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https://github.com/ikawrakow/ik_llama.cpp.git
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ed4e1a6588
* Fuse add+add+fused_rms * Try this * Macro to easily enable/disable fusion * Various: * Check that all tensors involved are on the same device before applying fusion * Fuse sigmoid+scale+sum_rows+div * Fix the fused bailingmoe2 experts selection The issue there was that the bias was not per row, but per expert group, so only the first n_per_group biases were used for al experts. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
93 lines
3.4 KiB
Plaintext
93 lines
3.4 KiB
Plaintext
//
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// Copyright (C) 2023-2024 The ggml authors
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// Copyright (C) 2024 Iwan Kawrakow
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// MIT license
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// SPDX-License-Identifier: MIT
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//
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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_MULTI_ADD_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_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_biased_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_swiglu_oai_cuda_f32(const float * x, const float * g, float * dst, const int64_t k, const int64_t n,
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const int64_t o0, const int64_t o1, const float alpha, const float limit, cudaStream_t stream);
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void ggml_cuda_op_fused_mul_unary(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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void ggml_fused_mul_unary(ggml_backend_cuda_context & ctx, ggml_unary_op op,
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int64_t nelements, const float * x, const float * y, float * z);
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void ggml_cuda_op_multi_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
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