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[CONTRIB] TFLite Runtime (apache#4439)
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# 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. | ||
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if(NOT USE_TFLITE STREQUAL "OFF") | ||
message(STATUS "Build with contrib.tflite") | ||
if (USE_TENSORFLOW_PATH STREQUAL "none") | ||
set(USE_TENSORFLOW_PATH ${CMAKE_CURRENT_SOURCE_DIR}/tensorflow) | ||
endif() | ||
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file(GLOB TFLITE_CONTRIB_SRC src/runtime/contrib/tflite/*.cc) | ||
list(APPEND RUNTIME_SRCS ${TFLITE_CONTRIB_SRC}) | ||
include_directories(${USE_TENSORFLOW_PATH}) | ||
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if (USE_TFLITE STREQUAL "ON") | ||
set(USE_TFLITE ${USE_TENSORFLOW_PATH}/tensorflow/lite/tools/make/gen/*/lib) | ||
endif() | ||
find_library(TFLITE_CONTRIB_LIB libtensorflow-lite.a ${USE_TFLITE}) | ||
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list(APPEND TVM_RUNTIME_LINKER_LIBS ${TFLITE_CONTRIB_LIB}) | ||
list(APPEND TVM_RUNTIME_LINKER_LIBS rt dl flatbuffers) | ||
endif() |
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# 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. | ||
"""TFLite runtime that load and run tflite models.""" | ||
from .._ffi.function import get_global_func | ||
from ..rpc import base as rpc_base | ||
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def create(tflite_model_bytes, ctx): | ||
"""Create a runtime executor module given a tflite model and context. | ||
Parameters | ||
---------- | ||
tflite_model_byte : bytes | ||
The tflite model to be deployed in bytes string format. | ||
ctx : TVMContext | ||
The context to deploy the module. It can be local or remote when there | ||
is only one TVMContext. | ||
Returns | ||
------- | ||
tflite_runtime : TFLiteModule | ||
Runtime tflite module that can be used to execute the tflite model. | ||
""" | ||
device_type = ctx.device_type | ||
if device_type >= rpc_base.RPC_SESS_MASK: | ||
fcreate = ctx._rpc_sess.get_function("tvm.tflite_runtime.create") | ||
return TFLiteModule(fcreate(bytearray(tflite_model_bytes), ctx)) | ||
fcreate = get_global_func("tvm.tflite_runtime.create") | ||
return TFLiteModule(fcreate(bytearray(tflite_model_bytes), ctx)) | ||
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class TFLiteModule(object): | ||
"""Wrapper runtime module. | ||
This is a thin wrapper of the underlying TVM module. | ||
you can also directly call set_input, run, and get_output | ||
of underlying module functions | ||
Parameters | ||
---------- | ||
module : Module | ||
The interal tvm module that holds the actual tflite functions. | ||
Attributes | ||
---------- | ||
module : Module | ||
The interal tvm module that holds the actual tflite functions. | ||
""" | ||
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def __init__(self, module): | ||
self.module = module | ||
self._set_input = module["set_input"] | ||
self._invoke = module["invoke"] | ||
self._get_output = module["get_output"] | ||
self._allocate_tensors = module["allocate_tensors"] | ||
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def set_input(self, index, value): | ||
"""Set inputs to the module via kwargs | ||
Parameters | ||
---------- | ||
key : int or str | ||
The input key | ||
value : the input value. | ||
The input key | ||
params : dict of str to NDArray | ||
Additonal arguments | ||
""" | ||
self._set_input(index, value) | ||
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def invoke(self): | ||
"""Invoke forward execution of the model | ||
Parameters | ||
---------- | ||
input_dict: dict of str to NDArray | ||
List of input values to be feed to | ||
""" | ||
self._invoke() | ||
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def allocate_tensors(self): | ||
"""Allocate space for all tensors. | ||
""" | ||
self._allocate_tensors() | ||
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def get_output(self, index): | ||
"""Get index-th output to out | ||
Parameters | ||
---------- | ||
index : int | ||
The output index | ||
""" | ||
return self._get_output(index) |
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/* | ||
* 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. | ||
*/ | ||
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/*! | ||
* \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> | ||
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#include "tflite_runtime.h" | ||
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namespace tvm { | ||
namespace runtime { | ||
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#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; \ | ||
} | ||
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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); | ||
} | ||
} | ||
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void TFLiteRuntime::Init(const std::string& tflite_model_bytes, | ||
TVMContext ctx) { | ||
const char* buffer = tflite_model_bytes.c_str(); | ||
size_t buffer_size = tflite_model_bytes.size(); | ||
std::unique_ptr<tflite::FlatBufferModel> model = | ||
tflite::FlatBufferModel::BuildFromBuffer(buffer, buffer_size); | ||
tflite::ops::builtin::BuiltinOpResolver resolver; | ||
tflite::InterpreterBuilder(*model, resolver)(&interpreter_); | ||
ctx_ = ctx; | ||
} | ||
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void TFLiteRuntime::AllocateTensors() { | ||
interpreter_->AllocateTensors(); | ||
} | ||
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void TFLiteRuntime::Invoke() { | ||
interpreter_->Invoke(); | ||
} | ||
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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]; | ||
} | ||
}); | ||
} | ||
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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; | ||
} | ||
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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") { | ||
return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { | ||
this->Invoke(); | ||
}); | ||
} else if (name == "allocate_tensors") { | ||
return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { | ||
this->AllocateTensors(); | ||
}); | ||
} else { | ||
return PackedFunc(); | ||
} | ||
} | ||
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Module TFLiteRuntimeCreate(const std::string& tflite_model_bytes, | ||
TVMContext ctx) { | ||
auto exec = make_object<TFLiteRuntime>(); | ||
exec->Init(tflite_model_bytes, ctx); | ||
return Module(exec); | ||
} | ||
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TVM_REGISTER_GLOBAL("tvm.tflite_runtime.create") | ||
.set_body([](TVMArgs args, TVMRetValue* rv) { | ||
*rv = TFLiteRuntimeCreate(args[0], args[1]); | ||
}); | ||
} // namespace runtime | ||
} // namespace tvm |
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