forked from mindspore-Ecosystem/mindspore
!7529 complex arithmetic_simplify
Merge pull request !7529 from zhuxiaochen/1020_allsimplify_1.0
This commit is contained in:
Gitee
commit
8d39a8a4b2
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@ -14,18 +14,18 @@
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* limitations under the License.
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*/
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#include "backend/optimizer/graph_kernel/arithmetic_simplify.h"
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#include <list>
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#include "backend/optimizer/graph_kernel/graph_kernel_helper.h"
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#include "backend/kernel_compiler/common_utils.h"
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#include "backend/session/anf_runtime_algorithm.h"
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#include "ir/pattern_matcher.h"
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#include "frontend/operator/ops.h"
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#include "ir/pattern_matcher.h"
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#include "utils/convert_utils.h"
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#include "utils/utils.h"
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namespace mindspore {
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namespace opt {
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AnfNodePtr NewCNodeWithInfo(const AnfNodePtrList &inputs, const AnfNodePtr &ori_node) {
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auto func_graph = ori_node->func_graph();
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MS_EXCEPTION_IF_NULL(func_graph);
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@ -401,10 +401,236 @@ AnfNodePtr SimplifyDiv(const AnfNodePtr &node) {
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(FLAG) = true; \
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}
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bool TryTransposeToReshape(const AnfNodePtr &node) {
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auto perm = AnfAlgo::GetNodeAttr<std::vector<int>>(node, "perm");
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auto ori_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0);
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std::vector<int> remove_one_perm;
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for (auto idx : perm) {
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if (idx < 0 || IntToSize(idx) >= ori_shape.size()) {
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MS_EXCEPTION(ValueError);
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return false;
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}
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if (ori_shape[idx] != 1) {
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remove_one_perm.emplace_back(idx);
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}
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}
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if (remove_one_perm.size() < 2) {
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return true;
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}
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for (size_t idx = 1; idx < remove_one_perm.size(); idx++) {
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if (remove_one_perm[idx] < remove_one_perm[idx - 1]) {
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return false;
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}
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}
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return true;
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}
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AnfNodePtr SimplifyTranspose(const AnfNodePtr &node) {
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if (!IsPrimitiveCNode(node, prim::kPrimTranspose)) {
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return nullptr;
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}
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if (TryTransposeToReshape(node)) {
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auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimReshape), node->cast<CNodePtr>()->input(1)}, node);
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return new_cnode;
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}
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return nullptr;
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}
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AnfNodePtr SimplifyMatMul(const AnfNodePtr &node) {
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if (!IsPrimitiveCNode(node, prim::kPrimMatMul)) {
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return nullptr;
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}
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PatternNode<AnfNodePtr> x, y;
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auto matmul_transpose_lambda = [&node, &x, &y]() -> AnfNodePtr {
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auto new_matmul = NewCNodeWithInfo({NewValueNode(prim::kPrimMatMul), y.GetNode(node), x.GetNode(node)}, node);
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auto new_abstract = node->abstract()->Clone();
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auto ori_shape = node->abstract()->GetShapeTrack()->cast<abstract::ShapePtr>();
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auto shape_value = ori_shape->shape();
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ShapeVector new_shape_value;
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std::copy(shape_value.rbegin(), shape_value.rend(), std::back_inserter(new_shape_value));
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auto new_shape = std::make_shared<abstract::Shape>(new_shape_value);
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new_abstract->set_shape(new_shape);
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new_matmul->set_abstract(new_abstract);
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auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimTranspose), new_matmul}, node);
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auto transpose_a = AnfAlgo::GetNodeAttr<ValuePtr>(node, "transpose_a");
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auto transpose_b = AnfAlgo::GetNodeAttr<ValuePtr>(node, "transpose_b");
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auto transpose_x1 = AnfAlgo::GetNodeAttr<ValuePtr>(node, "transpose_x1");
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auto transpose_x2 = AnfAlgo::GetNodeAttr<ValuePtr>(node, "transpose_x2");
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auto perm = AnfAlgo::GetNodeAttr<ValuePtr>(node->cast<CNodePtr>()->input(1), "perm");
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AnfAlgo::SetNodeAttr("transpose_a", transpose_b, new_matmul);
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AnfAlgo::SetNodeAttr("transpose_b", transpose_a, new_matmul);
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AnfAlgo::SetNodeAttr("transpose_x1", transpose_x2, new_matmul);
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AnfAlgo::SetNodeAttr("transpose_x2", transpose_x1, new_matmul);
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AnfAlgo::SetNodeAttr("perm", perm, new_cnode);
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return new_cnode;
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};
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// MatMul(Transpose(x), Transpose(y)) ==> Transpose(MatMul(y, x))
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MATCH_REPLACE_LAMBDA(node,
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PBinOperation(prim::kPrimMatMul, PUnaryOperation(prim::kPrimTranspose, x),
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PUnaryOperation(prim::kPrimTranspose, y), false),
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matmul_transpose_lambda);
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return nullptr;
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}
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ShapeVector TransAxisValueToVector(const ValuePtr &value) {
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MS_EXCEPTION_IF_NULL(value);
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ShapeVector axis_vector;
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if (value->isa<Int32Imm>()) {
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axis_vector.emplace_back(GetValue<int>(value));
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}
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if (value->isa<ValueTuple>() || value->isa<ValueList>()) {
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axis_vector = GetValue<std::vector<int>>(value);
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}
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return axis_vector;
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}
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ShapeVector GetNodeShape(const AnfNodePtr &node) {
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auto base_shape = node->Shape()->cast<abstract::ShapePtr>();
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std::vector<int> shape;
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std::transform(base_shape->shape().begin(), base_shape->shape().end(), std::back_inserter(shape), IntToSize);
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return shape;
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}
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std::vector<std::pair<int, int>> GetUnmodifiedDim(const ShapeVector &a, const ShapeVector &b) {
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std::vector<std::pair<int, int>> unmodified;
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for (size_t i = 0, j = 0, patial_a = 1, patial_b = 1;;) {
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if (i >= a.size() && j >= b.size()) {
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break;
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}
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patial_a *= a[i];
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patial_b *= b[j];
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if (patial_a == patial_b && a[i] == b[j]) {
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unmodified.emplace_back(std::make_pair(i, j));
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++i;
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++j;
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continue;
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}
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if (patial_a < patial_b && b[j] > a[i]) {
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++i;
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patial_a *= a[i];
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if (patial_a == patial_b) {
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++i;
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++j;
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}
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continue;
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}
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if (patial_a > patial_b && b[j] < a[i]) {
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++j;
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patial_b *= b[j];
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if (patial_a == patial_b) {
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++i;
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++j;
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}
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continue;
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}
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}
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return unmodified;
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}
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AnfNodePtr SimplifyReduce(const AnfNodePtr &node) {
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if (!IsPrimitiveCNode(node, prim::kPrimReduceMax) && !IsPrimitiveCNode(node, prim::kPrimReduceMin) &&
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!IsPrimitiveCNode(node, prim::kPrimReduceSum)) {
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return nullptr;
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}
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PatternNode<AnfNodePtr> x;
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auto trans_reduce_lamda = [&node, &x](PrimitivePtr &operation) -> AnfNodePtr {
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auto shape = GetNodeShape(node);
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if (shape.size() != 0 && shape.size() != 1) {
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return node;
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} else {
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auto tmp_node = node->cast<CNodePtr>();
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auto transpose_node = tmp_node->input(1);
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auto transpose_dimensions = GetValue<std::vector<int>>(AnfAlgo::GetNodeAttr<ValuePtr>(transpose_node, "perm"));
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ShapeVector new_dimensions;
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auto reduce_dimensions = TransAxisValueToVector(AnfAlgo::GetNodeAttr<ValuePtr>(tmp_node, "axis"));
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std::transform(reduce_dimensions.begin(), reduce_dimensions.end(), std::back_inserter(new_dimensions),
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[&transpose_dimensions](const int &dim) { return transpose_dimensions[dim]; });
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std::sort(new_dimensions.begin(), new_dimensions.end());
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auto new_cnode = NewCNodeWithInfo({NewValueNode(operation), x.GetNode(node)}, node);
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AnfAlgo::SetNodeAttr("axis", MakeValue(new_dimensions), new_cnode);
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AnfAlgo::CopyNodeAttr("keep_dims", node, new_cnode);
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return new_cnode;
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}
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};
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auto reduce_reduce_lamda = [&node, &x](PrimitivePtr &operation) -> AnfNodePtr {
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auto tmp_node = node->cast<CNodePtr>();
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auto arg_node = tmp_node->input(1);
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auto arg_dimensions = TransAxisValueToVector(AnfAlgo::GetNodeAttr<ValuePtr>(arg_node, "axis"));
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auto reduce_dimensions = TransAxisValueToVector(AnfAlgo::GetNodeAttr<ValuePtr>(tmp_node, "axis"));
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ShapeVector new_dimensions;
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for (size_t i = 0; i < arg_dimensions.size(); ++i) {
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for (size_t j = 0; j < reduce_dimensions.size(); ++j) {
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if (reduce_dimensions[j] >= arg_dimensions[i]) {
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++reduce_dimensions[j];
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}
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}
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}
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std::merge(arg_dimensions.begin(), arg_dimensions.end(), reduce_dimensions.begin(), reduce_dimensions.end(),
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std::back_inserter(new_dimensions));
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auto new_cnode = NewCNodeWithInfo({NewValueNode(operation), x.GetNode(node)}, node);
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AnfAlgo::SetNodeAttr("axis", MakeValue(new_dimensions), new_cnode);
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AnfAlgo::CopyNodeAttr("keep_dims", node, new_cnode);
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return new_cnode;
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};
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auto reshape_reduce_lamda = [&node, &x](PrimitivePtr &operation) -> AnfNodePtr {
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auto tmp_node = node->cast<CNodePtr>();
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auto arg_node = tmp_node->input(1);
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auto input_shape = GetNodeShape(arg_node->cast<CNodePtr>()->input(1));
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auto re_shape = GetNodeShape(arg_node);
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auto reduce_dimensions = TransAxisValueToVector(AnfAlgo::GetNodeAttr<ValuePtr>(tmp_node, "axis"));
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auto unmodified_dim_pair = GetUnmodifiedDim(input_shape, re_shape);
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std::vector<bool> dim_in_output(re_shape.size(), true);
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std::vector<bool> dim_unmodified(re_shape.size(), false);
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for (auto dim : reduce_dimensions) {
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dim_in_output[dim] = false;
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}
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for (auto pair_dim : unmodified_dim_pair) {
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dim_unmodified[pair_dim.second] = true;
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}
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bool replace = true;
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for (size_t i = 0; i < dim_in_output.size(); ++i) {
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if (dim_in_output[i] && !dim_unmodified[i]) {
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replace = false;
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}
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}
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if (replace) {
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ShapeVector un_dimensions;
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for (auto pair_dim : unmodified_dim_pair) {
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if (dim_in_output[pair_dim.second]) {
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un_dimensions.emplace_back(pair_dim.first);
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}
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}
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ShapeVector new_dimensions;
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for (size_t i = 0; i < input_shape.size(); ++i) {
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if (std::find(un_dimensions.begin(), un_dimensions.end(), i) == un_dimensions.end()) {
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new_dimensions.emplace_back(i);
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}
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}
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auto new_cnode = NewCNodeWithInfo({NewValueNode(operation), x.GetNode(node)}, node);
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AnfAlgo::SetNodeAttr("axis", MakeValue(new_dimensions), new_cnode);
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AnfAlgo::CopyNodeAttr("keep_dims", node, new_cnode);
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return new_cnode;
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}
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return node;
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};
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std::list<PrimitivePtr> ReduceOperations = {prim::kPrimReduceSum, prim::kPrimReduceMax, prim::kPrimReduceMin};
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for (auto operation : ReduceOperations) {
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// Reduce(Transpose(A)) = Reduce(A) if result is a scalar or vector
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MATCH_REPLACE_LAMBDA_FLAG(node, PPrimitive(operation, PPrimitive(prim::kPrimTranspose, x)), trans_reduce_lamda,
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operation);
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// Reduce(Reduce(A)) = Reduce(A)
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MATCH_REPLACE_LAMBDA_FLAG(node, PPrimitive(operation, PPrimitive(operation, x)), reduce_reduce_lamda, operation);
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// Reduce(Reshape(A)) = Reduce(A) if reduce dimensions is not in reshape dimensions
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MATCH_REPLACE_LAMBDA_FLAG(node, PPrimitive(operation, PPrimitive(prim::kPrimReshape, x)), reshape_reduce_lamda,
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operation);
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}
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return nullptr;
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}
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AnfNodePtr TrySimplify(const AnfNodePtr &node) {
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std::list<std::function<AnfNodePtr(AnfNodePtr)>> SimplifyFuncList = {
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SimplifyAdd, SimplifyDiv, SimplifyLog, SimplifyMul, SimplifyNeg,
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SimplifyPow, SimplifyRsqrt, SimplifySelect, SimplifySqrt, SimplifySub};
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SimplifyAdd, SimplifyDiv, SimplifyLog, SimplifyMul, SimplifyNeg, SimplifyPow, SimplifyRsqrt,
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SimplifySelect, SimplifySqrt, SimplifySub, SimplifyTranspose, SimplifyMatMul, SimplifyReduce};
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for (auto f : SimplifyFuncList) {
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auto ret = f(node);
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if (ret != nullptr) {
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@ -22,8 +22,8 @@
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#include <tuple>
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#include <vector>
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#include "ir/visitor.h"
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#include "base/core_ops.h"
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#include "ir/visitor.h"
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#include "utils/shape_utils.h"
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namespace mindspore {
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@ -750,9 +750,18 @@ class PConstant : public PBase<PConstant<T> > {
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if (value->isa<tensor::Tensor>()) {
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tensor::TensorPtr tensor_ptr = dyn_cast<tensor::Tensor>(value);
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TypeId tensor_type = tensor_ptr->Dtype()->type_id();
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auto tensor_abstract = node->abstract()->cast<abstract::AbstractTensorPtr>();
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TypePtr tensor_type_ptr = tensor_abstract->element()->BuildType();
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ShapeVector tensor_shape = tensor_abstract->shape()->shape();
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auto new_tensor_ptr = std::make_shared<tensor::Tensor>(tensor_type_ptr->type_id(), tensor_shape);
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size_t mem_size = GetTypeByte(tensor_type_ptr) * IntToSize(new_tensor_ptr->ElementsNum());
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if ((tensor_type == TypeId::kNumberTypeFloat32) || (tensor_type == TypeId::kNumberTypeFloat) ||
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(tensor_type == TypeId::kNumberTypeFloat64)) {
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float *data2 = reinterpret_cast<float *>(tensor_ptr->data_c());
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float *data = reinterpret_cast<float *>(tensor_ptr->data_c());
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float *data2 = reinterpret_cast<float *>(new_tensor_ptr->data_c());
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if (memcpy_s(data2, mem_size, data, mem_size) != 0) {
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return nullptr;
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}
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for (int i = 0; i < tensor_ptr->DataSize(); i++) {
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if (data2[i] == 0 && calcu_type == prim::kPrimReciprocal) {
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return nullptr;
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@ -761,7 +770,11 @@ class PConstant : public PBase<PConstant<T> > {
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}
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}
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if ((tensor_type == TypeId::kNumberTypeInt32) || (tensor_type == TypeId::kNumberTypeInt)) {
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int *data2 = reinterpret_cast<int *>(tensor_ptr->data_c());
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int *data = reinterpret_cast<int *>(tensor_ptr->data_c());
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int *data2 = reinterpret_cast<int *>(new_tensor_ptr->data_c());
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if (memcpy_s(data2, mem_size, data, mem_size) != 0) {
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return nullptr;
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}
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for (int i = 0; i < tensor_ptr->DataSize(); i++) {
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if (data2[i] == 0 && calcu_type == prim::kPrimReciprocal) {
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return nullptr;
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@ -770,7 +783,11 @@ class PConstant : public PBase<PConstant<T> > {
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}
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}
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if (tensor_type == TypeId::kNumberTypeFloat64) {
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double *data2 = reinterpret_cast<double *>(tensor_ptr->data_c());
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double *data = reinterpret_cast<double *>(tensor_ptr->data_c());
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double *data2 = reinterpret_cast<double *>(new_tensor_ptr->data_c());
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if (memcpy_s(data2, mem_size, data, mem_size) != 0) {
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return nullptr;
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}
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for (int i = 0; i < tensor_ptr->DataSize(); i++) {
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if (data2[i] == 0 && calcu_type == prim::kPrimReciprocal) {
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return nullptr;
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@ -778,7 +795,9 @@ class PConstant : public PBase<PConstant<T> > {
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data2[i] = CalcuConstant(data2[i], calcu_type);
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}
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}
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return node;
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auto new_vnode = NewValueNode(new_tensor_ptr);
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new_vnode->set_abstract(tensor_ptr->ToAbstract());
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return new_vnode;
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}
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return nullptr;
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}
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@ -1005,6 +1024,14 @@ BIN_OPERATION_PATTERN(operator-, prim::kPrimSub, false);
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return rep; \
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} \
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}
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#define MATCH_REPLACE_LAMBDA_FLAG(OrigNode, CaptureNode, Lambda, Flag) \
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if ((CaptureNode).TryCapture(OrigNode)) { \
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auto rep = (Lambda)(Flag); \
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if (rep != nullptr) { \
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return rep; \
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} \
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}
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} // namespace mindspore
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#endif // MINDSPORE_CORE_IR_PATTERN_MATCHER_H_
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@ -20,7 +20,8 @@ from mindspore import Tensor
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from mindspore.nn import Cell
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import mindspore.ops.operations as P
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
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context.set_context(mode=context.GRAPH_MODE,
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enable_graph_kernel=True, device_target="GPU")
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class Net(Cell):
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@ -33,6 +34,8 @@ class Net(Cell):
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self.sqrt = P.Sqrt()
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self.pow = P.Pow()
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self.neg = P.Neg()
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self.reducemin = P.ReduceMin()
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self.reshape = P.Reshape()
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def construct(self, x, y):
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add_res1 = self.add(x, 4)
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@ -42,7 +45,9 @@ class Net(Cell):
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div_res = self.div(mul_res, self.sqrt(mul_res))
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pow_res = self.pow(y, 2)
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neg_res = self.neg(self.neg(pow_res))
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return self.add(div_res, neg_res)
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add_res3 = self.add(neg_res, div_res)
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resh_res = self.reshape(add_res3, (2, 12, 3))
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return self.reducemin(resh_res, 1)
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@pytest.mark.level0
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@ -58,10 +63,12 @@ def test_basic():
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div_res = np.sqrt(mul_res)
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pow_res = input_y * input_y
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neg_res = pow_res
|
||||
expect = div_res + neg_res
|
||||
add_res3 = neg_res + div_res
|
||||
expect = np.min(add_res3, (1, 2))
|
||||
|
||||
net = Net()
|
||||
result = net(Tensor(input_x), Tensor(input_y))
|
||||
|
||||
res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
|
||||
res = np.allclose(expect, result.asnumpy(), rtol=1.e-4,
|
||||
atol=1.e-7, equal_nan=True)
|
||||
assert res
|
||||
|
|
|
|||
Loading…
Reference in New Issue