MNN/source/backend/metal/MetalSoftmax.mm

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//
// MetalSoftmax.mm
// MNN
//
// Created by MNN on 2019/01/30.
// Copyright © 2018, Alibaba Group Holding Limited
//
#import "backend/metal/MNNMetalContext.h"
#if MNN_METAL_ENABLED
#import "backend/metal/MetalSoftmax.hpp"
#import "core/Macro.h"
#import "backend/metal/MetalBackend.hpp"
#import "core/TensorUtils.hpp"
namespace MNN {
MetalSoftmax::MetalSoftmax(Backend *backend, int32_t axis) : Execution(backend), mAxis(axis) {
// nothing to do
}
ErrorCode MetalSoftmax::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
auto backend = static_cast<MetalBackend *>(this->backend());
auto context = (__bridge MNNMetalContext *)backend->context();
auto input = inputs[0], output = outputs[0];
auto dimensions = input->buffer().dimensions;
auto realAxis = mAxis < 0 ? dimensions + mAxis : mAxis;
auto channelPacked = TensorUtils::getDescribe(input)->dimensionFormat == MNN_DATA_FORMAT_NC4HW4; // even dims != 4
auto reorder = realAxis == 1 && channelPacked;
// shape
auto inside = 1, flat = input->length(realAxis), axis = flat, outside = 1;
for (int i = 0; i < realAxis; i++) {
auto length = input->length(i);
if (1 == i && channelPacked) {
length = UP_DIV(length, 4);
}
outside *= length;
}
for (int i = realAxis + 1; i < input->dimensions(); i++) {
auto length = input->length(i);
if (1 == i && channelPacked) {
length = UP_DIV(length, 4);
}
inside *= length;
}
if (reorder) {
axis = UP_DIV(axis, 4);
}
auto shape = [context newDeviceBuffer:4 * sizeof(int) access:CPUWriteOnly];
((int *)shape.contents)[0] = inside;
((int *)shape.contents)[1] = axis;
((int *)shape.contents)[2] = outside;
((int *)shape.contents)[3] = flat;
auto multiplex = axis >= 128;
// encode
auto tf = input->getDimensionType() == Tensor::TENSORFLOW;
auto kernel = multiplex ? (tf ? @"softmax_m_tf" : reorder ? @"softmax_m_on_reorder" : @"softmax_m_off_reorder")
: (tf ? @"softmax_tf" : reorder ? @"softmax_on_reorder" : @"softmax_off_reorder");
auto encoder = [context encoder];
auto bandwidth = [context load:kernel encoder:encoder];
[encoder setBuffer:(__bridge id<MTLBuffer>)(void *)input->deviceId() offset:0 atIndex:0];
[encoder setBuffer:(__bridge id<MTLBuffer>)(void *)output->deviceId() offset:0 atIndex:1];
[encoder setBuffer:shape offset:0 atIndex:2];
if (multiplex) {
auto unit = (!tf && reorder) ? sizeof(float) : 4 * sizeof(float);
auto threads = MIN(pow(log2(UP_DIV(axis, 64)), 2), bandwidth.threadExecutionWidth);
if (unit * bandwidth.maxThreadsPerThreadgroup > context.maxThreadgroupMemoryLength) {
bandwidth.maxThreadsPerThreadgroup /= context.maxThreadgroupMemoryLength / unit;
}
bandwidth.zAxisProtected = YES;
[encoder setThreadgroupMemoryLength:unit * bandwidth.maxThreadsPerThreadgroup atIndex:0];
[context dispatchEncoder:encoder
threads:{(NSUInteger)threads, (NSUInteger)inside, (NSUInteger)outside}
bandwidth:bandwidth];
} else {
[context dispatchEncoder:encoder threads:{(NSUInteger)inside, (NSUInteger)outside, 1} bandwidth:bandwidth];
}
[encoder endEncoding];
MNN_PRINT_ENCODER(context, encoder);
return NO_ERROR;
}
class MetalSoftmaxCreator : public MetalBackend::Creator {
public:
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend) const {
auto softmax = op->main_as_Axis();
return new MetalSoftmax(backend, softmax->axis());
}
};
REGISTER_METAL_OP_CREATOR(MetalSoftmaxCreator, OpType_Softmax);
} // namespace MNN
#endif /* MNN_METAL_ENABLED */