Implementation of cuda kernel for fake_quant_with_min_max_vars_per_channels op.
parent
3504b0cda9
commit
02d8616692
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@ -55,7 +55,7 @@ namespace nd4j {
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" for quatization should be in between 2 and 16, but %i "
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"was given.", numBits);
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}
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helpers::fakeQuantWithMinMaxVarsPerChannel(x, min, max, numBits, narrowed, output);
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helpers::fakeQuantWithMinMaxVarsPerChannel(block.launchContext(), x, min, max, numBits, narrowed, output);
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return ND4J_STATUS_OK;
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}
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@ -103,7 +103,7 @@ namespace helpers {
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void fakeQuantWithMinMaxVars(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVars_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
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}
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void fakeQuantWithMinMaxVarsPerChannel(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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void fakeQuantWithMinMaxVarsPerChannel(LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVarsPerChannel_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
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}
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@ -34,7 +34,7 @@ namespace helpers {
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// output - output tensor
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//
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template <typename T>
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static void Nudge(T min, T max, int quant_min, int quant_max, T* scale, T* nudged_min, T* nudged_max) {
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static __host__ __device__ void Nudge(T min, T max, int quant_min, int quant_max, T* scale, T* nudged_min, T* nudged_max) {
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T quant_max_float = static_cast<T>(quant_max);
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T quant_min_float = static_cast<T>(quant_min);
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*scale = (max - min) / (quant_max_float - quant_min_float);
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@ -78,44 +78,54 @@ namespace helpers {
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}
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template <typename T>
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void fakeQuantWithMinMaxVarsPerChannel_(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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int lowIntBound = narrowed?1:0;
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int upperIntBound = (1 << numBits) - 1;
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min->syncToHost();
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max->syncToHost();
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T scale, nudged_min, nudged_max;
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auto channels = min->lengthOf();
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input->syncToHost();
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input->syncToDevice();
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output->syncToHost();
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for (auto i = 0; i < channels; i++) {
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Nudge(min->t<T>(i), max->t<T>(i), lowIntBound, upperIntBound, &scale, &nudged_min, &nudged_max);
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static __global__ void fakeQuantWithMinMaxKernel(T* input, Nd4jLong* inputShape, T* min, T* max,
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int lowIntBound, int upperIntBound, Nd4jLong channels,
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T* output, Nd4jLong* outputShape, Nd4jLong length) {
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for (auto i = blockIdx.x; i < (int)channels; i += gridDim.x) {
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T scale, nudged_min, nudged_max;
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Nudge(min[i], max[i], lowIntBound, upperIntBound, &scale, &nudged_min, &nudged_max);
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//auto wiseMinMaxAndSoOn = LAMBDA_T(x, nudged_min, nudged_max, scale) {
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for (auto e = 0; e < input->lengthOf(); e += channels) {
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T val = input->t<T>(e + i);
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for (auto e = threadIdx.x; e < (int)length; e += (int)channels) {
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T val = input[shape::getIndexOffset(e + i, inputShape)];
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if (val < nudged_min) {
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val = nudged_min;
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} else if (val > nudged_max) {
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val = nudged_max;
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}
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output->t<T>(e + i) = (math::nd4j_floor<T, T>((val - nudged_min) / scale + T(0.5)) * scale + nudged_min);
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output[shape::getIndexOffset(e + i, outputShape)] = (math::nd4j_floor<T, T>((val - nudged_min) / scale + T(0.5)) * scale + nudged_min);
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};
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}
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output->syncToDevice();
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output->tickWriteDevice();
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}
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template <typename T>
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void fakeQuantWithMinMaxVarsPerChannel_(LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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int lowIntBound = narrowed?1:0;
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int upperIntBound = (1 << numBits) - 1;
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auto channels = min->lengthOf();
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auto length = input->lengthOf();
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NDArray::prepareSpecialUse({output}, {min, max, input});
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auto stream = context->getCudaStream();
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T* inputBuf = input->dataBuffer()->specialAsT<T>();
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T* outputBuf = output->dataBuffer()->specialAsT<T>();
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T* minBuf = min->dataBuffer()->specialAsT<T>();
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T* maxBuf = max->dataBuffer()->specialAsT<T>();
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fakeQuantWithMinMaxKernel<<<1, 1, 256, *stream>>>(inputBuf, input->specialShapeInfo(),
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minBuf, maxBuf, lowIntBound, upperIntBound, channels, outputBuf, output->specialShapeInfo(), length);
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NDArray::registerSpecialUse({output}, {min, max, input});
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}
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void fakeQuantWithMinMaxVars(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVars_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
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}
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void fakeQuantWithMinMaxVarsPerChannel(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVarsPerChannel_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
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void fakeQuantWithMinMaxVarsPerChannel(LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVarsPerChannel_, (context, input, min, max, numBits, narrowed, output), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVars_, (NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output), FLOAT_TYPES);
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BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVarsPerChannel_, (NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output), FLOAT_TYPES);
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BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVarsPerChannel_, (LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output), FLOAT_TYPES);
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}
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}
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@ -27,7 +27,7 @@ namespace ops {
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namespace helpers {
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void fakeQuantWithMinMaxVars(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output);
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void fakeQuantWithMinMaxVarsPerChannel(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output);
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void fakeQuantWithMinMaxVarsPerChannel(LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output);
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}
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}
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}
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