forked from rinkugohil30897/NeuroBook
1325 lines
114 KiB
Common Lisp
1325 lines
114 KiB
Common Lisp
//+------------------------------------------------------------------+
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//| opencl_program.cl |
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//| Copyright 2021, MetaQuotes Ltd. |
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//| https://www.mql5.com |
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//+------------------------------------------------------------------+
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//--- by default some GPU doesn't support TYPEs
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//--- cl_khr_fp64 directive is used to enable work with TYPEs
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#pragma OPENCL EXTENSION cl_khr_fp64 : enable
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//+------------------------------------------------------------------+
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//| Activation functions |
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//+------------------------------------------------------------------+
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//| Linear activation function |
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//| Parameter 'value' Weighted sum of initial data |
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//| 'a' defines the angle of inclination of the line |
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//| 'b' - the vertical offset of the line |
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//+------------------------------------------------------------------+
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__kernel void LineActivation(__global TYPE* inputs,
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__global TYPE* outputs,
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const TYPE a, const TYPE b)
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{
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size_t i = get_global_id(0);
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outputs[i] = (a * inputs[i] + b);
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}
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//+------------------------------------------------------------------+
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//| Sigmoid activation function |
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//| Parameter 'value' Weighted sum of initial data |
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//| 'a' stretches the range of values of the function |
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//| from '0' to 'a' |
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//| 'b' shifts the resulting value |
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//+------------------------------------------------------------------+
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__kernel void SigmoidActivation(__global TYPE* inputs,
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__global TYPE* outputs,
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const TYPE a, const TYPE b)
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{
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size_t i = get_global_id(0);
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outputs[i] = a / (1 + exp(-inputs[i])) - b;
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}
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//+------------------------------------------------------------------+
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//| Derivative of Sigmoid activation function |
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//| Parameter 'value' current point (result of feed forward). |
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//| 'a' stretches the range of values of the function from |
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//| '0' to 'a' |
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//| 'b' shifts the resulting value |
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//+------------------------------------------------------------------+
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__kernel void SigmoidDerivative(__global TYPE* outputs,
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__global TYPE* output_gr,
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__global TYPE* input_gr,
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const TYPE a, const TYPE b
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)
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{
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size_t i = get_global_id(0);
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if(a == 0)
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input_gr[i] = 0;
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else
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{
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TYPE z = clamp(outputs[i] + b, (TYPE)0, a);
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input_gr[i] = z * (1 - z / a) * output_gr[i];
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}
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}
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//+------------------------------------------------------------------+
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//| TANH activation function |
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//| Parameter 'value' Weighted sum of initial data |
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//+------------------------------------------------------------------+
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__kernel void TanhActivation(__global TYPE* inputs,
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__global TYPE* outputs)
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{
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size_t i = get_global_id(0);
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outputs[i] = tanh(inputs[i]);
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}
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//+------------------------------------------------------------------+
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//| Derivative of TANH activation function |
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//| Parameter 'value' current point (result of feed forward). |
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//+------------------------------------------------------------------+
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__kernel void TanhDerivative(__global TYPE* outputs,
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__global TYPE* output_gr,
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__global TYPE* input_gr
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)
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{
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size_t i = get_global_id(0);
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input_gr[i] = (1 - pow(outputs[i], 2)) * output_gr[i];
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}
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//+------------------------------------------------------------------+
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//| LReLU activation function |
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//| Parameter 'value' current point (result of feed forward). |
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//| 'a' leak parameter |
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//+------------------------------------------------------------------+
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__kernel void LReLUActivation(__global TYPE* inputs,
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__global TYPE* outputs,
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const TYPE a)
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{
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size_t i = get_global_id(0);
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TYPE value = inputs[i];
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outputs[i] = (value > 0 ? value : a * value);
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}
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//+------------------------------------------------------------------+
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//| Derivative of LReLU activation function |
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//| Parameter 'value' current point (result of feed forward). |
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//| 'a' leak parameter |
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//+------------------------------------------------------------------+
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__kernel void LReLUDerivative(__global TYPE* outputs,
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__global TYPE* output_gr,
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__global TYPE* input_gr,
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const TYPE a)
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{
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size_t i = get_global_id(0);
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input_gr[i] = (outputs[i] > 0 ? (TYPE)1 : a) * output_gr[i];
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}
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//+------------------------------------------------------------------+
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//| Swish activation function |
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//| Parameter 'value' Weighted sum of initial data |
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//| 'b' affects the nonlinearity of the function |
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//+------------------------------------------------------------------+
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__kernel void SwishActivation(__global TYPE* inputs,
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__global TYPE* outputs,
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const TYPE b)
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{
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size_t i = get_global_id(0);
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TYPE value = inputs[i];
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outputs[i] = value / (1 + exp(-b * value));
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}
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//+------------------------------------------------------------------+
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//| Derivative of Swish activation function |
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//| Parameter 'value' current point (result of feed forward). |
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//| 'value_input' Weighted sum of initial data |
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//| 'b' affects the nonlinearity of the function |
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//+------------------------------------------------------------------+
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__kernel void SwishDerivative(__global TYPE* outputs,
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__global TYPE* output_gr,
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__global TYPE* input_gr,
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const TYPE b,
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__global TYPE* inputs)
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{
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size_t i = get_global_id(0);
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TYPE by = b * outputs[i];
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input_gr[i] = (by + (1 - by) / (1 + exp(-b * inputs[i]))) * output_gr[i];
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}
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//+------------------------------------------------------------------+
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//| Transfer 4 elements of TYPE vector to TYPE4 |
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//| Parameter 'array' source array of data |
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//| 'start' first position to copy |
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//| 'step' step between elements to copy |
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//| 'size' Size of source array |
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//| 'shift' Shift in source array to the 1-st copied element |
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//+------------------------------------------------------------------+
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TYPE4 ToVect4(__global TYPE *array, int start, int step, int size, int shift)
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{
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TYPE4 result = (TYPE4)(0, 0, 0, 0);
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step = max(1, step);
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int st = start * step + shift;
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if(st < size)
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{
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int k = (size - shift + step - 1) / step;
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switch(k)
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{
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case 0:
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break;
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case 1:
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result = (TYPE4)(array[st], 0, 0, 0);
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break;
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case 2:
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result = (TYPE4)(array[st], array[st + step], 0, 0);
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break;
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case 3:
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result = (TYPE4)(array[st], array[st + step], array[st + 2 * step], 0);
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break;
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default:
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result = (TYPE4)(array[st], array[st + step], array[st + 2 * step], array[st + 3 * step]);
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break;
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}
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}
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return result;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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TYPE Max4(TYPE4 vect, TYPE value)
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{
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TYPE result = fmax(vect.s0, value);
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result = fmax(vect.s1, result);
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result = fmax(vect.s2, result);
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return(fmax(vect.s3, result));
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}
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//+------------------------------------------------------------------+
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//| Transfer TYPE4 to 4 elements of TYPE vector |
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//| Parameter 'array' target array of data |
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//| 'value' source TYPE4 vector |
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//| 'start' first position to copy in target array |
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//| 'step' step between elements in target array |
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//| 'size' Size of target array |
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//| 'shift' Shift in target array to the 1-st copied element |
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//+------------------------------------------------------------------+
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void D4ToArray(__global TYPE *array, TYPE4 value, int start, int step, int size, int shift)
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{
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step = max(1, step);
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int st = start * step + shift;
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if(st < size)
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{
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int k = (size - shift) % step;
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k = (size - shift - k) / step - start + (k > 0 ? 1 : 0);
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switch(k)
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{
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case 3:
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array[st + 2 * step] = value.s2;
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case 2:
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array[st + step] = value.s1;
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case 1:
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array[st] = value.s0;
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break;
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default:
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array[st + 3 * step] = value.s3;
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array[st + 2 * step] = value.s2;
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array[st + step] = value.s1;
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array[st] = value.s0;
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break;
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}
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}
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return;
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}
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//+------------------------------------------------------------------+
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//| Kernel Feed Forward of perceptron |
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//| Parameter 'inputs' vector of inputs data |
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//| 'weights' matrix of weights |
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//| 'outputs' output data |
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//| 'inputs_total' size of inputs vector |
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//+------------------------------------------------------------------+
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__kernel void PerceptronFeedForward(__global TYPE *inputs,
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__global TYPE *weights,
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__global TYPE *outputs,
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int inputs_total)
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{
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const int n = get_global_id(0);
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const int weights_total = get_global_size(0) * (inputs_total + 1);
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int shift = n * (inputs_total + 1);
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TYPE s = weights[shift + inputs_total];
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for(int i = 0; i < inputs_total; i += 4)
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s += dot(ToVect4(inputs, i, 1, inputs_total, 0), ToVect4(weights, i, 1, weights_total, shift));
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outputs[n] = s;
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}
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//+------------------------------------------------------------------+
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//| Kernel of calculation output gradients |
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//| Parameter 'target' vector of target data |
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//| 'outputs' vector of previous FF outputs data |
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//| 'gradients' vector of gradients |
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//| 'loss_function' type of loss function |
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//+------------------------------------------------------------------+
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__kernel void CalcOutputGradient(__global TYPE *target,
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__global TYPE *outputs,
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__global TYPE *gradients,
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int loss_function)
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{
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const int n = get_global_id(0);
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switch(loss_function)
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{
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case 0:
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gradients[n] = target[n] - outputs[n];
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break;
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case 1:
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gradients[n] = 2 * (target[n] - outputs[n]);
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break;
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case 2:
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gradients[n] = -target[n] / (outputs[n] + 1e-37f) * log(outputs[n] + 1e-37f);
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break;
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case 3:
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gradients[n] = (target[n] - outputs[n]) / (outputs[n] * (outputs[n] - 1) + 1e-37f);
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break;
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default:
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gradients[n] = target[n] - outputs[n];
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break;
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}
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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__kernel void CalcHiddenGradient(__global TYPE *gradient_inputs,
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__global TYPE *weights,
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__global TYPE *gradients,
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int outputs_total)
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{
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const int n = get_global_id(0);
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const int inputs_total = get_global_size(0);
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int weights_total = (inputs_total + 1) * outputs_total;
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//---
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TYPE grad = 0;
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for(int o = 0; o < outputs_total; o += 4)
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grad += dot(ToVect4(gradients, o, 1, outputs_total, 0), ToVect4(weights, o, (inputs_total + 1), weights_total, n));
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gradient_inputs[n] = grad;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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__kernel void CalcDeltaWeights(__global TYPE *inputs,
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__global TYPE *delta_weights,
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__global TYPE *gradients)
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{
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const int n = get_global_id(0);
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const int outputs_total = get_global_size(0);
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const int i = get_global_id(1);
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const int inputs_total = get_global_size(1);
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//---
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TYPE grad = gradients[n];
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int shift = n * (inputs_total + 1);
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delta_weights[shift + i] = inputs[i] * grad + delta_weights[shift + i];
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if(i == 0)
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delta_weights[shift + inputs_total] += grad;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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__kernel void SGDUpdate(__global TYPE *delta_weights,
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__global TYPE *weights,
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int total,
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int batch_size,
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TYPE learningRate,
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TYPE Lambda1,
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TYPE Lambda2
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)
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{
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int start = 4 * get_global_id(0);
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TYPE4 delta4 = ToVect4(delta_weights, start, 1, total, 0);
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TYPE4 weights4 = ToVect4(weights, start, 1, total, 0);
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TYPE lr = learningRate / ((TYPE)batch_size);
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weights4 -= (TYPE4)(Lambda1) + Lambda2 * weights4;
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weights4 += (TYPE4)(lr) * delta4;
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D4ToArray(weights, weights4, start, 1, total, 0);
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D4ToArray(delta_weights, (TYPE4)(0), start, 1, total, 0);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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__kernel void MomentumUpdate(__global TYPE *delta_weights,
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__global TYPE *weights,
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__global TYPE *momentum,
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int total, int batch_size,
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TYPE learningRate,
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TYPE beta,
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TYPE Lambda1, TYPE Lambda2)
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{
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int start = 4 * get_global_id(0);
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//---
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TYPE4 delta4 = ToVect4(delta_weights, start, 1, total, 0) / ((TYPE4)(batch_size));
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TYPE4 weights4 = ToVect4(weights, start, 1, total, 0);
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TYPE4 momentum4 = ToVect4(momentum, start, 1, total, 0);
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weights4 -= (TYPE4)(Lambda1) + Lambda2 * weights4;
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momentum4 = (TYPE4)(learningRate) * delta4 + (TYPE4)(beta) * momentum4;
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weights4 += momentum4;
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D4ToArray(weights, weights4, start, 1, total, 0);
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D4ToArray(momentum, momentum4, start, 1, total, 0);
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D4ToArray(delta_weights, (TYPE4)(0), start, 1, total, 0);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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__kernel void AdaGradUpdate(__global TYPE *delta_weights,
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__global TYPE *weights,
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__global TYPE *momentum,
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int total, int batch_size,
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TYPE learningRate,
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TYPE Lambda1, TYPE Lambda2)
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{
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int start = 4 * get_global_id(0);
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//---
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TYPE4 delta4 = ToVect4(delta_weights, start, 1, total, 0) / ((TYPE4)(batch_size));
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TYPE4 weights4 = ToVect4(weights, start, 1, total, 0);
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TYPE4 momentum4 = ToVect4(momentum, start, 1, total, 0);
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//---
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weights4 -= (TYPE4)(Lambda1) + Lambda2 * weights4;
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momentum4 = momentum4 + pow(delta4, 2);
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weights4 += learningRate / sqrt(momentum4 + 1.0e-37f);
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D4ToArray(weights, weights4, start, 1, total, 0);
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D4ToArray(momentum, momentum4, start, 1, total, 0);
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D4ToArray(delta_weights, (TYPE4)(0), start, 1, total, 0);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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__kernel void RMSPropUpdate(__global TYPE *delta_weights,
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__global TYPE *weights,
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__global TYPE *momentum,
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int total, int batch_size,
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TYPE learningRate,
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TYPE beta,
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TYPE Lambda1, TYPE Lambda2)
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{
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int start = 4 * get_global_id(0);
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//---
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TYPE4 delta4 = ToVect4(delta_weights, start, 1, total, 0) / ((TYPE4)(batch_size));
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TYPE4 weights4 = ToVect4(weights, start, 1, total, 0);
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TYPE4 momentum4 = ToVect4(momentum, start, 1, total, 0);
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//---
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weights4 -= (TYPE4)(Lambda1) + Lambda2 * weights4;
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momentum4 = beta * momentum4 + (1 - beta) * pow(delta4, 2);
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weights4 += delta4 * learningRate / (sqrt(momentum4) + 1.0e-37f);
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D4ToArray(weights, weights4, start, 1, total, 0);
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D4ToArray(momentum, momentum4, start, 1, total, 0);
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D4ToArray(delta_weights, (TYPE4)(0), start, 1, total, 0);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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__kernel void AdaDeltaUpdate(__global TYPE *delta_weights,
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__global TYPE *weights,
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__global TYPE *momentumW,
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__global TYPE *momentumG,
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int total, int batch_size,
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TYPE beta1, TYPE beta2,
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TYPE Lambda1, TYPE Lambda2)
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{
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int start = 4 * get_global_id(0);
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//---
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TYPE4 delta4 = ToVect4(delta_weights, start, 1, total, 0) / ((TYPE4)(batch_size));
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TYPE4 weights4 = ToVect4(weights, start, 1, total, 0);
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TYPE4 momentumW4 = ToVect4(momentumW, start, 1, total, 0);
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TYPE4 momentumG4 = ToVect4(momentumG, start, 1, total, 0);
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//---
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weights4 -= (TYPE4)(Lambda1) + Lambda2 * weights4;
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momentumW4 = beta1 * momentumW4 + (1 - beta1) * pow(weights4, 2);
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momentumG4 = beta2 * momentumG4 + (1 - beta2) * pow(delta4, 2);
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weights4 += delta4 * sqrt(momentumW4) / (sqrt(momentumG4) + 1.0e-37f);
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D4ToArray(weights, weights4, start, 1, total, 0);
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D4ToArray(momentumW, momentumW4, start, 1, total, 0);
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D4ToArray(momentumG, momentumG4, start, 1, total, 0);
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D4ToArray(delta_weights, (TYPE4)(0), start, 1, total, 0);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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__kernel void AdamUpdate(__global TYPE *delta_weights,
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__global TYPE *weights,
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__global TYPE *momentumM,
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__global TYPE *momentumV,
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int total, int batch_size,
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TYPE learningRate,
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TYPE beta1, TYPE beta2,
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TYPE Lambda1, TYPE Lambda2)
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{
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int start = 4 * get_global_id(0);
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//---
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TYPE4 delta4 = ToVect4(delta_weights, start, 1, total, 0) / ((TYPE4)(batch_size));
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TYPE4 weights4 = ToVect4(weights, start, 1, total, 0);
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TYPE4 momentumM4 = ToVect4(momentumM, start, 1, total, 0);
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TYPE4 momentumV4 = ToVect4(momentumV, start, 1, total, 0);
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//---
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momentumM4 = beta1 * momentumM4 + (1 - beta1) * delta4;
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momentumV4 = beta2 * momentumV4 + (1 - beta2) * pow(delta4, 2);
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TYPE4 m = momentumM4 / (1 - beta1);
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TYPE4 v = momentumV4 / (1 - beta2);
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weights4 -= (TYPE4)(Lambda1) + Lambda2 * weights4;
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weights4 += learningRate * m / (sqrt(v) + 1.0e-37f);
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D4ToArray(weights, weights4, start, 1, total, 0);
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D4ToArray(momentumM, momentumM4, start, 1, total, 0);
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D4ToArray(momentumV, momentumV4, start, 1, total, 0);
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D4ToArray(delta_weights, (TYPE4)(0), start, 1, total, 0);
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}
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//+------------------------------------------------------------------+
| |
//| Feed-forward kernel of the convolutional layer |
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//| Parameters: 'inputs' input data vector |
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//| 'weights' weight matrix |
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//| 'outputs' vector of results |
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//| 'inputs_total' size of input data vector |
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//| 'window' size of input data analysis window |
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//| 'step' window moving step |
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//| 'window_out' number of filters |
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//+------------------------------------------------------------------+
| |
__kernel void ConvolutionFeedForward(__global TYPE *inputs,
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__global TYPE *weights,
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__global TYPE *outputs,
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int inputs_total,
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int window,
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int step,
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int window_out,
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int transposed_out)
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{
| |
const int n = get_global_id(0);
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const int neurons = get_global_size(0);
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const int weights_total = (window + 1) * window_out;
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int shift = n * step;
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for(int w = 0; w < window_out; w++)
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{
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int out = (transposed_out == 1 ? w + n * window_out : w * neurons + n);
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int shift_weights = w * (window + 1) ;
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if((shift_weights + window) >= weights_total)
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break;
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TYPE s = weights[shift_weights + window];
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for(int i = 0; i < window; i += 4)
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s += dot(ToVect4(inputs, i, 1, inputs_total, shift),
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ToVect4(weights, i, 1, shift_weights + window, shift_weights));
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outputs[out] = s;
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}
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}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void ConvolutionCalcHiddenGradient(__global TYPE *gradient_inputs,
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__global TYPE *weights,
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__global TYPE *gradients,
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int window,
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int step,
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int window_out,
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int neurons,
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int transposed_out)
| |
{
| |
const int n = get_global_id(0);
| |
const int inputs_total = get_global_size(0);
| |
int weights_total = (window + 1) * window_out;
| |
//---
| |
TYPE grad = 0;
| |
int w_start = n % step;
| |
int r_start = max((n - window + step) / step, 0);
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int total = (window - w_start + step - 1) / step;
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total = min((n + step) / step, total);
| |
for(int i = 0; i < total; i ++)
| |
{
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int row = r_start + i;
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if(row >= neurons)
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break;
| |
for(int wo = 0; wo < window_out; wo++)
| |
{
| |
int shift_g = (transposed_out == 1 ? row * window_out + wo : row + wo * neurons);
| |
int shift_w = w_start + (total - i - 1) * step + wo * (window + 1);
| |
grad += gradients[shift_g] * weights[shift_w];
| |
}
| |
}
| |
gradient_inputs[n] = grad;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void ConvolutionCalcDeltaWeights(__global TYPE *inputs,
| |
__global TYPE *delta_weights,
| |
__global TYPE *gradients,
| |
int inputs_total,
| |
int step,
| |
int neurons,
| |
int transposed_out)
| |
{
| |
const int inp_w = get_global_id(0);
| |
const int w = get_global_id(1);
| |
const int window = get_global_size(0) - 1;
| |
const int window_out = get_global_size(1);
| |
//---
| |
int shift_delt = w * (window + 1) + inp_w;
| |
TYPE value = 0;
| |
if(inp_w == window)
| |
{
| |
for(int n = 0; n < neurons; n ++)
| |
value += gradients[transposed_out == 1 ? w + n*window_out : w * neurons + n];
| |
}
| |
else
| |
for(int n = 0; n < neurons; n ++)
| |
{
| |
int shift_inp = n * step + inp_w;
| |
if(shift_inp >= inputs_total)
| |
break;
| |
value += inputs[shift_inp] * gradients[transposed_out == 1 ? w + n*window_out : w * neurons + n];
| |
}
| |
delta_weights[shift_delt] += value;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| Feed-forward kernel of the pooling layer |
| |
//| Parameters: 'inputs' input data vector |
| |
//| 'outputs' vector of results |
| |
//| 'inputs_total' size of input data vector |
| |
//| 'input_neurons' vector size of 1st input data filter |
| |
//| 'window' size of input data analysis window |
| |
//| 'step' window moving step |
| |
//| 'activation' type of activation function |
| |
//+------------------------------------------------------------------+
| |
__kernel void ProofFeedForward(__global TYPE *inputs,
| |
__global TYPE *outputs,
| |
int inputs_total,
| |
int input_neurons,
| |
int window,
| |
int step,
| |
int activation)
| |
{
| |
const int n = get_global_id(0);
| |
const int w = get_global_id(1);
| |
const int neurons = get_global_size(0);
| |
const int window_out = get_global_size(1);
| |
int shift = n * step;
| |
int out = w * neurons + n;
| |
int shift_inp = w * input_neurons;
| |
TYPE s = 0;
| |
TYPE k = (TYPE)1 / (TYPE)window;
| |
TYPE4 k4 = (TYPE4)(k);
| |
for(int i = 0; i < window; i += 4)
| |
switch(activation)
| |
{
| |
case 0:
| |
s += dot(ToVect4(inputs, i, 1, min(shift_inp + input_neurons, inputs_total), shift_inp + shift),
| |
k4);
| |
break;
| |
case 1:
| |
s = Max4(ToVect4(inputs, i, 1, min(shift_inp + input_neurons, inputs_total), shift_inp + shift), s);
| |
break;
| |
default:
| |
break;
| |
}
| |
outputs[out] = s;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| Backpropagation kernel of the pooling layer |
| |
//| Parameters: 'inputs' input data vector |
| |
//| 'gradient_inputs' previous layer gradients vector |
| |
//| 'outputs' vector of results |
| |
//| 'gradients' current layer gradients vector |
| |
//| 'inputs_total' size of input data vector |
| |
//| 'outputs_total' size of outputs vector |
| |
//| 'window' size of input data analysis window |
| |
//| 'step' window moving step |
| |
//| 'neurons' vector size if 1st filter of outputs |
| |
//| 'activation' type of activation function |
| |
//+------------------------------------------------------------------+
| |
__kernel void ProofCalcHiddenGradient(__global TYPE *inputs,
| |
__global TYPE *gradient_inputs,
| |
__global TYPE *outputs,
| |
__global TYPE *gradients,
| |
int inputs_total,
| |
int outputs_total,
| |
int window,
| |
int step,
| |
int neurons,
| |
int activation)
| |
{
| |
const int n = get_global_id(0);
| |
const int w = get_global_id(1);
| |
const int input_neurons = get_global_size(0);
| |
const int window_out = get_global_size(1);
| |
//---
| |
int start = max((n - window + step) / step, 0);
| |
int stop = min((n + step - 1) / step + 1, neurons);
| |
TYPE grad = 0;
| |
int shift_inp = w * input_neurons + n;
| |
if(shift_inp >= inputs_total)
| |
return;
| |
TYPE inp = inputs[shift_inp];
| |
int shift_out = w * neurons;
| |
for(int o = start; o < stop; o ++)
| |
{
| |
int shift_g = shift_out + o;
| |
if(shift_g >= outputs_total)
| |
break;
| |
switch(activation)
| |
{
| |
case 0:
| |
grad += gradients[shift_g] / (TYPE)window;
| |
break;
| |
case 1:
| |
grad += (outputs[shift_g] == inp ? gradients[shift_g] : 0);
| |
break;
| |
default:
| |
break;
| |
}
| |
}
| |
gradient_inputs[shift_inp] = grad;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| LSTM block feed-forward kernel |
| |
//| Parameters: 'forgetgate' forget gate |
| |
//| 'inputgate' input gate |
| |
//| 'outputgate' output gate |
| |
//| 'newcontent' new content |
| |
//| 'memory' memory stream |
| |
//| 'hiddenstate' hidden state stream |
| |
//| 'outputs_total' number of elements in data stream |
| |
//+------------------------------------------------------------------+
| |
__kernel void LSTMFeedForward(__global TYPE *forgetgate,
| |
__global TYPE *inputgate,
| |
__global TYPE *outputgate,
| |
__global TYPE *newcontent,
| |
__global TYPE *memory,
| |
__global TYPE *hiddenstate,
| |
int outputs_total)
| |
{
| |
const int n = get_global_id(0);
| |
const int shift = n * 4;
| |
TYPE4 fg = ToVect4(forgetgate, shift, 1, outputs_total, 0);
| |
TYPE4 ig = ToVect4(inputgate, shift, 1, outputs_total, 0);
| |
TYPE4 og = ToVect4(outputgate, shift, 1, outputs_total, 0);
| |
TYPE4 nc = ToVect4(newcontent, shift, 1, outputs_total, 0);
| |
TYPE4 mem = ToVect4(memory, shift, 1, outputs_total, 0);
| |
//---
| |
TYPE4 temp = mem * fg;
| |
temp += ig * nc;
| |
D4ToArray(memory, temp, shift, 1, outputs_total, 0);
| |
temp = tanh(temp) * og;
| |
D4ToArray(hiddenstate, temp, shift, 1, outputs_total, 0);
| |
}
| |
//+------------------------------------------------------------------+
| |
//| LSTM block backpropagation kernel |
| |
//| Parameters: 'outputs' vector of outputs |
| |
//| 'gradients' current layer gradients vector |
| |
//| 'inputgate' input gate |
| |
//| 'outputgate' output gate |
| |
//| 'newcontent' new content |
| |
//| 'memory' memory stream |
| |
//| 'fg_gradients' forget gate gradient |
| |
//| 'ig_gradients' input gate gradient |
| |
//| 'og_gradients' output gate gradient |
| |
//| 'nc_gradients' new content gradient |
| |
//| 'outputs_total' size of outputs vector |
| |
//+------------------------------------------------------------------+
| |
__kernel void LSTMCalcHiddenGradient(__global TYPE *outputs,
| |
__global TYPE *gradients,
| |
__global TYPE *inputgate,
| |
__global TYPE *outputgate,
| |
__global TYPE *newcontent,
| |
__global TYPE *memory,
| |
__global TYPE *fg_gradients,
| |
__global TYPE *ig_gradients,
| |
__global TYPE *og_gradients,
| |
__global TYPE *nc_gradients,
| |
int outputs_total)
| |
{
| |
const int n = get_global_id(0);
| |
int shift = n * 4;
| |
//---
| |
TYPE4 out = ToVect4(outputs, shift, 1, outputs_total, 0);
| |
TYPE4 grad = ToVect4(gradients, shift, 1, outputs_total, 0);
| |
TYPE4 ig = ToVect4(inputgate, shift, 1, outputs_total, 0);
| |
TYPE4 og = ToVect4(outputgate, shift, 1, outputs_total, 0);
| |
TYPE4 nc = ToVect4(newcontent, shift, 1, outputs_total, 0);
| |
TYPE4 mem = ToVect4(memory, shift, 1, outputs_total, 0);
| |
//---
| |
TYPE4 m = out / (og + 1.0e-37f);
| |
//--- OutputGate gradient
| |
TYPE4 temp = grad * m;
| |
D4ToArray(og_gradients, temp, shift, 1, outputs_total, 0);
| |
//--- Градиент памяти cкорректируем на производную TANH
| |
grad = grad * og * (1 - pow(m, 2));
| |
//--- InputGate gradient
| |
temp = grad * nc;
| |
D4ToArray(ig_gradients, temp, shift, 1, outputs_total, 0);
| |
//--- NewContent gradient
| |
temp = grad * ig;
| |
D4ToArray(nc_gradients, temp, shift, 1, outputs_total, 0);
| |
//--- ForgetGates gradient
| |
temp = grad * mem;
| |
D4ToArray(fg_gradients, temp, shift, 1, outputs_total, 0);
| |
}
| |
//+------------------------------------------------------------------+
| |
//| Kernel calculating Self-Attention block dependency coefficients |
| |
//| Parameters: 'querys' tensor of queries |
| |
//| 'keys' tensor of keys |
| |
//| 'values' tensor of values |
| |
//| 'scores' matrix of dependency coefficients |
| |
//| 'inputs' tensor of input data |
| |
//| 'outputs' tensor of output data |
| |
//| 'window' size of input data analysis window |
| |
//| 'key_size' size of key vector for one element |
| |
//+------------------------------------------------------------------+
| |
__kernel void AttentionFeedForward(__global TYPE *querys,
| |
__global TYPE *keys,
| |
__global TYPE *scores,
| |
__global TYPE *values,
| |
__global TYPE *outputs,
| |
int window,
| |
int key_size,
| |
int mask)
| |
{
| |
const int q = get_global_id(0);
| |
const int units = get_global_size(0);
| |
const int h = get_global_id(1);
| |
const int heads = get_global_size(1);
| |
int shift_query = key_size * (q * heads + h);
| |
int shift_scores = units * (q * heads + h);
| |
TYPE summ = 0;
| |
for(int s = 0; s < units; s++)
| |
{
| |
TYPE score = 0;
| |
if(mask > 0 && s > q)
| |
{
| |
scores[shift_scores + s] = score;
| |
continue;
| |
}
| |
int shift_key = key_size * (s * heads + h);
| |
for(int k = 0; k < key_size; k ++)
| |
score += querys[shift_query + k] * keys[shift_key + k];
| |
score = exp(score / sqrt((TYPE)key_size));
| |
summ += score;
| |
scores[shift_scores + s] = score;
| |
}
| |
for(int s = 0; s < units; s++)
| |
scores[shift_scores + s] /= summ;
| |
//---
| |
shift_query = window * (q * heads + h);
| |
for(int i = 0; i < window; i++)
| |
{
| |
TYPE query = 0;
| |
for(int v = 0; v < units; v++)
| |
query += values[window * (v * heads + h) + i] * scores[shift_scores + v];
| |
outputs[shift_query + i] = query;
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| Kernel to propagate gradient inside the Self-Attention block |
| |
//| up to the level of dependency coefficient matrix Score |
| |
//| Parameters: 'values' tensor of values |
| |
//| 'values_grad' tensor of gradients at level of values |
| |
//| 'scores' matrix of dependency coefficients |
| |
//| 'scores_grad' matrix of dependency coeff. matrix |
| |
//| 'outputs' tensor of output data |
| |
//| 'outputs_grad' tensor of gradients at output level |
| |
//| 'window' size of input data analysis window |
| |
//+------------------------------------------------------------------+
| |
__kernel void AttentionCalcScoreGradient(__global TYPE *scores,
| |
__global TYPE *scores_grad,
| |
__global TYPE *values,
| |
__global TYPE *values_grad,
| |
__global TYPE *outputs_grad,
| |
__global TYPE *scores_temp,
| |
int window)
| |
{
| |
const int q = get_global_id(0);
| |
const int units = get_global_size(0);
| |
const int h = get_global_id(1);
| |
const int heads = get_global_size(1);
| |
int shift_value = window * (q * heads + h);
| |
int shift_score = units * (q * heads + h);
| |
//--- Gradient propagation to Values
| |
for(int i = 0; i < window; i ++)
| |
{
| |
TYPE grad = 0;
| |
for(int g = 0; g < units; g++)
| |
grad += scores[units * (g * heads + h) + q] * outputs_grad[window * (g * heads + h) + i];
| |
values_grad[shift_value + i] = grad;
| |
}
| |
//--- Gradient propagation to Score
| |
for(int k = 0; k < units; k++)
| |
{
| |
TYPE grad = 0;
| |
for(int i = 0; i < window; i++)
| |
grad += outputs_grad[shift_value + i] * values[window * (k * heads + h) + i];
| |
scores_temp[shift_score + k] = grad;
| |
}
| |
//--- Adjust by Softmax derivative
| |
for(int k = 0; k < units; k++)
| |
{
| |
TYPE grad = 0;
| |
TYPE score = scores[shift_score + k];
| |
for(int i = 0; i < units; i++)
| |
grad += scores[shift_score + i] * ((int)(i == k) - score) * scores_temp[shift_score + i];
| |
scores_grad[shift_score + k] = grad;
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void AttentionCalcHiddenGradient(__global TYPE *querys,
| |
__global TYPE *querys_grad,
| |
__global TYPE *keys,
| |
__global TYPE *keys_grad,
| |
__global TYPE *scores_grad,
| |
int key_size)
| |
{
| |
const int q = get_global_id(0);
| |
const int units = get_global_size(0);
| |
const int h = get_global_id(1);
| |
const int heads = get_global_size(1);
| |
int shift_query = key_size * (q * heads + h);
| |
int shift_score = units * (q * heads + h);
| |
//--- Gradient prpagation to Querys and Keys
| |
const TYPE k = 1 / sqrt((TYPE)key_size);
| |
//---
| |
for(int i = 0; i < key_size; i++)
| |
{
| |
TYPE grad_q = 0;
| |
TYPE grad_k = 0;
| |
for(int s = 0; s < units; s++)
| |
{
| |
grad_q += keys[key_size * (s * heads + h) + i] * scores_grad[shift_score + s];
| |
grad_k += querys[key_size * (s * heads + h) + i] * scores_grad[units * (s * heads + h) + q];
| |
}
| |
querys_grad[shift_query + i] = grad_q * k;
| |
keys_grad[shift_query + i] = grad_k * k;
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void GPTFeedForward(__global TYPE *querys,
| |
__global TYPE *keys,
| |
__global TYPE *scores,
| |
__global TYPE *values,
| |
__global TYPE *outputs,
| |
int key_size,
| |
int units,
| |
int current)
| |
{
| |
const int h = get_global_id(0);
| |
const int heads = get_global_size(0);
| |
int shift_query = key_size * h;
| |
int shift_scores = units * h;
| |
TYPE summ = 0;
| |
for(int s = 0; s < units; s++)
| |
{
| |
TYPE score = 0;
| |
int shift_key = key_size * (s * heads + h);
| |
for(int k = 0; k < key_size; k ++)
| |
{
| |
if(s == current)
| |
keys[shift_key + k] = querys[shift_query + k + heads * key_size];
| |
score += querys[shift_query + k] * keys[shift_key + k];
| |
}
| |
score = exp(score / sqrt((TYPE)key_size));
| |
summ += score;
| |
scores[shift_scores + s] = score;
| |
}
| |
for(int s = 0; s < units; s++)
| |
scores[shift_scores + s] /= summ;
| |
//---
| |
shift_query = key_size * h;
| |
for(int i = 0; i < key_size; i++)
| |
{
| |
TYPE query = 0;
| |
for(int v = 0; v < units; v++)
| |
{
| |
if(v == current)
| |
values[key_size * (v * heads + h) + i] = querys[(2 * heads + h) * key_size + i];
| |
query += values[key_size * (v * heads + h) + i] * scores[shift_scores + v];
| |
}
| |
outputs[shift_query + i] = query;
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void GPTCalcScoreGradient(__global TYPE *scores,
| |
__global TYPE *scores_grad,
| |
__global TYPE *values,
| |
__global TYPE *values_grad,
| |
__global TYPE *outputs_grad,
| |
__global TYPE *scores_temp,
| |
int window,
| |
int units,
| |
int current)
| |
{
| |
const int h = get_global_id(0);
| |
const int heads = get_global_size(0);
| |
int shift_value = window * (2 * heads + h);
| |
int shift_score = units * h;
| |
//--- Gradient propagation to Values
| |
for(int i = 0; i < window; i ++)
| |
values_grad[shift_value + i] = scores[units * h + current] * outputs_grad[window * h + i];
| |
//--- Gradient propagation to Score
| |
for(int k = 0; k < units; k++)
| |
{
| |
TYPE grad = 0;
| |
for(int i = 0; i < window; i++)
| |
grad += outputs_grad[shift_value + i] * values[window * (k * heads + h) + i];
| |
scores_temp[shift_score + k] = grad;
| |
}
| |
//--- Adjust by Softmax derivative
| |
for(int k = 0; k < units; k++)
| |
{
| |
TYPE grad = 0;
| |
TYPE score = scores[shift_score + k];
| |
for(int i = 0; i < units; i++)
| |
grad += scores[shift_score + i] * ((int)(i == k) - score) * scores_temp[shift_score + i];
| |
scores_grad[shift_score + k] = grad;
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void GPTCalcHiddenGradient(__global TYPE *querys,
| |
__global TYPE *querys_grad,
| |
__global TYPE *keys,
| |
__global TYPE *scores_grad,
| |
int key_size,
| |
int units,
| |
int current)
| |
{
| |
const int h = get_global_id(0);
| |
const int heads = get_global_size(0);
| |
int shift_query = key_size * h;
| |
int shift_key = key_size * (heads + h);
| |
int shift_score = units * h;
| |
//--- Gradient prpagation to Querys and Keys
| |
const TYPE k = 1 / sqrt((TYPE)key_size);
| |
//---
| |
for(int i = 0; i < key_size; i++)
| |
{
| |
TYPE grad_q = 0;
| |
TYPE grad_k = 0;
| |
for(int s = 0; s < units; s++)
| |
{
| |
grad_q += keys[key_size * (s * heads + h) + i] * scores_grad[shift_score + s];
| |
if(s == current)
| |
grad_k += querys[key_size * h + i] * scores_grad[units * h + current];
| |
}
| |
querys_grad[shift_query + i] = grad_q * k;
| |
querys_grad[shift_key + i] = grad_k * k;
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void BatchNormFeedForward(__global TYPE *inputs,
| |
__global TYPE *options,
| |
__global TYPE *weights,
| |
__global TYPE *output,
| |
int batch,
| |
int total)
| |
{
| |
int n = get_global_id(0);
| |
if(batch <= 1)
| |
{
| |
D4ToArray(output, ToVect4(inputs, n * 4, 1, total, 0), n * 4, 1, total, 0);
| |
return;
| |
}
| |
int shift = n * 4;
| |
int shift_options = n * 3 * 4;
| |
int shift_weights = n * 2 * 4;
| |
//---
| |
TYPE4 inp = ToVect4(inputs, shift, 1, total, 0);
| |
TYPE4 mean = ToVect4(options, shift, 3, total * 3, 0) * ((TYPE)batch - 1) + inp ;
| |
if(options[shift_options ] > 0 && options[shift_options + 1] > 0)
| |
mean /= (TYPE4)batch;
| |
TYPE4 delt = inp - mean;
| |
TYPE4 variance = ToVect4(options, shift, 3, total * 3, 1) * ((TYPE)batch - 1) + pow(delt, 2);
| |
if(options[shift_options + 1] > 0)
| |
variance /= (TYPE4)batch;
| |
TYPE4 nx = delt / sqrt(variance + 1e-37f);
| |
//---
| |
if(weights[shift_weights] == 0)
| |
D4ToArray(weights, (TYPE4)1, shift, 2, total * 2, 0);
| |
//---
| |
TYPE4 res = ToVect4(weights, shift, 2, total * 2, 0) * nx + ToVect4(weights, shift, 2, total * 2, 1);
| |
//---
| |
D4ToArray(options, mean, shift, 3, total * 3, 0);
| |
D4ToArray(options, variance, shift, 3, total * 3, 1);
| |
D4ToArray(options, nx, shift, 3, total * 3, 2);
| |
D4ToArray(output, res, shift, 1, total, 0);
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void BatchNormCalcHiddenGradient(__global TYPE *options,
| |
__global TYPE *gradient,
| |
__global TYPE *inputs,
| |
__global TYPE *gradient_inputs,
| |
__global TYPE *weights,
| |
int batch,
| |
int total
| |
)
| |
{
| |
int n = get_global_id(0);
| |
int shift = n * 4;
| |
if(batch <= 1)
| |
{
| |
D4ToArray(gradient_inputs, ToVect4(gradient, shift, 1, total, 0), shift, 1, total, 0);
| |
return;
| |
}
| |
//---
| |
TYPE4 inp = ToVect4(inputs, shift, 1, total, 0);
| |
TYPE4 gnx = ToVect4(gradient, shift, 1, total, 0) * ToVect4(weights, shift, 2, total * 2, 0);
| |
TYPE4 temp = 1 / sqrt(ToVect4(options, shift, 3, total * 3, 1) + 1e-37f);
| |
TYPE4 delt = inp - ToVect4(options, shift, 3, total * 3, 0);
| |
TYPE4 gvar = delt / (-2 * pow(ToVect4(options, shift, 3, total * 3, 1) + 1.0e-37f, 3.0f / 2.0f)) * gnx;
| |
TYPE4 gmu = (-temp) * gnx - gvar * 2 * delt / (TYPE4)batch;
| |
TYPE4 gx = temp * gnx + gmu / (TYPE4)batch + gvar * 2 * delt / (TYPE4)batch;
| |
//---
| |
D4ToArray(gradient_inputs, gx, shift, 1, total, 0);
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void BatchNormCalcDeltaWeights(__global TYPE *options,
| |
__global TYPE *delta_weights,
| |
__global TYPE *gradients)
| |
{
| |
const int n = get_global_id(0);
| |
int shift_options = n * 3;
| |
int shift_weights = n * 2;
| |
//---
| |
TYPE grad = gradients[n];
| |
delta_weights[shift_weights] += grad * options[shift_options + 2];
| |
delta_weights[shift_weights + 1] += grad;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void MaskMult(__global TYPE *inputs,
| |
__global TYPE *mask,
| |
__global TYPE *outputs,
| |
int outputs_total)
| |
{
| |
const int n = get_global_id(0) * 4;
| |
//---
| |
TYPE4 out = ToVect4(inputs, n, 1, outputs_total, 0) * ToVect4(mask, n, 1, outputs_total, 0);
| |
D4ToArray(outputs, out, n, 1, outputs_total, 0);
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void Sum(__global TYPE *inputs1,
| |
__global TYPE *inputs2,
| |
__global TYPE *outputs)
| |
{
| |
const int n = get_global_id(0);
| |
//---
| |
outputs[n] = inputs1[n] + inputs2[n];
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void LayerNormalize(__global TYPE* inputs,
| |
__global TYPE* outputs,
| |
__global TYPE* stds,
| |
const int total,
| |
const int std_shift)
| |
{
| |
uint i = (uint)get_global_id(0);
| |
uint l = (uint)get_local_id(0);
| |
uint ls = min((uint)get_local_size(0), (uint)LOCAL_SIZE);
| |
__local TYPE temp[LOCAL_SIZE];
| |
//---
| |
uint count = 0;
| |
do
| |
{
| |
uint shift = count * ls + l;
| |
temp[l] = (count > 0 ? temp[l] : 0) + (shift < total ? inputs[shift] : 0);
| |
count++;
| |
}
| |
while((count * ls + l) < total);
| |
temp[l] /= (TYPE)total;
| |
barrier(CLK_LOCAL_MEM_FENCE);
| |
count = ls;
| |
do
| |
{
| |
count = (count + 1) / 2;
| |
temp[l] += (l < count ? temp[l + count] : 0);
| |
barrier(CLK_LOCAL_MEM_FENCE);
| |
}
| |
while(count > 1);
| |
//---
| |
TYPE mean = (TYPE) temp[0];
| |
//---
| |
count = 0;
| |
do
| |
{
| |
uint shift = count * ls + l;
| |
temp[l] = (count > 0 ? temp[l] : 0) + (shift < total ? (TYPE)pow(inputs[shift] - mean, 2) : 0);
| |
count++;
| |
}
| |
while((count * ls + l) < total);
| |
temp[l] /= (TYPE)total;
| |
barrier(CLK_LOCAL_MEM_FENCE);
| |
count = ls;
| |
do
| |
{
| |
count = (count + 1) / 2;
| |
temp[l] += (l < count ? temp[l + count] : 0);
| |
barrier(CLK_LOCAL_MEM_FENCE);
| |
}
| |
while(count > 1);
| |
//---
| |
TYPE std = (TYPE)sqrt(temp[0]);
| |
if(l == 0)
| |
stds[std_shift] = std;
| |
count = 0;
| |
while((count * ls + l) < total)
| |
{
| |
uint shift = count * ls + l;
| |
outputs[shift] = (inputs[shift] - mean) / (std + 1e-37f);
| |
count++;
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void LayerNormalizeGradient(__global TYPE* outputs,
| |
__global TYPE* out_gradient,
| |
__global TYPE* inp_gradient,
| |
__global TYPE* stds,
| |
const int total,
| |
const int std_shift)
| |
{
| |
uint i = (uint)get_global_id(0);
| |
uint l = (uint)get_local_id(0);
| |
//---
| |
uint ls = min((uint)get_local_size(0), (uint)LOCAL_SIZE);
| |
__local TYPE dSTD[LOCAL_SIZE];
| |
__local TYPE dMean1[LOCAL_SIZE];
| |
__local TYPE dMean2[LOCAL_SIZE];
| |
uint count = 0;
| |
do
| |
{
| |
uint shift = count * ls + l;
| |
dSTD[l] = (count > 0 ? dSTD[l] : 0) - (shift < total ? out_gradient[shift] * outputs[shift] / (2 * (pow(stds[std_shift], (TYPE)2) + 1e-37f)) : 0);
| |
dMean1[l] = (count > 0 ? dMean1[l] : 0) - (shift < total ? out_gradient[shift] / (stds[std_shift] + 1e-37f) : 0);
| |
dMean2[l] = (count > 0 ? dMean2[l] : 0) - (shift < total ? 2 * outputs[shift] * stds[std_shift] / (TYPE)total : 0);
| |
count++;
| |
}
| |
while((count * ls + l) < total);
| |
barrier(CLK_LOCAL_MEM_FENCE);
| |
count = ls;
| |
do
| |
{
| |
count = (count + 1) / 2;
| |
dSTD[l] += (l < count ? dSTD[l + count] : 0);
| |
dMean1[l] += (l < count ? dMean1[l + count] : 0);
| |
dMean2[l] += (l < count ? dMean2[l + count] : 0);
| |
barrier(CLK_LOCAL_MEM_FENCE);
| |
}
| |
while(count > 1);
| |
//---
| |
TYPE dstd = dSTD[0];
| |
TYPE dmean = dMean1[0] + dstd * dMean2[0];
| |
//---
| |
count = 0;
| |
while((count * ls + l) < total)
| |
{
| |
uint shift = count * ls + l;
| |
inp_gradient[shift] = out_gradient[shift] / (stds[std_shift] + 1e-32f) + (2 * dstd * outputs[shift] * stds[std_shift] + dmean) / total;
| |
count++;
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void SoftMaxActivation(__global TYPE* inputs,
| |
__global TYPE* outputs,
| |
const ulong total)
| |
{
| |
uint i = (uint)get_global_id(0);
| |
uint l = (uint)get_local_id(0);
| |
uint h = (uint)get_global_id(1);
| |
uint ls = min((uint)get_local_size(0), (uint)LOCAL_SIZE);
| |
//---
| |
__local TYPE temp[LOCAL_SIZE];
| |
uint count = 0;
| |
for(count = l; (count < total && l < ls); count += ls)
| |
{
| |
uint shift = h * total + count;
| |
temp[l] = (count > l ? temp[l] : 0) + exp(inputs[shift]);
| |
}
| |
barrier(CLK_LOCAL_MEM_FENCE);
| |
count = ls;
| |
do
| |
{
| |
count = (count + 1) / 2;
| |
temp[l] += (l < count && (l + count) < ls ? temp[l + count] : 0);
| |
barrier(CLK_LOCAL_MEM_FENCE);
| |
}
| |
while(count > 1);
| |
//---
| |
TYPE sum = temp[0];
| |
for(count = l; count < total; count += ls)
| |
{
| |
uint shift = h * total + count;
| |
outputs[shift] = exp(inputs[shift]) / (sum + 1e-37f);
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| Derivative of SoftMax activation function |
| |
//| Parameter 'outputs' vector of previous FF outputs data |
| |
//| 'gradients' vector of gradients |
| |
//| 'outputs_total' size of outputs vector |
| |
//+------------------------------------------------------------------+
| |
__kernel void SoftMaxDerivative(__global TYPE* outputs,
| |
__global TYPE* output_gr,
| |
__global TYPE* input_gr)
| |
{
| |
size_t i = get_global_id(0);
| |
size_t outputs_total = get_global_size(0);
| |
size_t shift = get_global_id(1) * outputs_total;
| |
TYPE output = outputs[shift + i];
| |
TYPE result = 0;
| |
for(int j = 0; j < outputs_total; j++)
| |
result += output * (i == j ? 1 - output : -outputs[shift + j]) * output_gr[shift + j];
| |
input_gr[shift + i] = result;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void Split(__global TYPE* source,
| |
__global TYPE* target1,
| |
__global TYPE* target2,
| |
const int total_source,
| |
const int total_target1,
| |
const int total_target2
| |
)
| |
{
| |
int n = get_global_id(0);
| |
int total = get_global_size(0);
| |
for(int i = n; i < total_source; i += total)
| |
{
| |
if(i < total_target1)
| |
target1[i] = source[i];
| |
else
| |
{
| |
int t2 = i - total_target1;
| |
if(t2 < total_target2)
| |
target2[t2] = source[i];
| |
}
| |
}
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
__kernel void Concatenate(__global TYPE* source1,
| |
__global TYPE* source2,
| |
__global TYPE* target,
| |
const int total_source1,
| |
const int total_source2,
| |
const int total_target
| |
)
| |
{
| |
int n = get_global_id(0);
| |
int total = get_global_size(0);
| |
for(int i = n; i < total_target; i += total)
| |
{
| |
if(i < total_source1)
| |
target[i] = source1[i];
| |
else
| |
{
| |
int t2 = i - total_source1;
| |
if(t2 < total_source2)
| |
target[i] = source2[t2];
| |
}
| |
}
| |
}
| |
//+------------------------------------------------------------------+
|