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adapt for multi-frontend cj 

Signed-off-by: qingshanxiaozi <qingshanxiaozi@163.com>
This commit is contained in:
qingshanxiaozi 2021-06-27 01:43:39 +08:00
parent 611a8d8945
commit 4aaac53e21
7 changed files with 27 additions and 16 deletions
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19
mindspore/ccsrc/backend/optimizer/ascend/mindir/sparse_softmax_cross_entropy_with_logits_unify_mindir.cc
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@ -419,8 +419,9 @@ const BaseRef GradSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR::DefinePattern(
VarPtr x1 = std::make_shared<Var>();
VarPtr x2 = std::make_shared<Var>();
VarPtr x3 = std::make_shared<Var>();
VarPtr x4 = std::make_shared<Var>();
VectorRef sparse_softmax_cross_entropy_with_logits_grad({prim::kPrimSparseSoftmaxCrossEntropyWithLogits, x1, x2});
VectorRef sparse_softmax_cross_entropy_with_logits({prim::kPrimSparseSoftmaxCrossEntropyWithLogits, x1, x2});
VectorRef sparse_softmax_cross_entropy_with_logits({prim::kPrimSparseSoftmaxCrossEntropyWithLogits, x1, x4});
VectorRef depend(
{prim::kPrimDepend, sparse_softmax_cross_entropy_with_logits_grad, sparse_softmax_cross_entropy_with_logits});
return VectorRef({prim::kPrimMul, depend, x3});
@ -518,19 +519,21 @@ const AnfNodePtr PynativeSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR::Process
MS_EXCEPTION_IF_NULL(sparse_softmax_node);
CheckCNodeInputSize(sparse_softmax_node, kSparseSoftmaxCrossEntropyWithLogitsInputTensorNum);
if (AnfAlgo::HasNodeAttr(kAttrIsGrad, sparse_softmax_node) &&
AnfAlgo::GetNodeAttr<bool>(sparse_softmax_node, kAttrIsGrad)) {
return nullptr;
}
CNodePtr softmax_node;
auto one_hot_node = CreateOneHot(graph, sparse_softmax_node, true);
softmax_node = CreateSoftmaxCrossEntropyWithLogits(graph, sparse_softmax_node, one_hot_node);
std::vector<AnfNodePtr> softmax_node_outputs;
CreateMultipleOutputsOfAnfNode(graph, softmax_node, kSoftmaxCrossEntropyWithLogitsOutputNum, &softmax_node_outputs);
auto reduce_node = CreateReduceMean(graph, sparse_softmax_node, softmax_node_outputs[0], true);
return reduce_node;
// Both of the forward loss function and the backward loss function from cangjie will match this pattern,
// the true branch is for the backward loss function, and the false branch is for the other one.
if (AnfAlgo::HasNodeAttr(kAttrIsGrad, sparse_softmax_node) &&
AnfAlgo::GetNodeAttr<bool>(sparse_softmax_node, kAttrIsGrad)) {
return softmax_node_outputs[1];
} else {
auto reduce_node = CreateReduceMean(graph, sparse_softmax_node, softmax_node_outputs[0], true);
return reduce_node;
}
}
const BaseRef PynativeGradSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR::DefinePattern() const {

4
mindspore/ccsrc/backend/session/ascend_session.cc
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@ -294,8 +294,10 @@ void AscendSession::UnifyMindIR(const KernelGraphPtr &graph) {
unify_mindir_pm->AddPass(std::make_shared<opt::GradSparseSoftmaxCrossEntropyWithLogitsUnifyMindIRV2>());
unify_mindir_pm->AddPass(std::make_shared<opt::SparseSoftmaxCrossEntropyWithLogitsUnifyMindIR>());
} else {
unify_mindir_pm->AddPass(std::make_shared<opt::PynativeSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR>());
// Add PynativeGradSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR pass first to avoid the backward loss function
// from the python frontend matching the pattern defined in PynativeSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR.
unify_mindir_pm->AddPass(std::make_shared<opt::PynativeGradSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR>());
unify_mindir_pm->AddPass(std::make_shared<opt::PynativeSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR>());
}
unify_mindir_pm->AddPass(std::make_shared<opt::DropoutUnifyMindIR1>());
unify_mindir_pm->AddPass(std::make_shared<opt::DropoutGradUnifyMindIR>());

3
mindspore/ccsrc/backend/session/session_basic.h
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@ -300,9 +300,6 @@ class SessionBasic : public std::enable_shared_from_this<SessionBasic> {
#if !defined(_WIN32) && !defined(_WIN64)
std::shared_ptr<Debugger> debugger_;
#endif
#if (ENABLE_CPU && !_WIN32)
bool initialized_ps_cache_{false};
#endif
};
using SessionPtr = std::shared_ptr<session::SessionBasic>;

5
mindspore/ccsrc/pipeline/jit/CMakeLists.txt
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@ -26,4 +26,9 @@ if(ENABLE_GE OR ENABLE_D)
list(APPEND _PIPELINE_SRC_FILES ${_PIPELINE_GE_SRC_FILES})
endif()
if("${ENABLE_HIDDEN}" STREQUAL "OFF")
string(REPLACE " -Werror " " " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE " -fvisibility=hidden" " -fvisibility=default" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
endif()
add_library(_mindspore_pipeline_jit_obj OBJECT ${_PIPELINE_SRC_FILES})

5
mindspore/ccsrc/runtime/device/kernel_runtime_manager.cc
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@ -51,6 +51,11 @@ void KernelRuntimeManager::ClearGraphResource(uint32_t graph_id, const std::vect
}
}
KernelRuntimeManager &KernelRuntimeManager::Instance() {
static KernelRuntimeManager instance{};
return instance;
}
void KernelRuntimeManager::Register(const std::string &device_name, KernelRuntimeCreator &&runtime_creator) {
if (runtime_creators_.find(device_name) == runtime_creators_.end()) {
(void)runtime_creators_.emplace(device_name, runtime_creator);

5
mindspore/ccsrc/runtime/device/kernel_runtime_manager.h
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@ -32,10 +32,7 @@ using KernelRuntimeCreator = std::function<std::shared_ptr<KernelRuntime>()>;
class KernelRuntimeManager {
public:
static KernelRuntimeManager &Instance() {
static KernelRuntimeManager instance;
return instance;
}
static KernelRuntimeManager &Instance();
void Register(const std::string &device_name, KernelRuntimeCreator &&runtime_creator);
KernelRuntime *GetKernelRuntime(const std::string &device_name, uint32_t device_id);
KernelRuntime *GetCurrentKernelRuntime();

2
mindspore/core/utils/ms_utils.cc
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@ -17,9 +17,11 @@
namespace mindspore {
namespace common {
namespace {
const int CACHED_STR_NUM = 1 << 8;
const int CACHED_STR_MASK = CACHED_STR_NUM - 1;
std::vector<std::string> STR_HOLDER(CACHED_STR_NUM);
} // namespace
const char *SafeCStr(const std::string &&str) {
static std::atomic<uint32_t> index{0};
uint32_t cur_index = index++;