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e53ab91439
Initial integration of WASI-NN based on #1225: - Implement the library core/iwasm/libraries/wasi-nn - Support TensorFlow, CPU, F32 at the first stage - Add cmake variable `-DWAMR_BUILD_WASI_NN` - Add test case based on Docker image and update document Refer to #1573
133 lines
3.9 KiB
C
133 lines
3.9 KiB
C
/*
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* Copyright (C) 2019 Intel Corporation. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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*/
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#ifndef WASI_NN_WASM_H
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#define WASI_NN_WASM_H
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#include "wasi_nn_common.h"
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/**
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* Following definition from:
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* [Aug 10th, 2022]
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* https://github.com/WebAssembly/wasi-nn/blob/e5e1a6c31f424c7cd63026cd270e9746775675a0/wasi-nn.wit.md
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*/
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/* The graph initialization data. */
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// This consists of an array of buffers because implementing backends may encode
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// their graph IR in parts (e.g., OpenVINO stores its IR and weights
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// separately).
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typedef struct {
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uint8_t *buf;
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uint32_t size;
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} graph_builder;
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typedef struct {
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graph_builder *buf;
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uint32_t size;
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} graph_builder_array;
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/* The dimensions of a tensor. */
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// The array length matches the tensor rank and each element in the array
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// describes the size of each dimension.
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typedef struct {
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uint32_t *buf;
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uint32_t size;
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} tensor_dimensions;
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/* The tensor data. */
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// Initially conceived as a sparse representation, each empty cell would be
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// filled with zeros and the array length must match the product of all of the
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// dimensions and the number of bytes in the type (e.g., a 2x2 tensor with
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// 4-byte f32 elements would have a data array of length 16). Naturally, this
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// representation requires some knowledge of how to lay out data in
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// memory--e.g., using row-major ordering--and could perhaps be improved.
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typedef uint8_t *tensor_data;
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/* A tensor. */
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typedef struct {
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// Describe the size of the tensor (e.g., 2x2x2x2 -> [2, 2, 2, 2]). To
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// represent a tensor containing a single value, use `[1]` for the tensor
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// dimensions.
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tensor_dimensions *dimensions;
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// Describe the type of element in the tensor (e.g., f32).
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tensor_type type;
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// Contains the tensor data.
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tensor_data data;
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} tensor;
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/**
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* @brief Load an opaque sequence of bytes to use for inference.
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*
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* @param builder Model builder.
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* @param encoding Model encoding.
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* @param target Execution target.
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* @param graph Graph.
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* @return error Execution status.
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*/
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error
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load(graph_builder_array *builder, graph_encoding encoding,
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execution_target target, graph *graph)
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__attribute__((export_module("wasi_nn")))
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__attribute__((import_module("wasi_nn")));
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/**
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* @brief Create an execution instance of a loaded graph.
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*
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* @param graph Graph.
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* @param ctx Execution context.
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* @return error Execution status.
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*/
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error
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init_execution_context(graph graph, graph_execution_context *ctx)
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__attribute__((export_module("wasi_nn")))
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__attribute__((import_module("wasi_nn")));
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/**
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* @brief Define the inputs to use for inference.
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*
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* @param ctx Execution context.
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* @param index Input tensor index.
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* @param tensor Input tensor.
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* @return error Execution status.
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*/
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error
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set_input(graph_execution_context ctx, uint32_t index, tensor *tensor)
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__attribute__((export_module("wasi_nn")))
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__attribute__((import_module("wasi_nn")));
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/**
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* @brief Compute the inference on the given inputs.
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*
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* @param ctx Execution context.
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* @return error Execution status.
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*/
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error
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compute(graph_execution_context ctx) __attribute__((export_module("wasi_nn")))
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__attribute__((import_module("wasi_nn")));
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/**
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* @brief Extract the outputs after inference.
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*
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* @param ctx Execution context.
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* @param index Output tensor index.
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* @param output_tensor Buffer where output tensor with index `index` is
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* copied.
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* @param output_tensor_size Pointer to `output_tensor` maximum size.
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* After the function call it is updated with the
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* copied number of bytes.
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* @return error Execution status.
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*/
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error
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get_output(graph_execution_context ctx, uint32_t index,
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tensor_data output_tensor, uint32_t *output_tensor_size)
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__attribute__((export_module("wasi_nn")))
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__attribute__((import_module("wasi_nn")));
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#endif
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