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Add an initial wasi-nn implementation for Wasmtime (#2208)

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* Add an initial wasi-nn implementation for Wasmtime

This change adds a crate, `wasmtime-wasi-nn`, that uses `wiggle` to expose the current state of the wasi-nn API and `openvino` to implement the exposed functions. It includes an end-to-end test demonstrating how to do classification using wasi-nn:
 - `crates/wasi-nn/tests/classification-example` contains Rust code that is compiled to the `wasm32-wasi` target and run with a Wasmtime embedding that exposes the wasi-nn calls
 - the example uses Rust bindings for wasi-nn contained in `crates/wasi-nn/tests/wasi-nn-rust-bindings`; this crate contains code generated by `witx-bindgen` and eventually should be its own standalone crate

* Test wasi-nn as a CI step

This change adds:
 - a GitHub action for installing OpenVINO
 - a script, `ci/run-wasi-nn-example.sh`, to run the classification example
This commit is contained in:
Andrew Brown
2020-11-16 10:54:00 -08:00
committed by GitHub
parent 61a0bcbdc6
commit a61f068c64
33 changed files with 1554 additions and 1 deletions
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29
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[package]
name = "wasmtime-wasi-nn"
version = "0.21.0"
authors = ["The Wasmtime Project Developers"]
description = "Wasmtime implementation of the wasi-nn API"
documentation = "https://docs.rs/wasmtime-wasi-nn"
license = "Apache-2.0 WITH LLVM-exception"
categories = ["wasm", "computer-vision"]
keywords = ["webassembly", "wasm", "neural network"]
repository = "https://github.com/bytecodealliance/wasmtime"
readme = "README.md"
edition = "2018"
[dependencies]
# These dependencies are necessary for the witx-generation macros to work:
anyhow = "1.0"
log = { version = "0.4", default-features = false }
wasmtime = { path = "../wasmtime", version = "0.21.0", default-features = false }
wasmtime-runtime = { path = "../runtime", version = "0.21.0" }
wasmtime-wiggle = { path = "../wiggle/wasmtime", version = "0.21.0" }
wasmtime-wasi = { path = "../wasi", version = "0.21.0" }
wiggle = { path = "../wiggle", version = "0.21.0" }
# These dependencies are necessary for the wasi-nn implementation:
openvino = "0.1.5"
thiserror = "1.0"
[badges]
maintenance = { status = "experimental" }

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crates/wasi-nn/README.md Normal file
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# wasmtime-wasi-nn
This crate enables support for the [wasi-nn] API in Wasmtime. Currently it contains an implementation of [wasi-nn] using
OpenVINO™ but in the future it could support multiple machine learning backends. Since the [wasi-nn] API is expected
to be an optional feature of WASI, this crate is currently separate from the [wasi-common] crate. This crate is
experimental and its API, functionality, and location could quickly change.
[examples]: examples
[openvino]: https://crates.io/crates/openvino
[wasi-nn]: https://github.com/WebAssembly/wasi-nn
[wasi-common]: ../wasi-common
### Use
Use the Wasmtime APIs to instantiate a Wasm module and link in the `WasiNn` implementation as follows:
```
let wasi_nn = WasiNn::new(&store, WasiNnCtx::new()?);
wasi_nn.add_to_linker(&mut linker)?;
```
### Build
This crate should build as usual (i.e. `cargo build`) but note that using an existing installation of OpenVINO™, rather
than building from source, will drastically improve the build times. See the [openvino] crate for more information
### Example
An end-to-end example demonstrating ML classification is included in [examples]:
- `tests/wasi-nn-rust-bindings` contains ergonomic bindings for writing Rust code against the [wasi-nn] APIs
- `tests/classification-example` contains a standalone Rust project that uses the [wasi-nn] APIs and is compiled to the
`wasm32-wasi` target using the `wasi-nn-rust-bindings`
Run the example from the Wasmtime project directory:
```
ci/run-wasi-nn-example.sh
```

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//! This build script:
//! - has the configuration necessary for the wiggle and witx macros.
use std::path::PathBuf;
fn main() {
// This is necessary for Wiggle/Witx macros.
let wasi_root = PathBuf::from("./spec").canonicalize().unwrap();
println!("cargo:rustc-env=WASI_ROOT={}", wasi_root.display());
}

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See `ci/run-wasi-nn-example.sh` for how the classification example is tested during CI.

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# This file is automatically @generated by Cargo.
# It is not intended for manual editing.
[[package]]
name = "wasi-nn"
version = "0.1.0"
[[package]]
name = "wasi-nn-example"
version = "0.19.0"
dependencies = [
"wasi-nn 0.1.0",
]

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[package]
name = "wasi-nn-example"
version = "0.19.0"
authors = ["The Wasmtime Project Developers"]
readme = "README.md"
edition = "2018"
publish = false
[dependencies]
wasi-nn = { path = "../wasi-nn-rust-bindings", version = "0.1.0" }
# This crate is built with the wasm32-wasi target, so it's separate
# from the main Wasmtime build, so use this directive to exclude it
# from the parent directory's workspace.
[workspace]

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This example project demonstrates using the `wasi-nn` API to perform ML inference. It consists of Rust code that is
built using the `wasm32-wasi` target. See `ci/run-wasi-nn-example.sh` for how this is used.

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use std::convert::TryInto;
use std::fs;
use wasi_nn;
pub fn main() {
let xml = fs::read_to_string("fixture/frozen_inference_graph.xml").unwrap();
println!("First 50 characters of graph: {}", &xml[..50]);
let weights = fs::read("fixture/frozen_inference_graph.bin").unwrap();
println!("Size of weights: {}", weights.len());
let graph = unsafe {
wasi_nn::load(
&[&xml.into_bytes(), &weights],
wasi_nn::GRAPH_ENCODING_OPENVINO,
wasi_nn::EXECUTION_TARGET_CPU,
)
.unwrap()
};
println!("Graph handle ID: {}", graph);
let context = unsafe { wasi_nn::init_execution_context(graph).unwrap() };
println!("Execution context ID: {}", context);
// Load a tensor that precisely matches the graph input tensor (see
// `fixture/frozen_inference_graph.xml`).
let tensor_data = fs::read("fixture/tensor-1x3x300x300-f32.bgr").unwrap();
println!("Tensor bytes: {}", tensor_data.len());
let tensor = wasi_nn::Tensor {
dimensions: &[1, 3, 300, 300],
r#type: wasi_nn::TENSOR_TYPE_F32,
data: &tensor_data,
};
unsafe {
wasi_nn::set_input(context, 0, tensor).unwrap();
}
// Execute the inference.
unsafe {
wasi_nn::compute(context).unwrap();
}
// Retrieve the output (TODO output looks incorrect).
let mut output_buffer = vec![0f32; 1 << 20];
unsafe {
wasi_nn::get_output(
context,
0,
&mut output_buffer[..] as *mut [f32] as *mut u8,
(output_buffer.len() * 4).try_into().unwrap(),
);
}
println!("output tensor: {:?}", &output_buffer[..1000])
}

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/target

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# This file is automatically @generated by Cargo.
# It is not intended for manual editing.
[[package]]
name = "wasi-nn"
version = "0.1.0"

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[package]
name = "wasi-nn"
version = "0.1.0"
authors = ["The Wasmtime Project Developers"]
readme = "README.md"
edition = "2018"
publish = false
[dependencies]
# This crate is only used when building the example, so it's separate
# from the main Wasmtime build, so use this directive to exclude it
# from the parent directory's workspace.
[workspace]

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Apache License
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"License" shall mean the terms and conditions for use, reproduction,
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publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
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or a Contribution incorporated within the Work constitutes direct
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as of the date such litigation is filed.
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Work or Derivative Works thereof in any medium, with or without
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meet the following conditions:
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Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
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Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
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names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
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Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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PARTICULAR PURPOSE. You are solely responsible for determining the
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of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
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file or class name and description of purpose be included on the
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65
crates/wasi-nn/examples/wasi-nn-rust-bindings/README.md Normal file
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@@ -0,0 +1,65 @@
wasi-nn Rust Bindings
=====================
This crate contains API bindings for [wasi-nn] system calls in Rust. It is similar in purpose to the [wasi bindings] but
this crate provides access to the optional neural network functionality from WebAssembly.
[wasi-nn]: https://github.com/WebAssembly/wasi-nn
[wasi bindings]: https://github.com/bytecodealliance/wasi
> __NOTE__: These bindings are experimental (use at your own risk) and subject to upstream changes in the wasi-nn
> specification.
> __NOTE__: In the future this crate may be (should be) moved to its own repository, like the [wasi bindings].
### Use
Depend on this crate in your `Cargo.toml`:
```toml
[dependencies]
wasi-nn = "0.1.0"
```
Use the wasi-nn APIs in your application:
```rust
use wasi_nn;
unsafe {
wasi_nn::load(
&[&xml.into_bytes(), &weights],
wasi_nn::GRAPH_ENCODING_OPENVINO,
wasi_nn::EXECUTION_TARGET_CPU,
)
.unwrap()
}
```
Compile the application to WebAssembly:
```shell script
cargo build --target=wasm32-wasi
```
Run the generated Wasm in a runtime supporting wasi-nn. Currently Wasmtime has experimental support using the Wasmtime
APIs; see [main.rs](../main.rs) for an example of how this is accomplished.
### Generation
This crate contains code ([`src/generated.rs`](src/generated.rs)) generated by
[`witx-bindgen`](https://github.com/bytecodealliance/wasi/tree/main/crates/witx-bindgen).
To regenerate this code, run `witx-bindgen` against the [`wasi-nn` WITX file](https://github.com/WebAssembly/wasi-nn/blob/master/phases/ephemeral/witx/wasi_ephemeral_nn.witx):
```shell script
.../crates/witx-bindgen$ cargo run .../wasi-nn/phases/ephemeral/witx/wasi_ephemeral_nn.witx
```
### License
This project is licensed under the Apache 2.0 license. See [LICENSE](LICENSE) for more details.
### Contribution
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you,
as defined in the Apache-2.0 license, shall be licensed as above, without any additional terms or conditions.

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use super::NnErrno;
use core::fmt;
use core::num::NonZeroU16;
/// A raw error returned by wasi-nn APIs, internally containing a 16-bit error
/// code.
#[derive(Copy, Clone, PartialEq, Eq, Ord, PartialOrd)]
pub struct Error {
code: NonZeroU16,
}
impl Error {
/// Constructs a new error from a raw error code, returning `None` if the
/// error code is zero (which means success).
pub fn from_raw_error(error: NnErrno) -> Option<Error> {
Some(Error {
code: NonZeroU16::new(error)?,
})
}
/// Returns the raw error code that this error represents.
pub fn raw_error(&self) -> u16 {
self.code.get()
}
}
impl fmt::Display for Error {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(f, "{} (error {})", strerror(self.code.get()), self.code)?;
Ok(())
}
}
impl fmt::Debug for Error {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("Error")
.field("code", &self.code)
.field("message", &strerror(self.code.get()))
.finish()
}
}
/// This should be generated automatically by witx-bindgen but is not yet for enums other than
/// `Errno` (this API uses `NnErrno` to avoid naming conflicts). TODO: https://github.com/bytecodealliance/wasi/issues/52.
fn strerror(code: u16) -> &'static str {
match code {
super::NN_ERRNO_SUCCESS => "No error occurred.",
super::NN_ERRNO_INVALID_ARGUMENT => "Caller module passed an invalid argument.",
super::NN_ERRNO_MISSING_MEMORY => "Caller module is missing a memory export.",
super::NN_ERRNO_BUSY => "Device or resource busy.",
_ => "Unknown error.",
}
}
#[cfg(feature = "std")]
extern crate std;
#[cfg(feature = "std")]
impl std::error::Error for Error {}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn error_from_success_code() {
assert_eq!(None, Error::from_raw_error(0));
}
#[test]
fn error_from_invalid_argument_code() {
assert_eq!(
"Caller module passed an invalid argument. (error 1)",
Error::from_raw_error(1).unwrap().to_string()
);
}
}

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// This file is automatically generated, DO NOT EDIT
//
// To regenerate this file run the `crates/witx-bindgen` command
use core::mem::MaybeUninit;
pub use crate::error::Error;
pub type Result<T, E = Error> = core::result::Result<T, E>;
pub type BufferSize = u32;
pub type NnErrno = u16;
/// No error occurred.
pub const NN_ERRNO_SUCCESS: NnErrno = 0;
/// Caller module passed an invalid argument.
pub const NN_ERRNO_INVALID_ARGUMENT: NnErrno = 1;
/// Caller module is missing a memory export.
pub const NN_ERRNO_MISSING_MEMORY: NnErrno = 2;
/// Device or resource busy.
pub const NN_ERRNO_BUSY: NnErrno = 3;
pub type TensorDimensions<'a> = &'a [u32];
pub type TensorType = u8;
pub const TENSOR_TYPE_F16: TensorType = 0;
pub const TENSOR_TYPE_F32: TensorType = 1;
pub const TENSOR_TYPE_U8: TensorType = 2;
pub const TENSOR_TYPE_I32: TensorType = 3;
pub type TensorData<'a> = &'a [u8];
#[repr(C)]
#[derive(Copy, Clone, Debug)]
pub struct Tensor<'a> {
/// Describe the size of the tensor (e.g. 2x2x2x2 -> [2, 2, 2, 2]). To represent a tensor containing a single value,
/// use `[1]` for the tensor dimensions.
pub dimensions: TensorDimensions<'a>,
pub r#type: TensorType,
/// Contains the tensor data.
pub data: TensorData<'a>,
}
pub type GraphBuilder<'a> = &'a [u8];
pub type GraphBuilderArray<'a> = &'a [GraphBuilder<'a>];
pub type Graph = u32;
pub type GraphEncoding = u8;
/// TODO document buffer order
pub const GRAPH_ENCODING_OPENVINO: GraphEncoding = 0;
pub type ExecutionTarget = u8;
pub const EXECUTION_TARGET_CPU: ExecutionTarget = 0;
pub const EXECUTION_TARGET_GPU: ExecutionTarget = 1;
pub const EXECUTION_TARGET_TPU: ExecutionTarget = 2;
pub type GraphExecutionContext = u32;
/// Load an opaque sequence of bytes to use for inference.
///
/// This allows runtime implementations to support multiple graph encoding formats. For unsupported graph encodings,
/// return `errno::inval`.
///
/// ## Parameters
///
/// * `builder` - The bytes necessary to build the graph.
/// * `encoding` - The encoding of the graph.
/// * `target` - Where to execute the graph.
pub unsafe fn load(
builder: GraphBuilderArray,
encoding: GraphEncoding,
target: ExecutionTarget,
) -> Result<Graph> {
let mut graph = MaybeUninit::uninit();
let rc = wasi_ephemeral_nn::load(
builder.as_ptr(),
builder.len(),
encoding,
target,
graph.as_mut_ptr(),
);
if let Some(err) = Error::from_raw_error(rc) {
Err(err)
} else {
Ok(graph.assume_init())
}
}
/// TODO Functions like `describe_graph_inputs` and `describe_graph_outputs` (returning
/// an array of `$tensor_description`s) might be useful for introspecting the graph but are not yet included here.
/// Create an execution instance of a loaded graph.
/// TODO this may need to accept flags that might affect the compilation or execution of the graph.
pub unsafe fn init_execution_context(graph: Graph) -> Result<GraphExecutionContext> {
let mut context = MaybeUninit::uninit();
let rc = wasi_ephemeral_nn::init_execution_context(graph, context.as_mut_ptr());
if let Some(err) = Error::from_raw_error(rc) {
Err(err)
} else {
Ok(context.assume_init())
}
}
/// Define the inputs to use for inference.
///
/// This should return an $nn_errno (TODO define) if the input tensor does not match the expected dimensions and type.
///
/// ## Parameters
///
/// * `index` - The index of the input to change.
/// * `tensor` - The tensor to set as the input.
pub unsafe fn set_input(context: GraphExecutionContext, index: u32, tensor: Tensor) -> Result<()> {
let rc = wasi_ephemeral_nn::set_input(context, index, &tensor as *const _ as *mut _);
if let Some(err) = Error::from_raw_error(rc) {
Err(err)
} else {
Ok(())
}
}
/// Extract the outputs after inference.
///
/// This should return an $nn_errno (TODO define) if the inference has not yet run.
///
/// ## Parameters
///
/// * `index` - The index of the output to retrieve.
/// * `out_buffer` - An out parameter to which to copy the tensor data. The caller is responsible for allocating enough memory for
/// the tensor data or an error will be returned. Currently there is no dynamic way to extract the additional
/// tensor metadata (i.e. dimension, element type) but this should be added at some point.
///
/// ## Return
///
/// * `bytes_written` - The number of bytes of tensor data written to the `$out_buffer`.
pub unsafe fn get_output(
context: GraphExecutionContext,
index: u32,
out_buffer: *mut u8,
out_buffer_max_size: BufferSize,
) -> Result<BufferSize> {
let mut bytes_written = MaybeUninit::uninit();
let rc = wasi_ephemeral_nn::get_output(
context,
index,
out_buffer,
out_buffer_max_size,
bytes_written.as_mut_ptr(),
);
if let Some(err) = Error::from_raw_error(rc) {
Err(err)
} else {
Ok(bytes_written.assume_init())
}
}
/// Compute the inference on the given inputs (see `set_input`).
///
/// This should return an $nn_errno (TODO define) if the inputs are not all defined.
pub unsafe fn compute(context: GraphExecutionContext) -> Result<()> {
let rc = wasi_ephemeral_nn::compute(context);
if let Some(err) = Error::from_raw_error(rc) {
Err(err)
} else {
Ok(())
}
}
pub mod wasi_ephemeral_nn {
use super::*;
#[link(wasm_import_module = "wasi_ephemeral_nn")]
extern "C" {
/// Load an opaque sequence of bytes to use for inference.
///
/// This allows runtime implementations to support multiple graph encoding formats. For unsupported graph encodings,
/// return `errno::inval`.
pub fn load(
builder_ptr: *const GraphBuilder,
builder_len: usize,
encoding: GraphEncoding,
target: ExecutionTarget,
graph: *mut Graph,
) -> NnErrno;
/// TODO Functions like `describe_graph_inputs` and `describe_graph_outputs` (returning
/// an array of `$tensor_description`s) might be useful for introspecting the graph but are not yet included here.
/// Create an execution instance of a loaded graph.
/// TODO this may need to accept flags that might affect the compilation or execution of the graph.
pub fn init_execution_context(graph: Graph, context: *mut GraphExecutionContext)
-> NnErrno;
/// Define the inputs to use for inference.
///
/// This should return an $nn_errno (TODO define) if the input tensor does not match the expected dimensions and type.
pub fn set_input(
context: GraphExecutionContext,
index: u32,
tensor: *mut Tensor,
) -> NnErrno;
/// Extract the outputs after inference.
///
/// This should return an $nn_errno (TODO define) if the inference has not yet run.
pub fn get_output(
context: GraphExecutionContext,
index: u32,
out_buffer: *mut u8,
out_buffer_max_size: BufferSize,
bytes_written: *mut BufferSize,
) -> NnErrno;
/// Compute the inference on the given inputs (see `set_input`).
///
/// This should return an $nn_errno (TODO define) if the inputs are not all defined.
pub fn compute(context: GraphExecutionContext) -> NnErrno;
}
}

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mod error;
mod generated;
pub use generated::*;

1
crates/wasi-nn/spec Submodule

Submodule crates/wasi-nn/spec added at 68e73cf612

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//! Implements the base structure (i.e. [WasiNnCtx]) that will provide the implementation of the
//! wasi-nn API.
use crate::r#impl::UsageError;
use crate::witx::types::{Graph, GraphExecutionContext};
use openvino::InferenceError;
use std::cell::RefCell;
use std::collections::HashMap;
use std::hash::Hash;
use thiserror::Error;
use wiggle::GuestError;
/// Possible errors for interacting with [WasiNnCtx].
#[derive(Debug, Error)]
pub enum WasiNnError {
#[error("guest error")]
GuestError(#[from] GuestError),
#[error("openvino error")]
OpenvinoError(#[from] InferenceError),
#[error("usage error")]
UsageError(#[from] UsageError),
}
pub(crate) type WasiNnResult<T> = std::result::Result<T, WasiNnError>;
pub struct Table<K, V> {
entries: HashMap<K, V>,
next_key: u32,
}
impl<K, V> Default for Table<K, V> {
fn default() -> Self {
Self {
entries: HashMap::new(),
next_key: 0,
}
}
}
impl<K, V> Table<K, V>
where
K: Eq + Hash + From<u32> + Copy,
{
pub fn insert(&mut self, value: V) -> K {
let key = self.use_next_key();
self.entries.insert(key, value);
key
}
pub fn remove(&mut self, key: K) -> Option<V> {
self.entries.remove(&key)
}
pub fn get(&self, key: K) -> Option<&V> {
self.entries.get(&key)
}
pub fn get_mut(&mut self, key: K) -> Option<&mut V> {
self.entries.get_mut(&key)
}
pub fn len(&self) -> usize {
self.entries.len()
}
fn use_next_key(&mut self) -> K {
let current = self.next_key;
self.next_key += 1;
K::from(current)
}
}
pub struct ExecutionContext {
pub(crate) graph: Graph,
pub(crate) request: openvino::InferRequest,
}
impl ExecutionContext {
pub(crate) fn new(graph: Graph, request: openvino::InferRequest) -> Self {
Self { graph, request }
}
}
/// Capture the state necessary for calling into `openvino`.
pub struct Ctx {
pub(crate) core: openvino::Core,
pub(crate) graphs: Table<Graph, (openvino::CNNNetwork, openvino::ExecutableNetwork)>,
pub(crate) executions: Table<GraphExecutionContext, ExecutionContext>,
}
impl Ctx {
/// Make a new `WasiNnCtx` with the default settings.
pub fn new() -> WasiNnResult<Self> {
Ok(Self {
core: openvino::Core::new(None)?,
graphs: Table::default(),
executions: Table::default(),
})
}
}
/// This structure provides the Rust-side context necessary for implementing the wasi-nn API. At the
/// moment, it is specialized for a single inference implementation (i.e. OpenVINO) but conceivably
/// this could support more than one backing implementation.
pub struct WasiNnCtx {
pub(crate) ctx: RefCell<Ctx>,
}
impl WasiNnCtx {
/// Make a new `WasiNnCtx` with the default settings.
pub fn new() -> WasiNnResult<Self> {
Ok(Self {
ctx: RefCell::new(Ctx::new()?),
})
}
}
#[cfg(test)]
mod test {
use super::*;
#[test]
fn instantiate() {
WasiNnCtx::new().unwrap();
}
}

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//! Implements the wasi-nn API.
use crate::ctx::{ExecutionContext, WasiNnResult as Result};
use crate::witx::types::{
ExecutionTarget, Graph, GraphBuilderArray, GraphEncoding, GraphExecutionContext, Tensor,
TensorType,
};
use crate::witx::wasi_ephemeral_nn::WasiEphemeralNn;
use crate::WasiNnCtx;
use openvino::{Layout, Precision, TensorDesc};
use thiserror::Error;
use wiggle::GuestPtr;
#[derive(Debug, Error)]
pub enum UsageError {
#[error("Only OpenVINO's IR is currently supported, passed encoding: {0}")]
InvalidEncoding(GraphEncoding),
#[error("OpenVINO expects only two buffers (i.e. [ir, weights]), passed: {0}")]
InvalidNumberOfBuilders(u32),
#[error("Invalid graph handle; has it been loaded?")]
InvalidGraphHandle,
#[error("Invalid execution context handle; has it been initialized?")]
InvalidExecutionContextHandle,
#[error("Not enough memory to copy tensor data of size: {0}")]
NotEnoughMemory(u32),
}
impl<'a> WasiEphemeralNn for WasiNnCtx {
fn load<'b>(
&self,
builders: &GraphBuilderArray<'_>,
encoding: GraphEncoding,
target: ExecutionTarget,
) -> Result<Graph> {
if encoding != GraphEncoding::Openvino {
return Err(UsageError::InvalidEncoding(encoding).into());
}
if builders.len() != 2 {
return Err(UsageError::InvalidNumberOfBuilders(builders.len()).into());
}
let builders = builders.as_ptr();
let xml = builders.read()?.as_slice()?;
let weights = builders.add(1)?.read()?.as_slice()?;
let graph = self
.ctx
.borrow_mut()
.core
.read_network_from_buffer(&xml, &weights)?;
let executable_graph = self
.ctx
.borrow_mut()
.core
.load_network(&graph, map_execution_target_to_string(target))?;
let id = self
.ctx
.borrow_mut()
.graphs
.insert((graph, executable_graph));
Ok(id)
}
fn init_execution_context(&self, graph: Graph) -> Result<GraphExecutionContext> {
let request =
if let Some((_, executable_graph)) = self.ctx.borrow_mut().graphs.get_mut(graph) {
executable_graph.create_infer_request()?
} else {
return Err(UsageError::InvalidGraphHandle.into());
};
let execution_context = ExecutionContext::new(graph, request);
let handle = self.ctx.borrow_mut().executions.insert(execution_context);
Ok(handle)
}
fn set_input<'b>(
&self,
context: GraphExecutionContext,
index: u32,
tensor: &Tensor<'b>,
) -> Result<()> {
let graph = if let Some(execution) = self.ctx.borrow_mut().executions.get_mut(context) {
execution.graph
} else {
return Err(UsageError::InvalidExecutionContextHandle.into());
};
let input_name = if let Some((graph, _)) = self.ctx.borrow().graphs.get(graph) {
graph.get_input_name(index as usize)?
} else {
unreachable!("It should be impossible to attempt to access an execution's graph and for that graph not to exist--this is a bug.")
};
// Construct the blob structure.
let dimensions = tensor
.dimensions
.as_slice()?
.iter()
.map(|d| *d as u64)
.collect::<Vec<_>>();
let precision = match tensor.type_ {
TensorType::F16 => Precision::FP16,
TensorType::F32 => Precision::FP32,
TensorType::U8 => Precision::U8,
TensorType::I32 => Precision::I32,
};
// TODO There must be some good way to discover the layout here; this should not have to default to NHWC.
let desc = TensorDesc::new(Layout::NHWC, &dimensions, precision);
let data = tensor.data.as_slice()?;
let blob = openvino::Blob::new(desc, &data)?;
// Actually assign the blob to the request (TODO avoid duplication with the borrow above).
if let Some(execution) = self.ctx.borrow_mut().executions.get_mut(context) {
execution.request.set_blob(&input_name, blob)?;
} else {
return Err(UsageError::InvalidExecutionContextHandle.into());
}
Ok(())
}
fn compute(&self, context: GraphExecutionContext) -> Result<()> {
if let Some(execution) = self.ctx.borrow_mut().executions.get_mut(context) {
Ok(execution.request.infer()?)
} else {
return Err(UsageError::InvalidExecutionContextHandle.into());
}
}
fn get_output<'b>(
&self,
context: GraphExecutionContext,
index: u32,
out_buffer: &GuestPtr<'_, u8>,
out_buffer_max_size: u32,
) -> Result<u32> {
let graph = if let Some(execution) = self.ctx.borrow_mut().executions.get_mut(context) {
execution.graph
} else {
return Err(UsageError::InvalidExecutionContextHandle.into());
};
let output_name = if let Some((graph, _)) = self.ctx.borrow().graphs.get(graph) {
graph.get_output_name(index as usize)?
} else {
unreachable!("It should be impossible to attempt to access an execution's graph and for that graph not to exist--this is a bug.")
};
// Retrieve the tensor data.
let (mut blob, blob_size) =
if let Some(execution) = self.ctx.borrow_mut().executions.get_mut(context) {
let mut blob = execution.request.get_blob(&output_name)?; // TODO shouldn't need to be mut
let blob_size = blob.byte_len()? as u32;
if blob_size > out_buffer_max_size {
return Err(UsageError::NotEnoughMemory(blob_size).into());
}
(blob, blob_size)
} else {
return Err(UsageError::InvalidExecutionContextHandle.into());
};
// Copy the tensor data over to the `out_buffer`.
let mut out_slice = out_buffer.as_array(out_buffer_max_size).as_slice()?;
(&mut out_slice[..blob_size as usize]).copy_from_slice(blob.buffer()?);
Ok(blob_size)
}
}
/// Return the execution target string expected by OpenVINO from the `ExecutionTarget` enum provided
/// by wasi-nn.
fn map_execution_target_to_string(target: ExecutionTarget) -> &'static str {
match target {
ExecutionTarget::Cpu => "CPU",
ExecutionTarget::Gpu => "GPU",
ExecutionTarget::Tpu => unimplemented!("OpenVINO does not support TPU execution targets"),
}
}

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mod ctx;
mod r#impl;
mod witx;
pub use ctx::WasiNnCtx;
// Defines a `struct WasiNn` with member fields and appropriate APIs for dealing with all the
// various WASI exports.
wasmtime_wiggle::wasmtime_integration!({
// The wiggle code to integrate with lives here:
target: witx,
// This must be the same witx document as used above:
witx: ["$WASI_ROOT/phases/ephemeral/witx/wasi_ephemeral_nn.witx"],
// This must be the same ctx type as used for the target:
ctx: WasiNnCtx,
// This macro will emit a struct to represent the instance, with this name and docs:
modules: {
wasi_ephemeral_nn => {
name: WasiNn,
docs: "An instantiated instance of the wasi-nn exports.",
function_override: {}
}
},
// Error to return when caller module is missing memory export:
missing_memory: { witx::types::Errno::MissingMemory },
});

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crates/wasi-nn/src/witx.rs Normal file
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//! Contains the macro-generated implementation of wasi-nn from the its witx definition file.
use crate::ctx::WasiNnCtx;
use crate::ctx::WasiNnError;
// Generate the traits and types of wasi-nn in several Rust modules (e.g. `types`).
wiggle::from_witx!({
witx: ["$WASI_ROOT/phases/ephemeral/witx/wasi_ephemeral_nn.witx"],
ctx: WasiNnCtx,
errors: { errno => WasiNnError }
});
use types::Errno;
/// Wiggle generates code that performs some input validation on the arguments passed in by users of
/// wasi-nn. Here we convert the validation error into one (or more, eventually) of the error
/// variants defined in the witx.
impl types::GuestErrorConversion for WasiNnCtx {
fn into_errno(&self, e: wiggle::GuestError) -> Errno {
eprintln!("Guest error: {:?}", e);
Errno::InvalidArgument
}
}
impl<'a> types::UserErrorConversion for WasiNnCtx {
fn errno_from_wasi_nn_error(&self, e: WasiNnError) -> Errno {
eprintln!("Host error: {:?}", e);
match e {
WasiNnError::OpenvinoError(_) => unimplemented!(),
WasiNnError::GuestError(_) => unimplemented!(),
WasiNnError::UsageError(_) => unimplemented!(),
}
}
}
/// Additionally, we must let Wiggle know which of our error codes represents a successful operation.
impl wiggle::GuestErrorType for Errno {
fn success() -> Self {
Self::Success
}
}