mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-20 05:07:52 +00:00
5582039d0a
* CUDA: quantized KV support for FA vec * try CI fix * fix commented-out kernel variants * add q8_0 q4_0 tests * fix nwarps > batch size * split fattn compile via extern templates * fix flake8 * fix metal tests * fix cmake * make generate_cu_files.py executable * add autogenerated .cu files * fix AMD * error if type_v != FP16 and not flash_attn * remove obsolete code
350 lines
14 KiB
Plaintext
350 lines
14 KiB
Plaintext
#include "common.cuh"
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#include "fattn-common.cuh"
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#include "fattn-tile-f16.cuh"
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#include "fattn-tile-f32.cuh"
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#include "fattn-vec-f16.cuh"
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#include "fattn-vec-f32.cuh"
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#include "fattn-wmma-f16.cuh"
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#include "fattn.cuh"
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#include <cstdint>
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static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * KQV = dst;
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const ggml_tensor * Q = dst->src[0];
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const int32_t precision = KQV->op_params[2];
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if (precision != GGML_PREC_DEFAULT) {
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if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
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constexpr int cols_per_block = 16;
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switch (Q->ne[0]) {
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case 64:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
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break;
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case 80:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
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break;
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case 96:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
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break;
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case 112:
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ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
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break;
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case 128:
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ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
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break;
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case 256:
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ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
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break;
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default:
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GGML_ASSERT(false);
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break;
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}
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} else {
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constexpr int cols_per_block = 32;
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switch (Q->ne[0]) {
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case 64:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
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break;
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case 80:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
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break;
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case 96:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
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break;
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case 112:
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ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
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break;
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case 128:
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ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
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break;
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// case 256:
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// ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
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// break;
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default:
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GGML_ASSERT(false);
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break;
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}
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}
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return;
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}
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if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
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constexpr int cols_per_block = 8;
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switch (Q->ne[0]) {
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case 64:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
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break;
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case 96:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
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break;
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case 128:
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ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
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break;
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case 256:
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ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
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break;
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default:
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GGML_ASSERT(false);
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break;
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}
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return;
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}
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if (Q->ne[1] <= 32) {
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constexpr int cols_per_block = 16;
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switch (Q->ne[0]) {
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case 64:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
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break;
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case 80:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
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break;
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case 96:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
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break;
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case 112:
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ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
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break;
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case 128:
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ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
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break;
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case 256:
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ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
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break;
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default:
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GGML_ASSERT(false);
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break;
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}
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return;
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}
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constexpr int cols_per_block = 32;
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switch (Q->ne[0]) {
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case 64:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
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break;
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case 80:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
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break;
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case 96:
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ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
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break;
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case 112:
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ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
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break;
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case 128:
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ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
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break;
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case 256:
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ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
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break;
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default:
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GGML_ASSERT(false);
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break;
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}
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}
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#define FATTN_VEC_F16_CASE(D, type_K, type_V) \
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if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \
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ggml_cuda_flash_attn_ext_vec_f16_case<D, type_K, type_V>(ctx, dst); \
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return; \
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} \
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static void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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ggml_tensor * Q = dst->src[1];
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ggml_tensor * K = dst->src[1];
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ggml_tensor * V = dst->src[2];
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#ifdef GGML_CUDA_FA_ALL_QUANTS
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FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0)
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FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1)
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FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0)
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FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1)
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FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0)
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FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16 )
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
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FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
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#else
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
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FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
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FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
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FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
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#endif // GGML_CUDA_FA_ALL_QUANTS
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on_no_fattn_vec_case(Q->ne[0]);
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}
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#define FATTN_VEC_F32_CASE(D, type_K, type_V) \
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if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \
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ggml_cuda_flash_attn_ext_vec_f32_case<D, type_K, type_V>(ctx, dst); \
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return; \
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} \
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static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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ggml_tensor * Q = dst->src[1];
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ggml_tensor * K = dst->src[1];
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ggml_tensor * V = dst->src[2];
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#ifdef GGML_CUDA_FA_ALL_QUANTS
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FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0)
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FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1)
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FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0)
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FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1)
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FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0)
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FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
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#else
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
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FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
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FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
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#endif // GGML_CUDA_FA_ALL_QUANTS
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on_no_fattn_vec_case(Q->ne[0]);
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}
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void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * KQV = dst;
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const ggml_tensor * Q = dst->src[0];
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const ggml_tensor * K = dst->src[1];
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const ggml_tensor * V = dst->src[2];
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ggml_cuda_set_device(ctx.device);
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const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
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const int32_t precision = KQV->op_params[2];
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const bool quantized_KV = ggml_is_quantized(K->type) || ggml_is_quantized(V->type);
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// On AMD the tile kernels perform poorly, use the vec kernel instead:
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if (cc >= CC_OFFSET_AMD || quantized_KV) {
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if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
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ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
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} else {
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ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
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}
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return;
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}
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if (!fast_fp16_available(cc)) {
|
|
if (Q->ne[1] <= 8) {
|
|
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
|
|
} else {
|
|
ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
|
|
}
|
|
return;
|
|
}
|
|
|
|
if (!fp16_mma_available(cc)) {
|
|
if (Q->ne[1] <= 8) {
|
|
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
|
|
} else {
|
|
ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
|
|
}
|
|
return;
|
|
}
|
|
|
|
if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
|
|
if (precision == GGML_PREC_DEFAULT) {
|
|
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
|
|
return;
|
|
} else if(Q->ne[0] <= 128) {
|
|
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
|
|
return;
|
|
}
|
|
}
|
|
|
|
ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
|
|
}
|