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Code Issues Actions 4 Packages Projects Releases Wiki Activity

Refactor WASI-NN to simplify the support for multiple frameworks (#1834)

Browse Source 
- Reorganize the library structure
- Use the latest version of `wasi-nn` wit (Oct 25, 2022):
    0f77c48ec1/wasi-nn.wit.md
- Split logic that converts WASM structs to native structs in a separate file
- Simplify addition of new frameworks
This commit is contained in:
tonibofarull 2023-01-25 11:32:40 +01:00 committed by GitHub
parent 965edff4df
commit 9eed6686df
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
24 changed files with 911 additions and 504 deletions
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10
build-scripts/runtime_lib.cmake
View File

@ -96,9 +96,13 @@ if (WAMR_BUILD_LIB_PTHREAD_SEMAPHORE EQUAL 1)
endif ()
if (WAMR_BUILD_WASI_NN EQUAL 1)
execute_process(COMMAND ${WAMR_ROOT_DIR}/core/deps/install_tensorflow.sh
RESULT_VARIABLE TENSORFLOW_RESULT
)
if (NOT EXISTS "${WAMR_ROOT_DIR}/core/deps/tensorflow-src")
execute_process(COMMAND ${WAMR_ROOT_DIR}/core/deps/install_tensorflow.sh
RESULT_VARIABLE TENSORFLOW_RESULT
)
else ()
message("Tensorflow is already downloaded.")
endif()
set(TENSORFLOW_SOURCE_DIR "${WAMR_ROOT_DIR}/core/deps/tensorflow-src")
include_directories (${CMAKE_CURRENT_BINARY_DIR}/flatbuffers/include)
include_directories (${TENSORFLOW_SOURCE_DIR})

20
core/iwasm/aot/aot_runtime.c
View File

@ -1083,6 +1083,17 @@ aot_instantiate(AOTModule *module, bool is_sub_inst, uint32 stack_size,
}
#endif
#if WASM_ENABLE_WASI_NN != 0
if (!is_sub_inst) {
if (!(((AOTModuleInstanceExtra *)module_inst->e)->wasi_nn_ctx =
wasi_nn_initialize())) {
set_error_buf(error_buf, error_buf_size,
"wasi nn initialization failed");
goto fail;
}
}
#endif
/* Initialize the thread related data */
if (stack_size == 0)
stack_size = DEFAULT_WASM_STACK_SIZE;
@ -1194,6 +1205,15 @@ aot_deinstantiate(AOTModuleInstance *module_inst, bool is_sub_inst)
wasm_runtime_free(
((AOTModuleInstanceExtra *)module_inst->e)->c_api_func_imports);
#if WASM_ENABLE_WASI_NN != 0
if (!is_sub_inst) {
WASINNContext *wasi_nn_ctx =
((AOTModuleInstanceExtra *)module_inst->e)->wasi_nn_ctx;
if (wasi_nn_ctx)
wasi_nn_destroy(wasi_nn_ctx);
}
#endif
wasm_runtime_free(module_inst);
}

7
core/iwasm/aot/aot_runtime.h
View File

@ -11,6 +11,10 @@
#include "../interpreter/wasm_runtime.h"
#include "../compilation/aot.h"
#if WASM_ENABLE_WASI_NN != 0
#include "../libraries/wasi-nn/src/wasi_nn_private.h"
#endif
#ifdef __cplusplus
extern "C" {
#endif
@ -75,6 +79,9 @@ typedef struct AOTFunctionInstance {
typedef struct AOTModuleInstanceExtra {
CApiFuncImport *c_api_func_imports;
#if WASM_ENABLE_WASI_NN != 0
WASINNContext *wasi_nn_ctx;
#endif
} AOTModuleInstanceExtra;
#if defined(OS_ENABLE_HW_BOUND_CHECK) && defined(BH_PLATFORM_WINDOWS)

18
core/iwasm/interpreter/wasm_runtime.c
View File

@ -1803,6 +1803,16 @@ wasm_instantiate(WASMModule *module, bool is_sub_inst, uint32 stack_size,
}
#endif
#if WASM_ENABLE_WASI_NN != 0
if (!is_sub_inst) {
if (!(module_inst->e->wasi_nn_ctx = wasi_nn_initialize())) {
set_error_buf(error_buf, error_buf_size,
"wasi nn initialization failed");
goto fail;
}
}
#endif
#if WASM_ENABLE_DEBUG_INTERP != 0 \
|| (WASM_ENABLE_FAST_JIT != 0 && WASM_ENABLE_JIT != 0 \
&& WASM_ENABLE_LAZY_JIT != 0)
@ -1984,6 +1994,14 @@ wasm_deinstantiate(WASMModuleInstance *module_inst, bool is_sub_inst)
if (module_inst->e->c_api_func_imports)
wasm_runtime_free(module_inst->e->c_api_func_imports);
#if WASM_ENABLE_WASI_NN != 0
if (!is_sub_inst) {
WASINNContext *wasi_nn_ctx = module_inst->e->wasi_nn_ctx;
if (wasi_nn_ctx)
wasi_nn_destroy(wasi_nn_ctx);
}
#endif
wasm_runtime_free(module_inst);
}

8
core/iwasm/interpreter/wasm_runtime.h
View File

@ -11,6 +11,10 @@
#include "../common/wasm_runtime_common.h"
#include "../common/wasm_exec_env.h"
#if WASM_ENABLE_WASI_NN != 0
#include "../libraries/wasi-nn/src/wasi_nn_private.h"
#endif
#ifdef __cplusplus
extern "C" {
#endif
@ -242,6 +246,10 @@ typedef struct WASMModuleInstanceExtra {
&& WASM_ENABLE_LAZY_JIT != 0)
WASMModuleInstance *next;
#endif
#if WASM_ENABLE_WASI_NN != 0
WASINNContext *wasi_nn_ctx;
#endif
} WASMModuleInstanceExtra;
struct AOTFuncPerfProfInfo;

1
core/iwasm/libraries/wasi-nn/.dockerignore
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@ -1 +0,0 @@
**/Dockerfile

10
core/iwasm/libraries/wasi-nn/README.md
View File

@ -37,7 +37,11 @@ Tests: passed!
## What is missing
* Only 1 model at a time is supported.
Supported:
* Only 1 WASM app at a time.
* Only 1 model at a time.
* `graph` and `graph-execution-context` are ignored.
* Only `tensorflow` (lite) is supported.
* Only `cpu` is supported.
* Graph encoding: `tensorflowlite`.
* Execution target: `cpu`.
* Tensor type: `fp32`.

55
core/iwasm/libraries/wasi-nn/logger.h
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@ -1,55 +0,0 @@
/*
* 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

63
core/iwasm/libraries/wasi-nn/src/utils/logger.h Normal file
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@ -0,0 +1,63 @@
/*
* 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, ...) \
do { \
printf("[%s:%d] " fmt, __FILENAME__, __LINE__, ##__VA_ARGS__); \
printf("\n"); \
fflush(stdout); \
} while (0)
#else
#define NN_ERR_PRINTF(fmt, ...)
#endif
#ifdef ENABLE_WARN_LOG
#define NN_WARN_PRINTF(fmt, ...) \
do { \
printf("[%s:%d] " fmt, __FILENAME__, __LINE__, ##__VA_ARGS__); \
printf("\n"); \
fflush(stdout); \
} while (0)
#else
#define NN_WARN_PRINTF(fmt, ...)
#endif
#ifdef ENABLE_DBG_LOG
#define NN_DBG_PRINTF(fmt, ...) \
do { \
printf("[%s:%d] " fmt, __FILENAME__, __LINE__, ##__VA_ARGS__); \
printf("\n"); \
fflush(stdout); \
} while (0)
#else
#define NN_DBG_PRINTF(fmt, ...)
#endif
#ifdef ENABLE_INFO_LOG
#define NN_INFO_PRINTF(fmt, ...) \
do { \
printf("[%s:%d] " fmt, __FILENAME__, __LINE__, ##__VA_ARGS__); \
printf("\n"); \
fflush(stdout); \
} while (0)
#else
#define NN_INFO_PRINTF(fmt, ...)
#endif
#endif

163
core/iwasm/libraries/wasi-nn/src/utils/wasi_nn_app_native.c Normal file
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@ -0,0 +1,163 @@
/*
* Copyright (C) 2019 Intel Corporation. All rights reserved.
* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
*/
#include "wasi_nn_app_native.h"
static error
graph_builder_app_native(wasm_module_inst_t instance,
graph_builder_wasm *builder_wasm,
graph_builder *builder)
{
if (!wasm_runtime_validate_app_addr(instance, builder_wasm->buf_offset,
builder_wasm->size * sizeof(uint8_t))) {
NN_ERR_PRINTF("builder_wasm->buf_offset is invalid");
return invalid_argument;
}
builder->buf = (uint8_t *)wasm_runtime_addr_app_to_native(
instance, builder_wasm->buf_offset);
builder->size = builder_wasm->size;
return success;
}
error
graph_builder_array_app_native(wasm_module_inst_t instance,
graph_builder_array_wasm *builder_array_wasm,
graph_builder_array *builder_array)
{
if (!wasm_runtime_validate_native_addr(instance, builder_array_wasm,
sizeof(graph_builder_array_wasm))) {
NN_ERR_PRINTF("builder_array_wasm is invalid");
return invalid_argument;
}
NN_DBG_PRINTF("Graph builder array contains %d elements",
builder_array_wasm->size);
if (!wasm_runtime_validate_app_addr(
instance, builder_array_wasm->buf_offset,
builder_array_wasm->size * sizeof(graph_builder_wasm))) {
NN_ERR_PRINTF("builder_array_wasm->buf_offset is invalid");
return invalid_argument;
}
graph_builder_wasm *builder_wasm =
(graph_builder_wasm *)wasm_runtime_addr_app_to_native(
instance, builder_array_wasm->buf_offset);
graph_builder *builder = (graph_builder *)wasm_runtime_malloc(
builder_array_wasm->size * sizeof(graph_builder));
if (builder == NULL)
return missing_memory;
for (uint32_t i = 0; i < builder_array_wasm->size; ++i) {
error res;
if (success
!= (res = graph_builder_app_native(instance, &builder_wasm[i],
&builder[i]))) {
wasm_runtime_free(builder);
return res;
}
NN_DBG_PRINTF("Graph builder %d contains %d elements", i,
builder->size);
}
builder_array->buf = builder;
builder_array->size = builder_array_wasm->size;
return success;
}
static error
tensor_data_app_native(wasm_module_inst_t instance, uint32_t total_elements,
tensor_wasm *input_tensor_wasm, tensor_data *data)
{
if (!wasm_runtime_validate_app_addr(
instance, input_tensor_wasm->data_offset, total_elements)) {
NN_ERR_PRINTF("input_tensor_wasm->data_offset is invalid");
return invalid_argument;
}
*data = (tensor_data)wasm_runtime_addr_app_to_native(
instance, input_tensor_wasm->data_offset);
return success;
}
static error
tensor_dimensions_app_native(wasm_module_inst_t instance,
tensor_wasm *input_tensor_wasm,
tensor_dimensions **dimensions)
{
if (!wasm_runtime_validate_app_addr(instance,
input_tensor_wasm->dimensions_offset,
sizeof(tensor_dimensions_wasm))) {
NN_ERR_PRINTF("input_tensor_wasm->dimensions_offset is invalid");
return invalid_argument;
}
tensor_dimensions_wasm *dimensions_wasm =
(tensor_dimensions_wasm *)wasm_runtime_addr_app_to_native(
instance, input_tensor_wasm->dimensions_offset);
if (!wasm_runtime_validate_app_addr(instance, dimensions_wasm->buf_offset,
sizeof(tensor_dimensions))) {
NN_ERR_PRINTF("dimensions_wasm->buf_offset is invalid");
return invalid_argument;
}
*dimensions =
(tensor_dimensions *)wasm_runtime_malloc(sizeof(tensor_dimensions));
if (dimensions == NULL)
return missing_memory;
(*dimensions)->size = dimensions_wasm->size;
(*dimensions)->buf = (uint32_t *)wasm_runtime_addr_app_to_native(
instance, dimensions_wasm->buf_offset);
NN_DBG_PRINTF("Number of dimensions: %d", (*dimensions)->size);
return success;
}
error
tensor_app_native(wasm_module_inst_t instance, tensor_wasm *input_tensor_wasm,
tensor *input_tensor)
{
NN_DBG_PRINTF("Converting tensor_wasm to tensor");
if (!wasm_runtime_validate_native_addr(instance, input_tensor_wasm,
sizeof(tensor_wasm))) {
NN_ERR_PRINTF("input_tensor_wasm is invalid");
return invalid_argument;
}
error res;
tensor_dimensions *dimensions = NULL;
if (success
!= (res = tensor_dimensions_app_native(instance, input_tensor_wasm,
&dimensions))) {
NN_ERR_PRINTF("error when parsing dimensions");
return res;
}
uint32_t total_elements = 1;
for (uint32_t i = 0; i < dimensions->size; ++i) {
total_elements *= dimensions->buf[i];
NN_DBG_PRINTF("Dimension %d: %d", i, dimensions->buf[i]);
}
NN_DBG_PRINTF("Tensor type: %d", input_tensor_wasm->type);
NN_DBG_PRINTF("Total number of elements: %d", total_elements);
tensor_data data = NULL;
if (success
!= (res = tensor_data_app_native(instance, total_elements,
input_tensor_wasm, &data))) {
wasm_runtime_free(dimensions);
return res;
}
input_tensor->type = input_tensor_wasm->type;
input_tensor->dimensions = dimensions;
input_tensor->data = data;
return success;
}

51
core/iwasm/libraries/wasi-nn/src/utils/wasi_nn_app_native.h Normal file
View File

@ -0,0 +1,51 @@
/*
* Copyright (C) 2019 Intel Corporation. All rights reserved.
* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
*/
#ifndef WASI_NN_APP_NATIVE
#define WASI_NN_APP_NATIVE
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#include <errno.h>
#include <string.h>
#include "wasi_nn.h"
#include "logger.h"
#include "bh_platform.h"
#include "wasm_export.h"
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 buf_offset;
uint32_t size;
} tensor_dimensions_wasm;
typedef struct {
uint32_t dimensions_offset;
tensor_type type;
uint32_t data_offset;
} tensor_wasm;
error
graph_builder_array_app_native(wasm_module_inst_t instance,
graph_builder_array_wasm *builder,
graph_builder_array *builder_native);
error
tensor_app_native(wasm_module_inst_t instance, tensor_wasm *input_tensor,
tensor *input_tensor_native);
#endif

302
core/iwasm/libraries/wasi-nn/src/wasi_nn.c Normal file
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@ -0,0 +1,302 @@
/*
* Copyright (C) 2019 Intel Corporation. All rights reserved.
* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
*/
#include <stdio.h>
#include <stdlib.h>
#include <stdbool.h>
#include <assert.h>
#include <errno.h>
#include <string.h>
#include "wasi_nn.h"
#include "wasi_nn_app_native.h"
#include "logger.h"
#include "wasi_nn_tensorflowlite.hpp"
#include "bh_platform.h"
#include "wasm_export.h"
#include "wasm_runtime.h"
#include "aot_runtime.h"
/* Definition of 'wasi_nn.h' structs in WASM app format (using offset) */
typedef error (*LOAD)(graph_builder_array *, graph_encoding, execution_target,
graph *);
typedef error (*INIT_EXECUTION_CONTEXT)(graph, graph_execution_context *);
typedef error (*SET_INPUT)(graph_execution_context, uint32_t, tensor *);
typedef error (*COMPUTE)(graph_execution_context);
typedef error (*GET_OUTPUT)(graph_execution_context, uint32_t, tensor_data,
uint32_t *);
typedef struct {
LOAD load;
INIT_EXECUTION_CONTEXT init_execution_context;
SET_INPUT set_input;
COMPUTE compute;
GET_OUTPUT get_output;
} api_function;
/* Global variables */
static api_function lookup[] = {
{ NULL, NULL, NULL, NULL, NULL },
{ NULL, NULL, NULL, NULL, NULL },
{ NULL, NULL, NULL, NULL, NULL },
{ NULL, NULL, NULL, NULL, NULL },
{ tensorflowlite_load, tensorflowlite_init_execution_context,
tensorflowlite_set_input, tensorflowlite_compute,
tensorflowlite_get_output }
};
/* Utils */
static bool
is_encoding_implemented(graph_encoding encoding)
{
return lookup[encoding].load && lookup[encoding].init_execution_context
&& lookup[encoding].set_input && lookup[encoding].compute
&& lookup[encoding].get_output;
}
static error
is_model_initialized(WASINNContext *wasi_nn_ctx)
{
if (!wasi_nn_ctx->is_initialized) {
NN_ERR_PRINTF("Model not initialized.");
return runtime_error;
}
return success;
}
WASINNContext *
wasm_runtime_get_wasi_nn_ctx(wasm_module_inst_t instance)
{
WASINNContext *wasi_nn_ctx = NULL;
#if WASM_ENABLE_INTERP != 0
if (instance->module_type == Wasm_Module_Bytecode) {
NN_DBG_PRINTF("Getting ctx from WASM");
WASMModuleInstance *module_inst = (WASMModuleInstance *)instance;
wasi_nn_ctx = ((WASMModuleInstanceExtra *)module_inst->e)->wasi_nn_ctx;
}
#endif
#if WASM_ENABLE_AOT != 0
if (instance->module_type == Wasm_Module_AoT) {
NN_DBG_PRINTF("Getting ctx from AOT");
AOTModuleInstance *module_inst = (AOTModuleInstance *)instance;
wasi_nn_ctx = ((AOTModuleInstanceExtra *)module_inst->e)->wasi_nn_ctx;
}
#endif
bh_assert(wasi_nn_ctx != NULL);
NN_DBG_PRINTF("Returning ctx");
return wasi_nn_ctx;
}
/* 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 *g)
{
NN_DBG_PRINTF("Running wasi_nn_load [encoding=%d, target=%d]...", encoding,
target);
if (!is_encoding_implemented(encoding)) {
NN_ERR_PRINTF("Encoding not supported.");
return invalid_encoding;
}
wasm_module_inst_t instance = wasm_runtime_get_module_inst(exec_env);
bh_assert(instance);
error res;
graph_builder_array builder_native = { 0 };
if (success
!= (res = graph_builder_array_app_native(instance, builder,
&builder_native)))
return res;
if (!wasm_runtime_validate_native_addr(instance, g, sizeof(graph))) {
NN_ERR_PRINTF("graph is invalid");
res = invalid_argument;
goto fail;
}
res = lookup[encoding].load(&builder_native, encoding, target, g);
NN_DBG_PRINTF("wasi_nn_load finished with status %d [graph=%d]", res, *g);
WASINNContext *wasi_nn_ctx = wasm_runtime_get_wasi_nn_ctx(instance);
wasi_nn_ctx->current_encoding = encoding;
wasi_nn_ctx->is_initialized = true;
fail:
// XXX: Free intermediate structure pointers
if (builder_native.buf)
wasm_runtime_free(builder_native.buf);
return res;
}
error
wasi_nn_init_execution_context(wasm_exec_env_t exec_env, graph g,
graph_execution_context *ctx)
{
NN_DBG_PRINTF("Running wasi_nn_init_execution_context [graph=%d]...", g);
wasm_module_inst_t instance = wasm_runtime_get_module_inst(exec_env);
bh_assert(instance);
WASINNContext *wasi_nn_ctx = wasm_runtime_get_wasi_nn_ctx(instance);
error res;
if (success != (res = is_model_initialized(wasi_nn_ctx)))
return res;
if (!wasm_runtime_validate_native_addr(instance, ctx,
sizeof(graph_execution_context))) {
NN_ERR_PRINTF("ctx is invalid");
return invalid_argument;
}
res = lookup[wasi_nn_ctx->current_encoding].init_execution_context(g, ctx);
*ctx = g;
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);
wasm_module_inst_t instance = wasm_runtime_get_module_inst(exec_env);
bh_assert(instance);
WASINNContext *wasi_nn_ctx = wasm_runtime_get_wasi_nn_ctx(instance);
error res;
if (success != (res = is_model_initialized(wasi_nn_ctx)))
return res;
tensor input_tensor_native = { 0 };
if (success
!= (res = tensor_app_native(instance, input_tensor,
&input_tensor_native)))
return res;
res = lookup[wasi_nn_ctx->current_encoding].set_input(ctx, index,
&input_tensor_native);
// XXX: Free intermediate structure pointers
if (input_tensor_native.dimensions)
wasm_runtime_free(input_tensor_native.dimensions);
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);
wasm_module_inst_t instance = wasm_runtime_get_module_inst(exec_env);
bh_assert(instance);
WASINNContext *wasi_nn_ctx = wasm_runtime_get_wasi_nn_ctx(instance);
error res;
if (success != (res = is_model_initialized(wasi_nn_ctx)))
return res;
res = lookup[wasi_nn_ctx->current_encoding].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);
wasm_module_inst_t instance = wasm_runtime_get_module_inst(exec_env);
bh_assert(instance);
WASINNContext *wasi_nn_ctx = wasm_runtime_get_wasi_nn_ctx(instance);
error res;
if (success != (res = is_model_initialized(wasi_nn_ctx)))
return res;
if (!wasm_runtime_validate_native_addr(instance, output_tensor_size,
sizeof(uint32_t))) {
NN_ERR_PRINTF("output_tensor_size is invalid");
return invalid_argument;
}
res = lookup[wasi_nn_ctx->current_encoding].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;
}
/* Non-exposed public functions */
WASINNContext *
wasi_nn_initialize()
{
NN_DBG_PRINTF("Initializing wasi-nn");
WASINNContext *wasi_nn_ctx =
(WASINNContext *)wasm_runtime_malloc(sizeof(WASINNContext));
if (wasi_nn_ctx == NULL) {
NN_ERR_PRINTF("Error when allocating memory for WASI-NN context");
return NULL;
}
wasi_nn_ctx->is_initialized = true;
wasi_nn_ctx->current_encoding = 3;
return wasi_nn_ctx;
}
void
wasi_nn_destroy(WASINNContext *wasi_nn_ctx)
{
if (wasi_nn_ctx == NULL) {
NN_ERR_PRINTF(
"Error when deallocating memory. WASI-NN context is NULL");
return;
}
NN_DBG_PRINTF("Freeing wasi-nn");
NN_DBG_PRINTF("-> is_initialized: %d", wasi_nn_ctx->is_initialized);
NN_DBG_PRINTF("-> current_encoding: %d", wasi_nn_ctx->current_encoding);
tensorflowlite_destroy();
wasm_runtime_free(wasi_nn_ctx);
}
/* 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);
}

30
core/iwasm/libraries/wasi-nn/src/wasi_nn_private.h Normal file
View File

@ -0,0 +1,30 @@
/*
* Copyright (C) 2019 Intel Corporation. All rights reserved.
* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
*/
#ifndef WASI_NN_PRIVATE_H
#define WASI_NN_PRIVATE_H
#include "wasi_nn_types.h"
typedef struct {
bool is_initialized;
graph_encoding current_encoding;
} WASINNContext;
/**
* @brief Initialize wasi-nn
*
*/
WASINNContext *
wasi_nn_initialize();
/**
* @brief Destroy wasi-nn on app exists
*
*/
void
wasi_nn_destroy(WASINNContext *wasi_nn_ctx);
#endif

58
core/iwasm/libraries/wasi-nn/wasi_nn_tensorflow.cpp → core/iwasm/libraries/wasi-nn/src/wasi_nn_tensorflowlite.cpp
View File

@ -3,8 +3,10 @@
* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
*/
#include "wasi_nn_tensorflow.hpp"
#include "wasi_nn_common.h"
#include "wasi_nn.h"
#include "wasi_nn_tensorflowlite.hpp"
#include "logger.h"
#include "bh_common.h"
#include "bh_platform.h"
#include "platform_common.h"
@ -25,21 +27,21 @@ static char *model_pointer = NULL;
/* WASI-NN (tensorflow) implementation */
error
tensorflow_load(graph_builder_array builder, graph_encoding encoding,
execution_target target, graph *graph)
tensorflowlite_load(graph_builder_array *builder, graph_encoding encoding,
execution_target target, graph *g)
{
if (model_pointer != NULL) {
wasm_runtime_free(model_pointer);
model_pointer = NULL;
}
if (builder.size != 1) {
if (builder->size != 1) {
NN_ERR_PRINTF("Unexpected builder format.");
return invalid_argument;
}
if (encoding != tensorflow) {
NN_ERR_PRINTF("Encoding is not tensorflow.");
if (encoding != tensorflowlite) {
NN_ERR_PRINTF("Encoding is not tensorflowlite.");
return invalid_argument;
}
@ -48,7 +50,7 @@ tensorflow_load(graph_builder_array builder, graph_encoding encoding,
return invalid_argument;
}
uint32_t size = builder.buf[0].size;
uint32_t size = builder->buf[0].size;
model_pointer = (char *)wasm_runtime_malloc(size);
if (model_pointer == NULL) {
@ -56,7 +58,7 @@ tensorflow_load(graph_builder_array builder, graph_encoding encoding,
return missing_memory;
}
bh_memcpy_s(model_pointer, size, builder.buf[0].buf, size);
bh_memcpy_s(model_pointer, size, builder->buf[0].buf, size);
model = tflite::FlatBufferModel::BuildFromBuffer(model_pointer, size, NULL);
if (model == NULL) {
@ -81,7 +83,7 @@ tensorflow_load(graph_builder_array builder, graph_encoding encoding,
}
error
tensorflow_init_execution_context(graph graph)
tensorflowlite_init_execution_context(graph g, graph_execution_context *ctx)
{
if (interpreter == NULL) {
NN_ERR_PRINTF("Non-initialized interpreter.");
@ -92,8 +94,8 @@ tensorflow_init_execution_context(graph graph)
}
error
tensorflow_set_input(graph_execution_context ctx, uint32_t index,
tensor *input_tensor)
tensorflowlite_set_input(graph_execution_context ctx, uint32_t index,
tensor *input_tensor)
{
if (interpreter == NULL) {
NN_ERR_PRINTF("Non-initialized interpreter.");
@ -113,11 +115,11 @@ tensorflow_set_input(graph_execution_context ctx, uint32_t index,
}
uint32_t model_tensor_size = 1;
for (int i = 0; i < (int)tensor->dims->size; ++i)
for (int i = 0; i < 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++)
for (uint32_t 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) {
@ -136,7 +138,7 @@ tensorflow_set_input(graph_execution_context ctx, uint32_t index,
}
error
tensorflow_compute(graph_execution_context ctx)
tensorflowlite_compute(graph_execution_context ctx)
{
if (interpreter == NULL) {
NN_ERR_PRINTF("Non-initialized interpreter.");
@ -147,8 +149,9 @@ 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)
tensorflowlite_get_output(graph_execution_context ctx, uint32_t index,
tensor_data output_tensor,
uint32_t *output_tensor_size)
{
if (interpreter == NULL) {
NN_ERR_PRINTF("Non-initialized interpreter.");
@ -178,7 +181,7 @@ tensorflow_get_output(graph_execution_context context, uint32_t index,
}
float *tensor_f = interpreter->typed_output_tensor<float>(index);
for (int i = 0; i < model_tensor_size; ++i)
for (uint32_t i = 0; i < model_tensor_size; ++i)
NN_DBG_PRINTF("output: %f", tensor_f[i]);
*output_tensor_size = model_tensor_size;
@ -186,3 +189,22 @@ tensorflow_get_output(graph_execution_context context, uint32_t index,
model_tensor_size * sizeof(float));
return success;
}
void
tensorflowlite_destroy()
{
/*
TensorFlow Lite memory is man
Related issues:
* https://github.com/tensorflow/tensorflow/issues/15880
*/
NN_DBG_PRINTF("Freeing memory.");
model.reset(nullptr);
model = NULL;
interpreter.reset(nullptr);
interpreter = NULL;
wasm_runtime_free(model_pointer);
model_pointer = NULL;
NN_DBG_PRINTF("Memory free'd.");
}

41
core/iwasm/libraries/wasi-nn/src/wasi_nn_tensorflowlite.hpp Normal file
View File

@ -0,0 +1,41 @@
/*
* Copyright (C) 2019 Intel Corporation. All rights reserved.
* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
*/
#ifndef WASI_NN_TENSORFLOWLITE_HPP
#define WASI_NN_TENSORFLOWLITE_HPP
#include "wasi_nn.h"
#ifdef __cplusplus
extern "C" {
#endif
error
tensorflowlite_load(graph_builder_array *builder, graph_encoding encoding,
execution_target target, graph *g);
error
tensorflowlite_init_execution_context(graph g, graph_execution_context *ctx);
error
tensorflowlite_set_input(graph_execution_context ctx, uint32_t index,
tensor *input_tensor);
error
tensorflowlite_compute(graph_execution_context ctx);
error
tensorflowlite_get_output(graph_execution_context ctx, uint32_t index,
tensor_data output_tensor,
uint32_t *output_tensor_size);
void
tensorflowlite_destroy();
#ifdef __cplusplus
}
#endif
#endif

1
core/iwasm/libraries/wasi-nn/test/.dockerignore Normal file
View File

@ -0,0 +1 @@
Dockerfile

16
core/iwasm/libraries/wasi-nn/test/Dockerfile
View File

@ -8,18 +8,24 @@ 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
ARG WASI_SDK_VER=16
RUN wget -c --progress=dot:giga https://github.com/WebAssembly/wasi-sdk/releases/download/wasi-sdk-${WASI_SDK_VER}/wasi-sdk-${WASI_SDK_VER}.0-linux.tar.gz -P /opt \
&& tar xf /opt/wasi-sdk-${WASI_SDK_VER}.0-linux.tar.gz -C /opt \
&& ln -fs /opt/wasi-sdk-${WASI_SDK_VER}.0 /opt/wasi-sdk \
&& rm /opt/wasi-sdk-${WASI_SDK_VER}.0-linux.tar.gz
WORKDIR /home/wamr
COPY core/deps/install_tensorflow.sh core/deps/install_tensorflow.sh
RUN ./core/deps/install_tensorflow.sh
COPY core/iwasm/libraries/wasi-nn/test/requirements.txt .
RUN pip3 install -r requirements.txt
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 ..

15
core/iwasm/libraries/wasi-nn/test/test_tensorflow.c
View File

@ -28,7 +28,7 @@ typedef struct {
// WASI-NN wrappers
error
wasm_load(char *model_name, graph *graph)
wasm_load(char *model_name, graph *g)
{
FILE *pFile = fopen(model_name, "r");
if (pFile == NULL)
@ -64,7 +64,7 @@ wasm_load(char *model_name, graph *graph)
arr.buf[0].size = result;
arr.buf[0].buf = buffer;
error res = load(&arr, tensorflow, cpu, graph);
error res = load(&arr, tensorflowlite, cpu, g);
fclose(pFile);
free(buffer);
@ -73,13 +73,13 @@ wasm_load(char *model_name, graph *graph)
}
error
wasm_init_execution_context(graph graph, graph_execution_context *ctx)
wasm_init_execution_context(graph g, graph_execution_context *ctx)
{
return init_execution_context(graph, ctx);
return init_execution_context(g, ctx);
}
error
wasm_input(graph_execution_context ctx, float *input_tensor, uint32_t *dim)
wasm_set_input(graph_execution_context ctx, float *input_tensor, uint32_t *dim)
{
tensor_dimensions dims;
dims.size = INPUT_TENSOR_DIMS;
@ -130,7 +130,7 @@ run_inference(float *input, uint32_t *input_size, uint32_t *output_size,
exit(1);
}
if (wasm_input(ctx, input, input_size) != success) {
if (wasm_set_input(ctx, input, input_size) != success) {
fprintf(stderr, "Error when setting input tensor.");
exit(1);
}
@ -151,7 +151,7 @@ run_inference(float *input, uint32_t *input_size, uint32_t *output_size,
*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 .");
fprintf(stderr, "Error when getting output .");
exit(1);
}
@ -295,7 +295,6 @@ main()
test_mult_dimensions();
printf("################### Testing multiple outputs...\n");
test_mult_outputs();
printf("Tests: passed!\n");
return 0;
}

11
core/iwasm/libraries/wasi-nn/wasi_nn.cmake
View File

@ -5,6 +5,15 @@ 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 ${WASI_NN_DIR}/wasi_nn_tensorflow.cpp)
include_directories (${WASI_NN_DIR})
include_directories (${WASI_NN_DIR}/src)
include_directories (${WASI_NN_DIR}/src/utils)
set (
LIBC_WASI_NN_SOURCE
${WASI_NN_DIR}/src/wasi_nn.c
${WASI_NN_DIR}/src/wasi_nn_tensorflowlite.cpp
${WASI_NN_DIR}/src/utils/wasi_nn_app_native.c
)
set (TENSORFLOW_LIB tensorflow-lite)

81
core/iwasm/libraries/wasi-nn/wasi_nn.h
View File

@ -3,63 +3,17 @@
* 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
* [Oct 25th, 2022]
* https://github.com/WebAssembly/wasi-nn/blob/0f77c48ec195748990ff67928a4b3eef5f16c2de/wasi-nn.wit.md
*/
/* The graph initialization data. */
#ifndef WASI_NN_H
#define WASI_NN_H
// 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;
#include <stdint.h>
#include "wasi_nn_types.h"
/**
* @brief Load an opaque sequence of bytes to use for inference.
@ -67,25 +21,31 @@ typedef struct {
* @param builder Model builder.
* @param encoding Model encoding.
* @param target Execution target.
* @param graph Graph.
* @param g Graph.
* @return error Execution status.
*/
error
load(graph_builder_array *builder, graph_encoding encoding,
execution_target target, graph *graph)
__attribute__((export_module("wasi_nn")))
execution_target target, graph *g)
__attribute__((import_module("wasi_nn")));
/**
* INFERENCE
*
*/
// Bind a `graph` to the input and output tensors for an inference.
typedef uint32_t graph_execution_context;
/**
* @brief Create an execution instance of a loaded graph.
*
* @param graph Graph.
* @param g Graph.
* @param ctx Execution context.
* @return error Execution status.
*/
error
init_execution_context(graph graph, graph_execution_context *ctx)
__attribute__((export_module("wasi_nn")))
init_execution_context(graph g, graph_execution_context *ctx)
__attribute__((import_module("wasi_nn")));
/**
@ -98,7 +58,6 @@ init_execution_context(graph graph, graph_execution_context *ctx)
*/
error
set_input(graph_execution_context ctx, uint32_t index, tensor *tensor)
__attribute__((export_module("wasi_nn")))
__attribute__((import_module("wasi_nn")));
/**
@ -108,8 +67,7 @@ set_input(graph_execution_context ctx, uint32_t index, tensor *tensor)
* @return error Execution status.
*/
error
compute(graph_execution_context ctx) __attribute__((export_module("wasi_nn")))
__attribute__((import_module("wasi_nn")));
compute(graph_execution_context ctx) __attribute__((import_module("wasi_nn")));
/**
* @brief Extract the outputs after inference.
@ -126,7 +84,6 @@ __attribute__((import_module("wasi_nn")));
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

44
core/iwasm/libraries/wasi-nn/wasi_nn_common.h
View File

@ -1,44 +0,0 @@
/*
* 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

264
core/iwasm/libraries/wasi-nn/wasi_nn_native.c
View File

@ -1,264 +0,0 @@
/*
* 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);
}

40
core/iwasm/libraries/wasi-nn/wasi_nn_tensorflow.hpp
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@ -1,40 +0,0 @@
/*
* 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

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/*
* Copyright (C) 2019 Intel Corporation. All rights reserved.
* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
*/
#ifndef WASI_NN_TYPES_H
#define WASI_NN_TYPES_H
/**
* ERRORS
*
*/
// 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;
/**
* TENSOR
*
*/
// 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 type of the elements in a tensor.
typedef enum { fp16 = 0, fp32, up8, ip32 } tensor_type;
// 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;
/**
* GRAPH
*
*/
// 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;
// An execution graph for performing inference (i.e., a model).
typedef uint32_t graph;
// 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,
tensorflowlite
} graph_encoding;
// Define where the graph should be executed.
typedef enum execution_target { cpu = 0, gpu, tpu } execution_target;
#endif