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llama : offload KV cache per-layer

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Georgi Gerganov 2023-12-03 17:18:15 +02:00
parent c294c78eb7
commit 986b3da76a
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GPG Key ID: 449E073F9DC10735
2 changed files with 86 additions and 151 deletions
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235
llama.cpp
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@ -1,7 +1,3 @@
// TODO: move to context params
bool offload_k = true;
bool offload_v = true;
#define LLAMA_API_INTERNAL
#include "llama.h"
@ -1249,6 +1245,9 @@ struct llama_cparams {
float yarn_beta_slow;
bool mul_mat_q;
bool offload_k;
bool offload_v;
};
struct llama_layer {
@ -1331,8 +1330,10 @@ struct llama_kv_cache {
#ifdef GGML_USE_CUBLAS
if (ggml_cublas_loaded()) {
ggml_cuda_free_data(k);
ggml_cuda_free_data(v);
for (size_t i = 0; i < k_l.size(); ++i) {
ggml_cuda_free_data(k_l[i]);
ggml_cuda_free_data(v_l[i]);
}
}
#endif
}
@ -1524,7 +1525,9 @@ static bool llama_kv_cache_init(
struct llama_kv_cache & cache,
ggml_type wtype,
uint32_t n_ctx,
int n_gpu_layers) {
int n_gpu_layers,
bool offload_k,
bool offload_v) {
const uint32_t n_embd = hparams.n_embd_gqa();
const uint32_t n_layer = hparams.n_layer;
@ -2782,14 +2785,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -2859,14 +2855,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -2929,14 +2918,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -3006,14 +2988,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -3083,21 +3058,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
#ifdef GGML_USE_CUBLAS
if (n_gpu_layers > int(n_layer + 1)) {
LLAMA_LOG_ERROR("%s: CUDA backend missing Persimmon CUDA ops, can offload at most %ld layers. See: https://github.com/ggerganov/llama.cpp/issues/4038\n",
__func__, n_layer + 1);
throw std::runtime_error("Persimmon CUDA offload failed");
}
#endif
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -3156,14 +3117,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -3234,14 +3188,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -3301,14 +3248,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -3373,14 +3313,7 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
@ -3456,8 +3389,8 @@ static void llm_load_tensors(
}
#ifdef GGML_USE_CUBLAS
const int max_backend_supported_layers = hparams.n_layer + 3;
const int max_offloadable_layers = hparams.n_layer + 3;
const int max_backend_supported_layers = hparams.n_layer + 1;
const int max_offloadable_layers = hparams.n_layer + 1;
#elif GGML_USE_CLBLAST
const int max_backend_supported_layers = hparams.n_layer + 1;
const int max_offloadable_layers = hparams.n_layer + 1;
@ -3981,16 +3914,16 @@ struct llm_build_context {
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
struct ggml_tensor * inp_pos_host = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos_host, "inp_pos_host", -1);
// KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
cb(KQ_scale, "KQ_scale", -1);
struct ggml_tensor * KQ_scale_host = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
cb(KQ_scale_host, "KQ_scale_host", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
struct ggml_tensor * KQ_mask_host = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask_host, "KQ_mask_host", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
@ -3998,6 +3931,16 @@ struct llm_build_context {
}
for (int il = 0; il < n_layer; ++il) {
// offloaded mirrors
struct ggml_tensor * inp_pos = ggml_view_tensor(ctx0, inp_pos_host);
cb(inp_pos, "inp_pos", il);
struct ggml_tensor * KQ_scale = ggml_view_tensor(ctx0, KQ_scale_host);
cb(KQ_scale, "KQ_scale", il);
struct ggml_tensor * KQ_mask = ggml_view_tensor(ctx0, KQ_mask_host);
cb(KQ_mask, "KQ_mask", il);
struct ggml_tensor * inpSA = inpL;
// norm
@ -5165,8 +5108,6 @@ struct llm_build_context {
enum llm_offload_func_e {
OFFLOAD_FUNC_NOP,
OFFLOAD_FUNC,
OFFLOAD_FUNC_KQ,
OFFLOAD_FUNC_V,
OFFLOAD_FUNC_NR,
OFFLOAD_FUNC_EMB,
OFFLOAD_FUNC_OUT,
@ -5252,11 +5193,15 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
//{ "inp_embd", OFFLOAD_FUNC_NR }, // TODO: missing K-quants get_rows kernel
{ "pos_embd", OFFLOAD_FUNC_NR },
{ "inp_pos", OFFLOAD_FUNC_KQ }, // this is often used for KQ ops (e.g. rope)
{ "KQ_scale", OFFLOAD_FUNC_KQ },
{ "KQ_mask", OFFLOAD_FUNC_KQ },
{ "K_shift", OFFLOAD_FUNC_KQ },
{ "K_shifted", OFFLOAD_FUNC_KQ },
{ "inp_pos_host", OFFLOAD_FUNC_NOP }, // this is often used for KQ ops (e.g. rope)
{ "KQ_scale_host", OFFLOAD_FUNC_NOP },
{ "KQ_mask_host", OFFLOAD_FUNC_NOP },
{ "inp_pos", OFFLOAD_FUNC }, // these are offloaded versions of the tensors
{ "KQ_scale", OFFLOAD_FUNC },
{ "KQ_mask", OFFLOAD_FUNC },
{ "K_shift", OFFLOAD_FUNC },
{ "K_shifted", OFFLOAD_FUNC },
{ "inp_norm", OFFLOAD_FUNC_NR },
{ "inp_norm_w", OFFLOAD_FUNC_NR },
@ -5269,38 +5214,38 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
{ "attn_norm", OFFLOAD_FUNC },
{ "attn_norm_2", OFFLOAD_FUNC },
{ "wqkv", OFFLOAD_FUNC_KQ },
{ "bqkv", OFFLOAD_FUNC_KQ },
{ "wqkv_clamped", OFFLOAD_FUNC_KQ },
{ "wqkv", OFFLOAD_FUNC },
{ "bqkv", OFFLOAD_FUNC },
{ "wqkv_clamped", OFFLOAD_FUNC },
{ "tmpk", OFFLOAD_FUNC_KQ },
{ "tmpq", OFFLOAD_FUNC_KQ },
{ "tmpv", OFFLOAD_FUNC_V },
{ "Kcur", OFFLOAD_FUNC_KQ },
{ "Qcur", OFFLOAD_FUNC_KQ },
{ "Vcur", OFFLOAD_FUNC_V },
{ "tmpk", OFFLOAD_FUNC },
{ "tmpq", OFFLOAD_FUNC },
{ "tmpv", OFFLOAD_FUNC },
{ "Kcur", OFFLOAD_FUNC },
{ "Qcur", OFFLOAD_FUNC },
{ "Vcur", OFFLOAD_FUNC },
{ "krot", OFFLOAD_FUNC_KQ },
{ "qrot", OFFLOAD_FUNC_KQ },
{ "kpass", OFFLOAD_FUNC_KQ },
{ "qpass", OFFLOAD_FUNC_KQ },
{ "krotated", OFFLOAD_FUNC_KQ },
{ "qrotated", OFFLOAD_FUNC_KQ },
{ "krot", OFFLOAD_FUNC },
{ "qrot", OFFLOAD_FUNC },
{ "kpass", OFFLOAD_FUNC },
{ "qpass", OFFLOAD_FUNC },
{ "krotated", OFFLOAD_FUNC },
{ "qrotated", OFFLOAD_FUNC },
{ "q", OFFLOAD_FUNC_KQ },
{ "k", OFFLOAD_FUNC_KQ },
{ "kq", OFFLOAD_FUNC_KQ },
{ "kq_scaled", OFFLOAD_FUNC_KQ },
{ "kq_scaled_alibi", OFFLOAD_FUNC_KQ },
{ "kq_masked", OFFLOAD_FUNC_KQ },
{ "kq_soft_max", OFFLOAD_FUNC_V },
{ "kq_soft_max_ext", OFFLOAD_FUNC_V },
{ "v", OFFLOAD_FUNC_V },
{ "kqv", OFFLOAD_FUNC_V },
{ "kqv_merged", OFFLOAD_FUNC_V },
{ "kqv_merged_cont", OFFLOAD_FUNC_V },
{ "kqv_wo", OFFLOAD_FUNC_V },
{ "kqv_out", OFFLOAD_FUNC_V },
{ "q", OFFLOAD_FUNC },
{ "k", OFFLOAD_FUNC },
{ "kq", OFFLOAD_FUNC },
{ "kq_scaled", OFFLOAD_FUNC },
{ "kq_scaled_alibi", OFFLOAD_FUNC },
{ "kq_masked", OFFLOAD_FUNC },
{ "kq_soft_max", OFFLOAD_FUNC },
{ "kq_soft_max_ext", OFFLOAD_FUNC },
{ "v", OFFLOAD_FUNC },
{ "kqv", OFFLOAD_FUNC },
{ "kqv_merged", OFFLOAD_FUNC },
{ "kqv_merged_cont", OFFLOAD_FUNC },
{ "kqv_wo", OFFLOAD_FUNC },
{ "kqv_out", OFFLOAD_FUNC },
{ "ffn_inp", OFFLOAD_FUNC },
{ "ffn_norm", OFFLOAD_FUNC },
@ -5390,7 +5335,7 @@ static struct ggml_cgraph * llama_build_graph(
alloc_inp_embd = true;
}
if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) {
if (!alloc_inp_pos && strcmp(name, "inp_pos_host") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) {
@ -5406,7 +5351,7 @@ static struct ggml_cgraph * llama_build_graph(
alloc_inp_pos = true;
}
if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) {
if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale_host") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
@ -5417,7 +5362,7 @@ static struct ggml_cgraph * llama_build_graph(
alloc_inp_KQ_scale = true;
}
if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) {
if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask_host") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
@ -5493,14 +5438,10 @@ static struct ggml_cgraph * llama_build_graph(
{ OFFLOAD_FUNC_OUT, "CPU" },
#ifdef GGML_USE_CUBLAS
{ OFFLOAD_FUNC, "GPU (CUDA)" },
{ OFFLOAD_FUNC_KQ, "GPU (CUDA) KQ" },
{ OFFLOAD_FUNC_V, "GPU (CUDA) V" },
{ OFFLOAD_FUNC_NR, "GPU (CUDA) NR" },
{ OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" },
#else
{ OFFLOAD_FUNC, "CPU" },
{ OFFLOAD_FUNC_KQ, "CPU" },
{ OFFLOAD_FUNC_V, "CPU" },
{ OFFLOAD_FUNC_NR, "CPU" },
{ OFFLOAD_FUNC_EMB, "CPU" },
#endif // GGML_USE_CUBLAS
@ -5538,16 +5479,6 @@ static struct ggml_cgraph * llama_build_graph(
func_e = OFFLOAD_FUNC_NOP;
}
break;
case OFFLOAD_FUNC_V:
if (n_gpu_layers <= n_layer + 1) {
func_e = OFFLOAD_FUNC_NOP;
}
break;
case OFFLOAD_FUNC_KQ:
if (n_gpu_layers <= n_layer + 2) {
func_e = OFFLOAD_FUNC_NOP;
}
break;
case OFFLOAD_FUNC_EMB:
if (!offload_emb || n_gpu_layers < n_layer) {
func_e = OFFLOAD_FUNC_NOP;
@ -5569,8 +5500,6 @@ static struct ggml_cgraph * llama_build_graph(
case OFFLOAD_FUNC_NOP:
case OFFLOAD_FUNC_OUT: func = ggml_offload_nop; break;
case OFFLOAD_FUNC:
case OFFLOAD_FUNC_KQ:
case OFFLOAD_FUNC_V:
case OFFLOAD_FUNC_NR:
case OFFLOAD_FUNC_EMB: func = ggml_offload_gpu; break;
default: GGML_ASSERT(false);
@ -5806,7 +5735,7 @@ static int llama_decode_internal(
n_threads = std::min(4, n_threads);
}
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 1;
if (ggml_cpu_has_cublas() && fully_offloaded) {
n_threads = 1;
}
@ -8644,6 +8573,8 @@ struct llama_context_params llama_context_default_params() {
/*.f16_kv =*/ true,
/*.logits_all =*/ false,
/*.embedding =*/ false,
/*.offload_k =*/ true,
/*.offload_q =*/ true,
};
return result;
@ -8760,6 +8691,8 @@ struct llama_context * llama_new_context_with_model(
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.mul_mat_q = params.mul_mat_q;
cparams.offload_k = params.offload_k;
cparams.offload_v = params.offload_v;
cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx;
cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base;
@ -8797,7 +8730,7 @@ struct llama_context * llama_new_context_with_model(
// reserve memory for context buffers
if (!hparams.vocab_only) {
if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, cparams.n_ctx, model->n_gpu_layers)) {
if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, cparams.n_ctx, model->n_gpu_layers, cparams.offload_k, cparams.offload_v)) {
LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
llama_free(ctx);
return nullptr;

2
llama.h
View File

@ -196,6 +196,8 @@ extern "C" {
bool f16_kv; // use fp16 for KV cache, fp32 otherwise
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool embedding; // embedding mode only
bool offload_k;
bool offload_v;
};
// model quantization parameters