wasi-nn: Support uint8 quantized networks (#2433)

Support (non-full) uint8 quantized networks.
Inputs and outputs are still required to be `float`. The (de)quantization is done internally by wasi-nn.

Example generated from `quantized_model.py`:
![Screenshot from 2023-08-07 17-57-05](https://github.com/bytecodealliance/wasm-micro-runtime/assets/80318361/91f12ff6-870c-427a-b1dc-e307f7d1f5ee)

Visualization with [netron](https://netron.app/).

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

@@ -0,0 +1,2 @@
+**/*.wasm
+**/*.tflite

core/iwasm/libraries/wasi-nn/src/wasi_nn_tensorflowlite.cpp

@@ -285,14 +285,37 @@ tensorflowlite_set_input(void *tflite_ctx, graph_execution_context ctx,
         return invalid_argument;
     }
 
-    auto *input =
-        tfl_ctx->interpreters[ctx].interpreter->typed_input_tensor<float>(
-            index);
-    if (input == NULL)
-        return missing_memory;
+    if (tensor->quantization.type == kTfLiteNoQuantization) {
+        NN_DBG_PRINTF("No quantization information. Using float as default");
+        float *it =
+            tfl_ctx->interpreters[ctx].interpreter->typed_input_tensor<float>(
+                index);
+
+        int size = model_tensor_size * sizeof(float);
+        bh_memcpy_s(it, size, input_tensor->data, size);
+    }
+    else { // TODO: Assumming uint8 quantized networks.
+        TfLiteAffineQuantization *quant_info =
+            (TfLiteAffineQuantization *)tensor->quantization.params;
+        if (quant_info->scale->size != 1 || quant_info->zero_point->size != 1) {
+            NN_ERR_PRINTF("Quantization per channel is not supported");
+            return runtime_error;
+        }
+        uint8_t *it =
+            tfl_ctx->interpreters[ctx].interpreter->typed_input_tensor<uint8_t>(
+                index);
+
+        float scale = quant_info->scale->data[0];
+        float zero_point = (float)quant_info->zero_point->data[0];
+        NN_DBG_PRINTF("input tensor: (scale, offset) = (%f, %f)", scale,
+                      zero_point);
+
+        float *input_tensor_f = (float *)input_tensor->data;
+        for (uint32_t i = 0; i < model_tensor_size; ++i) {
+            it[i] = (uint8_t)(input_tensor_f[i] / scale + zero_point);
+        }
+    }
 
-    bh_memcpy_s(input, model_tensor_size * sizeof(float), input_tensor->data,
-                model_tensor_size * sizeof(float));
     return success;
 }
 
@@ -325,6 +348,7 @@ tensorflowlite_get_output(void *tflite_ctx, graph_execution_context ctx,
     NN_DBG_PRINTF("Number of tensors (%d)", num_output_tensors);
 
     if (index + 1 > num_output_tensors) {
+        NN_ERR_PRINTF("Index %d is invalid.", index);
         return runtime_error;
     }
 
@@ -343,15 +367,37 @@ tensorflowlite_get_output(void *tflite_ctx, graph_execution_context ctx,
         return missing_memory;
     }
 
-    float *tensor_f =
-        tfl_ctx->interpreters[ctx].interpreter->typed_output_tensor<float>(
-            index);
-    for (uint32_t i = 0; i < model_tensor_size; ++i)
-        NN_DBG_PRINTF("output: %f", tensor_f[i]);
+    if (tensor->quantization.type == kTfLiteNoQuantization) {
+        NN_DBG_PRINTF("No quantization information");
+        float *ot =
+            tfl_ctx->interpreters[ctx].interpreter->typed_output_tensor<float>(
+                index);
+
+        int size = model_tensor_size * sizeof(float);
+        bh_memcpy_s(output_tensor, size, ot, size);
+    }
+    else { // TODO: Assumming uint8 quantized networks.
+        TfLiteAffineQuantization *quant_info =
+            (TfLiteAffineQuantization *)tensor->quantization.params;
+        if (quant_info->scale->size != 1 || quant_info->zero_point->size != 1) {
+            NN_ERR_PRINTF("Quantization per channel is not supported");
+            return runtime_error;
+        }
+        uint8_t *ot = tfl_ctx->interpreters[ctx]
+                          .interpreter->typed_output_tensor<uint8_t>(index);
+
+        float scale = quant_info->scale->data[0];
+        float zero_point = (float)quant_info->zero_point->data[0];
+        NN_DBG_PRINTF("output tensor: (scale, offset) = (%f, %f)", scale,
+                      zero_point);
+
+        float *output_tensor_f = (float *)output_tensor;
+        for (uint32_t i = 0; i < model_tensor_size; ++i) {
+            output_tensor_f[i] = (ot[i] - zero_point) * scale;
+        }
+    }
 
     *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/test/Dockerfile.cpu

@@ -30,7 +30,6 @@ RUN make -j "$(grep -c ^processor /proc/cpuinfo)"
 
 FROM ubuntu:22.04
 
-COPY --from=base /home/wamr/product-mini/platforms/linux/build/libvmlib.so /libvmlib.so
 COPY --from=base /home/wamr/product-mini/platforms/linux/build/iwasm /iwasm
 
 ENTRYPOINT [ "/iwasm" ]

core/iwasm/libraries/wasi-nn/test/Dockerfile.nvidia-gpu

@@ -44,7 +44,6 @@ RUN mkdir -p /etc/OpenCL/vendors && \
 ENV NVIDIA_VISIBLE_DEVICES=all
 ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
 
-COPY --from=base /home/wamr/product-mini/platforms/linux/build/libvmlib.so /libvmlib.so
 COPY --from=base /home/wamr/product-mini/platforms/linux/build/iwasm /iwasm
 
 ENTRYPOINT [ "/iwasm" ]

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

@@ -1,6 +1,10 @@
+#!/bin/sh
+
 # Copyright (C) 2019 Intel Corporation.  All rights reserved.
 # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 
+CURR_PATH=$(cd $(dirname $0) && pwd -P)
+
 # WASM application that uses WASI-NN
 
 /opt/wasi-sdk/bin/clang \
@@ -13,9 +17,25 @@
 
 # TFLite models to use in the tests
 
-cd models
+cd ${CURR_PATH}/models
 python3 average.py
 python3 max.py
 python3 mult_dimension.py
 python3 mult_outputs.py
 python3 sum.py
+
+# Specific tests for TPU
+
+cd ${CURR_PATH}
+/opt/wasi-sdk/bin/clang \
+    -Wl,--allow-undefined \
+    -Wl,--strip-all,--no-entry \
+    --sysroot=/opt/wasi-sdk/share/wasi-sysroot \
+    -I../include -I../src/utils \
+    -o test_tensorflow_quantized.wasm \
+    test_tensorflow_quantized.c utils.c
+
+cd ${CURR_PATH}/models
+python3 quantized.py
+
+cd ${CURR_PATH}

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

@@ -0,0 +1,30 @@
+# 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
+import pathlib
+
+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)
+
+])
+
+def representative_dataset():
+    for _ in range(1000):
+      data = np.random.randint(0, 25, (1, 5, 5, 1))
+      yield [data.astype(np.float32)]
+
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.representative_dataset = representative_dataset
+converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+converter.inference_input_type = tf.uint8  # or tf.int8
+converter.inference_output_type = tf.uint8  # or tf.int8
+tflite_model = converter.convert()
+
+tflite_models_dir = pathlib.Path("./")
+tflite_model_file = tflite_models_dir / "quantized_model.tflite"
+tflite_model_file.write_bytes(tflite_model)

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

@@ -0,0 +1,63 @@
+/*
+ * Copyright (C) 2019 Intel Corporation.  All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <assert.h>
+#include <string.h>
+#include <math.h>
+
+#include "utils.h"
+#include "logger.h"
+
+#undef EPSILON
+#define EPSILON 1e-2
+
+void
+test_average_quantized(execution_target target)
+{
+    int dims[] = { 1, 5, 5, 1 };
+    input_info input = create_input(dims);
+
+    uint32_t output_size = 0;
+    float *output =
+        run_inference(target, input.input_tensor, input.dim, &output_size,
+                      "./models/quantized_model.tflite", 1);
+
+    NN_INFO_PRINTF("Output size: %d", output_size);
+    NN_INFO_PRINTF("Result: average is %f", output[0]);
+    // NOTE: 11.95 instead of 12 because of errors due quantization
+    assert(fabs(output[0] - 11.95) < EPSILON);
+
+    free(input.dim);
+    free(input.input_tensor);
+    free(output);
+}
+
+int
+main()
+{
+    char *env = getenv("TARGET");
+    if (env == NULL) {
+        NN_INFO_PRINTF("Usage:\n--env=\"TARGET=[cpu|gpu|tpu]\"");
+        return 1;
+    }
+    execution_target target;
+    if (strcmp(env, "cpu") == 0)
+        target = cpu;
+    else if (strcmp(env, "gpu") == 0)
+        target = gpu;
+    else if (strcmp(env, "tpu") == 0)
+        target = tpu;
+    else {
+        NN_ERR_PRINTF("Wrong target!");
+        return 1;
+    }
+    NN_INFO_PRINTF("################### Testing quantized model...");
+    test_average_quantized(target);
+
+    NN_INFO_PRINTF("Tests: passed!");
+    return 0;
+}