forked from jiuyuan/InfiniTensor
add trigon function operation on mlu: sin,cos,tan,asin,sinh,asinh
This commit is contained in:
wanghailu
parent
392427cca6
commit
820d855ec8
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@ -42,6 +42,18 @@ enum class OpType {
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Tanh,
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TanhBackward,
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Abs,
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Sin,
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Cos,
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Tan,
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ASin,
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ACos,
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ATan,
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SinH,
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CosH,
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TanH,
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ASinH,
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ACosH,
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ATanH,
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Resize,
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//
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MemBound = 300,
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@ -94,6 +106,18 @@ class OpRegistry {
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FOP(Tanh);
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FOP(TanhBackward);
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FOP(Abs);
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FOP(Sin);
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FOP(Cos);
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FOP(Tan);
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FOP(ASin);
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FOP(ACos);
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FOP(ATan);
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FOP(SinH);
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FOP(CosH);
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FOP(TanH);
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FOP(ASinH);
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FOP(ACosH);
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FOP(ATanH);
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//
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FOP(MemBound);
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default:
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@ -28,4 +28,17 @@ DEFINE_UNARY_OBJ(Sigmoid, OpType::Sigmoid)
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DEFINE_UNARY_OBJ(Tanh, OpType::Tanh)
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DEFINE_UNARY_OBJ(Softmax, OpType::Softmax)
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DEFINE_UNARY_OBJ(Abs, OpType::Abs)
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DEFINE_UNARY_OBJ(Sin, OpType::Sin)
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DEFINE_UNARY_OBJ(Cos, OpType::Cos)
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DEFINE_UNARY_OBJ(Tan, OpType::Tan)
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DEFINE_UNARY_OBJ(ASin, OpType::ASin)
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DEFINE_UNARY_OBJ(ACos, OpType::ACos)
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DEFINE_UNARY_OBJ(ATan, OpType::ATan)
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DEFINE_UNARY_OBJ(SinH, OpType::SinH)
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DEFINE_UNARY_OBJ(CosH, OpType::CosH)
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DEFINE_UNARY_OBJ(TanH, OpType::TanH)
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DEFINE_UNARY_OBJ(ASinH, OpType::ASinH)
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DEFINE_UNARY_OBJ(ACosH, OpType::ACosH)
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DEFINE_UNARY_OBJ(ATanH, OpType::ATanH)
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}; // namespace infini
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@ -0,0 +1,186 @@
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#include "bang/bang_kernel_without_config.h"
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#include "bang/bang_runtime.h"
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#include "operators/unary.h"
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namespace infini {
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class TrigonCnnl : public BangKernelWithoutConfig {
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virtual cnnlTrigonFunctionMode_t getOpType() const = 0;
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virtual cnnlComputationPreference_t getPrefer() const = 0;
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void compute(const Operator &_op,
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const RuntimeObj *_context) const override {
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auto op = as<UnaryObj>(_op);
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auto context = dynamic_cast<const BangRuntimeObj *>(_context);
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void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
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void *const cData = (op->getOutput()->getRawDataPtr<void *>());
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cnnlTensorDescriptor_t aDesc, cDesc;
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auto dim = op->getInputs(0)->getDims();
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if (dim.size() != 4)
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IT_TODO_HALT();
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int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
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// get inputs
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checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
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checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
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CNNL_DTYPE_FLOAT, 4, dim_array));
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// get outputs
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checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
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checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
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CNNL_DTYPE_FLOAT, 4, dim_array));
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// get op descriptor
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cnnlTrigonDescriptor_t opDesc;
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checkCnnlError(cnnlCreateTrigonDescriptor(&opDesc));
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checkCnnlError(cnnlSetTrigonDescriptor(
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opDesc, getOpType()));
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cnnlStatus_t stat =
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cnnlTrigonForward(context->cnnlHandle(), opDesc, aDesc,
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aData, cDesc, cData);
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if (stat != CNNL_STATUS_SUCCESS)
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return;
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// Destories in BANG does not require sync. But cnnl does not state
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// whether sync is required before destories.
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checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
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checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
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checkCnnlError(cnnlDestroyTrigonDescriptor(opDesc));
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}
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};
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class SinCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_SIN;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class CosCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_COS;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class TanCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_TAN;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class ASinCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_ASIN;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class ACosCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_ACOS;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class ATanCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_ATAN;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class SinHCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_SINH;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class CosHCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_COSH;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class TanHCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_TANH;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class ASinHCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_ASINH;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class ACosHCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_ACOSH;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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class ATanHCnnl : public TrigonCnnl {
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cnnlTrigonFunctionMode_t getOpType() const override {
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return CNNL_TRIGON_ATANH;
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}
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cnnlComputationPreference_t getPrefer() const override {
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return CNNL_COMPUTATION_HIGH_PRECISION;
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}
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};
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REGISTER_KERNEL(Device::BANG, OpType::Sin, DataType::Float32, SinCnnl,
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"Sin_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::Cos, DataType::Float32, CosCnnl,
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"Cos_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::Tan, DataType::Float32, TanCnnl,
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"Tan_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::ASin, DataType::Float32, ASinCnnl,
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"ASin_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::ACos, DataType::Float32, ACosCnnl,
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"ACos_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::ATan, DataType::Float32, ATanCnnl,
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"ATan_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::SinH, DataType::Float32, SinHCnnl,
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"SinH_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::CosH, DataType::Float32, CosHCnnl,
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"CosH_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::TanH, DataType::Float32, TanHCnnl,
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"TanH_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::ASinH, DataType::Float32, ASinHCnnl,
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"ASinH_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::ACosH, DataType::Float32, ACosHCnnl,
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"ACosH_cnnl_BANG_Float32");
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REGISTER_KERNEL(Device::BANG, OpType::ATanH, DataType::Float32, ATanHCnnl,
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"ATanH_cnnl_BANG_Float32");
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}; // namespace infini
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@ -0,0 +1,52 @@
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#include "bang/bang_runtime.h"
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#include "core/graph.h"
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#include "core/kernel.h"
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#include "core/runtime.h"
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#include "operators/unary.h"
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#include "test.h"
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namespace infini {
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template <class T>
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void testTrigon(const std::function<void(void *, size_t, DataType)> &generator,
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const Shape &shape) {
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// Runtime
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Runtime cpuRuntime = CpuRuntimeObj::getInstance();
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auto bangRuntime = make_ref<BangRuntimeObj>();
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// Build input data on CPU
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Tensor inputCpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
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inputCpu->dataMalloc();
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inputCpu->setData(generator);
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// GPU
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Graph bangGraph = make_ref<GraphObj>(bangRuntime);
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auto inputGpu = bangGraph->cloneTensor(inputCpu);
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auto gpuOp = bangGraph->addOp<T>(inputGpu, nullptr);
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bangGraph->dataMalloc();
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bangRuntime->run(bangGraph);
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auto outputGpu = gpuOp->getOutput();
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auto outputGpu2Cpu = outputGpu->clone(cpuRuntime);
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// CPU
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inputCpu->printData();
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outputGpu2Cpu->printData();
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EXPECT_TRUE(1);
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}
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TEST(cnnl_Trigon, run) {
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testTrigon<SinObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<CosObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<TanObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<ASinObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<ACosObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<ATanObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<SinHObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<CosHObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<TanHObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<ASinHObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<ACosHObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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testTrigon<ATanHObj>(IncrementalGenerator(), Shape{1, 1, 2, 3});
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}
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} // namespace infini
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