diff --git a/paddle/fluid/framework/ir/CMakeLists.txt b/paddle/fluid/framework/ir/CMakeLists.txt index 6c1337d3bd78ca408a8a13753e4a0807256cf78a..10afd3c60b27d8efe384235dbf6dd8ddb1145425 100644 --- a/paddle/fluid/framework/ir/CMakeLists.txt +++ b/paddle/fluid/framework/ir/CMakeLists.txt @@ -108,6 +108,7 @@ if(WITH_MKLDNN) pass_library(cpu_bfloat16_placement_pass inference DIR mkldnn) pass_library(cpu_bfloat16_pass inference DIR mkldnn) pass_library(fc_mkldnn_pass inference DIR mkldnn) + pass_library(interpolate_mkldnn_pass inference DIR mkldnn) pass_library(fc_act_mkldnn_fuse_pass inference DIR mkldnn) pass_library(cpu_quantize_placement_pass base DIR mkldnn) pass_library(cpu_quantize_pass inference DIR mkldnn) diff --git a/paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.cc b/paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.cc new file mode 100644 index 0000000000000000000000000000000000000000..06df1caca35b922ac96d7d886296a6dee6bfb764 --- /dev/null +++ b/paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.cc @@ -0,0 +1,67 @@ +// Copyright (c) 2018 PaddlePaddle 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. + +#include "paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.h" +#include +#include +#include "paddle/fluid/platform/enforce.h" + +namespace paddle { +namespace framework { +class OpDesc; +} // namespace framework +} // namespace paddle + +namespace paddle { +namespace framework { +namespace ir { + +class Graph; + +void InterpolateMKLDNNPass::ApplyImpl(ir::Graph* graph) const { + PADDLE_ENFORCE_NOT_NULL(graph, + platform::errors::InvalidArgument( + "Pointer to graph argument should not be NULL.")); + if (!(graph->Has("use_mkldnn") && graph->Get("use_mkldnn"))) { + VLOG(3) << "Do not handle interpolate_mkldnn_pass"; + return; + } + VLOG(4) << "Handle interpolate_mkldnn_pass"; + + Init("interpolate_mkldnn_pass", graph); + + int found_count = 0; + const std::vector interpolate_op_types = { + "bilinear_interp", "nearest_interp", "trilinear_interp", "bicubic_interp", + "linear_interp"}; + + for (const Node* node : graph->Nodes()) { + if (node->IsOp() && + std::find(interpolate_op_types.begin(), interpolate_op_types.end(), + node->Name()) != interpolate_op_types.end()) { + auto* op_desc = node->Op(); + op_desc->SetAttr("use_mkldnn", true); + ++found_count; + } + } + + AddStatis(found_count); +} + +} // namespace ir +} // namespace framework +} // namespace paddle + +REGISTER_PASS(interpolate_mkldnn_pass, + paddle::framework::ir::InterpolateMKLDNNPass); diff --git a/paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.h b/paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.h new file mode 100644 index 0000000000000000000000000000000000000000..c18ed16fe595a58bc7568ec63a5f21637bd30b56 --- /dev/null +++ b/paddle/fluid/framework/ir/mkldnn/interpolate_mkldnn_pass.h @@ -0,0 +1,41 @@ +// Copyright (c) 2018 PaddlePaddle 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. +#pragma once +#include + +#include "paddle/fluid/framework/ir/fuse_pass_base.h" +#include "paddle/fluid/framework/ir/graph.h" +#include "paddle/fluid/framework/ir/graph_pattern_detector.h" +#include "paddle/fluid/framework/ir/pass.h" + +namespace paddle { +namespace framework { +namespace ir { + +/* + * Change the interpolate op to run MKLDNN. + */ +class Graph; + +class InterpolateMKLDNNPass : public FusePassBase { + public: + virtual ~InterpolateMKLDNNPass() {} + + protected: + void ApplyImpl(ir::Graph* graph) const override; +}; + +} // namespace ir +} // namespace framework +} // namespace paddle diff --git a/paddle/fluid/framework/ir/placement_pass_base.cc b/paddle/fluid/framework/ir/placement_pass_base.cc index 1ac7e4d6a11385dc8082083aacab4d276399907c..f0c28133a8c4a803a98f03c72cd61e7caa1ac5ff 100644 --- a/paddle/fluid/framework/ir/placement_pass_base.cc +++ b/paddle/fluid/framework/ir/placement_pass_base.cc @@ -15,6 +15,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/placement_pass_base.h" #include #include +#include #include "paddle/fluid/framework/operator.h" namespace paddle { @@ -33,7 +34,7 @@ void PlacementPassBase::ApplyImpl(ir::Graph* graph) const { auto* op = n->Op(); if ((op->HasAttr(attr_name) || op->HasProtoAttr(attr_name)) && IsSupport(op->Type())) { - if (op_types_list.empty()) { + if (op_types_list.empty() && IsDefaultOpTypes(op->Type())) { op->SetAttr(attr_name, true); } else if (std::find(op_types_list.begin(), op_types_list.end(), n->Name()) != op_types_list.end()) { @@ -59,7 +60,30 @@ bool PlacementPassBase::IsSupport(const std::string& op_type) const { } } } else if (GetAttrName() == "use_mkldnn") { + // This ops have use_mkldnn attr, but not support for now. + const std::vector op_types = { + "trilinear_interp", "bicubic_interp", "linear_interp"}; + return std::find(op_types.begin(), op_types.end(), op_type) == + op_types.end(); + } + return false; +} + +bool PlacementPassBase::IsDefaultOpTypes(const std::string& op_type) const { + if (GetAttrName() == "use_cudnn") { return true; + } else if (GetAttrName() == "use_mkldnn") { + // For interpolate ops, there's a little difference between Paddle and + // MKLDNN. + // If run MKLDNN interpolate ops, manual set AnalysisConfig and apply + // the corresponding pass. + const std::vector not_default_op_types = { + "bilinear_interp", "nearest_interp", "trilinear_interp", + "bicubic_interp", "linear_interp"}; + bool is_interpolate_op = + std::find(not_default_op_types.begin(), not_default_op_types.end(), + op_type) != not_default_op_types.end(); + return !is_interpolate_op; } return false; } diff --git a/paddle/fluid/framework/ir/placement_pass_base.h b/paddle/fluid/framework/ir/placement_pass_base.h index ef1a920db3fd169904d4ebbd8fe0635444d17bd7..6927c031dcca38a1e1fd9153963b7646b0dbd32d 100644 --- a/paddle/fluid/framework/ir/placement_pass_base.h +++ b/paddle/fluid/framework/ir/placement_pass_base.h @@ -38,6 +38,7 @@ class PlacementPassBase : public Pass { private: bool IsSupport(const std::string& op_type) const; + bool IsDefaultOpTypes(const std::string& op_type) const; #if PADDLE_WITH_TESTING friend class PlacementPassTest; diff --git a/paddle/fluid/operators/interpolate_op.cc b/paddle/fluid/operators/interpolate_op.cc index e8a9ed878e9bd502b9bd7e7d82f574fb5740bb5d..f3699d0d7b6ed2db290c50bf3fb3f594e8c372e1 100644 --- a/paddle/fluid/operators/interpolate_op.cc +++ b/paddle/fluid/operators/interpolate_op.cc @@ -14,6 +14,9 @@ #include #include #include "paddle/fluid/framework/op_registry.h" +#ifdef PADDLE_WITH_MKLDNN +#include "paddle/fluid/platform/mkldnn_helper.h" +#endif namespace paddle { namespace operators { @@ -302,7 +305,6 @@ class InterpolateOp : public framework::OperatorWithKernel { platform::errors::Unimplemented( "Input(X) dimension must be 3, 4 or 5, but got dimension = %d .", dim_x.size())); - if (dim_x.size() == 3) { // shape check for 1D interpolate for input tensor shape NCHW Interpolate1DInferShapeCheck(ctx); @@ -318,13 +320,42 @@ class InterpolateOp : public framework::OperatorWithKernel { protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { + framework::DataLayout layout = framework::DataLayout::kAnyLayout; + framework::LibraryType library = framework::LibraryType::kPlain; + +#ifdef PADDLE_WITH_MKLDNN + auto interp_method = ctx.Attr("interp_method"); + // TODO(danqing): support other interp_method + if (this->CanMKLDNNBeUsed(ctx) && + (interp_method == "nearest" || interp_method == "bilinear")) { + layout = framework::DataLayout::kMKLDNN; + library = framework::LibraryType::kMKLDNN; + } +#endif + return framework::OpKernelType( - OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace()); + OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace(), + layout, library); } framework::OpKernelType GetKernelTypeForVar( const std::string& var_name, const Tensor& tensor, const framework::OpKernelType& expected_kernel_type) const override { +#ifdef PADDLE_WITH_MKLDNN + if ((expected_kernel_type.data_layout_ == framework::DataLayout::kMKLDNN) && + (tensor.layout() != framework::DataLayout::kMKLDNN)) { + auto attrs = Attrs(); + auto ar = paddle::framework::AttrReader(attrs); + const std::string data_format = ar.Get("data_layout"); + auto dl = framework::StringToDataLayout(data_format); + // Some models may have intentionally set "AnyLayout" for pool + // op. Treat this as NCHW (default data_format value) + if (dl != framework::DataLayout::kAnyLayout) { + return framework::OpKernelType(expected_kernel_type.data_type_, + tensor.place(), dl); + } + } +#endif if (var_name == "SizeTensor" || var_name == "Scale") { return expected_kernel_type; } @@ -394,6 +425,9 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker { "can be \'0\' for src_idx = scale*(dst_indx+0.5)-0.5 , " "can be \'1\' for src_idx = scale*dst_index .") .SetDefault(1); + AddAttr("use_mkldnn", + "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); AddComment(R"DOC( This operator samples input X to given output shape by using specified interpolation method, the interpolation methods can be \"nearest\" diff --git a/paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..f7df19ead9921a8a0fe7c1617777a41811f793a2 --- /dev/null +++ b/paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc @@ -0,0 +1,174 @@ +/* Copyright (c) 2020 PaddlePaddle 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. */ + +#include "paddle/fluid/framework/data_layout_transform.h" +#include "paddle/fluid/operators/interpolate_op.h" +#include "paddle/fluid/platform/mkldnn_reuse.h" + +namespace paddle { +namespace operators { + +using framework::DataLayout; +using dnnl::memory; +using dnnl::primitive; +using dnnl::reorder; +using dnnl::stream; +using dnnl::resampling_forward; +using platform::GetMKLDNNFormat; +using platform::to_void_cast; + +template +class InterpolateMKLDNNHandler + : public platform::MKLDNNHandlerT { + public: + InterpolateMKLDNNHandler(const dnnl::algorithm algo, + const paddle::platform::MKLDNNDeviceContext& dev_ctx, + const dnnl::engine engine, platform::Place cpu_place, + const Tensor* x, Tensor* z, + const std::string& uniq_name) + : platform::MKLDNNHandlerT( + dev_ctx, engine, cpu_place, + platform::CreateKey(dev_ctx, framework::vectorize(x->dims()), + uniq_name)) { + if (!this->isCached()) { + const auto src_x_tz = framework::vectorize(x->dims()); + const auto dst_tz = framework::vectorize(z->dims()); + const auto src_md = dnnl::memory::desc( + src_x_tz, platform::MKLDNNGetDataType(), x->format()); + const auto dst_md = memory::desc(dst_tz, platform::MKLDNNGetDataType(), + MKLDNNMemoryFormat::any); + this->AcquireForwardPrimitiveDescriptor( + dnnl::prop_kind::forward_inference, algo, src_md, dst_md); + } + } +}; + +template +class InterpolateMKLDNNKernel : public framework::OpKernel { + std::vector ComputeOutputShape( + const framework::ExecutionContext& ctx) const { + const auto* x = ctx.Input("X"); + auto in_dims = x->dims(); + const bool is_channel_last = false; // In mkldnn kernel, always use NCHW + + framework::DDim in_dhw_dims; + if (is_channel_last) { // NDHWC, NHWC, NWC + in_dhw_dims = framework::slice_ddim(in_dims, 1, in_dims.size() - 1); + } else { // NCDHW, NCHW, NCW + in_dhw_dims = framework::slice_ddim(in_dims, 2, in_dims.size()); + } + + std::vector out_dims; + if (in_dhw_dims.size() == 1) { + out_dims.push_back(ctx.Attr("out_w")); + } else if (in_dhw_dims.size() == 2) { + out_dims.push_back(ctx.Attr("out_h")); + out_dims.push_back(ctx.Attr("out_w")); + } else if (in_dhw_dims.size() == 3) { + out_dims.push_back(ctx.Attr("out_d")); + out_dims.push_back(ctx.Attr("out_h")); + out_dims.push_back(ctx.Attr("out_w")); + } + + auto list_new_size_tensor = ctx.MultiInput("SizeTensor"); + auto out_size = ctx.Input("OutSize"); + if (list_new_size_tensor.size() > 0) { + auto new_size = get_new_shape(list_new_size_tensor); + if (new_size.size() == out_dims.size()) { + out_dims = new_size; + } + } else if (out_size != nullptr) { + auto out_size_data = get_new_data_from_tensor(out_size); + if (out_size_data.size() == out_dims.size()) { + out_dims = out_size_data; + } + } else { + float scale; + auto scale_tensor = ctx.Input("Scale"); + if (scale_tensor != nullptr) { + auto scale_data = get_new_data_from_tensor(scale_tensor); + scale = scale_data[0]; + } else { + scale = ctx.Attr("scale"); + } + if (scale > 0) { + std::vector in_dhw_vec = framework::vectorize(in_dhw_dims); + std::transform( + in_dhw_vec.begin(), in_dhw_vec.end(), out_dims.begin(), + [&](int64_t i) -> int { return static_cast(i * scale); }); + } + } + + PADDLE_ENFORCE_GT(std::all_of(out_dims.begin(), out_dims.end(), + [](int i) { return i > 0; }), + 0, platform::errors::InvalidArgument( + "out_d, out_h, out_w of Op(interpolate) " + "should be greater than 0.")); + + out_dims.insert(out_dims.begin(), in_dims[0]); + if (is_channel_last) { + out_dims.push_back(in_dims[in_dims.size() - 1]); + } else { + out_dims.insert(out_dims.begin() + 1, in_dims[1]); + } + return out_dims; + } + + public: + void Compute(const framework::ExecutionContext& ctx) const override { + const auto& dev_ctx = + ctx.template device_context(); + const auto& mkldnn_engine = dev_ctx.GetEngine(); + + const auto* x = ctx.Input("X"); + std::vector scale_prior; + auto* z = ctx.Output("Out"); + + auto interp_method = ctx.Attr("interp_method"); + dnnl::algorithm algo = (interp_method == "nearest") + ? dnnl::algorithm::resampling_nearest + : dnnl::algorithm::resampling_linear; + + auto out_dims_vec = ComputeOutputShape(ctx); + framework::DDim dim_out = framework::make_ddim(out_dims_vec); + z->mutable_data(dim_out, ctx.GetPlace()); + + InterpolateMKLDNNHandler handler(algo, dev_ctx, mkldnn_engine, + ctx.GetPlace(), x, z, + ctx.OutputName("Out")); + + auto src_memory_p = handler.AcquireSrcMemory(x); + auto dst_memory_p = handler.AcquireDstMemory(z); + + auto resampling_prim = handler.AcquireForwardPrimitive(); + const std::unordered_map args = { + {DNNL_ARG_SRC, *src_memory_p}, {DNNL_ARG_DST, *dst_memory_p}}; + mkldnn::stream astream(mkldnn_engine); + resampling_prim->execute(astream, args); + astream.wait(); + + z->set_layout(DataLayout::kMKLDNN); + z->set_format(platform::GetMKLDNNFormat(*dst_memory_p)); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_KERNEL(nearest_interp, MKLDNN, ::paddle::platform::CPUPlace, + ops::InterpolateMKLDNNKernel); +REGISTER_OP_KERNEL(bilinear_interp, MKLDNN, ::paddle::platform::CPUPlace, + ops::InterpolateMKLDNNKernel); diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_mkldnn_op.py new file mode 100644 index 0000000000000000000000000000000000000000..e86273ea1c28ef56cd5786ca41715efe80ea6f5b --- /dev/null +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_bilinear_interp_mkldnn_op.py @@ -0,0 +1,201 @@ +# Copyright (c) 2018 PaddlePaddle 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. + +from __future__ import print_function + +import unittest +import numpy as np +import math +import paddle +import paddle.fluid.core as core +import paddle.fluid as fluid +from paddle.fluid.tests.unittests.op_test import OpTest +from paddle.fluid.tests.unittests.op_test import skip_check_grad_ci + + +def bilinear_interp_mkldnn_np(input, + out_h, + out_w, + out_size=None, + actual_shape=None, + data_layout='NCHW'): + """bilinear interpolation implement in shape [N, C, H, W]""" + if data_layout == "NHWC": + input = np.transpose(input, (0, 3, 1, 2)) # NHWC => NCHW + if out_size is not None: + out_h = out_size[0] + out_w = out_size[1] + if actual_shape is not None: + out_h = actual_shape[0] + out_w = actual_shape[1] + batch_size, channel, in_h, in_w = input.shape + + out = np.zeros((batch_size, channel, out_h, out_w)) + + for oh in range(out_h): + h0 = int(math.floor((oh + 0.5) * in_h / out_h - 0.5)) + h1 = int(math.ceil((oh + 0.5) * in_h / out_h - 0.5)) + h0 = max(h0, 0) + h1 = min(h1, in_h - 1) + Wh = (oh + 0.5) * in_h / out_h - 0.5 - h0 + for ow in range(out_w): + w0 = int(math.floor((ow + 0.5) * in_w / out_w - 0.5)) + w1 = int(math.ceil((ow + 0.5) * in_w / out_w - 0.5)) + w0 = max(w0, 0) + w1 = min(w1, in_w - 1) + Ww = (ow + 0.5) * in_w / out_w - 0.5 - w0 + input_h0_w0 = input[:, :, h0, w0] + input_h1_w0 = input[:, :, h1, w0] + input_h0_w1 = input[:, :, h0, w1] + input_h1_w1 = input[:, :, h1, w1] + out[:, :, oh, ow] = input_h0_w0 * (1 - Wh) * ( + 1 - Ww) + input_h1_w0 * Wh * (1 - Ww) + input_h0_w1 * ( + 1 - Wh) * Ww + input_h1_w1 * Wh * Ww + + if data_layout == "NHWC": + out = np.transpose(out, (0, 2, 3, 1)) # NCHW => NHWC + + return out.astype(input.dtype) + + +@skip_check_grad_ci(reason="Haven not implement interpolate grad kernel.") +class TestBilinearInterpMKLDNNOp(OpTest): + def init_test_case(self): + pass + + def setUp(self): + self.op_type = "bilinear_interp" + self.interp_method = 'bilinear' + self._cpu_only = True + self.use_mkldnn = True + self.input_shape = [1, 1, 2, 2] + self.data_layout = 'NCHW' + # priority: actual_shape > out_size > scale > out_h & out_w + self.out_h = 1 + self.out_w = 1 + self.scale = 2.0 + self.out_size = None + self.actual_shape = None + + self.init_test_case() + + input_np = np.random.random(self.input_shape).astype("float32") + if self.data_layout == "NCHW": + in_h = self.input_shape[2] + in_w = self.input_shape[3] + else: + in_h = self.input_shape[1] + in_w = self.input_shape[2] + + if self.scale > 0: + out_h = int(in_h * self.scale) + out_w = int(in_w * self.scale) + else: + out_h = self.out_h + out_w = self.out_w + + output_np = bilinear_interp_mkldnn_np(input_np, out_h, out_w, + self.out_size, self.actual_shape, + self.data_layout) + + self.inputs = {'X': input_np} + if self.out_size is not None: + self.inputs['OutSize'] = self.out_size + if self.actual_shape is not None: + self.inputs['OutSize'] = self.actual_shape + self.attrs = { + 'interp_method': self.interp_method, + 'out_h': self.out_h, + 'out_w': self.out_w, + 'scale': self.scale, + 'data_layout': self.data_layout, + 'use_mkldnn': self.use_mkldnn + } + self.outputs = {'Out': output_np} + + def test_check_output(self): + self.check_output(check_dygraph=False) + + +class TestBilinearInterpOpMKLDNNNHWC(TestBilinearInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [3, 2, 32, 16] + self.out_h = 27 + self.out_w = 49 + self.scale = 2.0 + self.data_layout = 'NHWC' + + +class TestBilinearNeighborInterpMKLDNNCase2(TestBilinearInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [3, 3, 9, 6] + self.out_h = 12 + self.out_w = 12 + self.scale = 1. + + +class TestBilinearNeighborInterpDataLayout(TestBilinearInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [2, 4, 4, 5] + self.out_h = 6 + self.out_w = 7 + self.scale = 0. + self.data_layout = "NHWC" + + +class TestBilinearNeighborInterpCase3(TestBilinearInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [1, 1, 32, 64] + self.out_h = 64 + self.out_w = 128 + self.scale = 0. + + +class TestBilinearNeighborInterpCase4(TestBilinearInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [4, 1, 7, 8] + self.out_h = 1 + self.out_w = 1 + self.scale = 0. + self.out_size = np.array([2, 2]).astype("int32") + + +class TestBilinearNeighborInterpCase5(TestBilinearInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [1, 1, 9, 6] + self.out_h = 12 + self.out_w = 12 + self.scale = 0. + self.out_size = np.array([13, 13]).astype("int32") + + +class TestBilinearNeighborInterpCase6(TestBilinearInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [1, 1, 32, 64] + self.out_h = 64 + self.out_w = 32 + self.scale = 0. + self.out_size = np.array([65, 129]).astype("int32") + + +class TestBilinearNeighborInterpSame(TestBilinearInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [2, 3, 32, 64] + self.out_h = 32 + self.out_w = 64 + self.scale = 0. + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_mkldnn_op.py new file mode 100755 index 0000000000000000000000000000000000000000..1e4bfd5f0cf017359a88d3b4c3754becb61ab77e --- /dev/null +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_mkldnn_op.py @@ -0,0 +1,166 @@ +# Copyright (c) 2018 PaddlePaddle 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. + +from __future__ import print_function + +import unittest +import numpy as np +import paddle +import paddle.fluid.core as core +import paddle.fluid as fluid +from paddle.fluid.tests.unittests.op_test import OpTest +from paddle.fluid.tests.unittests.op_test import skip_check_grad_ci + + +def nearest_neighbor_interp_mkldnn_np(X, + out_h, + out_w, + out_size=None, + actual_shape=None, + data_layout='NCHW'): + """nearest neighbor interpolation implement in shape [N, C, H, W]""" + if data_layout == "NHWC": + X = np.transpose(X, (0, 3, 1, 2)) # NHWC => NCHW + if out_size is not None: + out_h = out_size[0] + out_w = out_size[1] + if actual_shape is not None: + out_h = actual_shape[0] + out_w = actual_shape[1] + + n, c, in_h, in_w = X.shape + + fh = fw = 0.0 + if (out_h > 1): + fh = out_h * 1.0 / in_h + if (out_w > 1): + fw = out_w * 1.0 / in_w + + out = np.zeros((n, c, out_h, out_w)) + + for oh in range(out_h): + ih = int(round((oh + 0.5) / fh - 0.5)) + for ow in range(out_w): + iw = int(round((ow + 0.5) / fw - 0.5)) + out[:, :, oh, ow] = X[:, :, ih, iw] + + if data_layout == "NHWC": + out = np.transpose(out, (0, 2, 3, 1)) # NCHW => NHWC + + return out.astype(X.dtype) + + +@skip_check_grad_ci(reason="Haven not implement interpolate grad kernel.") +class TestNearestInterpMKLDNNOp(OpTest): + def init_test_case(self): + pass + + def setUp(self): + self.op_type = "nearest_interp" + self.interp_method = 'nearest' + self._cpu_only = True + self.use_mkldnn = True + self.input_shape = [1, 1, 2, 2] + self.data_layout = 'NCHW' + # priority: actual_shape > out_size > scale > out_h & out_w + self.out_h = 1 + self.out_w = 1 + self.scale = 2.0 + self.out_size = None + self.actual_shape = None + + self.init_test_case() + + input_np = np.random.random(self.input_shape).astype("float32") + if self.data_layout == "NCHW": + in_h = self.input_shape[2] + in_w = self.input_shape[3] + else: + in_h = self.input_shape[1] + in_w = self.input_shape[2] + + if self.scale > 0: + out_h = int(in_h * self.scale) + out_w = int(in_w * self.scale) + else: + out_h = self.out_h + out_w = self.out_w + + output_np = nearest_neighbor_interp_mkldnn_np( + input_np, out_h, out_w, self.out_size, self.actual_shape, + self.data_layout) + + self.inputs = {'X': input_np} + if self.out_size is not None: + self.inputs['OutSize'] = self.out_size + if self.actual_shape is not None: + self.inputs['OutSize'] = self.actual_shape + self.attrs = { + 'interp_method': self.interp_method, + 'out_h': self.out_h, + 'out_w': self.out_w, + 'scale': self.scale, + 'data_layout': self.data_layout, + 'use_mkldnn': self.use_mkldnn + } + self.outputs = {'Out': output_np} + + def test_check_output(self): + self.check_output(check_dygraph=False) + + +class TestNearestInterpOpMKLDNNNHWC(TestNearestInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [3, 2, 32, 16] + self.out_h = 27 + self.out_w = 49 + self.scale = 2.0 + self.data_layout = 'NHWC' + + +class TestNearestNeighborInterpMKLDNNCase2(TestNearestInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [3, 3, 9, 6] + self.out_h = 12 + self.out_w = 12 + self.scale = 1. + + +class TestNearestNeighborInterpCase3(TestNearestInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [1, 1, 32, 64] + self.out_h = 64 + self.out_w = 128 + self.scale = 0. + + +class TestNearestNeighborInterpCase4(TestNearestInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [1, 1, 32, 64] + self.out_h = 64 + self.out_w = 32 + self.scale = 0. + self.out_size = np.array([65, 129]).astype("int32") + + +class TestNearestNeighborInterpSame(TestNearestInterpMKLDNNOp): + def init_test_case(self): + self.input_shape = [2, 3, 32, 64] + self.out_h = 32 + self.out_w = 64 + self.scale = 0. + + +if __name__ == "__main__": + unittest.main() diff --git a/tools/static_mode_white_list.py b/tools/static_mode_white_list.py index 7d9f44f90503511083a16bcc77c850dac3cd002a..ba510d49a8c3bb3e60e0168923f223cd3a7d207c 100644 --- a/tools/static_mode_white_list.py +++ b/tools/static_mode_white_list.py @@ -597,6 +597,8 @@ STATIC_MODE_TESTING_LIST = [ 'test_elementwise_mul_bf16_mkldnn_op', 'test_fc_mkldnn_op', 'test_fc_bf16_mkldnn_op', + 'test_nearest_interp_mkldnn_op', + 'test_bilinear_interp_mkldnn_op', 'test_fusion_gru_int8_mkldnn_op', 'test_fusion_gru_mkldnn_op', 'test_gaussian_random_mkldnn_op',