mirror of
https://github.com/jart/cosmopolitan.git
synced 2025-01-31 11:37:35 +00:00
8fdb31681a
llama.com can now load weights that use the new file format which was introduced a few weeks ago. Note that, unlike llama.cpp, we will keep support for old file formats in our tool so you don't need to convert your weights when the upstream project makes breaking changes. Please note that using ggjt v3 does make avx2 inference go 5% faster for me.
152 lines
6.9 KiB
C
152 lines
6.9 KiB
C
/*-*- mode:c;indent-tabs-mode:nil;c-basic-offset:4;tab-width:8;coding:utf-8 -*-│
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│vi: set net ft=c ts=4 sts=4 sw=4 fenc=utf-8 :vi│
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╞══════════════════════════════════════════════════════════════════════════════╡
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│ Copyright 2023 Justine Alexandra Roberts Tunney │
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│ │
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│ Permission to use, copy, modify, and/or distribute this software for │
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│ any purpose with or without fee is hereby granted, provided that the │
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│ above copyright notice and this permission notice appear in all copies. │
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│ │
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│ THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL │
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│ WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED │
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│ WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE │
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│ AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL │
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│ DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR │
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│ PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER │
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│ TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR │
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│ PERFORMANCE OF THIS SOFTWARE. │
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╚─────────────────────────────────────────────────────────────────────────────*/
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#include "libc/assert.h"
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#include "third_party/ggml/ggjt.v2.q4_0.h"
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#include "third_party/ggml/ggjt.v2.q4_1.h"
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#include "third_party/ggml/ggjt.v2.q5_0.h"
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#include "third_party/ggml/ggjt.v2.q5_1.h"
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#include "third_party/ggml/ggjt.v2.q8_0.h"
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#include "third_party/ggml/ggjt.v2.q8_1.h"
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#include "third_party/ggml/ggml.h"
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// clang-format off
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static const int ggjt_v2_blck_size[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F32] = 1,
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[GGML_TYPE_F16] = 1,
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[GGML_TYPE_Q4_0] = V2_QK4_0,
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[GGML_TYPE_Q4_1] = V2_QK4_1,
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[GGML_TYPE_Q5_0] = V2_QK5_0,
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[GGML_TYPE_Q5_1] = V2_QK5_1,
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[GGML_TYPE_Q8_0] = V2_QK8_0,
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[GGML_TYPE_Q8_1] = V2_QK8_1,
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[GGML_TYPE_I8] = 1,
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[GGML_TYPE_I16] = 1,
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[GGML_TYPE_I32] = 1,
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};
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static const size_t ggjt_v2_type_size[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F32] = sizeof(float),
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[GGML_TYPE_F16] = sizeof(ggml_fp16_t),
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[GGML_TYPE_Q4_0] = sizeof(block_v2_q4_0),
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[GGML_TYPE_Q4_1] = sizeof(block_v2_q4_1),
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[GGML_TYPE_Q5_0] = sizeof(block_v2_q5_0),
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[GGML_TYPE_Q5_1] = sizeof(block_v2_q5_1),
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[GGML_TYPE_Q8_0] = sizeof(block_v2_q8_0),
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[GGML_TYPE_Q8_1] = sizeof(block_v2_q8_1),
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[GGML_TYPE_I8] = sizeof(int8_t),
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[GGML_TYPE_I16] = sizeof(int16_t),
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[GGML_TYPE_I32] = sizeof(int32_t),
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};
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static const char *const ggjt_v2_type_name[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F32] = "f32",
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[GGML_TYPE_F16] = "f16",
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[GGML_TYPE_Q4_0] = "q4_0",
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[GGML_TYPE_Q4_1] = "q4_1",
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[GGML_TYPE_Q4_2] = "q4_2",
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[GGML_TYPE_Q5_0] = "q5_0",
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[GGML_TYPE_Q5_1] = "q5_1",
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[GGML_TYPE_Q8_0] = "q8_0",
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[GGML_TYPE_Q8_1] = "q8_1",
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[GGML_TYPE_I8] = "i8",
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[GGML_TYPE_I16] = "i16",
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[GGML_TYPE_I32] = "i32",
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};
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static const bool ggjt_v2_is_quantized[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F32] = false,
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[GGML_TYPE_F16] = false,
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[GGML_TYPE_Q4_0] = true,
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[GGML_TYPE_Q4_1] = true,
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[GGML_TYPE_Q5_0] = true,
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[GGML_TYPE_Q5_1] = true,
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[GGML_TYPE_Q8_0] = true,
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[GGML_TYPE_Q8_1] = true,
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[GGML_TYPE_I8] = false,
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[GGML_TYPE_I16] = false,
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[GGML_TYPE_I32] = false,
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};
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static const quantize_chunk_f *const ggjt_v2_quantize_chunk[GGML_TYPE_COUNT] = {
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[GGML_TYPE_Q4_0] = (void *)ggml_quantize_v2_q4_0,
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[GGML_TYPE_Q4_1] = (void *)ggml_quantize_v2_q4_1,
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[GGML_TYPE_Q5_0] = (void *)ggml_quantize_v2_q5_0,
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[GGML_TYPE_Q5_1] = (void *)ggml_quantize_v2_q5_1,
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[GGML_TYPE_Q8_0] = (void *)ggml_quantize_v2_q8_0,
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};
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static const quantize_fns_t ggjt_v2_quantize_fns[GGML_TYPE_COUNT] = {
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[GGML_TYPE_Q4_0] = {
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.dequantize_row_q = dequantize_row_v2_q4_0,
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.quantize_row_q = quantize_row_v2_q4_0,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_v2_q4_0_reference,
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.quantize_row_q_dot = quantize_row_v2_q8_0,
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.vec_dot_q = ggml_vec_dot_v2_q4_0_q8_0,
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.vec_dot_type = GGML_TYPE_Q8_0,
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},
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[GGML_TYPE_Q4_1] = {
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.dequantize_row_q = dequantize_row_v2_q4_1,
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.quantize_row_q = quantize_row_v2_q4_1,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_v2_q4_1_reference,
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.quantize_row_q_dot = quantize_row_v2_q8_1,
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.vec_dot_q = ggml_vec_dot_v2_q4_1_q8_1,
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.vec_dot_type = GGML_TYPE_Q8_1,
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},
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[GGML_TYPE_Q5_0] = {
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.dequantize_row_q = dequantize_row_v2_q5_0,
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.quantize_row_q = quantize_row_v2_q5_0,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_v2_q5_0_reference,
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.quantize_row_q_dot = quantize_row_v2_q8_0,
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.vec_dot_q = ggml_vec_dot_v2_q5_0_q8_0,
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.vec_dot_type = GGML_TYPE_Q8_0,
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},
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[GGML_TYPE_Q5_1] = {
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.dequantize_row_q = dequantize_row_v2_q5_1,
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.quantize_row_q = quantize_row_v2_q5_1,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_v2_q5_1_reference,
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.quantize_row_q_dot = quantize_row_v2_q8_1,
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.vec_dot_q = ggml_vec_dot_v2_q5_1_q8_1,
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.vec_dot_type = GGML_TYPE_Q8_1,
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},
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[GGML_TYPE_Q8_0] = {
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.dequantize_row_q = dequantize_row_v2_q8_0,
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.quantize_row_q = quantize_row_v2_q8_0,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_v2_q8_0_reference,
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.quantize_row_q_dot = quantize_row_v2_q8_0,
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.vec_dot_q = ggml_vec_dot_v2_q8_0_q8_0,
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.vec_dot_type = GGML_TYPE_Q8_0,
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},
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[GGML_TYPE_Q8_1] = {
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.dequantize_row_q = NULL, // TODO
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.quantize_row_q = quantize_row_v2_q8_1,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_v2_q8_1_reference,
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.quantize_row_q_dot = quantize_row_v2_q8_1,
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.vec_dot_q = NULL, // TODO
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.vec_dot_type = GGML_TYPE_Q8_1,
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},
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};
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void ggjt_v2(void) {
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GGML_BLCK_SIZE = ggjt_v2_blck_size;
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GGML_TYPE_SIZE = ggjt_v2_type_size;
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GGML_TYPE_NAME = ggjt_v2_type_name;
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GGML_IS_QUANTIZED = ggjt_v2_is_quantized;
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quantize_fns = ggjt_v2_quantize_fns;
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GGML_QUANTIZE_CHUNK = ggjt_v2_quantize_chunk;
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}
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