Update WIT syntax with semicolons (#57)

* Update WIT syntax with semicolons

In preparation for when it's eventually required

* Add another semicolon

* Regenerate ml.md

.github/workflows/main.yml

@@ -11,6 +11,4 @@ jobs:
  runs-on: ubuntu-latest
  steps:
  - uses: actions/checkout@v2
- - uses: WebAssembly/wit-abi-up-to-date@v13
- with:
- wit-abi-tag: wit-abi-0.11.0
+ - uses: WebAssembly/wit-abi-up-to-date@v16

ml.md

@@ -20,6 +20,11 @@ Then, the user passes <em>tensor</em> inputs to the <em>graph</em>, computes the
 <p>All inputs and outputs to an ML inference are represented as <a href="#tensor"><code>tensor</code></a>s.</p>
 <hr />
 <h3>Types</h3>
+<h4><a name="tensor_dimensions"><code>type tensor-dimensions</code></a></h4>
+<p><a href="#tensor_dimensions"><a href="#tensor_dimensions"><code>tensor-dimensions</code></a></a></p>
+<p>The dimensions of a tensor.
+<p>The array length matches the tensor rank and each element in the array describes the size of
+each dimension</p>
 <h4><a name="tensor_type"><code>enum tensor-type</code></a></h4>
 <p>The type of the elements in a tensor.</p>
 <h5>Enum Cases</h5>
@@ -32,11 +37,6 @@ Then, the user passes <em>tensor</em> inputs to the <em>graph</em>, computes the
 <li><a name="tensor_type.i32"><code>I32</code></a></li>
 <li><a name="tensor_type.i64"><code>I64</code></a></li>
 </ul>
-<h4><a name="tensor_dimensions"><code>type tensor-dimensions</code></a></h4>
-<p><a href="#tensor_dimensions"><a href="#tensor_dimensions"><code>tensor-dimensions</code></a></a></p>
-<p>The dimensions of a tensor.
-<p>The array length matches the tensor rank and each element in the array describes the size of
-each dimension</p>
 <h4><a name="tensor_data"><code>type tensor-data</code></a></h4>
 <p><a href="#tensor_data"><a href="#tensor_data"><code>tensor-data</code></a></a></p>
 <p>The tensor data.
@@ -78,7 +78,11 @@ framework (e.g., TensorFlow):</p>
 #### <a name="tensor">`type tensor`</a>
 [`tensor`](#tensor)
 <p>
-#### <a name="graph_encoding">`enum graph-encoding`</a>
+#### <a name="graph">`type graph`</a>
+`u32`
+<p>An execution graph for performing inference (i.e., a model).
+<p>TODO: replace with <code>resource</code> (https://github.com/WebAssembly/wasi-nn/issues/47).</p>
+<h4><a name="graph_encoding"><code>enum graph-encoding</code></a></h4>
 <p>Describes the encoding of the graph. This allows the API to be implemented by various
 backends that encode (i.e., serialize) their graph IR with different formats.</p>
 <h5>Enum Cases</h5>
@@ -90,15 +94,6 @@ backends that encode (i.e., serialize) their graph IR with different formats.</p
 <li><a name="graph_encoding.tensorflowlite"><code>tensorflowlite</code></a></li>
 <li><a name="graph_encoding.autodetect"><code>autodetect</code></a></li>
 </ul>
-<h4><a name="graph_builder"><code>type graph-builder</code></a></h4>
-<p><a href="#graph_builder"><a href="#graph_builder"><code>graph-builder</code></a></a></p>
-<p>The graph initialization data.
-<p>This gets bundled up into an array of buffers because implementing backends may encode their
-graph IR in parts (e.g., OpenVINO stores its IR and weights separately).</p>
-<h4><a name="graph"><code>type graph</code></a></h4>
-<p><code>u32</code></p>
-<p>An execution graph for performing inference (i.e., a model).
-<p>TODO: replace with <code>resource</code> (https://github.com/WebAssembly/wasi-nn/issues/47).</p>
 <h4><a name="execution_target"><code>enum execution-target</code></a></h4>
 <p>Define where the graph should be executed.</p>
 <h5>Enum Cases</h5>
@@ -107,6 +102,11 @@ graph IR in parts (e.g., OpenVINO stores its IR and weights separately).</p>
 <li><a name="execution_target.gpu"><code>gpu</code></a></li>
 <li><a name="execution_target.tpu"><code>tpu</code></a></li>
 </ul>
+<h4><a name="graph_builder"><code>type graph-builder</code></a></h4>
+<p><a href="#graph_builder"><a href="#graph_builder"><code>graph-builder</code></a></a></p>
+<p>The graph initialization data.
+<p>This gets bundled up into an array of buffers because implementing backends may encode their
+graph IR in parts (e.g., OpenVINO stores its IR and weights separately).</p>
 <hr />
 <h3>Functions</h3>
 <h4><a name="load"><code>load: func</code></a></h4>

wit/wasi-nn.wit

@@ -1,4 +1,4 @@
-package wasi:nn
+package wasi:nn;
 
 /// `wasi-nn` is a WASI API for performing machine learning (ML) inference. The API is not (yet)
 /// capable of performing ML training. WebAssembly programs that want to use a host's ML
@@ -9,10 +9,10 @@ package wasi:nn
 ///
 /// This example world shows how to use these primitives together.
 world ml {
- import tensor
- import graph
- import inference
- import errors
+ import tensor;
+ import graph;
+ import inference;
+ import errors;
 }
 
 /// All inputs and outputs to an ML inference are represented as `tensor`s.
@@ -21,7 +21,7 @@ interface tensor {
  ///
  /// The array length matches the tensor rank and each element in the array describes the size of
  /// each dimension
- type tensor-dimensions = list<u32>
+ type tensor-dimensions = list<u32>;
 
  /// The type of the elements in a tensor.
  enum tensor-type {
@@ -41,7 +41,7 @@ interface tensor {
  /// in the type (e.g., a 2x2 tensor with 4-byte f32 elements would have a data array of length
  /// 16). Naturally, this representation requires some knowledge of how to lay out data in
  /// memory--e.g., using row-major ordering--and could perhaps be improved.
- type tensor-data = list<u8>
+ type tensor-data = list<u8>;
 
  record tensor {
  // Describe the size of the tensor (e.g., 2x2x2x2 -> [2, 2, 2, 2]). To represent a tensor
@@ -59,13 +59,13 @@ interface tensor {
 /// A `graph` is a loaded instance of a specific ML model (e.g., MobileNet) for a specific ML
 /// framework (e.g., TensorFlow):
 interface graph {
- use errors.{error}
- use tensor.{tensor}
+ use errors.{error};
+ use tensor.{tensor};
 
  /// An execution graph for performing inference (i.e., a model).
  ///
  /// TODO: replace with `resource` (https://github.com/WebAssembly/wasi-nn/issues/47).
- type graph = u32
+ type graph = u32;
 
  /// Describes the encoding of the graph. This allows the API to be implemented by various
  /// backends that encode (i.e., serialize) their graph IR with different formats.
@@ -89,45 +89,45 @@ interface graph {
  ///
  /// This gets bundled up into an array of buffers because implementing backends may encode their
  /// graph IR in parts (e.g., OpenVINO stores its IR and weights separately).
- type graph-builder = list<u8>
+ type graph-builder = list<u8>;
 
  /// Load a `graph` from an opaque sequence of bytes to use for inference.
- load: func(builder: list<graph-builder>, encoding: graph-encoding, target: execution-target) -> result<graph, error>
+ load: func(builder: list<graph-builder>, encoding: graph-encoding, target: execution-target) -> result<graph, error>;
 
  /// Load a `graph` by name.
  ///
  /// How the host expects the names to be passed and how it stores the graphs for retrieval via
  /// this function is **implementation-specific**. This allows hosts to choose name schemes that
  /// range from simple to complex (e.g., URLs?) and caching mechanisms of various kinds.
- load-by-name: func(name: string) -> result<graph, error>
+ load-by-name: func(name: string) -> result<graph, error>;
 }
 
 /// An inference "session" is encapsulated by a `graph-execution-context`. This structure binds a
 /// `graph` to input tensors before `compute`-ing an inference:
 interface inference {
- use errors.{error}
- use tensor.{tensor, tensor-data}
- use graph.{graph}
+ use errors.{error};
+ use tensor.{tensor, tensor-data};
+ use graph.{graph};
 
  /// Bind a `graph` to the input and output tensors for an inference.
  ///
  /// TODO: this is no longer necessary in WIT (https://github.com/WebAssembly/wasi-nn/issues/43)
- type graph-execution-context = u32
+ type graph-execution-context = u32;
 
  /// Create an execution instance of a loaded graph.
- init-execution-context: func(graph: graph) -> result<graph-execution-context, error>
+ init-execution-context: func(graph: graph) -> result<graph-execution-context, error>;
 
  /// Define the inputs to use for inference.
- set-input: func(ctx: graph-execution-context, index: u32, tensor: tensor) -> result<_, error>
+ set-input: func(ctx: graph-execution-context, index: u32, tensor: tensor) -> result<_, error>;
 
  /// Compute the inference on the given inputs.
  ///
  /// Note the expected sequence of calls: `set-input`, `compute`, `get-output`. TODO: this
  /// expectation could be removed as a part of https://github.com/WebAssembly/wasi-nn/issues/43.
- compute: func(ctx: graph-execution-context) -> result<_, error>
+ compute: func(ctx: graph-execution-context) -> result<_, error>;
 
  /// Extract the outputs after inference.
- get-output: func(ctx: graph-execution-context, index: u32) -> result<tensor-data, error>
+ get-output: func(ctx: graph-execution-context, index: u32) -> result<tensor-data, error>;
 }
 
 /// TODO: create function-specific errors (https://github.com/WebAssembly/wasi-nn/issues/42)