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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/build.yml
#	ci/run.sh
#	docs/function-calling.md
#	examples/gritlm/gritlm.cpp
#	ggml/CMakeLists.txt
#	ggml/cmake/common.cmake
#	ggml/src/CMakeLists.txt
#	ggml/src/ggml-cpu/CMakeLists.txt
#	ggml/src/ggml-cpu/ggml-cpu.c
#	ggml/src/ggml-hip/CMakeLists.txt
#	ggml/src/ggml-vulkan/CMakeLists.txt
#	ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt
#	requirements/requirements-compare-llama-bench.txt
#	scripts/compare-llama-bench.py
#	tests/CMakeLists.txt
This commit is contained in:
Concedo 2025-06-18 00:16:54 +08:00
commit 4356a00f4a
47 changed files with 1526 additions and 301 deletions
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9
common/arg.cpp
View file

@ -990,10 +990,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
params.tensor_buft_overrides.push_back({nullptr, nullptr});
}
if (params.reranking && params.embedding) {
throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both");
}
if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
throw std::runtime_error(string_format(
"error: the supplied chat template is not supported: %s%s\n",
@ -2749,9 +2745,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
add_opt(common_arg(
{"--reranking", "--rerank"},
string_format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"),
string_format("enable reranking endpoint on server (default: %s)", "disabled"),
[](common_params & params) {
params.reranking = true;
params.embedding = true;
params.pooling_type = LLAMA_POOLING_TYPE_RANK;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING"));
add_opt(common_arg(

5
common/chat-parser.cpp
View file

@ -49,6 +49,7 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::
// LOG_DBG("Tool call arguments:\n\traw: %s\n\tresult: %s\n", arguments.c_str(), tool_call.arguments.c_str());
result_.tool_calls.emplace_back(tool_call);
return true;
}
bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
@ -378,3 +379,7 @@ std::optional<common_chat_msg_parser::consume_json_result> common_chat_msg_parse
/* .is_partial = */ found_healing_marker,
};
}
void common_chat_msg_parser::clear_tools() {
result_.tool_calls.clear();
}

2
common/chat-parser.h
View file

@ -115,4 +115,6 @@ class common_chat_msg_parser {
const std::vector<std::vector<std::string>> & args_paths = {},
const std::vector<std::vector<std::string>> & content_paths = {}
);
void clear_tools();
};

6
common/chat.cpp
View file

@ -1838,7 +1838,7 @@ static common_chat_params common_chat_templates_apply_legacy(
if (res < 0) {
// if the custom "tmpl" is not supported, we throw an error
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
throw std::runtime_error("this custom template is not supported");
throw std::runtime_error("this custom template is not supported, try using --jinja");
}
// if it turns out that our buffer is too small, we resize it
@ -1921,7 +1921,9 @@ common_chat_msg common_chat_parse(const std::string & input, bool is_partial, co
} catch (const common_chat_msg_partial_exception & ex) {
LOG_DBG("Partial parse: %s\n", ex.what());
if (!is_partial) {
throw std::runtime_error(ex.what());
builder.clear_tools();
builder.move_to(0);
common_chat_parse_content_only(builder);
}
}
auto msg = builder.result();

78
common/common.cpp
View file

@ -775,6 +775,9 @@ bool fs_validate_filename(const std::string & filename) {
return true;
}
#include <iostream>
// returns true if successful, false otherwise
bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
@ -792,9 +795,16 @@ bool fs_create_directory_with_parents(const std::string & path) {
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
const std::wstring subpath = wpath.substr(0, pos_slash);
const wchar_t * test = subpath.c_str();
const bool success = CreateDirectoryW(test, NULL);
pos_slash += 1;
// skip the drive letter, in some systems it can return an access denied error
if (subpath.length() == 2 && subpath[1] == ':') {
continue;
}
const bool success = CreateDirectoryW(subpath.c_str(), NULL);
if (!success) {
const DWORD error = GetLastError();
@ -808,8 +818,6 @@ bool fs_create_directory_with_parents(const std::string & path) {
return false;
}
}
pos_slash += 1;
}
return true;
@ -905,34 +913,6 @@ struct common_init_result common_init_from_params(common_params & params) {
const llama_vocab * vocab = llama_model_get_vocab(model);
if (params.reranking) {
bool ok = true;
if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__);
ok = false;
}
bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
if (!has_eos && !has_sep) {
LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
ok = false;
} else if (!has_eos) {
LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
} else if (!has_sep) {
LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
ok = false;
}
if (!ok) {
llama_model_free(model);
return iparams;
}
}
auto cparams = common_context_params_to_llama(params);
llama_context * lctx = llama_init_from_model(model, cparams);
@ -974,6 +954,35 @@ struct common_init_result common_init_from_params(common_params & params) {
}
}
if (llama_pooling_type(lctx) == LLAMA_POOLING_TYPE_RANK) {
bool ok = true;
if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__);
ok = false;
}
bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
if (!has_eos && !has_sep) {
LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
ok = false;
} else if (!has_eos) {
LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
} else if (!has_sep) {
LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
ok = false;
}
if (!ok) {
llama_free(lctx);
llama_model_free(model);
return iparams;
}
}
// load and optionally apply lora adapters
for (auto & la : params.lora_adapters) {
llama_adapter_lora_ptr lora;
@ -1151,11 +1160,6 @@ struct llama_context_params common_context_params_to_llama(const common_params &
cparams.op_offload = !params.no_op_offload;
cparams.swa_full = params.swa_full;
if (params.reranking) {
cparams.embeddings = true;
cparams.pooling_type = LLAMA_POOLING_TYPE_RANK;
}
cparams.type_k = params.cache_type_k;
cparams.type_v = params.cache_type_v;

1
common/common.h
View file

@ -351,7 +351,6 @@ struct common_params {
int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
std::string embd_sep = "\n"; // separator of embeddings
bool reranking = false; // enable reranking support on server
// server params
int32_t port = 8080; // server listens on this network port

72
convert_hf_to_gguf.py
View file

@ -519,7 +519,7 @@ class TextModel(ModelBase):
def set_gguf_parameters(self):
self.gguf_writer.add_block_count(self.block_count)
if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions"], optional=True)) is not None:
if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length"], optional=True)) is not None:
self.gguf_writer.add_context_length(n_ctx)
logger.info(f"gguf: context length = {n_ctx}")
@ -2020,6 +2020,20 @@ class LlamaModel(TextModel):
raise ValueError(f"Unprocessed experts: {experts}")
@ModelBase.register("ArceeForCausalLM")
class ArceeModel(LlamaModel):
model_arch = gguf.MODEL_ARCH.ARCEE
def set_gguf_parameters(self):
super().set_gguf_parameters()
self._try_set_pooling_type()
rope_scaling = self.hparams.get("rope_scaling") or {}
if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
@ModelBase.register(
"LlavaForConditionalGeneration", # pixtral
"Mistral3ForConditionalGeneration", # mistral small 3.1
@ -4062,6 +4076,34 @@ class NomicBertModel(BertModel):
raise ValueError(f"unknown tokenizer: {toktyp}")
@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification")
class NeoBert(BertModel):
model_arch = gguf.MODEL_ARCH.NEO_BERT
def set_gguf_parameters(self):
super().set_gguf_parameters()
# NeoBERT uses 2/3 of the intermediate size as feed forward length
self.gguf_writer.add_feed_forward_length(int(2 * self.hparams["intermediate_size"] / 3))
self.gguf_writer.add_rope_freq_base(10000.0) # default value for NeoBERT
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
f_rms_eps = self.hparams.get("norm_eps", 1e-6) # default value for NeoBERT
self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
self.gguf_writer.add_pooling_type(gguf.PoolingType.CLS) # https://huggingface.co/chandar-lab/NeoBERT#how-to-use
def modify_tensors(self, data_torch, name, bid):
if name.startswith("decoder."):
return []
if name.startswith("model."):
name = name[6:]
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
class XLMRobertaModel(BertModel):
model_arch = gguf.MODEL_ARCH.BERT
@ -5262,6 +5304,34 @@ class DeepseekV2Model(TextModel):
raise ValueError(f"Unprocessed experts: {experts}")
@ModelBase.register("Dots1ForCausalLM")
class Dots1Model(Qwen2MoeModel):
model_arch = gguf.MODEL_ARCH.DOTS1
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.hparams["num_experts"] = self.hparams["n_routed_experts"]
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
if self.hparams["scoring_func"] == "noaux_tc":
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
else:
raise ValueError(f"Unsupported scoring_func value: {self.hparams['scoring_func']}")
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
if name.endswith("e_score_correction_bias"):
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
if "shared_experts" in name:
return [(self.map_tensor_name(name), data_torch)]
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("PLMForCausalLM")
class PLMModel(TextModel):
model_arch = gguf.MODEL_ARCH.PLM

184
ggml/src/ggml-cpu/arch-fallback.h Normal file
View file

@ -0,0 +1,184 @@
#pragma once
// Rename `_generic` functions if no native implementation is available.
// This effectively selects the generic implementation.
#if defined(GGML_CPU_GENERIC)
// quants.c
#define quantize_row_q8_0_generic quantize_row_q8_0
#define quantize_row_q8_1_generic quantize_row_q8_1
#define quantize_row_q8_K_generic quantize_row_q8_K
#define ggml_vec_dot_q4_0_q8_0_generic ggml_vec_dot_q4_0_q8_0
#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
#define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
#define ggml_vec_dot_q3_K_q8_K_generic ggml_vec_dot_q3_K_q8_K
#define ggml_vec_dot_q4_K_q8_K_generic ggml_vec_dot_q4_K_q8_K
#define ggml_vec_dot_q5_K_q8_K_generic ggml_vec_dot_q5_K_q8_K
#define ggml_vec_dot_q6_K_q8_K_generic ggml_vec_dot_q6_K_q8_K
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
// repack.cpp
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
// repack.cpp
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
#elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
// repack.cpp
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#elif defined(__POWERPC__) || defined(__powerpc__)
// ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679
// quants.c
#define quantize_row_q8_K_generic quantize_row_q8_K
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
// repack.cpp
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#elif defined(__loongarch64)
// quants.c
#define quantize_row_q8_K_generic quantize_row_q8_K
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
// repack.cpp
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#elif defined(__riscv)
// quants.c
#define quantize_row_q8_K_generic quantize_row_q8_K
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
// repack.cpp
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#elif defined(__s390x__)
// quants.c
#define quantize_row_q8_K_generic quantize_row_q8_K
#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
// repack.cpp
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#elif defined(__wasm__)
// quants.c
#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
// repack.cpp
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#endif

28
ggml/src/ggml-cpu/ggml-cpu-impl.h
View file

@ -503,31 +503,9 @@ static __m256 __lasx_xvreplfr2vr_s(const float val) {
// TODO: move to ggml-threading
void ggml_barrier(struct ggml_threadpool * tp);
void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value);
int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value);
#ifdef __cplusplus
}
#endif
#define GGML_DO_PRAGMA_(x) _Pragma (#x)
#define GGML_DO_PRAGMA(x) GGML_DO_PRAGMA_(x)
#if defined(GGML_CPU_GENERIC) || defined(__HIPCC__)
// Note for Apple targets:
// - clang: aliases are not supported on darwin
// - all native kernels need to be implemented in both x86 and arm files
// - on iOS, tvOS, and visionOS, if cmake cannot determine the target architecture, all `_generic` names are replaced by defines
# define GGML_WEAK_ALIAS(name, alias)
#elif defined(__GNUC__)
// GCC/Clang on *nix
# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(weak name = alias) // NOLINT
#elif defined(_MSC_VER) && defined(_WIN64)
// MSVC
// Note: C name mangling varies across different calling conventions
// see https://learn.microsoft.com/en-us/cpp/build/reference/decorated-names?view=msvc-170
# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(comment(linker, "/alternatename:" #name "=" #alias))
#elif defined(_MSC_VER) && defined(WIN32)
// ref: https://github.com/ggml-org/whisper.cpp/pull/3239#issuecomment-2958224591
# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(comment(linker, "/alternatename:_" #name "=_" #alias))
#else
# error "Unsupported compiler for GGML_WEAK_ALIAS"
#endif
#define GGML_CPU_NATIVE_IMPL(name) GGML_WEAK_ALIAS(name, name ## _generic)

8
ggml/src/ggml-cpu/ggml-cpu.c
View file

@ -563,6 +563,14 @@ void ggml_barrier(struct ggml_threadpool * tp) {
#endif
}
void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value) {
atomic_store_explicit(&tp->current_chunk, value, memory_order_relaxed);
}
int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value) {
return atomic_fetch_add_explicit(&tp->current_chunk, value, memory_order_relaxed);
}
#if defined(__gnu_linux__) && !defined(__BIONIC__)
static cpu_set_t ggml_get_numa_affinity(void) {
cpu_set_t cpuset;

8
ggml/src/ggml-cpu/llamafile/sgemm.cpp
View file

@ -53,7 +53,6 @@
#include "ggml-cpu-impl.h"
#include "ggml-quants.h"
#include <atomic>
#include <array>
#include <type_traits>
@ -394,8 +393,6 @@ class tinyBLAS {
template <int RM, int RN, int BM>
NOINLINE void gemm(int64_t m, int64_t n, int64_t BN) {
static std::atomic<int64_t> current_chunk;
GGML_ASSERT(m % (RM * BM) == 0);
const int64_t ytiles = m / (RM * BM);
const int64_t xtiles = (n + RN -1) / RN;
@ -410,7 +407,7 @@ class tinyBLAS {
if (params->ith == 0) {
GGML_ASSERT( jj_BN * SIZE_BN + (NB_BN - jj_BN) * (SIZE_BN - 1) == xtiles);
// Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start.
std::atomic_store_explicit(&current_chunk, (int64_t)params->nth, std::memory_order_relaxed);
ggml_threadpool_chunk_set(params->threadpool, params->nth);
}
ggml_barrier(params->threadpool);
@ -439,8 +436,7 @@ class tinyBLAS {
GGML_ASSERT(jj == jj2);
}
// next step.
job = std::atomic_fetch_add_explicit(&current_chunk, (int64_t)1, std::memory_order_relaxed);
job = ggml_threadpool_chunk_add(params->threadpool, 1);
}
ggml_barrier(params->threadpool);

26
ggml/src/ggml-cpu/quants.c
View file

@ -5,6 +5,8 @@
#include "ggml-quants.h"
#include "quants.h"
#include "arch-fallback.h"
#include <string.h>
#include <assert.h>
#include <float.h>
@ -38,12 +40,10 @@ void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
void quantize_row_q8_0_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
quantize_row_q8_0_ref(x, y, k);
}
GGML_CPU_NATIVE_IMPL(quantize_row_q8_0)
void quantize_row_q8_1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
quantize_row_q8_1_ref(x, y, k);
}
GGML_CPU_NATIVE_IMPL(quantize_row_q8_1)
//
// 2-6 bit quantization in super-blocks
@ -104,7 +104,6 @@ void quantize_row_tq2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy,
void quantize_row_q8_K_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
quantize_row_q8_K_ref(x, y, k);
}
GGML_CPU_NATIVE_IMPL(quantize_row_q8_K)
//===================================== Dot products =================================
@ -143,7 +142,6 @@ void ggml_vec_dot_q4_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_0_q8_0)
// TODO: add WASM SIMD
void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@ -181,7 +179,6 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_1_q8_1)
void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
const int qk = QK8_0;
@ -225,7 +222,6 @@ void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_0_q8_0)
void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
const int qk = QK8_1;
@ -269,7 +265,6 @@ void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_1_q8_1)
void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
const int qk = QK8_0;
@ -300,7 +295,6 @@ void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q8_0_q8_0)
void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(nrc == 1);
@ -353,7 +347,6 @@ void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_tq1_0_q8_K)
void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(nrc == 1);
@ -386,7 +379,6 @@ void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_tq2_0_q8_K)
void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(nrc == 1);
@ -439,7 +431,6 @@ void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
}
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q2_K_q8_K)
void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -519,7 +510,6 @@ void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
for (int l = 0; l < 8; ++l) sumf += sums[l];
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q3_K_q8_K)
void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -595,7 +585,6 @@ void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
for (int l = 0; l < 8; ++l) sumf += sums[l];
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_K_q8_K)
void ggml_vec_dot_q5_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -676,7 +665,6 @@ void ggml_vec_dot_q5_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
for (int l = 0; l < 8; ++l) sumf += sums[l];
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_K_q8_K)
void ggml_vec_dot_q6_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -732,7 +720,6 @@ void ggml_vec_dot_q6_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
for (int l = 0; l < 8; ++l) sumf += sums[l];
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q6_K_q8_K)
void ggml_vec_dot_iq2_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -775,7 +762,6 @@ void ggml_vec_dot_iq2_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs
}
*s = 0.125f * sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_xxs_q8_K)
void ggml_vec_dot_iq2_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -826,7 +812,6 @@ void ggml_vec_dot_iq2_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
*s = 0.125f * sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_xs_q8_K)
void ggml_vec_dot_iq2_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -879,7 +864,6 @@ void ggml_vec_dot_iq2_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
*s = 0.125f * sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_s_q8_K)
void ggml_vec_dot_iq3_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -924,7 +908,6 @@ void ggml_vec_dot_iq3_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs
}
*s = 0.25f * sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq3_xxs_q8_K)
void ggml_vec_dot_iq3_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -981,7 +964,6 @@ void ggml_vec_dot_iq3_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq3_s_q8_K)
void ggml_vec_dot_iq1_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -1025,7 +1007,6 @@ void ggml_vec_dot_iq1_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq1_s_q8_K)
void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
@ -1087,7 +1068,6 @@ void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq1_m_q8_K)
void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(nrc == 1);
@ -1117,7 +1097,6 @@ void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq4_nl_q8_0)
void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(nrc == 1);
@ -1164,7 +1143,6 @@ void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
*s = sumf;
}
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq4_xs_q8_K)
// ============================ 4-bit non-linear quants

27
ggml/src/ggml-cpu/quants.h
View file

@ -84,33 +84,6 @@ void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
#if defined(GGML_CPU_GENERIC)
#define quantize_row_q8_0_generic quantize_row_q8_0
#define quantize_row_q8_1_generic quantize_row_q8_1
#define quantize_row_q8_K_generic quantize_row_q8_K
#define ggml_vec_dot_q4_0_q8_0_generic ggml_vec_dot_q4_0_q8_0
#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
#define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
#define ggml_vec_dot_q3_K_q8_K_generic ggml_vec_dot_q3_K_q8_K
#define ggml_vec_dot_q4_K_q8_K_generic ggml_vec_dot_q4_K_q8_K
#define ggml_vec_dot_q5_K_q8_K_generic ggml_vec_dot_q5_K_q8_K
#define ggml_vec_dot_q6_K_q8_K_generic ggml_vec_dot_q6_K_q8_K
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
#endif
#ifdef __cplusplus
}
#endif

15
ggml/src/ggml-cpu/repack.cpp
View file

@ -8,6 +8,8 @@
#include "ggml-cpu-impl.h"
#include "traits.h"
#include "arch-fallback.h"
#include <cmath>
#include <cstring>
#include <cassert>
@ -83,7 +85,6 @@ void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GG
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_0_4x4)
void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
assert(QK8_0 == 32);
@ -122,7 +123,6 @@ void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GG
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_0_4x8)
void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
assert(QK_K == 256);
@ -174,7 +174,6 @@ void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GG
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_K_4x8)
} // extern "C"
@ -244,7 +243,6 @@ void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_4x4_q8_0)
void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
@ -289,7 +287,6 @@ void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_4x8_q8_0)
void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
@ -336,7 +333,6 @@ void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_8x8_q8_0)
void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK_K;
@ -415,7 +411,6 @@ void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_K_8x8_q8_K)
void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
@ -462,7 +457,6 @@ void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemv_iq4_nl_4x4_q8_0)
void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
@ -519,7 +513,6 @@ void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_4x4_q8_0)
void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
@ -574,7 +567,6 @@ void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_4x8_q8_0)
void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
@ -629,7 +621,6 @@ void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_8x8_q8_0)
void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK_K;
@ -719,7 +710,6 @@ void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_K_8x8_q8_K)
void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
@ -776,7 +766,6 @@ void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
}
}
}
GGML_CPU_NATIVE_IMPL(ggml_gemm_iq4_nl_4x4_q8_0)
} // extern "C"

21
ggml/src/ggml-cpu/repack.h
View file

@ -64,10 +64,6 @@ static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wro
extern "C" {
#endif
// Workaround for clang:
// clang++ complains: ``error: call to 'ggml_gemm_q4_0_4x4_q8_0' is ambiguous''
// repro: https://godbolt.org/z/oKdeWKonM (ICE), https://godbolt.org/z/1szq6P36v (ambiguous call)
#if defined(GGML_CPU_CLANG_WORKAROUND) || !(defined(__GNUC__) && defined(__clang__)) || defined(__HIPCC__)
void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
@ -81,7 +77,6 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
#endif // !defined(__clang__)
// Native implementations
void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
@ -98,22 +93,6 @@ void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
#if defined(GGML_CPU_GENERIC)
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#endif
#if defined(__cplusplus)
} // extern "C"
#endif

10
ggml/src/ggml-cuda/common.cuh
View file

@ -211,9 +211,9 @@ typedef float2 dfloat2;
#define FP16_MMA_AVAILABLE
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || defined(RDNA4))
#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
#define FP16_MMA_AVAILABLE
#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || defined(RDNA4))
#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
#define NEW_MMA_AVAILABLE
@ -266,11 +266,11 @@ static bool cp_async_available(const int cc) {
}
static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
return __AMDGCN_WAVEFRONT_SIZE;
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__))
return 64;
#else
return 32;
#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__))
}
[[noreturn]]

4
ggml/src/ggml-cuda/ggml-cuda.cu
View file

@ -2669,7 +2669,9 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft) || (integrated && ggml_backend_buft_is_cuda_host(node->src[j]->buffer->buft)));
}
}
#endif
#else
GGML_UNUSED(integrated);
#endif // NDEBUG
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
if (!ok) {

10
ggml/src/ggml-cuda/ssm-scan.cu
View file

@ -10,6 +10,8 @@ __global__ void __launch_bounds__(splitD, 2)
float * __restrict__ dst, const int64_t L) {
GGML_UNUSED(src1_nb0);
GGML_UNUSED(src2_nb0);
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
const int bidx = blockIdx.x; // split along B
const int bidy = blockIdx.y; // split along D
const int tid = threadIdx.x;
@ -44,16 +46,16 @@ __global__ void __launch_bounds__(splitD, 2)
if (N == 16) {
#pragma unroll
for (size_t i = 0; i < splitD / 4; i += 2) {
float value = A_block[(wid * warpSize + i) * stride_A + wtid];
float value = A_block[(wid * warp_size + i) * stride_A + wtid];
// todo: bank conflict
// I am always confused with how to use the swizzling method to solve
// bank conflit. Hoping somebody can tell me.
smem_A[(wid * warpSize + i) * stride_sA + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
smem_A[(wid * warp_size + i) * stride_sA + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
}
#pragma unroll
for (size_t i = 0; i < splitD / 4; i += 2) {
float value = s0_block[(wid * warpSize + i) * stride_s0 + wtid];
smem_s0[(wid * warpSize + i) * stride_ss0 + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
float value = s0_block[(wid * warp_size + i) * stride_s0 + wtid];
smem_s0[(wid * warp_size + i) * stride_ss0 + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value;
}
}

7
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View file

@ -184,6 +184,11 @@ struct vk_command_pool {
vk_queue *q;
};
// Prevent simultaneous submissions to the same queue.
// This could be per vk_queue if we stopped having two vk_queue structures
// sharing the same vk::Queue.
static std::mutex queue_mutex;
struct vk_queue {
uint32_t queue_family_index;
vk::Queue queue;
@ -1282,6 +1287,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_device& device, vk_command
static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
if (ctx->seqs.empty()) {
if (fence) {
std::lock_guard<std::mutex> guard(queue_mutex);
ctx->p->q->queue.submit({}, fence);
}
return;
@ -1351,6 +1357,7 @@ static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
}
}
std::lock_guard<std::mutex> guard(queue_mutex);
ctx->p->q->queue.submit(submit_infos, fence);
ctx->seqs.clear();

57
gguf-py/gguf/constants.py
View file

@ -291,6 +291,7 @@ class MODEL_ARCH(IntEnum):
BERT = auto()
NOMIC_BERT = auto()
NOMIC_BERT_MOE = auto()
NEO_BERT = auto()
JINA_BERT_V2 = auto()
BLOOM = auto()
STABLELM = auto()
@ -343,6 +344,8 @@ class MODEL_ARCH(IntEnum):
WAVTOKENIZER_DEC = auto()
PLM = auto()
BAILINGMOE = auto()
DOTS1 = auto()
ARCEE = auto()
class VISION_PROJECTOR_TYPE(IntEnum):
@ -571,6 +574,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.BERT: "bert",
MODEL_ARCH.NOMIC_BERT: "nomic-bert",
MODEL_ARCH.NOMIC_BERT_MOE: "nomic-bert-moe",
MODEL_ARCH.NEO_BERT: "neo-bert",
MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2",
MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm",
@ -623,6 +627,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
MODEL_ARCH.PLM: "plm",
MODEL_ARCH.BAILINGMOE: "bailingmoe",
MODEL_ARCH.DOTS1: "dots1",
MODEL_ARCH.ARCEE: "arcee",
}
VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@ -1077,6 +1083,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_UP_EXP,
MODEL_TENSOR.LAYER_OUT_NORM,
],
MODEL_ARCH.NEO_BERT: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.ENC_OUTPUT_NORM,
MODEL_TENSOR.CLS,
MODEL_TENSOR.CLS_OUT,
],
MODEL_ARCH.JINA_BERT_V2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.TOKEN_EMBD_NORM,
@ -2044,6 +2062,45 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
MODEL_ARCH.DOTS1: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_EXP_PROBS_B,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_GATE_SHEXP,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_UP_EXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
MODEL_ARCH.ARCEE: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
# TODO
}

2
gguf-py/gguf/gguf_writer.py
View file

@ -271,7 +271,7 @@ class GGUFWriter:
def add_key_value(self, key: str, val: Any, vtype: GGUFValueType, sub_type: GGUFValueType | None = None) -> None:
if any(key in kv_data for kv_data in self.kv_data):
raise ValueError(f'Duplicated key name {key!r}')
logger.warning(f'Duplicated key name {key!r}, overwriting it with new value {val!r} of type {vtype.name}')
self.kv_data[0][key] = GGUFValue(value=val, type=vtype, sub_type=sub_type)

11
gguf-py/gguf/tensor_mapping.py
View file

@ -31,6 +31,7 @@ class TensorNameMap:
"model.embeddings", # rwkv7
"model.word_embeddings", # bailingmoe
"language_model.model.embed_tokens", # llama4
"encoder", # neobert
),
# Token type embeddings
@ -134,6 +135,7 @@ class TensorNameMap:
"rwkv.blocks.{bid}.ln1", # rwkv6
"model.layers.{bid}.ln1", # rwkv7
"model.layers.{bid}.input_layernorm", # llama4
"transformer_encoder.{bid}.attention_norm", # neobert
),
# Attention norm 2
@ -161,6 +163,7 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.qkv_proj", # phi3
"encoder.layers.{bid}.self_attention.query_key_value", # chatglm
"transformer.layers.{bid}.attn.qkv_proj", # openelm
"transformer_encoder.{bid}.qkv", # neobert
),
# Attention query
@ -236,6 +239,7 @@ class TensorNameMap:
"transformer.layers.{bid}.attn.out_proj", # openelm
"transformer.h.{bid}.attn.attention.out_proj", # exaone
"model.layers.{bid}.self_attn.o_proj", # llama4
"transformer_encoder.{bid}.wo", # neobert
),
# Attention output norm
@ -276,6 +280,7 @@ class TensorNameMap:
"encoder.layers.{bid}.post_attention_layernorm", # chatglm
"transformer.layers.{bid}.ffn_norm", # openelm
"model.layers.{bid}.post_attention_layernorm", # llama4
"transformer_encoder.{bid}.ffn_norm", # neobert
),
# Post feed-forward norm
@ -305,7 +310,7 @@ class TensorNameMap:
),
MODEL_TENSOR.FFN_EXP_PROBS_B: (
"model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3
"model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1
),
# Feed-forward up
@ -340,6 +345,7 @@ class TensorNameMap:
"encoder.layers.{bid}.mlp.dense_h_to_4h", # chatglm
"transformer.h.{bid}.mlp.c_fc_1", # exaone
"model.layers.{bid}.feed_forward.up_proj", # llama4
"transformer_encoder.{bid}.ffn.w12", # neobert
),
MODEL_TENSOR.FFN_UP_EXP: (
@ -422,6 +428,7 @@ class TensorNameMap:
"encoder.layers.{bid}.mlp.dense_4h_to_h", # chatglm
"model.layers.h.{bid}.mlp.c_proj", # exaone
"model.layers.{bid}.feed_forward.down_proj", # llama4
"transformer_encoder.{bid}.ffn.w3", # neobert
),
MODEL_TENSOR.FFN_DOWN_EXP: (
@ -832,12 +839,14 @@ class TensorNameMap:
# TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg
MODEL_TENSOR.ENC_OUTPUT_NORM: (
"encoder.final_layer_norm", # t5
"layer_norm", # neobert
),
MODEL_TENSOR.CLS: (
"classifier", # jina
"classifier.dense", # roberta
"pre_classifier", # distillbert
"dense", # neobert
),
MODEL_TENSOR.CLS_OUT: (

17
include/llama.h
View file

@ -246,18 +246,21 @@ extern "C" {
typedef bool (*llama_progress_callback)(float progress, void * user_data);
// Input data for llama_decode
// Input data for llama_encode/llama_decode
// A llama_batch object can contain input about one or many sequences
// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
//
// - token : the token ids of the input (used when embd is NULL)
// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
// - pos : the positions of the respective token in the sequence
// (if set to NULL, the token position will be tracked automatically by llama_decode)
// (if set to NULL, the token position will be tracked automatically by llama_encode/llama_decode)
// - seq_id : the sequence to which the respective token belongs
// (if set to NULL, the sequence ID will be assumed to be 0)
// - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
// (if set to NULL, only the logits for last token will be returned)
// (if set to NULL:
// - if embeddings: all tokens are output
// - if not: only the last token is output
// )
//
typedef struct llama_batch {
int32_t n_tokens;
@ -265,8 +268,8 @@ extern "C" {
llama_token * token;
float * embd;
llama_pos * pos;
int32_t * n_seq_id; // TODO: remove, should belong to only 1 sequence
llama_seq_id ** seq_id; // TODO: become llama_seq_id * seq_id;
int32_t * n_seq_id;
llama_seq_id ** seq_id;
int8_t * logits; // TODO: rename this to "output"
} llama_batch;
@ -964,8 +967,8 @@ extern "C" {
// Get the number of threads used for prompt and batch processing (multiple token).
LLAMA_API int32_t llama_n_threads_batch(struct llama_context * ctx);
// Set whether the model is in embeddings mode or not
// If true, embeddings will be returned but logits will not
// Set whether the context outputs embeddings or not
// TODO: rename to avoid confusion with llama_get_embeddings()
LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
// Set whether to use causal attention or not

64
src/llama-arch.cpp
View file

@ -20,6 +20,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_BERT, "bert" },
{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
{ LLM_ARCH_NOMIC_BERT_MOE, "nomic-bert-moe" },
{ LLM_ARCH_NEO_BERT, "neo-bert" },
{ LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" },
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
@ -72,6 +73,8 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
{ LLM_ARCH_PLM, "plm" },
{ LLM_ARCH_BAILINGMOE, "bailingmoe" },
{ LLM_ARCH_DOTS1, "dots1" },
{ LLM_ARCH_ARCEE, "arcee" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -243,6 +246,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_ARCEE,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_LLAMA4,
{
@ -494,6 +515,21 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_NEO_BERT,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" },
{ LLM_TENSOR_CLS, "cls" },
{ LLM_TENSOR_CLS_OUT, "cls.output" },
},
},
{
LLM_ARCH_JINA_BERT_V2,
{
@ -1555,6 +1591,34 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
{
LLM_ARCH_DOTS1,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
{ LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
}
},
{
LLM_ARCH_UNKNOWN,
{

3
src/llama-arch.h
View file

@ -24,6 +24,7 @@ enum llm_arch {
LLM_ARCH_BERT,
LLM_ARCH_NOMIC_BERT,
LLM_ARCH_NOMIC_BERT_MOE,
LLM_ARCH_NEO_BERT,
LLM_ARCH_JINA_BERT_V2,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
@ -76,6 +77,8 @@ enum llm_arch {
LLM_ARCH_WAVTOKENIZER_DEC,
LLM_ARCH_PLM,
LLM_ARCH_BAILINGMOE,
LLM_ARCH_DOTS1,
LLM_ARCH_ARCEE,
LLM_ARCH_UNKNOWN,
};

181
src/llama-batch.cpp
View file

@ -3,6 +3,7 @@
#include "llama-impl.h"
#include "llama-cparams.h"
#include "llama-vocab.h"
#include "llama-memory.h"
#include <cassert>
#include <cstring>
@ -287,21 +288,28 @@ llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple
llama_batch_allocr::llama_batch_allocr() {
const char * LLAMA_BATCH_DEBUG = getenv("LLAMA_BATCH_DEBUG");
debug = LLAMA_BATCH_DEBUG ? atoi(LLAMA_BATCH_DEBUG) : 0;
seq_pos.resize(LLAMA_MAX_SEQ);
seq_cpl.resize(LLAMA_MAX_SEQ);
for (auto & cur : seq_cpl) {
cur.resize(LLAMA_MAX_SEQ);
}
}
bool llama_batch_allocr::init(const llama_batch & batch_inp, const llama_vocab & vocab, llama_pos p0) {
bool llama_batch_allocr::init(
const llama_batch & batch_inp,
const llama_vocab & vocab,
const llama_memory_i * memory,
bool embd_all) {
clear();
batch = batch_inp;
GGML_ASSERT(batch.n_tokens > 0);
if (!batch.pos) {
if (batch.seq_id) {
LLAMA_LOG_ERROR("%s: pos == NULL, but seq_id != NULL\n", __func__);
return false;
}
}
//
// validate input batch
//
if (batch.token) {
for (int32_t i = 0; i < batch.n_tokens; ++i) {
@ -315,22 +323,17 @@ bool llama_batch_allocr::init(const llama_batch & batch_inp, const llama_vocab &
if (batch.seq_id) {
for (int32_t i = 0; i < batch.n_tokens; ++i) {
for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
if (batch.seq_id && (batch.seq_id[i][s] < 0 || batch.seq_id[i][s] >= LLAMA_MAX_PARALLEL_SEQUENCES)) {
LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d > %d\n", __func__, i, s, batch.seq_id[i][s], LLAMA_MAX_PARALLEL_SEQUENCES);
if (batch.seq_id && (batch.seq_id[i][s] < 0 || batch.seq_id[i][s] >= LLAMA_MAX_SEQ)) {
LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d > %d\n", __func__, i, s, batch.seq_id[i][s], LLAMA_MAX_SEQ);
return false;
}
}
}
}
if (!batch.pos) {
assert(p0 >= 0);
pos.resize(batch.n_tokens);
for (int32_t i = 0; i < batch.n_tokens; i++) {
pos[i] = p0 + i;
}
batch.pos = pos.data();
}
//
// auto-generate missing fields
//
if (!batch.n_seq_id) {
n_seq_id.resize(batch.n_tokens);
@ -349,19 +352,89 @@ bool llama_batch_allocr::init(const llama_batch & batch_inp, const llama_vocab &
batch.seq_id = seq_id.data();
}
if (!batch.pos) {
pos.resize(batch.n_tokens);
// initialize the starting position for each sequence based on the positions in the memory
llama_pos p0[LLAMA_MAX_SEQ];
for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (!memory) {
p0[s] = 0;
} else {
p0[s] = memory->seq_pos_max(s) + 1;
}
}
for (int32_t i = 0; i < batch.n_tokens; i++) {
const llama_seq_id seq_id = batch.seq_id[i][0];
pos[i] = p0[seq_id];
for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
p0[batch.seq_id[i][s]] = pos[i] + 1;
}
}
batch.pos = pos.data();
}
if (!batch.logits) {
// by default return the output only for the last token
output.resize(batch.n_tokens);
if (embd_all) {
// return the output for all tokens
output.resize(batch.n_tokens, true);
} else {
// return the output only for the last token
output.resize(batch.n_tokens, false);
output[output.size() - 1] = true;
}
batch.logits = output.data();
} else if (embd_all) {
bool warn = false;
for (int32_t i = 0; i < batch.n_tokens; ++i) {
if (batch.logits[i] == 0) {
warn = true;
}
}
if (warn) {
LLAMA_LOG_WARN("%s: embeddings required but some input tokens were not marked as outputs -> overriding\n", __func__);
output.resize(batch.n_tokens, true);
batch.logits = output.data();
}
}
//
// compute stats
//
for (int32_t i = 0; i < batch.n_tokens; ++i) {
n_outputs += batch.logits[i] != 0;
}
// determine coupled sequences
// these are pairs of sequences that have at least one token in the input batch that is assigned to both of them
for (int32_t i = 0; i < batch.n_tokens; ++i) {
for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
seq_pos[batch.seq_id[i][s]].insert(batch.pos[i]);
if (s > 0) {
const llama_seq_id s0 = batch.seq_id[i][0];
const llama_seq_id s1 = batch.seq_id[i][s];
// mark that sequence s1 is coupled to s0
seq_cpl[s1][s0] = true;
// note: the other way around is not necessary for now
//seq_cpl[s0][s1] = true;
}
}
}
if (debug > 0) {
LLAMA_LOG_DEBUG("%s: input batch info (p0 = %d):\n", __func__, p0);
LLAMA_LOG_DEBUG("%s: input batch info:\n", __func__);
LLAMA_LOG_DEBUG("%s: n_tokens = %d\n", __func__, batch.n_tokens);
LLAMA_LOG_DEBUG("%s: token = %p\n", __func__, (void *) batch.token);
LLAMA_LOG_DEBUG("%s: embd = %p\n", __func__, (void *) batch.embd);
@ -404,6 +477,58 @@ bool llama_batch_allocr::init(const llama_batch & batch_inp, const llama_vocab &
batch.pos[i], batch.n_seq_id[i], ss.str().c_str(), batch.logits[i]);
}
LLAMA_LOG_DEBUG("%s: ]\n", __func__);
LLAMA_LOG_DEBUG("%s: seq = [\n", __func__);
for (int s0 = 0; s0 < (int) seq_pos.size(); ++s0) {
if (seq_pos[s0].empty()) {
continue;
}
std::stringstream ss;
for (int s1 = 0; s1 < (int) seq_cpl[s0].size(); ++s1) {
if (seq_cpl[s0][s1]) {
ss << s1 << " ";
}
}
LLAMA_LOG_DEBUG("%s: %4d: pos = [%4d, %4d], cpl = %s\n",
__func__, s0, seq_pos_min(s0), seq_pos_max(s0), ss.str().empty() ? "-" : ss.str().c_str());
}
LLAMA_LOG_DEBUG("%s: ]\n", __func__);
}
}
//
// consistency checks
//
for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq_pos[s].empty()) {
continue;
}
if (memory && seq_pos_min(s) != memory->seq_pos_max(s) + 1) {
LLAMA_LOG_ERROR("%s: sequence %d does not start from the last position stored in the memory\n", __func__, s);
return false;
}
if (seq_pos_max(s) - seq_pos_min(s) + 1 > (int) seq_pos[s].size()) {
LLAMA_LOG_ERROR("%s: sequence %d positions are not continuous\n", __func__, s);
return false;
}
}
if (memory) {
for (int32_t s0 = 0; s0 < LLAMA_MAX_SEQ; ++s0) {
for (int32_t s1 = 0; s1 < LLAMA_MAX_SEQ; ++s1) {
if (seq_cpl[s0][s1]) {
if (memory->seq_pos_min(s0) != memory->seq_pos_min(s1) ||
memory->seq_pos_max(s0) != memory->seq_pos_max(s1)) {
LLAMA_LOG_ERROR("%s: sequence %d is coupled to %d in the input batch, but have divereged\n", __func__, s0, s1);
return false;
}
}
}
}
}
@ -418,6 +543,14 @@ uint32_t llama_batch_allocr::get_n_outputs() const {
return n_outputs;
}
llama_pos llama_batch_allocr::seq_pos_min(llama_seq_id seq_id) const {
return seq_pos[seq_id].empty() ? -1 : *seq_pos[seq_id].begin();
}
llama_pos llama_batch_allocr::seq_pos_max(llama_seq_id seq_id) const {
return seq_pos[seq_id].empty() ? -1 : *seq_pos[seq_id].rbegin();
}
void llama_batch_allocr::clear() {
n_outputs = 0;
@ -426,6 +559,14 @@ void llama_batch_allocr::clear() {
n_seq_id.clear();
seq_id.clear();
output.clear();
for (auto & cur : seq_pos) {
cur.clear();
}
for (auto & cur : seq_cpl) {
std::fill(cur.begin(), cur.end(), false);
}
}
//

18
src/llama-batch.h
View file

@ -4,6 +4,7 @@
#include <array>
#include <vector>
#include <set>
// very similar to llama_batch,
// but has more metadata about sequences
@ -77,18 +78,26 @@ struct llama_sbatch {
llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false);
};
// temporary allocate memory for the input batch if needed
// a helper for sanitizing and fulfilling a batch
class llama_batch_allocr {
public:
llama_batch_allocr();
// optionally fulfill the batch returned by llama_batch_get_one
bool init(const llama_batch & batch_inp, const llama_vocab & vocab, llama_pos p0);
// sanitize and auto-gen missing data in the input batch
// memory is optional. if provided will be used to check for sequence continuity and to determine the positions
bool init(
const llama_batch & batch_inp,
const llama_vocab & vocab,
const llama_memory_i * memory,
bool embd_all);
const llama_batch & get_batch() const;
uint32_t get_n_outputs() const;
llama_pos seq_pos_min(llama_seq_id seq_id) const;
llama_pos seq_pos_max(llama_seq_id seq_id) const;
private:
void clear();
@ -103,5 +112,8 @@ private:
std::vector<llama_seq_id *> seq_id;
std::vector<int8_t> output;
std::vector<std::set<llama_pos>> seq_pos; // seq_pos[s]: the set of positions in sequence s
std::vector<std::vector<bool>> seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1
int debug;
};

17
src/llama-chat.cpp
View file

@ -183,6 +183,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
return LLM_CHAT_TEMPLATE_BAILING;
} else if (tmpl_contains("<|header_start|>") && tmpl_contains("<|header_end|>")) {
return LLM_CHAT_TEMPLATE_LLAMA4;
} else if (tmpl_contains("<|endofuserprompt|>")) {
return LLM_CHAT_TEMPLATE_DOTS1;
}
return LLM_CHAT_TEMPLATE_UNKNOWN;
}
@ -643,6 +645,21 @@ int32_t llm_chat_apply_template(
if (add_ass) {
ss << "Assistant:";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_DOTS1) {
// dots.llm1.inst (DOTS1)
for (auto message : chat) {
std::string role(message->role);
if (role == "system") {
ss << "<|system|>" << message->content << "<|endofsystem|>";
} else if (role == "user") {
ss << "<|userprompt|>" << message->content << "<|endofuserprompt|>";
} else {
ss << "<|response|>" << message->content << "<|endofresponse|>";
}
}
if (add_ass) {
ss << "<|response|>";
}
} else {
// template not supported
return -1;

1
src/llama-chat.h
View file

@ -43,6 +43,7 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_BAILING,
LLM_CHAT_TEMPLATE_LLAMA4,
LLM_CHAT_TEMPLATE_SMOLVLM,
LLM_CHAT_TEMPLATE_DOTS1,
LLM_CHAT_TEMPLATE_UNKNOWN,
};

38
src/llama-context.cpp
View file

@ -29,8 +29,8 @@ llama_context::llama_context(
const auto & hparams = model.hparams;
cparams.n_seq_max = std::max(1u, params.n_seq_max);
if (cparams.n_seq_max > LLAMA_MAX_PARALLEL_SEQUENCES) {
throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_PARALLEL_SEQUENCES));
if (cparams.n_seq_max > LLAMA_MAX_SEQ) {
throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_SEQ));
}
cparams.n_threads = params.n_threads;
@ -727,9 +727,8 @@ int llama_context::encode(const llama_batch & batch_inp) {
return -1;
}
// temporary allocate memory for the input batch if needed
// note: during encode, we always pass the full sequence starting from pos = 0
if (!batch_allocr->init(batch_inp, model.vocab, batch_inp.pos ? -1 : 0)) {
if (!batch_allocr->init(batch_inp, model.vocab, nullptr, true)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return -1;
}
@ -895,8 +894,10 @@ int llama_context::decode(const llama_batch & batch_inp) {
return -1;
}
// temporary allocate memory for the input batch if needed
if (!batch_allocr->init(batch_inp, model.vocab, batch_inp.pos ? -1 : memory->seq_pos_max(0) + 1)) {
// when computing embeddings, all tokens are output
const bool embd_all = cparams.embeddings;
if (!batch_allocr->init(batch_inp, model.vocab, memory.get(), embd_all)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return -1;
}
@ -913,12 +914,9 @@ int llama_context::decode(const llama_batch & batch_inp) {
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
// this indicates we are doing pooled embedding
const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
const uint32_t n_outputs_all = batch_allocr->get_n_outputs();
if (embd_pooled) {
if (embd_all) {
// require that all tokens are output
if (n_outputs_all != n_tokens_all) {
LLAMA_LOG_ERROR("%s: pooled embedding requires that all tokens are output (n_outputs_all = %d, n_tokens_all = %d)\n",
@ -947,7 +945,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
llama_memory_state_ptr mstate;
while (true) {
mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled);
mstate = memory->init_batch(batch, cparams.n_ubatch, embd_all);
if (!mstate) {
return -2;
}
@ -1025,8 +1023,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
if (!res) {
// the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
llama_pos pos_min[LLAMA_MAX_PARALLEL_SEQUENCES];
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
llama_pos pos_min[LLAMA_MAX_SEQ];
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
pos_min[s] = std::numeric_limits<llama_pos>::max();
}
@ -1037,7 +1035,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
pos_min[seq_id] = std::min(pos_min[seq_id], ubatch.pos[i]);
}
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (pos_min[s] == std::numeric_limits<llama_pos>::max()) {
continue;
}
@ -1060,7 +1058,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
//}
auto * t_logits = cparams.embeddings ? nullptr : res->get_logits();
auto * t_logits = res->get_logits();
auto * t_embd = cparams.embeddings ? res->get_embd() : nullptr;
if (t_embd && res->get_embd_pooled()) {
@ -1224,9 +1222,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
const auto n_vocab = vocab.n_tokens();
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
bool has_logits = !cparams.embeddings;
bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
bool has_logits = true;
bool has_embd = cparams.embeddings;
// TODO: hacky enc-dec support
if (model.arch == LLM_ARCH_T5) {
@ -2046,14 +2043,11 @@ void llama_context::opt_epoch_iter(
n_queued_tokens += n_tokens_all;
// this indicates we are doing pooled embedding
const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
embd_seq.clear();
uint32_t n_outputs_all = n_tokens_all;
auto mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled);
auto mstate = memory->init_batch(batch, cparams.n_ubatch, true);
if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__);
break;

2
src/llama-cparams.cpp
View file

@ -1,5 +1,5 @@
#include "llama-cparams.h"
size_t llama_max_parallel_sequences(void) {
return LLAMA_MAX_PARALLEL_SEQUENCES;
return LLAMA_MAX_SEQ;
}

2
src/llama-cparams.h
View file

@ -4,7 +4,7 @@
#include <cstdint>
#define LLAMA_MAX_PARALLEL_SEQUENCES 64
#define LLAMA_MAX_SEQ 64
struct llama_cparams {
uint32_t n_ctx; // context size used during inference

8
src/llama-kv-cache-recurrent.cpp
View file

@ -359,9 +359,7 @@ llama_pos llama_kv_cache_recurrent::seq_pos_max(llama_seq_id seq_id) const {
return result;
}
llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled) {
GGML_UNUSED(embd_pooled);
llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) {
auto sbatch = llama_sbatch(batch, hparams.n_embd, false);
std::vector<llama_ubatch> ubatches;
@ -369,8 +367,8 @@ llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch &
while (sbatch.n_tokens > 0) {
llama_ubatch ubatch;
if (embd_pooled) {
// Pooled embeddings cannot be split across ubatches (yet)
if (embd_all) {
// if all tokens are output, split by sequence
ubatch = sbatch.split_seq(n_ubatch);
} else {
ubatch = sbatch.split_equal(n_ubatch);

2
src/llama-kv-cache-recurrent.h
View file

@ -32,7 +32,7 @@ public:
llama_memory_state_ptr init_batch(
const llama_batch & batch,
uint32_t n_ubatch,
bool embd_pooled) override;
bool embd_all) override;
llama_memory_state_ptr init_full() override;

4
src/llama-kv-cache-unified-iswa.cpp
View file

@ -95,8 +95,8 @@ llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
return kv_swa->seq_pos_max(seq_id);
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled) {
GGML_UNUSED(embd_pooled);
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) {
GGML_UNUSED(embd_all);
// first try simple split
do {

2
src/llama-kv-cache-unified-iswa.h
View file

@ -34,7 +34,7 @@ public:
llama_memory_state_ptr init_batch(
const llama_batch & batch,
uint32_t n_ubatch,
bool embd_pooled) override;
bool embd_all) override;
llama_memory_state_ptr init_full() override;

14
src/llama-kv-cache-unified.cpp
View file

@ -310,8 +310,8 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
llama_memory_state_ptr llama_kv_cache_unified::init_batch(
const llama_batch & batch,
uint32_t n_ubatch,
bool embd_pooled) {
GGML_UNUSED(embd_pooled);
bool embd_all) {
GGML_UNUSED(embd_all);
do {
auto sbatch = llama_sbatch(batch, hparams.n_embd, true);
@ -572,7 +572,7 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
}
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (cells.seq_pos_min(s) < 0) {
continue;
}
@ -652,8 +652,8 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
// keep track of the max sequence position that we would overwrite with this ubatch
// for non-SWA cache, this would be always empty
llama_seq_id seq_pos_max_rm[LLAMA_MAX_PARALLEL_SEQUENCES];
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ];
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
seq_pos_max_rm[s] = -1;
}
@ -684,7 +684,7 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
// note: we want to preserve the invariant that all positions between [pos_min, pos_max] for each sequence
// will be present in the cache. so we have to purge any position which is less than those we would overwrite
// ref: https://github.com/ggml-org/llama.cpp/pull/13746#issuecomment-2916057092
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq_pos_max_rm[s] == -1) {
continue;
}
@ -1739,7 +1739,7 @@ llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_context * lctx,
bool do_shift,
defrag_info dinfo) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), lctx(lctx), do_shift(do_shift), dinfo(std::move(dinfo)) {
if (!do_shift && dinfo.empty()) {
if (!do_shift && this->dinfo.empty()) {
status = LLAMA_MEMORY_STATUS_NO_UPDATE;
}
}

2
src/llama-kv-cache-unified.h
View file

@ -59,7 +59,7 @@ public:
llama_memory_state_ptr init_batch(
const llama_batch & batch,
uint32_t n_ubatch,
bool embd_pooled) override;
bool embd_all) override;
llama_memory_state_ptr init_full() override;

16
src/llama-kv-cells.h
View file

@ -23,7 +23,7 @@ public:
used.clear();
for (uint32_t s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
for (uint32_t s = 0; s < LLAMA_MAX_SEQ; ++s) {
seq_pos[s].clear();
}
}
@ -240,7 +240,7 @@ public:
llama_seq_id seq_get(uint32_t i) const {
assert(seq[i].count() == 1);
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq[i].test(s)) {
return s;
}
@ -253,7 +253,7 @@ public:
// return -1 if the sequence is not present
llama_pos seq_pos_min(llama_seq_id seq_id) const {
assert(seq_id >= 0);
assert(seq_id < LLAMA_MAX_PARALLEL_SEQUENCES);
assert(seq_id < LLAMA_MAX_SEQ);
if (seq_pos[seq_id].empty()) {
return -1;
@ -266,7 +266,7 @@ public:
// return -1 if the sequence is not present
llama_pos seq_pos_max(llama_seq_id seq_id) const {
assert(seq_id >= 0);
assert(seq_id < LLAMA_MAX_PARALLEL_SEQUENCES);
assert(seq_id < LLAMA_MAX_SEQ);
if (seq_pos[seq_id].empty()) {
return -1;
@ -384,20 +384,20 @@ private:
//
std::vector<llama_pos> shift;
using bits_t = std::bitset<LLAMA_MAX_PARALLEL_SEQUENCES>;
using bits_t = std::bitset<LLAMA_MAX_SEQ>;
// the bitset seq[i] tells us which sequences are currently occupying the i-th cell
std::vector<bits_t> seq;
// the set seq_pos[s] tells us which positions are currently present for sequence s
// this way seq_pos[s].begin() and seq_pos[s].rbegin() give us the min/max positions currently in the cache
std::set<llama_pos> seq_pos[LLAMA_MAX_PARALLEL_SEQUENCES];
std::set<llama_pos> seq_pos[LLAMA_MAX_SEQ];
// helper functions for updating `seq_pos`, once cell at a time:
// remove cell i
void seq_pos_rm(uint32_t i) {
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq[i].test(s)) {
seq_pos[s].erase(pos[i]);
}
@ -406,7 +406,7 @@ private:
// add cell i
void seq_pos_add(uint32_t i) {
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq[i].test(s)) {
seq_pos[s].insert(pos[i]);
}

2
src/llama-memory.h
View file

@ -73,7 +73,7 @@ struct llama_memory_i {
virtual llama_memory_state_ptr init_batch(
const llama_batch & batch,
uint32_t n_ubatch,
bool embd_pooled) = 0;
bool embd_all) = 0;
// simulate full cache, used for allocating worst-case compute buffers
virtual llama_memory_state_ptr init_full() = 0;

556
src/llama-model.cpp
View file

@ -85,6 +85,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_40B: return "40B";
case LLM_TYPE_65B: return "65B";
case LLM_TYPE_70B: return "70B";
case LLM_TYPE_142B: return "142B";
case LLM_TYPE_236B: return "236B";
case LLM_TYPE_290B: return "290B";
case LLM_TYPE_314B: return "314B";
@ -603,6 +604,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.use_kq_norm = false;
}
} break;
case LLM_ARCH_ARCEE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
// Arcee uses the same structure as Llama
switch (hparams.n_layer) {
case 36: type = LLM_TYPE_4B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_DECI:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -743,6 +754,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
}
} break;
case LLM_ARCH_NEO_BERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
if (hparams.n_layer == 28) {
type = LLM_TYPE_250M;
}
} break;
case LLM_ARCH_BLOOM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@ -1449,6 +1470,20 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_DOTS1:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
switch (hparams.n_layer) {
case 62: type = LLM_TYPE_142B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
default: throw std::runtime_error("unsupported model architecture");
}
@ -2283,6 +2318,32 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0);
}
} break;
case LLM_ARCH_NEO_BERT:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd}, TENSOR_NOT_REQUIRED);
cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff*2}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
}
} break;
case LLM_ARCH_JINA_BERT_V2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // word_embeddings
@ -4219,6 +4280,89 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
}
} break;
case LLM_ARCH_DOTS1:
{
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_expert_shared = hparams.n_expert_shared;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
if (i < (int) hparams.n_layer_dense_lead) {
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
} else {
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
if (n_expert == 0) {
throw std::runtime_error("n_expert must be > 0");
}
if (n_expert_used == 0) {
throw std::runtime_error("n_expert_used must be > 0");
}
// MoE branch
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
// Shared expert branch
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
}
}
} break;
case LLM_ARCH_ARCEE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@ -6174,6 +6318,117 @@ struct llm_build_bert : public llm_graph_context {
}
};
struct llm_build_neo_bert : public llm_graph_context {
llm_build_neo_bert(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
ggml_tensor * cur;
ggml_tensor * inpL;
ggml_tensor * inp_pos = build_inp_pos();
// construct input embeddings (token, type, position)
inpL = build_inp_embd(model.tok_embd);
cb(inpL, "inp_embd", -1);
auto * inp_attn = build_attn_inp_no_cache();
// iterate layers
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * cur = inpL;
ggml_tensor * Qcur;
ggml_tensor * Kcur;
ggml_tensor * Vcur;
// pre-norm
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
// self-attention
cur = build_lora_mm(model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
// RoPE
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn, gf,
model.layers[il].wo, nullptr,
Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
cb(cur, "kqv_out", il);
if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
// skip computing output for unused tokens
ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
// re-add the layer input
cur = ggml_add(ctx0, cur, inpL);
ggml_tensor * ffn_inp = cur;
cb(ffn_inp, "ffn_inp", il);
// pre-norm
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
// feed-forward network
cur = build_ffn(cur,
model.layers[il].ffn_up,
NULL, NULL, NULL, NULL, NULL,
model.layers[il].ffn_down,
NULL, NULL, NULL,
LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
// attentions bypass the intermediate layer
cur = ggml_add(ctx0, cur, ffn_inp);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.output_norm_enc, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_embd", -1);
res->t_embd = cur;
ggml_build_forward_expand(gf, cur);
}
};
struct llm_build_bloom : public llm_graph_context {
llm_build_bloom(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
@ -13294,6 +13549,291 @@ struct llm_build_bailingmoe : public llm_graph_context {
}
};
struct llm_build_dots1 : public llm_graph_context {
llm_build_dots1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv_unified();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// norm
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self_attention
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn, gf,
model.layers[il].wo, model.layers[il].bo,
Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// MoE branch
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
if ((uint32_t) il < hparams.n_layer_dense_lead) {
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
ggml_tensor * moe_out =
build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
true, hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il);
cb(moe_out, "ffn_moe_out", il);
{
ggml_tensor * ffn_shexp = build_ffn(cur,
model.layers[il].ffn_up_shexp, NULL, NULL,
model.layers[il].ffn_gate_shexp, NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
}
cur = ggml_add(ctx0, cur, ffn_inp);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
};
struct llm_build_arcee : public llm_graph_context {
llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv_unified();
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// norm
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
// rope freq factors for llama3; may return nullptr for llama2 and other models
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn, gf,
model.layers[il].wo, model.layers[il].bo,
Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
// ARCEE uses relu^2 instead of silu
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
NULL, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
cb(cur, "ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
};
llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
llama_memory_i * res;
@ -13302,6 +13842,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_WAVTOKENIZER_DEC:
{
res = nullptr;
@ -13410,6 +13951,10 @@ llm_graph_result_ptr llama_model::build_graph(
{
llm = std::make_unique<llm_build_bert>(*this, params, gf);
} break;
case LLM_ARCH_NEO_BERT:
{
llm = std::make_unique<llm_build_neo_bert>(*this, params, gf);
} break;
case LLM_ARCH_BLOOM:
{
llm = std::make_unique<llm_build_bloom>(*this, params, gf);
@ -13632,6 +14177,14 @@ llm_graph_result_ptr llama_model::build_graph(
{
llm = std::make_unique<llm_build_bailingmoe>(*this, params, gf);
} break;
case LLM_ARCH_DOTS1:
{
llm = std::make_unique<llm_build_dots1>(*this, params, gf);
} break;
case LLM_ARCH_ARCEE:
{
llm = std::make_unique<llm_build_arcee>(*this, params, gf);
} break;
default:
GGML_ABORT("fatal error");
}
@ -13781,6 +14334,8 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
case LLM_ARCH_BAILINGMOE:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_ARCEE:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
@ -13814,6 +14369,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_NEMOTRON:
case LLM_ARCH_EXAONE:
case LLM_ARCH_MINICPM3:
case LLM_ARCH_DOTS1:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:

1
src/llama-model.h
View file

@ -73,6 +73,7 @@ enum llm_type {
LLM_TYPE_40B,
LLM_TYPE_65B,
LLM_TYPE_70B,
LLM_TYPE_142B,
LLM_TYPE_236B,
LLM_TYPE_290B,
LLM_TYPE_314B,

3
src/llama-quant.cpp
View file

@ -588,7 +588,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
gguf_set_val_f32(ctx_out.get(), o.key, o.val_f64);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
gguf_set_val_i32(ctx_out.get(), o.key, o.val_i64);
// Setting type to UINT32. See https://github.com/ggml-org/llama.cpp/pull/14182 for context
gguf_set_val_u32(ctx_out.get(), o.key, (uint32_t)abs(o.val_i64));
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
gguf_set_val_bool(ctx_out.get(), o.key, o.val_bool);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {

1
src/llama-vocab.cpp
View file

@ -2226,6 +2226,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|| t.first == "<|eom_id|>"
|| t.first == "<EOT>"
|| t.first == "_<EOT>"
|| t.first == "<|end_of_text|>"
) {
special_eog_ids.insert(t.second);
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {

8
src/llama.cpp
View file

@ -228,8 +228,11 @@ static struct llama_model * llama_model_load_from_file_impl(
// if using single GPU mode, remove all except the main GPU
if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
if (params.main_gpu < 0 || params.main_gpu >= (int)model->devices.size()) {
LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %d)\n", __func__, params.main_gpu, (int)model->devices.size());
if (params.main_gpu < 0) {
model->devices.clear();
} else {
if (params.main_gpu >= (int)model->devices.size()) {
LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %zu)\n", __func__, params.main_gpu, model->devices.size());
llama_model_free(model);
return nullptr;
}
@ -237,6 +240,7 @@ static struct llama_model * llama_model_load_from_file_impl(
model->devices.clear();
model->devices.push_back(main_gpu);
}
}
for (auto * dev : model->devices) {
size_t free, total; // NOLINT

152
tests/test-thread-safety.cpp Normal file
View file

@ -0,0 +1,152 @@
// thread safety test
// - Loads a copy of the same model on each GPU, plus a copy on the CPU
// - Creates n_parallel (--parallel) contexts per model
// - Runs inference in parallel on each context
#include <thread>
#include <vector>
#include <atomic>
#include "llama.h"
#include "arg.h"
#include "common.h"
#include "log.h"
#include "sampling.h"
int main(int argc, char ** argv) {
common_params params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
common_init();
llama_backend_init();
llama_numa_init(params.numa);
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
//llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
// if (level == GGML_LOG_LEVEL_ERROR) {
// common_log_add(common_log_main(), level, "%s", text);
// }
//}, NULL);
auto cparams = common_context_params_to_llama(params);
int dev_count = ggml_backend_dev_count();
int gpu_dev_count = 0;
for (int i = 0; i < dev_count; ++i) {
auto * dev = ggml_backend_dev_get(i);
if (dev && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
gpu_dev_count++;
}
}
const int num_models = gpu_dev_count + 1 + 1; // GPUs + 1 CPU model + 1 layer split
//const int num_models = std::max(1, gpu_dev_count);
const int num_contexts = std::max(1, params.n_parallel);
std::vector<llama_model_ptr> models;
std::vector<std::thread> threads;
std::atomic<bool> failed = false;
for (int m = 0; m < num_models; ++m) {
auto mparams = common_model_params_to_llama(params);
if (m < gpu_dev_count) {
mparams.split_mode = LLAMA_SPLIT_MODE_NONE;
mparams.main_gpu = m;
} else if (m == gpu_dev_count) {
mparams.split_mode = LLAMA_SPLIT_MODE_NONE;
mparams.main_gpu = -1; // CPU model
} else {
mparams.split_mode = LLAMA_SPLIT_MODE_LAYER;;
}
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
if (model == NULL) {
LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
return 1;
}
models.emplace_back(model);
}
for (int m = 0; m < num_models; ++m) {
auto * model = models[m].get();
for (int c = 0; c < num_contexts; ++c) {
threads.emplace_back([&, m, c, model]() {
LOG_INF("Creating context %d/%d for model %d/%d\n", c + 1, num_contexts, m + 1, num_models);
llama_context_ptr ctx { llama_init_from_model(model, cparams) };
if (ctx == NULL) {
LOG_ERR("failed to create context\n");
failed.store(true);
return;
}
std::unique_ptr<common_sampler, decltype(&common_sampler_free)> sampler { common_sampler_init(model, params.sampling), common_sampler_free };
if (sampler == NULL) {
LOG_ERR("failed to create sampler\n");
failed.store(true);
return;
}
llama_batch batch = {};
{
auto prompt = common_tokenize(ctx.get(), params.prompt, true);
if (prompt.empty()) {
LOG_ERR("failed to tokenize prompt\n");
failed.store(true);
return;
}
batch = llama_batch_get_one(prompt.data(), prompt.size());
if (llama_decode(ctx.get(), batch)) {
LOG_ERR("failed to decode prompt\n");
failed.store(true);
return;
}
}
const auto * vocab = llama_model_get_vocab(model);
std::string result = params.prompt;
for (int i = 0; i < params.n_predict; i++) {
llama_token token;
if (batch.n_tokens > 0) {
token = common_sampler_sample(sampler.get(), ctx.get(), batch.n_tokens - 1);
} else {
token = llama_vocab_bos(vocab);
}
result += common_token_to_piece(ctx.get(), token);
if (llama_vocab_is_eog(vocab, token)) {
break;
}
batch = llama_batch_get_one(&token, 1);
if (llama_decode(ctx.get(), batch)) {
LOG_ERR("Model %d/%d, Context %d/%d: failed to decode\n", m + 1, num_models, c + 1, num_contexts);
failed.store(true);
return;
}
}
LOG_INF("Model %d/%d, Context %d/%d: %s\n\n", m + 1, num_models, c + 1, num_contexts, result.c_str());
});
}
}
for (auto & thread : threads) {
thread.join();
}
if (failed) {
LOG_ERR("One or more threads failed.\n");
return 1;
}
LOG_INF("All threads finished without errors.\n");
return 0;
}

104
tools/server/server.cpp
View file

@ -88,6 +88,26 @@ enum error_type {
ERROR_TYPE_NOT_SUPPORTED, // custom error
};
static bool server_task_type_need_embd(server_task_type task_type) {
switch (task_type) {
case SERVER_TASK_TYPE_EMBEDDING:
case SERVER_TASK_TYPE_RERANK:
return true;
default:
return false;
}
}
static bool server_task_type_need_logits(server_task_type task_type) {
switch (task_type) {
case SERVER_TASK_TYPE_COMPLETION:
case SERVER_TASK_TYPE_INFILL:
return true;
default:
return false;
}
}
struct slot_params {
bool stream = true;
bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt
@ -1330,13 +1350,24 @@ struct server_slot {
n_draft_accepted = 0;
}
bool is_non_causal() const {
return task_type == SERVER_TASK_TYPE_EMBEDDING || task_type == SERVER_TASK_TYPE_RERANK;
bool need_embd() const {
return server_task_type_need_embd(task_type);
}
bool need_logits() const {
return server_task_type_need_logits(task_type);
}
// if the context does not have a memory module then all embeddings have to be computed within a single ubatch
// also we cannot split if the pooling would require any past tokens
bool can_split() const {
return
!need_embd() ||
(llama_get_memory(ctx) && llama_pooling_type(ctx) == LLAMA_POOLING_TYPE_LAST);
}
bool can_batch_with(server_slot & other_slot) const {
return is_non_causal() == other_slot.is_non_causal()
&& are_lora_equal(lora, other_slot.lora);
return task_type == other_slot.task_type && are_lora_equal(lora, other_slot.lora);
}
bool has_budget(const common_params & global_params) {
@ -1480,7 +1511,6 @@ struct server_slot {
{"n_ctx", n_ctx},
{"speculative", can_speculate()},
{"is_processing", is_processing()},
{"non_causal", is_non_causal()},
{"params", params.to_json()},
{"prompt", prompt_tokens.detokenize(ctx, true)},
{"next_token",
@ -2730,6 +2760,7 @@ struct server_context {
queue_tasks.defer(std::move(task));
break;
}
if (slot->is_processing()) {
// if requested slot is unavailable, we defer this task for processing later
SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id);
@ -3092,7 +3123,14 @@ struct server_context {
continue;
}
if (slot.is_non_causal()) {
// TODO: support memory-less logits computation
if (slot.need_logits() && !llama_get_memory(ctx)) {
slot.release();
send_error(slot, "the current context does not logits computation. skipping", ERROR_TYPE_SERVER);
continue;
}
if (!slot.can_split()) {
if (slot.n_prompt_tokens > n_ubatch) {
slot.release();
send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
@ -3227,8 +3265,7 @@ struct server_context {
}
if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) {
// we have to evaluate at least 1 token to generate logits.
SLT_WRN(slot, "need to evaluate at least 1 token to generate logits, n_past = %d, n_prompt_tokens = %d\n", slot.n_past, slot.n_prompt_tokens);
SLT_WRN(slot, "need to evaluate at least 1 token for each active slot, n_past = %d, n_prompt_tokens = %d\n", slot.n_past, slot.n_prompt_tokens);
slot.n_past--;
}
@ -3236,8 +3273,7 @@ struct server_context {
slot.n_prompt_tokens_processed = 0;
}
// non-causal tasks require to fit the entire prompt in the physical batch
if (slot.is_non_causal()) {
if (!slot.can_split()) {
// cannot fit the prompt in the current batch - will try next iter
if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
continue;
@ -3259,8 +3295,7 @@ struct server_context {
slot.cache_tokens.keep_first(slot.n_past);
// check if we should process the image
if (slot.n_past < slot.n_prompt_tokens
&& slot.prompt_tokens[slot.n_past] == LLAMA_TOKEN_NULL) {
if (slot.n_past < slot.n_prompt_tokens && slot.prompt_tokens[slot.n_past] == LLAMA_TOKEN_NULL) {
// process the image
int32_t new_n_past;
int32_t res = slot.prompt_tokens.process_chunk(ctx, mctx, slot.n_past, slot.id, new_n_past);
@ -3291,8 +3326,8 @@ struct server_context {
break; // end of text chunk
}
// without pooling, we want to output the embeddings for all the tokens in the batch
const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE;
// embedding requires all tokens in the batch to be output
const bool need_embd = server_task_type_need_embd(slot.task_type);
common_batch_add(batch, cur_tok, slot.n_past, { slot.id }, need_embd);
slot.cache_tokens.push_back(cur_tok);
@ -3346,17 +3381,15 @@ struct server_context {
SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens);
if (slot_batched) {
// make sure we're in the right embedding mode
llama_set_embeddings(ctx, slot_batched->is_non_causal());
// apply lora, only need to do it once per batch
common_set_adapter_lora(ctx, slot_batched->lora);
}
const bool do_encode = (params_base.embedding || params_base.reranking);
llama_set_embeddings(ctx, slot_batched->need_embd());
}
// pad the batch so that batch.n_tokens >= n_slots
// TODO: temporary workaround for https://github.com/ggml-org/llama.cpp/issues/13689
if (do_encode) {
if (slot_batched->need_embd()) {
const int n_slots = slots.size();
if (batch.n_tokens < n_slots) {
@ -3378,8 +3411,11 @@ struct server_context {
SRV_WRN("adding %d dummy tokens to the batch, seq_id = %d\n", n_add, seq_id);
for (int j = 0; j < n_add; ++j) {
common_batch_add(batch, 0, j, { seq_id }, false);
common_batch_add(batch, 0, j, { seq_id }, true);
}
slots[seq_id].cache_tokens.clear();
llama_memory_seq_rm(llama_get_memory(ctx), seq_id, -1, -1);
}
}
@ -4174,11 +4210,6 @@ int main(int argc, char ** argv) {
oaicompat_type oaicompat) -> void {
GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL);
if (ctx_server.params_base.embedding) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
auto completion_id = gen_chatcmplid();
std::unordered_set<int> task_ids;
try {
@ -4433,12 +4464,8 @@ int main(int argc, char ** argv) {
OAICOMPAT_TYPE_NONE); // infill is not OAI compatible
};
const auto handle_chat_completions = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
const auto handle_chat_completions = [&ctx_server, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
LOG_DBG("request: %s\n", req.body.c_str());
if (ctx_server.params_base.embedding) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
auto body = json::parse(req.body);
std::vector<raw_buffer> files;
@ -4566,13 +4593,18 @@ int main(int argc, char ** argv) {
};
const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) {
const json body = json::parse(req.body);
if (!ctx_server.params_base.embedding) {
res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
return;
}
const json body = json::parse(req.body);
// for the shape of input/content, see tokenize_input_prompts()
json prompt;
if (body.count("input") != 0) {
@ -4662,8 +4694,8 @@ int main(int argc, char ** argv) {
};
const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
if (!ctx_server.params_base.reranking || ctx_server.params_base.embedding) {
res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED));
if (!ctx_server.params_base.embedding || ctx_server.params_base.pooling_type != LLAMA_POOLING_TYPE_RANK) {
res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
@ -4878,7 +4910,9 @@ int main(int argc, char ** argv) {
};
bool was_bound = false;
bool is_sock = false;
if (string_ends_with(std::string(params.hostname), ".sock")) {
is_sock = true;
LOG_INF("%s: setting address family to AF_UNIX\n", __func__);
svr->set_address_family(AF_UNIX);
// bind_to_port requires a second arg, any value other than 0 should
@ -4956,7 +4990,9 @@ int main(int argc, char ** argv) {
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
LOG_INF("%s: server is listening on http://%s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port);
LOG_INF("%s: server is listening on %s - starting the main loop\n", __func__,
is_sock ? string_format("unix://%s", params.hostname.c_str()).c_str() :
string_format("http://%s:%d", params.hostname.c_str(), params.port).c_str());
// this call blocks the main thread until queue_tasks.terminate() is called
ctx_server.queue_tasks.start_loop();