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simplify transpose matmul reduce

This commit is contained in:
zhu_xiaochen 2020-10-20 17:17:24 +08:00
parent 15f57029a5
commit c739f14038
3 changed files with 273 additions and 13 deletions
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234
mindspore/ccsrc/backend/optimizer/graph_kernel/arithmetic_simplify.cc
View File

@ -14,18 +14,18 @@
* limitations under the License.
*/
#include "backend/optimizer/graph_kernel/arithmetic_simplify.h"
#include <list>
#include "backend/optimizer/graph_kernel/graph_kernel_helper.h"
#include "backend/kernel_compiler/common_utils.h"
#include "backend/session/anf_runtime_algorithm.h"
#include "ir/pattern_matcher.h"
#include "frontend/operator/ops.h"
#include "ir/pattern_matcher.h"
#include "utils/convert_utils.h"
#include "utils/utils.h"
namespace mindspore {
namespace opt {
AnfNodePtr NewCNodeWithInfo(const AnfNodePtrList &inputs, const AnfNodePtr &ori_node) {
auto func_graph = ori_node->func_graph();
MS_EXCEPTION_IF_NULL(func_graph);
@ -401,10 +401,236 @@ AnfNodePtr SimplifyDiv(const AnfNodePtr &node) {
(FLAG) = true; \
}
bool TryTransposeToReshape(const AnfNodePtr &node) {
auto perm = AnfAlgo::GetNodeAttr<std::vector<int>>(node, "perm");
auto ori_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, 0);
std::vector<int> remove_one_perm;
for (auto idx : perm) {
if (idx < 0 || IntToSize(idx) >= ori_shape.size()) {
MS_EXCEPTION(ValueError);
return false;
}
if (ori_shape[idx] != 1) {
remove_one_perm.emplace_back(idx);
}
}
if (remove_one_perm.size() < 2) {
return true;
}
for (size_t idx = 1; idx < remove_one_perm.size(); idx++) {
if (remove_one_perm[idx] < remove_one_perm[idx - 1]) {
return false;
}
}
return true;
}
AnfNodePtr SimplifyTranspose(const AnfNodePtr &node) {
if (!IsPrimitiveCNode(node, prim::kPrimTranspose)) {
return nullptr;
}
if (TryTransposeToReshape(node)) {
auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimReshape), node->cast<CNodePtr>()->input(1)}, node);
return new_cnode;
}
return nullptr;
}
AnfNodePtr SimplifyMatMul(const AnfNodePtr &node) {
if (!IsPrimitiveCNode(node, prim::kPrimMatMul)) {
return nullptr;
}
PatternNode<AnfNodePtr> x, y;
auto matmul_transpose_lambda = [&node, &x, &y]() -> AnfNodePtr {
auto new_matmul = NewCNodeWithInfo({NewValueNode(prim::kPrimMatMul), y.GetNode(node), x.GetNode(node)}, node);
auto new_abstract = node->abstract()->Clone();
auto ori_shape = node->abstract()->GetShapeTrack()->cast<abstract::ShapePtr>();
auto shape_value = ori_shape->shape();
ShapeVector new_shape_value;
std::copy(shape_value.rbegin(), shape_value.rend(), std::back_inserter(new_shape_value));
auto new_shape = std::make_shared<abstract::Shape>(new_shape_value);
new_abstract->set_shape(new_shape);
new_matmul->set_abstract(new_abstract);
auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimTranspose), new_matmul}, node);
auto transpose_a = AnfAlgo::GetNodeAttr<ValuePtr>(node, "transpose_a");
auto transpose_b = AnfAlgo::GetNodeAttr<ValuePtr>(node, "transpose_b");
auto transpose_x1 = AnfAlgo::GetNodeAttr<ValuePtr>(node, "transpose_x1");
auto transpose_x2 = AnfAlgo::GetNodeAttr<ValuePtr>(node, "transpose_x2");
auto perm = AnfAlgo::GetNodeAttr<ValuePtr>(node->cast<CNodePtr>()->input(1), "perm");
AnfAlgo::SetNodeAttr("transpose_a", transpose_b, new_matmul);
AnfAlgo::SetNodeAttr("transpose_b", transpose_a, new_matmul);
AnfAlgo::SetNodeAttr("transpose_x1", transpose_x2, new_matmul);
AnfAlgo::SetNodeAttr("transpose_x2", transpose_x1, new_matmul);
AnfAlgo::SetNodeAttr("perm", perm, new_cnode);
return new_cnode;
};
// MatMul(Transpose(x), Transpose(y)) ==> Transpose(MatMul(y, x))
MATCH_REPLACE_LAMBDA(node,
PBinOperation(prim::kPrimMatMul, PUnaryOperation(prim::kPrimTranspose, x),
PUnaryOperation(prim::kPrimTranspose, y), false),
matmul_transpose_lambda);
return nullptr;
}
ShapeVector TransAxisValueToVector(const ValuePtr &value) {
MS_EXCEPTION_IF_NULL(value);
ShapeVector axis_vector;
if (value->isa<Int32Imm>()) {
axis_vector.emplace_back(GetValue<int>(value));
}
if (value->isa<ValueTuple>() || value->isa<ValueList>()) {
axis_vector = GetValue<std::vector<int>>(value);
}
return axis_vector;
}
ShapeVector GetNodeShape(const AnfNodePtr &node) {
auto base_shape = node->Shape()->cast<abstract::ShapePtr>();
std::vector<int> shape;
std::transform(base_shape->shape().begin(), base_shape->shape().end(), std::back_inserter(shape), IntToSize);
return shape;
}
std::vector<std::pair<int, int>> GetUnmodifiedDim(const ShapeVector &a, const ShapeVector &b) {
std::vector<std::pair<int, int>> unmodified;
for (size_t i = 0, j = 0, patial_a = 1, patial_b = 1;;) {
if (i >= a.size() && j >= b.size()) {
break;
}
patial_a *= a[i];
patial_b *= b[j];
if (patial_a == patial_b && a[i] == b[j]) {
unmodified.emplace_back(std::make_pair(i, j));
++i;
++j;
continue;
}
if (patial_a < patial_b && b[j] > a[i]) {
++i;
patial_a *= a[i];
if (patial_a == patial_b) {
++i;
++j;
}
continue;
}
if (patial_a > patial_b && b[j] < a[i]) {
++j;
patial_b *= b[j];
if (patial_a == patial_b) {
++i;
++j;
}
continue;
}
}
return unmodified;
}
AnfNodePtr SimplifyReduce(const AnfNodePtr &node) {
if (!IsPrimitiveCNode(node, prim::kPrimReduceMax) && !IsPrimitiveCNode(node, prim::kPrimReduceMin) &&
!IsPrimitiveCNode(node, prim::kPrimReduceSum)) {
return nullptr;
}
PatternNode<AnfNodePtr> x;
auto trans_reduce_lamda = [&node, &x](PrimitivePtr &operation) -> AnfNodePtr {
auto shape = GetNodeShape(node);
if (shape.size() != 0 && shape.size() != 1) {
return node;
} else {
auto tmp_node = node->cast<CNodePtr>();
auto transpose_node = tmp_node->input(1);
auto transpose_dimensions = GetValue<std::vector<int>>(AnfAlgo::GetNodeAttr<ValuePtr>(transpose_node, "perm"));
ShapeVector new_dimensions;
auto reduce_dimensions = TransAxisValueToVector(AnfAlgo::GetNodeAttr<ValuePtr>(tmp_node, "axis"));
std::transform(reduce_dimensions.begin(), reduce_dimensions.end(), std::back_inserter(new_dimensions),
[&transpose_dimensions](const int &dim) { return transpose_dimensions[dim]; });
std::sort(new_dimensions.begin(), new_dimensions.end());
auto new_cnode = NewCNodeWithInfo({NewValueNode(operation), x.GetNode(node)}, node);
AnfAlgo::SetNodeAttr("axis", MakeValue(new_dimensions), new_cnode);
AnfAlgo::CopyNodeAttr("keep_dims", node, new_cnode);
return new_cnode;
}
};
auto reduce_reduce_lamda = [&node, &x](PrimitivePtr &operation) -> AnfNodePtr {
auto tmp_node = node->cast<CNodePtr>();
auto arg_node = tmp_node->input(1);
auto arg_dimensions = TransAxisValueToVector(AnfAlgo::GetNodeAttr<ValuePtr>(arg_node, "axis"));
auto reduce_dimensions = TransAxisValueToVector(AnfAlgo::GetNodeAttr<ValuePtr>(tmp_node, "axis"));
ShapeVector new_dimensions;
for (size_t i = 0; i < arg_dimensions.size(); ++i) {
for (size_t j = 0; j < reduce_dimensions.size(); ++j) {
if (reduce_dimensions[j] >= arg_dimensions[i]) {
++reduce_dimensions[j];
}
}
}
std::merge(arg_dimensions.begin(), arg_dimensions.end(), reduce_dimensions.begin(), reduce_dimensions.end(),
std::back_inserter(new_dimensions));
auto new_cnode = NewCNodeWithInfo({NewValueNode(operation), x.GetNode(node)}, node);
AnfAlgo::SetNodeAttr("axis", MakeValue(new_dimensions), new_cnode);
AnfAlgo::CopyNodeAttr("keep_dims", node, new_cnode);
return new_cnode;
};
auto reshape_reduce_lamda = [&node, &x](PrimitivePtr &operation) -> AnfNodePtr {
auto tmp_node = node->cast<CNodePtr>();
auto arg_node = tmp_node->input(1);
auto input_shape = GetNodeShape(arg_node->cast<CNodePtr>()->input(1));
auto re_shape = GetNodeShape(arg_node);
auto reduce_dimensions = TransAxisValueToVector(AnfAlgo::GetNodeAttr<ValuePtr>(tmp_node, "axis"));
auto unmodified_dim_pair = GetUnmodifiedDim(input_shape, re_shape);
std::vector<bool> dim_in_output(re_shape.size(), true);
std::vector<bool> dim_unmodified(re_shape.size(), false);
for (auto dim : reduce_dimensions) {
dim_in_output[dim] = false;
}
for (auto pair_dim : unmodified_dim_pair) {
dim_unmodified[pair_dim.second] = true;
}
bool replace = true;
for (size_t i = 0; i < dim_in_output.size(); ++i) {
if (dim_in_output[i] && !dim_unmodified[i]) {
replace = false;
}
}
if (replace) {
ShapeVector un_dimensions;
for (auto pair_dim : unmodified_dim_pair) {
if (dim_in_output[pair_dim.second]) {
un_dimensions.emplace_back(pair_dim.first);
}
}
ShapeVector new_dimensions;
for (size_t i = 0; i < input_shape.size(); ++i) {
if (std::find(un_dimensions.begin(), un_dimensions.end(), i) == un_dimensions.end()) {
new_dimensions.emplace_back(i);
}
}
auto new_cnode = NewCNodeWithInfo({NewValueNode(operation), x.GetNode(node)}, node);
AnfAlgo::SetNodeAttr("axis", MakeValue(new_dimensions), new_cnode);
AnfAlgo::CopyNodeAttr("keep_dims", node, new_cnode);
return new_cnode;
}
return node;
};
std::list<PrimitivePtr> ReduceOperations = {prim::kPrimReduceSum, prim::kPrimReduceMax, prim::kPrimReduceMin};
for (auto operation : ReduceOperations) {
// Reduce(Transpose(A)) = Reduce(A) if result is a scalar or vector
MATCH_REPLACE_LAMBDA_FLAG(node, PPrimitive(operation, PPrimitive(prim::kPrimTranspose, x)), trans_reduce_lamda,
operation);
// Reduce(Reduce(A)) = Reduce(A)
MATCH_REPLACE_LAMBDA_FLAG(node, PPrimitive(operation, PPrimitive(operation, x)), reduce_reduce_lamda, operation);
// Reduce(Reshape(A)) = Reduce(A) if reduce dimensions is not in reshape dimensions
MATCH_REPLACE_LAMBDA_FLAG(node, PPrimitive(operation, PPrimitive(prim::kPrimReshape, x)), reshape_reduce_lamda,
operation);
}
return nullptr;
}
AnfNodePtr TrySimplify(const AnfNodePtr &node) {
std::list<std::function<AnfNodePtr(AnfNodePtr)>> SimplifyFuncList = {
SimplifyAdd, SimplifyDiv, SimplifyLog, SimplifyMul, SimplifyNeg,
SimplifyPow, SimplifyRsqrt, SimplifySelect, SimplifySqrt, SimplifySub};
SimplifyAdd, SimplifyDiv, SimplifyLog, SimplifyMul, SimplifyNeg, SimplifyPow, SimplifyRsqrt,
SimplifySelect, SimplifySqrt, SimplifySub, SimplifyTranspose, SimplifyMatMul, SimplifyReduce};
for (auto f : SimplifyFuncList) {
auto ret = f(node);
if (ret != nullptr) {

37
mindspore/core/ir/pattern_matcher.h
View File

@ -22,8 +22,8 @@
#include <tuple>
#include <vector>
#include "ir/visitor.h"
#include "base/core_ops.h"
#include "ir/visitor.h"
#include "utils/shape_utils.h"
namespace mindspore {
@ -750,9 +750,18 @@ class PConstant : public PBase<PConstant<T> > {
if (value->isa<tensor::Tensor>()) {
tensor::TensorPtr tensor_ptr = dyn_cast<tensor::Tensor>(value);
TypeId tensor_type = tensor_ptr->Dtype()->type_id();
auto tensor_abstract = node->abstract()->cast<abstract::AbstractTensorPtr>();
TypePtr tensor_type_ptr = tensor_abstract->element()->BuildType();
ShapeVector tensor_shape = tensor_abstract->shape()->shape();
auto new_tensor_ptr = std::make_shared<tensor::Tensor>(tensor_type_ptr->type_id(), tensor_shape);
size_t mem_size = GetTypeByte(tensor_type_ptr) * IntToSize(new_tensor_ptr->ElementsNum());
if ((tensor_type == TypeId::kNumberTypeFloat32) || (tensor_type == TypeId::kNumberTypeFloat) ||
(tensor_type == TypeId::kNumberTypeFloat64)) {
float *data2 = reinterpret_cast<float *>(tensor_ptr->data_c());
float *data = reinterpret_cast<float *>(tensor_ptr->data_c());
float *data2 = reinterpret_cast<float *>(new_tensor_ptr->data_c());
if (memcpy_s(data2, mem_size, data, mem_size) != 0) {
return nullptr;
}
for (int i = 0; i < tensor_ptr->DataSize(); i++) {
if (data2[i] == 0 && calcu_type == prim::kPrimReciprocal) {
return nullptr;
@ -761,7 +770,11 @@ class PConstant : public PBase<PConstant<T> > {
}
}
if ((tensor_type == TypeId::kNumberTypeInt32) || (tensor_type == TypeId::kNumberTypeInt)) {
int *data2 = reinterpret_cast<int *>(tensor_ptr->data_c());
int *data = reinterpret_cast<int *>(tensor_ptr->data_c());
int *data2 = reinterpret_cast<int *>(new_tensor_ptr->data_c());
if (memcpy_s(data2, mem_size, data, mem_size) != 0) {
return nullptr;
}
for (int i = 0; i < tensor_ptr->DataSize(); i++) {
if (data2[i] == 0 && calcu_type == prim::kPrimReciprocal) {
return nullptr;
@ -770,7 +783,11 @@ class PConstant : public PBase<PConstant<T> > {
}
}
if (tensor_type == TypeId::kNumberTypeFloat64) {
double *data2 = reinterpret_cast<double *>(tensor_ptr->data_c());
double *data = reinterpret_cast<double *>(tensor_ptr->data_c());
double *data2 = reinterpret_cast<double *>(new_tensor_ptr->data_c());
if (memcpy_s(data2, mem_size, data, mem_size) != 0) {
return nullptr;
}
for (int i = 0; i < tensor_ptr->DataSize(); i++) {
if (data2[i] == 0 && calcu_type == prim::kPrimReciprocal) {
return nullptr;
@ -778,7 +795,9 @@ class PConstant : public PBase<PConstant<T> > {
data2[i] = CalcuConstant(data2[i], calcu_type);
}
}
return node;
auto new_vnode = NewValueNode(new_tensor_ptr);
new_vnode->set_abstract(tensor_ptr->ToAbstract());
return new_vnode;
}
return nullptr;
}
@ -1005,6 +1024,14 @@ BIN_OPERATION_PATTERN(operator-, prim::kPrimSub, false);
return rep; \
} \
}
#define MATCH_REPLACE_LAMBDA_FLAG(OrigNode, CaptureNode, Lambda, Flag) \
if ((CaptureNode).TryCapture(OrigNode)) { \
auto rep = (Lambda)(Flag); \
if (rep != nullptr) { \
return rep; \
} \
}
} // namespace mindspore
#endif // MINDSPORE_CORE_IR_PATTERN_MATCHER_H_

15
tests/st/ops/graph_kernel/test_simplify.py
View File

@ -20,7 +20,8 @@ from mindspore import Tensor
from mindspore.nn import Cell
import mindspore.ops.operations as P
context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
context.set_context(mode=context.GRAPH_MODE,
enable_graph_kernel=True, device_target="GPU")
class Net(Cell):
@ -33,6 +34,8 @@ class Net(Cell):
self.sqrt = P.Sqrt()
self.pow = P.Pow()
self.neg = P.Neg()
self.reducemin = P.ReduceMin()
self.reshape = P.Reshape()
def construct(self, x, y):
add_res1 = self.add(x, 4)
@ -42,7 +45,9 @@ class Net(Cell):
div_res = self.div(mul_res, self.sqrt(mul_res))
pow_res = self.pow(y, 2)
neg_res = self.neg(self.neg(pow_res))
return self.add(div_res, neg_res)
add_res3 = self.add(neg_res, div_res)
resh_res = self.reshape(add_res3, (2, 12, 3))
return self.reducemin(resh_res, 1)
@pytest.mark.level0
@ -58,10 +63,12 @@ def test_basic():
div_res = np.sqrt(mul_res)
pow_res = input_y * input_y
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