[ util ] Implement softmax calculation function in util #2479
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skykongkong8
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Feb 20, 2024
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skykongkong8
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- Current activation functions are implemented as a function template, and fully computes with the function template parameter's precision unless using NEON intrinsics with inter-fp32-values explicitly.
- According to current papers, for safe convergence of mixed precision training, it is quite critical to calculate softmax with fp32 precision.
- This PR proposes a SIMD version of softmax calculation, and uses temporally higher precision when using half-precision
- For mathematical stability, applied linear translation (using minus values for the input of exponential function) to avoid precision overflow
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| float max(const unsigned int N, float *X) { | ||
| #ifdef USE_NEON | ||
| return nntrainer::neon::max(N, X); | ||
| #else |
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This is for your future reference.
Use STL properly
std::vector<float> v (X, X+N);
return *std::max_element(v.begin(), v.end());And if you compile it properly, you may get x64/SIMD (AVX/SSE) for free:
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Will apply this right away
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- Current softmax implementation does not consider fp32 use in half-precision softmax - Implement raw, and neon-simd version of softmax function with fp32 and fp16 with fp32 accumulation **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <[email protected]>
- Unlike isamax function of BLAS, this function returns the maximum 'value', not index - Note that this function is applicable only when the input data is continuous **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <[email protected]>
- For numerical stability, using negative values for the input of exponential function is recommended. (since negative output will range from 0 to 1) - Subtract the maximum value before calculating exponential vectors **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <[email protected]>
- Add exponential inplace function **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <[email protected]>
- For cleaner code use std::max_element instead of for-loop **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <[email protected]>
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| unsigned int i = 0; | ||
| float sum = 0.f; | ||
| float max_x = max(N, X); | ||
| while (i < N) { |
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It might depend on the size of Matrix N, but you can also optimize further using omp and temporal buffer to save X[i] - max_x. You can optimize it later cause we also have to consider not using the NEON case.
