fix batchnorm

metal/paddle-mobile/paddle-mobile/Operators/Kernels/BatchNormKernel.swift

@@ -19,27 +19,36 @@ class BatchNormKernel<P: PrecisionType>: Kernel, Computable {
     var newBias: MTLBuffer
     
     required init(device: MTLDevice, param: BatchNormParam<P>) {
+        guard let newScale = device.makeBuffer(length: param.inputScale.buffer.length) else {
+            fatalError()
+        }
+        guard let newBias = device.makeBuffer(length: param.inputBias.buffer.length) else {
+            fatalError()
+        }
+        self.newScale = newScale
+        self.newBias = newBias
+        
         super.init(device: device, inFunctionName: "batchnorm")
         
-        let varianceBuffer = param.inputVariance.buffer
+        let varianceBuffer : MTLBuffer = param.inputVariance.buffer
+        
         var invStd: [Float32] = Array(repeating: 0, count: varianceBuffer.length)
         let varianceContents = varianceBuffer.contents().assumingMemoryBound(to: P.self)
         for i in 0..<(varianceBuffer.length / MemoryLayout<P>.stride) {
-            invStd[i] = 1 / Float32(varianceContents[i] + param.epsilon).squareRoot()
+            invStd[i] = 1 / (Float32(varianceContents[i]) + param.epsilon).squareRoot()
         }
-        var newScale = device.makeBuffer(param.inputScale.buffer.length)
-        var newBias = device.makeBuffer(param.inputBias.buffer.length)
-        var newScaleContents = newScale.contents().assumingMemoryBound(to: P.self)
-        var newBiasContents = newBias.contents().assumingMemoryBound(to: P.self)
-        let scale = param.inputScale.buffer
+        
+        let newScaleContents = newScale.contents().assumingMemoryBound(to: P.self)
+        let newBiasContents = newBias.contents().assumingMemoryBound(to: P.self)
+        let scale : MTLBuffer = param.inputScale.buffer
         let scaleContents = scale.contents().assumingMemoryBound(to: P.self)
-        let bias = param.inputBias.buffer
+        let bias : MTLBuffer = param.inputBias.buffer
         let biasContents = bias.contents().assumingMemoryBound(to: P.self)
         let meanContents = param.inputMean.buffer.contents().assumingMemoryBound(to: P.self)
         
-        for i in 0..<(scaleContents.lengh / MemoryLayout<P>.stride) {
-            newScaleContents[i] = invStd[i] * scaleContents[i]
-            newBiasContents[i] = biasContents[i] - meanContents[i] * invStd[i] * scaleContents[i]
+        for i in 0..<(newScale.length / MemoryLayout<P>.stride) {
+            newScaleContents[i] = P(invStd[i] * Float32(scaleContents[i]))
+            newBiasContents[i] = P(Float32(biasContents[i]) - Float32(meanContents[i]) * invStd[i] * Float32(scaleContents[i]))
         }
     }