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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.devops/cpu.Dockerfile
#	.devops/cuda.Dockerfile
#	.devops/intel.Dockerfile
#	.devops/llama-cli-cann.Dockerfile
#	.devops/musa.Dockerfile
#	.devops/rocm.Dockerfile
#	.devops/vulkan.Dockerfile
#	examples/llama-bench/llama-bench.cpp
#	examples/rpc/rpc-server.cpp
#	scripts/compare-llama-bench.py
#	tests/test-quantize-stats.cpp
This commit is contained in:
Concedo 2025-04-30 17:22:18 +08:00
commit 8273739412
9 changed files with 210 additions and 98 deletions
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130
common/arg.cpp
View file

@ -44,6 +44,25 @@ std::initializer_list<enum llama_example> mmproj_examples = {
// TODO: add LLAMA_EXAMPLE_SERVER when it's ready
};
static std::string read_file(const std::string & fname) {
std::ifstream file(fname);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
}
std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
file.close();
return content;
}
static void write_file(const std::string & fname, const std::string & content) {
std::ofstream file(fname);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
}
file << content;
file.close();
}
common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) {
this->examples = std::move(examples);
return *this;
@ -201,9 +220,11 @@ struct curl_slist_ptr {
static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) {
int remaining_attempts = max_attempts;
char * method = nullptr;
curl_easy_getinfo(curl, CURLINFO_EFFECTIVE_METHOD, &method);
while (remaining_attempts > 0) {
LOG_INF("%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
CURLcode res = curl_easy_perform(curl);
if (res == CURLE_OK) {
@ -214,6 +235,7 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma
LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
remaining_attempts--;
if (remaining_attempts == 0) break;
std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
}
@ -232,8 +254,6 @@ static bool common_download_file_single(const std::string & url, const std::stri
return false;
}
bool force_download = false;
// Set the URL, allow to follow http redirection
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
@ -257,7 +277,7 @@ static bool common_download_file_single(const std::string & url, const std::stri
// If the file exists, check its JSON metadata companion file.
std::string metadata_path = path + ".json";
nlohmann::json metadata;
nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
std::string etag;
std::string last_modified;
@ -267,7 +287,7 @@ static bool common_download_file_single(const std::string & url, const std::stri
if (metadata_in.good()) {
try {
metadata_in >> metadata;
LOG_INF("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
if (metadata.contains("url") && metadata.at("url").is_string()) {
auto previous_url = metadata.at("url").get<std::string>();
if (previous_url != url) {
@ -297,7 +317,10 @@ static bool common_download_file_single(const std::string & url, const std::stri
};
common_load_model_from_url_headers headers;
bool head_request_ok = false;
bool should_download = !file_exists; // by default, we should download if the file does not exist
// get ETag to see if the remote file has changed
{
typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
@ -326,23 +349,28 @@ static bool common_download_file_single(const std::string & url, const std::stri
curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
// we only allow retrying once for HEAD requests
// this is for the use case of using running offline (no internet), retrying can be annoying
bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0);
if (!was_perform_successful) {
return false;
head_request_ok = false;
}
long http_code = 0;
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code != 200) {
// HEAD not supported, we don't know if the file has changed
// force trigger downloading
force_download = true;
LOG_ERR("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
if (http_code == 200) {
head_request_ok = true;
} else {
LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
head_request_ok = false;
}
}
bool should_download = !file_exists || force_download;
if (!should_download) {
// if head_request_ok is false, we don't have the etag or last-modified headers
// we leave should_download as-is, which is true if the file does not exist
if (head_request_ok) {
// check if ETag or Last-Modified headers are different
// if it is, we need to download the file again
if (!etag.empty() && etag != headers.etag) {
LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
should_download = true;
@ -351,6 +379,7 @@ static bool common_download_file_single(const std::string & url, const std::stri
should_download = true;
}
}
if (should_download) {
std::string path_temporary = path + ".downloadInProgress";
if (file_exists) {
@ -425,13 +454,15 @@ static bool common_download_file_single(const std::string & url, const std::stri
{"etag", headers.etag},
{"lastModified", headers.last_modified}
});
std::ofstream(metadata_path) << metadata.dump(4);
LOG_INF("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
write_file(metadata_path, metadata.dump(4));
LOG_DBG("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
return false;
}
} else {
LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
}
return true;
@ -606,16 +637,37 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
// Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
// User-Agent header is already set in common_remote_get_content, no need to set it here
// we use "=" to avoid clashing with other component, while still being allowed on windows
std::string cached_response_fname = "manifest=" + hf_repo + "=" + tag + ".json";
string_replace_all(cached_response_fname, "/", "_");
std::string cached_response_path = fs_get_cache_file(cached_response_fname);
// make the request
common_remote_params params;
params.headers = headers;
auto res = common_remote_get_content(url, params);
long res_code = res.first;
std::string res_str(res.second.data(), res.second.size());
long res_code = 0;
std::string res_str;
bool use_cache = false;
try {
auto res = common_remote_get_content(url, params);
res_code = res.first;
res_str = std::string(res.second.data(), res.second.size());
} catch (const std::exception & e) {
LOG_WRN("error: failed to get manifest: %s\n", e.what());
LOG_WRN("try reading from cache\n");
// try to read from cache
try {
res_str = read_file(cached_response_path);
res_code = 200;
use_cache = true;
} catch (const std::exception & e) {
throw std::runtime_error("error: failed to get manifest (check your internet connection)");
}
}
std::string ggufFile;
std::string mmprojFile;
if (res_code == 200) {
if (res_code == 200 || res_code == 304) {
// extract ggufFile.rfilename in json, using regex
{
std::regex pattern("\"ggufFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\"");
@ -632,6 +684,10 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
mmprojFile = match[1].str();
}
}
if (!use_cache) {
// if not using cached response, update the cache file
write_file(cached_response_path, res_str);
}
} else if (res_code == 401) {
throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
} else {
@ -1143,6 +1199,9 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
fprintf(stderr, "%s\n", ex.what());
ctx_arg.params = params_org;
return false;
} catch (std::exception & ex) {
fprintf(stderr, "%s\n", ex.what());
exit(1); // for other exceptions, we exit with status code 1
}
return true;
@ -1443,13 +1502,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"-f", "--file"}, "FNAME",
"a file containing the prompt (default: none)",
[](common_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
}
params.prompt = read_file(value);
// store the external file name in params
params.prompt_file = value;
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
if (!params.prompt.empty() && params.prompt.back() == '\n') {
params.prompt.pop_back();
}
@ -1459,11 +1514,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"-sysf", "--system-prompt-file"}, "FNAME",
"a file containing the system prompt (default: none)",
[](common_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.system_prompt));
params.system_prompt = read_file(value);
if (!params.system_prompt.empty() && params.system_prompt.back() == '\n') {
params.system_prompt.pop_back();
}
@ -1888,15 +1939,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--grammar-file"}, "FNAME",
"file to read grammar from",
[](common_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
}
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(params.sampling.grammar)
);
params.sampling.grammar = read_file(value);
}
).set_sparam());
add_opt(common_arg(
@ -2816,14 +2859,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
"list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
),
[](common_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
}
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(params.chat_template));
params.chat_template = read_file(value);
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
add_opt(common_arg(

22
examples/server/utils.hpp
View file

@ -642,9 +642,31 @@ static json oaicompat_completion_params_parse(
throw std::runtime_error("Cannot use custom grammar constraints with tools.");
}
// if the assistant message appears at the end of list, we do not add end-of-turn token
// for ex. this can be useful to modify the reasoning process in reasoning models
bool prefill_assistant_message = !inputs.messages.empty() && inputs.messages.back().role == "assistant";
common_chat_msg last_message;
if (prefill_assistant_message) {
last_message = inputs.messages.back();
inputs.messages.pop_back();
/* sanity check, max one assistant message at the end of the list */
if (!inputs.messages.empty() && inputs.messages.back().role == "assistant"){
throw std::runtime_error("Cannot have 2 or more assistant messages at the end of the list.");
}
inputs.extract_reasoning = false;
inputs.add_generation_prompt = true;
}
// Apply chat template to the list of messages
auto chat_params = common_chat_templates_apply(tmpls, inputs);
/* Append assistant prefilled message */
if (prefill_assistant_message) {
chat_params.prompt += last_message.content;
}
llama_params["chat_format"] = static_cast<int>(chat_params.format);
llama_params["prompt"] = chat_params.prompt;
if (!chat_params.grammar.empty()) {

53
ggml/src/ggml-cuda/convert.cu
View file

@ -1,6 +1,8 @@
#include "convert.cuh"
#include "dequantize.cuh"
#include <cstdint>
#define CUDA_Q8_0_NE_ALIGN 2048
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
@ -570,30 +572,46 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t
}
template <typename src_t, typename dst_t>
static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) {
const int64_t i = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
static __global__ void convert_unary(
const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02,
const int64_t s01, const int64_t s02, const int64_t s03) {
const int64_t i00 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
if (i00 >= ne00) {
return;
}
const int64_t i01 = blockIdx.y;
const int64_t i02 = blockIdx.z % ne02;
const int64_t i03 = blockIdx.z / ne02;
const src_t * x = (const src_t *) vx;
y[i] = float(x[i]);
const int64_t ix = i03*s03 + i02*s02 + i01*s01 + i00;
const int64_t iy = ((i03*ne02 + i02)*ne01 + i01)*ne00 + i00;
y[iy] = float(x[ix]);
}
template <typename src_t, typename dst_t>
static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
static void convert_unary_cuda(const void * vx, dst_t * y,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t s01, const int64_t s02, const int64_t s03, cudaStream_t stream) {
const dim3 num_blocks((ne00 + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE, ne01, ne02*ne03);
convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>
(vx, y, ne00, ne01, ne02, s01, s02, s03);
}
template <typename src_t, typename dst_t>
static void convert_unary_cont_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
convert_unary_cuda<src_t>(vx, y, k, 1, 1, 1, k, k, k, stream);
}
to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type) {
switch (type) {
case GGML_TYPE_F32:
return convert_unary_cuda<float>;
return convert_unary_cont_cuda<float>;
case GGML_TYPE_F16:
return convert_unary_cuda<half>;
return convert_unary_cont_cuda<half>;
default:
return nullptr;
}
@ -643,9 +661,9 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F32:
return convert_unary_cuda<float>;
return convert_unary_cont_cuda<float>;
case GGML_TYPE_BF16:
return convert_unary_cuda<nv_bfloat16>;
return convert_unary_cont_cuda<nv_bfloat16>;
default:
return nullptr;
}
@ -692,7 +710,18 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F16:
return convert_unary_cuda<half>;
return convert_unary_cont_cuda<half>;
case GGML_TYPE_BF16:
return convert_unary_cont_cuda<nv_bfloat16>;
default:
return nullptr;
}
}
to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type) {
switch (type) {
case GGML_TYPE_F32:
return convert_unary_cuda<float>;
case GGML_TYPE_BF16:
return convert_unary_cuda<nv_bfloat16>;
default:

12
ggml/src/ggml-cuda/convert.cuh
View file

@ -3,7 +3,7 @@
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
template<typename T>
using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int64_t k, cudaStream_t stream);
using to_t_cuda_t = void (*)(const void * x, T * y, int64_t k, cudaStream_t stream);
typedef to_t_cuda_t<float> to_fp32_cuda_t;
typedef to_t_cuda_t<half> to_fp16_cuda_t;
@ -14,3 +14,13 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type);
to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type);
to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type);
// TODO more general support for non-contiguous inputs
template<typename T>
using to_t_nc_cuda_t = void (*)(const void * x, T * y,
int64_t ne00, int64_t ne01, int64_t ne02, int64_t ne03,
int64_t s01, int64_t s02, int64_t s03, cudaStream_t stream);
typedef to_t_nc_cuda_t<half> to_fp16_nc_cuda_t;
to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type);

70
ggml/src/ggml-cuda/ggml-cuda.cu
View file

@ -1721,15 +1721,15 @@ static __global__ void k_compute_batched_ptrs(
size_t nb12, size_t nb13,
size_t nbd2, size_t nbd3,
int64_t r2, int64_t r3) {
int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x;
int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y;
const int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x;
const int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y;
if (i13 >= ne13 || i12 >= ne12) {
return;
}
int64_t i03 = i13 / r3;
int64_t i02 = i12 / r2;
const int64_t i03 = i13 / r3;
const int64_t i02 = i12 / r2;
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 + i13*nb13;
@ -1743,6 +1743,10 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
GGML_ASSERT(src0->type == GGML_TYPE_F16);
// Byte offsets and tensor dimensions are currently used in an inconsistent way for dst.
// As long as dst is contiguous this does not matter though.
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_TENSOR_BINARY_OP_LOCALS
const int64_t ne_dst = ggml_nelements(dst);
@ -1751,21 +1755,31 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(), main_stream));
void * src0_ddq = src0->data;
half * src0_f16 = (half *) src0_ddq;
float * src1_ddf = (float *) src1->data;
float * dst_ddf = (float *) dst->data;
const half * src0_f16 = (const half *) src0->data;
float * dst_ddf = (float *) dst->data;
const half * src1_f16 = (const half *) src1->data;
const size_t ts_src1 = ggml_type_size(src1->type);
GGML_ASSERT(nb10 == ts_src1);
int64_t s11 = nb11 / ts_src1;
int64_t s12 = nb12 / ts_src1;
int64_t s13 = nb13 / ts_src1;
ggml_cuda_pool_alloc<half> src1_f16_alloc(ctx.pool());
// convert src1 to fp16
ggml_cuda_pool_alloc<half> src1_f16_alloc(ctx.pool());
if (src1->type != GGML_TYPE_F16) {
const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
const to_fp16_nc_cuda_t to_fp16_cuda = ggml_get_to_fp16_nc_cuda(src1->type);
const int64_t ne_src1 = ggml_nelements(src1);
src1_f16_alloc.alloc(ne_src1);
GGML_ASSERT(to_fp16_cuda != nullptr);
to_fp16_cuda(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream);
to_fp16_cuda(src1_f16, src1_f16_alloc.get(), ne10, ne11, ne12, ne13, s11, s12, s13, main_stream);
src1_f16 = src1_f16_alloc.get();
s11 = ne10;
s12 = ne11*s11;
s13 = ne12*s12;
}
half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get();
ggml_cuda_pool_alloc<half> dst_f16(ctx.pool());
char * dst_t;
@ -1825,13 +1839,13 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
int i02 = i12 / r2;
CUBLAS_CHECK(
cublasGemmEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
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_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01,
cu_compute_type,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
cublasGemmEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
alpha, (const char *) src0_f16 + i03*nb03 + i02*nb02, CUDA_R_16F, nb01/sizeof(half),
src1_f16 + i13*s13 + i12*s12, CUDA_R_16F, s11,
beta, ( char *) dst_t + i13*nbd3 + i12*nbd2, cu_data_type, ne0,
cu_compute_type,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
}
}
}
@ -1842,15 +1856,15 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
CUBLAS_CHECK(
cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
alpha, (const char *) src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA
(const char *) src1_f16, CUDA_R_16F, nb11/nb10, nb12/nb10, // strideB
beta, ( char *) dst_t, cu_data_type, ne01, nb2/nb0, // strideC
alpha, src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA
src1_f16, CUDA_R_16F, s11, s12, // strideB
beta, dst_t, cu_data_type, ne0, ne1*ne0, // strideC
ne12*ne13,
cu_compute_type,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
} else {
// use cublasGemmBatchedEx
const int ne23 = ne12*ne13;
const int64_t ne23 = ne12*ne13;
ggml_cuda_pool_alloc<const void *> ptrs_src(ctx.pool(), 2*ne23);
ggml_cuda_pool_alloc< void *> ptrs_dst(ctx.pool(), 1*ne23);
@ -1862,8 +1876,8 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
ne12, ne13,
ne23,
nb02, nb03,
src1->type == GGML_TYPE_F16 ? nb12 : nb12/2,
src1->type == GGML_TYPE_F16 ? nb13 : nb13/2,
src1->type == GGML_TYPE_F16 ? nb12 : s12*sizeof(half),
src1->type == GGML_TYPE_F16 ? nb13 : s13*sizeof(half),
nbd2, nbd3,
r2, r3);
CUDA_CHECK(cudaGetLastError());
@ -1872,8 +1886,8 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
cublasGemmBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00,
(const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10,
beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01,
(const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, s11,
beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne0,
ne23,
cu_compute_type,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
@ -1937,7 +1951,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
} else if (!split && use_mul_mat_vec_q) {
ggml_cuda_mul_mat_vec_q(ctx, src0, src1, nullptr, dst);
} else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16) &&
dst->op_params[0] == GGML_PREC_DEFAULT && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
!ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
// general KQ + KQV multi-batch without FlashAttention
ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
} else if (use_mul_mat_vec) {

1
include/llama.h
View file

@ -1234,6 +1234,7 @@ extern "C" {
"will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)");
/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
/// Setting k <= 0 makes this a noop
LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k);
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751

9
src/llama-model.cpp
View file

@ -48,11 +48,13 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_770M: return "770M";
case LLM_TYPE_780M: return "780M";
case LLM_TYPE_0_5B: return "0.5B";
case LLM_TYPE_0_6B: return "0.6B";
case LLM_TYPE_1B: return "1B";
case LLM_TYPE_1_3B: return "1.3B";
case LLM_TYPE_1_4B: return "1.4B";
case LLM_TYPE_1_5B: return "1.5B";
case LLM_TYPE_1_6B: return "1.6B";
case LLM_TYPE_1_7B: return "1.7B";
case LLM_TYPE_1_8B: return "1.8B";
case LLM_TYPE_2B: return "2B";
case LLM_TYPE_2_8B: return "2.8B";
@ -71,6 +73,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_15B: return "15B";
case LLM_TYPE_16B: return "16B";
case LLM_TYPE_20B: return "20B";
case LLM_TYPE_27B: return "27B";
case LLM_TYPE_30B: return "30B";
case LLM_TYPE_32B: return "32B";
case LLM_TYPE_34B: return "34B";
@ -79,6 +82,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_65B: return "65B";
case LLM_TYPE_70B: return "70B";
case LLM_TYPE_236B: return "236B";
case LLM_TYPE_290B: return "290B";
case LLM_TYPE_314B: return "314B";
case LLM_TYPE_671B: return "671B";
case LLM_TYPE_SMALL: return "0.1B";
@ -93,12 +97,8 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_16x3_8B: return "16x3.8B";
case LLM_TYPE_10B_128x3_66B: return "10B+128x3.66B";
case LLM_TYPE_57B_A14B: return "57B.A14B";
case LLM_TYPE_27B: return "27B";
case LLM_TYPE_290B: return "290B";
case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
case LLM_TYPE_0_6B: return "0.6B";
case LLM_TYPE_1_7B: return "1.7B";
case LLM_TYPE_30B_A3B: return "30B.A3B";
case LLM_TYPE_235B_A22B: return "235B.A22B";
default: return "?B";
@ -10298,7 +10298,6 @@ struct llm_build_deepseek2 : public llm_graph_context {
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
ggml_mul_mat_set_prec(q_nope_absorbed, GGML_PREC_F32);
cb(q_nope_absorbed, "q_nope_absorbed", il);
// {kv_lora_rank, n_head, n_tokens}

8
src/llama-model.h
View file

@ -39,11 +39,13 @@ enum llm_type {
LLM_TYPE_770M,
LLM_TYPE_780M,
LLM_TYPE_0_5B,
LLM_TYPE_0_6B,
LLM_TYPE_1B,
LLM_TYPE_1_3B,
LLM_TYPE_1_4B,
LLM_TYPE_1_5B,
LLM_TYPE_1_6B,
LLM_TYPE_1_7B,
LLM_TYPE_1_8B,
LLM_TYPE_2B,
LLM_TYPE_2_8B,
@ -62,6 +64,7 @@ enum llm_type {
LLM_TYPE_15B,
LLM_TYPE_16B,
LLM_TYPE_20B,
LLM_TYPE_27B,
LLM_TYPE_30B,
LLM_TYPE_32B,
LLM_TYPE_34B,
@ -70,6 +73,7 @@ enum llm_type {
LLM_TYPE_65B,
LLM_TYPE_70B,
LLM_TYPE_236B,
LLM_TYPE_290B,
LLM_TYPE_314B,
LLM_TYPE_671B,
LLM_TYPE_SMALL,
@ -84,12 +88,8 @@ enum llm_type {
LLM_TYPE_16x3_8B,
LLM_TYPE_10B_128x3_66B,
LLM_TYPE_57B_A14B,
LLM_TYPE_27B,
LLM_TYPE_290B,
LLM_TYPE_17B_16E, // llama4 Scout
LLM_TYPE_17B_128E, // llama4 Maverick
LLM_TYPE_0_6B,
LLM_TYPE_1_7B,
LLM_TYPE_30B_A3B,
LLM_TYPE_235B_A22B,
};

3
src/llama-sampling.cpp
View file

@ -232,7 +232,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
// }
if (k <= 0) {
k = cur_p->size;
return;
}
k = std::min(k, (int) cur_p->size);
@ -298,6 +298,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
}
cur_p->sorted = true;
}
cur_p->size = k;
}