ConcatKernel.swift

/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
 http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */

import Foundation

struct ConcatTestParam: TestParam {
  var input: [MTLTexture]
  var output: MTLTexture
  var dims: [[Int]]
  var axis: Int
  var odim: [Int]
}

struct ConcatMetalParam {
  var odim: (Int32, Int32, Int32, Int32) = (1, 1, 1, 1)
  var axis: Int32 = 0
  var offset: Int32 = 0
  var trans: (Int32, Int32, Int32, Int32) = (0, 1, 2, 3)
  var vdim: (Int32, Int32, Int32, Int32, Int32, Int32) = (0, 0, 0, 0, 0, 0)
}

class ConcatKernel<P: PrecisionType>: Kernel, Computable{
  var v = "normal"
  var pm = ConcatMetalParam.init()
  func compute(commandBuffer: MTLCommandBuffer, param: ConcatParam<P>) throws {
    
    guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
      throw PaddleMobileError.predictError(message: " encode is nil")
    }
    let num = param.input.count
    for i in 0..<num {
      encoder.setTexture(param.input[i].metalTexture, index: i)
    }
    encoder.setTexture(param.output.metalTexture, index: num)
    if v == "normal" {
      encoder.setTexture(param.output.metalTexture, index: num + 1)
    }
    encoder.setBytes(&pm, length: MemoryLayout<ConcatMetalParam>.size, index: 0)
    encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
    encoder.endEncoding()
  }

  required init(device: MTLDevice, param: ConcatParam<P>) {
    param.output.initTexture(device: device, inTranspose: param.transpose, computePrecision: computePrecision)
    let orank = param.output.tensorDim.cout()
    let num = param.input.count
    assert(num <= 6)
    var axis = 4 - param.output.tensorDim.cout() + param.axis
    for i in 0..<4 {
      if param.transpose[i] == axis {
        axis = i
        break
      }
    }
    pm.axis = Int32(axis)
    pm.odim = (Int32(param.output.dim[0]), Int32(param.output.dim[1]), Int32(param.output.dim[2]), Int32(param.output.dim[3]))
    pm.trans = (Int32(param.output.transpose[0]), Int32(param.output.transpose[1]), Int32(param.output.transpose[2]), Int32(param.output.transpose[3]))
    var vdim: [Int] = [0, 0, 0, 0, 0, 0]
    for i in 0..<num {
      vdim[i] = param.input[i].dim[axis]
    }
    if orank == 4 {
      if axis == 1 {
        v = "y"
      } else if axis == 2 {
        v = "x"
      } else {
        if (param.output.dim[0] == 1) && axis == 3 {
          var vz = true
          for i in 0..<num {
            if vdim[i] % 4 != 0 {
              vz = false
              break
            }
          }
          if vz {
            v = "z"
            for i in 0..<num {
              vdim[i] = vdim[i] / 4
            }
          }
        }
      }
    } else if orank == 3 {
      if axis == 2 {
        v = "y"
      } else if axis == 3 {
        v = "x"
      } else if axis == 1 {
        var vz = true
        for i in 0..<num {
          if vdim[i] % 4 != 0 {
            vz = false
            break
          }
        }
        if vz {
          v = "z"
          for i in 0..<num {
            vdim[i] = vdim[i] / 4
          }
        }
      }
    } else {
      if axis == 2 {
        v = "y"
      } else if axis == 3 {
        var vx = true
        for i in 0..<num {
          if vdim[i] % 4 != 0 {
            vx = false
            break
          }
        }
        if vx {
          v = "x"
          for i in 0..<num {
            vdim[i] = vdim[i] / 4
          }
        }
      }
    }
    pm.vdim = (Int32(vdim[0]), Int32(vdim[1]), Int32(vdim[2]), Int32(vdim[3]), Int32(vdim[4]), Int32(vdim[5]))
    if computePrecision == .Float32 {
      super.init(device: device, inFunctionName: "concat_\(orank)_\(num)_\(v)_float")
    } else if computePrecision == .Float16 {
      super.init(device: device, inFunctionName: "concat_\(orank)_\(num)_\(v)_half")
    } else {
      fatalError()
    }
  }
  
  required init(device: MTLDevice, testParam: ConcatTestParam) {
    super.init(device: device, inFunctionName: "concat")
  }
}