Support RESHAPE operator in XNNPACK delegate
PiperOrigin-RevId: 320678814 Change-Id: I5229605df654b35c84a968db3eef8afd498487e9
This commit is contained in:
Marat Dukhan
TensorFlower Gardener
parent
9060253512
commit
37d20f87f3
tensorflow
lite/delegates/xnnpack
workspace.bzl@ -213,6 +213,22 @@ cc_library(
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],
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)
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cc_library(
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name = "reshape_tester",
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testonly = 1,
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srcs = ["reshape_tester.cc"],
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hdrs = ["reshape_tester.h"],
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deps = [
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"//tensorflow/lite:framework",
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"//tensorflow/lite:schema_fbs_version",
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"//tensorflow/lite/c:common",
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"//tensorflow/lite/kernels:builtin_ops",
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"//tensorflow/lite/schema:schema_fbs",
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"@com_google_googletest//:gtest",
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"@flatbuffers",
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],
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)
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cc_library(
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name = "softmax_tester",
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testonly = 1,
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@ -604,6 +620,21 @@ cc_test(
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],
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)
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cc_test(
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name = "reshape_test",
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srcs = ["reshape_test.cc"],
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linkopts = select({
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"//tensorflow:emscripten": EMSCRIPTEN_LINKOPTS,
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"//conditions:default": [],
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}),
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deps = [
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":reshape_tester",
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":test_main",
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":xnnpack_delegate_test_mode",
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"@com_google_googletest//:gtest",
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],
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)
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cc_test(
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name = "round_test",
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srcs = ["round_test.cc"],
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225
tensorflow/lite/delegates/xnnpack/reshape_test.cc
Normal file
225
tensorflow/lite/delegates/xnnpack/reshape_test.cc
Normal file
@ -0,0 +1,225 @@
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/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <algorithm>
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#include <cstdint>
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#include <functional>
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#include <memory>
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#include <random>
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#include <gtest/gtest.h>
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#include "tensorflow/lite/delegates/xnnpack/reshape_tester.h"
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#include "tensorflow/lite/delegates/xnnpack/xnnpack_delegate.h"
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namespace tflite {
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namespace xnnpack {
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TEST(Reshape, 4DShapeAsInput) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> input_shape{
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{shape_rng(), shape_rng(), shape_rng(), shape_rng()}};
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std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
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std::shuffle(output_shape.begin(), output_shape.end(), rng);
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ReshapeTester()
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.InputShape(input_shape)
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.OutputShape(output_shape)
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.OutputShapeAsInput(true)
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, 4DShapeAsParam) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> input_shape{
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{shape_rng(), shape_rng(), shape_rng(), shape_rng()}};
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std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
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std::shuffle(output_shape.begin(), output_shape.end(), rng);
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ReshapeTester()
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.InputShape(input_shape)
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.OutputShape(output_shape)
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.OutputShapeAsInput(false)
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, 3DShapeAsInput) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> input_shape{
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{shape_rng(), shape_rng(), shape_rng()}};
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std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
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std::shuffle(output_shape.begin(), output_shape.end(), rng);
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ReshapeTester()
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.InputShape(input_shape)
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.OutputShape(output_shape)
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.OutputShapeAsInput(true)
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, 3DShapeAsParam) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> input_shape{
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{shape_rng(), shape_rng(), shape_rng()}};
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std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
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std::shuffle(output_shape.begin(), output_shape.end(), rng);
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ReshapeTester()
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.InputShape(input_shape)
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.OutputShape(output_shape)
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.OutputShapeAsInput(false)
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, 2DShapeAsInput) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> input_shape{{shape_rng(), shape_rng()}};
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std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
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std::shuffle(output_shape.begin(), output_shape.end(), rng);
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ReshapeTester()
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.InputShape(input_shape)
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.OutputShape(output_shape)
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.OutputShapeAsInput(true)
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, 2DShapeAsParam) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> input_shape{{shape_rng(), shape_rng()}};
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std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
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std::shuffle(output_shape.begin(), output_shape.end(), rng);
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ReshapeTester()
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.InputShape(input_shape)
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.OutputShape(output_shape)
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.OutputShapeAsInput(false)
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, 1DShapeAsInput) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> shape({shape_rng()});
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ReshapeTester()
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.InputShape(shape)
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.OutputShape(shape)
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.OutputShapeAsInput(true)
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, 1DShapeAsParam) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> shape({shape_rng()});
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ReshapeTester()
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.InputShape(shape)
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.OutputShape(shape)
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.OutputShapeAsInput(false)
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, 0D) {
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(nullptr),
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TfLiteXNNPackDelegateDelete);
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ReshapeTester()
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.InputShape(std::vector<int32_t>())
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.OutputShape(std::vector<int32_t>())
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.Test(xnnpack_delegate.get());
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}
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TEST(Reshape, MultiThreading) {
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TfLiteXNNPackDelegateOptions delegate_options =
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TfLiteXNNPackDelegateOptionsDefault();
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delegate_options.num_threads = 2;
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std::unique_ptr<TfLiteDelegate, decltype(&TfLiteXNNPackDelegateDelete)>
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xnnpack_delegate(TfLiteXNNPackDelegateCreate(&delegate_options),
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TfLiteXNNPackDelegateDelete);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto shape_rng =
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std::bind(std::uniform_int_distribution<int32_t>(2, 10), std::ref(rng));
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const std::vector<int32_t> input_shape{
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{shape_rng(), shape_rng(), shape_rng(), shape_rng()}};
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std::vector<int32_t> output_shape(input_shape.cbegin(), input_shape.cend());
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std::shuffle(output_shape.begin(), output_shape.end(), rng);
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ReshapeTester()
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.InputShape(input_shape)
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.OutputShape(output_shape)
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.OutputShapeAsInput(true)
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.Test(xnnpack_delegate.get());
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}
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} // namespace xnnpack
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} // namespace tflite
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181
tensorflow/lite/delegates/xnnpack/reshape_tester.cc
Normal file
181
tensorflow/lite/delegates/xnnpack/reshape_tester.cc
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@ -0,0 +1,181 @@
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/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/delegates/xnnpack/reshape_tester.h"
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#include <array>
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#include <cstdint>
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#include <functional>
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#include <numeric>
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#include <random>
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#include <vector>
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#include <gtest/gtest.h>
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#include "flatbuffers/flatbuffers.h" // from @flatbuffers
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/kernels/register.h"
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#include "tensorflow/lite/model.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/version.h"
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namespace tflite {
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namespace xnnpack {
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void ReshapeTester::Test(TfLiteDelegate* delegate) const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng =
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std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
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ASSERT_EQ(InputSize(), OutputSize());
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std::vector<char> buffer = CreateTfLiteModel();
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const Model* model = GetModel(buffer.data());
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std::unique_ptr<Interpreter> delegate_interpreter;
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ASSERT_EQ(
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InterpreterBuilder(model, ::tflite::ops::builtin::BuiltinOpResolver())(
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&delegate_interpreter),
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kTfLiteOk);
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std::unique_ptr<Interpreter> default_interpreter;
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ASSERT_EQ(
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InterpreterBuilder(model, ::tflite::ops::builtin::BuiltinOpResolver())(
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&default_interpreter),
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kTfLiteOk);
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ASSERT_TRUE(delegate_interpreter);
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ASSERT_TRUE(default_interpreter);
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ASSERT_EQ(delegate_interpreter->inputs().size(), 1);
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ASSERT_EQ(default_interpreter->inputs().size(), 1);
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ASSERT_EQ(delegate_interpreter->outputs().size(), 1);
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ASSERT_EQ(default_interpreter->outputs().size(), 1);
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ASSERT_EQ(delegate_interpreter->AllocateTensors(), kTfLiteOk);
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ASSERT_EQ(default_interpreter->AllocateTensors(), kTfLiteOk);
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ASSERT_EQ(delegate_interpreter->ModifyGraphWithDelegate(delegate), kTfLiteOk);
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float* default_input_data = default_interpreter->typed_tensor<float>(
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default_interpreter->inputs()[0]);
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std::generate(default_input_data, default_input_data + InputSize(),
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std::ref(f32rng));
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float* delegate_input_data = delegate_interpreter->typed_tensor<float>(
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delegate_interpreter->inputs()[0]);
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std::copy(default_input_data, default_input_data + InputSize(),
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delegate_input_data);
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ASSERT_EQ(default_interpreter->Invoke(), kTfLiteOk);
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ASSERT_EQ(delegate_interpreter->Invoke(), kTfLiteOk);
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float* default_output_data = default_interpreter->typed_tensor<float>(
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default_interpreter->outputs()[0]);
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float* delegate_output_data = delegate_interpreter->typed_tensor<float>(
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delegate_interpreter->outputs()[0]);
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for (size_t i = 0; i < OutputSize(); i++) {
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ASSERT_EQ(delegate_output_data[i], default_output_data[i]);
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}
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}
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std::vector<char> ReshapeTester::CreateTfLiteModel() const {
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flatbuffers::FlatBufferBuilder builder;
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flatbuffers::Offset<OperatorCode> operator_code =
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CreateOperatorCode(builder, BuiltinOperator_RESHAPE, 0);
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std::vector<flatbuffers::Offset<Buffer>> buffers{{
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CreateBuffer(builder, builder.CreateVector({})),
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}};
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if (OutputShapeAsInput()) {
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buffers.emplace_back(CreateBuffer(
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builder, builder.CreateVector(
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reinterpret_cast<const uint8_t*>(OutputShape().data()),
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OutputShape().size() * sizeof(int32_t))));
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}
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std::vector<flatbuffers::Offset<Tensor>> tensors{{
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CreateTensor(builder,
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builder.CreateVector<int32_t>(InputShape().data(),
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InputShape().size()),
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TensorType_FLOAT32),
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CreateTensor(builder,
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builder.CreateVector<int32_t>(OutputShape().data(),
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OutputShape().size()),
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TensorType_FLOAT32),
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}};
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if (OutputShapeAsInput()) {
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const std::array<int32_t, 1> reshape_shape{
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{static_cast<int32_t>(InputShape().size())}};
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tensors.insert(tensors.begin() + 1,
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CreateTensor(builder,
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builder.CreateVector<int32_t>(
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reshape_shape.data(), reshape_shape.size()),
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TensorType_INT32, /*buffer=*/1));
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}
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std::vector<int32_t> op_inputs({0});
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if (OutputShapeAsInput()) {
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op_inputs.push_back(1);
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}
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const std::array<int32_t, 1> op_outputs{{OutputShapeAsInput() ? 2 : 1}};
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BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE;
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flatbuffers::Offset<void> builtin_options = 0;
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if (!OutputShapeAsInput()) {
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builtin_options_type = tflite::BuiltinOptions_ReshapeOptions;
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builtin_options =
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CreateReshapeOptions(
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builder, builder.CreateVector<int32_t>(OutputShape().data(),
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OutputShape().size()))
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.Union();
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}
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const flatbuffers::Offset<Operator> op = CreateOperator(
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builder, /*opcode_index=*/0,
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builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
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builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()),
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builtin_options_type, builtin_options);
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|
||||
const std::array<int32_t, 1> subgraph_inputs{{op_inputs.front()}};
|
||||
const std::array<int32_t, 1> subgraph_outputs{{op_outputs.front()}};
|
||||
flatbuffers::Offset<SubGraph> subgraph = CreateSubGraph(
|
||||
builder, builder.CreateVector(tensors.data(), tensors.size()),
|
||||
builder.CreateVector<int32_t>(subgraph_inputs.data(),
|
||||
subgraph_inputs.size()),
|
||||
builder.CreateVector<int32_t>(subgraph_outputs.data(),
|
||||
subgraph_outputs.size()),
|
||||
builder.CreateVector(&op, 1));
|
||||
|
||||
const flatbuffers::Offset<Model> model_buffer = CreateModel(
|
||||
builder, TFLITE_SCHEMA_VERSION, builder.CreateVector(&operator_code, 1),
|
||||
builder.CreateVector(&subgraph, 1), builder.CreateString("Reshape model"),
|
||||
builder.CreateVector(buffers.data(), buffers.size()));
|
||||
|
||||
builder.Finish(model_buffer);
|
||||
|
||||
return std::vector<char>(builder.GetBufferPointer(),
|
||||
builder.GetBufferPointer() + builder.GetSize());
|
||||
}
|
||||
|
||||
int32_t ReshapeTester::ComputeSize(const std::vector<int32_t>& shape) {
|
||||
return std::accumulate(shape.cbegin(), shape.cend(), 1,
|
||||
std::multiplies<int32_t>());
|
||||
}
|
||||
|
||||
} // namespace xnnpack
|
||||
} // namespace tflite
|
||||
87
tensorflow/lite/delegates/xnnpack/reshape_tester.h
Normal file
87
tensorflow/lite/delegates/xnnpack/reshape_tester.h
Normal file
@ -0,0 +1,87 @@
|
||||
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
|
||||
|
||||
Licensed 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.
|
||||
==============================================================================*/
|
||||
|
||||
#ifndef TENSORFLOW_LITE_DELEGATES_XNNPACK_RESHAPE_TESTER_H_
|
||||
#define TENSORFLOW_LITE_DELEGATES_XNNPACK_RESHAPE_TESTER_H_
|
||||
|
||||
#include <cstdint>
|
||||
#include <vector>
|
||||
|
||||
#include <gtest/gtest.h>
|
||||
#include "tensorflow/lite/c/common.h"
|
||||
|
||||
namespace tflite {
|
||||
namespace xnnpack {
|
||||
|
||||
class ReshapeTester {
|
||||
public:
|
||||
ReshapeTester() = default;
|
||||
ReshapeTester(const ReshapeTester&) = delete;
|
||||
ReshapeTester& operator=(const ReshapeTester&) = delete;
|
||||
|
||||
inline ReshapeTester& InputShape(const std::vector<int32_t>& input_shape) {
|
||||
for (int32_t input_dim : input_shape) {
|
||||
EXPECT_GT(input_dim, 0);
|
||||
}
|
||||
input_shape_ = std::vector<int32_t>(input_shape.begin(), input_shape.end());
|
||||
input_size_ = ReshapeTester::ComputeSize(input_shape);
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline const std::vector<int32_t>& InputShape() const { return input_shape_; }
|
||||
|
||||
inline ReshapeTester& OutputShape(const std::vector<int32_t>& output_shape) {
|
||||
for (int32_t output_dim : output_shape) {
|
||||
EXPECT_GT(output_dim, 0);
|
||||
}
|
||||
output_shape_ =
|
||||
std::vector<int32_t>(output_shape.begin(), output_shape.end());
|
||||
output_size_ = ReshapeTester::ComputeSize(output_shape);
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline const std::vector<int32_t>& OutputShape() const {
|
||||
return output_shape_;
|
||||
}
|
||||
|
||||
inline int32_t InputSize() const { return input_size_; }
|
||||
|
||||
inline int32_t OutputSize() const { return output_size_; }
|
||||
|
||||
inline ReshapeTester& OutputShapeAsInput(bool shape_as_input) {
|
||||
shape_as_input_ = shape_as_input;
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline bool OutputShapeAsInput() const { return shape_as_input_; }
|
||||
|
||||
void Test(TfLiteDelegate* delegate) const;
|
||||
|
||||
private:
|
||||
std::vector<char> CreateTfLiteModel() const;
|
||||
|
||||
static int32_t ComputeSize(const std::vector<int32_t>& shape);
|
||||
|
||||
std::vector<int32_t> input_shape_;
|
||||
std::vector<int32_t> output_shape_;
|
||||
int32_t input_size_ = 1;
|
||||
int32_t output_size_ = 1;
|
||||
bool shape_as_input_ = false;
|
||||
};
|
||||
|
||||
} // namespace xnnpack
|
||||
} // namespace tflite
|
||||
|
||||
#endif // TENSORFLOW_LITE_DELEGATES_XNNPACK_RESHAPE_TESTER_H_
|
||||
@ -156,9 +156,10 @@ class Subgraph {
|
||||
switch (registration->builtin_code) {
|
||||
case kTfLiteBuiltinMean:
|
||||
case kTfLiteBuiltinPad:
|
||||
// Ignore the second input (static padding, or axes), because it is
|
||||
// represented as parameters of the XNNPACK operator rather than
|
||||
// extra input.
|
||||
case kTfLiteBuiltinReshape:
|
||||
// Ignore the second input (axes, static padding, or new shape),
|
||||
// because it is represented as parameters of the XNNPACK operator
|
||||
// rather than extra input.
|
||||
{
|
||||
const int t = node->inputs->data[0];
|
||||
tensors[t] = t;
|
||||
@ -742,6 +743,20 @@ class Subgraph {
|
||||
return kTfLiteOk;
|
||||
}
|
||||
|
||||
static TfLiteStatus CheckShapeTensorShape(TfLiteContext* context,
|
||||
const TfLiteTensor& tensor,
|
||||
int tensor_index, int node_index) {
|
||||
if (tensor.dims->size != 1) {
|
||||
TF_LITE_MAYBE_KERNEL_LOG(context,
|
||||
"unexpected number of shape dimensions (%d) in "
|
||||
"shape tensor #%d in node #%d: "
|
||||
"expected a 1D tensor",
|
||||
tensor.dims->size, tensor_index, node_index);
|
||||
return kTfLiteError;
|
||||
}
|
||||
return kTfLiteOk;
|
||||
}
|
||||
|
||||
static TfLiteStatus CheckTensorNonDynamicAllocation(
|
||||
TfLiteContext* context, const TfLiteTensor& tensor, int tensor_index,
|
||||
int node_index) {
|
||||
@ -902,6 +917,14 @@ class Subgraph {
|
||||
case kTfLiteBuiltinRelu6:
|
||||
return VisitReluNode(subgraph, logging_context, node_index, node,
|
||||
context->tensors, 0.0f, 6.0f, xnnpack_tensors);
|
||||
case kTfLiteBuiltinReshape: {
|
||||
const TfLiteReshapeParams* reshape_params =
|
||||
static_cast<const TfLiteReshapeParams*>(node->builtin_data);
|
||||
|
||||
return VisitReshapeNode(subgraph, logging_context, node_index, node,
|
||||
context->tensors, reshape_params,
|
||||
xnnpack_tensors);
|
||||
}
|
||||
case kTfLiteBuiltinRound:
|
||||
return VisitRoundNode(subgraph, logging_context, node_index, node,
|
||||
context->tensors, xnnpack_tensors);
|
||||
@ -2343,6 +2366,80 @@ class Subgraph {
|
||||
return kTfLiteOk;
|
||||
}
|
||||
|
||||
static TfLiteStatus VisitReshapeNode(
|
||||
xnn_subgraph_t subgraph, TfLiteContext* logging_context, int node_index,
|
||||
TfLiteNode* node, const TfLiteTensor* tensors,
|
||||
const TfLiteReshapeParams* reshape_params,
|
||||
const std::vector<uint32_t>& xnnpack_tensors) {
|
||||
switch (node->inputs->size) {
|
||||
case 1:
|
||||
case 2:
|
||||
break;
|
||||
default:
|
||||
TF_LITE_MAYBE_KERNEL_LOG(
|
||||
logging_context,
|
||||
"unexpected number of inputs (%d) in node #%d: "
|
||||
"either one or two inputs expected",
|
||||
node->inputs->size, node_index);
|
||||
return kTfLiteError;
|
||||
}
|
||||
if (node->outputs->size != 1) {
|
||||
TF_LITE_MAYBE_KERNEL_LOG(
|
||||
logging_context,
|
||||
"unexpected number of outputs (%d) in node #%d: one output expected",
|
||||
node->outputs->size, node_index);
|
||||
return kTfLiteError;
|
||||
}
|
||||
|
||||
const TfLiteTensor& input_tensor = tensors[node->inputs->data[0]];
|
||||
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
|
||||
logging_context, input_tensor, node->inputs->data[0], node_index));
|
||||
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, input_tensor, 0,
|
||||
XNN_MAX_TENSOR_DIMS,
|
||||
node->inputs->data[0]));
|
||||
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
|
||||
logging_context, input_tensor, node->inputs->data[0], node_index));
|
||||
|
||||
if (node->inputs->size == 2) {
|
||||
const TfLiteTensor& shape_tensor = tensors[node->inputs->data[1]];
|
||||
TF_LITE_ENSURE_STATUS(CheckTensorType(logging_context, shape_tensor,
|
||||
kTfLiteInt32, node->inputs->data[1],
|
||||
node_index));
|
||||
TF_LITE_ENSURE_STATUS(CheckShapeTensorShape(
|
||||
logging_context, shape_tensor, node->inputs->data[1], node_index));
|
||||
TF_LITE_ENSURE_STATUS(CheckTensorStaticAllocation(
|
||||
logging_context, shape_tensor, node->inputs->data[1], node_index));
|
||||
}
|
||||
|
||||
const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
|
||||
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
|
||||
logging_context, output_tensor, node->outputs->data[0], node_index));
|
||||
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, output_tensor, 0,
|
||||
XNN_MAX_TENSOR_DIMS,
|
||||
node->outputs->data[0]));
|
||||
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
|
||||
logging_context, output_tensor, node->outputs->data[0], node_index));
|
||||
|
||||
if (subgraph != nullptr) {
|
||||
std::array<size_t, XNN_MAX_TENSOR_DIMS> new_shape;
|
||||
std::copy(&output_tensor.dims->data[0],
|
||||
&output_tensor.dims->data[output_tensor.dims->size],
|
||||
new_shape.begin());
|
||||
const xnn_status status = xnn_define_static_reshape(
|
||||
subgraph, static_cast<size_t>(output_tensor.dims->size),
|
||||
new_shape.data(),
|
||||
/*input_id=*/xnnpack_tensors[node->inputs->data[0]],
|
||||
/*output_id=*/xnnpack_tensors[node->outputs->data[0]], /*flags=*/0);
|
||||
if (status != xnn_status_success) {
|
||||
TF_LITE_KERNEL_LOG(logging_context,
|
||||
"failed to delegate RESHAPE node #%d", node_index);
|
||||
return kTfLiteError;
|
||||
}
|
||||
}
|
||||
|
||||
return kTfLiteOk;
|
||||
}
|
||||
|
||||
static TfLiteStatus VisitRoundNode(
|
||||
xnn_subgraph_t subgraph, TfLiteContext* logging_context, int node_index,
|
||||
TfLiteNode* node, const TfLiteTensor* tensors,
|
||||
|
||||
@ -164,11 +164,11 @@ def tf_repositories(path_prefix = "", tf_repo_name = ""):
|
||||
|
||||
tf_http_archive(
|
||||
name = "XNNPACK",
|
||||
sha256 = "e37a92154c2ff72c3ebf97247617ce2e159ccc23e648fd62ded44a71c3d68c6a",
|
||||
strip_prefix = "XNNPACK-51a01c66c78334c3d5abf4034e9a8a550a8ad4ad",
|
||||
sha256 = "bd4278ebbe3f6b104f46548717b00bdba95acaab3cbac3de4015c65d868259f8",
|
||||
strip_prefix = "XNNPACK-d27202dfeaa8d3a96670ba47f3dce2f19305a092",
|
||||
urls = [
|
||||
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/XNNPACK/archive/51a01c66c78334c3d5abf4034e9a8a550a8ad4ad.zip",
|
||||
"https://github.com/google/XNNPACK/archive/51a01c66c78334c3d5abf4034e9a8a550a8ad4ad.zip",
|
||||
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/XNNPACK/archive/d27202dfeaa8d3a96670ba47f3dce2f19305a092.zip",
|
||||
"https://github.com/google/XNNPACK/archive/d27202dfeaa8d3a96670ba47f3dce2f19305a092.zip",
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user