50 lines
1.7 KiB
Plaintext
50 lines
1.7 KiB
Plaintext
#include "ggml-impl.h"
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#include "opt-step-sgd.cuh"
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#include <cstdint>
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static __global__ void opt_step_sgd_f32(
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float * __restrict__ x, const float * __restrict__ g,
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const float * __restrict__ pars, const int64_t k) {
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const int64_t i = (int64_t) blockIdx.x*blockDim.x + threadIdx.x;
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if (i >= k) {
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return;
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}
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x[i] = x[i] * (1.0f - pars[0] * pars[1]) - pars[0] * g[i];
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}
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static void opt_step_sgd_f32_cuda(
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float * x, const float * g, const float * __restrict__ pars, const int64_t k, cudaStream_t stream) {
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const dim3 block_dims(CUDA_OPT_STEP_SGD_BLOCK_SIZE, 1, 1);
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const dim3 block_nums((k + CUDA_OPT_STEP_SGD_BLOCK_SIZE - 1) / CUDA_OPT_STEP_SGD_BLOCK_SIZE, 1, 1);
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opt_step_sgd_f32<<<block_nums, block_dims, 0, stream>>>(x, g, pars, k);
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}
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void ggml_cuda_opt_step_sgd(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const ggml_tensor * src0_grad = dst->src[1];
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const ggml_tensor * params = dst->src[2];
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT(src0_grad->type == GGML_TYPE_F32);
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GGML_ASSERT(params->type == GGML_TYPE_F32);
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GGML_ASSERT(ggml_is_contiguous(src0));
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GGML_ASSERT(ggml_is_contiguous(src0_grad));
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GGML_ASSERT(ggml_is_contiguous(params));
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GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
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GGML_ASSERT(ggml_nelements(params) == 2);
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float * src0_d = (float *) src0->data;
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const float * src0_grad_d = (const float *) src0_grad->data;
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const float * params_d = (const float *) params->data;
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cudaStream_t stream = ctx.stream();
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const int64_t ne = ggml_nelements(src0);
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opt_step_sgd_f32_cuda(src0_d, src0_grad_d, params_d, ne, stream);
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}
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