Integrate WASI-NN into WAMR (#1521)

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

.gitignore

@@ -1,6 +1,7 @@
 .cache
 .vs
 .vscode
+.venv
 /.idea
 **/cmake-build-*/
 **/*build/

build-scripts/config_common.cmake

@@ -291,3 +291,6 @@ if (WAMR_BUILD_SGX_IPFS EQUAL 1)
     add_definitions (-DWASM_ENABLE_SGX_IPFS=1)
     message ("     SGX IPFS enabled")
 endif ()
+if (WAMR_BUILD_WASI_NN EQUAL 1)
+  message ("     WASI-NN enabled")
+endif ()

build-scripts/runtime_lib.cmake

@@ -91,6 +91,19 @@ if (WAMR_BUILD_LIB_PTHREAD_SEMAPHORE EQUAL 1)
     set (WAMR_BUILD_LIB_PTHREAD 1)
 endif ()
 
+if (WAMR_BUILD_WASI_NN EQUAL 1)
+    execute_process(COMMAND ${WAMR_ROOT_DIR}/core/deps/install_tensorflow.sh
+                    RESULT_VARIABLE TENSORFLOW_RESULT
+    )
+    set(TENSORFLOW_SOURCE_DIR "${WAMR_ROOT_DIR}/core/deps/tensorflow-src")
+    include_directories (${CMAKE_CURRENT_BINARY_DIR}/flatbuffers/include)
+    include_directories (${TENSORFLOW_SOURCE_DIR})
+    add_subdirectory(
+        "${TENSORFLOW_SOURCE_DIR}/tensorflow/lite"
+        "${CMAKE_CURRENT_BINARY_DIR}/tensorflow-lite" EXCLUDE_FROM_ALL)
+    include (${IWASM_DIR}/libraries/wasi-nn/wasi_nn.cmake)
+endif ()
+
 if (WAMR_BUILD_LIB_PTHREAD EQUAL 1)
     include (${IWASM_DIR}/libraries/lib-pthread/lib_pthread.cmake)
     # Enable the dependent feature if lib pthread is enabled
@@ -152,6 +165,7 @@ set (source_all
     ${UTILS_SHARED_SOURCE}
     ${LIBC_BUILTIN_SOURCE}
     ${LIBC_WASI_SOURCE}
+    ${LIBC_WASI_NN_SOURCE}
     ${IWASM_COMMON_SOURCE}
     ${IWASM_INTERP_SOURCE}
     ${IWASM_AOT_SOURCE}

core/deps/install_tensorflow.sh

@@ -0,0 +1,11 @@
+#!/bin/sh
+
+DEPS_ROOT=$(cd "$(dirname "$0")/" && pwd)
+cd ${DEPS_ROOT}
+
+echo "Downloading tensorflow in ${PWD}..."
+
+git clone https://github.com/tensorflow/tensorflow.git tensorflow-src \
+    --branch v2.9.2
+
+exit 0

core/iwasm/common/wasm_native.c

@@ -33,6 +33,9 @@ get_spectest_export_apis(NativeSymbol **p_libc_builtin_apis);
 uint32
 get_libc_wasi_export_apis(NativeSymbol **p_libc_wasi_apis);
 
+uint32_t
+get_wasi_nn_export_apis(NativeSymbol **p_libc_wasi_apis);
+
 uint32
 get_base_lib_export_apis(NativeSymbol **p_base_lib_apis);
 
@@ -425,6 +428,13 @@ wasm_native_init()
         goto fail;
 #endif /* WASM_ENABLE_LIB_RATS */
 
+#if WASM_ENABLE_WASI_NN != 0
+    n_native_symbols = get_wasi_nn_export_apis(&native_symbols);
+    if (!wasm_native_register_natives("wasi_nn", native_symbols,
+                                      n_native_symbols))
+        return false;
+#endif
+
     return true;
 fail:
     wasm_native_destroy();

core/iwasm/libraries/wasi-nn/.dockerignore

@@ -0,0 +1 @@
+**/Dockerfile

core/iwasm/libraries/wasi-nn/README.md

@@ -0,0 +1,43 @@
+# WASI-NN
+
+## How to use
+
+Enable WASI-NN in the WAMR by spefiying it in the cmake building configuration as follows,
+
+```
+set (WAMR_BUILD_WASI_NN  1)
+```
+
+The definition of the functions provided by WASI-NN is in the header file `core/iwasm/libraries/wasi-nn/wasi_nn.h`.
+
+By only including this file in your WASM application you will bind WASI-NN into your module.
+
+## Tests
+
+To run the tests we assume that the current directory is the root of the repository.
+
+
+1. Build the docker image,
+
+```
+docker build -t wasi-nn -f core/iwasm/libraries/wasi-nn/test/Dockerfile .
+```
+
+2. Run the container
+
+```
+docker run wasi-nn
+```
+
+If all the tests have run properly you will the the following message in the terminal,
+
+```
+Tests: passed!
+```
+
+## What is missing
+
+* Only 1 model at a time is supported.
+    * `graph` and `graph-execution-context` are ignored.
+* Only `tensorflow` (lite) is supported.
+* Only `cpu` is supported.

core/iwasm/libraries/wasi-nn/logger.h

@@ -0,0 +1,55 @@
+/*
+ * Copyright (C) 2019 Intel Corporation.  All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+ */
+
+#ifndef WASI_NN_LOGGER_H
+#define WASI_NN_LOGGER_H
+
+#include <stdio.h>
+#include <string.h>
+
+#define __FILENAME__ \
+    (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__)
+
+/* Disable a level by removing the define */
+#define ENABLE_ERR_LOG
+#define ENABLE_WARN_LOG
+#define ENABLE_DBG_LOG
+#define ENABLE_INFO_LOG
+
+// Definition of the levels
+#ifdef ENABLE_ERR_LOG
+#define NN_ERR_PRINTF(fmt, ...)                                    \
+    printf("[%s:%d] " fmt, __FILENAME__, __LINE__, ##__VA_ARGS__); \
+    printf("\n");                                                  \
+    fflush(stdout)
+#else
+#define NN_ERR_PRINTF(fmt, ...)
+#endif
+#ifdef ENABLE_WARN_LOG
+#define NN_WARN_PRINTF(fmt, ...)                                   \
+    printf("[%s:%d] " fmt, __FILENAME__, __LINE__, ##__VA_ARGS__); \
+    printf("\n");                                                  \
+    fflush(stdout)
+#else
+#define NN_WARN_PRINTF(fmt, ...)
+#endif
+#ifdef ENABLE_DBG_LOG
+#define NN_DBG_PRINTF(fmt, ...)                                    \
+    printf("[%s:%d] " fmt, __FILENAME__, __LINE__, ##__VA_ARGS__); \
+    printf("\n");                                                  \
+    fflush(stdout)
+#else
+#define NN_DBG_PRINTF(fmt, ...)
+#endif
+#ifdef ENABLE_INFO_LOG
+#define NN_INFO_PRINTF(fmt, ...)                                   \
+    printf("[%s:%d] " fmt, __FILENAME__, __LINE__, ##__VA_ARGS__); \
+    printf("\n");                                                  \
+    fflush(stdout)
+#else
+#define NN_INFO_PRINTF(fmt, ...)
+#endif
+
+#endif

core/iwasm/libraries/wasi-nn/test/CMakeLists.txt

@@ -0,0 +1,178 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+cmake_minimum_required (VERSION 2.9)
+
+project (iwasm)
+
+set (CMAKE_VERBOSE_MAKEFILE OFF)
+# Reset default linker flags
+set (CMAKE_SHARED_LIBRARY_LINK_C_FLAGS "")
+set (CMAKE_SHARED_LIBRARY_LINK_CXX_FLAGS "")
+set (CMAKE_C_STANDARD 99)
+set (CMAKE_CXX_STANDARD 14)
+
+if (NOT DEFINED WAMR_BUILD_PLATFORM)
+  set (WAMR_BUILD_PLATFORM "linux")
+endif ()
+
+# Set WAMR_BUILD_TARGET, currently values supported:
+# "X86_64", "AMD_64", "X86_32", "AARCH64[sub]", "ARM[sub]", "THUMB[sub]",
+# "MIPS", "XTENSA", "RISCV64[sub]", "RISCV32[sub]"
+if (NOT DEFINED WAMR_BUILD_TARGET)
+  if (CMAKE_SYSTEM_PROCESSOR MATCHES "^(arm64|aarch64)")
+    set (WAMR_BUILD_TARGET "AARCH64")
+  elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "riscv64")
+    set (WAMR_BUILD_TARGET "RISCV64")
+  elseif (CMAKE_SIZEOF_VOID_P EQUAL 8)
+    # Build as X86_64 by default in 64-bit platform
+    set (WAMR_BUILD_TARGET "X86_64")
+  elseif (CMAKE_SIZEOF_VOID_P EQUAL 4)
+    # Build as X86_32 by default in 32-bit platform
+    set (WAMR_BUILD_TARGET "X86_32")
+  else ()
+    message(SEND_ERROR "Unsupported build target platform!")
+  endif ()
+endif ()
+
+if (NOT CMAKE_BUILD_TYPE)
+  set(CMAKE_BUILD_TYPE Release)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_INTERP)
+  # Enable Interpreter by default
+  set (WAMR_BUILD_INTERP 1)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_AOT)
+  # Enable AOT by default.
+  set (WAMR_BUILD_AOT 1)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_JIT)
+  # Disable JIT by default.
+  set (WAMR_BUILD_JIT 0)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_FAST_JIT)
+  # Disable Fast JIT by default
+  set (WAMR_BUILD_FAST_JIT 0)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_LIBC_BUILTIN)
+  # Enable libc builtin support by default
+  set (WAMR_BUILD_LIBC_BUILTIN 1)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_LIBC_WASI)
+  # Enable libc wasi support by default
+  set (WAMR_BUILD_LIBC_WASI 1)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_FAST_INTERP)
+  # Enable fast interpreter
+  set (WAMR_BUILD_FAST_INTERP 1)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_MULTI_MODULE)
+  # Disable multiple modules by default
+  set (WAMR_BUILD_MULTI_MODULE 0)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_LIB_PTHREAD)
+  # Disable pthread library by default
+  set (WAMR_BUILD_LIB_PTHREAD 0)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_MINI_LOADER)
+  # Disable wasm mini loader by default
+  set (WAMR_BUILD_MINI_LOADER 0)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_SIMD)
+  # Enable SIMD by default
+  set (WAMR_BUILD_SIMD 1)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_REF_TYPES)
+  # Disable reference types by default
+  set (WAMR_BUILD_REF_TYPES 0)
+endif ()
+
+if (NOT DEFINED WAMR_BUILD_DEBUG_INTERP)
+  # Disable Debug feature by default
+  set (WAMR_BUILD_DEBUG_INTERP 0)
+endif ()
+
+if (WAMR_BUILD_DEBUG_INTERP EQUAL 1)
+  set (WAMR_BUILD_FAST_INTERP 0)
+  set (WAMR_BUILD_MINI_LOADER 0)
+  set (WAMR_BUILD_SIMD 0)
+endif ()
+
+if (COLLECT_CODE_COVERAGE EQUAL 1)
+  set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fprofile-arcs -ftest-coverage")
+  set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage")
+endif ()
+
+set (WAMR_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../../../../..)
+
+include (${WAMR_ROOT_DIR}/build-scripts/runtime_lib.cmake)
+add_library(vmlib ${WAMR_RUNTIME_LIB_SOURCE})
+
+set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--gc-sections -pie -fPIE")
+
+set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -Wextra -Wformat -Wformat-security -Wshadow")
+# set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wconversion -Wsign-conversion")
+
+set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wextra -Wformat -Wformat-security -Wno-unused")
+
+if (WAMR_BUILD_TARGET MATCHES "X86_.*" OR WAMR_BUILD_TARGET STREQUAL "AMD_64")
+  if (NOT (CMAKE_C_COMPILER MATCHES ".*clang.*" OR CMAKE_C_COMPILER_ID MATCHES ".*Clang"))
+    set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mindirect-branch-register")
+    set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mindirect-branch-register")
+    # UNDEFINED BEHAVIOR, refer to https://en.cppreference.com/w/cpp/language/ub
+    if(CMAKE_BUILD_TYPE STREQUAL "Debug" AND NOT WAMR_BUILD_JIT EQUAL 1)
+      set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fsanitize=undefined \
+                                          -fno-sanitize=bounds,bounds-strict,alignment \
+                                          -fno-sanitize-recover")
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined \
+                                              -fno-sanitize=bounds,bounds-strict,alignment \
+                                              -fno-sanitize-recover")
+    endif()
+  else ()
+    # UNDEFINED BEHAVIOR, refer to https://en.cppreference.com/w/cpp/language/ub
+    if(CMAKE_BUILD_TYPE STREQUAL "Debug" AND NOT WAMR_BUILD_JIT EQUAL 1)
+      set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fsanitize=undefined \
+                                          -fno-sanitize=bounds,alignment \
+                                          -fno-sanitize-recover")
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined \
+                                              -fno-sanitize=bounds,alignment \
+                                              -fno-sanitize-recover")
+    endif()
+  endif ()
+endif ()
+
+# The following flags are to enhance security, but it may impact performance,
+# we disable them by default.
+#if (WAMR_BUILD_TARGET MATCHES "X86_.*" OR WAMR_BUILD_TARGET STREQUAL "AMD_64")
+#  set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -ftrapv -D_FORTIFY_SOURCE=2")
+#endif ()
+#set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fstack-protector-strong --param ssp-buffer-size=4")
+#set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wl,-z,noexecstack,-z,relro,-z,now")
+
+include (${SHARED_DIR}/utils/uncommon/shared_uncommon.cmake)
+
+add_executable (iwasm ${WAMR_ROOT_DIR}/product-mini/platforms/${WAMR_BUILD_PLATFORM}/main.c ${UNCOMMON_SHARED_SOURCE})
+
+install (TARGETS iwasm DESTINATION bin)
+
+target_link_libraries (iwasm vmlib ${LLVM_AVAILABLE_LIBS} ${UV_A_LIBS} ${TENSORFLOW_LIB} -lm -ldl -lpthread)
+
+add_library (libiwasm SHARED ${WAMR_RUNTIME_LIB_SOURCE})
+
+install (TARGETS libiwasm DESTINATION lib)
+
+set_target_properties (libiwasm PROPERTIES OUTPUT_NAME iwasm)
+
+target_link_libraries (libiwasm ${LLVM_AVAILABLE_LIBS} ${UV_A_LIBS} -lm -ldl -lpthread)

core/iwasm/libraries/wasi-nn/test/Dockerfile

@@ -0,0 +1,32 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+FROM ubuntu:22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && apt-get install -y \
+    cmake build-essential git wget python3.10 python3-pip
+
+RUN wget -q https://github.com/WebAssembly/wasi-sdk/releases/download/wasi-sdk-14/wasi-sdk-14.0-linux.tar.gz && \
+    tar xf wasi-sdk-*-linux.tar.gz -C /opt && rm -f wasi-sdk-*-linux.tar.gz && \
+    mv /opt/wasi-sdk-14.0 /opt/wasi-sdk
+
+WORKDIR /home/wamr
+
+COPY core core
+COPY build-scripts build-scripts
+COPY product-mini product-mini
+
+RUN pip3 install -r core/iwasm/libraries/wasi-nn/test/requirements.txt
+
+WORKDIR /home/wamr/core/iwasm/libraries/wasi-nn/test/build
+
+RUN cmake -DWAMR_BUILD_WASI_NN=1 ..
+RUN make -j $(grep -c ^processor /proc/cpuinfo)
+
+WORKDIR /home/wamr/core/iwasm/libraries/wasi-nn/test
+
+RUN ./build.sh
+
+ENTRYPOINT [ "./build/iwasm",  "--dir=.", "test_tensorflow.wasm" ]

core/iwasm/libraries/wasi-nn/test/build.sh

@@ -0,0 +1,20 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+# WASM application that uses WASI-NN
+
+/opt/wasi-sdk/bin/clang \
+    -Wl,--allow-undefined \
+    -Wl,--strip-all,--no-entry \
+    --sysroot=/opt/wasi-sdk/share/wasi-sysroot \
+    -I/home/wamr/core/iwasm/libraries/wasi-nn \
+    -o test_tensorflow.wasm test_tensorflow.c
+
+# TFLite models to use in the tests
+
+cd models
+python3 average.py
+python3 max.py
+python3 mult_dimension.py
+python3 mult_outputs.py
+python3 sum.py

core/iwasm/libraries/wasi-nn/test/models/average.py

@@ -0,0 +1,16 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+from utils import save_model
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[5, 5, 1]),
+    tf.keras.layers.AveragePooling2D(
+        pool_size=(5, 5), strides=None, padding="valid", data_format=None)
+
+])
+
+# Export model to tflite
+
+save_model(model, "average.tflite")

core/iwasm/libraries/wasi-nn/test/models/max.py

@@ -0,0 +1,17 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+
+from utils import save_model
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[5, 5, 1]),
+    tf.keras.layers.MaxPooling2D(
+        pool_size=(5, 5), strides=None, padding="valid", data_format=None)
+
+])
+
+# Export model to tflite
+
+save_model(model, "max.tflite")

core/iwasm/libraries/wasi-nn/test/models/mult_dimension.py

@@ -0,0 +1,15 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+from utils import save_model
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[3, 3, 1]),
+    tf.keras.layers.Conv2D(1, (1, 1), kernel_initializer=tf.keras.initializers.Constant(
+        value=1), bias_initializer='zeros'
+    )
+])
+# Export model to tflite
+
+save_model(model, "mult_dim.tflite")

core/iwasm/libraries/wasi-nn/test/models/mult_outputs.py

@@ -0,0 +1,33 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+import numpy as np
+from keras.layers import AveragePooling2D, Conv2D
+
+from tensorflow.keras import Input, Model
+
+from utils import save_model
+
+
+inputs = Input(shape=(4, 4, 1))
+
+output1 = Conv2D(1, (4, 1), kernel_initializer=tf.keras.initializers.Constant(
+    value=1), bias_initializer='zeros'
+)(inputs)
+output2 = AveragePooling2D(pool_size=(
+    4, 1), strides=None, padding="valid", data_format=None)(inputs)
+
+model = Model(inputs=inputs, outputs=[output1, output2])
+
+inp = np.arange(16).reshape((1, 4, 4, 1))
+
+print(inp)
+
+res = model.predict(inp)
+
+print(res)
+print(res[0].shape)
+print(res[1].shape)
+
+save_model(model, "mult_out.tflite")

core/iwasm/libraries/wasi-nn/test/models/sum.py

@@ -0,0 +1,17 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+
+from utils import save_model
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[5, 5, 1]),
+    tf.keras.layers.Conv2D(1, (5, 5), kernel_initializer=tf.keras.initializers.Constant(
+        value=1), bias_initializer='zeros'
+    )
+])
+
+# Export model to tflite
+
+save_model(model, "sum.tflite")

core/iwasm/libraries/wasi-nn/test/models/utils.py

@@ -0,0 +1,13 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+import pathlib
+
+
+def save_model(model, filename):
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    tflite_model = converter.convert()
+    tflite_models_dir = pathlib.Path("./")
+    tflite_model_file = tflite_models_dir/filename
+    tflite_model_file.write_bytes(tflite_model)

core/iwasm/libraries/wasi-nn/test/requirements.txt

@@ -0,0 +1 @@
+tensorflow==2.10.0

core/iwasm/libraries/wasi-nn/test/test_tensorflow.c

@@ -0,0 +1,301 @@
+/*
+ * Copyright (C) 2019 Intel Corporation.  All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <stdint.h>
+#include <math.h>
+#include <assert.h>
+#include "wasi_nn.h"
+
+#include <fcntl.h>
+#include <errno.h>
+
+#define MAX_MODEL_SIZE 85000000
+#define MAX_OUTPUT_TENSOR_SIZE 200
+#define INPUT_TENSOR_DIMS 4
+#define EPSILON 1e-8
+
+typedef struct {
+    float *input_tensor;
+    uint32_t *dim;
+    uint32_t elements;
+} input_info;
+
+// WASI-NN wrappers
+
+error
+wasm_load(char *model_name, graph *graph)
+{
+    FILE *pFile = fopen(model_name, "r");
+    if (pFile == NULL)
+        return invalid_argument;
+
+    uint8_t *buffer;
+    size_t result;
+
+    // allocate memory to contain the whole file:
+    buffer = (uint8_t *)malloc(sizeof(uint8_t) * MAX_MODEL_SIZE);
+    if (buffer == NULL) {
+        fclose(pFile);
+        return missing_memory;
+    }
+
+    result = fread(buffer, 1, MAX_MODEL_SIZE, pFile);
+    if (result <= 0) {
+        fclose(pFile);
+        free(buffer);
+        return missing_memory;
+    }
+
+    graph_builder_array arr;
+
+    arr.size = 1;
+    arr.buf = (graph_builder *)malloc(sizeof(graph_builder));
+    if (arr.buf == NULL) {
+        fclose(pFile);
+        free(buffer);
+        return missing_memory;
+    }
+
+    arr.buf[0].size = result;
+    arr.buf[0].buf = buffer;
+
+    error res = load(&arr, tensorflow, cpu, graph);
+
+    fclose(pFile);
+    free(buffer);
+    free(arr.buf);
+    return res;
+}
+
+error
+wasm_init_execution_context(graph graph, graph_execution_context *ctx)
+{
+    return init_execution_context(graph, ctx);
+}
+
+error
+wasm_input(graph_execution_context ctx, float *input_tensor, uint32_t *dim)
+{
+    tensor_dimensions dims;
+    dims.size = INPUT_TENSOR_DIMS;
+    dims.buf = (uint32_t *)malloc(dims.size * sizeof(uint32_t));
+    if (dims.buf == NULL)
+        return missing_memory;
+
+    tensor tensor;
+    tensor.dimensions = &dims;
+    for (int i = 0; i < tensor.dimensions->size; ++i)
+        tensor.dimensions->buf[i] = dim[i];
+    tensor.type = fp32;
+    tensor.data = (uint8_t *)input_tensor;
+    error err = set_input(ctx, 0, &tensor);
+
+    free(dims.buf);
+    return err;
+}
+
+error
+wasm_compute(graph_execution_context ctx)
+{
+    return compute(ctx);
+}
+
+error
+wasm_get_output(graph_execution_context ctx, uint32_t index, float *out_tensor,
+                uint32_t *out_size)
+{
+    return get_output(ctx, index, (uint8_t *)out_tensor, out_size);
+}
+
+// Inference
+
+float *
+run_inference(float *input, uint32_t *input_size, uint32_t *output_size,
+              char *model_name, uint32_t num_output_tensors)
+{
+    graph graph;
+    if (wasm_load(model_name, &graph) != success) {
+        fprintf(stderr, "Error when loading model.");
+        exit(1);
+    }
+
+    graph_execution_context ctx;
+    if (wasm_init_execution_context(graph, &ctx) != success) {
+        fprintf(stderr, "Error when initialixing execution context.");
+        exit(1);
+    }
+
+    if (wasm_input(ctx, input, input_size) != success) {
+        fprintf(stderr, "Error when setting input tensor.");
+        exit(1);
+    }
+
+    if (wasm_compute(ctx) != success) {
+        fprintf(stderr, "Error when running inference.");
+        exit(1);
+    }
+
+    float *out_tensor = (float *)malloc(sizeof(float) * MAX_OUTPUT_TENSOR_SIZE);
+    if (out_tensor == NULL) {
+        fprintf(stderr, "Error when allocating memory for output tensor.");
+        exit(1);
+    }
+
+    uint32_t offset = 0;
+    for (int i = 0; i < num_output_tensors; ++i) {
+        *output_size = MAX_OUTPUT_TENSOR_SIZE - *output_size;
+        if (wasm_get_output(ctx, i, &out_tensor[offset], output_size)
+            != success) {
+            fprintf(stderr, "Error when getting input .");
+            exit(1);
+        }
+
+        offset += *output_size;
+    }
+    *output_size = offset;
+    return out_tensor;
+}
+
+// UTILS
+
+input_info
+create_input(int *dims)
+{
+    input_info input = { .dim = NULL, .input_tensor = NULL, .elements = 1 };
+
+    input.dim = malloc(INPUT_TENSOR_DIMS * sizeof(uint32_t));
+    if (input.dim)
+        for (int i = 0; i < INPUT_TENSOR_DIMS; ++i) {
+            input.dim[i] = dims[i];
+            input.elements *= dims[i];
+        }
+
+    input.input_tensor = malloc(input.elements * sizeof(float));
+    for (int i = 0; i < input.elements; ++i)
+        input.input_tensor[i] = i;
+
+    return input;
+}
+
+// TESTS
+
+void
+test_sum()
+{
+    int dims[] = { 1, 5, 5, 1 };
+    input_info input = create_input(dims);
+
+    uint32_t output_size = 0;
+    float *output = run_inference(input.input_tensor, input.dim, &output_size,
+                                  "models/sum.tflite", 1);
+
+    assert(output_size == 1);
+    assert(fabs(output[0] - 300.0) < EPSILON);
+
+    free(input.dim);
+    free(input.input_tensor);
+    free(output);
+}
+
+void
+test_max()
+{
+    int dims[] = { 1, 5, 5, 1 };
+    input_info input = create_input(dims);
+
+    uint32_t output_size = 0;
+    float *output = run_inference(input.input_tensor, input.dim, &output_size,
+                                  "models/max.tflite", 1);
+
+    assert(output_size == 1);
+    assert(fabs(output[0] - 24.0) < EPSILON);
+    printf("Result: max is %f\n", output[0]);
+
+    free(input.dim);
+    free(input.input_tensor);
+    free(output);
+}
+
+void
+test_average()
+{
+    int dims[] = { 1, 5, 5, 1 };
+    input_info input = create_input(dims);
+
+    uint32_t output_size = 0;
+    float *output = run_inference(input.input_tensor, input.dim, &output_size,
+                                  "models/average.tflite", 1);
+
+    assert(output_size == 1);
+    assert(fabs(output[0] - 12.0) < EPSILON);
+    printf("Result: average is %f\n", output[0]);
+
+    free(input.dim);
+    free(input.input_tensor);
+    free(output);
+}
+
+void
+test_mult_dimensions()
+{
+    int dims[] = { 1, 3, 3, 1 };
+    input_info input = create_input(dims);
+
+    uint32_t output_size = 0;
+    float *output = run_inference(input.input_tensor, input.dim, &output_size,
+                                  "models/mult_dim.tflite", 1);
+
+    assert(output_size == 9);
+    for (int i = 0; i < 9; i++)
+        assert(fabs(output[i] - i) < EPSILON);
+
+    free(input.dim);
+    free(input.input_tensor);
+    free(output);
+}
+
+void
+test_mult_outputs()
+{
+    int dims[] = { 1, 4, 4, 1 };
+    input_info input = create_input(dims);
+
+    uint32_t output_size = 0;
+    float *output = run_inference(input.input_tensor, input.dim, &output_size,
+                                  "models/mult_out.tflite", 2);
+
+    assert(output_size == 8);
+    // first tensor check
+    for (int i = 0; i < 4; i++)
+        assert(fabs(output[i] - (i * 4 + 24)) < EPSILON);
+    // second tensor check
+    for (int i = 0; i < 4; i++)
+        assert(fabs(output[i + 4] - (i + 6)) < EPSILON);
+
+    free(input.dim);
+    free(input.input_tensor);
+    free(output);
+}
+
+int
+main()
+{
+    printf("################### Testing sum...\n");
+    test_sum();
+    printf("################### Testing max...\n");
+    test_max();
+    printf("################### Testing average...\n");
+    test_average();
+    printf("################### Testing multiple dimensions...\n");
+    test_mult_dimensions();
+    printf("################### Testing multiple outputs...\n");
+    test_mult_outputs();
+
+    printf("Tests: passed!\n");
+    return 0;
+}

core/iwasm/libraries/wasi-nn/wasi_nn.cmake

@@ -0,0 +1,10 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+set (WASI_NN_DIR ${CMAKE_CURRENT_LIST_DIR})
+
+add_definitions (-DWASM_ENABLE_WASI_NN=1)
+
+set (LIBC_WASI_NN_SOURCE ${WASI_NN_DIR}/wasi_nn_native.c ${WASI_NN_DIR}/wasi_nn_tensorflow.cpp)
+
+set (TENSORFLOW_LIB tensorflow-lite)

core/iwasm/libraries/wasi-nn/wasi_nn.h

@@ -0,0 +1,132 @@
+/*
+ * Copyright (C) 2019 Intel Corporation.  All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+ */
+
+#ifndef WASI_NN_WASM_H
+#define WASI_NN_WASM_H
+
+#include "wasi_nn_common.h"
+
+/**
+ * Following definition from:
+ * [Aug 10th, 2022]
+ * https://github.com/WebAssembly/wasi-nn/blob/e5e1a6c31f424c7cd63026cd270e9746775675a0/wasi-nn.wit.md
+ */
+
+/* The graph initialization data. */
+
+// This consists of an array of buffers because implementing backends may encode
+// their graph IR in parts (e.g., OpenVINO stores its IR and weights
+// separately).
+typedef struct {
+    uint8_t *buf;
+    uint32_t size;
+} graph_builder;
+
+typedef struct {
+    graph_builder *buf;
+    uint32_t size;
+} graph_builder_array;
+
+/* The dimensions of a tensor. */
+
+// The array length matches the tensor rank and each element in the array
+// describes the size of each dimension.
+typedef struct {
+    uint32_t *buf;
+    uint32_t size;
+} tensor_dimensions;
+
+/* The tensor data. */
+
+// Initially conceived as a sparse representation, each empty cell would be
+// filled with zeros and the array length must match the product of all of the
+// dimensions and the number of bytes 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.
+typedef uint8_t *tensor_data;
+
+/* A tensor. */
+
+typedef struct {
+    // 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.
+    tensor_dimensions *dimensions;
+    // Describe the type of element in the tensor (e.g., f32).
+    tensor_type type;
+    // Contains the tensor data.
+    tensor_data data;
+} tensor;
+
+/**
+ * @brief Load an opaque sequence of bytes to use for inference.
+ *
+ * @param builder   Model builder.
+ * @param encoding  Model encoding.
+ * @param target    Execution target.
+ * @param graph     Graph.
+ * @return error    Execution status.
+ */
+error
+load(graph_builder_array *builder, graph_encoding encoding,
+     execution_target target, graph *graph)
+    __attribute__((export_module("wasi_nn")))
+    __attribute__((import_module("wasi_nn")));
+
+/**
+ * @brief Create an execution instance of a loaded graph.
+ *
+ * @param graph     Graph.
+ * @param ctx       Execution context.
+ * @return error    Execution status.
+ */
+error
+init_execution_context(graph graph, graph_execution_context *ctx)
+    __attribute__((export_module("wasi_nn")))
+    __attribute__((import_module("wasi_nn")));
+
+/**
+ * @brief Define the inputs to use for inference.
+ *
+ * @param ctx       Execution context.
+ * @param index     Input tensor index.
+ * @param tensor    Input tensor.
+ * @return error    Execution status.
+ */
+error
+set_input(graph_execution_context ctx, uint32_t index, tensor *tensor)
+    __attribute__((export_module("wasi_nn")))
+    __attribute__((import_module("wasi_nn")));
+
+/**
+ * @brief Compute the inference on the given inputs.
+ *
+ * @param ctx       Execution context.
+ * @return error    Execution status.
+ */
+error
+compute(graph_execution_context ctx) __attribute__((export_module("wasi_nn")))
+__attribute__((import_module("wasi_nn")));
+
+/**
+ * @brief Extract the outputs after inference.
+ *
+ * @param ctx                   Execution context.
+ * @param index                 Output tensor index.
+ * @param output_tensor         Buffer where output tensor with index `index` is
+ * copied.
+ * @param output_tensor_size    Pointer to `output_tensor` maximum size.
+ *                              After the function call it is updated with the
+ * copied number of bytes.
+ * @return error                Execution status.
+ */
+error
+get_output(graph_execution_context ctx, uint32_t index,
+           tensor_data output_tensor, uint32_t *output_tensor_size)
+    __attribute__((export_module("wasi_nn")))
+    __attribute__((import_module("wasi_nn")));
+
+#endif

core/iwasm/libraries/wasi-nn/wasi_nn_common.h

@@ -0,0 +1,44 @@
+/*
+ * Copyright (C) 2019 Intel Corporation.  All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+ */
+
+#ifndef WASI_NN_COMMON_H
+#define WASI_NN_COMMON_H
+
+#include <stdint.h>
+
+// The type of the elements in a tensor.
+typedef enum { fp16 = 0, fp32, up8, ip32 } tensor_type;
+
+// 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.
+typedef enum { openvino = 0, onnx, tensorflow, pytorch } graph_encoding;
+
+// Define where the graph should be executed.
+typedef enum { cpu = 0, gpu, tpu } execution_target;
+
+// Error codes returned by functions in this API.
+typedef enum {
+    // No error occurred.
+    success = 0,
+    // Caller module passed an invalid argument.
+    invalid_argument,
+    // Invalid encoding.
+    invalid_encoding,
+    // Caller module is missing a memory export.
+    missing_memory,
+    // Device or resource busy.
+    busy,
+    // Runtime Error.
+    runtime_error,
+} error;
+
+// An execution graph for performing inference (i.e., a model).
+typedef uint32_t graph;
+
+// Bind a `graph` to the input and output tensors for an inference.
+typedef uint32_t graph_execution_context;
+
+#endif

core/iwasm/libraries/wasi-nn/wasi_nn_native.c

@@ -0,0 +1,264 @@
+/*
+ * Copyright (C) 2019 Intel Corporation.  All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+ */
+
+#include <stdio.h>
+#include <assert.h>
+#include <errno.h>
+#include <string.h>
+#include <stdlib.h>
+
+#include "wasi_nn_common.h"
+#include "wasm_export.h"
+#include "bh_platform.h"
+
+#include "wasi_nn.h"
+#include "wasi_nn_tensorflow.hpp"
+#include "logger.h"
+
+/* Definition of 'wasi_nn.h' structs in WASM app format (using offset) */
+
+typedef struct {
+    uint32_t buf_offset;
+    uint32_t size;
+} graph_builder_wasm;
+
+typedef struct {
+    uint32_t buf_offset;
+    uint32_t size;
+} graph_builder_array_wasm;
+
+typedef struct {
+    uint32_t dimensions_offset;
+    tensor_type type;
+    uint32_t data_offset;
+} tensor_wasm;
+
+typedef struct {
+    uint32_t buf_offset;
+    uint32_t size;
+} tensor_dimensions_wasm;
+
+/* Global variables */
+
+static uint8_t _is_initialized;
+static graph_encoding _encoding;
+
+/* Utils */
+
+static error
+check_initialized()
+{
+    if (!_is_initialized) {
+        NN_ERR_PRINTF("Model not initialized.");
+        return invalid_argument;
+    }
+    if (_encoding != tensorflow) {
+        NN_ERR_PRINTF("Model encoding is not tensorflow.");
+        return invalid_argument;
+    }
+    return success;
+}
+
+/* WASI-NN implementation */
+
+error
+wasi_nn_load(wasm_exec_env_t exec_env, graph_builder_array_wasm *builder,
+             graph_encoding encoding, execution_target target, graph *graph)
+{
+    NN_DBG_PRINTF("Running wasi_nn_load [encoding=%d, target=%d]...", encoding,
+                  target);
+
+    wasm_module_inst_t instance = wasm_runtime_get_module_inst(exec_env);
+    bh_assert(instance);
+
+    if (!wasm_runtime_validate_native_addr(instance, builder,
+                                           sizeof(graph_builder_array_wasm)))
+        return invalid_argument;
+
+    if (!wasm_runtime_validate_app_addr(instance, builder->buf_offset,
+                                        builder->size * sizeof(uint32_t)))
+        return invalid_argument;
+
+    NN_DBG_PRINTF("Graph builder array contains %d elements", builder->size);
+
+    graph_builder_wasm *gb_wasm =
+        (graph_builder_wasm *)wasm_runtime_addr_app_to_native(
+            instance, builder->buf_offset);
+
+    graph_builder *gb_native = (graph_builder *)wasm_runtime_malloc(
+        builder->size * sizeof(graph_builder));
+    if (gb_native == NULL)
+        return missing_memory;
+
+    for (int i = 0; i < builder->size; ++i) {
+        if (!wasm_runtime_validate_app_addr(instance, gb_wasm[i].buf_offset,
+                                            gb_wasm[i].size
+                                                * sizeof(uint8_t))) {
+            wasm_runtime_free(gb_native);
+            return invalid_argument;
+        }
+
+        gb_native[i].buf = (uint8_t *)wasm_runtime_addr_app_to_native(
+            instance, gb_wasm[i].buf_offset);
+        gb_native[i].size = gb_wasm[i].size;
+
+        NN_DBG_PRINTF("Graph builder %d contains %d elements", i,
+                      gb_wasm[i].size);
+    }
+
+    graph_builder_array gba_native = { .buf = gb_native,
+                                       .size = builder->size };
+
+    if (!wasm_runtime_validate_native_addr(instance, graph, sizeof(graph))) {
+        wasm_runtime_free(gb_native);
+        return invalid_argument;
+    }
+
+    switch (encoding) {
+        case tensorflow:
+            break;
+        default:
+            NN_ERR_PRINTF("Only tensorflow is supported.");
+            wasm_runtime_free(gb_native);
+            return invalid_argument;
+    }
+
+    _encoding = encoding;
+    _is_initialized = 1;
+
+    error res = tensorflow_load(gba_native, _encoding, target, graph);
+    NN_DBG_PRINTF("wasi_nn_load finished with status %d [graph=%d]", res,
+                  *graph);
+
+    wasm_runtime_free(gb_native);
+    return res;
+}
+
+error
+wasi_nn_init_execution_context(wasm_exec_env_t exec_env, graph graph,
+                               graph_execution_context *ctx)
+{
+    NN_DBG_PRINTF("Running wasi_nn_init_execution_context [graph=%d]...",
+                  graph);
+    error res;
+    if (success != (res = check_initialized()))
+        return res;
+    res = tensorflow_init_execution_context(graph);
+    *ctx = graph;
+    NN_DBG_PRINTF(
+        "wasi_nn_init_execution_context finished with status %d [ctx=%d]", res,
+        *ctx);
+    return res;
+}
+
+error
+wasi_nn_set_input(wasm_exec_env_t exec_env, graph_execution_context ctx,
+                  uint32_t index, tensor_wasm *input_tensor)
+{
+    NN_DBG_PRINTF("Running wasi_nn_set_input [ctx=%d, index=%d]...", ctx,
+                  index);
+
+    error res;
+    if (success != (res = check_initialized()))
+        return res;
+
+    wasm_module_inst_t instance = wasm_runtime_get_module_inst(exec_env);
+    bh_assert(instance);
+
+    if (!wasm_runtime_validate_native_addr(instance, input_tensor,
+                                           sizeof(tensor_wasm)))
+        return invalid_argument;
+
+    if (!wasm_runtime_validate_app_addr(
+            instance, input_tensor->dimensions_offset, sizeof(uint32_t)))
+        return invalid_argument;
+
+    tensor_dimensions_wasm *dimensions_w =
+        (tensor_dimensions_wasm *)wasm_runtime_addr_app_to_native(
+            instance, input_tensor->dimensions_offset);
+
+    if (!wasm_runtime_validate_app_addr(instance, dimensions_w->buf_offset,
+                                        dimensions_w->size * sizeof(uint32_t)))
+        return invalid_argument;
+
+    tensor_dimensions dimensions = {
+        .buf = (uint32_t *)wasm_runtime_addr_app_to_native(
+            instance, dimensions_w->buf_offset),
+        .size = dimensions_w->size
+    };
+
+    NN_DBG_PRINTF("Number of dimensions: %d", dimensions.size);
+    int total_elements = 1;
+    for (int i = 0; i < dimensions.size; ++i) {
+        NN_DBG_PRINTF("Dimension %d: %d", i, dimensions.buf[i]);
+        total_elements *= dimensions.buf[i];
+    }
+    NN_DBG_PRINTF("Tensor type: %d", input_tensor->type);
+
+    if (!wasm_runtime_validate_app_addr(instance, input_tensor->data_offset,
+                                        total_elements))
+        return invalid_argument;
+
+    tensor tensor = { .type = input_tensor->type,
+                      .dimensions = &dimensions,
+                      .data = (uint8_t *)wasm_runtime_addr_app_to_native(
+                          instance, input_tensor->data_offset) };
+
+    res = tensorflow_set_input(ctx, index, &tensor);
+    NN_DBG_PRINTF("wasi_nn_set_input finished with status %d", res);
+    return res;
+}
+
+error
+wasi_nn_compute(wasm_exec_env_t exec_env, graph_execution_context ctx)
+{
+    NN_DBG_PRINTF("Running wasi_nn_compute [ctx=%d]...", ctx);
+    error res;
+    if (success != (res = check_initialized()))
+        return res;
+
+    res = tensorflow_compute(ctx);
+    NN_DBG_PRINTF("wasi_nn_compute finished with status %d", res);
+    return res;
+}
+
+error
+wasi_nn_get_output(wasm_exec_env_t exec_env, graph_execution_context ctx,
+                   uint32_t index, tensor_data output_tensor,
+                   uint32_t *output_tensor_size)
+{
+    NN_DBG_PRINTF("Running wasi_nn_get_output [ctx=%d, index=%d]...", ctx,
+                  index);
+    error res;
+    if (success != (res = check_initialized()))
+        return res;
+
+    res = tensorflow_get_output(ctx, index, output_tensor, output_tensor_size);
+    NN_DBG_PRINTF("wasi_nn_get_output finished with status %d [data_size=%d]",
+                  res, *output_tensor_size);
+    return res;
+}
+
+/* Register WASI-NN in WAMR */
+
+/* clang-format off */
+#define REG_NATIVE_FUNC(func_name, signature) \
+    { #func_name, wasi_nn_##func_name, signature, NULL }
+/* clang-format on */
+
+static NativeSymbol native_symbols_wasi_nn[] = {
+    REG_NATIVE_FUNC(load, "(*ii*)i"),
+    REG_NATIVE_FUNC(init_execution_context, "(i*)i"),
+    REG_NATIVE_FUNC(set_input, "(ii*)i"),
+    REG_NATIVE_FUNC(compute, "(i)i"),
+    REG_NATIVE_FUNC(get_output, "(ii**)i"),
+};
+
+uint32_t
+get_wasi_nn_export_apis(NativeSymbol **p_libc_wasi_apis)
+{
+    *p_libc_wasi_apis = native_symbols_wasi_nn;
+    return sizeof(native_symbols_wasi_nn) / sizeof(NativeSymbol);
+}

core/iwasm/libraries/wasi-nn/wasi_nn_tensorflow.cpp

@@ -0,0 +1,188 @@
+/*
+ * Copyright (C) 2019 Intel Corporation.  All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+ */
+
+#include "wasi_nn_tensorflow.hpp"
+#include "wasi_nn_common.h"
+#include "bh_common.h"
+#include "bh_platform.h"
+#include "platform_common.h"
+
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <tensorflow/lite/optional_debug_tools.h>
+#include <tensorflow/lite/error_reporter.h>
+
+/* Global variables */
+
+static std::unique_ptr<tflite::Interpreter> interpreter;
+static std::unique_ptr<tflite::FlatBufferModel> model;
+
+static char *model_pointer = NULL;
+
+/* WASI-NN (tensorflow) implementation */
+
+error
+tensorflow_load(graph_builder_array builder, graph_encoding encoding,
+                execution_target target, graph *graph)
+{
+    if (model_pointer != NULL) {
+        wasm_runtime_free(model_pointer);
+        model_pointer = NULL;
+    }
+
+    if (builder.size != 1) {
+        NN_ERR_PRINTF("Unexpected builder format.");
+        return invalid_argument;
+    }
+
+    if (encoding != tensorflow) {
+        NN_ERR_PRINTF("Encoding is not tensorflow.");
+        return invalid_argument;
+    }
+
+    if (target != cpu) {
+        NN_ERR_PRINTF("Only CPU target is supported.");
+        return invalid_argument;
+    }
+
+    uint32_t size = builder.buf[0].size;
+
+    model_pointer = (char *)wasm_runtime_malloc(size);
+    if (model_pointer == NULL) {
+        NN_ERR_PRINTF("Error when allocating memory for model.");
+        return missing_memory;
+    }
+
+    bh_memcpy_s(model_pointer, size, builder.buf[0].buf, size);
+
+    model = tflite::FlatBufferModel::BuildFromBuffer(model_pointer, size, NULL);
+    if (model == NULL) {
+        NN_ERR_PRINTF("Loading model error.");
+        wasm_runtime_free(model_pointer);
+        model_pointer = NULL;
+        return missing_memory;
+    }
+
+    // Build the interpreter with the InterpreterBuilder.
+    tflite::ops::builtin::BuiltinOpResolver resolver;
+    tflite::InterpreterBuilder tflite_builder(*model, resolver);
+    tflite_builder(&interpreter);
+    if (interpreter == NULL) {
+        NN_ERR_PRINTF("Error when generating the interpreter.");
+        wasm_runtime_free(model_pointer);
+        model_pointer = NULL;
+        return missing_memory;
+    }
+
+    return success;
+}
+
+error
+tensorflow_init_execution_context(graph graph)
+{
+    if (interpreter == NULL) {
+        NN_ERR_PRINTF("Non-initialized interpreter.");
+        return runtime_error;
+    }
+    interpreter->AllocateTensors();
+    return success;
+}
+
+error
+tensorflow_set_input(graph_execution_context ctx, uint32_t index,
+                     tensor *input_tensor)
+{
+    if (interpreter == NULL) {
+        NN_ERR_PRINTF("Non-initialized interpreter.");
+        return runtime_error;
+    }
+
+    uint32_t num_tensors = interpreter->inputs().size();
+    NN_DBG_PRINTF("Number of tensors (%d)", num_tensors);
+    if (index + 1 > num_tensors) {
+        return runtime_error;
+    }
+
+    auto tensor = interpreter->input_tensor(index);
+    if (tensor == NULL) {
+        NN_ERR_PRINTF("Missing memory");
+        return missing_memory;
+    }
+
+    uint32_t model_tensor_size = 1;
+    for (int i = 0; i < (int)tensor->dims->size; ++i)
+        model_tensor_size *= (uint32_t)tensor->dims->data[i];
+
+    uint32_t input_tensor_size = 1;
+    for (int i = 0; i < input_tensor->dimensions->size; i++)
+        input_tensor_size *= (uint32_t)input_tensor->dimensions->buf[i];
+
+    if (model_tensor_size != input_tensor_size) {
+        NN_ERR_PRINTF("Input tensor shape from the model is different than the "
+                      "one provided");
+        return invalid_argument;
+    }
+
+    auto *input = interpreter->typed_input_tensor<float>(index);
+    if (input == NULL)
+        return missing_memory;
+
+    bh_memcpy_s(input, model_tensor_size * sizeof(float), input_tensor->data,
+                model_tensor_size * sizeof(float));
+    return success;
+}
+
+error
+tensorflow_compute(graph_execution_context ctx)
+{
+    if (interpreter == NULL) {
+        NN_ERR_PRINTF("Non-initialized interpreter.");
+        return runtime_error;
+    }
+    interpreter->Invoke();
+    return success;
+}
+
+error
+tensorflow_get_output(graph_execution_context context, uint32_t index,
+                      tensor_data output_tensor, uint32_t *output_tensor_size)
+{
+    if (interpreter == NULL) {
+        NN_ERR_PRINTF("Non-initialized interpreter.");
+        return runtime_error;
+    }
+
+    uint32_t num_output_tensors = interpreter->outputs().size();
+    NN_DBG_PRINTF("Number of tensors (%d)", num_output_tensors);
+
+    if (index + 1 > num_output_tensors) {
+        return runtime_error;
+    }
+
+    auto tensor = interpreter->output_tensor(index);
+    if (tensor == NULL) {
+        NN_ERR_PRINTF("Missing memory");
+        return missing_memory;
+    }
+
+    uint32_t model_tensor_size = 1;
+    for (int i = 0; i < (int)tensor->dims->size; ++i)
+        model_tensor_size *= (uint32_t)tensor->dims->data[i];
+
+    if (*output_tensor_size < model_tensor_size) {
+        NN_ERR_PRINTF("Insufficient memory to copy tensor %d", index);
+        return missing_memory;
+    }
+
+    float *tensor_f = interpreter->typed_output_tensor<float>(index);
+    for (int i = 0; i < model_tensor_size; ++i)
+        NN_DBG_PRINTF("output: %f", tensor_f[i]);
+
+    *output_tensor_size = model_tensor_size;
+    bh_memcpy_s(output_tensor, model_tensor_size * sizeof(float), tensor_f,
+                model_tensor_size * sizeof(float));
+    return success;
+}

core/iwasm/libraries/wasi-nn/wasi_nn_tensorflow.hpp

@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2019 Intel Corporation.  All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+ */
+
+#ifndef WASI_NN_TENSORFLOW_HPP
+#define WASI_NN_TENSORFLOW_HPP
+
+#include <stdio.h>
+
+#include "wasi_nn.h"
+#include "logger.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+error
+tensorflow_load(graph_builder_array builder, graph_encoding encoding,
+                execution_target target, graph *graph);
+
+error
+tensorflow_init_execution_context(graph graph);
+
+error
+tensorflow_set_input(graph_execution_context ctx, uint32_t index,
+                     tensor *input_tensor);
+
+error
+tensorflow_compute(graph_execution_context ctx);
+
+error
+tensorflow_get_output(graph_execution_context context, uint32_t index,
+                      tensor_data output_tensor, uint32_t *output_tensor_size);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif