forked from mindspore-Ecosystem/mindspore
!1696 Enable ConfusionMulGrad fusion pass
Merge pull request !1696 from huanghui/TMP
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cc9c004bc1
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@ -100,6 +100,7 @@ void AddAscendBackendOptionalIRFusion(PassManager *ir_fusion_pm) {
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ir_fusion_pm->AddPass(std::make_shared<ClipByNormNoDivSquareSumFusion>());
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ir_fusion_pm->AddPass(std::make_shared<LambUpdateWithLRRuleFusion>());
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ir_fusion_pm->AddPass(std::make_shared<SoftmaxGradExtFusion>());
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ir_fusion_pm->AddPass(std::make_shared<ConfusionMulGradFusion>());
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ir_fusion_pm->AddPass(std::make_shared<ConfusionSoftmaxGradRule>());
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ir_fusion_pm->AddPass(std::make_shared<LambNextMVWithDecayRuleCond1>());
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ir_fusion_pm->AddPass(std::make_shared<LambNextMVWithDecayRuleCond2>());
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@ -74,10 +74,21 @@ AnfNodePtr GetMul0(const FuncGraphPtr &graph, const AnfNodePtr &input2, const An
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}
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bool QuitFusion(const FuncGraphPtr &graph, const AnfNodePtr &mul0_anf, const AnfNodePtr &mul1_anf,
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const AnfNodePtr &reduce_sum) {
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const AnfNodePtr &reduce_sum, const AnfNodePtr &input2) {
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MS_EXCEPTION_IF_NULL(mul0_anf);
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MS_EXCEPTION_IF_NULL(mul1_anf);
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MS_EXCEPTION_IF_NULL(reduce_sum);
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MS_EXCEPTION_IF_NULL(input2);
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auto addn = input2->cast<CNodePtr>();
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if (addn == nullptr || AnfAlgo::GetCNodeName(addn) != prim::kPrimAddN->name()) {
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MS_LOG(INFO) << "mul's second input is not addn";
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return true;
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}
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std::vector<size_t> shape = AnfAlgo::GetOutputInferShape(addn, 0);
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if (shape.size() != 2 || !(shape[1] == 1024 || shape[1] == 768)) {
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MS_LOG(INFO) << "Addn's infer shape is not equal [x,1024] or [x,768]";
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return true;
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}
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if (!mul0_anf->isa<CNode>() || !mul1_anf->isa<CNode>()) {
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return true;
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}
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@ -86,11 +97,6 @@ bool QuitFusion(const FuncGraphPtr &graph, const AnfNodePtr &mul0_anf, const Anf
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auto mul0 = mul0_anf->cast<CNodePtr>();
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MS_EXCEPTION_IF_NULL(mul0);
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// when network is _VirtualDatasetCell, quit fusion
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if (mul0->fullname_with_scope().find("network-_VirtualDatasetCell") != std::string::npos) {
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return true;
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}
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if (IsDepend(graph, mul0->input(1), reduce_sum)) {
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MS_LOG(INFO) << "mul0->input(1) depends on reduce_sum, quit fusion";
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return true;
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@ -128,7 +134,7 @@ const AnfNodePtr ConfusionMulGradFusion::Process(const FuncGraphPtr &graph, cons
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MS_LOG(INFO) << "Mul0 do not exist, quit fusion";
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return nullptr;
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}
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if (QuitFusion(graph, mul0, mul1, node)) {
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if (QuitFusion(graph, mul0, mul1, node, input2)) {
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return nullptr;
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}
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@ -84,8 +84,9 @@ const AnfNodePtr MulAddFusion::Process(const FuncGraphPtr &graph, const AnfNodeP
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inputs.push_back(mul->input(index));
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}
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auto another_input_node = add->input(add->size() - mul_index);
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if (IsUsedByOthers(graph, another_input_node)) {
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MS_LOG(INFO) << "Add's another input node is used by others, do not fuse";
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if (another_input_node->isa<CNode>() &&
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AnfAlgo::GetCNodeName(another_input_node) == prim::kPrimTupleGetItem->name()) {
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MS_LOG(INFO) << "Add's another input node has multiple outputs, do not fuse";
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return nullptr;
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}
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inputs.push_back(another_input_node);
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@ -32,7 +32,7 @@ class TestHWOptimizeConfusionMulGradFusion : public BackendCommon {
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TEST_F(TestHWOptimizeConfusionMulGradFusion, test_fusion) {
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FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_confusion_mul_grad_fusion", "before");
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EXPECT_NE(g, nullptr);
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std::vector<int> shp{1, 1, 1, 1};
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std::vector<int> shp{10, 1024};
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auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
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AbstractBasePtrList args_spec_list;
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for (size_t i = 0; i < 3; ++i) {
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@ -49,6 +49,5 @@ TEST_F(TestHWOptimizeConfusionMulGradFusion, test_fusion) {
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FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_confusion_mul_grad_fusion", "after");
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EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
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}
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} // namespace opt
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} // namespace mindspore
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@ -15,12 +15,13 @@
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from mindspore.ops import Primitive
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from mindspore.ops import operations as P
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addn = P.AddN()
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mul = P.Mul()
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reduce_sum = P.ReduceSum()
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confusion_mul_grad = Primitive('ConfusionMulGrad')
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make_tuple = Primitive('make_tuple')
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tuple_getitem = Primitive('tuple_getitem')
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axis = 2
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axis = 1
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class FnDict:
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@ -39,8 +40,10 @@ def test_confusion_mul_grad_fusion(tag):
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@fns
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def before(input1, input2, input3):
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output1 = mul(input1, input2)
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mul1 = mul(input3, input2)
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addn0 = addn((input2, input2))
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output1 = mul(input1, addn0)
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mul1 = mul(input3, addn0)
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# input axis will be convert to attr in step ConstructKernelGraph
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output2 = reduce_sum(mul1, axis)
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res = make_tuple(output1, output2)
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@ -48,7 +51,8 @@ def test_confusion_mul_grad_fusion(tag):
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@fns
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def after(input1, input2, input3):
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res = confusion_mul_grad(input1, input2, input3)
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addn0 = addn(input2, input2)
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res = confusion_mul_grad(input1, addn0, input3)
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item0 = tuple_getitem(res, 0)
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item1 = tuple_getitem(res, 1)
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res = make_tuple(item0, item1)
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