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Merge branch 'cuda-cublas-opts' into gg/phi-2

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Georgi Gerganov 2023-12-17 08:41:46 +02:00
commit d2f1e0dacc
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8 changed files with 425 additions and 196 deletions
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86
convert-lora-to-ggml.py
View File

@ -3,7 +3,6 @@ from __future__ import annotations
import json
import os
import re
import struct
import sys
from typing import Any, BinaryIO, Sequence
@ -11,43 +10,15 @@ from typing import Any, BinaryIO, Sequence
import numpy as np
import torch
from pathlib import Path
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
import gguf
NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
HF_SUBLAYER_TO_GGML = {
"self_attn.q_proj": "attn_q",
"self_attn.k_proj": "attn_k",
"self_attn.v_proj": "attn_v",
"self_attn.o_proj": "attn_output",
"mlp.gate_proj": "ffn_gate",
"mlp.down_proj": "ffn_down",
"mlp.up_proj": "ffn_up",
"input_layernorm": "attn_norm",
"post_attention_layernorm": "ffn_norm",
}
def translate_tensor_name(t: str) -> str:
match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
if match:
nn = match.group(1)
sub_layer = match.group(2)
lora_type = match.group(3)
sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
if sub_layer_renamed is None:
print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
sys.exit(1)
output_string = (
f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
)
return output_string
else:
print(f"Error: unrecognized tensor {t}")
sys.exit(1)
def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
fout.write(b"ggla"[::-1]) # magic (ggml lora)
fout.write(struct.pack("i", 1)) # file version
@ -61,9 +32,7 @@ def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
fout.write(struct.pack("i", int(params["lora_alpha"])))
def write_tensor_header(
self, name: str, shape: Sequence[int], data_type: np.dtype[Any]
) -> None:
def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
sname = name.encode("utf-8")
fout.write(
struct.pack(
@ -78,11 +47,12 @@ def write_tensor_header(
fout.seek((fout.tell() + 31) & -32)
if len(sys.argv) != 2:
print(f"Usage: python {sys.argv[0]} <path>")
if len(sys.argv) < 2:
print(f"Usage: python {sys.argv[0]} <path> [arch]")
print(
"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
)
print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
sys.exit(1)
input_json = os.path.join(sys.argv[1], "adapter_config.json")
@ -90,6 +60,14 @@ input_model = os.path.join(sys.argv[1], "adapter_model.bin")
output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
model = torch.load(input_model, map_location="cpu")
arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
if arch_name not in gguf.MODEL_ARCH_NAMES.values():
print(f"Error: unsupported architecture {arch_name}")
sys.exit(1)
arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
with open(input_json, "r") as f:
params = json.load(f)
@ -117,6 +95,7 @@ with open(output_path, "wb") as fout:
write_file_header(fout, params)
for k, v in model.items():
orig_k = k
if k.endswith(".default.weight"):
k = k.replace(".default.weight", ".weight")
if k in ["llama_proj.weight", "llama_proj.bias"]:
@ -129,7 +108,32 @@ with open(output_path, "wb") as fout:
v = v.float()
t = v.detach().numpy()
tname = translate_tensor_name(k)
prefix = "base_model.model."
if k.startswith(prefix):
k = k[len(prefix) :]
lora_suffixes = (".lora_A.weight", ".lora_B.weight")
if k.endswith(lora_suffixes):
suffix = k[-len(lora_suffixes[0]):]
k = k[: -len(lora_suffixes[0])]
else:
print(f"Error: unrecognized tensor name {orig_k}")
sys.exit(1)
tname = name_map.get_name(k)
if tname is None:
print(f"Error: could not map tensor name {orig_k}")
print(" Note: the arch parameter must be specified if the model is not llama")
sys.exit(1)
if suffix == ".lora_A.weight":
tname += ".weight.loraA"
elif suffix == ".lora_B.weight":
tname += ".weight.loraB"
else:
assert False
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
write_tensor_header(fout, tname, t.shape, t.dtype)
t.tofile(fout)

3
examples/server/public/completion.js
View File

@ -34,7 +34,8 @@ export async function* llama(prompt, params = {}, config = {}) {
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream'
'Accept': 'text/event-stream',
...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
},
signal: controller.signal,
});

7
examples/server/public/index.html
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@ -235,7 +235,8 @@
grammar: '',
n_probs: 0, // no completion_probabilities,
image_data: [],
cache_prompt: true
cache_prompt: true,
api_key: ''
})
/* START: Support for storing prompt templates and parameters in browsers LocalStorage */
@ -790,6 +791,10 @@
<fieldset>
${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
</fieldset>
<fieldset>
<label for="api_key">API Key</label>
<input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
</fieldset>
</details>
</form>
`

70
examples/server/server.cpp
View File

@ -36,6 +36,7 @@ using json = nlohmann::json;
struct server_params
{
std::string hostname = "127.0.0.1";
std::string api_key;
std::string public_path = "examples/server/public";
int32_t port = 8080;
int32_t read_timeout = 600;
@ -1953,6 +1954,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
printf(" --port PORT port to listen (default (default: %d)\n", sparams.port);
printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n");
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
@ -2002,6 +2004,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
sparams.public_path = argv[i];
}
else if (arg == "--api-key")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.api_key = argv[i];
}
else if (arg == "--timeout" || arg == "-to")
{
if (++i >= argc)
@ -2669,6 +2680,32 @@ int main(int argc, char **argv)
httplib::Server svr;
// Middleware for API key validation
auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
// If API key is not set, skip validation
if (sparams.api_key.empty()) {
return true;
}
// Check for API key in the header
auto auth_header = req.get_header_value("Authorization");
std::string prefix = "Bearer ";
if (auth_header.substr(0, prefix.size()) == prefix) {
std::string received_api_key = auth_header.substr(prefix.size());
if (received_api_key == sparams.api_key) {
return true; // API key is valid
}
}
// API key is invalid or not provided
res.set_content("Unauthorized: Invalid API Key", "text/plain");
res.status = 401; // Unauthorized
LOG_WARNING("Unauthorized: Invalid API Key", {});
return false;
};
svr.set_default_headers({{"Server", "llama.cpp"},
{"Access-Control-Allow-Origin", "*"},
{"Access-Control-Allow-Headers", "content-type"}});
@ -2711,8 +2748,11 @@ int main(int argc, char **argv)
res.set_content(data.dump(), "application/json");
});
svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = json::parse(req.body);
const int task_id = llama.request_completion(data, false, false, -1);
if (!json_value(data, "stream", false)) {
@ -2799,8 +2839,11 @@ int main(int argc, char **argv)
});
// TODO: add mount point without "/v1" prefix -- how?
svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = oaicompat_completion_params_parse(json::parse(req.body));
const int task_id = llama.request_completion(data, false, false, -1);
@ -2869,8 +2912,11 @@ int main(int argc, char **argv)
}
});
svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = json::parse(req.body);
const int task_id = llama.request_completion(data, true, false, -1);
if (!json_value(data, "stream", false)) {
@ -3005,11 +3051,15 @@ int main(int argc, char **argv)
svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
{
if (res.status == 401)
{
res.set_content("Unauthorized", "text/plain");
}
if (res.status == 400)
{
res.set_content("Invalid request", "text/plain");
}
else if (res.status != 500)
else if (res.status == 404)
{
res.set_content("File Not Found", "text/plain");
res.status = 404;
@ -3032,11 +3082,15 @@ int main(int argc, char **argv)
// to make it ctrl+clickable:
LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
LOG_INFO("HTTP server listening", {
{"hostname", sparams.hostname},
{"port", sparams.port},
});
std::unordered_map<std::string, std::string> log_data;
log_data["hostname"] = sparams.hostname;
log_data["port"] = std::to_string(sparams.port);
if (!sparams.api_key.empty()) {
log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4);
}
LOG_INFO("HTTP server listening", log_data);
// run the HTTP server in a thread - see comment below
std::thread t([&]()
{

62
ggml-cuda.cu
View File

@ -7406,27 +7406,20 @@ inline void ggml_cuda_op_mul_mat_cublas(
to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
}
const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16;
size_t dst_as = 0;
half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
const half alpha_f16 = 1.0f;
const half beta_f16 = 0.0f;
const float alpha = 1.0f;
const float beta = 0.0f;
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
CUBLAS_CHECK(
cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
row_diff, src1_ncols, ne10,
&alpha_f16, src0_ptr, CUDA_R_16F, ne00,
src1_ptr, CUDA_R_16F, ne10,
&beta_f16, dst_f16, CUDA_R_16F, ldc,
CUBLAS_COMPUTE_16F,
&alpha, src0_ptr, CUDA_R_16F, ne00,
src1_ptr, CUDA_R_16F, ne10,
&beta, dst_dd_i, CUDA_R_32F, ldc,
CUBLAS_COMPUTE_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream);
ggml_cuda_pool_free(dst_f16, dst_as);
if (src0_as != 0) {
ggml_cuda_pool_free(src0_as_f16, src0_as);
}
@ -8306,8 +8299,8 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
}
static __global__ void k_compute_batched_ptrs(
const half * src0_as_f16, const half * src1_as_f16, half * dst_f16,
__global__ static void k_compute_batched_ptrs(
const half * src0_as_f16, const half * src1_as_f16, float * dst_f32,
const void ** ptrs_src, void ** ptrs_dst,
int ne12, int ne13,
int ne23,
@ -8327,7 +8320,7 @@ static __global__ void k_compute_batched_ptrs(
ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2;
ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f32 + i12* nb2 + i13* nb3 ;
}
static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@ -8382,9 +8375,6 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
size_t dst_as = 0;
half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);
@ -8392,8 +8382,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
const half alpha_f16 = 1.0f;
const half beta_f16 = 0.0f;
const float alpha = 1.0f;
const float beta = 0.0f;
#if 0
// use cublasGemmEx
@ -8406,10 +8396,10 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
CUBLAS_CHECK(
cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha_f16, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half),
(const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float),
&beta_f16, ( char *) dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2, CUDA_R_16F, ne01,
CUBLAS_COMPUTE_16F,
&alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half),
(const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float),
&beta, ( char *) dst_ddf + i12* dst->nb[2] + i13* dst->nb[3] , CUDA_R_32F, ne01,
CUBLAS_COMPUTE_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
}
}
@ -8421,11 +8411,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
CUBLAS_CHECK(
cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha_f16, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA
(const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
&beta_f16, ( char *) dst_f16, CUDA_R_16F, ne01, dst->nb[2]/sizeof(float), // strideC
&alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA
(const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
&beta, ( char *) dst_ddf, CUDA_R_32F, ne01, dst->nb[2]/sizeof(float), // strideC
ne12*ne13,
CUBLAS_COMPUTE_16F,
CUBLAS_COMPUTE_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
} else {
// use cublasGemmBatchedEx
@ -8442,7 +8432,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
dim3 block_dims(ne13, ne12);
k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
src0_as_f16, src1_as_f16, dst_f16,
src0_as_f16, src1_as_f16, dst_ddf,
ptrs_src, ptrs_dst,
ne12, ne13,
ne23,
@ -8455,11 +8445,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
CUBLAS_CHECK(
cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
(const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
&beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
&alpha, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
(const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
&beta, ( void **) (ptrs_dst + 0*ne23), CUDA_R_32F, ne01,
ne23,
CUBLAS_COMPUTE_16F,
CUBLAS_COMPUTE_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
if (ptrs_src_s != 0) {
@ -8471,11 +8461,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
}
#endif
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
ggml_cuda_pool_free(src1_as_f16, src1_as);
ggml_cuda_pool_free(dst_f16, dst_as);
}
static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {

237
ggml.c
View File

@ -9584,16 +9584,11 @@ static bool ggml_compute_forward_mul_mat_use_blas(
}
#endif
// off1 = offset in i11 and i1
// cne1 = ne11 and ne1
// in a normal matrix multiplication, off1 = 0 and cne1 = ne1
// during GGML_TASK_INIT, the full src1 is converted regardless of off1 and cne1
static void ggml_compute_forward_mul_mat(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst,
int64_t off1, int64_t cne1) {
struct ggml_tensor * dst) {
int64_t t0 = ggml_perf_time_us();
UNUSED(t0);
@ -9661,9 +9656,9 @@ static void ggml_compute_forward_mul_mat(
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
const float * y = (float *) ((char *) src1->data + off1*nb11 + i12*nb12 + i13*nb13);
float * d = (float *) ((char *) dst->data + off1*nb1 + i12*nb2 + i13*nb3);
const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
if (type != GGML_TYPE_F32) {
float * const wdata = params->wdata;
@ -9680,7 +9675,7 @@ static void ggml_compute_forward_mul_mat(
}
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
cne1, ne01, ne10,
ne1, ne01, ne10,
1.0f, y, ne10,
x, ne00,
0.0f, d, ne01);
@ -9721,8 +9716,8 @@ static void ggml_compute_forward_mul_mat(
const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
const int64_t nr0 = ne01; // src0 rows
const int64_t nr1 = cne1*ne12*ne13; // src1 rows
const int64_t nr0 = ne01; // src0 rows
const int64_t nr1 = ne1*ne12*ne13; // src1 rows
//printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
@ -9764,9 +9759,9 @@ static void ggml_compute_forward_mul_mat(
for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
const int64_t i13 = (ir1/(ne12*cne1));
const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
const int64_t i11 = (ir1 - i13*ne12*cne1 - i12*cne1) + off1;
const int64_t i13 = (ir1/(ne12*ne1));
const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1;
const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1);
// broadcast src0 into src1
const int64_t i03 = i13/r3;
@ -9806,28 +9801,191 @@ static void ggml_compute_forward_mul_mat(
static void ggml_compute_forward_mul_mat_id(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * ids,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
// during GGML_TASK_INIT the entire src1 is converted to vec_dot_type
ggml_compute_forward_mul_mat(params, dst->src[2], src1, dst, 0, dst->ne[1]);
return;
}
const struct ggml_tensor * src0 = dst->src[2]; // only for GGML_TENSOR_BINARY_OP_LOCALS
const struct ggml_tensor * ids = src0;
GGML_TENSOR_BINARY_OP_LOCALS
const int ith = params->ith;
const int nth = params->nth;
const enum ggml_type type = src0->type;
const bool src1_cont = ggml_is_contiguous(src1);
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
// broadcast factors
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
// row groups
const int id = ggml_get_op_params_i32(dst, 0);
const int n_as = ggml_get_op_params_i32(dst, 1);
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
char * wdata_src1_end = (src1->type == vec_dot_type) ?
(char *) params->wdata :
(char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
GGML_ASSERT(row_id >= 0 && row_id < n_as);
int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
int64_t * matrix_rows = matrix_row_counts + n_as; // [n_as][ne11]
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
ggml_compute_forward_mul_mat(params, src0_row, src1, dst, i01, 1);
#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
if (params->type == GGML_TASK_INIT) {
char * wdata = params->wdata;
if (src1->type != vec_dot_type) {
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
assert(params->wsize >= ne11*ne12*ne13*row_size);
assert(src1->type == GGML_TYPE_F32);
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
wdata += row_size;
}
}
}
}
// initialize matrix_row_counts
GGML_ASSERT(wdata == wdata_src1_end);
memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
// group rows by src0 matrix
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
GGML_ASSERT(row_id >= 0 && row_id < n_as);
MMID_MATRIX_ROW(row_id, matrix_row_counts[row_id]) = i01;
matrix_row_counts[row_id] += 1;
}
return;
}
if (params->type == GGML_TASK_FINALIZE) {
return;
}
// compute each matrix multiplication in sequence
for (int cur_a = 0; cur_a < n_as; ++cur_a) {
const int64_t cne1 = matrix_row_counts[cur_a];
if (cne1 == 0) {
continue;
}
const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
const int64_t nr0 = ne01; // src0 rows
const int64_t nr1 = cne1*ne12*ne13; // src1 rows
//printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
// distribute the thread work across the inner or outer loop based on which one is larger
const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
const int64_t ith0 = ith % nth0;
const int64_t ith1 = ith / nth0;
const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
const int64_t ir010 = dr0*ith0;
const int64_t ir011 = MIN(ir010 + dr0, nr0);
const int64_t ir110 = dr1*ith1;
const int64_t ir111 = MIN(ir110 + dr1, nr1);
//printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
// threads with no work simply yield (not sure if it helps)
if (ir010 >= ir011 || ir110 >= ir111) {
sched_yield();
continue;
}
assert(ne12 % ne02 == 0);
assert(ne13 % ne03 == 0);
// block-tiling attempt
const int64_t blck_0 = 16;
const int64_t blck_1 = 16;
// attempt to reduce false-sharing (does not seem to make a difference)
float tmp[16];
for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
const int64_t i13 = (ir1/(ne12*cne1)); // Note: currently, src1 is always a matrix
const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
const int64_t _i11 = (ir1 - i13*ne12*cne1 - i12*cne1);
const int64_t i11 = MMID_MATRIX_ROW(cur_a, _i11);
// broadcast src0 into src1
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
const int64_t i1 = i11;
const int64_t i2 = i12;
const int64_t i3 = i13;
const char * src0_row = (const char *) src0_cur->data + (0 + i02*nb02 + i03*nb03);
// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
// the original src1 data pointer, so we should index using the indices directly
// TODO: this is a bit of a hack, we should probably have a better way to handle this
const char * src1_col = (const char *) wdata +
(src1_cont || src1->type != vec_dot_type
? (i11 + i12*ne11 + i13*ne12*ne11)*row_size
: (i11*nb11 + i12*nb12 + i13*nb13));
float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
//for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
// vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
//}
for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
}
memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
}
}
}
}
#undef MMID_MATRIX_ROW
}
// ggml_compute_forward_out_prod
@ -14217,7 +14375,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
} break;
case GGML_OP_MUL_MAT:
{
ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor, 0, tensor->ne[1]);
ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
} break;
case GGML_OP_MUL_MAT_ID:
{
@ -16017,7 +16175,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_MUL_MAT_ID:
{
// FIXME: blas
n_tasks = n_threads;
} break;
case GGML_OP_OUT_PROD:
@ -16351,20 +16508,16 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
} break;
case GGML_OP_MUL_MAT_ID:
{
const struct ggml_tensor * a = node->src[2];
const struct ggml_tensor * b = node->src[1];
const enum ggml_type vec_dot_type = type_traits[a->type].vec_dot_type;
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(a, b, node)) {
if (a->type != GGML_TYPE_F32) {
// here we need memory just for single 2D matrix from src0
cur = ggml_type_size(GGML_TYPE_F32)*(a->ne[0]*a->ne[1]);
}
} else
#endif
if (b->type != vec_dot_type) {
cur = ggml_row_size(vec_dot_type, ggml_nelements(b));
const struct ggml_tensor * src0 = node->src[2];
const struct ggml_tensor * src1 = node->src[1];
const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
if (src1->type != vec_dot_type) {
cur = ggml_row_size(vec_dot_type, ggml_nelements(src1));
}
const int n_as = ggml_get_op_params_i32(node, 1);
cur = GGML_PAD(cur, sizeof(int64_t)); // align
cur += n_as * sizeof(int64_t); // matrix_row_counts
cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
} break;
case GGML_OP_OUT_PROD:
{

155
llama.cpp
View File

@ -1521,6 +1521,10 @@ struct llama_context {
// decode output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
#ifndef NDEBUG
// guard against access to unset logits
std::vector<bool> logits_valid;
#endif
bool logits_all = false;
// input embedding (1-dimensional array: [n_embd])
@ -6386,6 +6390,14 @@ static int llama_decode_internal(
{
auto & logits_out = lctx.logits;
#ifndef NDEBUG
auto & logits_valid = lctx.logits_valid;
logits_valid.clear();
logits_valid.resize(n_tokens);
logits_out.clear();
#endif
if (batch.logits) {
logits_out.resize(n_vocab * n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
@ -6393,13 +6405,22 @@ static int llama_decode_internal(
continue;
}
memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab);
#ifndef NDEBUG
logits_valid[i] = true;
#endif
}
} else if (lctx.logits_all) {
logits_out.resize(n_vocab * n_tokens);
memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens);
#ifndef NDEBUG
std::fill(logits_valid.begin(), logits_valid.end(), true);
#endif
} else {
logits_out.resize(n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab);
#ifndef NDEBUG
logits_valid[n_tokens - 1] = true;
#endif
}
}
@ -8862,53 +8883,60 @@ static int llama_apply_lora_from_file_internal(
const int64_t t_start_lora_us = ggml_time_us();
auto fin = std::ifstream(path_lora, std::ios::binary);
if (!fin) {
LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
return 1;
}
llama_file fin(path_lora, "rb");
// verify magic and version
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
uint32_t format_version;
fin.read((char *) &format_version, sizeof(format_version));
uint32_t magic = fin.read_u32();
if (magic != LLAMA_FILE_MAGIC_GGLA) {
LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
return 1;
}
uint32_t format_version = fin.read_u32();
if (format_version != 1) {
LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
return 1;
}
}
int32_t lora_r;
int32_t lora_alpha;
fin.read((char *) &lora_r, sizeof(lora_r));
fin.read((char *) &lora_alpha, sizeof(lora_alpha));
int32_t lora_r = fin.read_u32();
int32_t lora_alpha = fin.read_u32();
float scaling = scale * (float)lora_alpha / (float)lora_r;
LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
// create a name -> tensor map of the model to accelerate lookups
// find the max tensor size to estimate the required temporary buffer size
size_t max_tensor_size = 0;
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
for (const auto & kv : model.tensors_by_name) {
model_tensors.insert(kv);
size_t f32_size = ggml_nelements(kv.second) * sizeof(float);
max_tensor_size = std::max(max_tensor_size, f32_size);
}
// create a temporary ggml context to store the lora tensors
// todo: calculate size from biggest possible tensor
std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
// TODO: use ggml-alloc
size_t lora_ctx_size = max_tensor_size * 3;
LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0);
std::vector<uint8_t> lora_buf(lora_ctx_size);
struct ggml_init_params params;
params.mem_size = lora_buf.size();
params.mem_buffer = lora_buf.data();
params.no_alloc = false;
ggml_context * lora_ctx = ggml_init(params);
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
using unique_context = std::unique_ptr<ggml_context, decltype(&ggml_free)>;
// create a name -> tensor map of the model to accelerate lookups
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
for (const auto & kv : model.tensors_by_name) {
model_tensors.insert(kv);
}
unique_context lora_ctx(nullptr, ggml_free);
lora_ctx.reset(ggml_init(params));
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
// load base model
std::unique_ptr<llama_model_loader> ml;
ggml_context * base_ctx = NULL;
unique_context base_ctx(nullptr, ggml_free);
std::vector<uint8_t> base_buf;
if (path_base_model) {
LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
@ -8917,6 +8945,7 @@ static int llama_apply_lora_from_file_internal(
size_t ctx_size;
size_t mmapped_size;
ml->calc_sizes(ctx_size, mmapped_size);
base_buf.resize(ctx_size);
ggml_init_params base_params;
@ -8924,9 +8953,9 @@ static int llama_apply_lora_from_file_internal(
base_params.mem_buffer = base_buf.data();
base_params.no_alloc = ml->use_mmap;
base_ctx = ggml_init(base_params);
base_ctx.reset(ggml_init(base_params));
// maybe this should in llama_model_loader
// maybe this should be in llama_model_loader
if (ml->use_mmap) {
ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa()));
}
@ -8939,27 +8968,35 @@ static int llama_apply_lora_from_file_internal(
std::vector<uint8_t> work_buffer;
while (true) {
if (fin.tell() == fin.size) {
// eof
break;
}
int32_t n_dims;
int32_t length;
int32_t name_len;
int32_t ftype;
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
if (fin.eof()) {
break;
fin.read_raw(&n_dims, sizeof(n_dims));
fin.read_raw(&name_len, sizeof(name_len));
fin.read_raw(&ftype, sizeof(ftype));
if (n_dims != 1 && n_dims != 2) {
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
fin.read_raw(&ne[i], sizeof(ne[i]));
}
std::string name;
{
GGML_ASSERT(name_len <= 1024);
char buf[1024];
fin.read(buf, length);
name = std::string(buf, length);
fin.read_raw(buf, name_len);
name = std::string(buf, name_len);
}
// check for lora suffix and get the type of tensor
@ -8973,7 +9010,7 @@ static int llama_apply_lora_from_file_internal(
std::string lora_type = name.substr(pos + lora_suffix.length());
std::string base_name = name;
base_name.erase(pos);
// LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
// LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str());
if (model_tensors.find(base_name) == model_tensors.end()) {
LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
@ -8992,22 +9029,15 @@ static int llama_apply_lora_from_file_internal(
return false;
}
}
ggml_tensor * lora_tensor;
if (n_dims == 2) {
lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
}
else {
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
ggml_set_name(lora_tensor, "lora_tensor");
ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]);
ggml_set_name(lora_tensor, name.c_str());
// load tensor data
size_t offset = fin.tellg();
size_t offset = fin.tell();
size_t tensor_data_size = ggml_nbytes(lora_tensor);
offset = (offset + 31) & -32;
fin.seekg(offset);
fin.read((char*)lora_tensor->data, tensor_data_size);
fin.seek(offset, SEEK_SET);
fin.read_raw(lora_tensor->data, tensor_data_size);
lora_tensors[name] = lora_tensor;
@ -9037,13 +9067,11 @@ static int llama_apply_lora_from_file_internal(
// load from base model
if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
// TODO: throw
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
return 1;
}
// TODO: not tested!! maybe not working!
base_t = ml->create_tensor(base_ctx, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
base_t = ml->create_tensor(base_ctx.get(), base_name, { dest_t->ne[0], dest_t->ne[1] }, GGML_BACKEND_CPU);
ml->load_data_for(base_t);
} else {
base_t = dest_t;
@ -9072,43 +9100,45 @@ static int llama_apply_lora_from_file_internal(
}
// w = w + BA*s
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB);
offload_func(BA);
ggml_set_name(BA, "BA");
if (scaling != 1.0f) {
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx.get(), scaling);
ggml_set_name(scale_tensor, "scale_tensor");
BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
BA = ggml_scale_inplace(lora_ctx.get(), BA, scale_tensor);
offload_func(BA);
ggml_set_name(BA, "BA_scaled");
}
ggml_tensor * r;
if (base_t == dest_t) {
r = ggml_add_inplace(lora_ctx, dest_t, BA);
r = ggml_add_inplace(lora_ctx.get(), dest_t, BA);
offload_func_force_inplace(r);
ggml_set_name(r, "r_add_inplace");
}
else {
r = ggml_add(lora_ctx, base_t, BA);
r = ggml_add(lora_ctx.get(), base_t, BA);
offload_func(r);
ggml_set_name(r, "r_add");
r = ggml_cpy(lora_ctx, r, dest_t);
r = ggml_cpy(lora_ctx.get(), r, dest_t);
offload_func(r);
ggml_set_name(r, "r_cpy");
}
struct ggml_cgraph * gf = ggml_new_graph(lora_ctx);
struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get());
ggml_build_forward_expand(gf, r);
ggml_graph_compute_helper(work_buffer, gf, n_threads);
// the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other
GGML_ASSERT(lora_tensors.size() == 2);
// we won't need these tensors again, reset the context to save memory
ggml_free(lora_ctx);
lora_ctx = ggml_init(params);
lora_ctx.reset(ggml_init(params));
lora_tensors.clear();
n_tensors++;
@ -9118,12 +9148,6 @@ static int llama_apply_lora_from_file_internal(
}
}
// TODO: this should be in a destructor, it will leak on failure
ggml_free(lora_ctx);
if (base_ctx) {
ggml_free(base_ctx);
}
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
@ -10288,6 +10312,7 @@ float * llama_get_logits(struct llama_context * ctx) {
}
float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
assert(ctx->logits_valid.at(i));
return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
}

1
llama.h
View File

@ -39,6 +39,7 @@
#define LLAMA_MAX_RNG_STATE (64*1024)
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN