mirror of
https://github.com/ggml-org/llama.cpp.git
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99 lines
3.5 KiB
Plaintext
99 lines
3.5 KiB
Plaintext
#pragma once
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#include "common.cuh"
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#define CUDA_NEG_BLOCK_SIZE 256
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#define CUDA_STEP_BLOCK_SIZE 256
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#define CUDA_GELU_BLOCK_SIZE 256
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#define CUDA_SILU_BLOCK_SIZE 256
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#define CUDA_SILU_BACK_BLOCK_SIZE 256
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#define CUDA_TANH_BLOCK_SIZE 256
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#define CUDA_RELU_BLOCK_SIZE 256
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#define CUDA_SIGMOID_BLOCK_SIZE 256
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#define CUDA_HARDSIGMOID_BLOCK_SIZE 256
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#define CUDA_EXP_BLOCK_SIZE 256
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#define CUDA_HARDSWISH_BLOCK_SIZE 256
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#define CUDA_SQR_BLOCK_SIZE 256
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#define CUDA_SQRT_BLOCK_SIZE 256
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#define CUDA_SIN_BLOCK_SIZE 256
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#define CUDA_COS_BLOCK_SIZE 256
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#define CUDA_GLU_BLOCK_SIZE 256
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#define CUDA_XIELU_BLOCK_SIZE 256
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void ggml_cuda_op_abs(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_sgn(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_neg(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_step(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_silu_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_gelu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_exp(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_sin(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_cos(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_log(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_elu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_reglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_geglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_swiglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_geglu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_cuda_op_xielu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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__device__ __forceinline__ float ggml_cuda_op_silu_single(float x) {
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return x / (1.0f + expf(-x));
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}
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__device__ __forceinline__ float ggml_cuda_op_gelu_single(float x) {
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const float GELU_COEF_A = 0.044715f;
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const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
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return 0.5f * x * (1.0f + tanhf(SQRT_2_OVER_PI * x * (1.0f + GELU_COEF_A * x * x)));
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}
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__device__ __forceinline__ float ggml_cuda_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) {
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x = fminf(x, limit);
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g = fmaxf(fminf(g, limit), -limit);
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float out_glu = x / (1.0f + expf(-x * alpha));
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out_glu = out_glu * (1.0f + g);
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return out_glu;
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}
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