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2026-07-13 13:33:03 +08:00

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//
// ReshapeExecution.cu
// MNN
//
// Created by MNN on 2026/02/25.
// Copyright © 2026, Alibaba Group Holding Limited
//
#include "core/MusaBackend.hpp"
#include "core/TensorUtils.hpp"
#include "MNN_generated.h"
#include <musa_runtime.h>
namespace MNN {
namespace MUSA {
// MUSA kernel for reshape (copy with shape change)
__global__ void ReshapeKernel(const float* input, float* output, int size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= size) return;
output[idx] = input[idx];
}
class ReshapeExecution : public Execution {
public:
ReshapeExecution(Backend* backend) : Execution(backend) {}
virtual ErrorCode onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) override {
// Reshape doesn't change data, just the shape
return NO_ERROR;
}
virtual ErrorCode onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) override {
#ifdef LOG_VERBOSE
MNN_PRINT("start ReshapeExecution onExecute...\n");
#endif
auto input = inputs[0];
auto output = outputs[0];
void* inputPtr = (void*)input->deviceId();
void* outputPtr = (void*)output->deviceId();
int size = input->elementSize();
// If input and output are contiguous, just copy
if (size > 0) {
dim3 threadsPerBlock(256);
dim3 blocksPerGrid((size + 255) / 256);
ReshapeKernel<<<blocksPerGrid, threadsPerBlock>>>(
(const float*)inputPtr, (float*)outputPtr, size);
// Check for kernel launch errors
musaError_t err = musaGetLastError();
if (err != musaSuccess) {
MNN_ERROR("MUSA Reshape kernel launch failed: %s\n", musaGetErrorString(err));
}
// Synchronize to ensure completion
auto musaBackend = static_cast<MusaBackend*>(backend());
musaBackend->getMusaRuntime()->device_sync();
}
#ifdef LOG_VERBOSE
MNN_PRINT("end ReshapeExecution onExecute...\n");
#endif
return NO_ERROR;
}
};
// MUSA kernel for transpose
__global__ void TransposeKernel(const float* input, float* output,
const int* perm, const int* inputStrides, const int* outputStrides,
int ndim, int totalSize) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= totalSize) return;
// Decode output index to multi-dimensional index
int tempIdx = idx;
int multiIdx[8]; // Support up to 8 dimensions
for (int i = ndim - 1; i >= 0; --i) {
multiIdx[i] = tempIdx % outputStrides[i];
tempIdx /= outputStrides[i];
}
// Apply permutation to get input index
int inputIdx = 0;
for (int i = 0; i < ndim; ++i) {
inputIdx += multiIdx[perm[i]] * inputStrides[i];
}
output[idx] = input[inputIdx];
}
class TransposeExecution : public Execution {
public:
TransposeExecution(const std::vector<int>& perm, Backend* backend)
: Execution(backend), mPerm(perm) {}
virtual ErrorCode onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) override {
mNdim = inputs[0]->dimensions();
// Calculate input and output strides
mInputStrides.resize(mNdim);
mOutputStrides.resize(mNdim);
int inputStride = 1;
int outputStride = 1;
for (int i = mNdim - 1; i >= 0; --i) {
mInputStrides[i] = inputStride;
mOutputStrides[i] = outputStride;
inputStride *= inputs[0]->length(i);
outputStride *= outputs[0]->length(i);
}
return NO_ERROR;
}
virtual ErrorCode onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) override {
#ifdef LOG_VERBOSE
MNN_PRINT("start TransposeExecution onExecute...\n");
#endif
auto input = inputs[0];
auto output = outputs[0];
void* inputPtr = (void*)input->deviceId();
void* outputPtr = (void*)output->deviceId();
int totalSize = output->elementSize();
// Copy perm and strides to device
int* dPerm = nullptr;
int* dInputStrides = nullptr;
int* dOutputStrides = nullptr;
musaMalloc(&dPerm, sizeof(int) * mNdim);
musaMalloc(&dInputStrides, sizeof(int) * mNdim);
musaMalloc(&dOutputStrides, sizeof(int) * mNdim);
musaMemcpy(dPerm, mPerm.data(), sizeof(int) * mNdim, MNNMemcpyHostToDevice);
musaMemcpy(dInputStrides, mInputStrides.data(), sizeof(int) * mNdim, MNNMemcpyHostToDevice);
musaMemcpy(dOutputStrides, mOutputStrides.data(), sizeof(int) * mNdim, MNNMemcpyHostToDevice);
dim3 threadsPerBlock(256);
dim3 blocksPerGrid((totalSize + 255) / 256);
TransposeKernel<<<blocksPerGrid, threadsPerBlock>>>(
(const float*)inputPtr, (float*)outputPtr,
dPerm, dInputStrides, dOutputStrides, mNdim, totalSize);
// Check for kernel launch errors
musaError_t err = musaGetLastError();
if (err != musaSuccess) {
MNN_ERROR("MUSA Transpose kernel launch failed: %s\n", musaGetErrorString(err));
}
// Synchronize to ensure completion
auto musaBackend = static_cast<MusaBackend*>(backend());
musaBackend->getMusaRuntime()->device_sync();
// Free temporary device memory
musaFree(dPerm);
musaFree(dInputStrides);
musaFree(dOutputStrides);
#ifdef LOG_VERBOSE
MNN_PRINT("end TransposeExecution onExecute...\n");
#endif
return NO_ERROR;
}
private:
std::vector<int> mPerm;
int mNdim;
std::vector<int> mInputStrides;
std::vector<int> mOutputStrides;
};
// Creator for Reshape operations
class ReshapeCreator : public MusaBackend::Creator {
public:
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const MNN::Op* op, Backend* backend) const override {
return new ReshapeExecution(backend);
}
};
// Creator for Transpose operations
class TransposeCreator : public MusaBackend::Creator {
public:
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const MNN::Op* op, Backend* backend) const override {
std::vector<int> perm;
if (op->type() == OpType_Transpose) {
auto permVec = op->main_as_Transpose()->perm();
for (int i = 0; i < permVec->size(); ++i) {
perm.push_back(permVec->data()[i]);
}
}
return new TransposeExecution(perm, backend);
}
};
MusaCreatorRegister<ReshapeCreator> __ReshapeExecution(OpType_Reshape);
MusaCreatorRegister<ReshapeCreator> __ReshapeTranspose(OpType_Transpose);
} // namespace MUSA
} // namespace MNN