topic/vta/index.html)。
 

doc/read/index.md

@@ -8,6 +8,7 @@ te/index
 topi/index
 tir/index
 op/index
+layout/index
 attrs/index
 expr/index
 reflection

doc/read/layout/InferCorrectLayoutOutput.ipynb

@@ -0,0 +1,171 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# InferCorrectLayoutOutput"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import set_env"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tvm\n",
+    "from tvm import relay\n",
+    "from tvm.relay.testing.temp_op_attr import TempOpAttr\n",
+    "from tvm.relay.op import op as _op"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "op_name = \"custom_op\"\n",
+    "\n",
+    "_op.register(op_name, r\"code(Add two tensor with inner broadcasting.)code\")\n",
+    "_op.get(op_name).set_num_inputs(2)\n",
+    "_op.get(op_name).add_argument(\"data_0\", \"Tensor\", \"The input data tensor.\")\n",
+    "_op.get(op_name).add_argument(\"data_1\", \"Tensor\", \"The input data tensor.\")\n",
+    "# 调用默认关系函数\n",
+    "_op.get(op_name).add_type_rel(\"Identity\")\n",
+    "_op.get(op_name).set_support_level(1)\n",
+    "_op.register_pattern(op_name, _op.OpPattern.ELEMWISE)\n",
+    "_op.register_stateful(op_name, False)\n",
+    "\n",
+    "assert _op.get(op_name).name == op_name\n",
+    "assert _op.get(op_name).num_inputs == 2\n",
+    "assert _op.get(op_name).get_attr(\"TOpPattern\") == _op.OpPattern.ELEMWISE\n",
+    "assert _op.get(op_name).get_attr(\"TOpIsStateful\") == False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;31mSignature:\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_infer_correct_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_layout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mSource:\u001b[0m   \n",
+      "\u001b[0;32mdef\u001b[0m \u001b[0mregister_infer_correct_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_layout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"Register infer op layout function for an op\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m    Parameters\u001b[0m\n",
+      "\u001b[0;34m    ----------\u001b[0m\n",
+      "\u001b[0;34m    op_name : str\u001b[0m\n",
+      "\u001b[0;34m        The name of the operator\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m    infer_layout: function (attrs: Attrs, inputs: List[Layout]) -> InferCorrectLayoutOutput\u001b[0m\n",
+      "\u001b[0;34m        The function to infer correct layout\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m    level : int\u001b[0m\n",
+      "\u001b[0;34m        The priority level\u001b[0m\n",
+      "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mtvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_op_attr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"FInferCorrectLayout\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_layout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFile:\u001b[0m      /media/pc/data/lxw/ai/tvm/python/tvm/relay/op/op.py\n",
+      "\u001b[0;31mType:\u001b[0m      function"
+     ]
+    }
+   ],
+   "source": [
+    "_op.register_infer_correct_layout??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "{func}`~tvm.relay.op.op.register_infer_correct_layout` 用于为算子注册推断布局函数。\n",
+    "\n",
+    "参数说明：\n",
+    "- `op_name`：字符串类型，表示算子的名称。\n",
+    "- `infer_layout`：函数类型，接受两个参数 `attrs` 和 `inputs`，返回 {class}`~tvm.relay.transform.infer_layout_utils.InferCorrectLayoutOutput` 类型的对象。该函数用于推断正确的布局。\n",
+    "- `level`：整数类型，表示优先级级别。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.infer_layout(attrs: tvm.ir.attrs.Attrs, inputs: list[tvm.tir.data_layout.Layout])>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tvm.relay.transform.infer_layout_utils import InferCorrectLayoutOutput\n",
+    "from tvm.ir import Attrs\n",
+    "from tvm.tir.data_layout import Layout\n",
+    "\n",
+    "def infer_layout(attrs: Attrs, inputs: list[Layout]):\n",
+    "    return InferCorrectLayoutOutput()\n",
+    "_op.register_infer_correct_layout(op_name, infer_layout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;31mInit signature:\u001b[0m \u001b[0mInferCorrectLayoutOutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_layouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_layouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_attrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mDocstring:\u001b[0m      An output structure to hold results from FInferCorrectLayout calls.\n",
+      "\u001b[0;31mFile:\u001b[0m           /media/pc/data/lxw/ai/tvm/python/tvm/relay/transform/infer_layout_utils.py\n",
+      "\u001b[0;31mType:\u001b[0m           type\n",
+      "\u001b[0;31mSubclasses:\u001b[0m     "
+     ]
+    }
+   ],
+   "source": [
+    "InferCorrectLayoutOutput?"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "xin",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

doc/read/layout/index.md

@@ -0,0 +1,6 @@
+# 数据布局
+
+```{toctree}
+InferCorrectLayoutOutput
+```
+

doc/topics/extension/set_env.py → doc/read/layout/set_env.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 from tvm_book.config.env import set_tvm
-TVM_ROOT = Path(__file__).resolve().parents[5]
+# TVM_ROOT = Path(__file__).resolve().parents[5]
 # print(TVM_ROOT)
-# TVM_ROOT = "/media/pc/data/lxw/ai/tvm/"
+TVM_ROOT = "/media/pc/data/lxw/ai/tvm/"
 set_tvm(TVM_ROOT)

doc/topics/index.md

@@ -2,5 +2,4 @@
 
 ```{toctree}
 extension/index
-onnxscript/index
 ```

doc/tutorials/frontend/index.md

@@ -8,6 +8,7 @@ mxnet
 tensorflow-tvm/index
 pytorch-tvm/index
 onnx-tvm/index
+onnxscript/index
 caffe-tvm/index
 relax-fx
 ```

doc/tutorials/frontend/onnx-tvm/index.md

@@ -4,6 +4,4 @@
 onnx-format
 onnxruntime-start
 torch2onnx/index
-torch2onnx-test
-test
 ```

doc/tutorials/frontend/onnx-tvm/onnx-format.ipynb

@@ -757,7 +757,7 @@
     "之前的模型假设线性回归的系数也是模型的输入。那不太方便。它们应该作为常量或初始化项成为模型本身的一部分，以遵循 onnx 语义。下一个示例修改了前一个示例，将输入 `A` 和 `B` 更改为初始化式。（参见 [array](https://onnx.ai/onnx/api/numpy_helper.html#l-numpy-helper-onnx-array)）。\n",
     "\n",
     "- `onnx.numpy_helper.to_array`: 将 ONNX 转换为 NumPy 数组。\n",
-    "- `onnx.numpy_helper.from_array`: 将N umPy 数组转换为 ONNX。"
+    "- `onnx.numpy_helper.from_array`: 将 NumPy 数组转换为 ONNX。"
    ]
   },
   {