Sha256: d1f180ff5cda211ef3b0d0ca4d7b225b9c27806b88b1c1569d112d05a7dc3c80
Contents?: true
Size: 1.42 KB
Versions: 5
Compression:
Stored size: 1.42 KB
Contents
% c_dtype = dtype_to_c_type(dtype)
// same dimension add floating point op
__kernel void apply_adam_<%= dtype %>(const int M, const int N,
__global const <%= c_dtype %> *grad,
__global const <%= c_dtype %> *learning_rate,
__global const <%= c_dtype %> *beta1_power,
__global const <%= c_dtype %> *beta2_power,
__global const <%= c_dtype %> *beta1,
__global const <%= c_dtype %> *beta2,
__global const <%= c_dtype %> *epsilon,
__global <%= c_dtype %> *momentum,
__global <%= c_dtype %> *output, __global <%= c_dtype %> *v) {
// Get the index of the current element to be processed
const int globalRow = get_global_id(0); // Row ID of C (0..M)
const int globalCol = get_global_id(1); // Col ID of C (0..N)
const int index = globalRow * N + globalCol;
<%= c_dtype %> alpha = learning_rate[0] * sqrt(1.0 - beta2_power[0]) / (1.0 - beta1_power[0]);
momentum[index] += (grad[index] - momentum[index]) * (1.0 - beta1[0]);
v[index] += (grad[index] * grad[index] - v[index]) * (1.0 - beta2[0]);
output[index] -= (momentum[index] * alpha) / ( sqrt(v[index]) + epsilon[0] );
}
Version data entries
5 entries across 5 versions & 2 rubygems