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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2019/11/29 05:25:43 UTC
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request
#4439: [CONTRIB] TFLite Runtime
FrozenGene commented on a change in pull request #4439: [CONTRIB] TFLite Runtime
URL: https://github.com/apache/incubator-tvm/pull/4439#discussion_r351984740
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File path: src/runtime/contrib/tflite/tflite_runtime.cc
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@@ -0,0 +1,189 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file tflite_runtime.cc
+ */
+#include <tvm/runtime/registry.h>
+#include <tvm/dtype.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+
+
+#include "tflite_runtime.h"
+
+namespace tvm {
+namespace runtime {
+
+#define TVM_DTYPE_DISPATCH(type, DType, ...) \
+ if (type == Float(64)) { \
+ typedef double DType; \
+ {__VA_ARGS__} \
+ } else if (type == Float(32)) { \
+ typedef float DType; \
+ {__VA_ARGS__} \
+ } else if (type == Float(16)) { \
+ typedef uint16_t DType; \
+ {__VA_ARGS__} \
+ } else if (type == Int(64)) { \
+ typedef int64_t DType; \
+ {__VA_ARGS__} \
+ } else if (type == Int(32)) { \
+ typedef int32_t DType; \
+ {__VA_ARGS__} \
+ } else if (type == Int(16)) { \
+ typedef int16_t DType; \
+ {__VA_ARGS__} \
+ } else if (type == Int(8)) { \
+ typedef int8_t DType; \
+ {__VA_ARGS__} \
+ } else if (type == UInt(64)) { \
+ typedef uint64_t DType; \
+ {__VA_ARGS__} \
+ } else if (type == UInt(32)) { \
+ typedef uint32_t DType; \
+ {__VA_ARGS__} \
+ } else if (type == UInt(16)) { \
+ typedef uint16_t DType; \
+ {__VA_ARGS__} \
+ } else if (type == UInt(8)) { \
+ typedef uint8_t DType; \
+ {__VA_ARGS__} \
+ } else { \
+ LOG(FATAL) << "unknown data type " << type; \
+ }
+
+DataType TfLiteDType2TVMDType(TfLiteType dtype) {
+ switch (dtype) {
+ case kTfLiteFloat32:
+ return Float(32);
+ case kTfLiteInt32:
+ return Int(32);
+ case kTfLiteInt64:
+ return Int(64);
+ case kTfLiteInt16:
+ return Int(16);
+ case kTfLiteInt8:
+ return Int(8);
+ case kTfLiteUInt8:
+ return UInt(8);
+ case kTfLiteFloat16:
+ return Float(16);
+ default:
+ LOG(FATAL) << "tflite data type not support yet: " << dtype;
+ return Float(32);
+ }
+}
+
+
+void TFLiteRuntime::Init(const std::string& tflite_fname,
+ TVMContext ctx) {
+ std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile(tflite_fname.c_str());
+ tflite::ops::builtin::BuiltinOpResolver resolver;
+ tflite::InterpreterBuilder(*model, resolver)(&interpreter_);
+ ctx_ = ctx;
+}
+
+void TFLiteRuntime::AllocateTensors() {
+ interpreter_->AllocateTensors();
+}
+
+void TFLiteRuntime::Invoke() {
+ interpreter_->Invoke();
+}
+
+void TFLiteRuntime::SetInput(int index, DLTensor* data_in) {
+ DataType dtype(data_in->dtype);
+ TVM_DTYPE_DISPATCH(dtype, DType, {
+ DType* dest = interpreter_->typed_input_tensor<DType>(index);
+ DType* src = static_cast<DType*>(data_in->data);
+ CHECK(data_in->strides == NULL);
+ int64_t size = 1;
+ for (int64_t i = 0; i < data_in->ndim; ++i) {
+ size *= data_in->shape[i];
+ }
+ for (int64_t i = 0; i < size; ++i) {
+ dest[i] = src[i];
+ }
+ });
+}
+
+NDArray TFLiteRuntime::GetOutput(int index) const {
+ TfLiteTensor* output = interpreter_->output_tensor(index);
+ DataType dtype = TfLiteDType2TVMDType(output->type);
+ TfLiteIntArray* dims = output->dims;
+ int64_t size = 1;
+ std::vector<int64_t> shape;
+ for (int i = 0; i < dims->size; ++i) {
+ shape.push_back(dims->data[i]);
+ size *= dims->data[i];
+ }
+
+ NDArray ret = NDArray::Empty(shape, dtype, ctx_);
+ TVM_DTYPE_DISPATCH(dtype, DType, {
+ DType* dest = static_cast<DType*>(ret->data);
+ DType* src = interpreter_->typed_output_tensor<DType>(index);
+ for (int64_t i = 0; i < size; ++i) {
+ dest[i] = src[i];
+ }
+ });
+ return ret;
+}
+
+PackedFunc TFLiteRuntime::GetFunction(
+ const std::string& name,
+ const ObjectPtr<Object>& sptr_to_self) {
+ // Return member functions during query.
+ if (name == "set_input") {
+ return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
+ int in_idx = args[0];
+ CHECK_GE(in_idx, 0);
+ this->SetInput(in_idx, args[1]);
+ });
+ } else if (name == "get_output") {
+ return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
+ *rv = this->GetOutput(args[0]);
+ });
+ } else if (name == "invoke") {
Review comment:
could we change the name from `invoke` into `run`? The api interface would be like our graphruntime, set_input, get_output, run and so on.
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