diff --git a/docs/arch/arch/05-device_target_interactions.md b/docs/arch/arch/05-device_target_interactions.md index 4a9b4d05..fc4c862f 100644 --- a/docs/arch/arch/05-device_target_interactions.md +++ b/docs/arch/arch/05-device_target_interactions.md @@ -13,7 +13,7 @@ sidebar_position: 150 * [Target](#tvm-target-specific-target) 类描述了运行函数的设备。它既对 target 代码生成器公开，也对优化 pass 公开。 * [target 代码生成器](#tvm-target-specific-codegen) 从 IRModule 构造了一个 [模块](/docs/arch/arch/runtimes#module)，它由一个或多个 [PackedFunc](/docs/arch/arch/runtimes#PackedFunc) 组成。 -## DeviceAPI {#tvm-target-specific-device-api} +## DeviceAPI `DeviceAPI` 表示特定硬件设备 API 的句柄。（例如，`CUDADeviceAPI` 通过 CUDA 框架处理所有的交互。）大多数 `DeviceAPI` 方法接收一个 `device_id` 参数，来指定访问哪个设备。Python 中通常用 `tvm.runtime.device()` 函数访问它们，这个函数返回特定设备的句柄，通过特定 API 访问。（例如，`tvm.runtime.device()` 通过 CUDA API 访问物理设备 `0`。） @@ -52,7 +52,7 @@ TVM 框架若要使用新的 DeviceAPI，应该按照以下步骤注册： 2. 给 [device_api.h](https://github.com/apache/tvm/blob/main/include/tvm/runtime/device_api.h) 中的 `DeviceName` 添加一个案例，从而将枚举值转换为字符串表示形式。这个字符串表示应该和 `TVM_REGISTER_GLOBAL` 的名称匹配。 3. 将入口添加到 `tvm.runtime.Device` 的 `MASK2STR` 和 `STR2MASK` 字典，获取新的枚举值。 -## Target 定义 {#tvm-target-specific-target} +## Target 定义 `Target` 对象是属性（包含物理设备、其硬件/驱动程序限制，及其功能）的查找表。在优化和代码生成阶段都可以访问 `Target`。虽然相同的 `Target` 类适用于所有 runtime target，但每个 runtime target 可能需要添加特定于 target 的选项。 @@ -64,7 +64,7 @@ TVM 框架若要使用新的 DeviceAPI，应该按照以下步骤注册： 代码生成器中可以用 C++ 中的 `target->GetAttr(param_name)`，或是 Python 中的 `target.attrs` 字典来访问 target 属性。 -## Target 代码生成器 {#tvm-target-specific-codegen} +## Target 代码生成器 代码生成器采用优化的 `IRModule`，并将其转换为可执行表示。每个代码生成器注册后，才能被 TVM 框架使用。这是通过注册 `"target.build.foo"` 函数来完成的，其中 `foo` 与上面的 `TVM_REGISTER_TARGET_KIND` 定义中使用的名称相同。 diff --git a/docs/arch/arch/07-inferbound.md b/docs/arch/arch/07-inferbound.md index 05ba0143..f81765ae 100644 --- a/docs/arch/arch/07-inferbound.md +++ b/docs/arch/arch/07-inferbound.md @@ -146,7 +146,7 @@ InferBound pass 有两个不是很明显的属性： 消息传递函数被命名为「PassUp」或「PassDown」，取决于消息是从 DAG 中的子代传递给其父代（「PassUp」），还是从父代传递给其子代（「PassDown」）。例如，上图左侧的大箭头显示 PassDownDomain 从根 IterVar `i` 向其子 `i.outer` 和 `i.inner` 发送消息。 -## PassDownDomain {#passdowndomain} +## PassDownDomain PassDownDomain 的作用是为 root_iter_vars 取 InferRootBound 产生的 Range，并设置 stage 中所有其他 IterVars 的 Range。 @@ -193,7 +193,7 @@ InferBound 调用 InferRootBound，然后在 stage 计算图中的每个 stage *对于包含 compute_at 的 schedules**而言**，InferBound 更为复杂。因此首先**针对**不包含 compute_at 的 schedules**解读**InferBound。* -### 阶段 1：为 consumer 的 leaf_iter_vars 初始化 IntSet {#phase1} +### 阶段 1：为 consumer 的 leaf_iter_vars 初始化 IntSet ``` c++ /* @@ -212,7 +212,7 @@ InferBound 调用 InferRootBound，然后在 stage 计算图中的每个 stage 简单起见，假设 schedule 不包含线程轴。这种情况下，仅当 schedule 包含 compute_at 时，才和案例 2 相关。参阅 [InferBound 与 compute_at](#inferboundca) 节来进一步获取更多信息。 -### 阶段 2：将 IntSet 从 consumer 的 leaf 传到 consumer 的 root {#phase2} +### 阶段 2：将 IntSet 从 consumer 的 leaf 传到 consumer 的 root ```c++ /* @@ -293,7 +293,7 @@ IntSet 的并集是通过将每个 IntSet 转换为一个区间来计算的， ![图片](/img/docs/tvmai/tvmai.github.io/main/images/docs/inferbound/gatherbound_problem.png) -## InferBound 与 compute_at {#inferboundca} +## InferBound 与 compute_at 若 schedule 包含 compute_at，则 InferRootBound 的阶段 1-2 会变得更加复杂。 @@ -377,7 +377,7 @@ public: 再次考虑上面的例子。为了让 InferBound 确定必须计算 C 的多少元素，重要的是，要知道 C 的计算是发生在 D 的叶变量的范围内，还是在该范围之上。在例 1 中，C 的计算发生在 D 的所有叶变量的范围*之上*。在例 2，C 的计算发生在 D 的所有叶变量的范围*内*。在例 3，C 出现在D 的 i 维度的范围内，但在 D 的 j 维度的范围之上。 -CreateAttachPath 负责确定哪些作用域包含 stage C。这些作用域按从最内层到最外层的顺序排列。因此，对于每个 stage，CreateAttachPath 都会生成一个「附加路径」，其中列出了包含该 stage 从最里面到最外面的范围，在例 1，C 的附加路径为空。在例 2，C 的附加路径包含 {j，i}。在例 3，C 的附加路径是 {i}。 +CreateAttachPath 负责确定哪些作用域包含 stage C。这些作用域按从最内层到最外层的顺序排列。因此，对于每个 stage，CreateAttachPath 都会生成一个「附加路径」，其中列出了包含该 stage 从最里面到最外面的范围，在例 1，C 的附加路径为空。在例 2，C 的附加路径包含 `{j，i}`。在例 3，C 的附加路径是 `{i}`。 以下示例阐明了附加路径的概念，适用于更复杂的情况。 @@ -415,11 +415,11 @@ realize D([0, 4], [0, 5], [0, 16]) { } ``` -在这种情况下，C 的附加路径是 {dk, dj, di}。注意 C 没有使用 di，但 di 仍然出现在 C 的附加路径中。 +在这种情况下，C 的附加路径是 `{dk, dj, di}`。注意 C 没有使用 di，但 di 仍然出现在 C 的附加路径中。 **例 5** -根据上述定义，可以很自然地在拆分后应用 Compute_at。下面例子中，C 的附着点是 D 的 j_inner。C 的附着路径是 {j_inner, j_outer, i}。 +根据上述定义，可以很自然地在拆分后应用 Compute_at。下面例子中，C 的附着点是 D 的 j_inner。C 的附着路径是 `{j_inner, j_outer, i}`。 ``` python C = tvm.compute((5, 16), lambda i, j : tvm.const(5, "int32"), name='C') @@ -456,7 +456,7 @@ s[C].compute_at(s[D], D.op.axis[1]) s[D].compute_at(s[E], E.op.axis[1]) ``` -当 `debug_keep_trivial_loop=True` 时，C 的附加路径为 {dj,di,ej,ei}，D 的附加路径为 {ej,ei}： +当 `debug_keep_trivial_loop=True` 时，C 的附加路径为 `{dj,di,ej,ei}`，D 的附加路径为 `{ej,ei}`： ``` c++ // attr [D] storage_scope = "global" @@ -494,7 +494,7 @@ produce E { 若没有附加，则已经为 consumer 变量计算的 Range 定义了 consumer 需要多少 C。但是，若 stage 实际上在 consumer 变量 j 的一个范围内，那么一次只需要 j 的范围内的一个点。 -### 阶段 1：为 consumer 的 leaf_iter_vars 初始化 IntSet {#phase1ca} +### 阶段 1：为 consumer 的 leaf_iter_vars 初始化 IntSet ``` c++ /* @@ -512,7 +512,7 @@ produce E { * 案例 3：需要释放。这种情况下，leaf 的 Range 被简单地转换为 IntSet。 若在 consumer 中遇到 stage C 的附着点，就会发生案例 2。对于此 attach_ivar，以及 consumer 的所有更高叶变量，将应用案例 2。若 C 在叶变量的 Range 内，这将确保仅请求叶变量范围内的单个点。 -### 阶段 2：将 IntSet 从 consumer 的 leaf 传到 consumer 的 root {#phase2ca} +### 阶段 2：将 IntSet 从 consumer 的 leaf 传到 consumer 的 root ``` c++ /* @@ -530,7 +530,7 @@ produce E { PassUpDomain 将 up_state 向 consumer 传给所有 IterVars 后，将创建一个从 root_iter_vars 到 IntSet 的新映射。若 stage 没有附加到当前 consumer，那么对于 consumer 的 attach_path 中的每个变量 iv，将 iv 的 Range 添加到一个 `relax_set`。stage 的 root 变量是根据这个 `relax_set` 进行评估的。 -这是为了处理类似以下示例的情况，其中 C 没有附加到任何地方，但它的 consumer D 在 stage E 中附加。这种情况下，在确定 C 有多少需要计算时，必须考虑 D 的 attach_path，{ej，ei}。 +这是为了处理类似以下示例的情况，其中 C 没有附加到任何地方，但它的 consumer D 在 stage E 中附加。这种情况下，在确定 C 有多少需要计算时，必须考虑 D 的 attach_path，`{ej，ei}`。 ``` python C = tvm.compute((5, 16), lambda ci, cj : tvm.const(5, "int32"), name='C') diff --git a/docs/arch/arch/10-relay_op_strategy.md b/docs/arch/arch/10-relay_op_strategy.md index d5c93a1a..07174427 100644 --- a/docs/arch/arch/10-relay_op_strategy.md +++ b/docs/arch/arch/10-relay_op_strategy.md @@ -157,7 +157,7 @@ def conv2d_strategy_mytarget(attrs, inputs, out_type, target): ... ``` -## 从 Op 策略中选择实现 {#select-implementation-from-op-strategy} +## 从 Op 策略中选择实现 在编译过程中，Relay 编译引擎要确定当有多个算子时，使用哪个算子来实现。选择策略的工作原理如下。 diff --git a/docs/arch/arch/11-convert_layout.md b/docs/arch/arch/11-convert_layout.md index f3a8b7f5..14def826 100644 --- a/docs/arch/arch/11-convert_layout.md +++ b/docs/arch/arch/11-convert_layout.md @@ -213,7 +213,7 @@ ConvertLayout pass 非常易于使用。pass 不是默认 relay.build pipeline 为了指定要转换到的布局，创建一个映射，该映射由重度布局敏感的算子指向该算子期望布局的列表。以下第一个示例指定了数据布局，允许内核布局自动转换为 TVM 支持的布局（针对特定的数据布局和算子）。这是通过使用「default」关键字指定的。 -第二个示例显示了如何转换为指定内核布局。注意，以下示例将转换为相同的布局，即 {‘nn.conv2d’: [‘NCHW’, ‘default’]} == {‘nn.conv2d’: [‘NCHW’, ‘OIHW’]} +第二个示例显示了如何转换为指定内核布局。注意，以下示例将转换为相同的布局，即 `{‘nn.conv2d’: [‘NCHW’, ‘default’]} == {‘nn.conv2d’: [‘NCHW’, ‘OIHW’]}` ``` python # TFlite 框架到 Relay 解析器 - 默认布局是 NHWC diff --git a/docs/arch/arch/12-benchmark.md b/docs/arch/arch/12-benchmark.md index c0f0a45f..771cc3d0 100644 --- a/docs/arch/arch/12-benchmark.md +++ b/docs/arch/arch/12-benchmark.md @@ -20,18 +20,18 @@ sidebar_position: 220 | runtime_ms_std | 5.3 | statistics | | | timestamp | 1572282699.6 | metadata | indicates when this record is logged | | schema_version | “0.1” | metadata | ensure reproducibility as we iterate on this schema | -| metadata | { “docker_tag”:”gcr.io/…/0a680”, … } | metadata | docker_tag is optional | -| workload_args | {“input_name”: “Input3”, “input_shape”: [list_of_shape], “data_layout”: NHCW} | workload | | -| workload_metadata | {“class”: “vision”,”doc_url”: “https://github.com/.../README.md”, “opset”: 7,”type”: “body_analysis”,”url”: “https://onnxzoo...ferplus.tar.gz”, “md5”: “07fc7…”} | workload | source of workload | +| metadata | `{ “docker_tag”:”gcr.io/…/0a680”, … }` | metadata | docker_tag is optional | +| workload_args | `{“input_name”: “Input3”, “input_shape”: [list_of_shape], “data_layout”: NHCW}` | workload | | +| workload_metadata | `{“class”: “vision”,”doc_url”: “https://github.com/.../README.md”, “opset”: 7,”type”: “body_analysis”,”url”: “https://onnxzoo...ferplus.tar.gz”, “md5”: “07fc7…”}` | workload | source of workload | | engine_version | “1.0.5” | compiler | use semvar format | -| engine_config | {“llvm”: “llvm-8”, “nvcc”: 10.1, “accelerator”: “MLAS”, “relay_opt_level”: 3, “tvm_target”:”llvm -mcpu=cascadelake”} | compiler | fields are optionally specified | -| compilation_config | {“opt_level”: 3, “layer_schedules”:[]/ } | compiler | fields are optionally specified | -| software_config | {“os”: “ubuntu:18.04”,”pip”: { “docker”: “4.1.0”, “gitpython”: “3.0.4”, “numpy”: “1.17.4”, “onnx”: “1.6.0”}, “cudnn”: “cudnn-8”, “cuda_driver”: “480.10.1”} | backend | env dependency list | -| runtime_config | {“num_cpu_threads”: 3} | backend | info on non-hardware, non-software metadata | -| hardware_config | {“cpu_count”: 16, “cloud_machine_type”:”c2-standard-16”, “memory_GB”:64} | hardware | json descriptor of target hardware environment | -| execution_config | {“number”: 1, “repeat”: 10, “min_repeat_ms”, 0} | statistics | workload execution parameters | -| metrics | {“accuracy”: 48.5,“compilation_ms_mean”: 12} | statistics | other metrics | -| runtime_raw | [{“runtime_ms”: 12, …}, {“runtime_ms”:13,…},…] | statistics | optional raw metrics array | +| engine_config | `{“llvm”: “llvm-8”, “nvcc”: 10.1, “accelerator”: “MLAS”, “relay_opt_level”: 3, “tvm_target”:”llvm -mcpu=cascadelake”}` | compiler | fields are optionally specified | +| compilation_config | `{“opt_level”: 3, “layer_schedules”:[]/ }` | compiler | fields are optionally specified | +| software_config | `{“os”: “ubuntu:18.04”,”pip”: { “docker”: “4.1.0”, “gitpython”: “3.0.4”, “numpy”: “1.17.4”, “onnx”: “1.6.0”}, “cudnn”: “cudnn-8”, “cuda_driver”: “480.10.1”}` | backend | env dependency list | +| runtime_config | `{“num_cpu_threads”: 3}` | backend | info on non-hardware, non-software metadata | +| hardware_config | `{“cpu_count”: 16, “cloud_machine_type”:”c2-standard-16”, “memory_GB”:64}` | hardware | json descriptor of target hardware environment | +| execution_config | `{“number”: 1, “repeat”: 10, “min_repeat_ms”, 0}` | statistics | workload execution parameters | +| metrics | `{“accuracy”: 48.5,“compilation_ms_mean”: 12}` | statistics | other metrics | +| runtime_raw | `[{“runtime_ms”: 12, …}, {“runtime_ms”:13,…},…]` | statistics | optional raw metrics array | ## 存储格式 @@ -98,4 +98,4 @@ sidebar_position: 220 "execution_config":{}, "metrics":{} } -``` \ No newline at end of file +``` diff --git a/docs/arch/arch/17-model_library_format.md b/docs/arch/arch/17-model_library_format.md index fc4c9f46..4ba0b705 100644 --- a/docs/arch/arch/17-model_library_format.md +++ b/docs/arch/arch/17-model_library_format.md @@ -78,7 +78,7 @@ TVM 生成的所有代码都放在这个目录中。编写代码时，生成的 * `target`：将 `device_type`（潜在的整数，作为字符串）映射到描述用于该 `device_type` 的 Relay 后端的子 target 的字典。 * `version`：标识模型库格式中格式的数字版本号。当元数据结构或磁盘结构更改时，这个数字会增加。本文档反映的是第 `5` 版。 -### 内存使用总结 {#memory-usage-summary} +### 内存使用总结 它是具有如下子键的字典： diff --git a/docs/arch/arch/runtimes/index.md b/docs/arch/arch/runtimes/index.md index f3d5a169..3994f1e8 100644 --- a/docs/arch/arch/runtimes/index.md +++ b/docs/arch/arch/runtimes/index.md @@ -18,7 +18,7 @@ TVM 支持多种编程语言进行编译器堆栈开发和部署。本文档将 * 实验：将编译好的函数发送到嵌入式设备上直接运行。 我们期望能够用任何语言定义一个函数，然后用另一种语言调用。还期望将 runtime 内核最小化，部署到嵌入式设备。 -## PackedFunc {#PackedFunc} +## PackedFunc [PackedFunc](https://github.com/apache/tvm/blob/main/include/tvm/runtime/packed_func.h) 是一个简单而优雅的解决方案，它可以解决以上问题。单个 `PackedFunc` 对象代表一个函数调用，其调用者和被调用者可能使用不同的语言。 @@ -104,7 +104,7 @@ PackedFunc 用于编译器和部署堆栈： 与普通函数相比，调用 PackedFunc 的开销很小，因为它只在堆栈上保存了几个值，所以只要不包装小的函数即可。总之，PackedFunc 是 TVM 中的通用粘合剂，可以广泛使用它来支持编译器和部署。 -## 模块 {#module} +## 模块 由于 TVM 支持多种类型的设备，因此需要支持不同类型的驱动程序。必须用驱动程序 API 来加载内核，以打包格式设置参数，并执行内核启动。 @@ -209,6 +209,6 @@ PackedFunc 中的每个参数都包含一个关联值 [TVMValue](https://github. * [c_runtime_api.cc](https://github.com/apache/tvm/blob/main/src/runtime/c_runtime_api.cc#L262)，用于 C API 以及如何提供回调。 为了支持扩展类型，使用了注册表系统来注册类型相关信息，如 C++ 中对所有类型的支持，参阅 [扩展类型](https://github.com/apache/tvm/tree/main/apps/extension) 了解更多详细信息。 -# Runtime 特定信息 {#runtime-specific-information} +# Runtime 特定信息 * [Vulkan Runtime](vulkan) diff --git a/docs/arch/arch/runtimes/vulkan.md b/docs/arch/arch/runtimes/vulkan.md index bd6fbeee..d396cb31 100644 --- a/docs/arch/arch/runtimes/vulkan.md +++ b/docs/arch/arch/runtimes/vulkan.md @@ -30,7 +30,7 @@ TVM 支持用 Vulkan 计算着色器（Vulkan compute shaders）来查询。所 截至 2021 年 5 月，还有一些 Vulkan 的实现没支持。例如，要支持 64 位整数。若不支持 Vulkan target，则会在 SPIR-V 代码生成期间报错。我们正努力消除这些限制，并支持其他 Vulkan 实现。 -## SPIR-V 功能 {#tvm-table-vulkan-capabilities} +## SPIR-V 功能 某些特定于设备的功能还对应于 SPIR-V 功能或扩展，它们必须在着色器中声明，或对应于要使用某个功能所需的最低 SPIR-V 版本。TVM 生成的着色器将声明执行编译好的计算图所需的最小扩展/功能集，以及 SPIR-V 的最小允许版本。 diff --git a/docs/arch/index.md b/docs/arch/index.md index a8c4f716..7f2c921e 100644 --- a/docs/arch/index.md +++ b/docs/arch/index.md @@ -13,7 +13,7 @@ title: 设计与架构 本指南提供了架构的一些补充视图。首先研究端到端的编译流程，并讨论关键的数据结构和转换。这种基于 runtime 的视图侧重于运行编译器时每个组件的交互。接下来研究代码库的逻辑模块及其关系。这部分提供了设计的静态总体视图。 -## 编译流程示例 {#example-compilation-flow} +## 编译流程示例 本指南研究编译器中的编译流程示例，下图显示了流程。在高层次，它包含以下步骤： @@ -132,7 +132,7 @@ result = gmod["get_output"](0).numpy() * 编写一组自定义转换（例如自定义量化）。 * 用 TVM 的 Python API 直接操作 IR。 -## 逻辑架构组件 {#logical-architecture-components} +## 逻辑架构组件 ![/img/docs/tlc-pack/web-data/main/images/design/tvm_static_overview.svg](/img/docs/tlc-pack/web-data/main/images/design/tvm_static_overview.svg) diff --git a/docs/contribute/code_guide.md b/docs/contribute/code_guide.md index 0f232d59..894ca591 100644 --- a/docs/contribute/code_guide.md +++ b/docs/contribute/code_guide.md @@ -26,26 +26,26 @@ python tests/scripts/ci.py lint clang-format 并不完美，必要时可以在某些代码区域上禁用 clang-format。 ``` c++ -// clang-format off -void Test() { +// clang-format off +void Test() { // clang-format 将在这个区域禁用。 -} +} // clang-format on ``` 因为 clang-format 可能无法识别宏，所以建议像普通函数样式一样使用宏。 ``` c++ -#define MACRO_IMPL { custom impl; } +#define MACRO_IMPL { custom impl; } #define MACRO_FUNC(x) // 不是首选，因为 clang-format 可能会将其识别为类型。 -// 首选 +// 首选 virtual void Func2() MACRO_IMPL; void Func3() { - // 首选 + // 首选 MACRO_FUNC(xyz); } ``` @@ -68,7 +68,7 @@ def test_mytest(target, dev): ... ``` -用 `target="llvm"`、`target="cuda"` 和其他几个运行 `test_mytest`。这可以确保测试由 CI 在正确的硬件上运行。如果只想针对几个 target 进行测试，请使用 `@tvm.testing.parametrize_targets("target_1", "target_2")`。如果想在单个 target 上进行测试，请使用来自 `tvm.testing()`{.interpreted-text role="func"} 的相关装饰器。例如，CUDA 测试使用 `@tvm.testing.requires_cuda` 装饰器。 +用 `target="llvm"`、`target="cuda"` 和其他几个运行 `test_mytest`。这可以确保测试由 CI 在正确的硬件上运行。如果只想针对几个 target 进行测试，请使用 `@tvm.testing.parametrize_targets("target_1", "target_2")`。如果想在单个 target 上进行测试，请使用来自 `tvm.testing()` 的相关装饰器。例如，CUDA 测试使用 `@tvm.testing.requires_cuda` 装饰器。 ## 处理整型常量表达式 diff --git a/docs/contribute/document.md b/docs/contribute/document.md index 93e00467..52d0765d 100644 --- a/docs/contribute/document.md +++ b/docs/contribute/document.md @@ -126,8 +126,8 @@ My Section reStructuredText 的 [figure](https://docutils.sourceforge.io/docs/ref/rst/directives.html#figure) 和 [image](https://docutils.sourceforge.io/docs/ref/rst/directives.html#image) 元素允许文档包含图像 URL。 -为 TVM 文档创建的图像文件应该在 仓库中，而使用这些图像的 *.rst* 文件应该在 TVM 仓库（）。 +为 TVM 文档创建的图像文件应该在 [https://github.com/tlc-pack/web-data](https://github.com/tlc-pack/web-data) 仓库中，而使用这些图像的 *.rst* 文件应该在 TVM 仓库（[https://github.com/apache/tvm](https://github.com/apache/tvm)）。 这需要两个 GitHub Pull Request，一个用于图像文件，另一个用于 *.rst* 文件。contributor 与 reviewer 之间可能需要讨论来协调 review 的过程。 -重要提示：使用上述的两个 pull request 时，请先在 中 merge pull request，然后再在 中 merge pull request。这有助于确保 TVM 在线文档中的所有 URL 链接都是有效的。 +重要提示：使用上述的两个 pull request 时，请先在 [https://github.com/tlc-pack/web-data](https://github.com/tlc-pack/web-data) 中 merge pull request，然后再在 [https://github.com/apache/tvm](https://github.com/apache/tvm) 中 merge pull request。这有助于确保 TVM 在线文档中的所有 URL 链接都是有效的。 diff --git a/docs/contribute/pull_request.md b/docs/contribute/pull_request.md index 62edd700..04cf8da1 100644 --- a/docs/contribute/pull_request.md +++ b/docs/contribute/pull_request.md @@ -20,17 +20,17 @@ sidebar_position: 2 # 虽然使用的 lint 命令应该与在 CI 中运行的相同，但此命令会重现 # 准确的 CI lint 过程（通常有助于调试 lint 脚本错误或避免手动安装工具） python tests/scripts/ci.py lint - + # 运行所有的 lint 步骤。 docker/lint.sh - + # 要单独运行步骤，请在命令行中指定步骤名称。 # 一个不正确拼写的步骤名称会导致工具打印所有可用的步骤。 docker/lint.sh ... ``` - + 若 clang-format lint 检查失败，运行 git-clang-format 自动对代码重新格式化： - + ``` bash # 运行 clang-format 检查所有自 upstream/main 以来改变的文件 docker/bash.sh ci_lint ./tests/lint/git-clang-format.sh upstream/main @@ -54,7 +54,7 @@ sidebar_position: 2 使用 Docker 镜像，创建能部署到多台机器上的稳定的 CI 环境。按照 [这个 issue 模板](https://github.com/apache/tvm/issues/new?assignees=&labels=&template=ci-image.md&title=%5BCI+Image%5D+) 的步骤，更新 CI Docker 镜像。 -## 测试 {#pr-testing} +## 测试 尽管每个 pull request 都有自动运行单元测试的 hook，但推荐先在本地运行单元测试，以减少 reviewer 的负担并加快 review 过程。 diff --git a/docs/contribute/release_process.md b/docs/contribute/release_process.md index 50a442fc..8996320f 100644 --- a/docs/contribute/release_process.md +++ b/docs/contribute/release_process.md @@ -29,11 +29,11 @@ TVM 中的版本 manager 角色意味着要负责以下这些事情： 如果已经上传了密钥，则可以跳过这部分。 -参考 将生成的 gpg 密钥上传到公钥服务器。 +参考 [https://www.apache.org/dev/openpgp.html#generate-key](https://www.apache.org/dev/openpgp.html#generate-key) 将生成的 gpg 密钥上传到公钥服务器。 通过 `gpg --export` 和 `gpg --import` 命令可以将 gpg 密钥传输到另一台机器上发布。 -最后一步是使用你的代码签名密钥 更新 KEYS 文件。查看对 TVM 主分支及 ASF SVN 的更改， +最后一步是使用你的代码签名密钥 [https://www.apache.org/dev/openpgp.html#export-public-key](https://www.apache.org/dev/openpgp.html#export-public-key) 更新 KEYS 文件。查看对 TVM 主分支及 ASF SVN 的更改， ``` bash # 指定 --depth=files 参数将跳过检查已有文件夹 @@ -122,7 +122,7 @@ svn ci --username $ASF_USERNAME --password "$ASF_PASSWORD" -m "Add RC" ## 对候选版本投票 -第一次投票在 Apache TVM 开发者名单（）上进行。为了获得更多关注，可以创建一个以 “[VOTE]” 开头的 GitHub Issue，它会自动镜像到 dev@。可以查看以前的投票帖子来了解它是如何进行的。电子邮件应遵循以下格式： +第一次投票在 Apache TVM 开发者名单（[dev@tvm.apache.org](mailto:dev@tvm.apache.org)）上进行。为了获得更多关注，可以创建一个以 “[VOTE]” 开头的 GitHub Issue，它会自动镜像到 dev@。可以查看以前的投票帖子来了解它是如何进行的。电子邮件应遵循以下格式： - 在电子邮件中提供版本说明初稿的链接 - 提供候选版本工程的链接 @@ -157,8 +157,8 @@ git push --delete origin v0.6.0.rc2 ## 更新 TVM 网站 -网站仓库位于 。向下载页面中添加版本工程以及 GPG 签名和 SHA 哈希。 +网站仓库位于 [https://github.com/apache/tvm-site](https://github.com/apache/tvm-site)。向下载页面中添加版本工程以及 GPG 签名和 SHA 哈希。 ## 发布公告 -向 和 发送公告邮件。公告应包括发布说明和下载页面的链接。 +向 [announce@apache.org](mailto:announce@apache.org) 和 [dev@tvm.apache.org](mailto:dev@tvm.apache.org) 发送公告邮件。公告应包括发布说明和下载页面的链接。 diff --git a/docs/how_to/deploy/07-vitis_ai.md b/docs/how_to/deploy/07-vitis_ai.md index 68f67612..5ea3e553 100644 --- a/docs/how_to/deploy/07-vitis_ai.md +++ b/docs/how_to/deploy/07-vitis_ai.md @@ -28,7 +28,7 @@ TVM 中当前的 Vitis AI 流支持使用 [Zynq Ultrascale+ MPSoc](https://www.x [Vitis AI 系统要求页面](https://github.com/Xilinx/Vitis-AI/blob/master/docs/learn/system_requirements.md) 列出了运行 Docker 容器以及在 Alveo 卡上执行的系统要求。对于边缘设备（例如 Zynq），部署模型需要使用带有 Vitis AI 流程的 TVM 编译模型的主机，以及用于运行编译模型的边缘设备。主机系统要求与上面链接中指定的相同。 -## 设置说明 {#setup} +## 设置说明 本节介绍如何用 Vitis AI 流为云和边缘设置 TVM。支持 Vitis AI 的 TVM 是通过 Docker 容器提供的。提供的脚本和 Dockerfile 将 TVM 和 Vitis AI 编译为单个镜像。 @@ -70,7 +70,7 @@ TVM 中当前的 Vitis AI 流支持使用 [Zynq Ultrascale+ MPSoc](https://www.x 在这个 Docker 容器中可以为云和边缘目标编译模型。要在 docker 容器内的云 Alveo 或 Versal VCK5000 卡上运行，按照 [Alveo](#alveo-setup) 或者 [Versal VCK5000](#versal-vck5000-setup) 设置说明进行操作。分别参照 [Zynq](#zynq-setup) 和 [Versal VCK190](#versal-vck190-setup)，为推理过程设置 Zynq 或 Versal VCK190 评估单板。 -### Alveo 设置 {#alveo-setup} +### Alveo 设置 查看 [Alveo 设置](https://github.com/Xilinx/Vitis-AI/blob/v1.4/setup/alveo/README.md) 获取设置信息。 @@ -85,7 +85,7 @@ source setup.sh [DPU-IDENTIFIER] 可在此页面顶部的 DPU Targets 表的第二列中找到此 DPU 标识符。 -### Versal VCK5000 设置 {#versal-vck5000-setup} +### Versal VCK5000 设置 查看 [VCK5000 Setup](https://github.com/Xilinx/Vitis-AI/blob/v1.4/setup/vck5000/README.md) 获取设置信息。 @@ -98,7 +98,7 @@ cd Vitis-AI/setup/vck5000 source setup.sh ``` -### Zynq 设置 {#zynq-setup} +### Zynq 设置 除了构建 TVM - Vitis AI docker 之外，对于 Zynq 目标（DPUCZDX8G），编译阶段在主机上的 docker 内运行，不需要任何特定设置。执行模型时，首先要设置 Zynq 板，更多信息如下。 @@ -173,11 +173,11 @@ source setup.sh 可能会看到有关未找到 "cpu-tf" runtime 的警告，可以忽略。 ::: -### Versal VCK190 设置 {#versal-vck190-setup} +### Versal VCK190 设置 参考 [Zynq 设置](#zynq-setup) 设置 Versal VCK190，但在步骤 1 中参考 [VCK190 镜像](https://www.xilinx.com/member/forms/download/design-license-xef.html?filename=xilinx-vck190-dpu-v2020.2-v1.4.0.img.gz)。其他步骤相同。 -## 编译模型 {#compile} +## 编译模型 带有 Vitis AI 流的 TVM 包含编译和推理两个阶段。在编译期间，用户可以为当前支持的云或边缘目标设备选择要编译的模型。编译模型生成的文件可用于在 [推理](#inference) 阶段在指定的目标设备上运行模型。目前，采用 Vitis AI 流程的 TVM 支持选定数量的 Xilinx 数据中心和边缘设备。 @@ -290,13 +290,13 @@ lib_edge.export_library('deploy_lib_edge.so', **lib_kwargs) 使用 TVM 和 Vitis AI 编译模型的教程到此结束，有关如何运行已编译的模型，参阅下一节。 -## 推理 {#inference} +## 推理 带有 Vitis AI 流的 TVM 包含编译和推理两个阶段，在编译期间，用户可以选择为当前支持的任何目标设备编译模型。编译模型后生成的文件可用于在推理阶段在目标设备上运行模型。 查看 [在 Alveo 和 VCK5000 上运行](#running-on-alveo-and-vck5000) 以及 [在 Zynq 和 VCK190 上运行](#running-on-zynq-and-vck190) 部分，分别在云加速卡和边缘板上进行推理。 -### 在 Alveo 和 VCK5000 上运行 {#running-on-alveo-and-vck5000} +### 在 Alveo 和 VCK5000 上运行 按照编译模型部分中的步骤，可以在 Docker 内的新输入上执行如下命令以加速推理： @@ -325,7 +325,7 @@ module.set_input(input_name, input_data) module.run() ``` -### 在 Zynq 和 VCK190 上运行 {#running-on-zynq-and-vck190} +### 在 Zynq 和 VCK190 上运行 开始前按照 [Zynq](#zynq-setup) 或 [Versal VCK190](#versal-vck190-setup) 设置说明进行设置。 diff --git a/docs/how_to/microtvm/01-aot.md b/docs/how_to/microtvm/01-aot.md index e9232e12..8389cb4f 100644 --- a/docs/how_to/microtvm/01-aot.md +++ b/docs/how_to/microtvm/01-aot.md @@ -62,7 +62,7 @@ relay_mod, params = relay.frontend.from_tflite( # 使用 C runtime（crt），并通过将 system-lib 设置为 True 来启用静态链接 RUNTIME = Runtime("crt", {"system-lib": True}) -# 在主机上模拟一个微控制器。使用来自 `src/runtime/crt/host/main.cc `_ 的 main()。 +# 在主机上模拟一个微控制器。使用来自 `src/runtime/crt/host/main.cc [https://github.com/apache/tvm/blob/main/src/runtime/crt/host/main.cc](https://github.com/apache/tvm/blob/main/src/runtime/crt/host/main.cc)`_ 的 main()。 # 若要使用物理硬件，请将「host」替换为与你的硬件匹配的内容。 TARGET = tvm.target.target.micro("host") diff --git a/docs/how_to/microtvm/03-tvm_arm.md b/docs/how_to/microtvm/03-tvm_arm.md index 99353105..492d7344 100644 --- a/docs/how_to/microtvm/03-tvm_arm.md +++ b/docs/how_to/microtvm/03-tvm_arm.md @@ -46,7 +46,7 @@ TVMC - TVM driver command-line interface 运行 demo 需要一些额外的 Python 包。通过下面的 requirements.txt 文件，来安装这些额外的 Python 包： *requirements.txt*[¶](#requirements-txt) -{#requirements-txt} + ``` text attrs==21.2.0 cloudpickle==2.0.0 @@ -167,7 +167,7 @@ curl -sS https://s3.amazonaws.com/model-server/inputs/kitten.jpg -o ./kitten.jpg * `outputs.h` - 用一个全零的整数数组，为推理的输出保留 1001 个整数值。 *convert_image.py*[¶](#convert-image-py) -{#convert-image-py} + ``` python #!python ./convert_image.py import os @@ -236,7 +236,7 @@ python convert_image.py ./kitten.jpg 以下脚本将在 src 目录中创建一个 `labels.h` 头文件，之前下载的 labels.txt 文件会变成字符串数组。该数组将用于显示图像分类后为的标签。 *convert_labels.py*[¶](#convert-image) -{#convert-image} + ``` python #!python ./convert_labels.py import os @@ -272,7 +272,7 @@ python convert_labels.py 下面的 C 程序会在之前下载并转换为整数数组的图像上，运行 MobileNet v1 模型的单个推理。由于该模型是以「ethos-u ...」为 target 编译的，因此需要迁移 Ethos(TM)-U55 NPU 支持的算子，以便进行加速。一旦应用程序构建并运行，测试图像应该被正确地归类为「tabby」，并且结果会显示在控制台上。这个文件应该放在 `./src`。 demo.c[¶](#demo-c) -{#demo-c} + ``` c #include #include diff --git a/docs/how_to/microtvm/05-microtvm_tflite.md b/docs/how_to/microtvm/05-microtvm_tflite.md index a9d3ebf1..e183c81f 100644 --- a/docs/how_to/microtvm/05-microtvm_tflite.md +++ b/docs/how_to/microtvm/05-microtvm_tflite.md @@ -74,11 +74,11 @@ title: 支持 TFLite 模型的 microTVM **题外话：重新创建预训练 TFLite 模型** 本教程下载了预训练的 TFLite 模型。使用微控制器时，请注意这些设备的资源高度受限，像 MobileNet 这样的标准模型和小内存并不匹配。 - + 本教程使用 TF Micro 示例模型之一。 - + 若要复制训练步骤，参阅：https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train - + :::note 若不小心从 `wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip` 下载了示例预训练模型，会由于未实现的操作码（114）而失败。 ::: @@ -238,7 +238,7 @@ os.unlink(model_library_format_tar_path) # 模型库格式用作此过程的模型输入。 # 平台为嵌入时提供了集成，可以被 TVM 直接用于主机驱动 # 推理和自动调优。这种集成由 -# `microTVM 项目 API` _提供。 +# `microTVM 项目 API` [https://github.com/apache/tvm-rfcs/blob/main/rfcs/0008-microtvm-project-api.md](https://github.com/apache/tvm-rfcs/blob/main/rfcs/0008-microtvm-project-api.md)_提供。 # # 嵌入式平台需要提供一个包含 microTVM API Server 的模板项目（通常， # 存在于根目录中的“microtvm_api_server.py”文件中）。本教程使用示例“主机” @@ -328,4 +328,4 @@ result is: [[0.4443792]] [下载 Python 源代码：micro_tflite.py](https://tvm.apache.org/docs/_downloads/2fb9ae7bf124f72614a43137cf2919cb/micro_tflite.py) -[下载 Jupyter Notebook：micro_tflite.ipynb](https://tvm.apache.org/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb) \ No newline at end of file +[下载 Jupyter Notebook：micro_tflite.ipynb](https://tvm.apache.org/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb) diff --git a/docs/how_to/microtvm/06-microtvm_arduino.md b/docs/how_to/microtvm/06-microtvm_arduino.md index 3307f154..fa1bd39e 100644 --- a/docs/how_to/microtvm/06-microtvm_arduino.md +++ b/docs/how_to/microtvm/06-microtvm_arduino.md @@ -214,7 +214,7 @@ validation_dataset = full_dataset.skip(len(train_dataset)) 首先，可以将输入图像的大小从 224x224 减小到 96x96 或 64x64，Keras 可以轻松做到这一点。还可以将模型的 **alpha** 从 1.0 降低到 0.25，这使得网络的宽度（和过滤器的数量）缩小了四倍。如果真的空间有限，可以通过让模型拍摄灰度图像而不是 RGB 图像来减少**通道**数量。 -在本教程中，使用 RGB 64x64 输入图像和 alpha 0.25。效果虽然不是很理想，但它允许完成的模型适合 192 KB 的 RAM，同时仍然允许使用官方 TensorFlow 源模型执行迁移学习（如果使用 alpha <0.25 或灰度输入，无法做到这样）。 +在本教程中，使用 RGB 64x64 输入图像和 alpha 0.25。效果虽然不是很理想，但它允许完成的模型适合 192 KB 的 RAM，同时仍然允许使用官方 TensorFlow 源模型执行迁移学习（如果使用 alpha \<0.25 或灰度输入，无法做到这样）。 ### 什么是迁移学习？ diff --git a/docs/install/docker.md b/docs/install/docker.md index 937f79fa..bb2a6ff0 100644 --- a/docs/install/docker.md +++ b/docs/install/docker.md @@ -65,4 +65,4 @@ Docker 镜像。 你也可以利用非官方的第三方预建镜像，注意：这些镜像是用来测试的，并不是 ASF 的版本。 -. +[https://hub.docker.com/r/tlcpack/](https://hub.docker.com/r/tlcpack/). diff --git a/docs/install/from_source.md b/docs/install/from_source.md index e0c582ba..24817f72 100644 --- a/docs/install/from_source.md +++ b/docs/install/from_source.md @@ -26,7 +26,7 @@ git submodule init git submodule update ``` -## 构建共享库 {#build-shared-library} +## 构建共享库 我们的目标是构建共享库： @@ -184,7 +184,7 @@ brew install python@3.8 如果一切顺利，我们就可以去查看 [Python 包的安装](#python-package-installation) 了。 -### 使用 Conda 环境进行构建 {#build-with-conda} +### 使用 Conda 环境进行构建 Conda 可以用来获取运行 TVM 所需的必要依赖。如果没有安装 Conda，请参照 [Conda @@ -243,7 +243,7 @@ cmake --build build --config Release -- /m 中安装 HIP runtime。确保安装系统中已经安装了 ROCm。 - 安装 LLVM 的最新稳定版本（v6.0.1），以及 LLD，确保 `ld.lld` 可以通过命令行获取。 -## Python 包的安装 {#python-package-installation} +## Python 包的安装 ### TVM 包 @@ -325,7 +325,7 @@ pip install 'xgboost>=1.1.0' [NNPACK Contrib 安装](nnpack) -## 启用 C++ 测试 {#C++_tests} +## 启用 C++ 测试 可以用 [Google Test](https://github.com/google/googletest) 来驱动 TVM 中的 C++ 测试。安装 GTest 最简单的方法是从源代码安装： diff --git a/docs/reference/api/python/auto_scheduler.md b/docs/reference/api/python/auto_scheduler.md index 13772faf..2c7bf16f 100644 --- a/docs/reference/api/python/auto_scheduler.md +++ b/docs/reference/api/python/auto_scheduler.md @@ -2,6 +2,6 @@ title: tvm.auto_scheduler --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.auto_scheduler ::: diff --git a/docs/reference/api/python/autotvm.md b/docs/reference/api/python/autotvm.md index 7e5c2685..e6319ab4 100644 --- a/docs/reference/api/python/autotvm.md +++ b/docs/reference/api/python/autotvm.md @@ -16,11 +16,11 @@ tvm.autotvm.apply_history_best tvm.autotvm.measure.measure ::: -::: {.autoclass members=""} +::: tvm.autotvm.measure.MeasureInput ::: -::: {.autoclass members=""} +::: tvm.autotvm.measure.MeasureResult ::: @@ -46,58 +46,58 @@ tvm.autotvm.measure.measure_methods.LocalRunner ## tvm.autotvm.tuner -::: {.automodule members=""} +::: tvm.autotvm.tuner ::: -::: {.autoclass members=""} +::: tvm.autotvm.tuner.Tuner ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.autotvm.tuner.RandomTuner ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.autotvm.tuner.GridSearchTuner ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.autotvm.tuner.GATuner ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.autotvm.tuner.XGBTuner ::: -::: {.automodule members=""} +::: tvm.autotvm.tuner.callback ::: ## tvm.autotvm.task -::: {.automodule members=""} +::: tvm.autotvm.task ::: -::: {.automodule members=""} +::: tvm.autotvm.task.task ::: -::: {.automodule members=""} +::: tvm.autotvm.task.space ::: -::: {.automodule members=""} +::: tvm.autotvm.task.dispatcher ::: -::: {.automodule members=""} +::: tvm.autotvm.task.topi_integration ::: ## tvm.autotvm.record -::: {.automodule members=""} +::: tvm.autotvm.record ::: diff --git a/docs/reference/api/python/contrib.md b/docs/reference/api/python/contrib.md index ba49b3e7..c0b574b0 100644 --- a/docs/reference/api/python/contrib.md +++ b/docs/reference/api/python/contrib.md @@ -8,138 +8,138 @@ tvm.contrib ## tvm.contrib.cblas -::: {.automodule members=""} +::: tvm.contrib.cblas ::: ## tvm.contrib.clang -::: {.automodule members=""} +::: tvm.contrib.clang ::: ## tvm.contrib.cc -::: {.automodule members=""} +::: tvm.contrib.cc ::: ## tvm.contrib.cublas -::: {.automodule members=""} +::: tvm.contrib.cublas ::: ## tvm.contrib.dlpack -::: {.automodule members=""} +::: tvm.contrib.dlpack ::: ## tvm.contrib.emcc -::: {.automodule members=""} +::: tvm.contrib.emcc ::: ## tvm.contrib.miopen -::: {.automodule members=""} +::: tvm.contrib.miopen ::: ## tvm.contrib.mxnet -::: {.automodule members=""} +::: tvm.contrib.mxnet ::: ## tvm.contrib.ndk -::: {.automodule members=""} +::: tvm.contrib.ndk ::: ## tvm.contrib.nnpack -::: {.automodule members=""} +::: tvm.contrib.nnpack ::: ## tvm.contrib.nvcc -::: {.automodule members=""} +::: tvm.contrib.nvcc ::: ## tvm.contrib.pickle_memoize -::: {.automodule members=""} +::: tvm.contrib.pickle_memoize ::: ## tvm.contrib.random -::: {.automodule members=""} +::: tvm.contrib.random ::: ## tvm.contrib.relay_viz -::: {.automodule members=""} +::: tvm.contrib.relay_viz ::: -::: {.automodule members=""} +::: tvm.contrib.relay_viz.dot ::: -::: {.automodule members=""} +::: tvm.contrib.relay_viz.terminal ::: -::: {.automodule members=""} +::: tvm.contrib.relay_viz.interface ::: ## tvm.contrib.rocblas -::: {.automodule members=""} +::: tvm.contrib.rocblas ::: ## tvm.contrib.rocm -::: {.automodule members=""} +::: tvm.contrib.rocm ::: ## tvm.contrib.sparse -::: {.automodule members=""} +::: tvm.contrib.sparse ::: ## tvm.contrib.spirv -::: {.automodule members=""} +::: tvm.contrib.spirv ::: ## tvm.contrib.tar -::: {.automodule members=""} +::: tvm.contrib.tar ::: ## tvm.contrib.utils -::: {.automodule members=""} +::: tvm.contrib.utils ::: ## tvm.contrib.xcode -::: {.automodule members=""} +::: tvm.contrib.xcode ::: diff --git a/docs/reference/api/python/error.md b/docs/reference/api/python/error.md index 95d5dc0c..ba83dfd5 100644 --- a/docs/reference/api/python/error.md +++ b/docs/reference/api/python/error.md @@ -2,6 +2,6 @@ title: tvm.error --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.error ::: diff --git a/docs/reference/api/python/graph_executor.md b/docs/reference/api/python/graph_executor.md index 810338eb..90110e9b 100644 --- a/docs/reference/api/python/graph_executor.md +++ b/docs/reference/api/python/graph_executor.md @@ -2,6 +2,6 @@ title: tvm.contrib.graph_executor --- -::: {.automodule members=""} +::: tvm.contrib.graph_executor ::: diff --git a/docs/reference/api/python/index.md b/docs/reference/api/python/index.md index a7518508..ab82bf8d 100644 --- a/docs/reference/api/python/index.md +++ b/docs/reference/api/python/index.md @@ -2,9 +2,9 @@ title: Python API --- -::: {.toctree maxdepth="2"} +``` runtime ndarray error ir target tir te driver relay/index relay/frontend relay/nn relay/vision relay/image relay/transform relay/analysis relay/backend relay/dataflow_pattern relay/testing autotvm auto_scheduler rpc micro contrib graph_executor topi vta/index -::: +``` diff --git a/docs/reference/api/python/ir.md b/docs/reference/api/python/ir.md index 4c60d1fb..16eebd04 100644 --- a/docs/reference/api/python/ir.md +++ b/docs/reference/api/python/ir.md @@ -1,17 +1,17 @@ ## tvm.ir -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.ir ::: ## tvm.instrument -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.instrument ::: ## tvm.transform -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.transform ::: diff --git a/docs/reference/api/python/micro.md b/docs/reference/api/python/micro.md index ed2cac3b..69db2fda 100644 --- a/docs/reference/api/python/micro.md +++ b/docs/reference/api/python/micro.md @@ -2,6 +2,6 @@ title: tvm.micro --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.micro ::: diff --git a/docs/reference/api/python/ndarray.md b/docs/reference/api/python/ndarray.md index dcab1750..f144d9b5 100644 --- a/docs/reference/api/python/ndarray.md +++ b/docs/reference/api/python/ndarray.md @@ -6,7 +6,7 @@ title: tvm.runtime.ndarray tvm.runtime.ndarray ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.nd.NDArray ::: diff --git a/docs/reference/api/python/relay/analysis.md b/docs/reference/api/python/relay/analysis.md index cbc937a2..0a82d753 100644 --- a/docs/reference/api/python/relay/analysis.md +++ b/docs/reference/api/python/relay/analysis.md @@ -2,6 +2,6 @@ title: tvm.relay.analysis --- -::: {.automodule members="" imported-members="" exclude-members="Object, RelayExpr, IRModule, Type" autosummary=""} +::: tvm.relay.analysis ::: diff --git a/docs/reference/api/python/relay/backend.md b/docs/reference/api/python/relay/backend.md index e3df36cb..2d50a660 100644 --- a/docs/reference/api/python/relay/backend.md +++ b/docs/reference/api/python/relay/backend.md @@ -6,18 +6,18 @@ title: tvm.relay.backend tvm.relay.backend ::: -::: {.automodule members=""} +::: tvm.relay.backend.interpreter ::: -::: {.automodule members=""} +::: tvm.relay.backend.te_compiler ::: -::: {.automodule members=""} +::: tvm.relay.backend.graph_executor_codegen ::: -::: {.automodule members=""} +::: tvm.relay.backend.vm ::: diff --git a/docs/reference/api/python/relay/dataflow_pattern.md b/docs/reference/api/python/relay/dataflow_pattern.md index 19fabdc3..9c44104a 100644 --- a/docs/reference/api/python/relay/dataflow_pattern.md +++ b/docs/reference/api/python/relay/dataflow_pattern.md @@ -2,6 +2,6 @@ title: tvm.relay.dataflow_pattern --- -::: {.automodule members="" imported-members="" exclude-members="Object, Node" autosummary=""} +::: tvm.relay.dataflow_pattern ::: diff --git a/docs/reference/api/python/relay/frontend.md b/docs/reference/api/python/relay/frontend.md index 539268a4..ca06b419 100644 --- a/docs/reference/api/python/relay/frontend.md +++ b/docs/reference/api/python/relay/frontend.md @@ -2,6 +2,6 @@ title: tvm.relay.frontend --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.frontend ::: diff --git a/docs/reference/api/python/relay/image.md b/docs/reference/api/python/relay/image.md index aeaa7507..9c214971 100644 --- a/docs/reference/api/python/relay/image.md +++ b/docs/reference/api/python/relay/image.md @@ -2,6 +2,6 @@ title: tvm.relay.image --- -::: {.automodule members="" imported-members="" exclude-members="Expr, Constant" autosummary=""} +::: tvm.relay.image ::: diff --git a/docs/reference/api/python/relay/index.md b/docs/reference/api/python/relay/index.md index 656d2cac..ee6cb3e8 100644 --- a/docs/reference/api/python/relay/index.md +++ b/docs/reference/api/python/relay/index.md @@ -2,12 +2,6 @@ title: tvm.relay --- -::: {.automodule members="" imported-members="" exclude-members="RelayExpr, -Type, TypeKind, -TypeVar, GlobalTypeVar, TypeConstraint, FuncType, TupleType, IncompleteType, -TypeCall, TypeRelation, TensorType, RelayRefType, GlobalVar, SourceName, -Span, Var, Op, Constructor -:noindex: TypeData -:autosummary:"} +::: tvm.relay ::: diff --git a/docs/reference/api/python/relay/nn.md b/docs/reference/api/python/relay/nn.md index 34dc4754..0b3873f1 100644 --- a/docs/reference/api/python/relay/nn.md +++ b/docs/reference/api/python/relay/nn.md @@ -2,6 +2,6 @@ title: tvm.relay.nn --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.nn ::: diff --git a/docs/reference/api/python/relay/testing.md b/docs/reference/api/python/relay/testing.md index 17116aa1..d861cfe1 100644 --- a/docs/reference/api/python/relay/testing.md +++ b/docs/reference/api/python/relay/testing.md @@ -2,42 +2,42 @@ title: tvm.relay.testing --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.testing ::: -::: {.automodule members=""} +::: tvm.relay.testing.mlp ::: -::: {.automodule members=""} +::: tvm.relay.testing.resnet ::: -::: {.automodule members=""} +::: tvm.relay.testing.dcgan ::: -::: {.automodule members=""} +::: tvm.relay.testing.mobilenet ::: -::: {.automodule members=""} +::: tvm.relay.testing.lstm ::: -::: {.automodule members=""} +::: tvm.relay.testing.inception_v3 ::: -::: {.automodule members=""} +::: tvm.relay.testing.squeezenet ::: -::: {.automodule members=""} +::: tvm.relay.testing.vgg ::: -::: {.automodule members=""} +::: tvm.relay.testing.densenet ::: diff --git a/docs/reference/api/python/relay/transform.md b/docs/reference/api/python/relay/transform.md index 0fc59717..6578b10b 100644 --- a/docs/reference/api/python/relay/transform.md +++ b/docs/reference/api/python/relay/transform.md @@ -2,6 +2,6 @@ title: tvm.relay.transform --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.transform ::: diff --git a/docs/reference/api/python/relay/vision.md b/docs/reference/api/python/relay/vision.md index 732e05ab..9b8332e7 100644 --- a/docs/reference/api/python/relay/vision.md +++ b/docs/reference/api/python/relay/vision.md @@ -2,6 +2,6 @@ title: tvm.relay.vision --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.vision ::: diff --git a/docs/reference/api/python/rpc.md b/docs/reference/api/python/rpc.md index 380993af..9689387d 100644 --- a/docs/reference/api/python/rpc.md +++ b/docs/reference/api/python/rpc.md @@ -2,6 +2,6 @@ title: tvm.rpc --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.rpc ::: diff --git a/docs/reference/api/python/runtime.md b/docs/reference/api/python/runtime.md index 4f6ef926..cdaa27f3 100644 --- a/docs/reference/api/python/runtime.md +++ b/docs/reference/api/python/runtime.md @@ -2,6 +2,6 @@ title: tvm.runtime --- -::: {.automodule members="" imported-members="" exclude-members="NDArray" autosummary=""} +::: tvm.runtime ::: diff --git a/docs/reference/api/python/target.md b/docs/reference/api/python/target.md index 2bcb2aa5..e9ef930e 100644 --- a/docs/reference/api/python/target.md +++ b/docs/reference/api/python/target.md @@ -2,6 +2,6 @@ title: tvm.target --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.target ::: diff --git a/docs/reference/api/python/te.md b/docs/reference/api/python/te.md index 2666ba85..628f883f 100644 --- a/docs/reference/api/python/te.md +++ b/docs/reference/api/python/te.md @@ -1,11 +1,11 @@ ## tvm.te -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.te ::: ## tvm.te.hybrid -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.te.hybrid ::: diff --git a/docs/reference/api/python/tir.md b/docs/reference/api/python/tir.md index 215368b1..3f82297e 100644 --- a/docs/reference/api/python/tir.md +++ b/docs/reference/api/python/tir.md @@ -1,23 +1,23 @@ -## tvm.tir {#api-python-tir} +## tvm.tir -::: {.automodule members="" imported-members="" exclude-members="PrimExpr, const" autosummary=""} +::: tvm.tir ::: ## tvm.tir.transform -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.tir.transform ::: ## tvm.tir.analysis -::: {.automodule members="" imported-members="" noindex="Buffer, Stmt" autosummary=""} +::: tvm.tir.analysis ::: ## tvm.tir.stmt_functor -::: {.automodule members="" autosummary=""} +::: tvm.tir.stmt_functor ::: diff --git a/docs/reference/api/python/topi.md b/docs/reference/api/python/topi.md index 054b7f2a..ae391b50 100644 --- a/docs/reference/api/python/topi.md +++ b/docs/reference/api/python/topi.md @@ -2,24 +2,24 @@ title: tvm.topi --- -::: {.automodule members="" imported-members="" noindex="AssertStmt" autosummary=""} +::: tvm.topi ::: ## tvm.topi.nn -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.topi.nn ::: ## tvm.topi.image -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.topi.image ::: ## tvm.topi.sparse -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.topi.sparse ::: diff --git a/docs/reference/langref/index.md b/docs/reference/langref/index.md index 8fbb72ee..5cd9a1db 100644 --- a/docs/reference/langref/index.md +++ b/docs/reference/langref/index.md @@ -22,9 +22,9 @@ transformations and optimizations. The below pages describe the grammar, type system, algebraic data types, and operators in Relay, respectively. -::: {.toctree maxdepth="2"} +``` relay_expr relay_type relay_adt relay_op relay_pattern -::: +``` ## Hybrid Script @@ -32,6 +32,6 @@ The below page describes the TVM hybrid script front-end, which uses software emulation to support some constructs not officially supported in TVM. -::: {.toctree maxdepth="2"} +``` hybrid_script -::: +``` diff --git a/docs/reference/langref/relay_adt.md b/docs/reference/langref/relay_adt.md index 96cff08a..320cee40 100644 --- a/docs/reference/langref/relay_adt.md +++ b/docs/reference/langref/relay_adt.md @@ -53,7 +53,7 @@ variables) to the list of constructors for that ADT.* Below is a simple example of defining an ADT and using it in a function via a match expression: -``` +``` # Defines an ADT named "Numbers" data Numbers { Empty : () -> Numbers @@ -83,7 +83,7 @@ Note that ADTs are identified by name, meaning that two ADTs with structurally identical constructors will nevertheless be distinct data types from the point of view of the typechecker. -``` +``` # structurally identical constructors to Numbers data Numbers2 { Empty2 : () -> Numbers2 @@ -105,7 +105,7 @@ functions, ADTs can be polymorphic and take type parameters. For example, one of the standard ADTs commonly used in functional programming languages is the optional type, defined here: -``` +``` # a is a type parameter data Optional { None : () -> Optional @@ -129,7 +129,7 @@ thus given a type that contains the concrete type arguments for that instance, ensuring the information is kept around. Let the below example illustrate: -``` +``` # the signature for option indicates the type argument def @inc_scalar(%opt : Optional[Tensor[(), int32]]) -> Tensor[(), int32] { match(%opt) { @@ -184,7 +184,7 @@ Many commonly used ADTs involve recursion; some of these are given in [Common ADT Uses](#common-adt-uses). As an example here, we will examine the list ADT, ubiquitous in functional languages: -``` +``` data List { Nil : () -> List Cons : (a, List[a]) -> List @@ -202,7 +202,7 @@ empty list). Lists represented in this manner can easily be recursively processed. For example, the following function sums a list of integers: -``` +``` def @list_sum(%l : List[Tensor[(), int32]]) -> Tensor[(), int32] { match(%l) { case Nil() { 0 } @@ -217,7 +217,7 @@ share structures that can be factored out into generic, easily usable functions that will be discussed under [Common ADT Uses](#common-adt-uses). -## Pattern Matching in Match Expressions {#adt-pattern} +## Pattern Matching in Match Expressions Match expressions in Relay, as in other functional languages, are capable of more versatile pattern matching than simply having one case @@ -241,7 +241,7 @@ arguments to `Cons`. The below example uses a wildcard pattern to ignore one of the arguments to `Cons`: -``` +``` def @first(%l : List[a]) -> Optional[a] { match(%l) { case Nil() { None() } @@ -255,7 +255,7 @@ to avoid nested match expressions for a list option. A top-level wildcard pattern is also used to handle all cases that do not match the first clause: -``` +``` def @second_opt(%ll : Optional[List[a]]) -> Optional[a] { match(%ll) { # we only need the second member of the list if there is one @@ -275,7 +275,7 @@ are listed: the first clause whose pattern that matches the input value is the one that is evaluated. Here, a top-level variable pattern binds the whole input value: -``` +``` def @match_order_beware(%l : List[a]) -> List[a] { match(%l) { case %v { %v } @@ -313,7 +313,7 @@ Relay\'s Prelude. (These have all been extensively characterized in the functional programming literature, and we do not attempt to reproduce that work in this document.) -``` +``` # Map: for [h1, h2, ..., hn] returns [f(h1), f(h2), ..., f(hn)] def @map(%f : fn(a) -> b, %l : List[a]) -> List[b] { match(%l) { @@ -343,7 +343,7 @@ Using these iteration constructs, many common operations over lists can be expressed compactly. For example, the following map doubles all members of a list: -``` +``` # directly written def @double(%l : List[Tensor[(), int32]]) -> List[Tensor[(), int32]] { match(%l) { @@ -358,7 +358,7 @@ def @double(%l : List[Tensor[(), int32]]) -> List[Tensor[(), int32]] { The following right fold concatenates two lists: -``` +``` # directly written def @concat(%l1 : List[a], %l2 : List[a]) -> List[a] { match(%l1) { @@ -373,7 +373,7 @@ def @concat(%l1 : List[a], %l2 : List[a]) -> List[a] { The following left fold flattens a list of lists (using concatenation): -``` +``` # directly written def @flatten(%ll : List[List[a]]) -> List[a] { match(%ll) { @@ -407,14 +407,14 @@ recurrent neural net (RNN) cell, which takes in a past state and an input value and returns a new state and output value. In Relay, this would have the following signature: -``` +``` @cell : fn(state_type, in_type) -> (state_type, out_type) ``` We might consider a ReLU cell as a simple concrete example, with a trained version below: -``` +``` def @linear(%x, %w, %b) { %w*%x + %b } def @relu_cell(%w, # weights @@ -437,7 +437,7 @@ def @trained_cell(%w, %b) { Following Olah\'s example, we can encode a sequence (list) of inputs with the following left fold: -``` +``` def @encode(%cell, %input : List[in_type], %init : state_type) -> state_type { # not using the output @foldl(fn(%state, %in) { %cell(%state, %in).0 }, %init, %input) @@ -448,7 +448,7 @@ Using an *unfold* iterator (from Haskell\'s standard library), the same cell could be used to make a generator network (which takes a single input and produces a sequence of outputs): -``` +``` # included in Relay's Prelude def @unfoldr(%f : fn(b) -> Optional[(a, b)], %z : b) -> List[a] { match(%f(%z)) { @@ -478,7 +478,7 @@ An accumulating map (a fold that simultaneously updates an accumulator value and a list of outputs) can be used to write a general RNN (with an output for every input): -``` +``` def @map_accumr(%f : fn(a, b) -> (a, c), %acc : a, %l : List[b]) -> (a, List[c]) { match(%l) { case Nil() { (%acc, Nil()) } @@ -511,7 +511,7 @@ two sets of cells (which may have different weights) process the input in both directions and produce a single set of outputs. The following is a Relay implementation of that example: -``` +``` # creates a list of tuples from two lists # included in Relay's Prelude def @zip(%l : List[a], %m : List[b]) -> List[(a, b)] { diff --git a/docs/reference/langref/relay_expr.md b/docs/reference/langref/relay_expr.md index f39b197a..42706abe 100644 --- a/docs/reference/langref/relay_expr.md +++ b/docs/reference/langref/relay_expr.md @@ -62,7 +62,7 @@ contained in the globally visible environment, known as the [module](#module-and-global-functions). Global identifiers must be unique. -See :py`~tvm.relay.expr.GlobalVar`{.interpreted-text role="class"} for +See `GlobalVar` for its implementation and documentation. ### Local Variable @@ -79,7 +79,7 @@ to `%a` in the inner scope refer to the later definition, while references to `%a` in the outer scope continue to refer to the first one. -``` +``` let %a = 1; let %b = 2 * %a; // %b = 2 let %a = %a + %a; // %a = 2. %a is shadowed @@ -87,14 +87,13 @@ let %a = %a + %a; // %a = 2. %a is shadowed ``` (Note that in Relay\'s implementation, each definition of a local -variable creates a new :py`~tvm.relay.expr.Var`{.interpreted-text -role="class"}, so a shadowed local variable, despite having the same +variable creates a new `Var`, so a shadowed local variable, despite having the same name as one in an outer scope, will be a different object. This allows for comparing local variables by pointer identity with the knowledge that the same local variable object corresponds to a different binding site.) -See :py`~tvm.relay.expr.Var`{.interpreted-text role="class"} for its +See `Var` for its implementation and documentation. ## Functions @@ -110,24 +109,24 @@ argument to a function or returned by a function, as function expressions evaluate to closures (see the [Closures](#closures) subsection), which are values like tensors and tuples. -See :py`~tvm.relay.function.Function`{.interpreted-text role="class"} +See `Function` for the definition and documentation of function nodes. ### Syntax A definition minimally consists of the keyword `fn`, an empty set of parameters, and a body expression -(:py`~tvm.relay.expr.Expr`{.interpreted-text role="class"}) contained by +(`Expr`) contained by curly braces. -``` +``` fn() { body } ``` A definition may contain any number of parameters. For example, a simple function that invokes the `add` operator: -``` +``` fn(%x, %y) { add(%x, %y) } ``` @@ -137,7 +136,7 @@ variables, just like those bound in a `let` expression. One may also annotate explicit types on functions. For example, we can restrict the above function to only work on certain types: -``` +``` fn(%x : Tensor[(10, 10), float32], %y : Tensor[(10, 10), float32]) -> Tensor[(10, 10), float32] { add(%x, %y) @@ -147,7 +146,7 @@ fn(%x : Tensor[(10, 10), float32], %y : Tensor[(10, 10), float32]) The above function only takes arguments of type `Tensor[(10, 10), float32]` and returns a value of type `Tensor[(10, 10), float32]`. A function parameter is just a local -variable (:py`~tvm.relay.expr.LocalVar`{.interpreted-text role="class"}) +variable (`LocalVar`) optionally annotated with a type, written as `%x : T`. When the type information is omitted, Relay attempts to infer the most @@ -159,7 +158,7 @@ function body and call sites. A recursive function expression can be defined using a `let` binding, as here: -``` +``` let %fact = fn(%x : Tensor[(10, 10), float32]) -> Tensor[(10, 10), float32] { if (%x == Constant(0, (10, 10), float32)) { Constant(1, (10, 10), float32) @@ -181,7 +180,7 @@ For example, in the below example, the final result will be a tensor of zero values because the closure for `%f` stores the value of `%x` at the pointer where `%f` was defined. -``` +``` let %g = fn() { let %x = Constant(0, (10, 10), float32); // %x is a free variable in the below function @@ -208,13 +207,12 @@ Type parameters are classified by *kind* and can only appear in parts of the type signature where their kind is appropriate (e.g., type parameters of kind `Shape` can only appear where a shape would be expected in a tensor type); for a full discussion, see -`the documentation on type parameters `{.interpreted-text -role="ref"}. +`the documentation on type parameters `. For example, one can define a polymorphic identity function for any Relay type as follows: -``` +``` fn(%x : t) -> t { %x } @@ -223,7 +221,7 @@ fn(%x : t) -> t { The below definition is also polymorphic, but restricts its arguments to tensor types: -``` +``` fn(%x : Tensor[s, bt]) { %x } @@ -236,7 +234,7 @@ Notice that the return type is omitted and will be inferred. A function may also be subject to one or more type relations, such as in the following: -``` +``` fn(%x, %y) where Broadcast { add(%x, %y) } ``` @@ -252,8 +250,7 @@ constraints on types (especially tensor shapes). All function relations must hold at all call sites; type checking is thus treated as a constraint-solving problem. For more detail on type relations and their implementations, please see -`their section in the documentation on Relay's type system `{.interpreted-text -role="ref"}. +`their section in the documentation on Relay's type system `. ## Operators @@ -272,8 +269,7 @@ From the perspective of Relay\'s type system, an operator is a function, so operators may be called like any other function and have function types. In particular, operator types are registered using a single type relation (see -`the documentation on type relations `{.interpreted-text -role="ref"}), typically a relation specialized to that operator. For +`the documentation on type relations `, typically a relation specialized to that operator. For example, the `add` operator is registered with the `Broadcast` relation, indicating that the arguments of `add` must be tensors and that the return type is a tensor whose shape depends on those of its arguments. @@ -286,20 +282,19 @@ Note that common arithmetic operators such as `add` and `multiply` may be written using the corresponding arithmetic operators in the text format (e.g., `+` or `*`) as syntactic sugar. -See :py`~tvm.relay.op.Op`{.interpreted-text role="class"} for the +See `Op` for the definition and documentation of operator nodes, demonstrating the infrastructure for registering operator metadata. The other files in -:py`~tvm.relay.op`{.interpreted-text role="class"} give handles for +`op` give handles for generating a call to various pre-registered operators. The -`tutorial on adding operators to Relay `{.interpreted-text -role="ref"} shows how to add further operators into the language. +`tutorial on adding operators to Relay ` shows how to add further operators into the language. ## ADT Constructors Algebraic data types (ADTs) in Relay are described in detail in a -`separate overview`{.interpreted-text role="ref"} and +`separate overview` and their integration into the type system is described -`here`{.interpreted-text role="ref"}. +`here`. In this section, we will simply note that ADT constructors are given a function type and should be used inside call nodes like a function or @@ -326,7 +321,7 @@ well as the name (\"tag\") of the constructor. The tag will be used for deconstructing the instances and retrieving the values when [ADT Matching](#adt-matching). -See :py`~tvm.relay.adt.Constructor`{.interpreted-text role="class"} for +See `Constructor` for the definition and documentation. ## Call @@ -339,7 +334,7 @@ functions) and Relay operators. The syntax of calls follows that used in C-like languages, demonstrated in the example below: -``` +``` let %c = 1; let %f = fn(%x : Tensor[(), float32], %y : Tensor[(), float32]) { %x + %y + %c }; %f(10, 11) @@ -362,7 +357,7 @@ given, type inference will attempt to infer type arguments if possible. The following code gives examples of explicit and inferred type arguments: -``` +``` // %f : fn(a, b) -> c let %x1 = %f(True, False); // %x1 is of type Tensor[(), bool] @@ -381,15 +376,15 @@ has the `Broadcast` relation, then there are many different shapes that the arguments in the below call could have that would satisfy the type annotation: -``` +``` let %x : Tensor[(100, 100, 100), float32] = %f(%a, %b); %x ``` -See :py`~tvm.relay.expr.Call`{.interpreted-text role="class"} for its +See `Call` for its definition and documentation. -## Module and Global Functions {#module-description} +## Module and Global Functions Relay keeps a global data structure known as a \"module\" (often called an \"environment\" in other functional programming languages) to keep @@ -409,7 +404,7 @@ function definition includes a global identifier and is allowed to recursively refer to that identifier in the body, as in the following example: -``` +``` def @ackermann(%m : Tensor[(), int32], %n : Tensor[(), int32]) -> Tensor[(), int32] { if (%m == 0) { %n + 1 @@ -427,15 +422,14 @@ and body above. Any reference to the identifier `@ackermann` elsewhere in the code could then look up the identifier in the module and replace the function definition as needed. -See :py`~tvm.IRModule`{.interpreted-text role="class"} for the +See `IRModule` for the definition and documentation of a module. ## Constant This node represents a constant tensor value (see -:py`~tvm.relay.Value`{.interpreted-text role="mod"} for more details). A -constant is represented as a :py`~tvm.NDArray`{.interpreted-text -role="class"}, allowing Relay to utilize TVM operators for constant +`Value` for more details). A +constant is represented as a `NDArray`, allowing Relay to utilize TVM operators for constant evaluation. This node can also represent scalar constants, since scalars are tensors @@ -443,7 +437,7 @@ with a shape of `()`. In the text format, numerical and boolean literals are thus syntactic sugar for constants encoding a tensor type with a rank-zero shape. -See :py`~tvm.relay.expr.Constant`{.interpreted-text role="class"} for +See `Constant` for its definition and documentation. ## Tuples @@ -454,14 +448,14 @@ The tuple node builds a finite (that is, of statically known size) sequence of heterogeneous data. These tuples match Python\'s closely, and their fixed length allows for efficient projection of their members. -``` +``` fn(%a : Tensor[(10, 10), float32], %b : float32, %c : Tensor[(100, 100), float32]) { let %tup = (%a, %b); // type: (Tensor[(10, 10), float32], float32) ((%tup.0 + %tup.1), %c) // type: (Tensor[(10, 10), float32], Tensor[(100, 100), float32]) } ``` -See :py`~tvm.relay.expr.Tuple`{.interpreted-text role="class"} for its +See `Tuple` for its definition and documentation. ### Projection @@ -471,11 +465,11 @@ particular member of the tuple. Projections are 0-indexed. For example, the below projection evaluates to `%b`: -``` +``` (%a, %b, %c).1 ``` -See :py`~tvm.relay.expr.TupleGetItem`{.interpreted-text role="class"} +See `TupleGetItem` for its definition and documentation. ## Let Bindings @@ -499,7 +493,7 @@ evaluating the bindings it depends on. For example, in the following example the entire expression evaluates to a tensor of shape `(10, 10)` where all elements are 2: -``` +``` let %x : Tensor[(10, 10), float32] = Constant(1, (10, 10), float32); %x + %x ``` @@ -511,13 +505,13 @@ where neither depends on the other can be safely reordered. For example, the first and second `let` bindings below may be evaluated in either order because neither has a dataflow dependency on the other: -``` +``` let %x = %a + %b; let %y = %c + %d; %x * %y ``` -See :py`~tvm.relay.expr.Let`{.interpreted-text role="class"} for its +See `Let` for its definition and documentation. ## Graph Bindings @@ -536,15 +530,14 @@ already employed by NNVM and other dataflow graph-based input formats. The fact that the variables are not scoped offers some flexibility in evaluation order compared to `let` bindings, though this can also introduce some ambiguity in programs (the -`developer introduction to the Relay IR`{.interpreted-text -role="ref"} includes more detailed discussion of this nuance). +`developer introduction to the Relay IR` includes more detailed discussion of this nuance). *Note: Graph bindings are not currently parsed by the text format.* In Relay\'s text format, a graph binding can be written as below (note the lack of a `let` keyword and a semicolon): -``` +``` %1 = %a + %b %2 = %1 + %1 %2 * %2 @@ -558,7 +551,7 @@ corresponding Relay AST node and using the variables repeatedly, as below (a C++ program using the corresponding API bindings could accomplish the same thing): -``` +``` sum1 = relay.add(a, b) sum2 = relay.add(sum1, sum1) relay.multiply(sum2, sum2) @@ -578,7 +571,7 @@ Relay has a simple if-then-else expression that allows programs to branch on a single value of type `bool`, i.e., a zero-rank tensor of booleans (`Tensor[(), bool]`). -``` +``` if (%t == %u) { %t } else { @@ -593,13 +586,13 @@ evaluates to the value of the \"then\" branch if the condition value evaluates to `True` and evaluates to the value of the \"else\" branch if the condition value evaluates to `False`. -See :py`~tvm.relay.expr.If`{.interpreted-text role="class"} for its +See `If` for its definition and documentation. ## ADT Matching Instances of algebraic data types (ADTs), as discussed in the -`ADT overview`{.interpreted-text role="ref"}, are +`ADT overview`, are containers that store the arguments passed to the constructor used to create them, tagged by the constructor name. @@ -612,8 +605,7 @@ expressions are capable of more general pattern-matching than simply splitting by constructors: any ADT instance nested inside an instance (e.g., a list of lists) can be deconstructed at the same time as the outer instance, while the different fields of the instance can be bound -to variables. (See `this section`{.interpreted-text -role="ref"} for a detailed description of ADT pattern-matching.) +to variables. (See `this section` for a detailed description of ADT pattern-matching.) A match expression is defined using the input value (an expression) and a list of clauses, each of which consists of a pattern and an @@ -623,7 +615,7 @@ evaluated and returned. For example, suppose we have an ADT for natural numbers: -``` +``` data Nat { Z : () -> Nat # zero S : (Nat) -> Nat # successor (+1) to a nat @@ -632,7 +624,7 @@ data Nat { Then the following function subtracts one from a passed nat: -``` +``` fn(%v: Nat[]) -> Nat[] { match(%v) { case Z() { Z() } @@ -645,7 +637,7 @@ The following function subtracts two from its argument if it is at least two and returns the argument otherwise, using a nested constructor pattern: -``` +``` fn(%v : Nat[]) -> Nat[] { match(%v) { case S(S(%n)) { %n } @@ -659,7 +651,7 @@ As aforementioned, the ordering of match clauses is relevant. In the below example, the first clause will always match so those below it can never run: -``` +``` fn(%v : Nat[]) -> Nat[] { match(%v) { case _ { %v } @@ -670,7 +662,7 @@ fn(%v : Nat[]) -> Nat[] { } ``` -See :py`~tvm.relay.adt.Match`{.interpreted-text role="class"} for its +See `Match` for its definition and documentation. ## TempExprs @@ -688,5 +680,5 @@ For an example of `TempExpr` being used in a pass, see to store information about scaling parameters as the pass tries to fold these into the weights of a convolution. -See :py`~tvm.relay.expr.TempExpr`{.interpreted-text role="class"} for +See `TempExpr` for its definition and documentation. diff --git a/docs/reference/langref/relay_op.md b/docs/reference/langref/relay_op.md index f38049e5..bb237532 100644 --- a/docs/reference/langref/relay_op.md +++ b/docs/reference/langref/relay_op.md @@ -24,7 +24,7 @@ operators in the python frontend. This level enables fully connected multi-layer perceptron. -::: {.autosummary nosignatures=""} +::: tvm.relay.log tvm.relay.sqrt tvm.relay.rsqrt tvm.relay.exp tvm.relay.sigmoid tvm.relay.add tvm.relay.subtract tvm.relay.multiply tvm.relay.divide tvm.relay.mod tvm.relay.tanh tvm.relay.concatenate @@ -37,7 +37,7 @@ tvm.relay.nn.bias_add This level enables typical convnet models. -::: {.autosummary nosignatures=""} +::: tvm.relay.nn.conv2d tvm.relay.nn.conv2d_transpose tvm.relay.nn.conv3d tvm.relay.nn.conv3d_transpose tvm.relay.nn.dense tvm.relay.nn.max_pool2d tvm.relay.nn.max_pool3d tvm.relay.nn.avg_pool2d tvm.relay.nn.avg_pool3d @@ -56,7 +56,7 @@ tvm.relay.nn.contrib_conv3d_winograd_weight_transform This level enables additional math and transform operators. -::: {.autosummary nosignatures=""} +::: tvm.relay.nn.leaky_relu tvm.relay.nn.prelu tvm.relay.reshape tvm.relay.reshape_like tvm.relay.copy tvm.relay.transpose tvm.relay.squeeze tvm.relay.floor tvm.relay.ceil tvm.relay.sign @@ -72,7 +72,7 @@ tvm.relay.sparse_to_dense **Level 4: Broadcast and Reductions** -::: {.autosummary nosignatures=""} +::: tvm.relay.right_shift tvm.relay.left_shift tvm.relay.equal tvm.relay.not_equal tvm.relay.greater tvm.relay.greater_equal tvm.relay.less tvm.relay.less_equal tvm.relay.all tvm.relay.any @@ -87,7 +87,7 @@ tvm.relay.broadcast_to **Level 5: Vision/Image Operators** -::: {.autosummary nosignatures=""} +::: tvm.relay.image.resize1d tvm.relay.image.resize2d tvm.relay.image.resize3d tvm.relay.image.crop_and_resize tvm.relay.image.dilation2d tvm.relay.vision.multibox_prior @@ -97,7 +97,7 @@ tvm.relay.vision.yolo_reorg **Level 6: Algorithm Operators** -::: {.autosummary nosignatures=""} +::: tvm.relay.argsort tvm.relay.topk ::: @@ -106,7 +106,7 @@ tvm.relay.argsort tvm.relay.topk This level support backpropagation of broadcast operators. It is temporary. -::: {.autosummary nosignatures=""} +::: tvm.relay.broadcast_to_like tvm.relay.collapse_sum_like tvm.relay.slice_like tvm.relay.shape_of tvm.relay.ndarray_size tvm.relay.layout_transform tvm.relay.device_copy @@ -120,7 +120,7 @@ tvm.relay.one_hot This level supports dialect operators. -::: {.autosummary nosignatures=""} +::: tvm.relay.qnn.op.add tvm.relay.qnn.op.batch_matmul tvm.relay.qnn.op.concatenate tvm.relay.qnn.op.conv2d tvm.relay.qnn.op.conv2d_transpose tvm.relay.qnn.op.dense diff --git a/docs/reference/langref/relay_pattern.md b/docs/reference/langref/relay_pattern.md index 51500f48..ef92242f 100644 --- a/docs/reference/langref/relay_pattern.md +++ b/docs/reference/langref/relay_pattern.md @@ -160,8 +160,8 @@ def test_match_tuple(): The next example is matching a pattern of batch_norm -\> get(0) -\> relu. Note that you can also use -[is_tuple_get_item(bn_node)]{.title-ref} to match a -[TupleGetItem]{.title-ref} node with any index. +[is_tuple_get_item(bn_node)] to match a +[TupleGetItem] node with any index. ``` python def test_match_tuple_get_item(): @@ -332,7 +332,7 @@ doesn\'t correspond to a set of patterns nodes exactly 1-to-1. There are a few other places where we support such \"fuzzy\" matching. Tuples, Functions, and Call nodes with any number of inputs can be -matched by passing [None]{.title-ref} as the argument value, i.e.: +matched by passing [None] as the argument value, i.e.: tuple_pattern = is_tuple(None) func_pattern = FunctionPattern(None, wildcard() + wildcard()) @@ -345,9 +345,9 @@ arguments. Additionally, we support matching Functions with fuzzy bodies, i.e., a function body that is under constrained by the pattern. The pattern [FunctionPattern(\[is_var(), is_var()\], wildcard() + -wildcard()\])]{.title-ref} will match [relay.Function(\[x, y\], x + -y)]{.title-ref}, but it will also match [relay.Function(\[x, y\], x \* -x + y)]{.title-ref}. In the second case, the pattern doesn\'t perfectly +wildcard()\])] will match [relay.Function(\[x, y\], x + +y)], but it will also match [relay.Function(\[x, y\], x \* +x + y)]. In the second case, the pattern doesn\'t perfectly constrain the body of the function, so the resulting match is fuzzy. ## Pattern Language Design diff --git a/docs/reference/langref/relay_type.md b/docs/reference/langref/relay_type.md index 00ef5d52..4e469ee1 100644 --- a/docs/reference/langref/relay_type.md +++ b/docs/reference/langref/relay_type.md @@ -49,7 +49,7 @@ to Relay expressions. The base type for all Relay types. All Relay types are sub-classes of this base type. -See :py`~tvm.relay.ty.Type`{.interpreted-text role="class"} for its +See `Type` for its definition and documentation. ## Tensor Type @@ -71,11 +71,11 @@ shapes will be concrete before running a program. For example, here is a simple concrete tensor type corresponding to a 10-by-10 tensor of 32-bit floats: -``` +``` Tensor[(10, 10), float32] ``` -See :py`~tvm.relay.ty.TensorType`{.interpreted-text role="class"} for +See `TensorType` for its definition and documentation. ## Tuple Type @@ -93,13 +93,13 @@ For example, in the below code, `%t` is of type `(Tensor[(), bool], Tensor[(10, 10), float32])` and `%c` is of type `Tensor[(10, 10), float32]`. -``` +``` let %t = (False, Constant(1, (10, 10), float32)); let %c = %t.1; %c ``` -See :py`~tvm.relay.ty.TupleType`{.interpreted-text role="class"} for its +See `TupleType` for its definition and documentation. ## Type Parameter @@ -125,14 +125,14 @@ parameters at call sites. For example, `s` below is a type parameter of kind `Shape` and it will be substituted with `(10, 10)` at the call site below: -``` +``` def @plus(%t1 : Tensor[s, float32], %t2 : Tensor[s, float32]) { add(%t1, %t2) } plus<(10, 10)>(%a, %b) ``` -See :py`~tvm.relay.ty.TypeVar`{.interpreted-text role="class"} for its +See `TypeVar` for its definition and documentation. ## Type Constraint @@ -141,12 +141,12 @@ This is an abstract class representing a type constraint, to be elaborated upon in further releases. Currently, type relations are the only type constraints provided; they are discussed below. -See :py`~tvm.relay.ty.TypeConstraint`{.interpreted-text role="class"} +See `TypeConstraint` for its definition and documentation. ## Function Type -A function type in Relay, see [tvm/relay/type.h]{.title-ref} for more +A function type in Relay, see [tvm/relay/type.h] for more details. This is the type assigned to functions in Relay. A function type @@ -162,7 +162,7 @@ hold at every call site of the function. The type constraints typically take the function\'s argument types and the function\'s return type as arguments, but may take a subset instead. -See :py`~tvm.relay.ty.FuncType`{.interpreted-text role="class"} for its +See `FuncType` for its definition and documentation. ## Type Relation @@ -175,8 +175,8 @@ Relay to statically reason about the shapes in these cases. A type relation `R` describes a relationship between the input and output types of a Relay function. Namely, `R` is a function on types -that outputs [true]{.title-ref} if the relationship holds and -[false]{.title-ref} if it fails to hold. Types given to a relation may +that outputs [true] if the relationship holds and +[false] if it fails to hold. Types given to a relation may be incomplete or include shape variables, so type inference must assign appropriate values to incomplete types and shape variables for necessary relations to hold, if such values exist. @@ -200,7 +200,7 @@ Flatten(Tensor(sh, bt), O) :- If we have a relation like `Broadcast` it becomes possible to type operators like `add`: -``` +``` add : fn(t1, t2) -> t3 where Broadcast ``` @@ -234,7 +234,7 @@ Presently all of the relations used in Relay are implemented in C++. See the files in `src/relay/op` for examples of relations implemented in C++. -See :py`~tvm.relay.ty.TypeRelation`{.interpreted-text role="class"} for +See `TypeRelation` for its definition and documentation. ## Incomplete Type @@ -252,15 +252,15 @@ concrete type arguments (instantiated) at call sites, whereas incomplete types may appear anywhere in the program and are filled in during type inference. -See :py`~tvm.relay.ty.IncompleteType`{.interpreted-text role="class"} +See `IncompleteType` for its definition and documentation. -## Algebraic Data Types {#adt-typing} +## Algebraic Data Types *Note: ADTs are not currently supported in the text format.* Algebraic data types (ADTs) are described in more detail in -`their overview `{.interpreted-text role="ref"}; this +`their overview `; this section describes their implementation in the type system. An ADT is defined by a collection of named constructors, each of which @@ -298,7 +298,7 @@ Recursion in an ADT definition thus follows just like recursion for a global function: the constructor can simply reference the ADT handle (global type variable) in its definition. -See :py`~tvm.relay.ty.GlobalTypeVar`{.interpreted-text role="class"} for +See `GlobalTypeVar` for its definition and documentation. ### Definitions (Type Data) @@ -306,8 +306,7 @@ its definition and documentation. Besides a name, an ADT needs to store the constructors that are used to define it and any type parameters used within them. These are stored in the module, -`analogous to global function definitions`{.interpreted-text -role="ref"}. +`analogous to global function definitions`. While type-checking uses of ADTs, the type system sometimes must index into the module using the ADT name to look up information about @@ -316,7 +315,7 @@ a match expression clause, the type-checker must check the constructor\'s signature to ensure that any bound variables are being assigned the correct types. -See :py`~tvm.relay.adt.TypeData`{.interpreted-text role="class"} for its +See `TypeData` for its definition and documentation. ### Type Call @@ -340,7 +339,7 @@ one argument for each type parameter in the ADT definition. (An ADT definition with no arguments means that any instance will have no type arguments passed to the type call). -See :py`~tvm.relay.ty.TypeCall`{.interpreted-text role="class"} for its +See `TypeCall` for its definition and documentation. ### Example: List ADT @@ -349,7 +348,7 @@ This subsection uses the simple list ADT (included as a default ADT in Relay) to illustrate the constructs described in the previous sections. Its definition is as follows: -``` +``` data List { Nil : () -> List Cons : (a, List[a]) -> List @@ -366,7 +365,7 @@ the `Cons` constructor is accomplished by using the global type variable Below two instances of lists with their types given, using type calls: -``` +``` Cons(1, Cons(2, Nil())) # List[Tensor[(), int32]] Cons((1, 1), Cons((2, 2), Nil())) # List[(Tensor[(), int32], Tensor[(), int32])] ``` @@ -378,7 +377,7 @@ take any arguments that use a type parameter. (Nevertheless, for any Here are two lists that are rejected by the type system because the type parameters do not match: -``` +``` # attempting to put an integer on a list of int * int tuples Cons(1, Cons((1, 1), Nil())) # attempting to put a list of ints on a list of lists of int * int tuples diff --git a/docs/tutorial/06-tensor_expr.md b/docs/tutorial/06-tensor_expr.md index 57206cf7..04fb2d47 100644 --- a/docs/tutorial/06-tensor_expr.md +++ b/docs/tutorial/06-tensor_expr.md @@ -318,7 +318,7 @@ if run_cuda: # 编译后的 TVM 函数提供了一个任何语言都可调用的 C API。 # # 我们在 Python 中提供了最小数组 API 来进行快速测试以及制作原型。 - # 数组 API 基于 `DLPack `_ 标准。 + # 数组 API 基于 `DLPack [https://github.com/dmlc/dlpack](https://github.com/dmlc/dlpack)`_ 标准。 # # - 首先创建 GPU 设备。 # - 然后 tvm.nd.array 将数据复制到 GPU 上。 diff --git a/docs/tutorial/07-ops_AutoTVM.md b/docs/tutorial/07-ops_AutoTVM.md index c1ab10ee..2aa51a8d 100644 --- a/docs/tutorial/07-ops_AutoTVM.md +++ b/docs/tutorial/07-ops_AutoTVM.md @@ -297,7 +297,7 @@ Finish loading 10 records ## 总结 -本教程展示了如何构建算子模板，使得 TVM 能够搜索参数空间，并选择优化的调度配置。为了更深入地了解其工作原理，推荐基于 :ref: *张量表达式入门 _* 教程中演示的调度操作，向调度添加新的搜索参数。接下来的章节将演示 AutoScheduler，它是TVM 中一种优化常用算子的方法，同时无需用户提供自定义的模板。 +本教程展示了如何构建算子模板，使得 TVM 能够搜索参数空间，并选择优化的调度配置。为了更深入地了解其工作原理，推荐基于 *张量表达式入门 ``* 教程中演示的调度操作，向调度添加新的搜索参数。接下来的章节将演示 AutoScheduler，它是TVM 中一种优化常用算子的方法，同时无需用户提供自定义的模板。 [下载 Python 源代码：autotvm_matmul_x86.py](https://tvm.apache.org/docs/_downloads/8e7bbc9dbdda76ac573b24606b41c006/autotvm_matmul_x86.py) diff --git a/docs/tutorial/10-rpc.md b/docs/tutorial/10-rpc.md index 4f8c2d59..8c7b659a 100644 --- a/docs/tutorial/10-rpc.md +++ b/docs/tutorial/10-rpc.md @@ -98,11 +98,11 @@ func.export_library(path) 除了 `-mtriple`，还可设置其他编译选项，例如： -* **-mcpu=** +* **`-mcpu=`** 指定生成的代码运行的芯片架构。默认情况这是从 target 三元组推断出来的，并自动检测到当前架构。 -* **-mattr=a1,+a2,-a3,…** +* **`-mattr=a1,+a2,-a3,…`** 覆盖或控制 target 的指定属性，例如是否启用 SIMD 操作。默认属性集由当前 CPU 设置。要获取可用属性列表，执行： diff --git a/docs/tutorial/11-quick_start.md b/docs/tutorial/11-quick_start.md index f5b08223..3a4c0e1f 100644 --- a/docs/tutorial/11-quick_start.md +++ b/docs/tutorial/11-quick_start.md @@ -52,7 +52,7 @@ mod, params = relay.testing.resnet.get_workload( print(mod.astext(show_meta_data=False)) ``` -访问代码中的 mod.astext 更多信息： +访问代码中的 mod.astext 更多信息：[https://tvm.apache.org/docs/reference/api/python/ir.html#tvm.ir.Node.astext](https://tvm.apache.org/docs/reference/api/python/ir.html#tvm.ir.Node.astext) 输出结果： @@ -250,4 +250,4 @@ tvm.testing.assert_allclose(out_deploy, out, atol=1e-5) [下载 Python 源代码：relay_quick_start.py](https://tvm.apache.org/docs/_downloads/cc6d9aebd24d54d81752590cbc8f99f9/relay_quick_start.py) -[下载 Jupyter Notebook：relay_quick_start.ipynb](https://tvm.apache.org/docs/_downloads/3dd2108354ac3028c96bcd6a0c7899dd/relay_quick_start.ipynb) \ No newline at end of file +[下载 Jupyter Notebook：relay_quick_start.ipynb](https://tvm.apache.org/docs/_downloads/3dd2108354ac3028c96bcd6a0c7899dd/relay_quick_start.ipynb) diff --git a/docusaurus.config.js b/docusaurus.config.js deleted file mode 100644 index a5756b0b..00000000 --- a/docusaurus.config.js +++ /dev/null @@ -1,139 +0,0 @@ -// @ts-check -// Note: type annotations allow type checking and IDEs autocompletion - -const lightCodeTheme = require('prism-react-renderer/themes/github'); -const darkCodeTheme = require('prism-react-renderer/themes/dracula'); - -/** @type {import('@docusaurus/types').Config} */ -const config = { - title: 'Apache TVM 中文站', - tagline: 'Apache TVM 是一个端到端的深度学习编译框架，适用于 CPU、GPU 和各种机器学习加速芯片。', - url: 'https://tvm.hyper.ai', - baseUrl: '/', - onBrokenLinks: 'throw', - onBrokenMarkdownLinks: 'warn', - favicon: 'img/favicon.png', - organizationName: 'hyperai', // Usually your GitHub org/user name. - projectName: 'tvm-cn', // Usually your repo name. - - // Even if you don't use internalization, you can use this field to set useful - // metadata like html lang. For example, if your site is Chinese, you may want - // to replace "en" with "zh-Hans". - i18n: { - defaultLocale: 'zh-Hans', - locales: ['zh-Hans'], - }, - - presets: [ - [ - '@docusaurus/preset-classic', - /** @type {import('@docusaurus/preset-classic').Options} */ - ({ - docs: { - sidebarPath: require.resolve('./sidebars.js'), - // Please change this to your repo. - editUrl: 'https://github.com/hyperai/tvm-cn/edit/master/', - showLastUpdateAuthor: true, - showLastUpdateTime: true, - lastVersion: 'current', - versions: { - current: { - label: '0.12.0', - }, - } - }, - blog: { - showReadingTime: true, - // Please change this to your repo. - editUrl: - 'https://github.com/facebook/docusaurus/edit/main/website/blog/', - }, - theme: { - customCss: require.resolve('./src/css/app.scss'), - }, - }), - ], - ], - - themeConfig: - /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ - ({ - colorMode: { - defaultMode: 'light', - disableSwitch: false, - respectPrefersColorScheme: true, - }, - navbar: { - title: 'TVM 中文站', - hideOnScroll: false, - logo: { - alt: 'TVM Logo', - src: 'img/favicon-dark.svg', - srcDark: 'img/favicon.svg', - }, - items: [ - { - type: 'doc', - docId: 'index', - position: 'left', - label: '查看文档', - }, - // {to: '/docs', label: '查看文档', position: 'left'}, - {to: '/about', label: '关于', position: 'left'}, - {href: 'https://github.com/hyperai/tvm-cn', label: 'GitHub 项目', position: 'left'}, - {href: 'https://hyper.ai', label: '返回超神经', position: 'left'}, - - // https://github.com/facebook/docusaurus/blob/main/website/docusaurus.config.js#L535C1-L535C1 - { - type: 'docsVersionDropdown', - position: 'right', - dropdownActiveClassDisabled: true, - }, - ], - }, - footer: { - style: 'light', - links: [ - ], - copyright: `© ${new Date().getFullYear()} Apache Software Foundation and Hyper.AI for Chinese Simplified mirror`, - }, - prism: { - theme: lightCodeTheme, - darkTheme: darkCodeTheme, - }, - algolia: { - apiKey: 'f36b719e2245a23ecd89c7e9a41937f2', - indexName: 'docs', - - // Optional: see doc section below - contextualSearch: true, - - // Optional: see doc section below - appId: 'KU6TD2KAGA', - - // Optional: Algolia search parameters - searchParameters: { - attributesToSnippet: [ - 'content:50' - ], - }, - - //... other Algolia params - }, - }), - - plugins: [ - 'docusaurus-plugin-sass', - require.resolve('./src/plugins/typekit/'), - ], - scripts: [ - { - src: 'https://get.openbayes.net/js/script.js', - defer: true, - 'data-domain': 'tvm.hyper.ai' - } - ], - -}; - -module.exports = config; diff --git a/docusaurus.config.ts b/docusaurus.config.ts new file mode 100644 index 00000000..267743e1 --- /dev/null +++ b/docusaurus.config.ts @@ -0,0 +1,159 @@ +import type {Config} from '@docusaurus/types'; +import type * as Preset from '@docusaurus/preset-classic'; +import remarkMath from 'remark-math'; +import rehypeKatex from 'rehype-katex'; + +// @ts-check +// Note: type annotations allow type checking and IDEs autocompletion + +const {themes} = require('prism-react-renderer'); +const lightTheme = themes.github; +const darkTheme = themes.dracula; + +const config: Config = { + title: 'Apache TVM 中文站', + tagline: 'Apache TVM 是一个端到端的深度学习编译框架，适用于 CPU、GPU 和各种机器学习加速芯片。', + url: 'https://tvm.hyper.ai', + baseUrl: '/', + onBrokenLinks: 'throw', + onBrokenMarkdownLinks: 'warn', + favicon: 'img/favicon.png', + organizationName: 'hyperai', // Usually your GitHub org/user name. + projectName: 'tvm-cn', // Usually your repo name. + + // Even if you don't use internalization, you can use this field to set useful + // metadata like html lang. For example, if your site is Chinese, you may want + // to replace "en" with "zh-Hans". + i18n: { + defaultLocale: 'zh-Hans', + locales: ['zh-Hans'], + }, + + presets: [ + [ + '@docusaurus/preset-classic', + { + docs: { + sidebarPath: require.resolve('./sidebars.js'), + // Please change this to your repo. + editUrl: 'https://github.com/hyperai/tvm-cn/edit/master/', + showLastUpdateAuthor: true, + showLastUpdateTime: true, + lastVersion: 'current', + versions: { + current: { + label: '0.12.0', + }, + }, + remarkPlugins: [remarkMath], + // TODO: ts fix + // @ts-ignore + rehypePlugins: [rehypeKatex], + }, + blog: { + showReadingTime: true, + // Please change this to your repo. + editUrl: + 'https://github.com/facebook/docusaurus/edit/main/website/blog/', + }, + theme: { + customCss: require.resolve('./src/css/app.scss'), + }, + } satisfies Preset.Options, + ], + ], + + // https://docusaurus.io/docs/markdown-features/math-equations + stylesheets: [ + { + // href: 'https://cdn.jsdelivr.net/npm/katex@0.13.24/dist/katex.min.css', + href: 'https://experiments-hk.sparanoid.net/jsd/npm/katex@0.13.24/dist/katex.min.css', + type: 'text/css', + integrity: + 'sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM', + crossorigin: 'anonymous', + }, + ], + + themeConfig: { + colorMode: { + defaultMode: 'light', + disableSwitch: false, + respectPrefersColorScheme: true, + }, + navbar: { + title: 'TVM 中文站', + hideOnScroll: false, + logo: { + alt: 'TVM Logo', + src: 'img/favicon-dark.svg', + srcDark: 'img/favicon.svg', + }, + items: [ + { + type: 'doc', + docId: 'index', + position: 'left', + label: '查看文档', + }, + // {to: '/docs', label: '查看文档', position: 'left'}, + {to: '/about', label: '关于', position: 'left'}, + {href: 'https://github.com/hyperai/tvm-cn', label: 'GitHub 项目', position: 'left'}, + {href: 'https://hyper.ai', label: '返回超神经', position: 'left'}, + + // https://github.com/facebook/docusaurus/blob/main/website/docusaurus.config.js#L535C1-L535C1 + { + type: 'docsVersionDropdown', + position: 'right', + dropdownActiveClassDisabled: true, + }, + ], + }, + footer: { + style: 'light', + links: [ + ], + copyright: `© ${new Date().getFullYear()} Apache Software Foundation and Hyper.AI for Chinese Simplified mirror`, + }, + prism: { + theme: lightTheme, + darkTheme: darkTheme, + // https://docusaurus.io/docs/migration/v3#prism-react-renderer-v20 + additionalLanguages: ['bash', 'diff', 'json', 'python'], + }, + algolia: { + apiKey: 'f36b719e2245a23ecd89c7e9a41937f2', + indexName: 'docs', + + // Optional: see doc section below + contextualSearch: true, + + // Optional: see doc section below + appId: 'KU6TD2KAGA', + + // Optional: Algolia search parameters + searchParameters: { + attributesToSnippet: [ + 'content:50' + ], + }, + + //... other Algolia params + }, + } satisfies Preset.ThemeConfig, + + plugins: [ + 'docusaurus-plugin-sass', + require.resolve('./src/plugins/typekit/'), + ], + scripts: [ + { + src: 'https://get.openbayes.net/js/script.js', + defer: true, + 'data-domain': 'tvm.hyper.ai' + } + ], + +}; + +module.exports = config; diff --git a/package.json b/package.json index 3768acb5..a1003da0 100644 --- a/package.json +++ b/package.json @@ -16,22 +16,24 @@ "typecheck": "tsc" }, "dependencies": { - "@docusaurus/core": "2.4.3", - "@docusaurus/preset-classic": "2.4.3", - "@mdx-js/react": "1.6.22", + "@docusaurus/core": "3.0.0", + "@docusaurus/preset-classic": "3.0.0", + "@mdx-js/react": "3.0.0", "@openbayes/docsearch-css": "1.2.7", "@openbayes/docusaurus-enlight": "1.3.0", "clsx": "2.0.0", "docusaurus-plugin-sass": "0.2.5", "kladenets": "2.0.0-15", - "prism-react-renderer": "1.3.5", - "react": "17.0.2", - "react-dom": "17.0.2", + "prism-react-renderer": "2.1.0", + "react": "18.2.0", + "react-dom": "18.2.0", + "rehype-katex": "7", + "remark-math": "6", "sass": "1.69.5" }, "devDependencies": { - "@docusaurus/module-type-aliases": "2.4.3", - "@tsconfig/docusaurus": "2.0.2", + "@docusaurus/module-type-aliases": "3.0.0", + "@docusaurus/tsconfig": "3.0.0", "typescript": "5.2.2" }, "browserslist": { @@ -47,6 +49,6 @@ ] }, "engines": { - "node": ">=16.14" + "node": ">=18.0" } } diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index f954af65..bf578822 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -6,14 +6,14 @@ settings: dependencies: '@docusaurus/core': - specifier: 2.4.3 - version: 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) + specifier: 3.0.0 + version: 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) '@docusaurus/preset-classic': - specifier: 2.4.3 - version: 2.4.3(@algolia/client-search@4.19.1)(react-dom@17.0.2)(react@17.0.2)(search-insights@2.7.0)(typescript@5.2.2) + specifier: 3.0.0 + version: 3.0.0(@algolia/client-search@4.20.0)(@types/react@18.2.33)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.9.0)(typescript@5.2.2) '@mdx-js/react': - specifier: 1.6.22 - version: 1.6.22(react@17.0.2) + specifier: 3.0.0 + version: 3.0.0(@types/react@18.2.33)(react@18.2.0) '@openbayes/docsearch-css': specifier: 1.2.7 version: 1.2.7(kladenets@2.0.0-15) @@ -25,207 +25,178 @@ dependencies: version: 2.0.0 docusaurus-plugin-sass: specifier: 0.2.5 - version: 0.2.5(@docusaurus/core@2.4.3)(sass@1.69.5)(webpack@5.88.2) + version: 0.2.5(@docusaurus/core@3.0.0)(sass@1.69.5)(webpack@5.89.0) kladenets: specifier: 2.0.0-15 version: 2.0.0-15 prism-react-renderer: - 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engines: {node: '>=16.14'} + /@docusaurus/plugin-content-docs@3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2): + resolution: {integrity: sha512-MFZsOSwmeJ6rvoZMLieXxPuJsA9M9vn7/mUZmfUzSUTeHAeq+fEqvLltFOxcj4DVVDTYlQhgWYd+PISIWgamKw==} + engines: {node: '>=18.0'} peerDependencies: - react: ^16.8.4 || ^17.0.0 - react-dom: ^16.8.4 || ^17.0.0 + react: ^18.0.0 + react-dom: ^18.0.0 dependencies: - '@docusaurus/core': 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/logger': 2.4.3 - '@docusaurus/mdx-loader': 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/module-type-aliases': 2.4.3(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/types': 2.4.3(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/utils': 2.4.3(@docusaurus/types@2.4.3) - '@docusaurus/utils-validation': 2.4.3(@docusaurus/types@2.4.3) - '@types/react-router-config': 5.0.7 - combine-promises: 1.1.0 - fs-extra: 10.1.0 - import-fresh: 3.3.0 + 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+ '@docusaurus/core': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/mdx-loader': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/types': 3.0.0(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/utils': 3.0.0(@docusaurus/types@3.0.0) + '@docusaurus/utils-validation': 3.0.0(@docusaurus/types@3.0.0) + fs-extra: 11.1.1 + react: 18.2.0 + react-dom: 18.2.0(react@18.2.0) + tslib: 2.6.2 + webpack: 5.89.0 transitivePeerDependencies: - '@parcel/css' - '@swc/core' @@ -1966,21 +1851,21 @@ packages: - webpack-cli dev: false - /@docusaurus/plugin-debug@2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2): - resolution: {integrity: sha512-LkUbuq3zCmINlFb+gAd4ZvYr+bPAzMC0hwND4F7V9bZ852dCX8YoWyovVUBKq4er1XsOwSQaHmNGtObtn8Av8Q==} - engines: {node: '>=16.14'} + /@docusaurus/plugin-debug@3.0.0(@types/react@18.2.33)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2): + resolution: {integrity: 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tslib: 2.6.2 transitivePeerDependencies: - '@parcel/css' - '@swc/core' @@ -2001,19 +1886,19 @@ packages: - webpack-cli dev: false - /@docusaurus/plugin-google-analytics@2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2): - resolution: {integrity: sha512-KzBV3k8lDkWOhg/oYGxlK5o9bOwX7KpPc/FTWoB+SfKhlHfhq7qcQdMi1elAaVEIop8tgK6gD1E58Q+XC6otSQ==} - engines: {node: '>=16.14'} + /@docusaurus/plugin-google-analytics@3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2): + resolution: {integrity: sha512-0zcLK8w+ohmSm1fjUQCqeRsjmQc0gflvXnaVA/QVVCtm2yCiBtkrSGQXqt4MdpD7Xq8mwo3qVd5nhIcvrcebqw==} + engines: {node: '>=18.0'} peerDependencies: - react: ^16.8.4 || ^17.0.0 - react-dom: ^16.8.4 || ^17.0.0 + react: ^18.0.0 + react-dom: ^18.0.0 dependencies: - '@docusaurus/core': 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/types': 2.4.3(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/utils-validation': 2.4.3(@docusaurus/types@2.4.3) - react: 17.0.2 - react-dom: 17.0.2(react@17.0.2) - tslib: 2.6.0 + '@docusaurus/core': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/types': 3.0.0(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/utils-validation': 3.0.0(@docusaurus/types@3.0.0) + react: 18.2.0 + react-dom: 18.2.0(react@18.2.0) + tslib: 2.6.2 transitivePeerDependencies: - '@parcel/css' - '@swc/core' @@ -2032,19 +1917,20 @@ packages: - webpack-cli dev: false - /@docusaurus/plugin-google-gtag@2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2): - resolution: {integrity: sha512-5FMg0rT7sDy4i9AGsvJC71MQrqQZwgLNdDetLEGDHLfSHLvJhQbTCUGbGXknUgWXQJckcV/AILYeJy+HhxeIFA==} - engines: {node: '>=16.14'} + /@docusaurus/plugin-google-gtag@3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2): + resolution: {integrity: sha512-asEKavw8fczUqvXu/s9kG2m1epLnHJ19W6CCCRZEmpnkZUZKiM8rlkDiEmxApwIc2JDDbIMk+Y2TMkJI8mInbQ==} + engines: {node: '>=18.0'} peerDependencies: - react: ^16.8.4 || ^17.0.0 - react-dom: ^16.8.4 || ^17.0.0 + react: ^18.0.0 + react-dom: ^18.0.0 dependencies: - '@docusaurus/core': 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/types': 2.4.3(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/utils-validation': 2.4.3(@docusaurus/types@2.4.3) - react: 17.0.2 - react-dom: 17.0.2(react@17.0.2) - tslib: 2.6.0 + '@docusaurus/core': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/types': 3.0.0(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/utils-validation': 3.0.0(@docusaurus/types@3.0.0) + '@types/gtag.js': 0.0.12 + react: 18.2.0 + react-dom: 18.2.0(react@18.2.0) + tslib: 2.6.2 transitivePeerDependencies: - '@parcel/css' - '@swc/core' @@ -2063,19 +1949,19 @@ packages: - webpack-cli dev: false - /@docusaurus/plugin-google-tag-manager@2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2): - resolution: {integrity: 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3.0.0(@docusaurus/types@3.0.0) + react: 18.2.0 + react-dom: 18.2.0(react@18.2.0) + tslib: 2.6.2 transitivePeerDependencies: - '@parcel/css' - '@swc/core' @@ -2094,24 +1980,24 @@ packages: - webpack-cli dev: false - /@docusaurus/plugin-sitemap@2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2): - resolution: {integrity: sha512-LRQYrK1oH1rNfr4YvWBmRzTL0LN9UAPxBbghgeFRBm5yloF6P+zv1tm2pe2hQTX/QP5bSKdnajCvfnScgKXMZQ==} - engines: {node: '>=16.14'} + /@docusaurus/plugin-sitemap@3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2): + resolution: {integrity: sha512-cfcONdWku56Oi7Hdus2uvUw/RKRRlIGMViiHLjvQ21CEsEqnQ297MRoIgjU28kL7/CXD/+OiANSq3T1ezAiMhA==} + engines: {node: '>=18.0'} peerDependencies: - react: ^16.8.4 || ^17.0.0 - react-dom: ^16.8.4 || ^17.0.0 + react: ^18.0.0 + react-dom: ^18.0.0 dependencies: - '@docusaurus/core': 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/logger': 2.4.3 - '@docusaurus/types': 2.4.3(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/utils': 2.4.3(@docusaurus/types@2.4.3) - '@docusaurus/utils-common': 2.4.3(@docusaurus/types@2.4.3) - '@docusaurus/utils-validation': 2.4.3(@docusaurus/types@2.4.3) - fs-extra: 10.1.0 - react: 17.0.2 - react-dom: 17.0.2(react@17.0.2) + '@docusaurus/core': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/logger': 3.0.0 + '@docusaurus/types': 3.0.0(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/utils': 3.0.0(@docusaurus/types@3.0.0) + '@docusaurus/utils-common': 3.0.0(@docusaurus/types@3.0.0) + '@docusaurus/utils-validation': 3.0.0(@docusaurus/types@3.0.0) + fs-extra: 11.1.1 + react: 18.2.0 + react-dom: 18.2.0(react@18.2.0) sitemap: 7.1.1 - tslib: 2.6.0 + tslib: 2.6.2 transitivePeerDependencies: - '@parcel/css' - '@swc/core' @@ -2130,28 +2016,28 @@ packages: - webpack-cli dev: false - 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2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/plugin-google-tag-manager': 2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/plugin-sitemap': 2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/theme-classic': 2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/theme-common': 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/theme-search-algolia': 2.4.3(@algolia/client-search@4.19.1)(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(search-insights@2.7.0)(typescript@5.2.2) - '@docusaurus/types': 2.4.3(react-dom@17.0.2)(react@17.0.2) - react: 17.0.2 - react-dom: 17.0.2(react@17.0.2) + /@docusaurus/preset-classic@3.0.0(@algolia/client-search@4.20.0)(@types/react@18.2.33)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.9.0)(typescript@5.2.2): + resolution: {integrity: 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'@docusaurus/plugin-sitemap': 3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/theme-classic': 3.0.0(@types/react@18.2.33)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/theme-common': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/theme-search-algolia': 3.0.0(@algolia/client-search@4.20.0)(@docusaurus/types@3.0.0)(@types/react@18.2.33)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.9.0)(typescript@5.2.2) + '@docusaurus/types': 3.0.0(react-dom@18.2.0)(react@18.2.0) + react: 18.2.0 + react-dom: 18.2.0(react@18.2.0) transitivePeerDependencies: - '@algolia/client-search' - '@parcel/css' @@ -2174,53 +2060,54 @@ packages: - webpack-cli dev: false - /@docusaurus/react-loadable@5.5.2(react@17.0.2): + /@docusaurus/react-loadable@5.5.2(react@18.2.0): resolution: {integrity: sha512-A3dYjdBGuy0IGT+wyLIGIKLRE+sAk1iNk0f1HjNDysO7u8lhL4N3VEm+FAubmJbAztn94F7MxBTPmnixbiyFdQ==} peerDependencies: react: '*' 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'@docusaurus/theme-common': 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/theme-translations': 2.4.3 - '@docusaurus/types': 2.4.3(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/utils': 2.4.3(@docusaurus/types@2.4.3) - '@docusaurus/utils-common': 2.4.3(@docusaurus/types@2.4.3) - '@docusaurus/utils-validation': 2.4.3(@docusaurus/types@2.4.3) - '@mdx-js/react': 1.6.22(react@17.0.2) + react: 18.2.0 + + /@docusaurus/theme-classic@3.0.0(@types/react@18.2.33)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2): + resolution: {integrity: sha512-wWOHSrKMn7L4jTtXBsb5iEJ3xvTddBye5PjYBnWiCkTAlhle2yMdc4/qRXW35Ot+OV/VXu6YFG8XVUJEl99z0A==} + engines: {node: '>=18.0'} + peerDependencies: + react: ^18.0.0 + react-dom: ^18.0.0 + dependencies: + '@docusaurus/core': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/mdx-loader': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/module-type-aliases': 3.0.0(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/plugin-content-blog': 3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/plugin-content-docs': 3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/plugin-content-pages': 3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/theme-common': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/theme-translations': 3.0.0 + '@docusaurus/types': 3.0.0(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/utils': 3.0.0(@docusaurus/types@3.0.0) + '@docusaurus/utils-common': 3.0.0(@docusaurus/types@3.0.0) + '@docusaurus/utils-validation': 3.0.0(@docusaurus/types@3.0.0) + '@mdx-js/react': 3.0.0(@types/react@18.2.33)(react@18.2.0) clsx: 1.2.1 copy-text-to-clipboard: 3.2.0 infima: 0.2.0-alpha.43 lodash: 4.17.21 nprogress: 0.2.0 - postcss: 8.4.26 - prism-react-renderer: 1.3.5(react@17.0.2) + postcss: 8.4.31 + prism-react-renderer: 2.1.0(react@18.2.0) prismjs: 1.29.0 - react: 17.0.2 - react-dom: 17.0.2(react@17.0.2) - react-router-dom: 5.3.4(react@17.0.2) - rtlcss: 3.5.0 - tslib: 2.6.0 + react: 18.2.0 + react-dom: 18.2.0(react@18.2.0) + react-router-dom: 5.3.4(react@18.2.0) + rtlcss: 4.1.1 + tslib: 2.6.2 utility-types: 3.10.0 transitivePeerDependencies: - '@parcel/css' - '@swc/core' - '@swc/css' + - '@types/react' - bufferutil - csso - debug @@ -2235,30 +2122,29 @@ packages: - webpack-cli dev: false - /@docusaurus/theme-common@2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2): - resolution: {integrity: sha512-7KaDJBXKBVGXw5WOVt84FtN8czGWhM0lbyWEZXGp8AFfL6sZQfRTluFp4QriR97qwzSyOfQb+nzcDZZU4tezUw==} - engines: {node: '>=16.14'} + /@docusaurus/theme-common@3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2): + resolution: {integrity: sha512-PahRpCLRK5owCMEqcNtUeTMOkTUCzrJlKA+HLu7f+8osYOni617YurXvHASCsSTxurjXaLz/RqZMnASnqATxIA==} + engines: {node: '>=18.0'} peerDependencies: - react: ^16.8.4 || ^17.0.0 - react-dom: ^16.8.4 || ^17.0.0 + react: ^18.0.0 + react-dom: ^18.0.0 dependencies: - '@docusaurus/mdx-loader': 2.4.3(@docusaurus/types@2.4.3)(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/module-type-aliases': 2.4.3(react-dom@17.0.2)(react@17.0.2) - '@docusaurus/plugin-content-blog': 2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/plugin-content-docs': 2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/plugin-content-pages': 2.4.3(react-dom@17.0.2)(react@17.0.2)(typescript@5.2.2) - '@docusaurus/utils': 2.4.3(@docusaurus/types@2.4.3) - '@docusaurus/utils-common': 2.4.3(@docusaurus/types@2.4.3) + '@docusaurus/mdx-loader': 3.0.0(@docusaurus/types@3.0.0)(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/module-type-aliases': 3.0.0(react-dom@18.2.0)(react@18.2.0) + '@docusaurus/plugin-content-blog': 3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/plugin-content-docs': 3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/plugin-content-pages': 3.0.0(react-dom@18.2.0)(react@18.2.0)(typescript@5.2.2) + '@docusaurus/utils': 3.0.0(@docusaurus/types@3.0.0) + '@docusaurus/utils-common': 3.0.0(@docusaurus/types@3.0.0) '@types/history': 4.7.11 - '@types/react': 18.2.15 - '@types/react-router-config': 5.0.7 + '@types/react': 18.2.33 + '@types/react-router-config': 5.0.9 clsx: 1.2.1 parse-numeric-range: 1.3.0 - prism-react-renderer: 1.3.5(react@17.0.2) - react: 17.0.2 - react-dom: 17.0.2(react@17.0.2) - tslib: 2.6.0 - use-sync-external-store: 1.2.0(react@17.0.2) + prism-react-renderer: 2.1.0(react@18.2.0) + react: 18.2.0 + react-dom: 18.2.0(react@18.2.0) + tslib: 2.6.2 utility-types: 3.10.0 transitivePeerDependencies: - '@docusaurus/types' @@ -2279,30 +2165,30 @@ packages: - webpack-cli dev: false - 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bufferutil - debug @@ -8363,19 +9242,20 @@ packages: - utf-8-validate dev: false - /webpack-merge@5.9.0: - resolution: {integrity: sha512-6NbRQw4+Sy50vYNTw7EyOn41OZItPiXB8GNv3INSoe3PSFaHJEz3SHTrYVaRm2LilNGnFUzh0FAwqPEmU/CwDg==} + /webpack-merge@5.10.0: + resolution: {integrity: sha512-+4zXKdx7UnO+1jaN4l2lHVD+mFvnlZQP/6ljaJVb4SZiwIKeUnrT5l0gkT8z+n4hKpC+jpOv6O9R+gLtag7pSA==} engines: {node: '>=10.0.0'} dependencies: clone-deep: 4.0.1 + flat: 5.0.2 wildcard: 2.0.1 /webpack-sources@3.2.3: resolution: {integrity: sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w==} engines: {node: '>=10.13.0'} - /webpack@5.88.2: - resolution: {integrity: sha512-JmcgNZ1iKj+aiR0OvTYtWQqJwq37Pf683dY9bVORwVbUrDhLhdn/PlO2sHsFHPkj7sHNQF3JwaAkp49V+Sq1tQ==} + /webpack@5.89.0: + resolution: {integrity: sha512-qyfIC10pOr70V+jkmud8tMfajraGCZMBWJtrmuBymQKCrLTRejBI8STDp1MCyZu/QTdZSeacCQYpYNQVOzX5kw==} engines: {node: '>=10.13.0'} hasBin: true peerDependencies: @@ -8384,17 +9264,17 @@ packages: webpack-cli: optional: true dependencies: - '@types/eslint-scope': 3.7.4 - '@types/estree': 1.0.1 + '@types/eslint-scope': 3.7.6 + '@types/estree': 1.0.4 '@webassemblyjs/ast': 1.11.6 '@webassemblyjs/wasm-edit': 1.11.6 '@webassemblyjs/wasm-parser': 1.11.6 - acorn: 8.10.0 - acorn-import-assertions: 1.9.0(acorn@8.10.0) - browserslist: 4.21.10 + acorn: 8.11.2 + acorn-import-assertions: 1.9.0(acorn@8.11.2) + browserslist: 4.22.1 chrome-trace-event: 1.0.3 enhanced-resolve: 5.15.0 - es-module-lexer: 1.3.0 + es-module-lexer: 1.3.1 eslint-scope: 5.1.1 events: 3.3.0 glob-to-regexp: 0.4.1 @@ -8405,7 +9285,7 @@ packages: neo-async: 2.6.2 schema-utils: 3.3.0 tapable: 2.2.1 - terser-webpack-plugin: 5.3.9(webpack@5.88.2) + terser-webpack-plugin: 5.3.9(webpack@5.89.0) watchpack: 2.4.0 webpack-sources: 3.2.3 transitivePeerDependencies: @@ -8413,7 +9293,7 @@ packages: - esbuild - uglify-js - /webpackbar@5.0.2(webpack@5.88.2): + /webpackbar@5.0.2(webpack@5.89.0): resolution: {integrity: sha512-BmFJo7veBDgQzfWXl/wwYXr/VFus0614qZ8i9znqcl9fnEdiVkdbi0TedLQ6xAK92HZHDJ0QmyQ0fmuZPAgCYQ==} engines: {node: '>=12'} peerDependencies: @@ -8422,8 +9302,8 @@ packages: chalk: 4.1.2 consola: 2.15.3 pretty-time: 1.1.0 - std-env: 3.3.3 - webpack: 5.88.2 + std-env: 3.4.3 + webpack: 5.89.0 dev: false /websocket-driver@0.7.4: @@ -8462,13 +9342,6 @@ packages: isexe: 2.0.0 dev: false - /widest-line@3.1.0: - resolution: {integrity: sha512-NsmoXalsWVDMGupxZ5R08ka9flZjjiLvHVAWYOKtiKM8ujtZWr9cRffak+uSE48+Ob8ObalXpwyeUiyDD6QFgg==} - engines: {node: '>=8'} - dependencies: - string-width: 4.2.3 - dev: false - /widest-line@4.0.1: resolution: {integrity: sha512-o0cyEG0e8GPzT4iGHphIOh0cJOV8fivsXxddQasHPHfoZf1ZexrfeA21w2NaEN1RHE+fXlfISmOE8R9N3u3Qig==} engines: {node: '>=12'} @@ -8479,15 +9352,6 @@ packages: /wildcard@2.0.1: resolution: {integrity: sha512-CC1bOL87PIWSBhDcTrdeLo6eGT7mCFtrg0uIJtqJUFyK+eJnzl8A1niH56uu7KMa5XFrtiV+AQuHO3n7DsHnLQ==} - /wrap-ansi@7.0.0: - resolution: {integrity: sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==} - engines: {node: '>=10'} - dependencies: - ansi-styles: 4.3.0 - string-width: 4.2.3 - strip-ansi: 6.0.1 - dev: false - /wrap-ansi@8.1.0: resolution: {integrity: sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==} engines: {node: '>=12'} @@ -8523,8 +9387,8 @@ packages: optional: true dev: false - /ws@8.13.0: - resolution: {integrity: sha512-x9vcZYTrFPC7aSIbj7sRCYo7L/Xb8Iy+pW0ng0wt2vCJv7M9HOMy0UoN3rr+IFC7hb7vXoqS+P9ktyLLLhO+LA==} + /ws@8.14.2: + resolution: {integrity: sha512-wEBG1ftX4jcglPxgFCMJmZ2PLtSbJ2Peg6TmpJFTbe9GZYOQCDPdMYu/Tm0/bGZkw8paZnJY45J4K2PZrLYq8g==} engines: {node: '>=10.0.0'} peerDependencies: bufferutil: ^4.0.1 @@ -8536,21 +9400,16 @@ packages: optional: true dev: false - /xdg-basedir@4.0.0: - resolution: {integrity: sha512-PSNhEJDejZYV7h50BohL09Er9VaIefr2LMAf3OEmpCkjOi34eYyQYAXUTjEQtZJTKcF0E2UKTh+osDLsgNim9Q==} - engines: {node: '>=8'} + /xdg-basedir@5.1.0: + resolution: {integrity: sha512-GCPAHLvrIH13+c0SuacwvRYj2SxJXQ4kaVTT5xgL3kPrz56XxkF21IGhjSE1+W0aw7gpBWRGXLCPnPby6lSpmQ==} + engines: {node: '>=12'} dev: false /xml-js@1.6.11: resolution: {integrity: sha512-7rVi2KMfwfWFl+GpPg6m80IVMWXLRjO+PxTq7V2CDhoGak0wzYzFgUY2m4XJ47OGdXd8eLE8EmwfAmdjw7lC1g==} hasBin: true dependencies: - sax: 1.2.4 - dev: false - - /xtend@4.0.2: - resolution: {integrity: sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==} - engines: {node: '>=0.4'} + sax: 1.3.0 dev: false /yallist@3.1.1: @@ -8571,6 +9430,11 @@ packages: engines: {node: '>=10'} dev: false - /zwitch@1.0.5: - resolution: {integrity: sha512-V50KMwwzqJV0NpZIZFwfOD5/lyny3WlSzRiXgA0G7VUnRlqttta1L6UQIHzd6EuBY/cHGfwTIck7w1yH6Q5zUw==} + /yocto-queue@1.0.0: + resolution: {integrity: sha512-9bnSc/HEW2uRy67wc+T8UwauLuPJVn28jb+GtJY16iiKWyvmYJRXVT4UamsAEGQfPohgr2q4Tq0sQbQlxTfi1g==} + engines: {node: '>=12.20'} + dev: false + + /zwitch@2.0.4: + resolution: {integrity: sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==} dev: false diff --git a/rst2md.sh b/rst2md.sh index 98e60b6c..bd57dc69 100755 --- a/rst2md.sh +++ b/rst2md.sh @@ -9,7 +9,7 @@ # By default this will keep the original .rst file # # Original by https://gist.github.com/stupergenius/b43d38fbff0547c96322e3f44264a969 -# Modified by Tunghsiao Liu +# Modified by Sparanoid t@sparanoid.com set -o errexit set -o nounset diff --git a/tsconfig.json b/tsconfig.json index 6f475698..314eab8a 100644 --- a/tsconfig.json +++ b/tsconfig.json @@ -1,6 +1,6 @@ { // This file is not used in compilation. It is here just for a nice editor experience. - "extends": "@tsconfig/docusaurus/tsconfig.json", + "extends": "@docusaurus/tsconfig", "compilerOptions": { "baseUrl": "." } diff --git a/versioned_docs/version-0.10.0/arch/arch/05-device_target_interactions.md b/versioned_docs/version-0.10.0/arch/arch/05-device_target_interactions.md index 4a9b4d05..fc4c862f 100644 --- a/versioned_docs/version-0.10.0/arch/arch/05-device_target_interactions.md +++ b/versioned_docs/version-0.10.0/arch/arch/05-device_target_interactions.md @@ -13,7 +13,7 @@ sidebar_position: 150 * [Target](#tvm-target-specific-target) 类描述了运行函数的设备。它既对 target 代码生成器公开，也对优化 pass 公开。 * [target 代码生成器](#tvm-target-specific-codegen) 从 IRModule 构造了一个 [模块](/docs/arch/arch/runtimes#module)，它由一个或多个 [PackedFunc](/docs/arch/arch/runtimes#PackedFunc) 组成。 -## DeviceAPI {#tvm-target-specific-device-api} +## DeviceAPI `DeviceAPI` 表示特定硬件设备 API 的句柄。（例如，`CUDADeviceAPI` 通过 CUDA 框架处理所有的交互。）大多数 `DeviceAPI` 方法接收一个 `device_id` 参数，来指定访问哪个设备。Python 中通常用 `tvm.runtime.device()` 函数访问它们，这个函数返回特定设备的句柄，通过特定 API 访问。（例如，`tvm.runtime.device()` 通过 CUDA API 访问物理设备 `0`。） @@ -52,7 +52,7 @@ TVM 框架若要使用新的 DeviceAPI，应该按照以下步骤注册： 2. 给 [device_api.h](https://github.com/apache/tvm/blob/main/include/tvm/runtime/device_api.h) 中的 `DeviceName` 添加一个案例，从而将枚举值转换为字符串表示形式。这个字符串表示应该和 `TVM_REGISTER_GLOBAL` 的名称匹配。 3. 将入口添加到 `tvm.runtime.Device` 的 `MASK2STR` 和 `STR2MASK` 字典，获取新的枚举值。 -## Target 定义 {#tvm-target-specific-target} +## Target 定义 `Target` 对象是属性（包含物理设备、其硬件/驱动程序限制，及其功能）的查找表。在优化和代码生成阶段都可以访问 `Target`。虽然相同的 `Target` 类适用于所有 runtime target，但每个 runtime target 可能需要添加特定于 target 的选项。 @@ -64,7 +64,7 @@ TVM 框架若要使用新的 DeviceAPI，应该按照以下步骤注册： 代码生成器中可以用 C++ 中的 `target->GetAttr(param_name)`，或是 Python 中的 `target.attrs` 字典来访问 target 属性。 -## Target 代码生成器 {#tvm-target-specific-codegen} +## Target 代码生成器 代码生成器采用优化的 `IRModule`，并将其转换为可执行表示。每个代码生成器注册后，才能被 TVM 框架使用。这是通过注册 `"target.build.foo"` 函数来完成的，其中 `foo` 与上面的 `TVM_REGISTER_TARGET_KIND` 定义中使用的名称相同。 diff --git a/versioned_docs/version-0.10.0/arch/arch/07-inferbound.md b/versioned_docs/version-0.10.0/arch/arch/07-inferbound.md index 05ba0143..f81765ae 100644 --- a/versioned_docs/version-0.10.0/arch/arch/07-inferbound.md +++ b/versioned_docs/version-0.10.0/arch/arch/07-inferbound.md @@ -146,7 +146,7 @@ InferBound pass 有两个不是很明显的属性： 消息传递函数被命名为「PassUp」或「PassDown」，取决于消息是从 DAG 中的子代传递给其父代（「PassUp」），还是从父代传递给其子代（「PassDown」）。例如，上图左侧的大箭头显示 PassDownDomain 从根 IterVar `i` 向其子 `i.outer` 和 `i.inner` 发送消息。 -## PassDownDomain {#passdowndomain} +## PassDownDomain PassDownDomain 的作用是为 root_iter_vars 取 InferRootBound 产生的 Range，并设置 stage 中所有其他 IterVars 的 Range。 @@ -193,7 +193,7 @@ InferBound 调用 InferRootBound，然后在 stage 计算图中的每个 stage *对于包含 compute_at 的 schedules**而言**，InferBound 更为复杂。因此首先**针对**不包含 compute_at 的 schedules**解读**InferBound。* -### 阶段 1：为 consumer 的 leaf_iter_vars 初始化 IntSet {#phase1} +### 阶段 1：为 consumer 的 leaf_iter_vars 初始化 IntSet ``` c++ /* @@ -212,7 +212,7 @@ InferBound 调用 InferRootBound，然后在 stage 计算图中的每个 stage 简单起见，假设 schedule 不包含线程轴。这种情况下，仅当 schedule 包含 compute_at 时，才和案例 2 相关。参阅 [InferBound 与 compute_at](#inferboundca) 节来进一步获取更多信息。 -### 阶段 2：将 IntSet 从 consumer 的 leaf 传到 consumer 的 root {#phase2} +### 阶段 2：将 IntSet 从 consumer 的 leaf 传到 consumer 的 root ```c++ /* @@ -293,7 +293,7 @@ IntSet 的并集是通过将每个 IntSet 转换为一个区间来计算的， ![图片](/img/docs/tvmai/tvmai.github.io/main/images/docs/inferbound/gatherbound_problem.png) -## InferBound 与 compute_at {#inferboundca} +## InferBound 与 compute_at 若 schedule 包含 compute_at，则 InferRootBound 的阶段 1-2 会变得更加复杂。 @@ -377,7 +377,7 @@ public: 再次考虑上面的例子。为了让 InferBound 确定必须计算 C 的多少元素，重要的是，要知道 C 的计算是发生在 D 的叶变量的范围内，还是在该范围之上。在例 1 中，C 的计算发生在 D 的所有叶变量的范围*之上*。在例 2，C 的计算发生在 D 的所有叶变量的范围*内*。在例 3，C 出现在D 的 i 维度的范围内，但在 D 的 j 维度的范围之上。 -CreateAttachPath 负责确定哪些作用域包含 stage C。这些作用域按从最内层到最外层的顺序排列。因此，对于每个 stage，CreateAttachPath 都会生成一个「附加路径」，其中列出了包含该 stage 从最里面到最外面的范围，在例 1，C 的附加路径为空。在例 2，C 的附加路径包含 {j，i}。在例 3，C 的附加路径是 {i}。 +CreateAttachPath 负责确定哪些作用域包含 stage C。这些作用域按从最内层到最外层的顺序排列。因此，对于每个 stage，CreateAttachPath 都会生成一个「附加路径」，其中列出了包含该 stage 从最里面到最外面的范围，在例 1，C 的附加路径为空。在例 2，C 的附加路径包含 `{j，i}`。在例 3，C 的附加路径是 `{i}`。 以下示例阐明了附加路径的概念，适用于更复杂的情况。 @@ -415,11 +415,11 @@ realize D([0, 4], [0, 5], [0, 16]) { } ``` -在这种情况下，C 的附加路径是 {dk, dj, di}。注意 C 没有使用 di，但 di 仍然出现在 C 的附加路径中。 +在这种情况下，C 的附加路径是 `{dk, dj, di}`。注意 C 没有使用 di，但 di 仍然出现在 C 的附加路径中。 **例 5** -根据上述定义，可以很自然地在拆分后应用 Compute_at。下面例子中，C 的附着点是 D 的 j_inner。C 的附着路径是 {j_inner, j_outer, i}。 +根据上述定义，可以很自然地在拆分后应用 Compute_at。下面例子中，C 的附着点是 D 的 j_inner。C 的附着路径是 `{j_inner, j_outer, i}`。 ``` python C = tvm.compute((5, 16), lambda i, j : tvm.const(5, "int32"), name='C') @@ -456,7 +456,7 @@ s[C].compute_at(s[D], D.op.axis[1]) s[D].compute_at(s[E], E.op.axis[1]) ``` -当 `debug_keep_trivial_loop=True` 时，C 的附加路径为 {dj,di,ej,ei}，D 的附加路径为 {ej,ei}： +当 `debug_keep_trivial_loop=True` 时，C 的附加路径为 `{dj,di,ej,ei}`，D 的附加路径为 `{ej,ei}`： ``` c++ // attr [D] storage_scope = "global" @@ -494,7 +494,7 @@ produce E { 若没有附加，则已经为 consumer 变量计算的 Range 定义了 consumer 需要多少 C。但是，若 stage 实际上在 consumer 变量 j 的一个范围内，那么一次只需要 j 的范围内的一个点。 -### 阶段 1：为 consumer 的 leaf_iter_vars 初始化 IntSet {#phase1ca} +### 阶段 1：为 consumer 的 leaf_iter_vars 初始化 IntSet ``` c++ /* @@ -512,7 +512,7 @@ produce E { * 案例 3：需要释放。这种情况下，leaf 的 Range 被简单地转换为 IntSet。 若在 consumer 中遇到 stage C 的附着点，就会发生案例 2。对于此 attach_ivar，以及 consumer 的所有更高叶变量，将应用案例 2。若 C 在叶变量的 Range 内，这将确保仅请求叶变量范围内的单个点。 -### 阶段 2：将 IntSet 从 consumer 的 leaf 传到 consumer 的 root {#phase2ca} +### 阶段 2：将 IntSet 从 consumer 的 leaf 传到 consumer 的 root ``` c++ /* @@ -530,7 +530,7 @@ produce E { PassUpDomain 将 up_state 向 consumer 传给所有 IterVars 后，将创建一个从 root_iter_vars 到 IntSet 的新映射。若 stage 没有附加到当前 consumer，那么对于 consumer 的 attach_path 中的每个变量 iv，将 iv 的 Range 添加到一个 `relax_set`。stage 的 root 变量是根据这个 `relax_set` 进行评估的。 -这是为了处理类似以下示例的情况，其中 C 没有附加到任何地方，但它的 consumer D 在 stage E 中附加。这种情况下，在确定 C 有多少需要计算时，必须考虑 D 的 attach_path，{ej，ei}。 +这是为了处理类似以下示例的情况，其中 C 没有附加到任何地方，但它的 consumer D 在 stage E 中附加。这种情况下，在确定 C 有多少需要计算时，必须考虑 D 的 attach_path，`{ej，ei}`。 ``` python C = tvm.compute((5, 16), lambda ci, cj : tvm.const(5, "int32"), name='C') diff --git a/versioned_docs/version-0.10.0/arch/arch/10-relay_op_strategy.md b/versioned_docs/version-0.10.0/arch/arch/10-relay_op_strategy.md index d5c93a1a..07174427 100644 --- a/versioned_docs/version-0.10.0/arch/arch/10-relay_op_strategy.md +++ b/versioned_docs/version-0.10.0/arch/arch/10-relay_op_strategy.md @@ -157,7 +157,7 @@ def conv2d_strategy_mytarget(attrs, inputs, out_type, target): ... ``` -## 从 Op 策略中选择实现 {#select-implementation-from-op-strategy} +## 从 Op 策略中选择实现 在编译过程中，Relay 编译引擎要确定当有多个算子时，使用哪个算子来实现。选择策略的工作原理如下。 diff --git a/versioned_docs/version-0.10.0/arch/arch/11-convert_layout.md b/versioned_docs/version-0.10.0/arch/arch/11-convert_layout.md index f3a8b7f5..14def826 100644 --- a/versioned_docs/version-0.10.0/arch/arch/11-convert_layout.md +++ b/versioned_docs/version-0.10.0/arch/arch/11-convert_layout.md @@ -213,7 +213,7 @@ ConvertLayout pass 非常易于使用。pass 不是默认 relay.build pipeline 为了指定要转换到的布局，创建一个映射，该映射由重度布局敏感的算子指向该算子期望布局的列表。以下第一个示例指定了数据布局，允许内核布局自动转换为 TVM 支持的布局（针对特定的数据布局和算子）。这是通过使用「default」关键字指定的。 -第二个示例显示了如何转换为指定内核布局。注意，以下示例将转换为相同的布局，即 {‘nn.conv2d’: [‘NCHW’, ‘default’]} == {‘nn.conv2d’: [‘NCHW’, ‘OIHW’]} +第二个示例显示了如何转换为指定内核布局。注意，以下示例将转换为相同的布局，即 `{‘nn.conv2d’: [‘NCHW’, ‘default’]} == {‘nn.conv2d’: [‘NCHW’, ‘OIHW’]}` ``` python # TFlite 框架到 Relay 解析器 - 默认布局是 NHWC diff --git a/versioned_docs/version-0.10.0/arch/arch/12-benchmark.md b/versioned_docs/version-0.10.0/arch/arch/12-benchmark.md index c0f0a45f..771cc3d0 100644 --- a/versioned_docs/version-0.10.0/arch/arch/12-benchmark.md +++ b/versioned_docs/version-0.10.0/arch/arch/12-benchmark.md @@ -20,18 +20,18 @@ sidebar_position: 220 | runtime_ms_std | 5.3 | statistics | | | timestamp | 1572282699.6 | metadata | indicates when this record is logged | | schema_version | “0.1” | metadata | ensure reproducibility as we iterate on this schema | -| metadata | { “docker_tag”:”gcr.io/…/0a680”, … } | metadata | docker_tag is optional | -| workload_args | {“input_name”: “Input3”, “input_shape”: [list_of_shape], “data_layout”: NHCW} | workload | | -| workload_metadata | {“class”: “vision”,”doc_url”: “https://github.com/.../README.md”, “opset”: 7,”type”: “body_analysis”,”url”: “https://onnxzoo...ferplus.tar.gz”, “md5”: “07fc7…”} | workload | source of workload | +| metadata | `{ “docker_tag”:”gcr.io/…/0a680”, … }` | metadata | docker_tag is optional | +| workload_args | `{“input_name”: “Input3”, “input_shape”: [list_of_shape], “data_layout”: NHCW}` | workload | | +| workload_metadata | `{“class”: “vision”,”doc_url”: “https://github.com/.../README.md”, “opset”: 7,”type”: “body_analysis”,”url”: “https://onnxzoo...ferplus.tar.gz”, “md5”: “07fc7…”}` | workload | source of workload | | engine_version | “1.0.5” | compiler | use semvar format | -| engine_config | {“llvm”: “llvm-8”, “nvcc”: 10.1, “accelerator”: “MLAS”, “relay_opt_level”: 3, “tvm_target”:”llvm -mcpu=cascadelake”} | compiler | fields are optionally specified | -| compilation_config | {“opt_level”: 3, “layer_schedules”:[]/ } | compiler | fields are optionally specified | -| software_config | {“os”: “ubuntu:18.04”,”pip”: { “docker”: “4.1.0”, “gitpython”: “3.0.4”, “numpy”: “1.17.4”, “onnx”: “1.6.0”}, “cudnn”: “cudnn-8”, “cuda_driver”: “480.10.1”} | backend | env dependency list | -| runtime_config | {“num_cpu_threads”: 3} | backend | info on non-hardware, non-software metadata | -| hardware_config | {“cpu_count”: 16, “cloud_machine_type”:”c2-standard-16”, “memory_GB”:64} | hardware | json descriptor of target hardware environment | -| execution_config | {“number”: 1, “repeat”: 10, “min_repeat_ms”, 0} | statistics | workload execution parameters | -| metrics | {“accuracy”: 48.5,“compilation_ms_mean”: 12} | statistics | other metrics | -| runtime_raw | [{“runtime_ms”: 12, …}, {“runtime_ms”:13,…},…] | statistics | optional raw metrics array | +| engine_config | `{“llvm”: “llvm-8”, “nvcc”: 10.1, “accelerator”: “MLAS”, “relay_opt_level”: 3, “tvm_target”:”llvm -mcpu=cascadelake”}` | compiler | fields are optionally specified | +| compilation_config | `{“opt_level”: 3, “layer_schedules”:[]/ }` | compiler | fields are optionally specified | +| software_config | `{“os”: “ubuntu:18.04”,”pip”: { “docker”: “4.1.0”, “gitpython”: “3.0.4”, “numpy”: “1.17.4”, “onnx”: “1.6.0”}, “cudnn”: “cudnn-8”, “cuda_driver”: “480.10.1”}` | backend | env dependency list | +| runtime_config | `{“num_cpu_threads”: 3}` | backend | info on non-hardware, non-software metadata | +| hardware_config | `{“cpu_count”: 16, “cloud_machine_type”:”c2-standard-16”, “memory_GB”:64}` | hardware | json descriptor of target hardware environment | +| execution_config | `{“number”: 1, “repeat”: 10, “min_repeat_ms”, 0}` | statistics | workload execution parameters | +| metrics | `{“accuracy”: 48.5,“compilation_ms_mean”: 12}` | statistics | other metrics | +| runtime_raw | `[{“runtime_ms”: 12, …}, {“runtime_ms”:13,…},…]` | statistics | optional raw metrics array | ## 存储格式 @@ -98,4 +98,4 @@ sidebar_position: 220 "execution_config":{}, "metrics":{} } -``` \ No newline at end of file +``` diff --git a/versioned_docs/version-0.10.0/arch/arch/17-model_library_format.md b/versioned_docs/version-0.10.0/arch/arch/17-model_library_format.md index fc4c9f46..4ba0b705 100644 --- a/versioned_docs/version-0.10.0/arch/arch/17-model_library_format.md +++ b/versioned_docs/version-0.10.0/arch/arch/17-model_library_format.md @@ -78,7 +78,7 @@ TVM 生成的所有代码都放在这个目录中。编写代码时，生成的 * `target`：将 `device_type`（潜在的整数，作为字符串）映射到描述用于该 `device_type` 的 Relay 后端的子 target 的字典。 * `version`：标识模型库格式中格式的数字版本号。当元数据结构或磁盘结构更改时，这个数字会增加。本文档反映的是第 `5` 版。 -### 内存使用总结 {#memory-usage-summary} +### 内存使用总结 它是具有如下子键的字典： diff --git a/versioned_docs/version-0.10.0/arch/arch/runtimes/index.md b/versioned_docs/version-0.10.0/arch/arch/runtimes/index.md index f3d5a169..3994f1e8 100644 --- a/versioned_docs/version-0.10.0/arch/arch/runtimes/index.md +++ b/versioned_docs/version-0.10.0/arch/arch/runtimes/index.md @@ -18,7 +18,7 @@ TVM 支持多种编程语言进行编译器堆栈开发和部署。本文档将 * 实验：将编译好的函数发送到嵌入式设备上直接运行。 我们期望能够用任何语言定义一个函数，然后用另一种语言调用。还期望将 runtime 内核最小化，部署到嵌入式设备。 -## PackedFunc {#PackedFunc} +## PackedFunc [PackedFunc](https://github.com/apache/tvm/blob/main/include/tvm/runtime/packed_func.h) 是一个简单而优雅的解决方案，它可以解决以上问题。单个 `PackedFunc` 对象代表一个函数调用，其调用者和被调用者可能使用不同的语言。 @@ -104,7 +104,7 @@ PackedFunc 用于编译器和部署堆栈： 与普通函数相比，调用 PackedFunc 的开销很小，因为它只在堆栈上保存了几个值，所以只要不包装小的函数即可。总之，PackedFunc 是 TVM 中的通用粘合剂，可以广泛使用它来支持编译器和部署。 -## 模块 {#module} +## 模块 由于 TVM 支持多种类型的设备，因此需要支持不同类型的驱动程序。必须用驱动程序 API 来加载内核，以打包格式设置参数，并执行内核启动。 @@ -209,6 +209,6 @@ PackedFunc 中的每个参数都包含一个关联值 [TVMValue](https://github. * [c_runtime_api.cc](https://github.com/apache/tvm/blob/main/src/runtime/c_runtime_api.cc#L262)，用于 C API 以及如何提供回调。 为了支持扩展类型，使用了注册表系统来注册类型相关信息，如 C++ 中对所有类型的支持，参阅 [扩展类型](https://github.com/apache/tvm/tree/main/apps/extension) 了解更多详细信息。 -# Runtime 特定信息 {#runtime-specific-information} +# Runtime 特定信息 * [Vulkan Runtime](vulkan) diff --git a/versioned_docs/version-0.10.0/arch/arch/runtimes/vulkan.md b/versioned_docs/version-0.10.0/arch/arch/runtimes/vulkan.md index bd6fbeee..d396cb31 100644 --- a/versioned_docs/version-0.10.0/arch/arch/runtimes/vulkan.md +++ b/versioned_docs/version-0.10.0/arch/arch/runtimes/vulkan.md @@ -30,7 +30,7 @@ TVM 支持用 Vulkan 计算着色器（Vulkan compute shaders）来查询。所 截至 2021 年 5 月，还有一些 Vulkan 的实现没支持。例如，要支持 64 位整数。若不支持 Vulkan target，则会在 SPIR-V 代码生成期间报错。我们正努力消除这些限制，并支持其他 Vulkan 实现。 -## SPIR-V 功能 {#tvm-table-vulkan-capabilities} +## SPIR-V 功能 某些特定于设备的功能还对应于 SPIR-V 功能或扩展，它们必须在着色器中声明，或对应于要使用某个功能所需的最低 SPIR-V 版本。TVM 生成的着色器将声明执行编译好的计算图所需的最小扩展/功能集，以及 SPIR-V 的最小允许版本。 diff --git a/versioned_docs/version-0.10.0/arch/index.md b/versioned_docs/version-0.10.0/arch/index.md index a8c4f716..7f2c921e 100644 --- a/versioned_docs/version-0.10.0/arch/index.md +++ b/versioned_docs/version-0.10.0/arch/index.md @@ -13,7 +13,7 @@ title: 设计与架构 本指南提供了架构的一些补充视图。首先研究端到端的编译流程，并讨论关键的数据结构和转换。这种基于 runtime 的视图侧重于运行编译器时每个组件的交互。接下来研究代码库的逻辑模块及其关系。这部分提供了设计的静态总体视图。 -## 编译流程示例 {#example-compilation-flow} +## 编译流程示例 本指南研究编译器中的编译流程示例，下图显示了流程。在高层次，它包含以下步骤： @@ -132,7 +132,7 @@ result = gmod["get_output"](0).numpy() * 编写一组自定义转换（例如自定义量化）。 * 用 TVM 的 Python API 直接操作 IR。 -## 逻辑架构组件 {#logical-architecture-components} +## 逻辑架构组件 ![/img/docs/tlc-pack/web-data/main/images/design/tvm_static_overview.svg](/img/docs/tlc-pack/web-data/main/images/design/tvm_static_overview.svg) diff --git a/versioned_docs/version-0.10.0/contribute/code_guide.md b/versioned_docs/version-0.10.0/contribute/code_guide.md index 0f232d59..894ca591 100644 --- a/versioned_docs/version-0.10.0/contribute/code_guide.md +++ b/versioned_docs/version-0.10.0/contribute/code_guide.md @@ -26,26 +26,26 @@ python tests/scripts/ci.py lint clang-format 并不完美，必要时可以在某些代码区域上禁用 clang-format。 ``` c++ -// clang-format off -void Test() { +// clang-format off +void Test() { // clang-format 将在这个区域禁用。 -} +} // clang-format on ``` 因为 clang-format 可能无法识别宏，所以建议像普通函数样式一样使用宏。 ``` c++ -#define MACRO_IMPL { custom impl; } +#define MACRO_IMPL { custom impl; } #define MACRO_FUNC(x) // 不是首选，因为 clang-format 可能会将其识别为类型。 -// 首选 +// 首选 virtual void Func2() MACRO_IMPL; void Func3() { - // 首选 + // 首选 MACRO_FUNC(xyz); } ``` @@ -68,7 +68,7 @@ def test_mytest(target, dev): ... ``` -用 `target="llvm"`、`target="cuda"` 和其他几个运行 `test_mytest`。这可以确保测试由 CI 在正确的硬件上运行。如果只想针对几个 target 进行测试，请使用 `@tvm.testing.parametrize_targets("target_1", "target_2")`。如果想在单个 target 上进行测试，请使用来自 `tvm.testing()`{.interpreted-text role="func"} 的相关装饰器。例如，CUDA 测试使用 `@tvm.testing.requires_cuda` 装饰器。 +用 `target="llvm"`、`target="cuda"` 和其他几个运行 `test_mytest`。这可以确保测试由 CI 在正确的硬件上运行。如果只想针对几个 target 进行测试，请使用 `@tvm.testing.parametrize_targets("target_1", "target_2")`。如果想在单个 target 上进行测试，请使用来自 `tvm.testing()` 的相关装饰器。例如，CUDA 测试使用 `@tvm.testing.requires_cuda` 装饰器。 ## 处理整型常量表达式 diff --git a/versioned_docs/version-0.10.0/contribute/document.md b/versioned_docs/version-0.10.0/contribute/document.md index 93e00467..52d0765d 100644 --- a/versioned_docs/version-0.10.0/contribute/document.md +++ b/versioned_docs/version-0.10.0/contribute/document.md @@ -126,8 +126,8 @@ My Section reStructuredText 的 [figure](https://docutils.sourceforge.io/docs/ref/rst/directives.html#figure) 和 [image](https://docutils.sourceforge.io/docs/ref/rst/directives.html#image) 元素允许文档包含图像 URL。 -为 TVM 文档创建的图像文件应该在 仓库中，而使用这些图像的 *.rst* 文件应该在 TVM 仓库（）。 +为 TVM 文档创建的图像文件应该在 [https://github.com/tlc-pack/web-data](https://github.com/tlc-pack/web-data) 仓库中，而使用这些图像的 *.rst* 文件应该在 TVM 仓库（[https://github.com/apache/tvm](https://github.com/apache/tvm)）。 这需要两个 GitHub Pull Request，一个用于图像文件，另一个用于 *.rst* 文件。contributor 与 reviewer 之间可能需要讨论来协调 review 的过程。 -重要提示：使用上述的两个 pull request 时，请先在 中 merge pull request，然后再在 中 merge pull request。这有助于确保 TVM 在线文档中的所有 URL 链接都是有效的。 +重要提示：使用上述的两个 pull request 时，请先在 [https://github.com/tlc-pack/web-data](https://github.com/tlc-pack/web-data) 中 merge pull request，然后再在 [https://github.com/apache/tvm](https://github.com/apache/tvm) 中 merge pull request。这有助于确保 TVM 在线文档中的所有 URL 链接都是有效的。 diff --git a/versioned_docs/version-0.10.0/contribute/pull_request.md b/versioned_docs/version-0.10.0/contribute/pull_request.md index 62edd700..04cf8da1 100644 --- a/versioned_docs/version-0.10.0/contribute/pull_request.md +++ b/versioned_docs/version-0.10.0/contribute/pull_request.md @@ -20,17 +20,17 @@ sidebar_position: 2 # 虽然使用的 lint 命令应该与在 CI 中运行的相同，但此命令会重现 # 准确的 CI lint 过程（通常有助于调试 lint 脚本错误或避免手动安装工具） python tests/scripts/ci.py lint - + # 运行所有的 lint 步骤。 docker/lint.sh - + # 要单独运行步骤，请在命令行中指定步骤名称。 # 一个不正确拼写的步骤名称会导致工具打印所有可用的步骤。 docker/lint.sh ... ``` - + 若 clang-format lint 检查失败，运行 git-clang-format 自动对代码重新格式化： - + ``` bash # 运行 clang-format 检查所有自 upstream/main 以来改变的文件 docker/bash.sh ci_lint ./tests/lint/git-clang-format.sh upstream/main @@ -54,7 +54,7 @@ sidebar_position: 2 使用 Docker 镜像，创建能部署到多台机器上的稳定的 CI 环境。按照 [这个 issue 模板](https://github.com/apache/tvm/issues/new?assignees=&labels=&template=ci-image.md&title=%5BCI+Image%5D+) 的步骤，更新 CI Docker 镜像。 -## 测试 {#pr-testing} +## 测试 尽管每个 pull request 都有自动运行单元测试的 hook，但推荐先在本地运行单元测试，以减少 reviewer 的负担并加快 review 过程。 diff --git a/versioned_docs/version-0.10.0/contribute/release_process.md b/versioned_docs/version-0.10.0/contribute/release_process.md index 50a442fc..8996320f 100644 --- a/versioned_docs/version-0.10.0/contribute/release_process.md +++ b/versioned_docs/version-0.10.0/contribute/release_process.md @@ -29,11 +29,11 @@ TVM 中的版本 manager 角色意味着要负责以下这些事情： 如果已经上传了密钥，则可以跳过这部分。 -参考 将生成的 gpg 密钥上传到公钥服务器。 +参考 [https://www.apache.org/dev/openpgp.html#generate-key](https://www.apache.org/dev/openpgp.html#generate-key) 将生成的 gpg 密钥上传到公钥服务器。 通过 `gpg --export` 和 `gpg --import` 命令可以将 gpg 密钥传输到另一台机器上发布。 -最后一步是使用你的代码签名密钥 更新 KEYS 文件。查看对 TVM 主分支及 ASF SVN 的更改， +最后一步是使用你的代码签名密钥 [https://www.apache.org/dev/openpgp.html#export-public-key](https://www.apache.org/dev/openpgp.html#export-public-key) 更新 KEYS 文件。查看对 TVM 主分支及 ASF SVN 的更改， ``` bash # 指定 --depth=files 参数将跳过检查已有文件夹 @@ -122,7 +122,7 @@ svn ci --username $ASF_USERNAME --password "$ASF_PASSWORD" -m "Add RC" ## 对候选版本投票 -第一次投票在 Apache TVM 开发者名单（）上进行。为了获得更多关注，可以创建一个以 “[VOTE]” 开头的 GitHub Issue，它会自动镜像到 dev@。可以查看以前的投票帖子来了解它是如何进行的。电子邮件应遵循以下格式： +第一次投票在 Apache TVM 开发者名单（[dev@tvm.apache.org](mailto:dev@tvm.apache.org)）上进行。为了获得更多关注，可以创建一个以 “[VOTE]” 开头的 GitHub Issue，它会自动镜像到 dev@。可以查看以前的投票帖子来了解它是如何进行的。电子邮件应遵循以下格式： - 在电子邮件中提供版本说明初稿的链接 - 提供候选版本工程的链接 @@ -157,8 +157,8 @@ git push --delete origin v0.6.0.rc2 ## 更新 TVM 网站 -网站仓库位于 。向下载页面中添加版本工程以及 GPG 签名和 SHA 哈希。 +网站仓库位于 [https://github.com/apache/tvm-site](https://github.com/apache/tvm-site)。向下载页面中添加版本工程以及 GPG 签名和 SHA 哈希。 ## 发布公告 -向 和 发送公告邮件。公告应包括发布说明和下载页面的链接。 +向 [announce@apache.org](mailto:announce@apache.org) 和 [dev@tvm.apache.org](mailto:dev@tvm.apache.org) 发送公告邮件。公告应包括发布说明和下载页面的链接。 diff --git a/versioned_docs/version-0.10.0/how_to/deploy/07-vitis_ai.md b/versioned_docs/version-0.10.0/how_to/deploy/07-vitis_ai.md index 68f67612..5ea3e553 100644 --- a/versioned_docs/version-0.10.0/how_to/deploy/07-vitis_ai.md +++ b/versioned_docs/version-0.10.0/how_to/deploy/07-vitis_ai.md @@ -28,7 +28,7 @@ TVM 中当前的 Vitis AI 流支持使用 [Zynq Ultrascale+ MPSoc](https://www.x [Vitis AI 系统要求页面](https://github.com/Xilinx/Vitis-AI/blob/master/docs/learn/system_requirements.md) 列出了运行 Docker 容器以及在 Alveo 卡上执行的系统要求。对于边缘设备（例如 Zynq），部署模型需要使用带有 Vitis AI 流程的 TVM 编译模型的主机，以及用于运行编译模型的边缘设备。主机系统要求与上面链接中指定的相同。 -## 设置说明 {#setup} +## 设置说明 本节介绍如何用 Vitis AI 流为云和边缘设置 TVM。支持 Vitis AI 的 TVM 是通过 Docker 容器提供的。提供的脚本和 Dockerfile 将 TVM 和 Vitis AI 编译为单个镜像。 @@ -70,7 +70,7 @@ TVM 中当前的 Vitis AI 流支持使用 [Zynq Ultrascale+ MPSoc](https://www.x 在这个 Docker 容器中可以为云和边缘目标编译模型。要在 docker 容器内的云 Alveo 或 Versal VCK5000 卡上运行，按照 [Alveo](#alveo-setup) 或者 [Versal VCK5000](#versal-vck5000-setup) 设置说明进行操作。分别参照 [Zynq](#zynq-setup) 和 [Versal VCK190](#versal-vck190-setup)，为推理过程设置 Zynq 或 Versal VCK190 评估单板。 -### Alveo 设置 {#alveo-setup} +### Alveo 设置 查看 [Alveo 设置](https://github.com/Xilinx/Vitis-AI/blob/v1.4/setup/alveo/README.md) 获取设置信息。 @@ -85,7 +85,7 @@ source setup.sh [DPU-IDENTIFIER] 可在此页面顶部的 DPU Targets 表的第二列中找到此 DPU 标识符。 -### Versal VCK5000 设置 {#versal-vck5000-setup} +### Versal VCK5000 设置 查看 [VCK5000 Setup](https://github.com/Xilinx/Vitis-AI/blob/v1.4/setup/vck5000/README.md) 获取设置信息。 @@ -98,7 +98,7 @@ cd Vitis-AI/setup/vck5000 source setup.sh ``` -### Zynq 设置 {#zynq-setup} +### Zynq 设置 除了构建 TVM - Vitis AI docker 之外，对于 Zynq 目标（DPUCZDX8G），编译阶段在主机上的 docker 内运行，不需要任何特定设置。执行模型时，首先要设置 Zynq 板，更多信息如下。 @@ -173,11 +173,11 @@ source setup.sh 可能会看到有关未找到 "cpu-tf" runtime 的警告，可以忽略。 ::: -### Versal VCK190 设置 {#versal-vck190-setup} +### Versal VCK190 设置 参考 [Zynq 设置](#zynq-setup) 设置 Versal VCK190，但在步骤 1 中参考 [VCK190 镜像](https://www.xilinx.com/member/forms/download/design-license-xef.html?filename=xilinx-vck190-dpu-v2020.2-v1.4.0.img.gz)。其他步骤相同。 -## 编译模型 {#compile} +## 编译模型 带有 Vitis AI 流的 TVM 包含编译和推理两个阶段。在编译期间，用户可以为当前支持的云或边缘目标设备选择要编译的模型。编译模型生成的文件可用于在 [推理](#inference) 阶段在指定的目标设备上运行模型。目前，采用 Vitis AI 流程的 TVM 支持选定数量的 Xilinx 数据中心和边缘设备。 @@ -290,13 +290,13 @@ lib_edge.export_library('deploy_lib_edge.so', **lib_kwargs) 使用 TVM 和 Vitis AI 编译模型的教程到此结束，有关如何运行已编译的模型，参阅下一节。 -## 推理 {#inference} +## 推理 带有 Vitis AI 流的 TVM 包含编译和推理两个阶段，在编译期间，用户可以选择为当前支持的任何目标设备编译模型。编译模型后生成的文件可用于在推理阶段在目标设备上运行模型。 查看 [在 Alveo 和 VCK5000 上运行](#running-on-alveo-and-vck5000) 以及 [在 Zynq 和 VCK190 上运行](#running-on-zynq-and-vck190) 部分，分别在云加速卡和边缘板上进行推理。 -### 在 Alveo 和 VCK5000 上运行 {#running-on-alveo-and-vck5000} +### 在 Alveo 和 VCK5000 上运行 按照编译模型部分中的步骤，可以在 Docker 内的新输入上执行如下命令以加速推理： @@ -325,7 +325,7 @@ module.set_input(input_name, input_data) module.run() ``` -### 在 Zynq 和 VCK190 上运行 {#running-on-zynq-and-vck190} +### 在 Zynq 和 VCK190 上运行 开始前按照 [Zynq](#zynq-setup) 或 [Versal VCK190](#versal-vck190-setup) 设置说明进行设置。 diff --git a/versioned_docs/version-0.10.0/how_to/microtvm/01-aot.md b/versioned_docs/version-0.10.0/how_to/microtvm/01-aot.md index e9232e12..8389cb4f 100644 --- a/versioned_docs/version-0.10.0/how_to/microtvm/01-aot.md +++ b/versioned_docs/version-0.10.0/how_to/microtvm/01-aot.md @@ -62,7 +62,7 @@ relay_mod, params = relay.frontend.from_tflite( # 使用 C runtime（crt），并通过将 system-lib 设置为 True 来启用静态链接 RUNTIME = Runtime("crt", {"system-lib": True}) -# 在主机上模拟一个微控制器。使用来自 `src/runtime/crt/host/main.cc `_ 的 main()。 +# 在主机上模拟一个微控制器。使用来自 `src/runtime/crt/host/main.cc [https://github.com/apache/tvm/blob/main/src/runtime/crt/host/main.cc](https://github.com/apache/tvm/blob/main/src/runtime/crt/host/main.cc)`_ 的 main()。 # 若要使用物理硬件，请将「host」替换为与你的硬件匹配的内容。 TARGET = tvm.target.target.micro("host") diff --git a/versioned_docs/version-0.10.0/how_to/microtvm/03-tvm_arm.md b/versioned_docs/version-0.10.0/how_to/microtvm/03-tvm_arm.md index 99353105..492d7344 100644 --- a/versioned_docs/version-0.10.0/how_to/microtvm/03-tvm_arm.md +++ b/versioned_docs/version-0.10.0/how_to/microtvm/03-tvm_arm.md @@ -46,7 +46,7 @@ TVMC - TVM driver command-line interface 运行 demo 需要一些额外的 Python 包。通过下面的 requirements.txt 文件，来安装这些额外的 Python 包： *requirements.txt*[¶](#requirements-txt) -{#requirements-txt} + ``` text attrs==21.2.0 cloudpickle==2.0.0 @@ -167,7 +167,7 @@ curl -sS https://s3.amazonaws.com/model-server/inputs/kitten.jpg -o ./kitten.jpg * `outputs.h` - 用一个全零的整数数组，为推理的输出保留 1001 个整数值。 *convert_image.py*[¶](#convert-image-py) -{#convert-image-py} + ``` python #!python ./convert_image.py import os @@ -236,7 +236,7 @@ python convert_image.py ./kitten.jpg 以下脚本将在 src 目录中创建一个 `labels.h` 头文件，之前下载的 labels.txt 文件会变成字符串数组。该数组将用于显示图像分类后为的标签。 *convert_labels.py*[¶](#convert-image) -{#convert-image} + ``` python #!python ./convert_labels.py import os @@ -272,7 +272,7 @@ python convert_labels.py 下面的 C 程序会在之前下载并转换为整数数组的图像上，运行 MobileNet v1 模型的单个推理。由于该模型是以「ethos-u ...」为 target 编译的，因此需要迁移 Ethos(TM)-U55 NPU 支持的算子，以便进行加速。一旦应用程序构建并运行，测试图像应该被正确地归类为「tabby」，并且结果会显示在控制台上。这个文件应该放在 `./src`。 demo.c[¶](#demo-c) -{#demo-c} + ``` c #include #include diff --git a/versioned_docs/version-0.10.0/how_to/microtvm/05-microtvm_tflite.md b/versioned_docs/version-0.10.0/how_to/microtvm/05-microtvm_tflite.md index a9d3ebf1..e183c81f 100644 --- a/versioned_docs/version-0.10.0/how_to/microtvm/05-microtvm_tflite.md +++ b/versioned_docs/version-0.10.0/how_to/microtvm/05-microtvm_tflite.md @@ -74,11 +74,11 @@ title: 支持 TFLite 模型的 microTVM **题外话：重新创建预训练 TFLite 模型** 本教程下载了预训练的 TFLite 模型。使用微控制器时，请注意这些设备的资源高度受限，像 MobileNet 这样的标准模型和小内存并不匹配。 - + 本教程使用 TF Micro 示例模型之一。 - + 若要复制训练步骤，参阅：https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train - + :::note 若不小心从 `wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip` 下载了示例预训练模型，会由于未实现的操作码（114）而失败。 ::: @@ -238,7 +238,7 @@ os.unlink(model_library_format_tar_path) # 模型库格式用作此过程的模型输入。 # 平台为嵌入时提供了集成，可以被 TVM 直接用于主机驱动 # 推理和自动调优。这种集成由 -# `microTVM 项目 API` _提供。 +# `microTVM 项目 API` [https://github.com/apache/tvm-rfcs/blob/main/rfcs/0008-microtvm-project-api.md](https://github.com/apache/tvm-rfcs/blob/main/rfcs/0008-microtvm-project-api.md)_提供。 # # 嵌入式平台需要提供一个包含 microTVM API Server 的模板项目（通常， # 存在于根目录中的“microtvm_api_server.py”文件中）。本教程使用示例“主机” @@ -328,4 +328,4 @@ result is: [[0.4443792]] [下载 Python 源代码：micro_tflite.py](https://tvm.apache.org/docs/_downloads/2fb9ae7bf124f72614a43137cf2919cb/micro_tflite.py) -[下载 Jupyter Notebook：micro_tflite.ipynb](https://tvm.apache.org/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb) \ No newline at end of file +[下载 Jupyter Notebook：micro_tflite.ipynb](https://tvm.apache.org/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb) diff --git a/versioned_docs/version-0.10.0/how_to/microtvm/06-microtvm_arduino.md b/versioned_docs/version-0.10.0/how_to/microtvm/06-microtvm_arduino.md index 3307f154..fa1bd39e 100644 --- a/versioned_docs/version-0.10.0/how_to/microtvm/06-microtvm_arduino.md +++ b/versioned_docs/version-0.10.0/how_to/microtvm/06-microtvm_arduino.md @@ -214,7 +214,7 @@ validation_dataset = full_dataset.skip(len(train_dataset)) 首先，可以将输入图像的大小从 224x224 减小到 96x96 或 64x64，Keras 可以轻松做到这一点。还可以将模型的 **alpha** 从 1.0 降低到 0.25，这使得网络的宽度（和过滤器的数量）缩小了四倍。如果真的空间有限，可以通过让模型拍摄灰度图像而不是 RGB 图像来减少**通道**数量。 -在本教程中，使用 RGB 64x64 输入图像和 alpha 0.25。效果虽然不是很理想，但它允许完成的模型适合 192 KB 的 RAM，同时仍然允许使用官方 TensorFlow 源模型执行迁移学习（如果使用 alpha <0.25 或灰度输入，无法做到这样）。 +在本教程中，使用 RGB 64x64 输入图像和 alpha 0.25。效果虽然不是很理想，但它允许完成的模型适合 192 KB 的 RAM，同时仍然允许使用官方 TensorFlow 源模型执行迁移学习（如果使用 alpha \<0.25 或灰度输入，无法做到这样）。 ### 什么是迁移学习？ diff --git a/versioned_docs/version-0.10.0/install/docker.md b/versioned_docs/version-0.10.0/install/docker.md index 937f79fa..bb2a6ff0 100644 --- a/versioned_docs/version-0.10.0/install/docker.md +++ b/versioned_docs/version-0.10.0/install/docker.md @@ -65,4 +65,4 @@ Docker 镜像。 你也可以利用非官方的第三方预建镜像，注意：这些镜像是用来测试的，并不是 ASF 的版本。 -. +[https://hub.docker.com/r/tlcpack/](https://hub.docker.com/r/tlcpack/). diff --git a/versioned_docs/version-0.10.0/install/from_source.md b/versioned_docs/version-0.10.0/install/from_source.md index 66637f6c..a8cd6099 100644 --- a/versioned_docs/version-0.10.0/install/from_source.md +++ b/versioned_docs/version-0.10.0/install/from_source.md @@ -26,7 +26,7 @@ git submodule init git submodule update ``` -## 构建共享库 {#build-shared-library} +## 构建共享库 我们的目标是构建共享库： @@ -175,7 +175,7 @@ brew install python@3.8 如果一切顺利，我们就可以去查看 [Python 包的安装](#python-package-installation) 了。 -### 使用 Conda 环境进行构建 {#build-with-conda} +### 使用 Conda 环境进行构建 Conda 可以用来获取运行 TVM 所需的必要依赖。如果没有安装 Conda，请参照 [Conda @@ -234,7 +234,7 @@ cmake --build build --config Release -- /m 中安装 HIP runtime。确保安装系统中已经安装了 ROCm。 - 安装 LLVM 的最新稳定版本（v6.0.1），以及 LLD，确保 `ld.lld` 可以通过命令行获取。 -## Python 包的安装 {#python-package-installation} +## Python 包的安装 ### TVM 包 @@ -310,7 +310,7 @@ pip install xgboost [NNPACK Contrib 安装](nnpack) -## 启用 C++ 测试 {#C++_tests} +## 启用 C++ 测试 可以用 [Google Test](https://github.com/google/googletest) 来驱动 TVM 中的 C++ 测试。安装 GTest 最简单的方法是从源代码安装： diff --git a/versioned_docs/version-0.10.0/reference/api/python/auto_scheduler.md b/versioned_docs/version-0.10.0/reference/api/python/auto_scheduler.md index 13772faf..2c7bf16f 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/auto_scheduler.md +++ b/versioned_docs/version-0.10.0/reference/api/python/auto_scheduler.md @@ -2,6 +2,6 @@ title: tvm.auto_scheduler --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.auto_scheduler ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/autotvm.md b/versioned_docs/version-0.10.0/reference/api/python/autotvm.md index 7e5c2685..e6319ab4 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/autotvm.md +++ b/versioned_docs/version-0.10.0/reference/api/python/autotvm.md @@ -16,11 +16,11 @@ tvm.autotvm.apply_history_best tvm.autotvm.measure.measure ::: -::: {.autoclass members=""} +::: tvm.autotvm.measure.MeasureInput ::: -::: {.autoclass members=""} +::: tvm.autotvm.measure.MeasureResult ::: @@ -46,58 +46,58 @@ tvm.autotvm.measure.measure_methods.LocalRunner ## tvm.autotvm.tuner -::: {.automodule members=""} +::: tvm.autotvm.tuner ::: -::: {.autoclass members=""} +::: tvm.autotvm.tuner.Tuner ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.autotvm.tuner.RandomTuner ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.autotvm.tuner.GridSearchTuner ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.autotvm.tuner.GATuner ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.autotvm.tuner.XGBTuner ::: -::: {.automodule members=""} +::: tvm.autotvm.tuner.callback ::: ## tvm.autotvm.task -::: {.automodule members=""} +::: tvm.autotvm.task ::: -::: {.automodule members=""} +::: tvm.autotvm.task.task ::: -::: {.automodule members=""} +::: tvm.autotvm.task.space ::: -::: {.automodule members=""} +::: tvm.autotvm.task.dispatcher ::: -::: {.automodule members=""} +::: tvm.autotvm.task.topi_integration ::: ## tvm.autotvm.record -::: {.automodule members=""} +::: tvm.autotvm.record ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/contrib.md b/versioned_docs/version-0.10.0/reference/api/python/contrib.md index ba49b3e7..c0b574b0 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/contrib.md +++ b/versioned_docs/version-0.10.0/reference/api/python/contrib.md @@ -8,138 +8,138 @@ tvm.contrib ## tvm.contrib.cblas -::: {.automodule members=""} +::: tvm.contrib.cblas ::: ## tvm.contrib.clang -::: {.automodule members=""} +::: tvm.contrib.clang ::: ## tvm.contrib.cc -::: {.automodule members=""} +::: tvm.contrib.cc ::: ## tvm.contrib.cublas -::: {.automodule members=""} +::: tvm.contrib.cublas ::: ## tvm.contrib.dlpack -::: {.automodule members=""} +::: tvm.contrib.dlpack ::: ## tvm.contrib.emcc -::: {.automodule members=""} +::: tvm.contrib.emcc ::: ## tvm.contrib.miopen -::: {.automodule members=""} +::: tvm.contrib.miopen ::: ## tvm.contrib.mxnet -::: {.automodule members=""} +::: tvm.contrib.mxnet ::: ## tvm.contrib.ndk -::: {.automodule members=""} +::: tvm.contrib.ndk ::: ## tvm.contrib.nnpack -::: {.automodule members=""} +::: tvm.contrib.nnpack ::: ## tvm.contrib.nvcc -::: {.automodule members=""} +::: tvm.contrib.nvcc ::: ## tvm.contrib.pickle_memoize -::: {.automodule members=""} +::: tvm.contrib.pickle_memoize ::: ## tvm.contrib.random -::: {.automodule members=""} +::: tvm.contrib.random ::: ## tvm.contrib.relay_viz -::: {.automodule members=""} +::: tvm.contrib.relay_viz ::: -::: {.automodule members=""} +::: tvm.contrib.relay_viz.dot ::: -::: {.automodule members=""} +::: tvm.contrib.relay_viz.terminal ::: -::: {.automodule members=""} +::: tvm.contrib.relay_viz.interface ::: ## tvm.contrib.rocblas -::: {.automodule members=""} +::: tvm.contrib.rocblas ::: ## tvm.contrib.rocm -::: {.automodule members=""} +::: tvm.contrib.rocm ::: ## tvm.contrib.sparse -::: {.automodule members=""} +::: tvm.contrib.sparse ::: ## tvm.contrib.spirv -::: {.automodule members=""} +::: tvm.contrib.spirv ::: ## tvm.contrib.tar -::: {.automodule members=""} +::: tvm.contrib.tar ::: ## tvm.contrib.utils -::: {.automodule members=""} +::: tvm.contrib.utils ::: ## tvm.contrib.xcode -::: {.automodule members=""} +::: tvm.contrib.xcode ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/error.md b/versioned_docs/version-0.10.0/reference/api/python/error.md index 95d5dc0c..ba83dfd5 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/error.md +++ b/versioned_docs/version-0.10.0/reference/api/python/error.md @@ -2,6 +2,6 @@ title: tvm.error --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.error ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/graph_executor.md b/versioned_docs/version-0.10.0/reference/api/python/graph_executor.md index 810338eb..90110e9b 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/graph_executor.md +++ b/versioned_docs/version-0.10.0/reference/api/python/graph_executor.md @@ -2,6 +2,6 @@ title: tvm.contrib.graph_executor --- -::: {.automodule members=""} +::: tvm.contrib.graph_executor ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/index.md b/versioned_docs/version-0.10.0/reference/api/python/index.md index a7518508..ab82bf8d 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/index.md +++ b/versioned_docs/version-0.10.0/reference/api/python/index.md @@ -2,9 +2,9 @@ title: Python API --- -::: {.toctree maxdepth="2"} +``` runtime ndarray error ir target tir te driver relay/index relay/frontend relay/nn relay/vision relay/image relay/transform relay/analysis relay/backend relay/dataflow_pattern relay/testing autotvm auto_scheduler rpc micro contrib graph_executor topi vta/index -::: +``` diff --git a/versioned_docs/version-0.10.0/reference/api/python/ir.md b/versioned_docs/version-0.10.0/reference/api/python/ir.md index 4c60d1fb..16eebd04 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/ir.md +++ b/versioned_docs/version-0.10.0/reference/api/python/ir.md @@ -1,17 +1,17 @@ ## tvm.ir -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.ir ::: ## tvm.instrument -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.instrument ::: ## tvm.transform -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.transform ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/micro.md b/versioned_docs/version-0.10.0/reference/api/python/micro.md index ed2cac3b..69db2fda 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/micro.md +++ b/versioned_docs/version-0.10.0/reference/api/python/micro.md @@ -2,6 +2,6 @@ title: tvm.micro --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.micro ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/ndarray.md b/versioned_docs/version-0.10.0/reference/api/python/ndarray.md index dcab1750..f144d9b5 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/ndarray.md +++ b/versioned_docs/version-0.10.0/reference/api/python/ndarray.md @@ -6,7 +6,7 @@ title: tvm.runtime.ndarray tvm.runtime.ndarray ::: -::: {.autoclass members="" inherited-members=""} +::: tvm.nd.NDArray ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/analysis.md b/versioned_docs/version-0.10.0/reference/api/python/relay/analysis.md index cbc937a2..0a82d753 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/analysis.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/analysis.md @@ -2,6 +2,6 @@ title: tvm.relay.analysis --- -::: {.automodule members="" imported-members="" exclude-members="Object, RelayExpr, IRModule, Type" autosummary=""} +::: tvm.relay.analysis ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/backend.md b/versioned_docs/version-0.10.0/reference/api/python/relay/backend.md index e3df36cb..2d50a660 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/backend.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/backend.md @@ -6,18 +6,18 @@ title: tvm.relay.backend tvm.relay.backend ::: -::: {.automodule members=""} +::: tvm.relay.backend.interpreter ::: -::: {.automodule members=""} +::: tvm.relay.backend.te_compiler ::: -::: {.automodule members=""} +::: tvm.relay.backend.graph_executor_codegen ::: -::: {.automodule members=""} +::: tvm.relay.backend.vm ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/dataflow_pattern.md b/versioned_docs/version-0.10.0/reference/api/python/relay/dataflow_pattern.md index 19fabdc3..9c44104a 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/dataflow_pattern.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/dataflow_pattern.md @@ -2,6 +2,6 @@ title: tvm.relay.dataflow_pattern --- -::: {.automodule members="" imported-members="" exclude-members="Object, Node" autosummary=""} +::: tvm.relay.dataflow_pattern ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/frontend.md b/versioned_docs/version-0.10.0/reference/api/python/relay/frontend.md index 539268a4..ca06b419 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/frontend.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/frontend.md @@ -2,6 +2,6 @@ title: tvm.relay.frontend --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.frontend ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/image.md b/versioned_docs/version-0.10.0/reference/api/python/relay/image.md index aeaa7507..9c214971 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/image.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/image.md @@ -2,6 +2,6 @@ title: tvm.relay.image --- -::: {.automodule members="" imported-members="" exclude-members="Expr, Constant" autosummary=""} +::: tvm.relay.image ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/index.md b/versioned_docs/version-0.10.0/reference/api/python/relay/index.md index 656d2cac..ee6cb3e8 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/index.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/index.md @@ -2,12 +2,6 @@ title: tvm.relay --- -::: {.automodule members="" imported-members="" exclude-members="RelayExpr, -Type, TypeKind, -TypeVar, GlobalTypeVar, TypeConstraint, FuncType, TupleType, IncompleteType, -TypeCall, TypeRelation, TensorType, RelayRefType, GlobalVar, SourceName, -Span, Var, Op, Constructor -:noindex: TypeData -:autosummary:"} +::: tvm.relay ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/nn.md b/versioned_docs/version-0.10.0/reference/api/python/relay/nn.md index 34dc4754..0b3873f1 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/nn.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/nn.md @@ -2,6 +2,6 @@ title: tvm.relay.nn --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.nn ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/testing.md b/versioned_docs/version-0.10.0/reference/api/python/relay/testing.md index 17116aa1..d861cfe1 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/testing.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/testing.md @@ -2,42 +2,42 @@ title: tvm.relay.testing --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.testing ::: -::: {.automodule members=""} +::: tvm.relay.testing.mlp ::: -::: {.automodule members=""} +::: tvm.relay.testing.resnet ::: -::: {.automodule members=""} +::: tvm.relay.testing.dcgan ::: -::: {.automodule members=""} +::: tvm.relay.testing.mobilenet ::: -::: {.automodule members=""} +::: tvm.relay.testing.lstm ::: -::: {.automodule members=""} +::: tvm.relay.testing.inception_v3 ::: -::: {.automodule members=""} +::: tvm.relay.testing.squeezenet ::: -::: {.automodule members=""} +::: tvm.relay.testing.vgg ::: -::: {.automodule members=""} +::: tvm.relay.testing.densenet ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/transform.md b/versioned_docs/version-0.10.0/reference/api/python/relay/transform.md index 0fc59717..6578b10b 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/transform.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/transform.md @@ -2,6 +2,6 @@ title: tvm.relay.transform --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.transform ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/relay/vision.md b/versioned_docs/version-0.10.0/reference/api/python/relay/vision.md index 732e05ab..9b8332e7 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/relay/vision.md +++ b/versioned_docs/version-0.10.0/reference/api/python/relay/vision.md @@ -2,6 +2,6 @@ title: tvm.relay.vision --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.relay.vision ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/rpc.md b/versioned_docs/version-0.10.0/reference/api/python/rpc.md index 380993af..9689387d 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/rpc.md +++ b/versioned_docs/version-0.10.0/reference/api/python/rpc.md @@ -2,6 +2,6 @@ title: tvm.rpc --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.rpc ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/runtime.md b/versioned_docs/version-0.10.0/reference/api/python/runtime.md index 4f6ef926..cdaa27f3 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/runtime.md +++ b/versioned_docs/version-0.10.0/reference/api/python/runtime.md @@ -2,6 +2,6 @@ title: tvm.runtime --- -::: {.automodule members="" imported-members="" exclude-members="NDArray" autosummary=""} +::: tvm.runtime ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/target.md b/versioned_docs/version-0.10.0/reference/api/python/target.md index 2bcb2aa5..e9ef930e 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/target.md +++ b/versioned_docs/version-0.10.0/reference/api/python/target.md @@ -2,6 +2,6 @@ title: tvm.target --- -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.target ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/te.md b/versioned_docs/version-0.10.0/reference/api/python/te.md index 2666ba85..628f883f 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/te.md +++ b/versioned_docs/version-0.10.0/reference/api/python/te.md @@ -1,11 +1,11 @@ ## tvm.te -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.te ::: ## tvm.te.hybrid -::: {.automodule members="" imported-members="" autosummary=""} +::: tvm.te.hybrid ::: diff --git a/versioned_docs/version-0.10.0/reference/api/python/tir.md b/versioned_docs/version-0.10.0/reference/api/python/tir.md index 215368b1..88b44909 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/tir.md +++ b/versioned_docs/version-0.10.0/reference/api/python/tir.md @@ -1,23 +1,23 @@ -## tvm.tir {#api-python-tir} +## tvm.tir -::: {.automodule members="" imported-members="" exclude-members="PrimExpr, const" autosummary=""} +``` tvm.tir -::: +``` ## tvm.tir.transform -::: {.automodule members="" imported-members="" autosummary=""} +``` tvm.tir.transform -::: +``` ## tvm.tir.analysis -::: {.automodule members="" imported-members="" noindex="Buffer, Stmt" autosummary=""} +``` tvm.tir.analysis -::: +``` ## tvm.tir.stmt_functor -::: {.automodule members="" autosummary=""} +``` tvm.tir.stmt_functor -::: +``` diff --git a/versioned_docs/version-0.10.0/reference/api/python/topi.md b/versioned_docs/version-0.10.0/reference/api/python/topi.md index 054b7f2a..0629b9f0 100644 --- a/versioned_docs/version-0.10.0/reference/api/python/topi.md +++ b/versioned_docs/version-0.10.0/reference/api/python/topi.md @@ -2,24 +2,24 @@ title: tvm.topi --- -::: {.automodule members="" imported-members="" noindex="AssertStmt" autosummary=""} +``` tvm.topi -::: +``` ## tvm.topi.nn -::: {.automodule members="" imported-members="" autosummary=""} +``` tvm.topi.nn -::: +``` ## tvm.topi.image -::: {.automodule members="" imported-members="" autosummary=""} +``` tvm.topi.image -::: +``` ## tvm.topi.sparse -::: {.automodule members="" imported-members="" autosummary=""} +``` tvm.topi.sparse -::: +``` diff --git a/versioned_docs/version-0.10.0/reference/langref/index.md b/versioned_docs/version-0.10.0/reference/langref/index.md index 8fbb72ee..5cd9a1db 100644 --- a/versioned_docs/version-0.10.0/reference/langref/index.md +++ b/versioned_docs/version-0.10.0/reference/langref/index.md @@ -22,9 +22,9 @@ transformations and optimizations. The below pages describe the grammar, type system, algebraic data types, and operators in Relay, respectively. -::: {.toctree maxdepth="2"} +``` relay_expr relay_type relay_adt relay_op relay_pattern -::: +``` ## Hybrid Script @@ -32,6 +32,6 @@ The below page describes the TVM hybrid script front-end, which uses software emulation to support some constructs not officially supported in TVM. -::: {.toctree maxdepth="2"} +``` hybrid_script -::: +``` diff --git a/versioned_docs/version-0.10.0/reference/langref/relay_adt.md b/versioned_docs/version-0.10.0/reference/langref/relay_adt.md index 96cff08a..320cee40 100644 --- a/versioned_docs/version-0.10.0/reference/langref/relay_adt.md +++ b/versioned_docs/version-0.10.0/reference/langref/relay_adt.md @@ -53,7 +53,7 @@ variables) to the list of constructors for that ADT.* Below is a simple example of defining an ADT and using it in a function via a match expression: -``` +``` # Defines an ADT named "Numbers" data Numbers { Empty : () -> Numbers @@ -83,7 +83,7 @@ Note that ADTs are identified by name, meaning that two ADTs with structurally identical constructors will nevertheless be distinct data types from the point of view of the typechecker. -``` +``` # structurally identical constructors to Numbers data Numbers2 { Empty2 : () -> Numbers2 @@ -105,7 +105,7 @@ functions, ADTs can be polymorphic and take type parameters. For example, one of the standard ADTs commonly used in functional programming languages is the optional type, defined here: -``` +``` # a is a type parameter data Optional { None : () -> Optional @@ -129,7 +129,7 @@ thus given a type that contains the concrete type arguments for that instance, ensuring the information is kept around. Let the below example illustrate: -``` +``` # the signature for option indicates the type argument def @inc_scalar(%opt : Optional[Tensor[(), int32]]) -> Tensor[(), int32] { match(%opt) { @@ -184,7 +184,7 @@ Many commonly used ADTs involve recursion; some of these are given in [Common ADT Uses](#common-adt-uses). As an example here, we will examine the list ADT, ubiquitous in functional languages: -``` +``` data List { Nil : () -> List Cons : (a, List[a]) -> List @@ -202,7 +202,7 @@ empty list). Lists represented in this manner can easily be recursively processed. For example, the following function sums a list of integers: -``` +``` def @list_sum(%l : List[Tensor[(), int32]]) -> Tensor[(), int32] { match(%l) { case Nil() { 0 } @@ -217,7 +217,7 @@ share structures that can be factored out into generic, easily usable functions that will be discussed under [Common ADT Uses](#common-adt-uses). -## Pattern Matching in Match Expressions {#adt-pattern} +## Pattern Matching in Match Expressions Match expressions in Relay, as in other functional languages, are capable of more versatile pattern matching than simply having one case @@ -241,7 +241,7 @@ arguments to `Cons`. The below example uses a wildcard pattern to ignore one of the arguments to `Cons`: -``` +``` def @first(%l : List[a]) -> Optional[a] { match(%l) { case Nil() { None() } @@ -255,7 +255,7 @@ to avoid nested match expressions for a list option. A top-level wildcard pattern is also used to handle all cases that do not match the first clause: -``` +``` def @second_opt(%ll : Optional[List[a]]) -> Optional[a] { match(%ll) { # we only need the second member of the list if there is one @@ -275,7 +275,7 @@ are listed: the first clause whose pattern that matches the input value is the one that is evaluated. Here, a top-level variable pattern binds the whole input value: -``` +``` def @match_order_beware(%l : List[a]) -> List[a] { match(%l) { case %v { %v } @@ -313,7 +313,7 @@ Relay\'s Prelude. (These have all been extensively characterized in the functional programming literature, and we do not attempt to reproduce that work in this document.) -``` +``` # Map: for [h1, h2, ..., hn] returns [f(h1), f(h2), ..., f(hn)] def @map(%f : fn(a) -> b, %l : List[a]) -> List[b] { match(%l) { @@ -343,7 +343,7 @@ Using these iteration constructs, many common operations over lists can be expressed compactly. For example, the following map doubles all members of a list: -``` +``` # directly written def @double(%l : List[Tensor[(), int32]]) -> List[Tensor[(), int32]] { match(%l) { @@ -358,7 +358,7 @@ def @double(%l : List[Tensor[(), int32]]) -> List[Tensor[(), int32]] { The following right fold concatenates two lists: -``` +``` # directly written def @concat(%l1 : List[a], %l2 : List[a]) -> List[a] { match(%l1) { @@ -373,7 +373,7 @@ def @concat(%l1 : List[a], %l2 : List[a]) -> List[a] { The following left fold flattens a list of lists (using concatenation): -``` +``` # directly written def @flatten(%ll : List[List[a]]) -> List[a] { match(%ll) { @@ -407,14 +407,14 @@ recurrent neural net (RNN) cell, which takes in a past state and an input value and returns a new state and output value. In Relay, this would have the following signature: -``` +``` @cell : fn(state_type, in_type) -> (state_type, out_type) ``` We might consider a ReLU cell as a simple concrete example, with a trained version below: -``` +``` def @linear(%x, %w, %b) { %w*%x + %b } def @relu_cell(%w, # weights @@ -437,7 +437,7 @@ def @trained_cell(%w, %b) { Following Olah\'s example, we can encode a sequence (list) of inputs with the following left fold: -``` +``` def @encode(%cell, %input : List[in_type], %init : state_type) -> state_type { # not using the output @foldl(fn(%state, %in) { %cell(%state, %in).0 }, %init, %input) @@ -448,7 +448,7 @@ Using an *unfold* iterator (from Haskell\'s standard library), the same cell could be used to make a generator network (which takes a single input and produces a sequence of outputs): -``` +``` # included in Relay's Prelude def @unfoldr(%f : fn(b) -> Optional[(a, b)], %z : b) -> List[a] { match(%f(%z)) { @@ -478,7 +478,7 @@ An accumulating map (a fold that simultaneously updates an accumulator value and a list of outputs) can be used to write a general RNN (with an output for every input): -``` +``` def @map_accumr(%f : fn(a, b) -> (a, c), %acc : a, %l : List[b]) -> (a, List[c]) { match(%l) { case Nil() { (%acc, Nil()) } @@ -511,7 +511,7 @@ two sets of cells (which may have different weights) process the input in both directions and produce a single set of outputs. The following is a Relay implementation of that example: -``` +``` # creates a list of tuples from two lists # included in Relay's Prelude def @zip(%l : List[a], %m : List[b]) -> List[(a, b)] { diff --git a/versioned_docs/version-0.10.0/reference/langref/relay_expr.md b/versioned_docs/version-0.10.0/reference/langref/relay_expr.md index f39b197a..8b30189a 100644 --- a/versioned_docs/version-0.10.0/reference/langref/relay_expr.md +++ b/versioned_docs/version-0.10.0/reference/langref/relay_expr.md @@ -62,7 +62,7 @@ contained in the globally visible environment, known as the [module](#module-and-global-functions). Global identifiers must be unique. -See :py`~tvm.relay.expr.GlobalVar`{.interpreted-text role="class"} for +See `GlobalVar` for its implementation and documentation. ### Local Variable @@ -79,7 +79,7 @@ to `%a` in the inner scope refer to the later definition, while references to `%a` in the outer scope continue to refer to the first one. -``` +``` let %a = 1; let %b = 2 * %a; // %b = 2 let %a = %a + %a; // %a = 2. %a is shadowed @@ -87,14 +87,13 @@ let %a = %a + %a; // %a = 2. %a is shadowed ``` (Note that in Relay\'s implementation, each definition of a local -variable creates a new :py`~tvm.relay.expr.Var`{.interpreted-text -role="class"}, so a shadowed local variable, despite having the same +variable creates a new `Var`, so a shadowed local variable, despite having the same name as one in an outer scope, will be a different object. This allows for comparing local variables by pointer identity with the knowledge that the same local variable object corresponds to a different binding site.) -See :py`~tvm.relay.expr.Var`{.interpreted-text role="class"} for its +See `Var` for its implementation and documentation. ## Functions @@ -110,24 +109,24 @@ argument to a function or returned by a function, as function expressions evaluate to closures (see the [Closures](#closures) subsection), which are values like tensors and tuples. -See :py`~tvm.relay.function.Function`{.interpreted-text role="class"} +See `Function` for the definition and documentation of function nodes. ### Syntax A definition minimally consists of the keyword `fn`, an empty set of parameters, and a body expression -(:py`~tvm.relay.expr.Expr`{.interpreted-text role="class"}) contained by +(`Expr`) contained by curly braces. -``` +``` fn() { body } ``` A definition may contain any number of parameters. For example, a simple function that invokes the `add` operator: -``` +``` fn(%x, %y) { add(%x, %y) } ``` @@ -137,7 +136,7 @@ variables, just like those bound in a `let` expression. One may also annotate explicit types on functions. For example, we can restrict the above function to only work on certain types: -``` +``` fn(%x : Tensor[(10, 10), float32], %y : Tensor[(10, 10), float32]) -> Tensor[(10, 10), float32] { add(%x, %y) @@ -147,7 +146,7 @@ fn(%x : Tensor[(10, 10), float32], %y : Tensor[(10, 10), float32]) The above function only takes arguments of type `Tensor[(10, 10), float32]` and returns a value of type `Tensor[(10, 10), float32]`. A function parameter is just a local -variable (:py`~tvm.relay.expr.LocalVar`{.interpreted-text role="class"}) +variable (`LocalVar`) optionally annotated with a type, written as `%x : T`. When the type information is omitted, Relay attempts to infer the most @@ -159,7 +158,7 @@ function body and call sites. A recursive function expression can be defined using a `let` binding, as here: -``` +``` let %fact = fn(%x : Tensor[(10, 10), float32]) -> Tensor[(10, 10), float32] { if (%x == Constant(0, (10, 10), float32)) { Constant(1, (10, 10), float32) @@ -181,7 +180,7 @@ For example, in the below example, the final result will be a tensor of zero values because the closure for `%f` stores the value of `%x` at the pointer where `%f` was defined. -``` +``` let %g = fn() { let %x = Constant(0, (10, 10), float32); // %x is a free variable in the below function @@ -208,13 +207,12 @@ Type parameters are classified by *kind* and can only appear in parts of the type signature where their kind is appropriate (e.g., type parameters of kind `Shape` can only appear where a shape would be expected in a tensor type); for a full discussion, see -`the documentation on type parameters `{.interpreted-text -role="ref"}. +`the documentation on type parameters `. For example, one can define a polymorphic identity function for any Relay type as follows: -``` +``` fn(%x : t) -> t { %x } @@ -223,7 +221,7 @@ fn(%x : t) -> t { The below definition is also polymorphic, but restricts its arguments to tensor types: -``` +``` fn(%x : Tensor[s, bt]) { %x } @@ -236,7 +234,7 @@ Notice that the return type is omitted and will be inferred. A function may also be subject to one or more type relations, such as in the following: -``` +``` fn(%x, %y) where Broadcast { add(%x, %y) } ``` @@ -252,8 +250,7 @@ constraints on types (especially tensor shapes). All function relations must hold at all call sites; type checking is thus treated as a constraint-solving problem. For more detail on type relations and their implementations, please see -`their section in the documentation on Relay's type system `{.interpreted-text -role="ref"}. +`their section in the documentation on Relay's type system `. ## Operators @@ -272,8 +269,7 @@ From the perspective of Relay\'s type system, an operator is a function, so operators may be called like any other function and have function types. In particular, operator types are registered using a single type relation (see -`the documentation on type relations `{.interpreted-text -role="ref"}), typically a relation specialized to that operator. For +`the documentation on type relations `), typically a relation specialized to that operator. For example, the `add` operator is registered with the `Broadcast` relation, indicating that the arguments of `add` must be tensors and that the return type is a tensor whose shape depends on those of its arguments. @@ -286,20 +282,19 @@ Note that common arithmetic operators such as `add` and `multiply` may be written using the corresponding arithmetic operators in the text format (e.g., `+` or `*`) as syntactic sugar. -See :py`~tvm.relay.op.Op`{.interpreted-text role="class"} for the +See `Op` for the definition and documentation of operator nodes, demonstrating the infrastructure for registering operator metadata. The other files in -:py`~tvm.relay.op`{.interpreted-text role="class"} give handles for +`` give handles for generating a call to various pre-registered operators. The -`tutorial on adding operators to Relay `{.interpreted-text -role="ref"} shows how to add further operators into the language. +`tutorial on adding operators to Relay ` shows how to add further operators into the language. ## ADT Constructors Algebraic data types (ADTs) in Relay are described in detail in a -`separate overview`{.interpreted-text role="ref"} and +`separate overview` and their integration into the type system is described -`here`{.interpreted-text role="ref"}. +`here`. In this section, we will simply note that ADT constructors are given a function type and should be used inside call nodes like a function or @@ -326,7 +321,7 @@ well as the name (\"tag\") of the constructor. The tag will be used for deconstructing the instances and retrieving the values when [ADT Matching](#adt-matching). -See :py`~tvm.relay.adt.Constructor`{.interpreted-text role="class"} for +See `Constructor` for the definition and documentation. ## Call @@ -339,7 +334,7 @@ functions) and Relay operators. The syntax of calls follows that used in C-like languages, demonstrated in the example below: -``` +``` let %c = 1; let %f = fn(%x : Tensor[(), float32], %y : Tensor[(), float32]) { %x + %y + %c }; %f(10, 11) @@ -362,7 +357,7 @@ given, type inference will attempt to infer type arguments if possible. The following code gives examples of explicit and inferred type arguments: -``` +``` // %f : fn(a, b) -> c let %x1 = %f(True, False); // %x1 is of type Tensor[(), bool] @@ -381,15 +376,15 @@ has the `Broadcast` relation, then there are many different shapes that the arguments in the below call could have that would satisfy the type annotation: -``` +``` let %x : Tensor[(100, 100, 100), float32] = %f(%a, %b); %x ``` -See :py`~tvm.relay.expr.Call`{.interpreted-text role="class"} for its +See `Call` for its definition and documentation. -## Module and Global Functions {#module-description} +## Module and Global Functions Relay keeps a global data structure known as a \"module\" (often called an \"environment\" in other functional programming languages) to keep @@ -409,7 +404,7 @@ function definition includes a global identifier and is allowed to recursively refer to that identifier in the body, as in the following example: -``` +``` def @ackermann(%m : Tensor[(), int32], %n : Tensor[(), int32]) -> Tensor[(), int32] { if (%m == 0) { %n + 1 @@ -427,15 +422,14 @@ and body above. Any reference to the identifier `@ackermann` elsewhere in the code could then look up the identifier in the module and replace the function definition as needed. -See :py`~tvm.IRModule`{.interpreted-text role="class"} for the +See `IRModule` for the definition and documentation of a module. ## Constant This node represents a constant tensor value (see -:py`~tvm.relay.Value`{.interpreted-text role="mod"} for more details). A -constant is represented as a :py`~tvm.NDArray`{.interpreted-text -role="class"}, allowing Relay to utilize TVM operators for constant +`Value` for more details). A +constant is represented as a `NDArray`, allowing Relay to utilize TVM operators for constant evaluation. This node can also represent scalar constants, since scalars are tensors @@ -443,7 +437,7 @@ with a shape of `()`. In the text format, numerical and boolean literals are thus syntactic sugar for constants encoding a tensor type with a rank-zero shape. -See :py`~tvm.relay.expr.Constant`{.interpreted-text role="class"} for +See `Constant` for its definition and documentation. ## Tuples @@ -454,14 +448,14 @@ The tuple node builds a finite (that is, of statically known size) sequence of heterogeneous data. These tuples match Python\'s closely, and their fixed length allows for efficient projection of their members. -``` +``` fn(%a : Tensor[(10, 10), float32], %b : float32, %c : Tensor[(100, 100), float32]) { let %tup = (%a, %b); // type: (Tensor[(10, 10), float32], float32) ((%tup.0 + %tup.1), %c) // type: (Tensor[(10, 10), float32], Tensor[(100, 100), float32]) } ``` -See :py`~tvm.relay.expr.Tuple`{.interpreted-text role="class"} for its +See `Tuple` for its definition and documentation. ### Projection @@ -471,11 +465,11 @@ particular member of the tuple. Projections are 0-indexed. For example, the below projection evaluates to `%b`: -``` +``` (%a, %b, %c).1 ``` -See :py`~tvm.relay.expr.TupleGetItem`{.interpreted-text role="class"} +See `TupleGetItem` for its definition and documentation. ## Let Bindings @@ -499,7 +493,7 @@ evaluating the bindings it depends on. For example, in the following example the entire expression evaluates to a tensor of shape `(10, 10)` where all elements are 2: -``` +``` let %x : Tensor[(10, 10), float32] = Constant(1, (10, 10), float32); %x + %x ``` @@ -511,13 +505,13 @@ where neither depends on the other can be safely reordered. For example, the first and second `let` bindings below may be evaluated in either order because neither has a dataflow dependency on the other: -``` +``` let %x = %a + %b; let %y = %c + %d; %x * %y ``` -See :py`~tvm.relay.expr.Let`{.interpreted-text role="class"} for its +See `Let` for its definition and documentation. ## Graph Bindings @@ -536,15 +530,14 @@ already employed by NNVM and other dataflow graph-based input formats. The fact that the variables are not scoped offers some flexibility in evaluation order compared to `let` bindings, though this can also introduce some ambiguity in programs (the -`developer introduction to the Relay IR`{.interpreted-text -role="ref"} includes more detailed discussion of this nuance). +`developer introduction to the Relay IR` includes more detailed discussion of this nuance). *Note: Graph bindings are not currently parsed by the text format.* In Relay\'s text format, a graph binding can be written as below (note the lack of a `let` keyword and a semicolon): -``` +``` %1 = %a + %b %2 = %1 + %1 %2 * %2 @@ -558,7 +551,7 @@ corresponding Relay AST node and using the variables repeatedly, as below (a C++ program using the corresponding API bindings could accomplish the same thing): -``` +``` sum1 = relay.add(a, b) sum2 = relay.add(sum1, sum1) relay.multiply(sum2, sum2) @@ -578,7 +571,7 @@ Relay has a simple if-then-else expression that allows programs to branch on a single value of type `bool`, i.e., a zero-rank tensor of booleans (`Tensor[(), bool]`). -``` +``` if (%t == %u) { %t } else { @@ -593,13 +586,13 @@ evaluates to the value of the \"then\" branch if the condition value evaluates to `True` and evaluates to the value of the \"else\" branch if the condition value evaluates to `False`. -See :py`~tvm.relay.expr.If`{.interpreted-text role="class"} for its +See `If` for its definition and documentation. ## ADT Matching Instances of algebraic data types (ADTs), as discussed in the -`ADT overview`{.interpreted-text role="ref"}, are +`ADT overview`, are containers that store the arguments passed to the constructor used to create them, tagged by the constructor name. @@ -612,8 +605,7 @@ expressions are capable of more general pattern-matching than simply splitting by constructors: any ADT instance nested inside an instance (e.g., a list of lists) can be deconstructed at the same time as the outer instance, while the different fields of the instance can be bound -to variables. (See `this section`{.interpreted-text -role="ref"} for a detailed description of ADT pattern-matching.) +to variables. (See `this section` for a detailed description of ADT pattern-matching.) A match expression is defined using the input value (an expression) and a list of clauses, each of which consists of a pattern and an @@ -623,7 +615,7 @@ evaluated and returned. For example, suppose we have an ADT for natural numbers: -``` +``` data Nat { Z : () -> Nat # zero S : (Nat) -> Nat # successor (+1) to a nat @@ -632,7 +624,7 @@ data Nat { Then the following function subtracts one from a passed nat: -``` +``` fn(%v: Nat[]) -> Nat[] { match(%v) { case Z() { Z() } @@ -645,7 +637,7 @@ The following function subtracts two from its argument if it is at least two and returns the argument otherwise, using a nested constructor pattern: -``` +``` fn(%v : Nat[]) -> Nat[] { match(%v) { case S(S(%n)) { %n } @@ -659,7 +651,7 @@ As aforementioned, the ordering of match clauses is relevant. In the below example, the first clause will always match so those below it can never run: -``` +``` fn(%v : Nat[]) -> Nat[] { match(%v) { case _ { %v } @@ -670,7 +662,7 @@ fn(%v : Nat[]) -> Nat[] { } ``` -See :py`~tvm.relay.adt.Match`{.interpreted-text role="class"} for its +See `Match` for its definition and documentation. ## TempExprs @@ -688,5 +680,5 @@ For an example of `TempExpr` being used in a pass, see to store information about scaling parameters as the pass tries to fold these into the weights of a convolution. -See :py`~tvm.relay.expr.TempExpr`{.interpreted-text role="class"} for +See `TempExpr` for its definition and documentation. diff --git a/versioned_docs/version-0.10.0/reference/langref/relay_op.md b/versioned_docs/version-0.10.0/reference/langref/relay_op.md index f38049e5..2bab53a7 100644 --- a/versioned_docs/version-0.10.0/reference/langref/relay_op.md +++ b/versioned_docs/version-0.10.0/reference/langref/relay_op.md @@ -9,11 +9,7 @@ front-end frameworks and provide basic building blocks for optimization. Since deep learning is a fast evolving field, it is possible to have operators that are not in here. -::: note -::: title -Note -::: - +:::warning This document will directly list the function signature of these operators in the python frontend. ::: @@ -24,20 +20,20 @@ operators in the python frontend. This level enables fully connected multi-layer perceptron. -::: {.autosummary nosignatures=""} +``` tvm.relay.log tvm.relay.sqrt tvm.relay.rsqrt tvm.relay.exp tvm.relay.sigmoid tvm.relay.add tvm.relay.subtract tvm.relay.multiply tvm.relay.divide tvm.relay.mod tvm.relay.tanh tvm.relay.concatenate tvm.relay.expand_dims tvm.relay.nn.softmax tvm.relay.nn.log_softmax tvm.relay.nn.relu tvm.relay.nn.dropout tvm.relay.nn.batch_norm tvm.relay.nn.bias_add -::: +``` **Level 2: Convolutions** This level enables typical convnet models. -::: {.autosummary nosignatures=""} +``` tvm.relay.nn.conv2d tvm.relay.nn.conv2d_transpose tvm.relay.nn.conv3d tvm.relay.nn.conv3d_transpose tvm.relay.nn.dense tvm.relay.nn.max_pool2d tvm.relay.nn.max_pool3d tvm.relay.nn.avg_pool2d tvm.relay.nn.avg_pool3d @@ -50,13 +46,13 @@ tvm.relay.nn.contrib_conv2d_winograd_without_weight_transform tvm.relay.nn.contrib_conv2d_winograd_weight_transform tvm.relay.nn.contrib_conv3d_winograd_without_weight_transform tvm.relay.nn.contrib_conv3d_winograd_weight_transform -::: +``` **Level 3: Additional Math And Transform Operators** This level enables additional math and transform operators. -::: {.autosummary nosignatures=""} +``` tvm.relay.nn.leaky_relu tvm.relay.nn.prelu tvm.relay.reshape tvm.relay.reshape_like tvm.relay.copy tvm.relay.transpose tvm.relay.squeeze tvm.relay.floor tvm.relay.ceil tvm.relay.sign @@ -68,11 +64,11 @@ tvm.relay.split tvm.relay.arange tvm.relay.meshgrid tvm.relay.stack tvm.relay.repeat tvm.relay.tile tvm.relay.reverse tvm.relay.reverse_sequence tvm.relay.unravel_index tvm.relay.sparse_to_dense -::: +``` **Level 4: Broadcast and Reductions** -::: {.autosummary nosignatures=""} +``` tvm.relay.right_shift tvm.relay.left_shift tvm.relay.equal tvm.relay.not_equal tvm.relay.greater tvm.relay.greater_equal tvm.relay.less tvm.relay.less_equal tvm.relay.all tvm.relay.any @@ -83,30 +79,30 @@ tvm.relay.sum tvm.relay.max tvm.relay.min tvm.relay.mean tvm.relay.variance tvm.relay.std tvm.relay.mean_variance tvm.relay.mean_std tvm.relay.prod tvm.relay.strided_slice tvm.relay.broadcast_to -::: +``` **Level 5: Vision/Image Operators** -::: {.autosummary nosignatures=""} +``` tvm.relay.image.resize1d tvm.relay.image.resize2d tvm.relay.image.resize3d tvm.relay.image.crop_and_resize tvm.relay.image.dilation2d tvm.relay.vision.multibox_prior tvm.relay.vision.multibox_transform_loc tvm.relay.vision.nms tvm.relay.vision.yolo_reorg -::: +``` **Level 6: Algorithm Operators** -::: {.autosummary nosignatures=""} +``` tvm.relay.argsort tvm.relay.topk -::: +``` **Level 10: Temporary Operators** This level support backpropagation of broadcast operators. It is temporary. -::: {.autosummary nosignatures=""} +``` tvm.relay.broadcast_to_like tvm.relay.collapse_sum_like tvm.relay.slice_like tvm.relay.shape_of tvm.relay.ndarray_size tvm.relay.layout_transform tvm.relay.device_copy @@ -114,13 +110,13 @@ tvm.relay.annotation.on_device tvm.relay.reverse_reshape tvm.relay.sequence_mask tvm.relay.nn.batch_matmul tvm.relay.nn.adaptive_max_pool2d tvm.relay.nn.adaptive_avg_pool2d tvm.relay.one_hot -::: +``` **Level 11: Dialect Operators** This level supports dialect operators. -::: {.autosummary nosignatures=""} +``` tvm.relay.qnn.op.add tvm.relay.qnn.op.batch_matmul tvm.relay.qnn.op.concatenate tvm.relay.qnn.op.conv2d tvm.relay.qnn.op.conv2d_transpose tvm.relay.qnn.op.dense @@ -128,4 +124,4 @@ tvm.relay.qnn.op.dequantize tvm.relay.qnn.op.mul tvm.relay.qnn.op.quantize tvm.relay.qnn.op.requantize tvm.relay.qnn.op.rsqrt tvm.relay.qnn.op.simulated_dequantize tvm.relay.qnn.op.simulated_quantize tvm.relay.qnn.op.subtract -::: +``` diff --git a/versioned_docs/version-0.10.0/reference/langref/relay_pattern.md b/versioned_docs/version-0.10.0/reference/langref/relay_pattern.md index 51500f48..ef92242f 100644 --- a/versioned_docs/version-0.10.0/reference/langref/relay_pattern.md +++ b/versioned_docs/version-0.10.0/reference/langref/relay_pattern.md @@ -160,8 +160,8 @@ def test_match_tuple(): The next example is matching a pattern of batch_norm -\> get(0) -\> relu. Note that you can also use -[is_tuple_get_item(bn_node)]{.title-ref} to match a -[TupleGetItem]{.title-ref} node with any index. +[is_tuple_get_item(bn_node)] to match a +[TupleGetItem] node with any index. ``` python def test_match_tuple_get_item(): @@ -332,7 +332,7 @@ doesn\'t correspond to a set of patterns nodes exactly 1-to-1. There are a few other places where we support such \"fuzzy\" matching. Tuples, Functions, and Call nodes with any number of inputs can be -matched by passing [None]{.title-ref} as the argument value, i.e.: +matched by passing [None] as the argument value, i.e.: tuple_pattern = is_tuple(None) func_pattern = FunctionPattern(None, wildcard() + wildcard()) @@ -345,9 +345,9 @@ arguments. Additionally, we support matching Functions with fuzzy bodies, i.e., a function body that is under constrained by the pattern. The pattern [FunctionPattern(\[is_var(), is_var()\], wildcard() + -wildcard()\])]{.title-ref} will match [relay.Function(\[x, y\], x + -y)]{.title-ref}, but it will also match [relay.Function(\[x, y\], x \* -x + y)]{.title-ref}. In the second case, the pattern doesn\'t perfectly +wildcard()\])] will match [relay.Function(\[x, y\], x + +y)], but it will also match [relay.Function(\[x, y\], x \* +x + y)]. In the second case, the pattern doesn\'t perfectly constrain the body of the function, so the resulting match is fuzzy. ## Pattern Language Design diff --git a/versioned_docs/version-0.10.0/reference/langref/relay_type.md b/versioned_docs/version-0.10.0/reference/langref/relay_type.md index 00ef5d52..4e469ee1 100644 --- a/versioned_docs/version-0.10.0/reference/langref/relay_type.md +++ b/versioned_docs/version-0.10.0/reference/langref/relay_type.md @@ -49,7 +49,7 @@ to Relay expressions. The base type for all Relay types. All Relay types are sub-classes of this base type. -See :py`~tvm.relay.ty.Type`{.interpreted-text role="class"} for its +See `Type` for its definition and documentation. ## Tensor Type @@ -71,11 +71,11 @@ shapes will be concrete before running a program. For example, here is a simple concrete tensor type corresponding to a 10-by-10 tensor of 32-bit floats: -``` +``` Tensor[(10, 10), float32] ``` -See :py`~tvm.relay.ty.TensorType`{.interpreted-text role="class"} for +See `TensorType` for its definition and documentation. ## Tuple Type @@ -93,13 +93,13 @@ For example, in the below code, `%t` is of type `(Tensor[(), bool], Tensor[(10, 10), float32])` and `%c` is of type `Tensor[(10, 10), float32]`. -``` +``` let %t = (False, Constant(1, (10, 10), float32)); let %c = %t.1; %c ``` -See :py`~tvm.relay.ty.TupleType`{.interpreted-text role="class"} for its +See `TupleType` for its definition and documentation. ## Type Parameter @@ -125,14 +125,14 @@ parameters at call sites. For example, `s` below is a type parameter of kind `Shape` and it will be substituted with `(10, 10)` at the call site below: -``` +``` def @plus(%t1 : Tensor[s, float32], %t2 : Tensor[s, float32]) { add(%t1, %t2) } plus<(10, 10)>(%a, %b) ``` -See :py`~tvm.relay.ty.TypeVar`{.interpreted-text role="class"} for its +See `TypeVar` for its definition and documentation. ## Type Constraint @@ -141,12 +141,12 @@ This is an abstract class representing a type constraint, to be elaborated upon in further releases. Currently, type relations are the only type constraints provided; they are discussed below. -See :py`~tvm.relay.ty.TypeConstraint`{.interpreted-text role="class"} +See `TypeConstraint` for its definition and documentation. ## Function Type -A function type in Relay, see [tvm/relay/type.h]{.title-ref} for more +A function type in Relay, see [tvm/relay/type.h] for more details. This is the type assigned to functions in Relay. A function type @@ -162,7 +162,7 @@ hold at every call site of the function. The type constraints typically take the function\'s argument types and the function\'s return type as arguments, but may take a subset instead. -See :py`~tvm.relay.ty.FuncType`{.interpreted-text role="class"} for its +See `FuncType` for its definition and documentation. ## Type Relation @@ -175,8 +175,8 @@ Relay to statically reason about the shapes in these cases. A type relation `R` describes a relationship between the input and output types of a Relay function. Namely, `R` is a function on types -that outputs [true]{.title-ref} if the relationship holds and -[false]{.title-ref} if it fails to hold. Types given to a relation may +that outputs [true] if the relationship holds and +[false] if it fails to hold. Types given to a relation may be incomplete or include shape variables, so type inference must assign appropriate values to incomplete types and shape variables for necessary relations to hold, if such values exist. @@ -200,7 +200,7 @@ Flatten(Tensor(sh, bt), O) :- If we have a relation like `Broadcast` it becomes possible to type operators like `add`: -``` +``` add : fn(t1, t2) -> t3 where Broadcast ``` @@ -234,7 +234,7 @@ Presently all of the relations used in Relay are implemented in C++. See the files in `src/relay/op` for examples of relations implemented in C++. -See :py`~tvm.relay.ty.TypeRelation`{.interpreted-text role="class"} for +See `TypeRelation` for its definition and documentation. ## Incomplete Type @@ -252,15 +252,15 @@ concrete type arguments (instantiated) at call sites, whereas incomplete types may appear anywhere in the program and are filled in during type inference. -See :py`~tvm.relay.ty.IncompleteType`{.interpreted-text role="class"} +See `IncompleteType` for its definition and documentation. -## Algebraic Data Types {#adt-typing} +## Algebraic Data Types *Note: ADTs are not currently supported in the text format.* Algebraic data types (ADTs) are described in more detail in -`their overview `{.interpreted-text role="ref"}; this +`their overview `; this section describes their implementation in the type system. An ADT is defined by a collection of named constructors, each of which @@ -298,7 +298,7 @@ Recursion in an ADT definition thus follows just like recursion for a global function: the constructor can simply reference the ADT handle (global type variable) in its definition. -See :py`~tvm.relay.ty.GlobalTypeVar`{.interpreted-text role="class"} for +See `GlobalTypeVar` for its definition and documentation. ### Definitions (Type Data) @@ -306,8 +306,7 @@ its definition and documentation. Besides a name, an ADT needs to store the constructors that are used to define it and any type parameters used within them. These are stored in the module, -`analogous to global function definitions`{.interpreted-text -role="ref"}. +`analogous to global function definitions`. While type-checking uses of ADTs, the type system sometimes must index into the module using the ADT name to look up information about @@ -316,7 +315,7 @@ a match expression clause, the type-checker must check the constructor\'s signature to ensure that any bound variables are being assigned the correct types. -See :py`~tvm.relay.adt.TypeData`{.interpreted-text role="class"} for its +See `TypeData` for its definition and documentation. ### Type Call @@ -340,7 +339,7 @@ one argument for each type parameter in the ADT definition. (An ADT definition with no arguments means that any instance will have no type arguments passed to the type call). -See :py`~tvm.relay.ty.TypeCall`{.interpreted-text role="class"} for its +See `TypeCall` for its definition and documentation. ### Example: List ADT @@ -349,7 +348,7 @@ This subsection uses the simple list ADT (included as a default ADT in Relay) to illustrate the constructs described in the previous sections. Its definition is as follows: -``` +``` data List { Nil : () -> List Cons : (a, List[a]) -> List @@ -366,7 +365,7 @@ the `Cons` constructor is accomplished by using the global type variable Below two instances of lists with their types given, using type calls: -``` +``` Cons(1, Cons(2, Nil())) # List[Tensor[(), int32]] Cons((1, 1), Cons((2, 2), Nil())) # List[(Tensor[(), int32], Tensor[(), int32])] ``` @@ -378,7 +377,7 @@ take any arguments that use a type parameter. (Nevertheless, for any Here are two lists that are rejected by the type system because the type parameters do not match: -``` +``` # attempting to put an integer on a list of int * int tuples Cons(1, Cons((1, 1), Nil())) # attempting to put a list of ints on a list of lists of int * int tuples diff --git a/versioned_docs/version-0.10.0/tutorial/06-tensor_expr.md b/versioned_docs/version-0.10.0/tutorial/06-tensor_expr.md index 57206cf7..04fb2d47 100644 --- a/versioned_docs/version-0.10.0/tutorial/06-tensor_expr.md +++ b/versioned_docs/version-0.10.0/tutorial/06-tensor_expr.md @@ -318,7 +318,7 @@ if run_cuda: # 编译后的 TVM 函数提供了一个任何语言都可调用的 C API。 # # 我们在 Python 中提供了最小数组 API 来进行快速测试以及制作原型。 - # 数组 API 基于 `DLPack `_ 标准。 + # 数组 API 基于 `DLPack [https://github.com/dmlc/dlpack](https://github.com/dmlc/dlpack)`_ 标准。 # # - 首先创建 GPU 设备。 # - 然后 tvm.nd.array 将数据复制到 GPU 上。 diff --git a/versioned_docs/version-0.10.0/tutorial/07-ops_AutoTVM.md b/versioned_docs/version-0.10.0/tutorial/07-ops_AutoTVM.md index c1ab10ee..80e4b1e2 100644 --- a/versioned_docs/version-0.10.0/tutorial/07-ops_AutoTVM.md +++ b/versioned_docs/version-0.10.0/tutorial/07-ops_AutoTVM.md @@ -297,7 +297,7 @@ Finish loading 10 records ## 总结 -本教程展示了如何构建算子模板，使得 TVM 能够搜索参数空间，并选择优化的调度配置。为了更深入地了解其工作原理，推荐基于 :ref: *张量表达式入门 _* 教程中演示的调度操作，向调度添加新的搜索参数。接下来的章节将演示 AutoScheduler，它是TVM 中一种优化常用算子的方法，同时无需用户提供自定义的模板。 +本教程展示了如何构建算子模板，使得 TVM 能够搜索参数空间，并选择优化的调度配置。为了更深入地了解其工作原理，推荐基于 :ref: *张量表达式入门 ``* 教程中演示的调度操作，向调度添加新的搜索参数。接下来的章节将演示 AutoScheduler，它是TVM 中一种优化常用算子的方法，同时无需用户提供自定义的模板。 [下载 Python 源代码：autotvm_matmul_x86.py](https://tvm.apache.org/docs/_downloads/8e7bbc9dbdda76ac573b24606b41c006/autotvm_matmul_x86.py) diff --git a/versioned_docs/version-0.10.0/tutorial/10-rpc.md b/versioned_docs/version-0.10.0/tutorial/10-rpc.md index 4f8c2d59..8c7b659a 100644 --- a/versioned_docs/version-0.10.0/tutorial/10-rpc.md +++ b/versioned_docs/version-0.10.0/tutorial/10-rpc.md @@ -98,11 +98,11 @@ func.export_library(path) 除了 `-mtriple`，还可设置其他编译选项，例如： -* **-mcpu=** +* **`-mcpu=`** 指定生成的代码运行的芯片架构。默认情况这是从 target 三元组推断出来的，并自动检测到当前架构。 -* **-mattr=a1,+a2,-a3,…** +* **`-mattr=a1,+a2,-a3,…`** 覆盖或控制 target 的指定属性，例如是否启用 SIMD 操作。默认属性集由当前 CPU 设置。要获取可用属性列表，执行： diff --git a/versioned_docs/version-0.10.0/tutorial/11-quick_start.md b/versioned_docs/version-0.10.0/tutorial/11-quick_start.md index f5b08223..3a4c0e1f 100644 --- a/versioned_docs/version-0.10.0/tutorial/11-quick_start.md +++ b/versioned_docs/version-0.10.0/tutorial/11-quick_start.md @@ -52,7 +52,7 @@ mod, params = relay.testing.resnet.get_workload( print(mod.astext(show_meta_data=False)) ``` -访问代码中的 mod.astext 更多信息： +访问代码中的 mod.astext 更多信息：[https://tvm.apache.org/docs/reference/api/python/ir.html#tvm.ir.Node.astext](https://tvm.apache.org/docs/reference/api/python/ir.html#tvm.ir.Node.astext) 输出结果： @@ -250,4 +250,4 @@ tvm.testing.assert_allclose(out_deploy, out, atol=1e-5) [下载 Python 源代码：relay_quick_start.py](https://tvm.apache.org/docs/_downloads/cc6d9aebd24d54d81752590cbc8f99f9/relay_quick_start.py) -[下载 Jupyter Notebook：relay_quick_start.ipynb](https://tvm.apache.org/docs/_downloads/3dd2108354ac3028c96bcd6a0c7899dd/relay_quick_start.ipynb) \ No newline at end of file +[下载 Jupyter Notebook：relay_quick_start.ipynb](https://tvm.apache.org/docs/_downloads/3dd2108354ac3028c96bcd6a0c7899dd/relay_quick_start.ipynb)