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Add "load_by_name" API at wasi-nn #4267

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1df9b7c
add wasm load by name for WASI-NN
HongxiaWangSSSS May 12, 2025
c5414fd
Merge branch 'bytecodealliance:main' into feature/support_wasm_load_b…
HongxiaWangSSSS May 12, 2025
ff4ed2f
remove wasm_load_by_name from old wasi-nn
HongxiaWangSSSS May 19, 2025
2831ff2
add wasi_ephemeral_nn in header file
HongxiaWangSSSS May 27, 2025
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2 changes: 1 addition & 1 deletion core/iwasm/libraries/wasi-nn/include/wasi_nn.h
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Expand Up @@ -30,7 +30,7 @@ load(graph_builder_array *builder, graph_encoding encoding,
__attribute__((import_module("wasi_nn")));

wasi_nn_error
load_by_name(const char *name, graph *g)
load_by_name(char *name, uint32_t name_len, graph *g)
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@yamt yamt May 12, 2025

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is this a bug fix?

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@lum1n0us lum1n0us May 12, 2025

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wasi_nn.h is a header for WebAssembly applications written in the C language. Is there a specific reason that we need to change it?

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@HongxiaWangSSSS HongxiaWangSSSS May 13, 2025

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yes, its a bugfix,

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@lum1n0us lum1n0us May 14, 2025

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Since we have two sets of APIs for historical reasons, we might remove one in another PR. For now, let's ensure both are functional.

  • I suggest we use WASM_ENABLE_WASI_EPHEMERAL_NN on the wasm side.
  • With this flag, we declare two sets of APIs in wasi_nn.h. Please align with the content of native_symbols_wasi_nn in wasi_nn.c

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@ayakoakasaka ayakoakasaka May 26, 2025

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@lum1n0us
Where can I find the function prototypes for wasi_ephemeral_nn?
For functions like get_output, the signatures are different, so simply replacing wasi_nn with wasi_ephemeral_nn mechanically doesn't seem to work.

wasm-micro-runtime/core/iwasm/libraries/wasi-nn/src/wasi_nn.c

Lines 690 to 704 in c018b8a

#if WASM_ENABLE_WASI_EPHEMERAL_NN != 0
REG_NATIVE_FUNC(load, "(*iii*)i"),
REG_NATIVE_FUNC(load_by_name, "(*i*)i"),
REG_NATIVE_FUNC(load_by_name_with_config, "(*i*i*)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*)i"),
#else /* WASM_ENABLE_WASI_EPHEMERAL_NN == 0 */
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"),
#endif /* WASM_ENABLE_WASI_EPHEMERAL_NN != 0 */

Even when referring to the wasi-nn specification, the signatures declared there don’t appear to match what is used for wasi_ephemeral_nn
https://github.com/WebAssembly/wasi-nn/blob/71320d95b8c6d43f9af7f44e18b1839db85d89b4/wasi-nn.witx#L59-L86

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@lum1n0us lum1n0us May 27, 2025

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@HongxiaWangSSSS I suggest we use WASM_ENABLE_WASI_EPHEMERAL_NN on the wasm side and there will be two sets for wasi_ephemeral_nn and wasi_nn. wasi_nn will be deprecated in the future.

#if WASM_ENABLE_WASI_EPHEMERAL_NN != 0
wasi_nn_error
load(graph_builder_array *builder, graph_encoding encoding,
     execution_target target, graph *g)
    __attribute__((import_module("wasi_ephemeral_nn")));

wasi_nn_error
load_by_name(const char *name, uint32_t len, graph *g)
    __attribute__((import_module("wasi_ephemeral_nn")));

wasi_nn_error
load_by_name_with_config(const char *name, uint32_t name_len, void *config, uint32_t config_len, graph *g)     __attribute__((import_module("wasi_ephemeral_nn")));;

wasi_nn_error
init_execution_context(graph g, graph_execution_context *exec_ctx)     __attribute__((import_module("wasi_ephemeral_nn")));;

wasi_nn_error
set_input(graph_execution_context ctx, uint32_t index, tensor *tensor)     __attribute__((import_module("wasi_ephemeral_nn")));;

wasi_nn_error
compute(graph_execution_context ctx)     __attribute__((import_module("wasi_ephemeral_nn")));;

wasi_nn_error
get_output(graph_execution_context ctx, uint32_t index,
           tensor_data output_tensor, uint32_t *output_tensor_size)     __attribute__((import_module("wasi_ephemeral_nn")));;

#else

wasi_nn_error
load(graph_builder_array *builder, graph_encoding encoding,
     execution_target target, graph *g)
    __attribute__((import_module("wasi_nn")));

wasi_nn_error
init_execution_context(graph g, graph_execution_context *ctx)
    __attribute__((import_module("wasi_nn")));

wasi_nn_error
set_input(graph_execution_context ctx, uint32_t index, tensor *tensor)
    __attribute__((import_module("wasi_nn")));

wasi_nn_error
compute(graph_execution_context ctx) __attribute__((import_module("wasi_nn")));

wasi_nn_error
get_output(graph_execution_context ctx, uint32_t index,
           tensor_data output_tensor, uint32_t *output_tensor_size)
    __attribute__((import_module("wasi_nn")));
#endif

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@ayakoakasaka ayakoakasaka May 27, 2025

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Is there a mismatch in the function signature?

wasm-micro-runtime/core/iwasm/libraries/wasi-nn/src/wasi_nn.c

Line 697 in c018b8a

REG_NATIVE_FUNC(get_output, "(ii*i*)i"),

and

wasi_nn_error
get_output(graph_execution_context ctx, uint32_t index,
tensor_data output_tensor, uint32_t *output_tensor_size) attribute((import_module("wasi_ephemeral_nn")));

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@lum1n0us lum1n0us May 27, 2025 •
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YES. there should be two versions, one for wasi_ephemeral_nn, another for wasi_nn. please refer to:

#if WASM_ENABLE_WASI_EPHEMERAL_NN != 0
wasi_nn_error
wasi_nn_load(wasm_exec_env_t exec_env, graph_builder_wasm *builder,
             uint32_t builder_wasm_size, graph_encoding encoding,
             execution_target target, graph *g)
#else  /* WASM_ENABLE_WASI_EPHEMERAL_NN == 0 */
wasi_nn_error
wasi_nn_load(wasm_exec_env_t exec_env, graph_builder_array_wasm *builder,
             graph_encoding encoding, execution_target target, graph *g)
#endif /* WASM_ENABLE_WASI_EPHEMERAL_NN != 0 */


#if WASM_ENABLE_WASI_EPHEMERAL_NN != 0
wasi_nn_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_len, uint32_t *output_tensor_size)
#else  /* WASM_ENABLE_WASI_EPHEMERAL_NN == 0 */
wasi_nn_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)
#endif /* WASM_ENABLE_WASI_EPHEMERAL_NN != 0 */

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@ayakoakasaka ayakoakasaka May 27, 2025

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If so, we need to define it at wasi_nn.h

wasi_nn_error
 get_output(graph_execution_context ctx, uint32_t index,
           tensor_data output_tensor, uint32_t output_tensor_len, uint32_t *output_tensor_size)     __attribute__((import_module("wasi_ephemeral_nn")));

But the backend definition doesn't look like it matches (signature might be ok)

wasm-micro-runtime/core/iwasm/libraries/wasi-nn/src/wasi_nn_tensorflowlite.cpp

Lines 366 to 368 in c018b8a

__attribute__((visibility("default"))) wasi_nn_error
get_output(void *tflite_ctx, graph_execution_context ctx, uint32_t index,
tensor_data output_tensor, uint32_t *output_tensor_size)

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@lum1n0us lum1n0us May 27, 2025 •
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Yes. I am suggestion this #4267 (comment) as the new content of wasi_nn.h. Plus, #4267 (comment).

__attribute__((import_module("wasi_nn")));

/**
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7 changes: 2 additions & 5 deletions core/iwasm/libraries/wasi-nn/src/wasi_nn_tensorflowlite.cpp
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Expand Up @@ -85,14 +85,11 @@ is_valid_graph(TFLiteContext *tfl_ctx, graph g)
NN_ERR_PRINTF("Invalid graph: %d >= %d.", g, MAX_GRAPHS_PER_INST);
return runtime_error;
}
if (tfl_ctx->models[g].model_pointer == NULL) {
if (tfl_ctx->models[g].model_pointer == NULL
&& tfl_ctx->models[g].model == NULL) {
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The original version can output different information based on various invalid argument cases. Is there a specific reason we need to merge them?

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@HongxiaWangSSSS HongxiaWangSSSS May 13, 2025

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If call load_by_name(), there is no need to save the tflite buf to model_pointer.
https://github.com/HongxiaWangSSSS/wasm-micro-runtime/blob/c5414fd28baf973e3c95db1318de4d26f88007d3/core/iwasm/libraries/wasi-nn/src/wasi_nn_tensorflowlite.cpp#L141C49-L141C68

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@HongxiaWangSSSS HongxiaWangSSSS May 13, 2025

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I'm not sure why not free model_point after below operation in load()
https://github.com/HongxiaWangSSSS/wasm-micro-runtime/blob/c5414fd28baf973e3c95db1318de4d26f88007d3/core/iwasm/libraries/wasi-nn/src/wasi_nn_tensorflowlite.cpp#L151

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@lum1n0us lum1n0us May 13, 2025

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It is required to validate TFLitesContext.models[g] for both cases, using load() and load_by_name(). It will not be acceptable if the change disables one of these cases.

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@HongxiaWangSSSS HongxiaWangSSSS May 13, 2025

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Whether it is load or load_by_name, the check of models[g].model_pointerdoes not seem to be necessary, just make sure the models[g].model is not NULL maybe is enough.
Do you have any idea?

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@lum1n0us lum1n0us May 13, 2025

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does not seem to be necessary.

Why is that?

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@HongxiaWangSSSS HongxiaWangSSSS May 19, 2025

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after this operation, the models[g].model_pointer's connect has been saved in models[g].model.

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@lum1n0us lum1n0us May 27, 2025

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https://github.com/tensorflow/tensorflow/blob/02896e880298894beedc71f8666f6949ad0e174f/tensorflow/compiler/mlir/lite/core/model_builder_base.h#L169-#L184

  /// Builds a model based on a pre-loaded flatbuffer.
  /// Caller retains ownership of the buffer and should keep it alive until
  /// the returned object is destroyed. Caller also retains ownership of
  /// `error_reporter` and must ensure its lifetime is longer than the
  /// FlatBufferModelBase instance.
  /// Returns a nullptr in case of failure.
  /// NOTE: this does NOT validate the buffer so it should NOT be called on
  /// invalid/untrusted input. Use VerifyAndBuildFromBuffer in that case
  static std::unique_ptr<T> BuildFromBuffer(
      const char* caller_owned_buffer, size_t buffer_size,
      ErrorReporter* error_reporter = T::GetDefaultErrorReporter()) {
    error_reporter = ValidateErrorReporter(error_reporter);
    std::unique_ptr<Allocation> allocation(
        new MemoryAllocation(caller_owned_buffer, buffer_size, error_reporter));
    return BuildFromAllocation(std::move(allocation), error_reporter);
  }

If I understand correctly, model_pointer acts as a pre-allocated buffer, and its ownership is still held by the caller, in our case, tfl_ctx. Meanwhile, model holds the ownership of the result from tflite::FlatBufferModel::BuildFromBuffer(). Therefore, both are required.

NN_ERR_PRINTF("Context (model) non-initialized.");
return runtime_error;
}
if (tfl_ctx->models[g].model == NULL) {
NN_ERR_PRINTF("Context (tflite model) non-initialized.");
return runtime_error;
}
return success;
}

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7 changes: 6 additions & 1 deletion core/iwasm/libraries/wasi-nn/test/utils.c
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Expand Up @@ -58,7 +58,7 @@ wasm_load(char *model_name, graph *g, execution_target target)
wasi_nn_error
wasm_load_by_name(const char *model_name, graph *g)
{
wasi_nn_error res = load_by_name(model_name, g);
wasi_nn_error res = load_by_name(model_name, strlen(model_name), g);
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@lum1n0us lum1n0us May 27, 2025

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better be

#if WASM_ENABLE_WASI_EPHEMERAL_NN != 0
wasi_nn_error
wasm_load_by_name(const char *model_name, graph *g)
{
    wasi_nn_error res = load_by_name(model_name, strlen(model_name), g);
    return res;
}
#endif

return res;
}

Expand Down Expand Up @@ -108,7 +108,12 @@ run_inference(execution_target target, float *input, uint32_t *input_size,
uint32_t num_output_tensors)
{
graph graph;

#if WASM_ENABLE_WASI_EPHEMERAL_NN == 0
if (wasm_load(model_name, &graph, target) != success) {
#else
if (wasm_load_by_name(model_name, &graph) != success) {
#endif
NN_ERR_PRINTF("Error when loading model.");
exit(1);
}
Expand Down
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