From 012de7695dfd8a2d490095852f2e96c08f7802b2 Mon Sep 17 00:00:00 2001 From: Megvii Engine Team Date: Tue, 28 Sep 2021 13:58:48 +0800 Subject: [PATCH] feat(mgb/gopt): add profiler cache In order to improve performance of the profiling procedure. Make layout transform testcase stable. The profiling result in ci environment will be cached in files. GitOrigin-RevId: ba2743f35fcdbd554b7cd82e70f433ccdcc66fa4 --- .gitattributes | 1 + sdk/load-and-run/BUILD | 4 - sdk/load-and-run/src/mgblar.cpp | 2 +- .../impl/utils}/infile_persistent_cache.cpp | 4 +- .../megbrain/utils}/infile_persistent_cache.h | 3 +- .../include/megbrain/utils/persistent_cache.h | 2 + .../global_layout_transform/opr_safe_dump.cpp | 96 +++++++ .../global_layout_transform/opr_safe_dump.h | 30 +++ .../profiler_cache.cpp | 184 ++++++++++++++ .../global_layout_transform/profiler_impl.cpp | 232 ++++++++--------- src/gopt/include/megbrain/gopt/profiler.h | 239 +++++++++++++++++- src/gopt/test/cache_data.h | Bin 0 -> 570073 bytes src/gopt/test/embed_cache.py | 93 +++++++ src/gopt/test/layout_transform_pass.cpp | 207 ++++++++++----- 14 files changed, 905 insertions(+), 192 deletions(-) rename {sdk/load-and-run/src => src/core/impl/utils}/infile_persistent_cache.cpp (98%) rename {sdk/load-and-run/src => src/core/include/megbrain/utils}/infile_persistent_cache.h (95%) create mode 100644 src/gopt/impl/global_layout_transform/opr_safe_dump.cpp create mode 100644 src/gopt/impl/global_layout_transform/opr_safe_dump.h create mode 100644 src/gopt/impl/global_layout_transform/profiler_cache.cpp create mode 100644 src/gopt/test/cache_data.h create mode 100644 src/gopt/test/embed_cache.py diff --git a/.gitattributes b/.gitattributes index 082d51c09..0f1dbf43e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -9,6 +9,7 @@ dnn/src/cuda/matrix_mul/fp32_simt/kimpl/* binary dnn/src/cuda/sass/prebuilt/map_defs.cpp binary dnn/src/cuda/convolution/backward_data/int8/kimpl/* binary dnn/src/cuda/elemwise_multi_type/kimpl/* binary +src/gopt/test/cache_data.h binary tools/mlir/mlir-tblgen filter=lfs diff=lfs merge=lfs -text imperative/python/test/integration/data/*.mge filter=lfs diff=lfs merge=lfs -text ci/resource/models/float/mobilenet_v2.pkl filter=lfs diff=lfs merge=lfs -text diff --git a/sdk/load-and-run/BUILD b/sdk/load-and-run/BUILD index 54b98e680..29c79b220 100644 --- a/sdk/load-and-run/BUILD +++ b/sdk/load-and-run/BUILD @@ -2,13 +2,11 @@ cc_library( name = "mgblar", copts = ["-std=c++14"], srcs = [ - "src/infile_persistent_cache.cpp", "src/mgblar.cpp", "src/json_loader.cpp", "src/text_table.cpp", ], hdrs = [ - "src/infile_persistent_cache.h", "src/mgblar.h", "src/json_loader.h", "src/text_table.h", @@ -57,11 +55,9 @@ cc_megvii_binary( cc_library( name = "megbrain_ios_lar_lib", srcs = [ - "src/infile_persistent_cache.cpp", "src/mgblar.cpp", ], hdrs = [ - "src/infile_persistent_cache.h", "src/mgblar.h", ], copts = ["-DMGB_NO_MAIN=1"], diff --git a/sdk/load-and-run/src/mgblar.cpp b/sdk/load-and-run/src/mgblar.cpp index b3d901b74..da41f919e 100644 --- a/sdk/load-and-run/src/mgblar.cpp +++ b/sdk/load-and-run/src/mgblar.cpp @@ -10,7 +10,6 @@ */ #include "./mgblar.h" -#include "./infile_persistent_cache.h" #include "./json_loader.h" #include "./npy.h" #include "./text_table.h" @@ -30,6 +29,7 @@ #include "megbrain/serialization/extern_c_opr.h" #include "megbrain/serialization/serializer.h" #include "megbrain/utils/debug.h" +#include "megbrain/utils/infile_persistent_cache.h" #include "megbrain/system.h" #include "megbrain/version.h" diff --git a/sdk/load-and-run/src/infile_persistent_cache.cpp b/src/core/impl/utils/infile_persistent_cache.cpp similarity index 98% rename from sdk/load-and-run/src/infile_persistent_cache.cpp rename to src/core/impl/utils/infile_persistent_cache.cpp index 81444b7ae..5171ff789 100644 --- a/sdk/load-and-run/src/infile_persistent_cache.cpp +++ b/src/core/impl/utils/infile_persistent_cache.cpp @@ -1,5 +1,5 @@ /** - * \file sdk/load-and-run/src/infile_persistent_cache.cpp + * \file src/core/impl/utils/infile_persistent_cache.cpp * MegEngine is Licensed under the Apache License, Version 2.0 (the "License") * * Copyright (c) 2014-2021 Megvii Inc. All rights reserved. @@ -9,7 +9,7 @@ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. */ -#include "./infile_persistent_cache.h" +#include "megbrain/utils/infile_persistent_cache.h" #if defined(_WIN32) #include diff --git a/sdk/load-and-run/src/infile_persistent_cache.h b/src/core/include/megbrain/utils/infile_persistent_cache.h similarity index 95% rename from sdk/load-and-run/src/infile_persistent_cache.h rename to src/core/include/megbrain/utils/infile_persistent_cache.h index d9dc5bf09..28b28bef0 100644 --- a/sdk/load-and-run/src/infile_persistent_cache.h +++ b/src/core/include/megbrain/utils/infile_persistent_cache.h @@ -1,5 +1,5 @@ /** - * \file sdk/load-and-run/src/infile_persistent_cache.h + * \file src/core/include/megbrain/utils/infile_persistent_cache.h * MegEngine is Licensed under the Apache License, Version 2.0 (the "License") * * Copyright (c) 2014-2021 Megvii Inc. All rights reserved. @@ -70,6 +70,7 @@ public: Maybe get(const std::string& category, const Blob& key) override; void put(const std::string& category, const Blob& key, const Blob& value) override; + bool support_dump_cache() override { return true; } }; } // namespace mgb diff --git a/src/core/include/megbrain/utils/persistent_cache.h b/src/core/include/megbrain/utils/persistent_cache.h index ce25c59d6..8c881aea7 100644 --- a/src/core/include/megbrain/utils/persistent_cache.h +++ b/src/core/include/megbrain/utils/persistent_cache.h @@ -39,6 +39,8 @@ public: virtual void put( const std::string& category, const Blob& key, const Blob& value) = 0; + virtual bool support_dump_cache() { return false; } + //! set an implementation; return the original implementation static std::shared_ptr set_impl( std::shared_ptr impl); diff --git a/src/gopt/impl/global_layout_transform/opr_safe_dump.cpp b/src/gopt/impl/global_layout_transform/opr_safe_dump.cpp new file mode 100644 index 000000000..28c187918 --- /dev/null +++ b/src/gopt/impl/global_layout_transform/opr_safe_dump.cpp @@ -0,0 +1,96 @@ +/** + * \file src/gopt/impl/global_layout_transform/opr_safe_dump.cpp + * MegEngine is Licensed under the Apache License, Version 2.0 (the "License") + * + * Copyright (c) 2014-2021 Megvii Inc. All rights reserved. + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or + * implied. + */ + +#include "./opr_safe_dump.h" +#include "megbrain/opr/basic_arith.h" +#include "megbrain/opr/dnn/convolution.h" +#include "megbrain/opr/dnn/pooling.h" +#include "megbrain/opr/imgproc.h" +#include "megbrain/opr/nn_int.h" +#include "megbrain/opr/tensor_manip.h" + +#include "midout.h" +MIDOUT_DECL(megbrain_opr_safe_dump) +#define MIDOUT_B(...) MIDOUT_BEGIN(megbrain_opr_safe_dump, __VA_ARGS__) { +#define MIDOUT_E \ + } \ + MIDOUT_END(); + +using namespace mgb; +using namespace opr; + +namespace { +template +void write_param(std::string& data, const Param& param) { + megdnn::Algorithm::serialize_write_pod(param, data); +} + +template <> +void write_param(std::string& /* data */, const DType& /* dtype */) {} + +template +struct OprDumpImpl { + static std::string dump(const cg::OperatorNodeBase* opr_) { + MIDOUT_B(Opr) + auto&& opr = opr_->cast_final_safe(); + std::string data; + write_param(data, opr.param()); + return data; + MIDOUT_E + } +}; + +#define INST(_Opr) \ + template <> \ + struct OprDumpImpl<_Opr> { \ + static std::string dump(const cg::OperatorNodeBase* opr_) { \ + MIDOUT_B(_Opr) \ + auto&& opr = opr_->cast_final_safe<_Opr>(); \ + std::string data; \ + write_param(data, opr.param()); \ + using ExecutionPolicy = megdnn::param::ExecutionPolicy; \ + ExecutionPolicy policy{ \ + opr.execution_policy_transient().strategy, \ + opr.execution_policy_transient().workspace_limit}; \ + write_param(data, policy); \ + return data; \ + MIDOUT_E \ + } \ + }; +INST(Convolution); +INST(ConvBiasForward); +INST(ConvolutionBackwardData); +INST(PoolingForward); +#undef INST +} // namespace + +namespace mgb { +namespace gopt { +namespace intl { + +std::string opr_safe_dump(const cg::OperatorNodeBase* opr) { +#define cb(_Opr) \ + if (opr->dyn_typeinfo() == _Opr::typeinfo()) { \ + return OprDumpImpl<_Opr>::dump(opr); \ + } else + FOREACH_SUPPORTED_OPR(cb) { + mgb_throw(InternalError, "unsupported operator(got:%s)", + opr->dyn_typeinfo()->name); + } +#undef cb +} + +} // namespace intl +} // namespace gopt +} // namespace mgb + +// vim: syntax=cpp.doxygen diff --git a/src/gopt/impl/global_layout_transform/opr_safe_dump.h b/src/gopt/impl/global_layout_transform/opr_safe_dump.h new file mode 100644 index 000000000..d25be51d2 --- /dev/null +++ b/src/gopt/impl/global_layout_transform/opr_safe_dump.h @@ -0,0 +1,30 @@ +/** + * \file src/gopt/impl/global_layout_transform/opr_safe_dump.h + * MegEngine is Licensed under the Apache License, Version 2.0 (the "License") + * + * Copyright (c) 2014-2021 Megvii Inc. All rights reserved. + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or + * implied. + */ + +#pragma once +#include "megbrain/graph.h" + +namespace mgb { +namespace gopt { +namespace intl { +#define FOREACH_SUPPORTED_OPR(cb) \ + cb(Convolution) cb(ConvBiasForward) cb(ConvolutionBackwardData) \ + cb(PoolingForward) cb(WarpPerspective) cb(Resize) cb(Elemwise) \ + cb(ElemwiseMultiType) cb(Concat) cb(PowC) cb(TypeCvt) + +std::string opr_safe_dump(const cg::OperatorNodeBase* opr); + +} // namespace intl +} // namespace gopt +} // namespace mgb + +// vim: syntax=cpp.doxygen diff --git a/src/gopt/impl/global_layout_transform/profiler_cache.cpp b/src/gopt/impl/global_layout_transform/profiler_cache.cpp new file mode 100644 index 000000000..cfa703400 --- /dev/null +++ b/src/gopt/impl/global_layout_transform/profiler_cache.cpp @@ -0,0 +1,184 @@ +/** + * \file src/gopt/impl/profiler_cache.cpp + * MegEngine is Licensed under the Apache License, Version 2.0 (the "License") + * + * Copyright (c) 2014-2021 Megvii Inc. All rights reserved. + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or + * implied. + */ + +#include "./opr_safe_dump.h" +#include "megbrain/gopt/profiler.h" +#include "megbrain/comp_node_env.h" + +using namespace mgb; +using namespace gopt; +using ReformatKey = ReformatManager::ReformatKey; + +// =================== ProfilerCache ====================== +void ProfilerCache::Key::build_blob_from_opr() { + auto&& opr = m_key_impl.opr_key.opr; + // process opr type + auto type = opr->dyn_typeinfo()->name; + size_t type_size = strlen(type); + + // process opr param + auto data = intl::opr_safe_dump(opr); + size_t param_size = data.size(); + + size_t nr_inputs = opr->input().size(); + size_t nr_outputs = opr->usable_output().size(); + size_t nr_layouts = nr_inputs + nr_outputs; + m_blob_storage.reserve(sizeof(TensorLayout) * 3 * nr_layouts + type_size + + param_size); + + // serialize opr type + m_blob_storage.append(type, type_size); + + // serialize param + const char* data_ptr = reinterpret_cast(data.data()); + m_blob_storage.append(data_ptr, param_size); + + // serialize layouts + auto append_layout = [this](const VarNode* v) { + TensorLayout ly{v->shape(), v->dtype(), v->format()}; + for (size_t i = 0; i < ly.ndim; ++i) { + if (i) + m_blob_storage.push_back(','); + m_blob_storage.append(std::to_string(ly.shape[i])); + } + if (!ly.is_contiguous()) { + m_blob_storage.push_back(';'); + for (size_t i = 0; i < ly.ndim; ++i) { + if (i) + m_blob_storage.push_back(','); + m_blob_storage.append(std::to_string(ly.stride[i])); + } + } + m_blob_storage.push_back(';'); + m_blob_storage.append(ly.dtype.name()); + m_blob_storage.push_back('|'); + }; + for (size_t i = 0; i < nr_inputs; ++i) { + append_layout(opr->input(i)); + } + for (size_t i = 0; i < nr_outputs; ++i) { + append_layout(opr->output(i)); + } + + // serialize opr_format + m_blob_storage.append(std::to_string( + static_cast(m_key_impl.opr_key.opr_format))); + + // serialize extra_attribute + m_blob_storage.append(std::to_string( + static_cast(m_key_impl.opr_key.extra_attribute))); +} + +void ProfilerCache::Key::build_category(CompNode cn) { + m_category = "layout_transform_profile:"; + auto&& env = CompNodeEnv::from_comp_node(cn); + switch (env.property().type) { +#if MGB_CUDA + case CompNode::DeviceType::CUDA: { + auto&& prop = env.cuda_env().device_prop; + m_category += ssprintf("plat=cuda;dev=%s;cap=%d.%d", prop.name, + prop.major, prop.minor); + break; + } +#endif + case CompNode::DeviceType::CPU: + m_category += "plat=cpu"; + break; + default: + mgb_throw(MegBrainError, + "unsupported comp node for global layout transform " + "profiler cache category"); + } +} + +void ProfilerCache::Key::build_blob_from_var() { + auto v = m_key_impl.var_key.var; + + // serialize layouts + auto append_layout = [this](const VarNode* v) { + TensorLayout ly{v->shape(), v->dtype(), v->format()}; + for (size_t i = 0; i < ly.ndim; ++i) { + if (i) + m_blob_storage.push_back(','); + m_blob_storage.append(std::to_string(ly.shape[i])); + } + if (!ly.is_contiguous()) { + m_blob_storage.push_back(';'); + for (size_t i = 0; i < ly.ndim; ++i) { + if (i) + m_blob_storage.push_back(','); + m_blob_storage.append(std::to_string(ly.stride[i])); + } + } + m_blob_storage.push_back(';'); + m_blob_storage.append(ly.dtype.name()); + m_blob_storage.push_back('|'); + }; + append_layout(v); + + // serialze reformat key + m_blob_storage.append(m_key_impl.var_key.key.to_string()); +} + +const std::string& ProfilerCache::Key::category() const { + mgb_assert(!m_category.empty()); + return m_category; +} + +PersistentCache::Blob ProfilerCache::Key::blob() const { + mgb_assert(!m_blob_storage.empty()); + return {m_blob_storage.data(), m_blob_storage.size()}; +} + +ProfilerCache& ProfilerCache::inst() { + static ProfilerCache inst; + return inst; +} + +ProfilerCache& ProfilerCache::set_impl(std::unique_ptr impl) { + mgb_assert(impl != nullptr); + m_impl.swap(impl); + return *this; +} + +void ProfilerCache::dump_cache(const char* path) { + mgb_assert(m_impl->support_dump_cache(), + "current impl of ProfilerCache does not support dump cache to " + "file."); + auto cache = static_cast(m_impl.get()); + cache->dump_cache(path); +} + +Maybe ProfilerCache::get(const Key& key) { + auto raw_buf = m_impl->get(key.category(), key.blob()); + if (!raw_buf.valid()) + return None; + // data type of cost is float + auto buf = static_cast(raw_buf->ptr); + auto size = raw_buf->size; + mgb_assert(buf && size == sizeof(float), + "ProfileCache invalid value: ptr=%p, size=%zu", buf, size); + auto read_f32 = [&]() { + auto ret = *reinterpret_cast(buf); + return ret; + }; + auto cost = read_f32(); + return cost; +} + +void ProfilerCache::put(const Key& key, Result& result) { + std::string val; + megdnn::Algorithm::serialize_write_pod(result, val); + m_impl->put(key.category(), key.blob(), {val.data(), val.size()}); +} + +// vim: syntax=cpp.doxygen diff --git a/src/gopt/impl/global_layout_transform/profiler_impl.cpp b/src/gopt/impl/global_layout_transform/profiler_impl.cpp index 43a463e71..99a585070 100644 --- a/src/gopt/impl/global_layout_transform/profiler_impl.cpp +++ b/src/gopt/impl/global_layout_transform/profiler_impl.cpp @@ -154,69 +154,61 @@ void MarkInputContiguous::init_output_static_infer_desc() { } // namespace /* ================== ProfilerImpl =================*/ -class ProfilerImpl final : public ProfilerBase { -public: - ProfilerImpl(int runs = 10) : m_runs{runs} {}; - ~ProfilerImpl() = default; - ProfilingResult profile(const Problem& problem) const override; - -private: - static constexpr float PROFILE_TIME_OUT = 1e7; - using ReformatAttribute = ReformatKey::Attribute; - /*! - * \brief profile opr format agnostic operators (like elemwise, elemwise - * multi type, typecvt etc.) - * - * \param opr pointer to the operator node to be profiled - * \param base_format the original tensor format of the operator node. - * \param available_tensor_formats the available tensor formats - * \return the operator node record - */ - OperatorNodeRecord profile_operator( - const OperatorNodeBase* opr, TensorFormats base_format, - const SmallVector& available_tensor_formats, - ReformatAttribute extra_attribute = ReformatAttribute::DEFAULT) const; - float profile_operator( - const OperatorNodeBase* opr, TensorFormats base_format, - TensorFormats tensor_format, - ReformatAttribute extra_attribute = ReformatAttribute::DEFAULT) const; - /*! - * \brief profile opr format aware operators (like conv, deconv, conv_bias, - * etc.) - * - * \param opr pointer to the operator node to be profiled - * \param base_config the tensor formats configuration of base opr format - * \param config all the available configuration - * \return the operator node record - */ - OperatorNodeRecord profile_operator( - const OperatorNodeBase* opr, - const OprTensorFormatsConfiguration& base_config, - const SmallVector& available_configs, - ReformatAttribute extra_attribute = ReformatAttribute::DEFAULT) const; - float profile_operator( - const OperatorNodeBase* opr, - const OprTensorFormatsConfiguration& base_config, - const OprTensorFormatsConfiguration& config, - ReformatAttribute extra_attribute = ReformatAttribute::DEFAULT) const; - /*! - * \brief profile layout transform of the var node - * - * \param var pointer to the var node to be profiled - * \param base_format the original tensor formats in which the var node is - * stored \param available_tensor_formats the available tensor formats - * \param extra_attribute the extra attributes (options) of the problem - * \return the var node record - */ - VarNodeRecord profile_var_node( - const VarNode* var, TensorFormats base_format, - const SmallVector& available_tensor_formats, - ReformatAttribute extra_attribute = ReformatAttribute::DEFAULT) const; - float profile_var_node( - const VarNode* var, TensorFormats base_format, - const ReformatKey& key) const; - int m_runs; /// sample times of the profiler -}; +ProfilerImpl::ProfilerImpl(int runs, float opr_threshold, + float var_node_threshold) + : m_opr_threshold{opr_threshold}, + m_var_node_threshold{var_node_threshold}, + m_runs{runs} { + m_opr_filter = [this](const OperatorNodeBase* opr, + OperatorNodeBase* new_opr) { + /// \note: for the considerations of performance, we skip nchw(naive) + /// kernels for conv bias on CUDA platform. to remove this later + if (auto conv = try_cast_as_op(new_opr)) { + if (conv->output(0)->comp_node().device_type() == + CompNode::DeviceType::CUDA && + conv->input(0)->dtype().category() == + DTypeCategory::QUANTIZED && + conv->param().format == OprFormat::NCHW) { + return false; + } + } + float comp1 = m_opr_footprint.get_computation( + const_cast(opr)); + float comp2 = m_opr_footprint.get_computation(new_opr); + if (comp2 > m_opr_threshold * comp1) + return false; + return true; + }; + m_var_node_filter = [this](const VarNode* var, TensorShape from, + TensorShape to, ReformatKey key) { + /// \note: due to the alignment requirement of low-bit tensor, we skip + /// some layout transform for low-bit tensors. The skipped layout + /// transforms do not have corresponding dnn kernel and cannot be + /// implemented by tensor manip operators (like reshape, dimshuffle, + /// subtensor, etc.). + if (var->dtype().enumv() == DTypeEnum::QuantizedS4 || + var->dtype().enumv() == DTypeEnum::Quantized4Asymm) { + if (key.input_format == TensorFormats::NCHW && + key.output_format != TensorFormats::NHWC && + key.output_format != TensorFormats::NCHWc64) { + return false; + } + if (key.output_format == TensorFormats::NCHW && + key.input_format != TensorFormats::NHWC && + key.input_format != TensorFormats::NCHWc64) { + return false; + } + } + TensorLayout orig_ly = {var->shape(), var->dtype()}, + from_ly = {from, var->dtype()}, to_ly = {to, var->dtype()}; + float orig_memory = orig_ly.span().dist_byte() * 2.f; + float reformat_memory = + from_ly.span().dist_byte() + to_ly.span().dist_byte(); + if (reformat_memory > orig_memory * m_var_node_threshold) + return false; + return true; + }; +} ProfilerImpl::OperatorNodeRecord ProfilerImpl::profile_operator( const OperatorNodeBase* opr, TensorFormats base_format, @@ -507,56 +499,6 @@ ProfilerImpl::ProfilingResult ProfilerImpl::profile(const Problem& problem) cons } /* ================== ProfilerBase =================*/ -ProfilerBase::ProfilerBase(float opr_threshold, float var_node_threshold) - : m_opr_threshold{opr_threshold}, m_var_node_threshold{var_node_threshold} { - m_opr_filter = [this](const OperatorNodeBase* opr, OperatorNodeBase* new_opr) { - /// \note: for the considerations of performance, we skip nchw(naive) - /// kernels for conv bias on CUDA platform. to remove this later - if (auto conv = try_cast_as_op(new_opr)) { - if (conv->output(0)->comp_node().device_type() == - CompNode::DeviceType::CUDA && - conv->input(0)->dtype().category() == DTypeCategory::QUANTIZED && - conv->param().format == OprFormat::NCHW) { - return false; - } - } - float comp1 = - m_opr_footprint.get_computation(const_cast(opr)); - float comp2 = m_opr_footprint.get_computation(new_opr); - if (comp2 > m_opr_threshold * comp1) - return false; - return true; - }; - m_var_node_filter = [this](const VarNode* var, TensorShape from, TensorShape to, - ReformatKey key) { - /// \note: due to the alignment requirement of low-bit tensor, we skip - /// some layout transform for low-bit tensors. The skipped layout - /// transforms do not have corresponding dnn kernel and cannot be - /// implemented by tensor manip operators (like reshape, dimshuffle, - /// subtensor, etc.). - if (var->dtype().enumv() == DTypeEnum::QuantizedS4 || - var->dtype().enumv() == DTypeEnum::Quantized4Asymm) { - if (key.input_format == TensorFormats::NCHW && - key.output_format != TensorFormats::NHWC && - key.output_format != TensorFormats::NCHWc64) { - return false; - } - if (key.output_format == TensorFormats::NCHW && - key.input_format != TensorFormats::NHWC && - key.input_format != TensorFormats::NCHWc64) { - return false; - } - } - TensorLayout orig_ly = {var->shape(), var->dtype()}, - from_ly = {from, var->dtype()}, to_ly = {to, var->dtype()}; - float orig_memory = orig_ly.span().dist_byte() * 2.f; - float reformat_memory = from_ly.span().dist_byte() + to_ly.span().dist_byte(); - if (reformat_memory > orig_memory * m_var_node_threshold) - return false; - return true; - }; -} - std::string ProfilerBase::OperatorNodeRecord::to_string() const { auto str = ssprintf( "\nopr type: %s\nopr name: %s\ninputs:\n", opr->dyn_typeinfo()->name, @@ -595,4 +537,68 @@ std::unique_ptr ProfilerBase::make_profiler() { return std::make_unique(); } +std::unique_ptr ProfilerBase::make_cached_profiler( + const char* path) { + return std::make_unique(path); +} + +/* ================== CachedProfiler =================*/ +CachedProfiler::CachedProfiler(const char* path, int runs, float opr_threshold, + float var_node_threshold) + : ProfilerImpl(runs, opr_threshold, var_node_threshold), m_path{path} { + if (m_path != nullptr) { // file cache + ProfilerCache::inst().set_impl( + std::make_unique(m_path)); + } +} + +CachedProfiler::ProfilingResult CachedProfiler::profile( + const Problem& problem) const { + auto ret = ProfilerImpl::profile(problem); + if (m_path != nullptr) + ProfilerCache::inst().dump_cache(m_path); + return ret; +} + +float CachedProfiler::profile_operator( + const OperatorNodeBase* opr, TensorFormats base_format, + TensorFormats tensor_format, ReformatAttribute extra_attribute) const { + ProfilerCache::Key key{opr, tensor_formats_to_opr_format(tensor_format), + extra_attribute}; + auto ret = ProfilerCache::inst().get(key); + if (ret.valid()) + return ret.val(); + auto rst = ProfilerImpl::profile_operator(opr, base_format, tensor_format, + extra_attribute); + ProfilerCache::inst().put(key, rst); + return rst; +} + +float CachedProfiler::profile_operator( + const OperatorNodeBase* opr, + const OprTensorFormatsConfiguration& base_config, + const OprTensorFormatsConfiguration& config, + ReformatAttribute extra_attribute) const { + ProfilerCache::Key key{opr, config.opr_format, extra_attribute}; + auto ret = ProfilerCache::inst().get(key); + if (ret.valid()) + return ret.val(); + auto rst = ProfilerImpl::profile_operator(opr, base_config, config, + extra_attribute); + ProfilerCache::inst().put(key, rst); + return rst; +} + +float CachedProfiler::profile_var_node(const VarNode* var, + TensorFormats base_format, + const ReformatKey& key) const { + ProfilerCache::Key pf_key{var, key}; + auto ret = ProfilerCache::inst().get(pf_key); + if (ret.valid()) + return ret.val(); + auto rst = ProfilerImpl::profile_var_node(var, base_format, key); + ProfilerCache::inst().put(pf_key, rst); + return rst; +} + // vim: syntax=cpp.doxygen diff --git a/src/gopt/include/megbrain/gopt/profiler.h b/src/gopt/include/megbrain/gopt/profiler.h index 3d4d61ef1..299312c34 100644 --- a/src/gopt/include/megbrain/gopt/profiler.h +++ b/src/gopt/include/megbrain/gopt/profiler.h @@ -18,11 +18,13 @@ #include "megbrain/gopt/subgraph_extractor.h" #include "megbrain/opr/dnn/convolution.h" #include "megbrain/plugin/opr_footprint.h" +#include "megbrain/utils/infile_persistent_cache.h" namespace mgb { namespace gopt { class Problem; +class CachedProfiler; /*! * \brief A profiler that collects all the performance data to describe the @@ -75,22 +77,245 @@ public: using VarNodeFilter = thin_function; - ProfilerBase(float opr_threshold = 2.f, float var_node_threshold = 2.f); - ProfilerBase(OprFilter opr_filter, VarNodeFilter var_node_filter = {}) - : m_opr_filter{std::move(opr_filter)}, - m_var_node_filter{std::move(var_node_filter)} {} + ProfilerBase() = default; + virtual ~ProfilerBase() = default; + virtual ProfilingResult profile(const Problem& problem) const = 0; + + ProfilerBase& set_opr_filter(const OprFilter& opr_filter) { + m_opr_filter = opr_filter; + return *this; + } + + ProfilerBase& set_var_node_filter(const VarNodeFilter& var_node_filter) { + m_var_node_filter = var_node_filter; + return *this; + } + static std::unique_ptr make_profiler(); + static std::unique_ptr make_cached_profiler( + const char* path = nullptr); protected: OprFilter m_opr_filter; VarNodeFilter m_var_node_filter; - float m_opr_threshold; - float m_var_node_threshold; +}; -private: + +/*! \brief A default profiler impl + */ +class ProfilerImpl : public ProfilerBase { +public: + ProfilerImpl(int runs = 10, float opr_threshold = 2.f, + float var_node_threshold = 2.f); + ~ProfilerImpl() = default; + ProfilingResult profile(const Problem& problem) const override; + +protected: + static constexpr float PROFILE_TIME_OUT = 1e7; + using ReformatKey = ReformatManager::ReformatKey; + using ReformatAttribute = ReformatKey::Attribute; + /*! + * \brief profile opr format agnostic operators (like elemwise, elemwise + * multi type, typecvt etc.) + * + * \param opr pointer to the operator node to be profiled + * \param base_format the original tensor format of the operator node. + * \param available_tensor_formats the available tensor formats + * \return the operator node record + */ + OperatorNodeRecord profile_operator( + const OperatorNodeBase* opr, TensorFormats base_format, + const SmallVector& available_tensor_formats, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const; + /*! + * \brief prfile opr format agnostic operators (like elemwise, elemwise multi type, typecvt etc.) + * + * \param opr pointer to the operator to be profiled + * \param base_format the original tensor format of the operator node. + * \param tensor_format the tensor format to be profiled + * \param extra_attribute identify whether to use image object for OpenCL or automatically padding nhwc layout + * \return elapsed time of operator in the given tensor format configuration + */ + virtual float profile_operator( + const OperatorNodeBase* opr, TensorFormats base_format, + TensorFormats tensor_format, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const; + /*! + * \brief profile opr format aware operators (like conv, deconv, conv_bias, + * etc.) + * + * \param opr pointer to the operator node to be profiled + * \param base_config the tensor formats configuration of base opr format + * \param config all the available configuration + * \return the operator node record + */ + OperatorNodeRecord profile_operator( + const OperatorNodeBase* opr, + const OprTensorFormatsConfiguration& base_config, + const SmallVector& available_configs, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const; + /*! + * \brief prfile opr format aware operators (like conv, deconv, conv_bias, resize, warp etc.) + * + * \param opr pointer to the operator to be profiled + * \param base_config the original opr format configuration of the operator node, + * \param config the opr format configuration to be profiled + * \param extra_attribute identify whether to use image object for OpenCL or automatically padding nhwc layout + * \return elapsed time of operator in the given opr format configuration + */ + virtual float profile_operator(const OperatorNodeBase* opr, + const OprTensorFormatsConfiguration& base_config, + const OprTensorFormatsConfiguration& config, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const; + /*! + * \brief profile layout transform of the var node + * + * \param var pointer to the var node to be profiled + * \param base_format the original tensor formats in which the var node is + * stored + * \param available_tensor_formats the available tensor formats + * \param extra_attribute the extra attributes (options) of the problem + * \return the var node record + */ + VarNodeRecord profile_var_node( + const VarNode* var, TensorFormats base_format, + const SmallVector& available_tensor_formats, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const; + /*! + * \brief profile layout transform of the var node + * + * \param var pointer to the var node to be profiled + * \param base_format the original tensor formats in which the var node is + * stored + * \param key type of ReformatKey, identify the information/attributes of the layout transoform + * \return elapsed time of the layout transform + */ + virtual float profile_var_node(const VarNode* var, + TensorFormats base_format, + const ReformatKey& key) const; OprFootprint m_opr_footprint; + float m_opr_threshold; /// a threshold, when the computation of the newly + /// created operator that is built in some opr + /// format configuration is as greater as + /// m_opr_threshold times of the original operator, + /// the opr format configuration will be skipped + /// (i.e. the cost is infinite) + float m_var_node_threshold; /// a threshold, when the memory footprint of + /// the layout transform of the var node is as + /// larger as m_var_node_threshold as the var + /// node itself, the layout transform will be + /// skipped (i.e. the cost is infinite) + int m_runs; /// sample times of the profiler +}; + +/*! + * \brief a ProfilerCache that manages the profiling results of operator in + * different layouts and of layout transform of var nodes. + */ +class ProfilerCache : public NonCopyableObj { + ProfilerCache() : m_impl{std::make_unique()} {}; + +public: + using ReformatKey = ReformatManager::ReformatKey; + using ReformatAttribute = ReformatKey::Attribute; + using OprFormat = ProfilerBase::OprFormat; + class Key final : public NonCopyableObj { + std::string m_blob_storage; + std::string m_category; + + struct OprKey { + const OperatorNodeBase* opr; + OprFormat opr_format; + ReformatAttribute extra_attribute; + }; + + struct VarKey { + const VarNode* var; + ReformatKey key; + }; + + union KeyImpl { + OprKey opr_key; + VarKey var_key; + + KeyImpl() { std::memset(this, 0, sizeof(KeyImpl)); } + }; + + KeyImpl m_key_impl; + + void build_blob_from_opr(); + void build_blob_from_var(); + void build_category(CompNode cn); + + public: + Key(const OperatorNodeBase* opr, OprFormat opr_format, + ReformatAttribute extra_attribute = ReformatAttribute::DEFAULT) { + m_key_impl.opr_key = {opr, opr_format, extra_attribute}; + build_blob_from_opr(); + mgb_assert( + opr->node_prop().contain( + cg::OperatorNodeProp::Flag::SINGLE_COMP_NODE), + "operator with multiple comp node is not supported(opr:%s)", + opr->cname()); + // here, we assume that the operator to be profiled has only one + // comp node + build_category(opr->output(0)->comp_node()); + } + + Key(const VarNode* var, ReformatKey key) { + m_key_impl.var_key = {var, key}; + build_blob_from_var(); + build_category(var->comp_node()); + } + + const std::string& category() const; + PersistentCache::Blob blob() const; + }; + + using Result = float; + +public: + static ProfilerCache& inst(); + + ProfilerCache& set_impl(std::unique_ptr impl); + + void dump_cache(const char* path); + + Maybe get(const Key& key); + + void put(const Key& key, Result& result); + +private: + std::unique_ptr m_impl; +}; + +class CachedProfiler final : public ProfilerImpl { +public: + CachedProfiler(const char* path = nullptr, int runs = 10, + float opr_threshold = 2.f, float var_node_threshold = 2.f); + ProfilingResult profile(const Problem& problem) const override; + +private: + float profile_operator(const OperatorNodeBase* opr, + TensorFormats base_format, + TensorFormats tensor_format, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const override; + float profile_operator(const OperatorNodeBase* opr, + const OprTensorFormatsConfiguration& base_config, + const OprTensorFormatsConfiguration& config, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const override; + float profile_var_node(const VarNode* var, TensorFormats base_format, + const ReformatKey& key) const override; + const char* m_path; }; } // namespace gopt diff --git a/src/gopt/test/cache_data.h b/src/gopt/test/cache_data.h new file mode 100644 index 0000000000000000000000000000000000000000..f049589e93605153451273ca3e70c0c71be857e3 GIT binary patch literal 570073 zcmeFaOV1=Zk{!B_Us1r_CZzPptbDX)dNqr?n2n&`H|kc`NDbVn61uVn7!35^?=c$9 zNaNE;caJYGBy?AXe-Ysx+j!w!25sr1AqM={*G^a`SZ6w{_v+?e*gB*KmGe}fB*KUzy0*<@Bi}K z-~RQ_|Mbf@T=cKM{rZ3Z^3z{__{Sgq<8i!uAO830Q#?4{e|h)y^Sk4DygLs1568!+ zcVB-DFURpQe0sdcpLpy26R-5Gk2wCy-=eqhC%sJ{zo)N#!GFiQAMtX~Tl^Ea?5E@1 z*ROaJKl1)PUHM1+g(Ld*aeSfAKht&a^8VdJeu_@PTOXe9zTp2K-+lQ)$3MP*_wmcS zA3xE*^bs}bN4g-sPCdde<8nX#`0nGUcRzl3ci_F)_b+ll*X5>CZ;pfhrjAn=&#0Bux`8VtjZT${V zdA@smjJ^E$`P~zv`w?*910evx6~>&#=Xm#t0HfgooKq^>5JcIr#6f05)~=Gxvh=4%k0={HRx7KJ00ur2OJp_+4?4D`zYhQz7waSS^Akvc z80z>DBM%NhF1Ys>DWpm;U$rHX{D~-_{Jy-(dGfjb-BHMgI9DZ+9{A`!emx>6)KWEpG%M3)5T8D59!_H0PK-qI)ob%`j$v{OBaq~zi zE_!gsFg%Xu2jVXxTl}B<31^Sqe!)A?^)K`E{v(iz#=@w|v{ zI29L}jxUW1z8BF$6O#@vo|~U4@h=+qL^lpx9;&6OjKZu3e!M|QTri6tQ|t2<5Lqw7 zJHXrkTL-B`dQtH%K$^2Z^f>_SBR0jyXgQ^$PAlseS&O55=_W_RM+16WxE~FpDDhr% zFeiE|#6Cd1Ba}@s z5-5a}e$_M(Mj|%(^%H_sWSS^0K*gg^zsseYNc%FIyu+nGt_YVf_Q3Q2P4R+S2LDf~ zagx58q;e_0s$_i~o5os6{2pzLy%cOnSPwF*V4zGW)qe#@p|;h-s5>3K5@d8G zZN-Gif?K)6113Ty96np5;5L5N158q~P{X;&Mw2qOuxUV(xR*{1>PJFeu<<)nMoMrv zC42A`n@-lNGEQx~=jN{UG*bm&1MfA);f|jlfCNxL%!dEVRMTvx$vEqM=fP$}Kd->+ z!Twc~Kd1t4x_U|i4y+FO05b4Y(@O5g1WD?rXkGhF6BB8p0((yoJcPBx^O+)s3<7CpS9x3MF?3-ClMqqVW2w0 z6Z2Sg%7f8U^Pz(~G9IOZR9q`zF1%_6>u|Gb9wQB zV)n2es1>xg^-2c-#HY~HS)A}f*@-++fIt(A*;D>*8!DJ)5xOfiukghAT!h%`F!|dG z^}V3AIRyT-g&1K3O`6bbNcNzMEwkmq@>X2;Cu_u%pCx=q} z1I}?3jh^-jg_3dkjIX0(k9OYI`*o}Qq5512|3rtp#^tMRTgmo8aj_0XRqWbOZbF3) zfrSUHeh1uj{B&9PL`%9YZQ3?rvGl}G=<|h!q*HnYN{E;aS{R9$3shQOv~rkgh|y?e zK1*KZeR&oCA)o8t`_o!s>3MeNLR+0{X3;R`29`Jk()UEZdzm6ii+|>q8CLs)otYNp zP1~%xGB@a|@&1C#{Wm%8j>xEUGZ}xvuK){C%F;@}eULC+`ow)yR62rS4;C~K)3c-|4Dwqq zK$`+Cbzn}87sMTQ*v@L^y460l%}UJatXnl42PFe>ZFc1fXW?T4trI~t0;n*TT>UN? zW%XvKqjKXzE|{d{{hLdA5t}%E?FZeCQ0UFf+}ar7I_OgGcLyJ7pHX~%;wrT2A0>jI z*JwNh5n{jYFLpTqhX%6$VTTPtMlMKxhJ zOIXDJC7Pyl{i<~WyMoa*`3XvVHhZU(;2`~qlYrUS!adHA82^cdnt4!Wg z&uY_h2(4yJ?Q@0FbW;)LwZ4W9-CB!aWel7PCLXMwr>KEtUsl@+!dh^~!x(|on&|CG zLAyIG^X!;*D9gnB=|!Qffoe(;8eOu;EJy8)lSCkFK7MGHl&k*nxM&^rQ-4O`1Oz=s zlQJdF$}{cyZqHSdW6O#lEZRpt=!r0G)GoG?;mL};CN>Rk$`}G=%EZZNrmnl?>!yhI_Ec2hJ?tj2~U|Q%^ z&EH0z!_Hxepe6p@Sb<~X@AZ*QHbCdr$T0}>pl>_KettmB@#`0Wgh-H9id>(Ym4U@{ ziYh--6jrc`FB2e)>EqE5|k&lpqg2rQkacE*8B9&3H=S(u|wHPcF`BqBad4 z=FnU3p(aq*jpNGx*iREpq_Z+ij81yn;bIFnS#%T=Ry4tf5CKhlXa@2+jGSyBBE?k+ zZF#!g&)O)wng6Gqj`G_2#RY(5NX`Vr+TcgPC|U0!$=W*OB3=7bAz=Zn%ul8?Sm{`3 z{AJK4P^;KR&65PGVYmrS*0JvfRGLvhyb#irp!>I_PPJ}X&T#-Wv~=>;2DvjKgDDj> zEfvWEM$4BqguJ9&u+=7XFLvCt)Kx>Bl-sVi)cyt+Jwx4IM%aV4ve&VTbx;vZl!7%z z`2=VuP8DU8^~)ju)kOm;hZOJ-kNeYZ-q4{3?-p~jgsD_VBcfh-w{v6bSem)ROMJE( zvT+esBx?~h@Qjw5wZ-TH`oR=1>49BNkvd>*cp5*SSr$ZSoN&W~Q;j0LW=TD!GM*o? zd*^f=M(u*)lKQ#fwntqId4R_(qDiOrtv}rGrqPgL>foOLc9Sn4YGLS^U&`!z!RPna z&kDW@;@V`JwfP{F5lToyArs|vlo#cT?r?WiM@Guk9z}earw1GIDoJUyFE^jL9B$)$^njg$zrujSB_Dij?H$;Esg?H3?yw zJjtDI>@b0t0lY3=nF;b&i9QT^dP@fi;k20v?V5#^5Eyi}YWm}%jlKDTQ3vJv@+xaU z&_>b0)n-E1Z0vF$3ej(Hzg5#>otweXxwV9%cdjvPvt#2_m@8Zw{wrNfltJOX z8CEa_q{i4^P3?xDHP)2^x`7P7A*jwpXr7de@Xo@RxnoT6Qv*cM1tT_0>glW|pV^CO z+-#8d(seDg4bO^*J^}Y#gl)xmrlT&UC@=TvI7ZIKFLpjCe3k`2-wA$@U40OUjcQ(Y z`(%+4Wf*}-Au5qcdTb2ZJ<$3$c$d(_0n zscB~oU$0^C3B8X5l!7DM6Rx9Lmo>6wL$R8+FY(-ra3jTZJvV<>KzfEr8I;v9^-8Na zD#sXuut80?x=W)&nkARlwzMeB)h|+nP`#;L{D{dM3=J(y;$CtmbB!h29hgUb@lGGv zBNdG|zQqnXThlDhE+zn~o({Wj&0r8t5*~YzZD+YijZw|;793Knz?c|ixgggIap6Wh zISD5vrBX_ngEdgKbObIn7SdQy3#M6z6#dbXVtYd$e6RI%h3^Ym1gugWbcOBQA$_b$QK-d$BBiJ1Dm!szNp2%)k?LGbhos{aSEGEG$U)5(BSU zsMFOUfZKxhV&6?Z z7#MD^*^qefz>VmbatXQ8V5(ss(u>0(r{OOcb_^@vbSQ7+LYa%5ZNI6&+LcaMuob}& zqTQ))v}{e9IgKPOh4)Pyz`R5)^`3sU{FVF+Rt}LUx3_7Fn?=T#r$iSNnYf;&JkrAq zUr-Vo&_o@EN!?&C^<%f3zQk*trvnz5L(LW)@bW@s%)YR-&A(@S9b~FKv|6Z64cCij zyUAX$YuR*1?XHXvt#2-G0k?QhcXUfM?+gfWJbYx$cUnkVgw~Pbw4uFq3oD)@k7ZPu zDV4F5S9xDv#k0xh`nPu`6{0<>I8EVCA+B0>^abwcINF`%aTx@;dNKYptf9ML+H@Yv z_j2{r&-8p`0z8Y2>5*VNa{!fxvZl3cISnO~n}D*=g3B^wbs@5|R#!ENq^f$eZ72xq zn2C-RQasFoglGlKe`78`jZ97&_BQ+D-If)o2O4Rf{(FR*53y%1?j^(RxYBuTOSFc5 z<+?Y4&|^U>5SLA$@}VReB<@tMtQ}miL+dpOEa-jI=0d3R+FL9X7&d@Qm@{-ses_x%)JGtpLL1&fHt}ZB*QbEnZHqiBy zZ%-7-MXD6qoBHk*Mb4oscL3B(mYL|J)FU3^YX+1T8EIZ35*x*>1a$8|Kkq1)#Bz1} z{f=g-E)kK9XnrRPOULUQv69;^~OEaQmtL96G=ghyLYt_qI$4sz^; 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under the Apache License, Version 2.0 (the "License") +# +# Copyright (c) 2014-2021 Megvii Inc. All rights reserved. +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + +# 为了保证全局图优化里的 profiling 结果不受到 ci 环境的影响，所以把写死的 profiling 结果存到了 cache 里去， +# 每次跑测试会从内存里读取 cache 里的 profiling 结果，然后根据 profiling 结果去做全局图优化。 +# 这个脚本用来把 dump 出去的 cache 文件转化成 cache 的头文件，用于测试时读取数据。 +# 如果在 src/gopt/test/layout_transform_pass.cpp 里添加了全局图优化相关的测试，则需要考虑用这个脚本来 +# 处理一下 profiling 数据。 +# 1. 首先将 src/gopt/test/layout_transform_pass.cpp 中的 `#define MGB_WITH_CACHED_TEST 1` 修改为 +# `#define MGB_WITH_CACHED_TEST 0` +# 2. 编译megbrain_test，并运行所有全局图优化相关测试： +# ./megbrain_test --gtest_filter="*LayoutTransform*" +# 3. 用这个脚本把所有的cache文件打包在一起 +# python3 embed_cache.py -o cache_data.h $(ls /path/to/cache/*.cache) +# 4. 将步骤1中的 define 改回去，这样 profile 过程用到的是 cache 下来的数据。随后可以重新构建 megbrain_test ， +# 验证测试是否正确。 +import os.path +import logging +import hashlib +import argparse +import struct +import itertools +import sys +import subprocess + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.WARNING, format='%(asctime)-15s %(message)s') + +CHAR_MAP = {i: r'{}'.format(i) for i in range(256)} + +def _u32(data): + return struct.unpack('\n') + fout.write('#include \n') + for k, v in sorted(src_map.items()): + fout.write(""" +static const std::vector {} = {{ +""".format(k.replace('.', '_'))) + fout.write('{}'.format(v)) + fout.write('};\n') + + def invoke(self, output): + logger.info('generate cache_data.h ...') + fname2cache_data = {} + for fname in self._cache_files: + base, ext = os.path.splitext(os.path.basename(fname)) + assert ext == ".cache", "ext: {}, fname {}".format(ext, fname) + assert base not in fname2cache_data, "duplicated kernel: " + base + fname2cache_data[base] = self.gen_cache_data(fname) + with open(output, 'w') as fout: + self.gen_cache_data_header(fout, fname2cache_data) + logger.info('done') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description='embed cache into cache header file', + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('-o', '--output', help='output source file', + required=True) + parser.add_argument('cache', help='cache files to be embedded', nargs='+') + args = parser.parse_args() + cache_generator = CacheDataGenerator(args.cache) + cache_generator.invoke(args.output) diff --git a/src/gopt/test/layout_transform_pass.cpp b/src/gopt/test/layout_transform_pass.cpp index e9cfb7e1f..8cba8b16d 100644 --- a/src/gopt/test/layout_transform_pass.cpp +++ b/src/gopt/test/layout_transform_pass.cpp @@ -23,6 +23,12 @@ #include "megbrain/plugin/profiler.h" #include "megbrain/serialization/serializer.h" +#define MGB_WITH_CACHED_TEST 1 + +#if MGB_WITH_CACHED_TEST +#include "./cache_data.h" +#endif + using namespace mgb; using namespace gopt; using namespace serialization; @@ -53,6 +59,78 @@ size_t find_opr_num(SymbolVar endpoint) { cg::DepOprIter{cb}.add(endpoint.node()->owner_opr()); return opr_num; } + +using OprFormat = Problem::OprFormat; +OprFormat tensor_formats_to_opr_format(TensorFormats tensor_format) { + switch (tensor_format) { + case TensorFormats::NCHW: + return OprFormat::NCHW; + case TensorFormats::NCHWc4: + return OprFormat::NCHW4; + case TensorFormats::NCHWc8: + return OprFormat::NCHW8; + case TensorFormats::NCHWc32: + return OprFormat::NCHW32; + case TensorFormats::NCHWc64: + return OprFormat::NCHW64; + case TensorFormats::NHWC: + return OprFormat::NHWC; + case TensorFormats::CHWNc4: + return OprFormat::CHWN4; + default: + mgb_throw(MegBrainError, "tensor format(%u) is not supported", + static_cast(tensor_format)); + } +} + +class ProfilerMock : public ProfilerImpl { +public: + ProfilerMock(const uint8_t* bin, size_t size) { + mgb_assert(bin != nullptr); + ProfilerCache::inst().set_impl( + std::make_unique(bin, size)); + } + ~ProfilerMock() { + // reset in memory cache + ProfilerCache::inst().set_impl( + std::make_unique()); + } + +private: + float profile_operator(const OperatorNodeBase* opr, + TensorFormats base_format, + TensorFormats tensor_format, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const override { + ProfilerCache::Key key{opr, tensor_formats_to_opr_format(tensor_format), + extra_attribute}; + auto ret = ProfilerCache::inst().get(key); + if (ret.valid()) + return ret.val(); + mgb_assert(false); + } + float profile_operator(const OperatorNodeBase* opr, + const OprTensorFormatsConfiguration& base_config, + const OprTensorFormatsConfiguration& config, + ReformatAttribute extra_attribute = + ReformatAttribute::DEFAULT) const override { + ProfilerCache::Key key{opr, config.opr_format, extra_attribute}; + std::string tmp; + tmp.reserve(key.blob().size); + auto ret = ProfilerCache::inst().get(key); + if (ret.valid()) + return ret.val(); + mgb_assert(false); + } + float profile_var_node(const VarNode* var, TensorFormats base_format, + const ReformatKey& key) const override { + ProfilerCache::Key pf_key{var, key}; + auto ret = ProfilerCache::inst().get(pf_key); + if (ret.valid()) + return ret.val(); + mgb_assert(false); + } +}; } // namespace #if MGB_CUDA @@ -96,15 +174,23 @@ TEST(TestLayoutTransform, Resnet18_QS8) { OprFormat::NCHW, TensorFormats::NCHW, Target::UNSPEC, ReformatAttribute::AUTO_PADDING_NHWC}; auto ctx = std::make_unique( - std::move(opr_list), std::move(available_tensor_formats), attribute); - ctx->add_opr_config( - opr::ConvBiasForward::typeinfo(), - {OprFormat::NCHW4, OprFormat::NCHW32, OprFormat::CHWN4, OprFormat::NHWC}) - .add_opr_config( - opr::PoolingForward::typeinfo(), - {OprFormat::NCHW4, OprFormat::NCHW32, OprFormat::NHWC, - OprFormat::CHWN4}); - auto profiler = ProfilerBase::make_profiler(); + std::move(opr_list), std::move(available_tensor_formats), + attribute); + ctx->add_opr_config(opr::ConvBiasForward::typeinfo(), + {OprFormat::NCHW4, OprFormat::NCHW32, OprFormat::CHWN4, + OprFormat::NHWC}) + .add_opr_config(opr::PoolingForward::typeinfo(), + {OprFormat::NCHW4, OprFormat::NCHW32, + OprFormat::NHWC, OprFormat::CHWN4}); +#if MGB_WITH_CACHED_TEST + auto profiler = std::make_unique( + static_cast( + TestLayoutTransform_Resnet18_QS8.data()), + TestLayoutTransform_Resnet18_QS8.size()); +#else + auto profiler = ProfilerBase::make_cached_profiler( + "TestLayoutTransform.Resnet18_QS8.cache"); +#endif std::unique_ptr solver{ new DynamicProgrammingSolver(std::move(profiler))}; auto new_output = @@ -190,7 +276,15 @@ TEST(TestLayoutTransform, Resnet18_QS4) { opr::PoolingForward::typeinfo(), {OprFormat::NCHW4, OprFormat::NCHW32, OprFormat::NCHW64, OprFormat::NHWC, OprFormat::CHWN4}); - auto profiler = ProfilerBase::make_profiler(); +#if MGB_WITH_CACHED_TEST + auto profiler = std::make_unique( + static_cast( + TestLayoutTransform_Resnet18_QS4.data()), + TestLayoutTransform_Resnet18_QS4.size()); +#else + auto profiler = ProfilerBase::make_cached_profiler( + "TestLayoutTransform.Resnet18_QS4.cache"); +#endif std::unique_ptr solver{ new DynamicProgrammingSolver(std::move(profiler))}; auto new_output = @@ -305,7 +399,15 @@ TEST(TestLayoutTransform, Detection_QS8) { opr::PoolingForward::typeinfo(), {OprFormat::NCHW4, OprFormat::NCHW32, OprFormat::NCHW64, OprFormat::NHWC, OprFormat::CHWN4}); - auto profiler = ProfilerBase::make_profiler(); +#if MGB_WITH_CACHED_TEST + auto profiler = std::make_unique( + static_cast( + TestLayoutTransform_Detection_QS8.data()), + TestLayoutTransform_Detection_QS8.size()); +#else + auto profiler = ProfilerBase::make_cached_profiler( + "TestLayoutTransform.Detection_QS8.cache"); +#endif std::unique_ptr solver{ new DynamicProgrammingSolver(std::move(profiler))}; auto new_outputs = @@ -375,7 +477,15 @@ TEST(TestLayoutTransform, Detection_QS4) { opr::PoolingForward::typeinfo(), {OprFormat::NCHW4, OprFormat::NCHW32, OprFormat::NCHW64, OprFormat::NHWC, OprFormat::CHWN4}); - auto profiler = ProfilerBase::make_profiler(); +#if MGB_WITH_CACHED_TEST + auto profiler = std::make_unique( + static_cast( + TestLayoutTransform_Detection_QS4.data()), + TestLayoutTransform_Detection_QS4.size()); +#else + auto profiler = ProfilerBase::make_cached_profiler( + "TestLayoutTransform.Detection_QS4.cache"); +#endif std::unique_ptr solver{ new DynamicProgrammingSolver(std::move(profiler))}; auto new_outputs = @@ -443,10 +553,18 @@ TEST(TestLayoutTransform, Wide) { OprFormat::NCHW, TensorFormats::NCHW, Target::UNSPEC, ReformatAttribute::DEFAULT}; auto ctx = std::make_unique( - std::move(opr_list), std::move(available_tensor_formats), attribute); - ctx->add_opr_config( - opr::ConvBiasForward::typeinfo(), {OprFormat::NCHW, OprFormat::NHWC}); - auto profiler = ProfilerBase::make_profiler(); + std::move(opr_list), std::move(available_tensor_formats), + attribute); + ctx->add_opr_config(opr::ConvBiasForward::typeinfo(), + {OprFormat::NCHW, OprFormat::NHWC}); +#if MGB_WITH_CACHED_TEST + auto profiler = std::make_unique( + static_cast(TestLayoutTransform_Wide.data()), + TestLayoutTransform_Wide.size()); +#else + auto profiler = ProfilerBase::make_cached_profiler( + "TestLayoutTransform.Wide.cache"); +#endif std::unique_ptr solver{ new DynamicProgrammingSolver(std::move(profiler))}; auto v = gopt::GraphOptimizer{} @@ -463,12 +581,8 @@ TEST(TestLayoutTransform, Wide) { auto func = network.graph->compile({{sym_o, {}}}); func->execute(); gprof.to_json_full(func.get())->writeto_fpath(output_file("wide.json")); - /// check global layout transform pass, no dimshuffle - /// disable the following check, to make ci stable. -#if 0 auto nr_dimshuffle = find_opr_num(sym_o); ASSERT_EQ(nr_dimshuffle, 0u); -#endif auto nr_param_merge = find_opr_num(sym_o); ASSERT_EQ(nr_param_merge, 1u); /// check first conv format @@ -477,48 +591,6 @@ TEST(TestLayoutTransform, Wide) { ASSERT_EQ(cast.param().format, opr::ConvBias::Param::Format::NCHW); } -TEST(TestLayoutTransform, ElemwiseMultiType) { - REQUIRE_GPU(1); - auto cn = CompNode::load("gpu0"); - Network network(cn); - auto x = network.add_var("x", {64, 64, 1, 2}); - auto y = network.add_var("y", {64, 64, 1, 2}); - x = network.add_type_cvt(x, dtype::QuantizedS4{1.f}); - y = network.add_type_cvt(y, dtype::QuantizedS4{1.f}); - auto x_ = network.add_type_cvt(x, dtype::Float32()); - auto y_ = network.add_type_cvt(y, dtype::Float32()); - auto z = network.add_elemwise( - {x_, y_}, dtype::Float32(), opr::Elemwise::Mode::FUSE_ADD_RELU); - z = network.add_type_cvt(z, dtype::QuantizedS4{1.f}); - z = network.add_type_cvt(z, dtype::Float32()); - auto z2 = network.add_elemwise( - {x, y}, dtype::QuantizedS4{1.f}, opr::Elemwise::Mode::FUSE_ADD_RELU); - z2 = network.add_type_cvt(z2, dtype::Float32()); - HostTensorND t1; - auto func1 = network.graph->compile({make_callback_copy(z, t1)}); - func1->execute(); - - HostTensorND t3; - auto func3 = network.graph->compile({make_callback_copy(z2, t3)}); - func3->execute(); - - auto alter_x = opr::RelayoutFormat::make( - x, megdnn::param::RelayoutFormat::Mode::NCHW_NCHW64); - auto alter_y = opr::RelayoutFormat::make( - y, megdnn::param::RelayoutFormat::Mode::NCHW_NCHW64); - auto alter_z = network.add_elemwise( - {alter_x, alter_y}, dtype::QuantizedS4{1.f}, - opr::Elemwise::Mode::FUSE_ADD_RELU); - alter_z = opr::RelayoutFormat::make( - alter_z, megdnn::param::RelayoutFormat::Mode::NCHW64_NCHW); - alter_z = network.add_type_cvt(alter_z, dtype::Float32()); - HostTensorND t2; - auto func2 = network.graph->compile({make_callback_copy(alter_z, t2)}); - func2->execute(); - // MGB_ASSERT_TENSOR_EQ(t1, t3); - MGB_ASSERT_TENSOR_EQ(t2, t3); -} - #if CUDA_VERSION >= 10020 TEST(TestLayoutTransform, DetectionHead) { REQUIRE_GPU(1); @@ -600,8 +672,15 @@ TEST(TestLayoutTransform, DetectionHead) { .add_opr_config( opr::WarpPerspectiveForward::typeinfo(), {OprFormat::NHWC, OprFormat::NCHW4, OprFormat::NCHW64}); - - auto profiler = ProfilerBase::make_profiler(); +#if MGB_WITH_CACHED_TEST + auto profiler = std::make_unique( + static_cast( + TestLayoutTransform_DetectionHead.data()), + TestLayoutTransform_DetectionHead.size()); +#else + auto profiler = ProfilerBase::make_cached_profiler( + "TestLayoutTransform.DetectionHead.cache"); +#endif std::unique_ptr solver{ new DynamicProgrammingSolver(std::move(profiler))}; auto new_out_vars = -- GitLab