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InfiniTensor
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support Dynamic tensor infer shape and fix memory pool (#176) 

* feat: support dynamic tensor part1

* feat: support dynamic-tensor part2

* feat: support dynamic tensor part 3

* fix: fix some ..

* - add kvcache example

* feat: support concat to identity kernel

* add a simple mempory pool for allocator

* fix: rebase to master

* fix bug after merging

* - remove outdated script

* fix: fix as review

---------

Co-authored-by: kilinchange <kilinchange@163.com>
Co-authored-by: Haojie Wang <haojie0429@gmail.com>
This commit is contained in:
zhangyunze 2023-11-23 13:11:50 +08:00 committed by GitHub
parent 965df4e294
commit 331f7ab2b8
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
78 changed files with 605 additions and 229 deletions
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9
include/core/graph.h
View File

@ -53,6 +53,7 @@ class GraphObj : public Object {
const TensorVec &getTensors() const { return tensors; }
const OpVec &getOperators() const { return ops; }
OpVec getComputeOps() const;
Tensor getTensor(int) const;
/**
* Sort the nodes in topological order.
@ -64,7 +65,13 @@ class GraphObj : public Object {
void optimize();
void dataMalloc(bool useNaiveAllocator = false);
void shape_infer();
void dataMalloc(bool useNaiveAllocator = false, size_t memPoolSize = 0);
Tensor cloneKV(Tensor &tensor);
void freeHeap();
/**
* @brief Add an operator and create its outputs. Output tensor arguments

13
include/core/graph_handler.h
View File

@ -81,6 +81,7 @@ class GraphHandlerObj {
Tensor cast(Tensor input, Tensor output, int to);
Tensor expand(Tensor input, Tensor output, Shape dims);
Tensor where(Tensor inputX, Tensor inputY, Tensor condition, Tensor output);
std::vector<int> getDims(Tensor x) { return x->getDims(); }
Tensor allReduceSum(Tensor input, Tensor output);
Tensor allReduceProd(Tensor input, Tensor output);
@ -98,9 +99,19 @@ class GraphHandlerObj {
inline void optimize() { g->optimize(); }
inline void shape_infer() { g->shape_infer(); }
void change_shape(const vector<int> &shape, int tensorId);
//------ runtime
inline void data_malloc() { g->dataMalloc(); }
inline void data_malloc(bool useNaiveAllocator = false,
size_t memPoolSize = 0) {
g->dataMalloc(useNaiveAllocator, memPoolSize);
}
inline Tensor clone_KV(Tensor &tensor) { return g->cloneKV(tensor); }
inline void free_heap() { g->freeHeap(); }
inline void tune() { g->getRuntime()->run(g, true); }

19
include/core/lazy_allocator.h
View File

@ -26,14 +26,23 @@ class LazyAllocator {
size_t weightPeak = 0;
size_t heapPeak = 0;
size_t alignment;
bool hasMemPool = false;
size_t memPoolSize = 0;
// pointer to the memory actually allocated
void *ptr = nullptr;
// pointer to the weight memory space
void *weightPtr = nullptr;
// memory pool ptr
void *memPoolPtr = nullptr;
// // a cache designed for a batch size that has already occurred
// std::unordered_map<size_t, std::unordered_map<TensorObj *, size_t>>
// batchsizeToTensorOffset;
@ -68,6 +77,10 @@ class LazyAllocator {
void init();
void setMemPool(size_t memPoolSize);
bool getMemPoolStatus();
// function: simulate memory allocation
// arguments：
// size: size of memory block to be allocated
@ -76,6 +89,10 @@ class LazyAllocator {
size_t allocWeight(size_t size);
size_t heapAlloc(size_t size);
void freeHeap();
// function: simulate memory free
// arguments:
// addr: head address offset of memory block to be free
@ -92,6 +109,8 @@ class LazyAllocator {
void *getWeightPtr();
void *getHeapPtr();
void info();
private:

5
include/core/operator.h
View File

@ -55,8 +55,7 @@ class OperatorObj : public Object {
public:
OperatorObj(OpType opType, TensorVec inputs, TensorVec outputs);
virtual optional<vector<Shape>>
inferShape(const TensorVec &inputs) const = 0;
virtual optional<vector<Shape>> inferShape(const TensorVec &inputs) = 0;
virtual vector<DataType> inferDataType(const TensorVec &inputs) const;
/**
* @brief Constructs outputs (if requried) and check whether the operator is
@ -105,7 +104,7 @@ class OperatorObj : public Object {
const TensorVec &newOutputs) const = 0;
protected:
optional<vector<Shape>> inferShape() const;
optional<vector<Shape>> inferShape();
vector<DataType> inferDataType() const;
private:

1
include/core/tensor.h
View File

@ -31,6 +31,7 @@ class TensorObj : public TensorBaseObj {
size_t getBytes() const { return _size * dtype.getSize(); }
Shape getDims() const { return shape; }
void setShape(Shape shape_);
size_t getRank() const { return shape.size(); }
Shape getStride() const;
size_t getOffset(const vector<int> &ds) const;

9
include/cuda/cuda_element_wise.h
View File

@ -1,8 +1,13 @@
#pragma once
namespace infini {
void div_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
void div_kernel(void *a, void *b, void *c, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3, int c0, int c1, int c2, int c3);
void pow_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
void add_kernel(void *a, void *b, void *c, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3, int c0, int c1, int c2, int c3);
void pow_kernel(void *a, void *b, void *c, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3, int c0, int c1, int c2, int c3);
void less_kernel(void *a, void *b, void *c, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3, int c0, int c1, int c2,
int c3);
}; // namespace infini

3
include/cuda/cuda_pad_slice.h
View File

@ -10,10 +10,11 @@ typedef struct {
int wholeNDim[MAX_DIM]; // dim size after padding or before slicing
int partNDim[MAX_DIM]; // dim size before padding or after slicing
int partStride[MAX_DIM]; // stride before padding or after slicing
int DType;
} TransMetaData;
namespace infini {
void pad_slice_kernel(float *partData, float *wholeData,
void pad_slice_kernel(void *partData, void *wholeData,
const TransMetaData &metadata, int nDims, int num,
bool isPad);
} // namespace infini

2
include/operators/G2BMM.h
View File

@ -35,7 +35,7 @@ class G2BMMObj : public OperatorObj {
OP_CLONE(G2BMMObj);
std::string toString() const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
int numInputs() const override { return 2; }
int numOutputs() const override { return 1; }

2
include/operators/GBMM.h
View File

@ -33,7 +33,7 @@ class GBMMObj : public OperatorObj {
OP_CLONE(GBMMObj);
std::string toString() const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
int numInputs() const override { return 2; }
int numOutputs() const override { return 1; }

2
include/operators/activation_backward.h
View File

@ -7,7 +7,7 @@ class ActivationBackwardObj : public OperatorObj {
ActivationBackwardObj(OpType type, GraphObj *graph, Tensor y, Tensor diff_y,
Tensor x, Tensor diff_x);
OP_CLONE(ActivationBackwardObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 3; }

2
include/operators/all_gather.h
View File

@ -27,7 +27,7 @@ class AllGatherObj : public OperatorObj {
int numInputs() const override { return 1; }
int numOutputs() const override { return world_size; }
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;

2
include/operators/all_reduce.h
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@ -33,7 +33,7 @@ class AllReduceBaseObj : public OperatorObj {
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override {
optional<vector<Shape>> inferShape(const TensorVec &inputs) override {
return {{inputs[0]->getDims()}};
};

2
include/operators/attention_kvcache.h
View File

@ -29,7 +29,7 @@ class AttentionKVCacheObj : public OperatorObj {
Tensor output_matmul);
OP_CLONE(AttentionKVCacheObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 6; }

2
include/operators/batch_norm.h
View File

@ -34,7 +34,7 @@ class BatchNormObj : public OperatorObj {
Tensor var, Tensor scale, Tensor bias, float momentum = 0.9,
float eps = 1e-5, bool trainingMode = false);
OP_CLONE(BatchNormObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
// output size will be 3 when training

2
include/operators/broadcast.h
View File

@ -26,7 +26,7 @@ class BroadcastObj : public OperatorObj {
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override {
optional<vector<Shape>> inferShape(const TensorVec &inputs) override {
return {{inputs[0]->getDims()}};
};

2
include/operators/concat.h
View File

@ -22,7 +22,7 @@ class ConcatObj : public OperatorObj {
ConcatObj(GraphObj *graph, TensorVec inputs, Tensor output, int dim);
OP_CLONE(ConcatObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return inputs.size(); }

8
include/operators/conv.h
View File

@ -142,7 +142,7 @@ class ConvObj : public ConvBaseObj {
ActType act = ActType::None);
OP_CLONE(ConvObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
int getNumGroups() const override { return c / getChannelPerGroup(); }
private:
@ -164,7 +164,7 @@ class ConvBackwardFilterObj : public ConvBaseObj {
int sh = 1, int sw = 1, int dh = 1, int dw = 1,
Tensor bias = nullptr, ActType act = ActType::None);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
ActType getAct() const { return act; }
int getNumGroups() const override { return c / getChannelPerGroup(); }
@ -191,7 +191,7 @@ class ConvTransposed2dObj : public ConvBaseObj {
Tensor bias = nullptr, ActType act = ActType::None);
OP_CLONE(ConvTransposed2dObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
int getNumGroups() const override { return group; }
std::pair<int, int> getOutputPadding() const { return {oph, opw}; }
@ -218,7 +218,7 @@ class ConvTransposed2dNHWCObj : public ConvBaseObj {
Tensor bias = nullptr, ActType act = ActType::None);
OP_CLONE(ConvTransposed2dNHWCObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
int getNumGroups() const override { return group; }
private:

2
include/operators/det.h
View File

@ -7,7 +7,7 @@ class DetObj : public OperatorObj {
enum Mode { NormalDet = 0, LogDet };
DetObj(GraphObj *graph, Tensor input, Tensor output, Mode mode);
OP_CLONE(DetObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }

2
include/operators/dropout.h
View File

@ -37,7 +37,7 @@ class DropoutObj : public OperatorObj {
DropoutObj(GraphObj *graph, Tensor data, Tensor output, Tensor mask,
float ratio, bool training_mode);
OP_CLONE(DropoutObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }

4
include/operators/element_wise.h
View File

@ -21,7 +21,7 @@ class ElementWiseObj : public OperatorObj {
*/
ElementWiseObj(OpType type, GraphObj *graph, Tensor input0, Tensor input1,
Tensor output);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 2; }
@ -38,7 +38,7 @@ class MSELossObj : public OperatorObj {
MSELossObj(GraphObj *graph, Tensor input0, Tensor input1,
Reduction reduction, Tensor output);
OP_CLONE(MSELossObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
Reduction getReduction() const { return reductionMode; }
std::string toString() const override;

2
include/operators/expand.h
View File

@ -21,7 +21,7 @@ class ExpandObj : public OperatorObj {
*/
ExpandObj(GraphObj *graph, Tensor input, Tensor output, Shape dims);
OP_CLONE(ExpandObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }

2
include/operators/extend.h
View File

@ -23,7 +23,7 @@ class ExtendObj : public OperatorObj {
ExtendObj(GraphObj *graph, Tensor input, Tensor output, int dim,
int num = 1);
OP_CLONE(ExtendObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }

4
include/operators/gather.h
View File

@ -39,7 +39,7 @@ class GatherObj : public GatherBaseObj {
int axis);
OP_CLONE(GatherObj);
std::string toString() const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
vector<DataType> inferDataType(const TensorVec &inputs) const override;
private:
@ -69,7 +69,7 @@ class GatherElementsObj : public GatherBaseObj {
Tensor output, int axis);
OP_CLONE(GatherElementsObj);
std::string toString() const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
vector<DataType> inferDataType(const TensorVec &inputs) const override;
private:

2
include/operators/matmul.h
View File

@ -45,7 +45,7 @@ class MatmulObj : public OperatorObj {
OP_CLONE(MatmulObj);
std::string toString() const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
int numInputs() const override { return inputs.size(); }
int numOutputs() const override { return 1; }

2
include/operators/membound.h
View File

@ -21,7 +21,7 @@ class MemBoundObj : public OperatorObj {
OP_CLONE(MemBoundObj);
std::string toString() const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
int numInputs() const override { return inputs.size(); }
int numOutputs() const override { return outputs.size(); }

2
include/operators/pad.h
View File

@ -27,7 +27,7 @@ class PadObj : public OperatorObj {
const vector<int> &pads, const optional<vector<int>> &axes);
OP_CLONE(PadObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }

2
include/operators/pooling.h
View File

@ -41,7 +41,7 @@ class PoolingObj : public OperatorObj {
int ceilMode);
OP_CLONE(PoolingObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }

2
include/operators/reduce_mean.h
View File

@ -23,7 +23,7 @@ class ReduceMeanObj : public OperatorObj {
ReduceMeanObj(GraphObj *graph, Tensor input, Tensor output,
const optional<vector<int>> &axes, bool keepDims = true);
OP_CLONE(ReduceMeanObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }

13
include/operators/reshape.h
View File

@ -9,6 +9,7 @@ namespace infini {
*/
class ReshapeObj : public OperatorObj {
Shape dims;
Shape outputShape;
public:
/**
@ -17,18 +18,20 @@ class ReshapeObj : public OperatorObj {
* @param graph The computation graph that this operator belongs to.
* @param input The input tensor.
* @param output The output tensor.
* @param dims The shape of the output tensor.
* @param dims The shape to infer the output shape.
* @param outputShape The real shape of output tensor.
*/
ReshapeObj(GraphObj *graph, Tensor input, Tensor output, Shape dims);
OP_CLONE(ReshapeObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
inline Shape getShape() const { return dims; }
inline Shape getShape() const { return outputShape; }
inline Shape getDims() const { return dims; }
private:
vector<int> getWorkloadVector() const override;
@ -55,7 +58,7 @@ class FlattenObj : public OperatorObj {
FlattenObj(GraphObj *graph, Tensor input, Tensor output, int axis);
OP_CLONE(FlattenObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }
@ -85,7 +88,7 @@ class IdentityObj : public OperatorObj {
IdentityObj(GraphObj *graph, Tensor input, Tensor output);
OP_CLONE(IdentityObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }

2
include/operators/resize.h
View File

@ -60,7 +60,7 @@ class ResizeObj : public OperatorObj {
// Operator clone(TensorVec inputs, TensorVec outputs) override;
vector<DataType> inferDataType(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return inputs.size(); }
int numOutputs() const override { return 1; }

2
include/operators/slice.h
View File

@ -32,7 +32,7 @@ class SliceObj : public OperatorObj {
const optional<vector<int>> &steps);
OP_CLONE(SliceObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
inline int numInputs() const override { return 1; }
inline int numOutputs() const override { return 1; }

2
include/operators/softmax.h
View File

@ -10,7 +10,7 @@ class SoftmaxObj : public OperatorObj {
OP_CLONE(SoftmaxObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override {
optional<vector<Shape>> inferShape(const TensorVec &inputs) override {
return {{inputs[0]->getDims()}};
};

2
include/operators/split.h
View File

@ -37,7 +37,7 @@ class SplitObj : public OperatorObj {
int dim, const vector<int> &ratio);
OP_CLONE(SplitObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }

4
include/operators/transpose.h
View File

@ -7,7 +7,7 @@ class TransposeObj : public OperatorObj {
TransposeObj(GraphObj *graph, Tensor input, Tensor output,
vector<int> permute);
OP_CLONE(TransposeObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }
@ -25,7 +25,7 @@ class DepthToSpaceObj : public OperatorObj {
DepthToSpaceObj(GraphObj *graph, Tensor input, Tensor output, int blocksize,
std::string mode);
OP_CLONE(DepthToSpaceObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }

24
include/operators/unary.h
View File

@ -17,7 +17,7 @@ class UnaryObj : public OperatorObj {
* @param output The output tensor.
*/
UnaryObj(OpType type, GraphObj *graph, Tensor input, Tensor output);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }
@ -33,7 +33,7 @@ class ClipObj : public OperatorObj {
ClipObj(GraphObj *graph, Tensor input, Tensor output,
std::optional<float> min, std::optional<float> max);
OP_CLONE(ClipObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
std::optional<float> getMin() const { return minValue; };
@ -52,7 +52,7 @@ class HardtanhObj : public OperatorObj {
HardtanhObj(GraphObj *graph, Tensor input, Tensor output, float min,
float max);
OP_CLONE(HardtanhObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
float getMin() const { return minValue; };
@ -70,7 +70,7 @@ class FlipObj : public OperatorObj {
public:
FlipObj(GraphObj *graph, Tensor input, Tensor output, vector<int> axis);
OP_CLONE(FlipObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
vector<int> getAxis() const { return axisValue; };
@ -87,7 +87,7 @@ class FillObj : public OperatorObj {
public:
FillObj(GraphObj *graph, Tensor input, Tensor output, float value);
OP_CLONE(FillObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
float getValue() const { return setValue; };
@ -104,7 +104,7 @@ class L2LossObj : public OperatorObj {
public:
L2LossObj(GraphObj *graph, Tensor input, Tensor output);
OP_CLONE(L2LossObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }
@ -120,7 +120,7 @@ class TransformObj : public OperatorObj {
TransformObj(GraphObj *graph, Tensor input, Tensor output, float alpha,
float beta);
OP_CLONE(TransformObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
float getAlpha() const { return alphaValue; }
@ -165,7 +165,7 @@ class CastObj : public OperatorObj {
public:
CastObj(GraphObj *graph, Tensor input, Tensor output, CastType type);
OP_CLONE(CastObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
vector<DataType> inferDataType(const TensorVec &inputs) const override;
std::string toString() const override;
@ -185,7 +185,7 @@ class CumsumObj : public OperatorObj {
CumsumObj(GraphObj *graph, Tensor input, Tensor output, int axis,
bool exclusive, bool reverse);
OP_CLONE(CumsumObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int getAxis() const { return axisValue; }
@ -205,7 +205,7 @@ class ShapeObj : public OperatorObj {
public:
ShapeObj(GraphObj *graph, Tensor input, Tensor output);
OP_CLONE(ShapeObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 1; }
@ -216,7 +216,7 @@ class PReluObj : public OperatorObj {
public:
PReluObj(GraphObj *graph, Tensor input, Tensor alpha, Tensor output);
OP_CLONE(PReluObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return 2; }
@ -236,7 +236,7 @@ class LogObj : public OperatorObj {
};
LogObj(GraphObj *graph, Tensor input, Tensor output, LogType type);
OP_CLONE(LogObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
LogType getType() const { return logType; }

2
include/operators/where.h
View File

@ -22,7 +22,7 @@ class WhereObj : public OperatorObj {
Tensor output);
OP_CLONE(WhereObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
std::string toString() const override;
int numInputs() const override { return inputs.size(); }

34
pyinfinitensor/src/pyinfinitensor/onnx.py
View File

@ -48,7 +48,7 @@ class OnnxStub:
pass
except RuntimeError:
pass
self.inputs: Dict[str, backend.Tensor] = {}
self.outputs: Dict[str, backend.Tensor] = {}
self.initializer: Dict[int, TensorProto] = {}
@ -510,19 +510,11 @@ class OnnxStub:
mode,
)
elif node.op_type == "Reshape":
dims = _search_shape(model, node.input[0])
size = reduce(lambda acc, x: acc * x, dims)
input_shape = _parse_data(data[node.input[1]])
for i, x in enumerate(input_shape):
if x == 0:
input_shape[i] = dims[i]
temp = reduce(lambda acc, x: acc * x, input_shape, 1)
if temp < 0:
input_shape[input_shape.index(-1)] = size // -temp
shape = _parse_data(data[node.input[1]])
tensors[node.output[0]] = self.handler.reshape(
tensors[node.input[0]],
tensors.get(node.output[0]),
input_shape,
shape,
)
elif node.op_type == "Squeeze":
input_shape = _search_shape(model, node.input[0])
@ -1112,6 +1104,26 @@ class OnnxStub:
def optimize(self) -> None:
self.handler.optimize()
def clone_KV(self, tensor: backend.Tensor) -> backend.Tensor:
return self.handler.clone_KV(tensor)
def free_heap(self) -> None:
self.handler.free_heap()
def set_input(self, inputShapes: List[int]) -> None:
for newInput, oldInput in zip(inputShapes, self.inputs):
oldTensor = self.inputs[oldInput]
self.handler.change_shape(newInput, oldTensor.fuid())
self.handler.shape_infer()
self.handler.data_malloc()
def getShape(self, name: str) -> List[int]:
if name in self.inputs:
ans = self.handler.getDims(self.inputs[name])
else:
ans = self.handler.getDims(self.outputs[name])
return ans
def tune(self) -> None:
self.handler.tune()

18
pyinfinitensor/tests/test_onnx.py
View File

@ -209,6 +209,7 @@ class TestStringMethods(unittest.TestCase):
make_and_import_model(make_graph([relu], "relu", [x], [y]))
"""Gelu operator is not supported by onnx 14.1 currently."""
def test_gelu(self):
pass
# x = make_tensor_value_info("x", TensorProto.FLOAT, [1, 3, 5, 7])
@ -500,5 +501,22 @@ class TestStringMethods(unittest.TestCase):
make_and_import_model(make_graph([where], "where", [x, y, con], [output]))
class TestDynamicTensor(unittest.TestCase):
def test_dynamic_tensor(self):
filename = r"resnet18-v2-7.onnx"
current_path = os.getcwd()
model_file = ""
for root, dirs, files in os.walk(current_path):
if filename in files:
model_file = os.path.join(root, filename)
model = OnnxStub(onnx.load(model_file), backend.cpu_runtime())
output_key = list(model.outputs.keys())[0]
old_output_shape = model.getShape(output_key)
self.assertEqual(old_output_shape, ([1, 1000]))
model.set_input([[5, 3, 224, 224]])
new_output_shape = model.getShape(output_key)
self.assertEqual(new_output_shape, ([5, 1000]))
if __name__ == "__main__":
unittest.main()

58
src/core/graph.cc
View File

@ -1,5 +1,7 @@
#include "core/graph.h"
#include "operators/reshape.h"
#include <algorithm>
#include <numeric>
#include <queue>
namespace infini {
@ -123,10 +125,40 @@ void GraphObj::optimize() {
}
}
void GraphObj::dataMalloc(bool useNaiveAllocator) {
Tensor GraphObj::getTensor(int fuid) const {
for (auto tensor : tensors) {
if (tensor->getFuid() == fuid) {
return tensor;
}
}
return nullptr;
}
void GraphObj::shape_infer() {
for (auto &op : ops) {
auto ans = op->inferShape();
IT_ASSERT(ans.has_value());
auto oldOutputs = op->getOutputs();
IT_ASSERT(ans.value().size() == oldOutputs.size());
// replace the old outputshape and size with new one
for (int i = 0; i < (int)ans.value().size(); ++i) {
auto newShape = ans.value()[i];
auto oldShape = oldOutputs[i]->getDims();
auto fuid = oldOutputs[i]->getFuid();
if (newShape != oldShape) {
auto tensor = this->getTensor(fuid);
tensor->setShape(newShape);
}
}
}
}
void GraphObj::dataMalloc(bool useNaiveAllocator, size_t memPoolSize) {
// topological sorting first
IT_ASSERT(topo_sort() == true);
if (useNaiveAllocator) {
// can not set memory pool when use naive allocator
IT_ASSERT(memPoolSize == 0);
// used for debugging memory out-of-bounds access, tensors will not be
// released correctly
// note: behavior may not match running in non-naive mode, and it may
@ -136,6 +168,9 @@ void GraphObj::dataMalloc(bool useNaiveAllocator) {
}
return;
}
if (memPoolSize > 0) {
allocator.setMemPool(memPoolSize);
}
// count the number of times all tensors are used
std::unordered_map<TensorObj *, size_t> tensorToRefCount;
// record the memory address offsets of all tensors to be allocated
@ -222,6 +257,27 @@ void GraphObj::dataMalloc(bool useNaiveAllocator) {
}
}
Tensor GraphObj::cloneKV(Tensor &tensor) {
auto obj = tensor->clone();
if (allocator.getMemPoolStatus()) {
if (tensor->hasData()) {
obj->setDataBlob(make_ref<BlobObj>(
tensor->runtime,
static_cast<uint8_t *>(allocator.getHeapPtr()) +
allocator.heapAlloc(tensor->getBytes())));
obj->copyData(tensor);
}
} else {
if (tensor->hasData()) {
obj->dataMalloc();
obj->copyData(tensor);
}
}
return obj;
}
void GraphObj::freeHeap() { this->allocator.freeHeap(); }
Tensor GraphObj::addTensor(Shape dim, DataType dtype) {
return tensors.emplace_back(make_ref<TensorObj>(dim, dtype, runtime));
}

8
src/core/graph_handler.cc
View File

@ -20,6 +20,7 @@
#include "operators/transpose.h"
#include "operators/unary.h"
#include "operators/where.h"
#include <numeric>
namespace infini {
@ -555,4 +556,11 @@ static DataType dtype_repr_convert(int dtype) {
}
}
void GraphHandlerObj::change_shape(const vector<int> &shape, int tensorId) {
auto tensor = g->getTensor(tensorId);
IT_ASSERT(tensor != nullptr);
IT_ASSERT(shape.size() != 0);
tensor->setShape(shape);
}
} // namespace infini

68
src/core/lazy_allocator.cc
View File

@ -30,6 +30,9 @@ LazyAllocator::~LazyAllocator() {
if (this->weightPtr != nullptr) {
runtime->dealloc(this->weightPtr);
}
if (this->memPoolPtr != nullptr) {
runtime->dealloc(this->memPoolPtr);
}
}
void LazyAllocator::init() {
@ -44,6 +47,17 @@ void LazyAllocator::init() {
this->ptr = nullptr;
}
void LazyAllocator::setMemPool(size_t memPoolSize) {
IT_ASSERT(memPoolSize > 0);
if (!this->hasMemPool) {
this->hasMemPool = true;
this->memPoolSize = memPoolSize;
this->memPoolPtr = runtime->alloc(memPoolSize);
}
}
bool LazyAllocator::getMemPoolStatus() { return this->hasMemPool; }
size_t LazyAllocator::alloc(size_t size) {
// pad the size to the multiple of alignment
size = this->getAlignedSize(size);
@ -102,6 +116,17 @@ size_t LazyAllocator::allocWeight(size_t size) {
return retAddr;
}
size_t LazyAllocator::heapAlloc(size_t size) {
size = this->getAlignedSize(size);
this->heapPeak += size;
IT_ASSERT(this->memPoolSize >=
this->weightPeak + this->peak + this->heapPeak);
size_t retAddr = this->memPoolSize - this->heapPeak;
return retAddr;
}
void LazyAllocator::freeHeap() { this->heapPeak = 0; }
void LazyAllocator::free(size_t addr, size_t size) {
IT_ASSERT(this->ptr == nullptr);
size = getAlignedSize(size);
@ -143,25 +168,40 @@ void LazyAllocator::free(size_t addr, size_t size) {
}
void *LazyAllocator::getPtr() {
if (this->ptr == nullptr) {
this->ptr = runtime->alloc(this->peak);
// #ifdef DEBUG_MODE
// printf("LazyAllocator really alloc non-weight: %p %lu
// bytes\n", this->ptr, peak);
// #endif
if (!hasMemPool) {
if (this->ptr == nullptr) {
this->ptr = runtime->alloc(this->peak);
// #ifdef DEBUG_MODE
// printf("LazyAllocator really alloc non-weight: %p %lu
// bytes\n", this->ptr, peak);
// #endif
}
return this->ptr;
} else {
IT_ASSERT(this->memPoolSize >= this->weightPeak + this->peak);
return static_cast<uint8_t *>(this->memPoolPtr) + weightPeak;
}
return this->ptr;
}
void *LazyAllocator::getWeightPtr() {
if (this->weightPtr == nullptr) {
this->weightPtr = runtime->alloc(this->weightPeak);
// #ifdef DEBUG_MODE
// printf("LazyAllocator really alloc weight: %p %lu bytes\n",
// this->weightPtr, weightPeak);
// #endif
if (!hasMemPool) {
if (this->weightPtr == nullptr) {
this->weightPtr = runtime->alloc(this->weightPeak);
// #ifdef DEBUG_MODE
// printf("LazyAllocator really alloc weight: %p %lu
// bytes\n",
// this->weightPtr, weightPeak);
// #endif
}
return this->weightPtr;
} else {
return this->memPoolPtr;
}
return this->weightPtr;
}
void *LazyAllocator::getHeapPtr() {
IT_ASSERT(hasMemPool);
return this->memPoolPtr;
}
size_t LazyAllocator::getAlignedSize(size_t size) {

4
src/core/operator.cc
View File

@ -77,9 +77,7 @@ bool OperatorObj::checkValid(GraphObj *graph) {
return true;
}
optional<vector<Shape>> OperatorObj::inferShape() const {
return inferShape(inputs);
}
optional<vector<Shape>> OperatorObj::inferShape() { return inferShape(inputs); }
vector<DataType> OperatorObj::inferDataType(const TensorVec &inputs) const {
auto dataType = inputs[0]->getDType();

7
src/core/tensor.cc
View File

@ -59,6 +59,13 @@ Shape TensorObj::getStride() const {
return stride;
}
void TensorObj::setShape(Shape shape_) {
shape = shape_;
size_t size = std::accumulate(shape.begin(), shape.end(), 1,
[](auto acc, auto x) { return acc * x; });
_size = size;
}
void TensorObj::printData() const {
IT_ASSERT(data != nullptr);
if (!runtime->isCpu())

15
src/ffi/ffi_infinitensor.cc
View File

@ -446,7 +446,10 @@ void init_graph_builder(py::module &m) {
})
.def("has_target", &TensorObj::hasTarget, policy::automatic)
.def("src", &TensorObj::getSource, policy::move)
.def("printData", &TensorObj::printData, policy::automatic);
.def("printData", &TensorObj::printData, policy::automatic)
.def("copy_data",
py::overload_cast<const Tensor &>(&TensorObj::copyData),
policy::move);
py::class_<OperatorObj, std::shared_ptr<OperatorObj>>(m, "Operator")
.def("op_type", &OperatorObj::getOpType, policy::automatic)
.def("inputs", py::overload_cast<>(&OperatorObj::getInputs, py::const_),
@ -466,6 +469,7 @@ void init_graph_builder(py::module &m) {
.def("add", &Handler::add, policy::move)
.def("sub", &Handler::sub, policy::move)
.def("mul", &Handler::mul, policy::move)
.def("max", &Handler::max, policy::move)
.def("div", &Handler::div, policy::move)
.def("pow", &Handler::pow, policy::move)
.def("min", &Handler::min, policy::move)
@ -510,10 +514,17 @@ void init_graph_builder(py::module &m) {
.def("topo_sort", &Handler::topo_sort, policy::automatic)
.def("optimize", &Handler::optimize, policy::automatic)
.def("operators", &Handler::operators, policy::move)
.def("data_malloc", &Handler::data_malloc, policy::automatic)
.def("data_malloc", &Handler::data_malloc,
py::arg("useNaiveAllocator") = false, py::arg("memPoolSize") = 0,
policy::automatic)
.def("clone_KV", &Handler::clone_KV, policy::move)
.def("free_heap", &Handler::free_heap, policy::move)
.def("get_perf_time", &Handler::get_perf_time, policy::automatic)
.def("tune", &Handler::tune, policy::automatic)
.def("run", &Handler::run, policy::automatic)
.def("shape_infer", &Handler::shape_infer, policy::automatic)
.def("change_shape", &Handler::change_shape, policy::automatic)
.def("getDims", &Handler::getDims, policy::automatic)
.def("get_perf_time", &Handler::get_perf_time, policy::automatic);
}

19
src/kernels/cuda/element_wise.cc
View File

@ -44,7 +44,6 @@ class ElementWiseCudnn : public CudaKernelWithoutConfig {
std::copy(a_dim.begin(), a_dim.end(), a + (4 - a_dim.size()));
std::copy(b_dim.begin(), b_dim.end(), b + (4 - b_dim.size()));
std::copy(c_dim.begin(), c_dim.end(), c + (4 - c_dim.size()));
// get inputs
checkCudnnError(cudnnCreateTensorDescriptor(&aDesc));
checkCudnnError(cudnnSetTensor4dDescriptor(aDesc, CUDNN_TENSOR_NCHW,
@ -110,9 +109,9 @@ class ElementWiseCuda : public CudaKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
float *const aData = (op->getInputs(0)->getRawDataPtr<float *>());
float *const bData = (op->getInputs(1)->getRawDataPtr<float *>());
float *const cData = (op->getOutput()->getRawDataPtr<float *>());
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
@ -134,7 +133,13 @@ class ElementWiseCuda : public CudaKernelWithoutConfig {
else if (op->getOpType() == OpType::Pow)
pow_kernel(aData, bData, cData, a[0], a[1], a[2], a[3], b[0], b[1],
b[2], b[3], c[0], c[1], c[2], c[3]);
else
else if (op->getOpType() == OpType::Add) {
add_kernel(aData, bData, cData, a[0], a[1], a[2], a[3], b[0], b[1],
b[2], b[3], c[0], c[1], c[2], c[3]);
} else if (op->getOpType() == OpType::Less) {
less_kernel(aData, bData, cData, a[0], a[1], a[2], a[3], b[0], b[1],
b[2], b[3], c[0], c[1], c[2], c[3]);
} else
IT_TODO_HALT();
}
};
@ -152,6 +157,10 @@ REGISTER_KERNEL(Device::CUDA, OpType::Max, DataType::Float32, MaxCudnn,
REGISTER_KERNEL(Device::CUDA, OpType::Div, DataType::Float32, ElementWiseCuda,
"Div_CUDA_Float32");
REGISTER_KERNEL(Device::CUDA, OpType::Add, DataType::Int64, ElementWiseCuda,
"Add_CUDA_Int64");
REGISTER_KERNEL(Device::CUDA, OpType::Pow, DataType::Float32, ElementWiseCuda,
"Pow__CUDA_Float32");
REGISTER_KERNEL(Device::CUDA, OpType::Less, DataType::Int64, ElementWiseCuda,
"Less__CUDA_Int64");
}; // namespace infini

125
src/kernels/cuda/element_wise.cu
View File

@ -5,9 +5,10 @@ constexpr unsigned int num_threads() { return 32 * 4; }
constexpr int thread_work_size() { return 4; }
constexpr int block_work_size() { return thread_work_size() * num_threads(); }
__global__ void _div_kernel(float *x, float *y, float *z, int a0, int a1,
int a2, int a3, int b0, int b1, int b2, int b3,
int c0, int c1, int c2, int c3) {
template <class T>
__global__ void _div_kernel(void *x, void *y, void *z, int a0, int a1, int a2,
int a3, int b0, int b1, int b2, int b3, int c0,
int c1, int c2, int c3) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
int n = c0 * c1 * c2 * c3;
@ -27,16 +28,17 @@ __global__ void _div_kernel(float *x, float *y, float *z, int a0, int a1,
int b1_index = c1_index % b1;
int b2_index = c2_index % b2;
int b3_index = c3_index % b3;
z[i] = x[a0_index * a1 * a2 * a3 + a1_index * a2 * a3 + a2_index * a3 +
a3_index] /
y[b0_index * b1 * b2 * b3 + b1_index * b2 * b3 + b2_index * b3 +
b3_index];
((T *)z)[i] = ((T *)x)[a0_index * a1 * a2 * a3 + a1_index * a2 * a3 +
a2_index * a3 + a3_index] /
((T *)y)[b0_index * b1 * b2 * b3 + b1_index * b2 * b3 +
b2_index * b3 + b3_index];
}
}
__global__ void _pow_kernel(float *x, float *y, float *z, int a0, int a1,
int a2, int a3, int b0, int b1, int b2, int b3,
int c0, int c1, int c2, int c3) {
template <class T>
__global__ void _add_kernel(void *x, void *y, void *z, int a0, int a1, int a2,
int a3, int b0, int b1, int b2, int b3, int c0,
int c1, int c2, int c3) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
int n = c0 * c1 * c2 * c3;
@ -56,32 +58,115 @@ __global__ void _pow_kernel(float *x, float *y, float *z, int a0, int a1,
int b1_index = c1_index % b1;
int b2_index = c2_index % b2;
int b3_index = c3_index % b3;
z[i] = pow(x[a0_index * a1 * a2 * a3 + a1_index * a2 * a3 +
a2_index * a3 + a3_index],
y[b0_index * b1 * b2 * b3 + b1_index * b2 * b3 +
b2_index * b3 + b3_index]);
((T *)z)[i] = ((T *)x)[a0_index * a1 * a2 * a3 + a1_index * a2 * a3 +
a2_index * a3 + a3_index] +
((T *)y)[b0_index * b1 * b2 * b3 + b1_index * b2 * b3 +
b2_index * b3 + b3_index];
}
}
template <class T>
__global__ void _pow_kernel(void *x, void *y, void *z, int a0, int a1, int a2,
int a3, int b0, int b1, int b2, int b3, int c0,
int c1, int c2, int c3) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
int n = c0 * c1 * c2 * c3;
for (int i = index; i < n; i += stride) {
int c0_index = i / (c1 * c2 * c3);
int c1_index = (i % (c1 * c2 * c3)) / (c2 * c3);
int c2_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) / c3;
int c3_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) % c3;
int a0_index = c0_index % a0;
int a1_index = c1_index % a1;
int a2_index = c2_index % a2;
int a3_index = c3_index % a3;
int b0_index = c0_index % b0;
int b1_index = c1_index % b1;
int b2_index = c2_index % b2;
int b3_index = c3_index % b3;
((T *)z)[i] =
pow(((T *)x)[a0_index * a1 * a2 * a3 + a1_index * a2 * a3 +
a2_index * a3 + a3_index],
((T *)y)[b0_index * b1 * b2 * b3 + b1_index * b2 * b3 +
b2_index * b3 + b3_index]);
}
}
template <class T>
__global__ void _less_kernel(void *x, void *y, void *z, int a0, int a1, int a2,
int a3, int b0, int b1, int b2, int b3, int c0,
int c1, int c2, int c3) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
int n = c0 * c1 * c2 * c3;
for (int i = index; i < n; i += stride) {
int c0_index = i / (c1 * c2 * c3);
int c1_index = (i % (c1 * c2 * c3)) / (c2 * c3);
int c2_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) / c3;
int c3_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) % c3;
int a0_index = c0_index % a0;
int a1_index = c1_index % a1;
int a2_index = c2_index % a2;
int a3_index = c3_index % a3;
int b0_index = c0_index % b0;
int b1_index = c1_index % b1;
int b2_index = c2_index % b2;
int b3_index = c3_index % b3;
((bool *)z)[i] =
((T *)x)[a0_index * a1 * a2 * a3 + a1_index * a2 * a3 +
a2_index * a3 + a3_index] <
((T *)y)[b0_index * b1 * b2 * b3 + b1_index * b2 * b3 +
b2_index * b3 + b3_index]
? true
: false;
}
}
namespace infini {
void div_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
void div_kernel(void *a, void *b, void *c, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3, int c0, int c1, int c2,
int c3) {
int blocksize = block_work_size();
int num = c0 * c1 * c2 * c3;
int gridsize = (num + block_work_size() - 1) / block_work_size();
_div_kernel<<<gridsize, blocksize>>>(a, b, c, a0, a1, a2, a3, b0, b1, b2,
b3, c0, c1, c2, c3);
_div_kernel<float><<<gridsize, blocksize>>>(a, b, c, a0, a1, a2, a3, b0, b1,
b2, b3, c0, c1, c2, c3);
}
void pow_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
void add_kernel(void *a, void *b, void *c, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3, int c0, int c1, int c2,
int c3) {
int blocksize = block_work_size();
int num = c0 * c1 * c2 * c3;
int gridsize = (num + block_work_size() - 1) / block_work_size();
_add_kernel<int64_t><<<gridsize, blocksize>>>(a, b, c, a0, a1, a2, a3, b0,
b1, b2, b3, c0, c1, c2, c3);
}
void pow_kernel(void *a, void *b, void *c, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3, int c0, int c1, int c2,
int c3) {
int blocksize = block_work_size();
int num = c0 * c1 * c2 * c3;
int gridsize = (num + block_work_size() - 1) / block_work_size();
_pow_kernel<<<gridsize, blocksize>>>(a, b, c, a0, a1, a2, a3, b0, b1, b2,
b3, c0, c1, c2, c3);
_pow_kernel<float><<<gridsize, blocksize>>>(a, b, c, a0, a1, a2, a3, b0, b1,
b2, b3, c0, c1, c2, c3);
}
void less_kernel(void *a, void *b, void *c, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3, int c0, int c1, int c2,
int c3) {
int blocksize = block_work_size();
int num = c0 * c1 * c2 * c3;
int gridsize = (num + block_work_size() - 1) / block_work_size();
_less_kernel<int64_t><<<gridsize, blocksize>>>(a, b, c, a0, a1, a2, a3, b0,
b1, b2, b3, c0, c1, c2, c3);
}
}; // namespace infini

7
src/kernels/cuda/pad_slice.cc
View File

@ -16,8 +16,9 @@ class PadSliceCudaCompute {
metadata.partNDim[i] = partTensor->getDims()[i];
metadata.partStride[i] = partTensor->getStride()[i];
}
pad_slice_kernel(partTensor->getRawDataPtr<float *>(),
wholeTensor->getRawDataPtr<float *>(), metadata, nDims,
metadata.DType = partTensor->getDType().getIndex();
pad_slice_kernel(partTensor->getRawDataPtr<void *>(),
wholeTensor->getRawDataPtr<void *>(), metadata, nDims,
wholeTensor->size(), isPad);
}
};
@ -40,6 +41,8 @@ class SliceCuda : private PadSliceCudaCompute, public CudaKernelWithoutConfig {
REGISTER_KERNEL(Device::CUDA, OpType::Slice, DataType::Float32, SliceCuda,
"Slice__CUDA_Float32");
REGISTER_KERNEL(Device::CUDA, OpType::Slice, DataType::Int64, SliceCuda,
"Slice__CUDA_Int64");
REGISTER_KERNEL(Device::CUDA, OpType::Pad, DataType::Float32, PadCuda,
"Pad__CUDA_Float32");
} // namespace infini

19
src/kernels/cuda/pad_slice.cu
View File

@ -1,3 +1,4 @@
#include "core/data_type.h"
#include "cuda/cuda_common.h"
#include "cuda/cuda_pad_slice.h"
@ -19,9 +20,9 @@ __device__ int WholeTensorOffset2PartTensorOffset(int wholeOffset,
return offset;
}
__global__ void _pad_slice_kernel(float *part, float *whole,
TransMetaData metaData, int nDims, int num,
bool isPad) {
template <typename T>
__global__ void _pad_slice_kernel(T *part, T *whole, TransMetaData metaData,
int nDims, int num, bool isPad) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid >= num)
return;
@ -41,12 +42,18 @@ __global__ void _pad_slice_kernel(float *part, float *whole,
}
namespace infini {
void pad_slice_kernel(float *partData, float *wholeData,
void pad_slice_kernel(void *partData, void *wholeData,
const TransMetaData &metadata, int nDims, int num,
bool isPad) {
int blockSize = 32 * 16;
int gridSize = (num + blockSize - 1) / blockSize;
_pad_slice_kernel<<<gridSize, blockSize>>>(partData, wholeData, metadata,
nDims, num, isPad);
if (metadata.DType == DataType::Int64.getIndex()) {
_pad_slice_kernel<int64_t>
<<<gridSize, blockSize>>>((int64_t *)partData, (int64_t *)wholeData,
metadata, nDims, num, isPad);
} else if (metadata.DType == DataType::Float32.getIndex()) {
_pad_slice_kernel<float><<<gridSize, blockSize>>>(
(float *)partData, (float *)wholeData, metadata, nDims, num, isPad);
}
}
} // namespace infini

15
src/kernels/cuda/split_concat.cc
View File

@ -59,6 +59,21 @@ class CudaCompute {
class ConcatCuda : private CudaCompute, public CudaKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto inputs = _op->getInputs();
if (inputs.size() == 2) {
for (size_t i = 0; i < 2; i++) {
if (inputs[i]->size() == 0) {
auto inData =
_op->getInputs(1 - i)->getRawDataPtr<void *>();
auto outData =
_op->getOutputs()[0]->getRawDataPtr<void *>();
cudaMemcpyAsync(outData, inData,
_op->getInputs(1 - i)->getBytes(),
cudaMemcpyDeviceToDevice);
return;
}
}
}
do_compute(_op->getOutput(), _op->getInputs(),
as<ConcatObj>(_op)->getDim(), _op->getOutput()->getRank(),
false);

7
src/operators/G2BMM.cc
View File

@ -20,15 +20,18 @@ string G2BMMObj::toString() const {
return os.str();
}
optional<vector<Shape>> G2BMMObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> G2BMMObj::inferShape(const TensorVec &inputs) {
auto A = inputs[0], B = inputs[1];
b = A->getDims()[0];
m = A->getDims()[1];
k = A->getDims()[2];
IT_ASSERT(A->getRank() == 3 && B->getRank() == 3);
IT_ASSERT(A->getDims()[0] == B->getDims()[0]);
IT_ASSERT(A->getDims()[1] == B->getDims()[1]);
IT_ASSERT(A->getDims()[2] == B->getDims()[2]);
IT_ASSERT(width >= 0);
int b(A->getDims()[0]), m(A->getDims()[1]), n(2 * width + 1);
int n(2 * width + 1);
return {{{b, m, n}}};
}

9
src/operators/GBMM.cc
View File

@ -21,15 +21,18 @@ string GBMMObj::toString() const {
return os.str();
}
optional<vector<Shape>> GBMMObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> GBMMObj::inferShape(const TensorVec &inputs) {
auto A = inputs[0], B = inputs[1];
b = A->getDims()[0];
m = A->getDims()[1];
w = (A->getDims()[2] - 1) / 2;
n = B->getDims()[2];
IT_ASSERT(A->getRank() == 3 && B->getRank() == 3);
IT_ASSERT(A->getDims()[0] == B->getDims()[0]);
IT_ASSERT(A->getDims()[1] == B->getDims()[1]);
IT_ASSERT(A->getDims()[2] % 2 != 0);
int b(A->getDims()[0]), m(A->getDims()[1]), k(B->getDims()[2]);
return {{{b, m, k}}};
return {{{b, m, n}}};
}
vector<int> GBMMObj::getWorkloadVector() const {

2
src/operators/activation_backward.cc
View File

@ -9,7 +9,7 @@ ActivationBackwardObj::ActivationBackwardObj(OpType type, GraphObj *graph,
}
optional<vector<Shape>>
ActivationBackwardObj::inferShape(const TensorVec &inputs) const {
ActivationBackwardObj::inferShape(const TensorVec &inputs) {
return {{inputs[0]->getDims()}};
}

3
src/operators/all_gather.cc
View File

@ -10,8 +10,7 @@ AllGatherObj::AllGatherObj(GraphObj *graph, Tensor input,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
AllGatherObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> AllGatherObj::inferShape(const TensorVec &inputs) {
Shape input_shape = inputs[0]->getDims();
vector<Shape> output_shapes(getWorldSize(), input_shape);
return output_shapes;

2
src/operators/attention_kvcache.cc
View File

@ -18,7 +18,7 @@ AttentionKVCacheObj::AttentionKVCacheObj(GraphObj *graph, Tensor input_k_cache,
}
optional<vector<Shape>>
AttentionKVCacheObj::inferShape(const TensorVec &inputs) const {
AttentionKVCacheObj::inferShape(const TensorVec &inputs) {
IT_ASSERT(inputs.size() == 6);
Shape dims = inputs[0]->getDims();
ShapeElem n = dims.at(dim);

3
src/operators/batch_norm.cc
View File

@ -13,8 +13,7 @@ BatchNormObj::BatchNormObj(GraphObj *graph, Tensor input, Tensor output,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
BatchNormObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> BatchNormObj::inferShape(const TensorVec &inputs) {
auto input = inputs[0];
auto mean = inputs[1];
auto var = inputs[2];

9
src/operators/concat.cc
View File

@ -9,9 +9,16 @@ ConcatObj::ConcatObj(GraphObj *graph, TensorVec inputs, Tensor output, int _dim)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ConcatObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ConcatObj::inferShape(const TensorVec &inputs) {
Shape dims = inputs[0]->getDims();
auto rank = inputs[0]->getRank();
if (inputs.size() == 2) {
for (size_t i = 0; i < inputs.size(); ++i) {
if (inputs[i]->size() == 0) {
return {{inputs[1 - i]->getDims()}};
}
}
}
ShapeElem n = dims.at(dim);
for (auto itr = inputs.begin() + 1; itr != inputs.end(); ++itr) {
auto input = *itr;

65
src/operators/conv.cc
View File

@ -82,14 +82,15 @@ ConvObj::ConvObj(GraphObj *graph, Tensor input, Tensor weight, Tensor output,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ConvObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ConvObj::inferShape(const TensorVec &inputs) {
const auto &input = inputs[0], &weight = inputs[1];
auto n = input->getDims()[0];
auto h = input->getDims()[2];
auto w = input->getDims()[3];
auto f = weight->getDims()[0];
auto r = weight->getDims()[2];
auto s = weight->getDims()[3];
n = input->getDims()[0];
c = input->getDims()[1];
h = input->getDims()[2];
w = input->getDims()[3];
f = weight->getDims()[0];
r = weight->getDims()[2];
s = weight->getDims()[3];
int on = n, oc = f;
int oh = 0, ow = 0;
// For NCHW+FCRS layout, C of input is divisable by C of weight
@ -141,15 +142,15 @@ ConvTransposed2dObj::ConvTransposed2dObj(GraphObj *graph, Tensor input,
}
optional<vector<Shape>>
ConvTransposed2dObj::inferShape(const TensorVec &inputs) const {
ConvTransposed2dObj::inferShape(const TensorVec &inputs) {
const Tensor &input = inputs[0], &weight = inputs[1];
auto n = input->getDims()[0];
auto f = input->getDims()[1];
auto h = input->getDims()[2];
auto w = input->getDims()[3];
auto c = weight->getDims()[1];
auto r = weight->getDims()[2];
auto s = weight->getDims()[3];
n = input->getDims()[0];
f = input->getDims()[1];
h = input->getDims()[2];
w = input->getDims()[3];
c = weight->getDims()[1];
r = weight->getDims()[2];
s = weight->getDims()[3];
IT_ASSERT(f == weight->getDims()[0]);
int on = n, oc = c * group;
@ -219,14 +220,15 @@ ConvBackwardFilterObj::ConvBackwardFilterObj(GraphObj *graph, Tensor inputX,
}
optional<vector<Shape>>
ConvBackwardFilterObj::inferShape(const TensorVec &inputs) const {
ConvBackwardFilterObj::inferShape(const TensorVec &inputs) {
const auto &inputX = inputs[0], &diffY = inputs[1];
auto n = inputX->getDims()[0];
auto h = inputX->getDims()[2];
auto w = inputX->getDims()[3];
auto f = diffY->getDims()[0];
auto r = diffY->getDims()[2];
auto s = diffY->getDims()[3];
n = inputX->getDims()[0];
c = inputX->getDims()[1];
h = inputX->getDims()[2];
w = inputX->getDims()[3];
f = diffY->getDims()[0];
r = diffY->getDims()[2];
s = diffY->getDims()[3];
int on = n, oc = f;
int oh = 0, ow = 0;
// For NCHW+FCRS layout, C of input is divisable by C of weight
@ -280,17 +282,16 @@ ConvTransposed2dNHWCObj::ConvTransposed2dNHWCObj(GraphObj *graph, Tensor input,
}
optional<vector<Shape>>
ConvTransposed2dNHWCObj::inferShape(const TensorVec &inputs) const {
ConvTransposed2dNHWCObj::inferShape(const TensorVec &inputs) {
const Tensor &input = inputs[0], &weight = inputs[1];
auto n = input->getDims()[0];
auto f = input->getDims()[3];
auto h = input->getDims()[1];
auto w = input->getDims()[2];
auto c = weight->getDims()[3];
auto r = weight->getDims()[1];
auto s = weight->getDims()[2];
if (f != weight->getDims()[0])
return {};
n = input->getDims()[0];
f = input->getDims()[3];
h = input->getDims()[1];
w = input->getDims()[2];
c = weight->getDims()[3];
r = weight->getDims()[1];
s = weight->getDims()[2];
IT_ASSERT(f == weight->getDims()[0]);
int on = n, oc = c * group;
int oh = 0, ow = 0;

2
src/operators/det.cc
View File

@ -6,7 +6,7 @@ DetObj::DetObj(GraphObj *graph, Tensor input, Tensor output, Mode mode)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> DetObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> DetObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
auto input = A->getDims();
int rank = A->getRank();

2
src/operators/dropout.cc
View File

@ -10,7 +10,7 @@ DropoutObj::DropoutObj(GraphObj *graph, Tensor data, Tensor output, Tensor mask,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> DropoutObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> DropoutObj::inferShape(const TensorVec &inputs) {
auto shape = inputs[0]->getDims();
return {{shape, shape}};
}

5
src/operators/element_wise.cc
View File

@ -8,8 +8,7 @@ ElementWiseObj::ElementWiseObj(OpType type, GraphObj *graph, Tensor input0,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
ElementWiseObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ElementWiseObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0], B = inputs[1];
auto res = infer_broadcast(A->getDims(), B->getDims());
return {{res}};
@ -45,7 +44,7 @@ MSELossObj::MSELossObj(GraphObj *graph, Tensor input0, Tensor input1,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> MSELossObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> MSELossObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0], B = inputs[1];
IT_ASSERT(A->getRank() == B->getRank());
IT_ASSERT(A->getDims() == B->getDims());

2
src/operators/expand.cc
View File

@ -8,7 +8,7 @@ ExpandObj::ExpandObj(GraphObj *graph, Tensor input, Tensor output, Shape dims)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ExpandObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ExpandObj::inferShape(const TensorVec &inputs) {
auto shape_input = inputs[0]->getDims();
Shape ret = infer_broadcast(shape_input, dims);
return {{ret}};

2
src/operators/extend.cc
View File

@ -11,7 +11,7 @@ ExtendObj::ExtendObj(GraphObj *graph, Tensor input, Tensor output, int dim,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ExtendObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ExtendObj::inferShape(const TensorVec &inputs) {
auto ret = inputs[0]->getDims();
ret[dim] = ret[dim] * (num + 1);
return {{ret}};

2
src/operators/gather.cc
View File

@ -10,7 +10,7 @@ GatherObj::GatherObj(GraphObj *graph, Tensor input, Tensor indices,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> GatherObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> GatherObj::inferShape(const TensorVec &inputs) {
auto dims0 = inputs[0]->getDims();
auto dims1 = inputs[1]->getDims();

3
src/operators/gather_elements.cc
View File

@ -24,8 +24,7 @@ bool checkShape(Tensor input, Tensor indices, int axis) {
return true;
}
optional<vector<Shape>>
GatherElementsObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> GatherElementsObj::inferShape(const TensorVec &inputs) {
IT_ASSERT(checkShape(inputs[0], inputs[1], axis));
auto indicesDims = inputs[1]->getDims(); // output has same shape as indices
return {{indicesDims}};

32
src/operators/matmul.cc
View File

@ -9,25 +9,6 @@ MatmulObj::MatmulObj(GraphObj *graph, Tensor A, Tensor B, Tensor C, bool transA,
: OperatorObj(OpType::MatMul,
bias ? TensorVec{A, B, bias} : TensorVec{A, B}, {C}),
transA(transA), transB(transB), act(act), b(1) {
auto shape_a = A->getDims();
auto shape_b = B->getDims();
int rankA = A->getRank();
int rankB = B->getRank();
IT_ASSERT(rankA >= 2 && rankB >= 2);
Shape shape_a1(shape_a.begin(), shape_a.begin() + (rankA - 2));
Shape shape_b1(shape_b.begin(), shape_b.begin() + (rankB - 2));
auto ret = infer_broadcast(shape_a1, shape_b1);
if (ret.empty()) {
b = 1;
} else {
b = std::accumulate(ret.begin(), ret.end(), 1, std::multiplies<int>());
}
auto kA = *(transA ? shape_a.rbegin() + 1 : shape_a.rbegin());
auto kB = *(transB ? shape_b.rbegin() : shape_b.rbegin() + 1);
IT_ASSERT(kA == kB);
m = *(transA ? shape_a.rbegin() : shape_a.rbegin() + 1);
n = *(transB ? shape_b.rbegin() + 1 : shape_b.rbegin());
k = kA;
IT_ASSERT(checkValid(graph));
}
@ -40,7 +21,7 @@ string MatmulObj::toString() const {
return os.str();
}
optional<vector<Shape>> MatmulObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> MatmulObj::inferShape(const TensorVec &inputs) {
auto A = inputs[0], B = inputs[1];
auto shapeA = A->getDims();
auto shapeB = B->getDims();
@ -49,6 +30,17 @@ optional<vector<Shape>> MatmulObj::inferShape(const TensorVec &inputs) const {
Shape shapeA1(shapeA.begin(), shapeA.begin() + (rankA - 2));
Shape shapeB1(shapeB.begin(), shapeB.begin() + (rankB - 2));
Shape ret = infer_broadcast(shapeA1, shapeB1);
if (ret.empty()) {
b = 1;
} else {
b = std::accumulate(ret.begin(), ret.end(), 1, std::multiplies<int>());
}
auto kA = *(transA ? shapeA.rbegin() + 1 : shapeA.rbegin());
auto kB = *(transB ? shapeB.rbegin() : shapeB.rbegin() + 1);
IT_ASSERT(kA == kB);
m = *(transA ? shapeA.rbegin() : shapeA.rbegin() + 1);
n = *(transB ? shapeB.rbegin() + 1 : shapeB.rbegin());
k = kA;
ret.emplace_back(m);
ret.emplace_back(n);
return {{ret}};

2
src/operators/membound.cc
View File

@ -60,7 +60,7 @@ string MemBoundObj::toString() const {
return os.str();
}
optional<vector<Shape>> MemBoundObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> MemBoundObj::inferShape(const TensorVec &inputs) {
// inputs have to match nnetInputs excatly
if (inputs.size() != nnetInputs.size())
return {};

2
src/operators/pad.cc
View File

@ -22,7 +22,7 @@ PadObj::PadObj(GraphObj *graph, Tensor input, Tensor output,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> PadObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> PadObj::inferShape(const TensorVec &inputs) {
auto dims = inputs[0]->getDims();
int rank = inputs[0]->getRank();
IT_ASSERT(rank * 2 == (int)pads.size());

2
src/operators/pooling.cc
View File

@ -12,7 +12,7 @@ PoolingObj::PoolingObj(GraphObj *graph, OpType optype, Tensor input,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> PoolingObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> PoolingObj::inferShape(const TensorVec &inputs) {
const auto &input = inputs[0];
auto h = input->getDims()[input->getRank() - 2],
w = input->getDims()[input->getRank() - 1];

3
src/operators/reduce_mean.cc
View File

@ -21,8 +21,7 @@ bool ReduceMeanObj::isReduced(int idx) const {
return axes.find(idx) != axes.end();
}
optional<vector<Shape>>
ReduceMeanObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ReduceMeanObj::inferShape(const TensorVec &inputs) {
auto dims = inputs[0]->getDims();
auto rank = inputs[0]->getRank();

46
src/operators/reshape.cc
View File

@ -1,5 +1,6 @@
#include "operators/reshape.h"
#include "utils/operator_utils.h"
#include <numeric>
namespace infini {
ReshapeObj::ReshapeObj(GraphObj *graph, Tensor input, Tensor output, Shape dims)
@ -7,14 +8,37 @@ ReshapeObj::ReshapeObj(GraphObj *graph, Tensor input, Tensor output, Shape dims)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ReshapeObj::inferShape(const TensorVec &inputs) const {
size_t size = 1;
for (size_t i = 0; i < dims.size(); ++i) {
size *= dims.at(i);
optional<vector<Shape>> ReshapeObj::inferShape(const TensorVec &inputs) {
int count = 0;
for (auto x : dims) {
if (x == -1) {
count++;
}
IT_ASSERT(x == -1 || x >= 0);
}
IT_ASSERT(size == inputs[0]->size());
IT_ASSERT(count == 0 || count == 1);
auto inputShape = inputs[0]->getDims();
int size = inputs[0]->size();
int index = -1;
outputShape = dims;
for (int i = 0; i < (int)dims.size(); ++i) {
if (dims[i] == 0) {
outputShape[i] = inputShape[i];
}
if (dims[i] == -1) {
index = i;
}
}
if (index != -1) {
outputShape[index] =
size / (-std::accumulate(outputShape.begin(), outputShape.end(), 1,
[](auto acc, auto x) { return acc * x; }));
}
int outputSize = std::accumulate(outputShape.begin(), outputShape.end(), 1,
[](auto acc, auto x) { return acc * x; });
IT_ASSERT(outputSize == size);
return {{dims}};
return {{outputShape}};
}
std::string ReshapeObj::toString() const {
@ -22,7 +46,7 @@ std::string ReshapeObj::toString() const {
os << "Reshape[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "dims=" << vecToString(dims) << ",";
os << "outputShape=" << vecToString(outputShape) << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
@ -30,12 +54,12 @@ std::string ReshapeObj::toString() const {
vector<int> ReshapeObj::getWorkloadVector() const {
vector<int> ret = inputs[0]->getDims();
ret.insert(ret.end(), dims.begin(), dims.end());
ret.insert(ret.end(), outputShape.begin(), outputShape.end());
ret.emplace(ret.begin(), type.underlying());
return ret;
}
vector<int> ReshapeObj::getOpAttrVector() const {
vector<int> ret = dims;
vector<int> ret = outputShape;
ret.emplace(ret.begin(), type.underlying());
return ret;
}
@ -47,7 +71,7 @@ FlattenObj::FlattenObj(GraphObj *graph, Tensor input, Tensor output, int _axis)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> FlattenObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> FlattenObj::inferShape(const TensorVec &inputs) {
int sizeB = 1, sizeE = 1;
auto dims = getInputs(0)->getDims();
int rank = getInputs(0)->getRank();
@ -84,7 +108,7 @@ IdentityObj::IdentityObj(GraphObj *graph, Tensor input, Tensor output)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> IdentityObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> IdentityObj::inferShape(const TensorVec &inputs) {
return {{getInputs(0)->getDims()}};
}

2
src/operators/resize.cc
View File

@ -206,7 +206,7 @@ float ResizeObj::round_int(float x) const {
}
// output shape is related to sizes/scales value.
optional<vector<Shape>> ResizeObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ResizeObj::inferShape(const TensorVec &inputs) {
auto inDims = inputs[0]->getDims();
Shape ret = inDims;
int rank = inputs[0]->getRank();

2
src/operators/slice.cc
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@ -62,7 +62,7 @@ SliceObj::SliceObj(GraphObj *graph, Tensor input, Tensor output,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> SliceObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> SliceObj::inferShape(const TensorVec &inputs) {
Shape ans;
ans.reserve(axes.size());
for (const auto &range : axes) {

2
src/operators/split.cc
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@ -35,7 +35,7 @@ SplitObj::SplitObj(GraphObj *graph, Tensor input,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> SplitObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> SplitObj::inferShape(const TensorVec &inputs) {
IT_ASSERT(num != -1 && ratio.size() != 0);
auto inputDims = inputs[0]->getDims();
int totalSize = inputDims.at(dim);

6
src/operators/transpose.cc
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@ -16,8 +16,7 @@ TransposeObj::TransposeObj(GraphObj *graph, Tensor input, Tensor output,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
TransposeObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> TransposeObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
auto input_dim = A->getDims();
auto output_dim = input_dim;
@ -66,8 +65,7 @@ DepthToSpaceObj::DepthToSpaceObj(GraphObj *graph, Tensor input, Tensor output,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
DepthToSpaceObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> DepthToSpaceObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
auto inputDim = A->getDims();
IT_ASSERT(inputDim.size() == 4);

18
src/operators/unary.cc
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@ -6,7 +6,7 @@ UnaryObj::UnaryObj(OpType type, GraphObj *graph, Tensor input, Tensor output)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> UnaryObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> UnaryObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
return {{A->getDims()}};
}
@ -37,7 +37,7 @@ ClipObj::ClipObj(GraphObj *graph, Tensor input, Tensor output,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ClipObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ClipObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
return {{A->getDims()}};
}
@ -68,7 +68,7 @@ HardtanhObj::HardtanhObj(GraphObj *graph, Tensor input, Tensor output,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> HardtanhObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> HardtanhObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
return {{A->getDims()}};
}
@ -97,7 +97,7 @@ FillObj::FillObj(GraphObj *graph, Tensor input, Tensor output, float value)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> FillObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> FillObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
return {{A->getDims()}};
}
@ -124,7 +124,7 @@ L2LossObj::L2LossObj(GraphObj *graph, Tensor input, Tensor output)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> L2LossObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> L2LossObj::inferShape(const TensorVec &inputs) {
Shape temp = {1};
return {{temp}};
}
@ -159,7 +159,7 @@ vector<DataType> CastObj::inferDataType(const TensorVec &inputs) const {
return vector(numOutputs(), output_dataType);
}
optional<vector<Shape>> CastObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> CastObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
return {{A->getDims()}};
}
@ -241,7 +241,7 @@ ShapeObj::ShapeObj(GraphObj *graph, Tensor input, Tensor output)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ShapeObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> ShapeObj::inferShape(const TensorVec &inputs) {
return {{{static_cast<int>(inputs[0]->getRank())}}};
}
@ -257,7 +257,7 @@ PReluObj::PReluObj(GraphObj *graph, Tensor input, Tensor alpha, Tensor output)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> PReluObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> PReluObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
return {{A->getDims()}};
}
@ -286,7 +286,7 @@ LogObj::LogObj(GraphObj *graph, Tensor input, Tensor output, LogType type)
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> LogObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> LogObj::inferShape(const TensorVec &inputs) {
const auto A = inputs[0];
return {{A->getDims()}};
}

2
src/operators/where.cc
View File

@ -10,7 +10,7 @@ WhereObj::WhereObj(GraphObj *graph, Tensor inputX, Tensor inputY,
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> WhereObj::inferShape(const TensorVec &inputs) const {
optional<vector<Shape>> WhereObj::inferShape(const TensorVec &inputs) {
auto shapeX = inputs[0]->getDims();
auto shapeY = inputs[1]->getDims();
auto shapeCon = inputs[2]->getDims();

29
test/kernels/cuda/test_cuda_concat.cc
View File

@ -158,4 +158,33 @@ TEST(Concat, CudaHigh) {
12., 13., 14., 15., 16., 17., 1., 1., 1., 1., 1., 1.,
18., 19., 20., 21., 22., 23., 1., 1., 1., 1., 1., 1.}));
}
TEST(ConcatToIdentity, Cuda) {
Runtime runtime = NativeCpuRuntimeObj::getInstance();
Graph gCpu = make_ref<GraphObj>(runtime);
auto t1 = gCpu->addTensor({2, 2, 3, 1}, DataType::Float32);
auto t2 = gCpu->addTensor({0}, DataType::Float32);
gCpu->dataMalloc();
t1->setData(IncrementalGenerator());
t2->setData(OneGenerator());
auto cudaRuntime = make_ref<CudaRuntimeObj>();
Graph gCuda = make_ref<GraphObj>(cudaRuntime);
auto t1Gpu = gCuda->cloneTensor(t1);
auto t2Gpu = gCuda->cloneTensor(t2);
auto op = gCuda->addOp<ConcatObj>(TensorVec{t1Gpu, t2Gpu}, nullptr, 2);
gCuda->dataMalloc();
t1Gpu->setData(IncrementalGenerator());
t2Gpu->setData(OneGenerator());
cudaRuntime->run(gCuda);
// cudaPrintTensor(op->getOutput());
// copy output from CUDA to CPU
auto oCpu = gCpu->cloneTensor(op->getOutput());
EXPECT_TRUE(
oCpu->equalData(vector<float>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}));
}
} // namespace infini

9
test/operators/test_concat.cc
View File

@ -14,4 +14,13 @@ TEST(Concat, ShapeInfer) {
EXPECT_EQ(op->getOutput()->getDims(), (Shape{1, 3, 2, 9}));
}
TEST(Concat, ShapeInfer1) {
Runtime runtime = NativeCpuRuntimeObj::getInstance();
Graph g = make_ref<GraphObj>(runtime);
auto t1 = g->addTensor({1, 3, 2, 4}, DataType::Float32);
auto t2 = g->addTensor({0}, DataType::Float32);
auto op = g->addOp<ConcatObj>(TensorVec{t1, t2}, nullptr, 3);
EXPECT_EQ(op->getOutput()->getDims(), (Shape{1, 3, 2, 4}));
}
} // namespace infini