vbatts/llama.cpp
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Merge branch 'master' into server-probs

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Jhen 2023-08-23 09:26:28 +08:00
commit ec4a19c5af
20 changed files with 1443 additions and 436 deletions
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13
common/common.cpp
View file

@ -289,7 +289,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.n_batch = std::stoi(argv[i]);
params.n_batch = std::min(512, params.n_batch);
} else if (arg == "--keep") {
if (++i >= argc) {
invalid_param = true;
@ -388,11 +387,11 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--mul-mat-q" || arg == "-mmq") {
} else if (arg == "--no-mul-mat-q" || arg == "-nommq") {
#ifdef GGML_USE_CUBLAS
params.mul_mat_q = true;
params.mul_mat_q = false;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n");
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--low-vram" || arg == "-lv") {
#ifdef GGML_USE_CUBLAS
@ -602,9 +601,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
fprintf(stdout, " -nommq, --no-mul-mat-q\n");
fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
#endif
fprintf(stdout, " --mtest compute maximum memory usage\n");
fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");

2
common/common.h
View file

@ -68,7 +68,7 @@ struct gpt_params {
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
bool mul_mat_q = false; // if true, use experimental mul_mat_q kernels
bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs

27
convert.py
View file

@ -69,7 +69,10 @@ SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
'I32': DT_I32,
}
class GGMLFileType(enum.Enum):
# TODO: match this with `llama_ftype`
# TODO: rename to LLAMAFileType
# TODO: move to `gguf.py`
class GGMLFileType(enum.IntEnum):
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
@ -101,6 +104,8 @@ class Params:
n_head_kv: int
f_norm_eps: float
ftype: Optional[GGMLFileType] = None
@staticmethod
def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range
@ -738,6 +743,9 @@ class OutputFile:
self.gguf.add_head_count_kv (params.n_head_kv)
self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)
if params.ftype:
self.gguf.add_file_type(params.ftype)
def add_meta_vocab(self, vocab: Vocab) -> None:
tokens = []
scores = []
@ -956,7 +964,7 @@ def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, Sentence
path = path3
else:
raise FileNotFoundError(
f"Could not find tokenizer.model in {path} or its parent; "
f"Could not find {vocab_file} in {path} or its parent; "
"if it's in another directory, pass the directory as --vocab-dir")
print(f"Loading vocab file '{path}', type '{vocabtype}'")
@ -1020,6 +1028,12 @@ def main(args_in: Optional[List[str]] = None) -> None:
" - LLaMA v2: --ctx 4096\n")
params.n_ctx = args.ctx
if args.outtype:
params.ftype = {
"f32": GGMLFileType.AllF32,
"f16": GGMLFileType.MostlyF16,
}[args.outtype]
print(f"params = {params}")
vocab: Vocab
@ -1042,9 +1056,12 @@ def main(args_in: Optional[List[str]] = None) -> None:
model = model_plus.model
model = convert_model_names(model, params)
output_type = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, output_type)
outfile = args.outfile or default_outfile(model_plus.paths, output_type)
ftype = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, ftype)
outfile = args.outfile or default_outfile(model_plus.paths, ftype)
params.ftype = ftype
print(f"Writing {outfile}, format {ftype}")
OutputFile.write_all(outfile, params, model, vocab)
print(f"Wrote {outfile}")

15
examples/embedding/embedding.cpp
View file

@ -72,12 +72,20 @@ int main(int argc, char ** argv) {
fprintf(stderr, "\n");
}
if (params.embedding){
if (embd_inp.size() > 0) {
if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads)) {
if (embd_inp.size() > (size_t)params.n_ctx) {
fprintf(stderr, "%s: error: prompt is longer than the context window (%zu tokens, n_ctx = %d)\n",
__func__, embd_inp.size(), params.n_ctx);
return 1;
}
while (!embd_inp.empty()) {
int n_tokens = std::min(params.n_batch, (int) embd_inp.size());
if (llama_eval(ctx, embd_inp.data(), n_tokens, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
n_past += n_tokens;
embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_tokens);
}
const int n_embd = llama_n_embd(ctx);
@ -87,7 +95,6 @@ int main(int argc, char ** argv) {
printf("%f ", embeddings[i]);
}
printf("\n");
}
llama_print_timings(ctx);
llama_free(ctx);

10
examples/llama-bench/llama-bench.cpp
View file

@ -148,7 +148,7 @@ struct cmd_params {
};
static const cmd_params cmd_params_defaults = {
/* model */ {"models/7B/ggml-model-q4_0.bin"},
/* model */ {"models/7B/ggml-model-q4_0.gguf"},
/* n_prompt */ {512},
/* n_gen */ {128},
/* n_batch */ {512},
@ -179,12 +179,12 @@ static void print_usage(int /* argc */, char ** argv) {
fprintf(stdout, " -mg i, --main-gpu <n> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
fprintf(stdout, " -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str());
fprintf(stdout, " -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
fprintf(stdout, " -ts, --tensor_split <ts> \n");
fprintf(stdout, " -ts, --tensor_split <ts0/ts1/..> \n");
fprintf(stdout, " -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
fprintf(stdout, " -o, --output <csv|json|md|sql> (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : "md");
fprintf(stdout, " -o, --output <csv|json|md|sql> (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql");
fprintf(stdout, " -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
fprintf(stdout, "\n");
fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by repeating the parameter.\n");
fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
}
@ -728,7 +728,7 @@ struct markdown_printer : public printer {
if (!is_cpu_backend) {
fields.push_back("n_gpu_layers");
}
if (params.n_batch.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
fields.push_back("n_threads");
}
if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {

13
examples/server/server.cpp
View file

@ -672,12 +672,11 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
fprintf(stdout, " number of layers to store in VRAM\n");
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
fprintf(stdout, " -nommq, --no-mul-mat-q\n");
fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
#endif
fprintf(stdout, " -m FNAME, --model FNAME\n");
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
@ -868,12 +867,12 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--mul-mat-q" || arg == "-mmq")
else if (arg == "--no-mul-mat-q" || arg == "-nommq")
{
#ifdef GGML_USE_CUBLAS
params.mul_mat_q = true;
params.mul_mat_q = false;
#else
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n", {});
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--main-gpu" || arg == "-mg")

4
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -1868,10 +1868,10 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1)); assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head);
t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd)); assert_shape_2d(t11->grad, N*n_batch, n_embd);
t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3)); assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch);
t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode, n_ctx)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode, n_ctx, 10000.0f, 1.0f, 0.0f, false)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch)); assert_shape_2d(t08->grad, n_embd, N*n_batch);
t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3)); assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch);
t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode, n_ctx)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode, n_ctx, 10000.0f, 1.0f, 0.0f, false)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch)); assert_shape_2d(t05->grad, n_embd, N*n_batch);
t04->grad = expand(gb, ggml_add_inplace(ctx0,
ggml_add_inplace(ctx0,

4
ggml-alloc.c
View file

@ -76,7 +76,7 @@ struct ggml_allocr {
};
#ifdef GGML_ALLOCATOR_DEBUG
static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i] == NULL) {
alloc->allocated_tensors[i] = tensor;
@ -85,7 +85,7 @@ static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tens
}
GGML_ASSERT(!"out of allocated_tensors");
}
static void remove_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i] == tensor ||
(alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {

187
ggml-cuda.cu
View file

@ -259,6 +259,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
#define CUDA_CPY_BLOCK_SIZE 32
#define CUDA_SCALE_BLOCK_SIZE 256
#define CUDA_ROPE_BLOCK_SIZE 256
#define CUDA_ALIBI_BLOCK_SIZE 32
#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
#define CUDA_QUANTIZE_BLOCK_SIZE 256
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
@ -286,7 +287,7 @@ static int g_device_count = -1;
static int g_main_device = 0;
static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
static bool g_mul_mat_q = false;
static bool g_mul_mat_q = true;
static void * g_scratch_buffer = nullptr;
static size_t g_scratch_size = 1024*1024*1024; // 1 GB by default
@ -3886,13 +3887,13 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
// rope == RoPE == rotary positional embedding
static __global__ void rope_f32(const float * x, float * dst, const int ncols, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale) {
const int col = 2*(blockDim.x*blockIdx.x + threadIdx.x);
const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
if (col >= ncols) {
return;
}
const int row = blockDim.y*blockIdx.y + threadIdx.y;
const int row = blockDim.x*blockIdx.x + threadIdx.x;
const int i = row*ncols + col;
const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
@ -3940,9 +3941,32 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
}
static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
const int n_heads_log2_floor, const float m0, const float m1) {
const int col = blockDim.x*blockIdx.x + threadIdx.x;
if (col >= ncols) {
return;
}
const int row = blockDim.y*blockIdx.y + threadIdx.y;
const int i = row*ncols + col;
const int k = row/k_rows;
float m_k;
if (k < n_heads_log2_floor) {
m_k = powf(m0, k + 1);
} else {
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
}
dst[i] = col * m_k + x[i];
}
static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
const int col = blockDim.y*blockIdx.y + threadIdx.y;
const int row = blockDim.x*blockIdx.x + threadIdx.x;
if (col >= ncols) {
return;
@ -3955,24 +3979,29 @@ static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int
// the CUDA soft max implementation differs from the CPU implementation
// instead of doubles floats are used
// values are also not normalized to the maximum value by subtracting it in the exponential function
// theoretically these changes could cause problems with rounding error and arithmetic overflow but for LLaMa it seems to be fine
static __global__ void soft_max_f32(const float * x, float * dst, const int ncols) {
const int row = blockDim.y*blockIdx.y + threadIdx.y;
const int block_size = blockDim.x;
const int tid = threadIdx.x;
const int row = blockDim.x*blockIdx.x + threadIdx.x;
const int block_size = blockDim.y;
const int tid = threadIdx.y;
float tmp = 0.0;
float max_val = -INFINITY;
for (int block_start = 0; block_start < ncols; block_start += block_size) {
const int col = block_start + tid;
if (col >= ncols) {
break;
for (int col = tid; col < ncols; col += block_size) {
const int i = row*ncols + col;
max_val = max(max_val, x[i]);
}
// find the max value in the block
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
max_val = max(max_val, __shfl_xor_sync(0xffffffff, max_val, mask, 32));
}
float tmp = 0.f;
for (int col = tid; col < ncols; col += block_size) {
const int i = row*ncols + col;
const float val = expf(x[i]);
const float val = expf(x[i] - max_val);
tmp += val;
dst[i] = val;
}
@ -3983,15 +4012,11 @@ static __global__ void soft_max_f32(const float * x, float * dst, const int ncol
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
for (int block_start = 0; block_start < ncols; block_start += block_size) {
const int col = block_start + tid;
if (col >= ncols) {
break;
}
const float inv_tmp = 1.f / tmp;
for (int col = tid; col < ncols; col += block_size) {
const int i = row*ncols + col;
dst[i] /= tmp;
dst[i] *= inv_tmp;
}
}
@ -4752,9 +4777,9 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
GGML_ASSERT(nrows % 2 == 0);
const dim3 block_dims(2*CUDA_ROPE_BLOCK_SIZE, 1, 1);
const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(num_blocks_x, nrows, 1);
const dim3 block_nums(nrows, num_blocks_x, 1);
rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
}
@ -4766,16 +4791,25 @@ static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, con
rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p, block_p, theta_scale);
}
static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
const int k_rows, const int n_heads_log2_floor, const float m0,
const float m1, cudaStream_t stream) {
const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1);
const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE);
const dim3 block_nums(num_blocks_x, nrows, 1);
alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1);
}
static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) {
const dim3 block_dims(CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1, 1);
const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1);
const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE;
const dim3 block_nums(block_num_x, nrows_x, 1);
const dim3 block_nums(nrows_x, block_num_x, 1);
diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
}
static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, cudaStream_t stream) {
const dim3 block_dims(WARP_SIZE, 1, 1);
const dim3 block_nums(1, nrows_x, 1);
const dim3 block_dims(1, WARP_SIZE, 1);
const dim3 block_nums(nrows_x, 1, 1);
soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x);
}
@ -5501,6 +5535,41 @@ inline void ggml_cuda_op_rope(
(void) i1;
}
inline void ggml_cuda_op_alibi(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
cudaStream_t & cudaStream_main){
GGML_ASSERT(src0_ddf_i != nullptr);
GGML_ASSERT(dst_ddf_i != nullptr);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t i01_diff = i01_high - i01_low;
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
GGML_ASSERT(ne01 + n_past == ne00);
GGML_ASSERT(n_head == ne02);
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
// compute
alibi_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_heads_log2_floor, m0, m1, cudaStream_main);
(void) src1;
(void) src0_ddq_i;
(void) src1_ddf_i;
(void) i1;
}
inline void ggml_cuda_op_diag_mask_inf(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
@ -6121,6 +6190,11 @@ void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_ten
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm
}
void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_alibi, true, true);
}
void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
(void) src0;
(void) src1;
@ -6240,7 +6314,7 @@ static struct ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
return extra;
}
void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace) {
void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) {
if (scratch && g_scratch_size == 0) {
return;
}
@ -6249,14 +6323,19 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
const ggml_op src0_op = tensor->src[0]->op;
if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace);
ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
}
}
if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace);
ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
}
tensor->backend = GGML_BACKEND_GPU;
if (scratch && no_alloc) {
return;
}
struct ggml_tensor_extra_gpu * extra;
const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
@ -6308,16 +6387,48 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo
tensor->extra = extra;
}
void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset) {
if (g_scratch_size == 0) {
return;
}
if (g_scratch_buffer == nullptr) {
CUDA_CHECK(cudaMalloc(&g_scratch_buffer, g_scratch_size));
}
struct ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra();
const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
tensor->op == GGML_OP_VIEW;
if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
size_t view_offset = 0;
if (tensor->op == GGML_OP_VIEW) {
memcpy(&view_offset, tensor->op_params, sizeof(size_t));
}
extra->data_device[g_main_device] = src0_ddc + view_offset;
} else {
extra->data_device[g_main_device] = (char *) g_scratch_buffer + offset;
}
tensor->extra = extra;
}
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, true, false);
ggml_cuda_assign_buffers_impl(tensor, true, false, false);
}
void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, true, false, true);
}
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, false, false);
ggml_cuda_assign_buffers_impl(tensor, false, false, false);
}
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, false, true);
ggml_cuda_assign_buffers_impl(tensor, false, true, false);
}
void ggml_cuda_set_main_device(int main_device) {
@ -6456,6 +6567,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
}
func = ggml_cuda_rope;
break;
case GGML_OP_ALIBI:
if (!any_on_device) {
return false;
}
func = ggml_cuda_alibi;
break;
default:
return false;
}

5
ggml-cuda.h
View file

@ -16,9 +16,14 @@ GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const str
GGML_API void ggml_cuda_set_tensor_split(const float * tensor_split);
GGML_API void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
GGML_API void ggml_cuda_free_data(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor);
GGML_API void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
GGML_API void ggml_cuda_set_main_device(int main_device);
GGML_API void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
GGML_API void ggml_cuda_set_scratch_size(size_t scratch_size);

5
ggml-metal.metal
View file

@ -1850,6 +1850,7 @@ kernel void kernel_mul_mm(device const uchar * src0,
//load data and store to threadgroup memory
half4x4 temp_a;
dequantize_func(x, il, temp_a);
threadgroup_barrier(mem_flags::mem_threadgroup);
#pragma unroll(16)
for (int i = 0; i < 16; i++) {
*(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \
@ -1895,14 +1896,14 @@ kernel void kernel_mul_mm(device const uchar * src0,
}
} else {
// block is smaller than 64x32, we should avoid writing data outside of the matrix
threadgroup_barrier(mem_flags::mem_threadgroup);
threadgroup float *temp_str = ((threadgroup float *)shared_memory) \
+ 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M;
for (int i = 0; i < 8; i++) {
threadgroup_barrier(mem_flags::mem_device);
simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M);
}
threadgroup_barrier(mem_flags::mem_device);
threadgroup_barrier(mem_flags::mem_threadgroup);
device float *C = dst + BLOCK_SIZE_M * r0 + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0;
if (sgitg==0) {
for (int i = 0; i < n_rows; i++) {

927
ggml.c
View file

File diff suppressed because it is too large Load diff

120
ggml.h
View file

@ -211,6 +211,7 @@
#define GGML_MAX_OP_PARAMS 32
#define GGML_DEFAULT_N_THREADS 4
#define GGML_EXIT_SUCCESS 0
#define GGML_EXIT_ABORTED 1
@ -259,8 +260,9 @@
extern "C" {
#endif
#ifdef __ARM_NEON
// we use the built-in 16-bit float type
#if defined(__ARM_NEON) && defined(__CUDACC__)
typedef half ggml_fp16_t;
#elif defined(__ARM_NEON)
typedef __fp16 ggml_fp16_t;
#else
typedef uint16_t ggml_fp16_t;
@ -344,10 +346,12 @@ extern "C" {
GGML_OP_ARGMAX,
GGML_OP_REPEAT,
GGML_OP_REPEAT_BACK,
GGML_OP_CONCAT,
GGML_OP_SILU_BACK,
GGML_OP_NORM, // normalize
GGML_OP_RMS_NORM,
GGML_OP_RMS_NORM_BACK,
GGML_OP_GROUP_NORM,
GGML_OP_MUL_MAT,
GGML_OP_OUT_PROD,
@ -373,14 +377,19 @@ extern "C" {
GGML_OP_CLAMP,
GGML_OP_CONV_1D,
GGML_OP_CONV_2D,
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
GGML_OP_FLASH_ATTN_BACK,
GGML_OP_WIN_PART,
GGML_OP_WIN_UNPART,
GGML_OP_GET_REL_POS,
GGML_OP_ADD_REL_POS,
GGML_OP_UNARY,
@ -804,6 +813,13 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// concat a and b on dim 2
// used in stable-diffusion
GGML_API struct ggml_tensor * ggml_concat(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_abs(
struct ggml_context * ctx,
struct ggml_tensor * a);
@ -912,6 +928,19 @@ extern "C" {
struct ggml_tensor * a,
float eps);
// group normalize along ne0*ne1*n_groups
// used in stable-diffusion
// TODO: eps is hardcoded to 1e-6 for now
GGML_API struct ggml_tensor * ggml_group_norm(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_groups);
GGML_API struct ggml_tensor * ggml_group_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_groups);
// a - x
// b - dy
// TODO: update with configurable eps
@ -1212,6 +1241,15 @@ extern "C" {
float freq_base,
float freq_scale);
// xPos RoPE, in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
float base,
bool down);
// rotary position embedding backward, i.e compute dx from dy
// a - dy
GGML_API struct ggml_tensor * ggml_rope_back(
@ -1220,7 +1258,11 @@ extern "C" {
int n_past,
int n_dims,
int mode,
int n_ctx);
int n_ctx,
float freq_base,
float freq_scale,
float xpos_base,
bool xpos_down);
// alibi position embedding
// in-place, returns view(a)
@ -1247,6 +1289,15 @@ extern "C" {
int p0, // padding
int d0); // dilation
// conv_1d with padding = half
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
GGML_API struct ggml_tensor* ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s,
int d);
GGML_API struct ggml_tensor * ggml_conv_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1258,14 +1309,38 @@ extern "C" {
int d0,
int d1);
// conv_1d with padding = half
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
GGML_API struct ggml_tensor * ggml_conv_1d_ph(
// kernel size is a->ne[0] x a->ne[1]
// stride is equal to kernel size
// padding is zero
// example:
// a: 16 16 3 768
// b: 1024 1024 3 1
// res: 64 64 768 1
// used in sam
GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// kernel size is a->ne[0] x a->ne[1]
// stride is 1
// padding is half
// example:
// a: 3 3 256 256
// b: 64 64 256 1
// res: 64 64 256 1
// used in sam
GGML_API struct ggml_tensor * ggml_conv_2d_s1_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s,
int d);
int stride);
enum ggml_op_pool {
GGML_OP_POOL_MAX,
@ -1292,6 +1367,13 @@ extern "C" {
int p0,
int p1);
// nearest interpolate
// used in stable-diffusion
GGML_API struct ggml_tensor * ggml_upscale(
struct ggml_context * ctx,
struct ggml_tensor * a,
int scale_factor);
GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,
struct ggml_tensor * q,
@ -1345,6 +1427,27 @@ extern "C" {
struct ggml_tensor * a,
enum ggml_unary_op op);
// used in sam
GGML_API struct ggml_tensor * ggml_get_rel_pos(
struct ggml_context * ctx,
struct ggml_tensor * a,
int qh,
int kh);
// used in sam
GGML_API struct ggml_tensor * ggml_add_rel_pos(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * pw,
struct ggml_tensor * ph);
GGML_API struct ggml_tensor * ggml_add_rel_pos_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * pw,
struct ggml_tensor * ph);
// custom operators
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
@ -1499,6 +1602,7 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * tensor);
GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);

4
gguf.py
View file

@ -26,6 +26,7 @@ KEY_GENERAL_DESCRIPTION = "general.description"
KEY_GENERAL_LICENSE = "general.license"
KEY_GENERAL_SOURCE_URL = "general.source.url"
KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
KEY_GENERAL_FILE_TYPE = "general.file_type"
# LLM
KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
@ -595,6 +596,9 @@ class GGUFWriter:
def add_source_hf_repo(self, repo: str):
self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo)
def add_file_type(self, ftype: int):
self.add_uint32(KEY_GENERAL_FILE_TYPE, ftype)
def add_name(self, name: str):
self.add_string(KEY_GENERAL_NAME, name)

164
k_quants.c
View file

@ -77,6 +77,11 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t *
}
return 1/iscale;
}
bool return_early = false;
if (rmse_type < 0) {
rmse_type = -rmse_type;
return_early = true;
}
int weight_type = rmse_type%2;
float sumlx = 0;
float suml2 = 0;
@ -89,56 +94,9 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t *
suml2 += w*l*l;
}
float scale = sumlx/suml2;
if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale;
float best = scale * sumlx;
for (int itry = 0; itry < 3; ++itry) {
iscale = 1/scale;
float slx = 0;
float sl2 = 0;
bool changed = false;
for (int i = 0; i < n; ++i) {
int l = nearest_int(iscale * x[i]);
l = MAX(-nmax, MIN(nmax-1, l));
if (l + nmax != L[i]) { changed = true; }
float w = weight_type == 1 ? x[i] * x[i] : 1.f;
slx += w*x[i]*l;
sl2 += w*l*l;
}
if (!changed || sl2 == 0 || slx*slx <= best*sl2) { break; }
for (int i = 0; i < n; ++i) {
int l = nearest_int(iscale * x[i]);
L[i] = nmax + MAX(-nmax, MIN(nmax-1, l));
}
sumlx = slx; suml2 = sl2;
scale = sumlx/suml2;
best = scale * sumlx;
}
for (int itry = 0; itry < 5; ++itry) {
int n_changed = 0;
for (int i = 0; i < n; ++i) {
float w = weight_type == 1 ? x[i]*x[i] : 1;
int l = L[i] - nmax;
float slx = sumlx - w*x[i]*l;
if (slx > 0) {
float sl2 = suml2 - w*l*l;
int new_l = nearest_int(x[i] * sl2 / slx);
new_l = MAX(-nmax, MIN(nmax-1, new_l));
if (new_l != l) {
slx += w*x[i]*new_l;
sl2 += w*new_l*new_l;
if (sl2 > 0 && slx*slx*suml2 > sumlx*sumlx*sl2) {
L[i] = nmax + new_l; sumlx = slx; suml2 = sl2;
scale = sumlx / suml2; best = scale * sumlx;
++n_changed;
}
}
}
}
if (!n_changed) { break; }
}
if (rmse_type < 3) {
return scale;
}
for (int is = -4; is <= 4; ++is) {
for (int is = -9; is <= 9; ++is) {
if (is == 0) {
continue;
}
@ -221,12 +179,17 @@ static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t *
return 1/iscale;
}
static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min, int ntry) {
static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min,
int ntry, float alpha) {
float min = x[0];
float max = x[0];
float sum_x = 0;
float sum_x2 = 0;
for (int i = 1; i < n; ++i) {
if (x[i] < min) min = x[i];
if (x[i] > max) max = x[i];
sum_x += x[i];
sum_x2 += x[i]*x[i];
}
if (max == min) {
for (int i = 0; i < n; ++i) L[i] = 0;
@ -254,7 +217,7 @@ static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t
for (int i = 0; i < n; ++i) {
sum += x[i] - scale*L[i];
}
min = sum/n;
min = alpha*min + (1 - alpha)*sum/n;
if (min > 0) min = 0;
iscale = 1/scale;
if (!did_change) break;
@ -263,6 +226,82 @@ static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t
return scale;
}
static float make_qkx2_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux,
float rmin, float rdelta, int nstep, bool use_mad) {
float min = x[0];
float max = x[0];
float sum_w = weights[0];
float sum_x = sum_w * x[0];
for (int i = 1; i < n; ++i) {
if (x[i] < min) min = x[i];
if (x[i] > max) max = x[i];
float w = weights[i];
sum_w += w;
sum_x += w * x[i];
}
if (min > 0) min = 0;
if (max == min) {
for (int i = 0; i < n; ++i) L[i] = 0;
*the_min = -min;
return 0.f;
}
float iscale = nmax/(max - min);
float scale = 1/iscale;
float best_mad = 0;
for (int i = 0; i < n; ++i) {
int l = nearest_int(iscale*(x[i] - min));
L[i] = MAX(0, MIN(nmax, l));
float diff = scale * L[i] + min - x[i];
diff = use_mad ? fabsf(diff) : diff * diff;
float w = weights[i];
best_mad += w * diff;
}
if (nstep < 1) {
*the_min = -min;
return scale;
}
for (int is = 0; is <= nstep; ++is) {
iscale = (rmin + rdelta*is + nmax)/(max - min);
float sum_l = 0, sum_l2 = 0, sum_xl = 0;
for (int i = 0; i < n; ++i) {
int l = nearest_int(iscale*(x[i] - min));
l = MAX(0, MIN(nmax, l));
Laux[i] = l;
float w = weights[i];
sum_l += w*l;
sum_l2 += w*l*l;
sum_xl += w*l*x[i];
}
float D = sum_w * sum_l2 - sum_l * sum_l;
if (D > 0) {
float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D;
float this_min = (sum_l2 * sum_x - sum_l * sum_xl)/D;
if (this_min > 0) {
this_min = 0;
this_scale = sum_xl / sum_l2;
}
float mad = 0;
for (int i = 0; i < n; ++i) {
float diff = this_scale * Laux[i] + this_min - x[i];
diff = use_mad ? fabsf(diff) : diff * diff;
float w = weights[i];
mad += w * diff;
}
if (mad < best_mad) {
for (int i = 0; i < n; ++i) {
L[i] = Laux[i];
}
best_mad = mad;
scale = this_scale;
min = this_min;
}
}
}
*the_min = -min;
return scale;
}
#if QK_K == 256
static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) {
if (j < 4) {
@ -281,6 +320,8 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
const int nb = k / QK_K;
uint8_t L[QK_K];
uint8_t Laux[16];
float weights[16];
float mins[QK_K/16];
float scales[QK_K/16];
@ -291,7 +332,8 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/16; ++j) {
scales[j] = make_qkx1_quants(16, 3, x + 16*j, L + 16*j, &mins[j], 5);
for (int l = 0; l < 16; ++l) weights[l] = fabsf(x[16*j + l]);
scales[j] = make_qkx2_quants(16, 3, x + 16*j, weights, L + 16*j, &mins[j], Laux, -0.5f, 0.1f, 15, true);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;
@ -637,6 +679,8 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
const int nb = k / QK_K;
uint8_t L[QK_K];
uint8_t Laux[32];
float weights[32];
float mins[QK_K/32];
float scales[QK_K/32];
@ -645,7 +689,12 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/32; ++j) {
scales[j] = make_qkx1_quants(32, 15, x + 32*j, L + 32*j, &mins[j], 5);
//scales[j] = make_qkx1_quants(32, 15, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
float sum_x2 = 0;
for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
float av_x = sqrtf(sum_x2/32);
for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;
@ -798,6 +847,8 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
uint8_t L[QK_K];
float mins[QK_K/32];
float scales[QK_K/32];
float weights[32];
uint8_t Laux[32];
#else
int8_t L[QK_K];
float scales[QK_K/16];
@ -810,7 +861,12 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/32; ++j) {
scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 5);
//scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
float sum_x2 = 0;
for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
float av_x = sqrtf(sum_x2/32);
for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
scales[j] = make_qkx2_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.5f, 0.1f, 15, false);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;

291
llama.cpp
View file

@ -10,13 +10,7 @@
#include "ggml.h"
#if !defined(GGML_USE_CUBLAS)
#include "ggml-alloc.h"
# define LLAMA_USE_ALLOCATOR
#else
# define LLAMA_USE_SCRATCH
# define LLAMA_MAX_SCRATCH_BUFFERS 16
#endif
#ifdef GGML_USE_CUBLAS
# include "ggml-cuda.h"
@ -588,14 +582,6 @@ struct llama_state {
static llama_state g_state;
//
// memory sizes (calculated for n_batch == 512)
//
// computed for n_ctx == 2048
// TODO: dynamically determine these sizes
// needs modifications in ggml
// available llama models
enum e_model {
MODEL_UNKNOWN,
@ -610,76 +596,6 @@ enum e_model {
static const size_t kB = 1024;
static const size_t MB = 1024*1024;
static std::map<e_model, size_t> MEM_REQ_SCRATCH0(int n_ctx)
{
std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, ((size_t) n_ctx / 16ull + 92ull) * MB },
{ MODEL_7B, ((size_t) n_ctx / 16ull + 100ull) * MB },
{ MODEL_13B, ((size_t) n_ctx / 12ull + 120ull) * MB },
{ MODEL_30B, ((size_t) n_ctx / 9ull + 160ull) * MB },
{ MODEL_65B, ((size_t) n_ctx / 6ull + 256ull) * MB }, // guess
{ MODEL_70B, ((size_t) n_ctx / 7ull + 164ull) * MB },
};
return k_sizes;
}
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH1()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 128ull * MB },
{ MODEL_7B, 160ull * MB },
{ MODEL_13B, 192ull * MB },
{ MODEL_30B, 256ull * MB },
{ MODEL_65B, 384ull * MB }, // guess
{ MODEL_70B, 304ull * MB },
};
return k_sizes;
}
// used to store the compute graph tensors + non-scratch data
static const std::map<e_model, size_t> & MEM_REQ_EVAL()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 8ull * MB },
{ MODEL_7B, 10ull * MB },
{ MODEL_13B, 12ull * MB },
{ MODEL_30B, 16ull * MB },
{ MODEL_65B, 24ull * MB }, // guess
{ MODEL_70B, 24ull * MB },
};
return k_sizes;
}
// amount of VRAM needed per batch size to hold temporary results
// the values for 3b are not derived from testing but instead chosen conservatively
static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 512ull * kB },
{ MODEL_7B, 512ull * kB },
{ MODEL_13B, 640ull * kB },
{ MODEL_30B, 768ull * kB },
{ MODEL_65B, 1280ull * kB },
{ MODEL_70B, 1280ull * kB },
};
return k_sizes;
}
// amount of VRAM needed per batch size and context to hold temporary results
// the values for 3b are not derived from testing but instead chosen conservatively
static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_3B, 128ull },
{ MODEL_7B, 128ull },
{ MODEL_13B, 160ull },
{ MODEL_30B, 208ull },
{ MODEL_65B, 256ull },
{ MODEL_70B, 256ull },
};
return k_sizes;
}
// default hparams (LLaMA 7B)
struct llama_hparams {
uint32_t n_vocab = 32000;
@ -857,11 +773,9 @@ struct llama_context {
ggml_metal_free(ctx_metal);
}
#endif
#ifdef LLAMA_USE_ALLOCATOR
if (alloc) {
ggml_allocr_free(alloc);
}
#endif
}
std::mt19937 rng;
@ -901,17 +815,8 @@ struct llama_context {
// memory buffers used to evaluate the model
llama_buffer buf_compute;
#ifdef LLAMA_USE_ALLOCATOR
llama_buffer buf_alloc;
ggml_allocr * alloc = NULL;
#endif
#ifdef LLAMA_USE_SCRATCH
llama_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
int buf_last = 0;
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
#endif
#ifdef GGML_USE_METAL
ggml_metal_context * ctx_metal = NULL;
@ -920,37 +825,6 @@ struct llama_context {
#ifdef GGML_USE_MPI
ggml_mpi_context * ctx_mpi = NULL;
#endif
void use_buf(struct ggml_context * ctx, int i) { // NOLINT
#if defined(LLAMA_USE_SCRATCH)
size_t last_size = 0;
if (i == -1) {
last_size = ggml_set_scratch(ctx, { 0, 0, nullptr, });
} else {
auto & buf = buf_scratch[i];
last_size = ggml_set_scratch(ctx, { 0, buf.size, buf.data, });
}
if (buf_last >= 0) {
buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
}
buf_last = i;
#else
(void) i;
(void) ctx;
#endif
}
size_t get_buf_max_mem(int i) { // NOLINT
#if defined(LLAMA_USE_SCRATCH)
return buf_max_size[i];
#else
(void) i;
return 0;
#endif
}
};
//
@ -1121,6 +995,16 @@ struct llama_model_loader {
} break;
}
// this is a way to mark that we have "guessed" the file type
ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
{
const int kid = gguf_find_key(ctx_gguf, "general.file_type");
if (kid >= 0) {
ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
}
}
for (int i = 0; i < n_kv; i++) {
const char * name = gguf_get_key(ctx_gguf, i);
const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
@ -1323,7 +1207,11 @@ struct llama_model_loader {
// load LLaMA models
//
const char * llama_model_ftype_name(enum llama_ftype ftype) {
std::string llama_model_ftype_name(enum llama_ftype ftype) {
if (ftype & LLAMA_FTYPE_GUESSED) {
return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
}
switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
@ -1552,7 +1440,7 @@ static void llama_model_load_internal(
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype));
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str());
LLAMA_LOG_INFO("%s: model size = %.2f B\n", __func__, ml->n_elements*1e-9);
// general kv
@ -1620,7 +1508,6 @@ static void llama_model_load_internal(
// prepare memory for the weights
size_t vram_weights = 0;
size_t vram_scratch = 0;
{
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_embd_gqa = hparams.n_embd_gqa();
@ -1701,13 +1588,6 @@ static void llama_model_load_internal(
ctx_size +
mmapped_size - vram_weights; // weights in VRAM not in memory
#ifndef LLAMA_USE_ALLOCATOR
mem_required +=
MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL().at(model.type);
#endif
// this is the memory required by one llama_state
const size_t mem_required_state =
scale*hparams.kv_size();
@ -1715,24 +1595,7 @@ static void llama_model_load_internal(
LLAMA_LOG_INFO("%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
(void) vram_scratch;
(void) n_batch;
#ifdef GGML_USE_CUBLAS
if (low_vram) {
LLAMA_LOG_INFO("%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__);
ggml_cuda_set_scratch_size(0); // disable scratch
} else {
const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type);
const size_t vram_scratch_per_context = VRAM_REQ_SCRATCH_PER_CONTEXT().at(model.type);
vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context);
ggml_cuda_set_scratch_size(vram_scratch);
if (n_gpu_layers > 0) {
LLAMA_LOG_INFO("%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n",
__func__, vram_scratch_base / kB, vram_scratch_per_context,
(vram_scratch + MB - 1) / MB); // round up
}
}
#endif // GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
@ -1769,8 +1632,8 @@ static void llama_model_load_internal(
LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n",
__func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
LLAMA_LOG_INFO("%s: total VRAM used: %zu MB\n",
__func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up
LLAMA_LOG_INFO("%s: VRAM used: %zu MB\n",
__func__, (vram_weights + vram_kv_cache + MB - 1) / MB); // round up
#else
(void) n_gpu_layers;
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
@ -1875,9 +1738,7 @@ static struct ggml_cgraph * llama_build_graph(
/*.no_alloc =*/ false,
};
#ifdef LLAMA_USE_ALLOCATOR
params.no_alloc = true;
#endif
struct ggml_context * ctx0 = ggml_init(params);
@ -1889,14 +1750,10 @@ static struct ggml_cgraph * llama_build_graph(
if (tokens) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
}
#else
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
#endif
ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
@ -1907,14 +1764,10 @@ static struct ggml_cgraph * llama_build_graph(
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_alloc(lctx.alloc, inpL);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
}
#else
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
#endif
}
const int i_gpu_start = n_layer - n_gpu_layers;
@ -1931,25 +1784,21 @@ static struct ggml_cgraph * llama_build_graph(
#ifdef GGML_USE_CUBLAS
if (n_gpu_layers > n_layer) {
offload_func_nr = ggml_cuda_assign_buffers;
offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
}
if (n_gpu_layers > n_layer + 1) {
offload_func_v = ggml_cuda_assign_buffers;
offload_func_v = ggml_cuda_assign_buffers_no_alloc;
}
if (n_gpu_layers > n_layer + 2) {
offload_func_kq = ggml_cuda_assign_buffers;
offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
}
#endif // GGML_USE_CUBLAS
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
}
#else
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
#endif
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
for (int il = 0; il < n_layer; ++il) {
@ -1959,14 +1808,12 @@ static struct ggml_cgraph * llama_build_graph(
#ifdef GGML_USE_CUBLAS
if (il >= i_gpu_start) {
offload_func = ggml_cuda_assign_buffers;
offload_func = ggml_cuda_assign_buffers_no_alloc;
}
#endif // GGML_USE_CUBLAS
struct ggml_tensor * inpSA = inpL;
lctx.use_buf(ctx0, 0);
// norm
{
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
@ -2104,8 +1951,6 @@ static struct ggml_cgraph * llama_build_graph(
ggml_set_name(cur, "result_wo");
}
lctx.use_buf(ctx0, 1);
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
offload_func(inpFF);
ggml_set_name(inpFF, "inpFF");
@ -2160,8 +2005,6 @@ static struct ggml_cgraph * llama_build_graph(
inpL = cur;
}
lctx.use_buf(ctx0, 0);
// norm
{
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
@ -2178,8 +2021,6 @@ static struct ggml_cgraph * llama_build_graph(
cur = ggml_mul_mat(ctx0, model.output, cur);
ggml_set_name(cur, "result_output");
lctx.use_buf(ctx0, -1);
// logits -> probs
//cur = ggml_soft_max_inplace(ctx0, cur);
@ -2189,15 +2030,6 @@ static struct ggml_cgraph * llama_build_graph(
mem_per_token = ggml_used_mem(ctx0)/N;
}
#if 0
LLAMA_LOG_INFO("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
ggml_used_mem(ctx0)/1024.0/1024.0,
lctx.get_buf_max_mem(0)/1024.0/1024.0,
lctx.get_buf_max_mem(1)/1024.0/1024.0,
lctx.work_buffer.size()/1024.0/1024.0,
n_past, N);
#endif
ggml_free(ctx0);
return gf;
@ -2248,14 +2080,26 @@ static bool llama_eval_internal(
const int64_t n_embd = hparams.n_embd;
const int64_t n_vocab = hparams.n_vocab;
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_reset(lctx.alloc);
#endif
ggml_cgraph * gf = llama_build_graph(lctx, tokens, embd, n_tokens, n_past);
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_alloc_graph(lctx.alloc, gf);
#ifdef GGML_USE_CUBLAS
for (int i = 0; i < gf->n_leafs; i++) {
ggml_tensor * node = gf->leafs[i];
if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data);
}
}
for (int i = 0; i < gf->n_nodes; i++) {
ggml_tensor * node = gf->nodes[i];
if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data);
}
}
#endif
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
@ -2409,18 +2253,11 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
}
static std::string llama_escape_whitespace(const std::string& text) {
std::string result;
bool escaping = false;
result += "\xe2\x96\x81";
std::string result = "\xe2\x96\x81";
for (size_t offs = 0; offs < text.length(); ++offs) {
if (text[offs] == ' ') {
if (!escaping) {
result += "\xe2\x96\x81";
escaping = true;
}
}
else {
escaping = false;
} else {
result += text[offs];
}
}
@ -3620,6 +3457,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// copy the KV pairs from the input file
gguf_set_kv (ctx_out, model_loader->ctx_gguf);
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
gguf_set_val_u32(ctx_out, "general.file_type", ftype);
#ifdef GGML_USE_K_QUANTS
int n_attention_wv = 0;
@ -3717,24 +3555,40 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type = GGML_TYPE_Q6_K;
}
} else if (name.find("attn_v.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
use_more_bits(i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
++i_attention_wv;
} else if (name.find("ffn_down.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
//else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < n_feed_forward_w2/8) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < 4) new_type = GGML_TYPE_Q5_K;
++i_feed_forward_w2;
} else if (name.find("attn_output.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
}
else if (name.find("ffn_gate.weight") != std::string::npos || name.find("ffn_up.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
}
// This can be used to reduce the size of the Q5_K_S model.
// The associated PPL increase is fully in line with the size reduction
//else {
// if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
//}
bool convert_incompatible_tensor = false;
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
@ -4319,7 +4173,6 @@ struct llama_context * llama_new_context_with_model(
ctx->embedding.resize(hparams.n_embd);
}
#ifdef LLAMA_USE_ALLOCATOR
{
static const size_t tensor_alignment = 32;
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
@ -4350,13 +4203,6 @@ struct llama_context * llama_new_context_with_model(
LLAMA_LOG_INFO("%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
// debug - for comparison with scratch buffer
//size_t prev_req =
// MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) +
// MEM_REQ_SCRATCH1().at(ctx->model.type) +
// MEM_REQ_EVAL().at(ctx->model.type);
//LLAMA_LOG_INFO("%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
// recreate allocator with exact memory requirements
ggml_allocr_free(ctx->alloc);
@ -4367,16 +4213,17 @@ struct llama_context * llama_new_context_with_model(
ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
}
#endif
#ifdef GGML_USE_CUBLAS
if (params.low_vram) {
LLAMA_LOG_INFO("%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__);
ggml_cuda_set_scratch_size(0); // disable scratch
} else {
ggml_cuda_set_scratch_size(alloc_size);
LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MB\n", __func__, alloc_size / 1024.0 / 1024.0);
}
#else
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type) + ggml_graph_overhead());
#endif
#ifdef LLAMA_USE_SCRATCH
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type));
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
#endif
}
}
#ifdef GGML_USE_METAL
if (params.n_gpu_layers > 0) {
@ -4471,7 +4318,7 @@ int llama_model_n_embd(const struct llama_model * model) {
}
int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size) {
return snprintf(buf, buf_size, "LLaMA %s %s", llama_model_type_name(model->type), llama_model_ftype_name(model->ftype));
return snprintf(buf, buf_size, "LLaMA %s %s", llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str());
}
int llama_model_quantize(

2
llama.h
View file

@ -103,6 +103,8 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
typedef struct llama_token_data {

2
scripts/sync-ggml.sh
View file

@ -1,6 +1,7 @@
#!/bin/bash
cp -rpv ../ggml/src/ggml.c ./ggml.c
cp -rpv ../ggml/src/ggml-alloc.c ./ggml-alloc.c
cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h
cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h
@ -9,6 +10,7 @@ cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h
cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m
cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h
cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp
cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp

11
tests/test-tokenizer-0.cpp
View file

@ -17,6 +17,8 @@ static std::string unescape_whitespace(llama_context* ctx, const std::vector<lla
static const std::map<std::string, std::vector<llama_token>> & k_tests() {
static std::map<std::string, std::vector<llama_token>> _k_tests = {
{ " ", {1, 259, }, },
{ " ", { 1, 1678, }, },
{ " ", { 1, 268, }, },
{ "\t", { 1, 29871, 12, }, },
{ "\n", { 1, 29871, 13, }, },
{ "\t\n", { 1, 29871, 12, 13, }, },
@ -38,6 +40,12 @@ static const std::map<std::string, std::vector<llama_token>> & k_tests() {
243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598,
313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681,
313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, },
{ "Hello", { 1, 15043 }, },
{ " Hello", { 1, 29871, 15043 }, },
{ " Hello", { 1, 259, 15043 }, },
{ " Hello", { 1, 1678, 15043 }, },
{ " Hello", { 1, 268, 15043 }, },
{ " Hello\n Hello", { 1, 268, 15043, 13, 1678, 15043 }, },
};
return _k_tests;
@ -106,7 +114,8 @@ int main(int argc, char **argv) {
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s'\n", __func__, unescape_whitespace(ctx, test_kv.second).c_str());
fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
unescape_whitespace(ctx, res).c_str(), unescape_whitespace(ctx, test_kv.second).c_str());
fprintf(stderr, "%s : expected tokens: ", __func__);
for (const auto & t : test_kv.second) {
fprintf(stderr, "%6d, ", t);

11
tests/test-tokenizer-1.cpp
View file

@ -11,18 +11,11 @@
#include <locale>
static std::string escape_whitespace(const std::string& text) {
std::string result;
bool escaping = false;
result += "\xe2\x96\x81";
std::string result = "\xe2\x96\x81";
for (size_t offs = 0; offs < text.length(); ++offs) {
if (text[offs] == ' ') {
if (!escaping) {
result += "\xe2\x96\x81";
escaping = true;
}
}
else {
escaping = false;
} else {
result += text[offs];
}
}