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Llama cpp bump (df1b612): granite docling / mamba2 optimizations / multimodal encoding fixes (#12552)

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* feat: Bump llama.cpp to df1b612

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(mtmd): Correctly encode text chunks during mtmd tokenization

There can be text chunks that appear interspersed with the image embeddings
that contain template delimiter tokens for some models. These need to be
correctly translated to text tokens.

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* tests: Use MtmdChunk in image_test

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* style: Fix unnecessary conversion linting

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(ggml): Revert changes to ggml_hip.cpp

These changes were done largely by our code assistant and are likely wrong

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Revert changes in mem_nvml.cpp

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update sync point to 1deee0

This brings in several more optimization commits and model support for
EmbeddingGemma

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patches for 1deee0

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: sync for bump to 1deee0

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Bad patch updates with errant `+`

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Bump llama.cpp/ggml to 7049736

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: format-patches after latest bump

Branch: LlamaCPPBump-GraniteDocling

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
Gabe Goodhart 2025-10-13 16:26:18 -06:00 committed by GitHub
parent e638f2acb6
commit 4987f13d34
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99 changed files with 4580 additions and 2323 deletions
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2
Makefile.sync
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@ -1,6 +1,6 @@
UPSTREAM=https://github.com/ggml-org/llama.cpp.git
WORKDIR=llama/vendor
FETCH_HEAD=364a7a6d4a786e98947c8a90430ea581213c0ba9
FETCH_HEAD=7049736b2dd9011bf819e298b844ebbc4b5afdc9
.PHONY: help
help:

2
llama/build-info.cpp generated vendored
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@ -1,4 +1,4 @@
int LLAMA_BUILD_NUMBER = 0;
char const *LLAMA_COMMIT = "364a7a6d4a786e98947c8a90430ea581213c0ba9";
char const *LLAMA_COMMIT = "7049736b2dd9011bf819e298b844ebbc4b5afdc9";
char const *LLAMA_COMPILER = "";
char const *LLAMA_BUILD_TARGET = "";

1
llama/llama.cpp/common/common.cpp vendored
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@ -1133,6 +1133,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
mparams.use_mlock = params.use_mlock;
mparams.check_tensors = params.check_tensors;
mparams.use_extra_bufts = !params.no_extra_bufts;
mparams.no_host = params.no_host;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;

6
llama/llama.cpp/common/common.h vendored
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@ -392,6 +392,7 @@ struct common_params {
bool check_tensors = false; // validate tensor data
bool no_op_offload = false; // globally disable offload host tensor operations to device
bool no_extra_bufts = false; // disable extra buffer types (used for weight repacking)
bool no_host = false; // bypass host buffer allowing extra buffers to be used
bool single_turn = false; // single turn chat conversation
@ -424,7 +425,8 @@ struct common_params {
int32_t timeout_write = timeout_read; // http write timeout in seconds
int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool)
int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting
int32_t n_swa_checkpoints = 3; // max number of SWA checkpoints per slot
int32_t n_ctx_checkpoints = 8; // max number of context checkpoints per slot
int32_t cache_ram_mib = 8192; // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
std::string hostname = "127.0.0.1";
std::string public_path = ""; // NOLINT
@ -432,7 +434,7 @@ struct common_params {
std::string chat_template = ""; // NOLINT
bool use_jinja = false; // NOLINT
bool enable_chat_template = true;
common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO;
common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
int reasoning_budget = -1;
bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response

8
llama/llama.cpp/include/llama.h vendored
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@ -296,6 +296,7 @@ extern "C" {
bool use_mlock; // force system to keep model in RAM
bool check_tensors; // validate model tensor data
bool use_extra_bufts; // use extra buffer types (used for weight repacking)
bool no_host; // bypass host buffer allowing extra buffers to be used
};
// NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
@ -543,6 +544,9 @@ extern "C" {
// Returns true if the model is recurrent (like Mamba, RWKV, etc.)
LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model);
// Returns true if the model is hybrid (like Jamba, Granite, etc.)
LLAMA_API bool llama_model_is_hybrid(const struct llama_model * model);
// Returns true if the model is diffusion-based (like LLaDA, Dream, etc.)
LLAMA_API bool llama_model_is_diffusion(const struct llama_model * model);
@ -791,8 +795,12 @@ extern "C" {
size_t n_token_capacity,
size_t * n_token_count_out);
// for backwards-compat
#define LLAMA_STATE_SEQ_FLAGS_SWA_ONLY 1
// work only with partial states, such as SWA KV cache or recurrent cache (e.g. Mamba)
#define LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY 1
typedef uint32_t llama_state_seq_flags;
LLAMA_API size_t llama_state_seq_get_size_ext(

62
llama/llama.cpp/src/llama-arch.cpp vendored
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@ -94,12 +94,14 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_SMOLLM3, "smollm3" },
{ LLM_ARCH_OPENAI_MOE, "gpt-oss" },
{ LLM_ARCH_LFM2, "lfm2" },
{ LLM_ARCH_LFM2MOE, "lfm2moe" },
{ LLM_ARCH_DREAM, "dream" },
{ LLM_ARCH_SMALLTHINKER, "smallthinker" },
{ LLM_ARCH_LLADA, "llada" },
{ LLM_ARCH_LLADA_MOE, "llada-moe" },
{ LLM_ARCH_SEED_OSS, "seed_oss" },
{ LLM_ARCH_GROVEMOE, "grovemoe" },
{ LLM_ARCH_APERTUS, "apertus" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -219,6 +221,11 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
{ LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
// sentence-transformers dense modules feature dims
{ LLM_KV_DENSE_2_FEAT_IN, "%s.dense_2_feat_in" },
{ LLM_KV_DENSE_2_FEAT_OUT, "%s.dense_2_feat_out" },
{ LLM_KV_DENSE_3_FEAT_IN, "%s.dense_3_feat_in" },
{ LLM_KV_DENSE_3_FEAT_OUT, "%s.dense_3_feat_out" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
@ -258,6 +265,11 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ADAPTER_LORA_PROMPT_PREFIX, "adapter.lora.prompt_prefix" },
{ LLM_KV_ADAPTER_ALORA_INVOCATION_TOKENS, "adapter.alora.invocation_tokens" },
{ LLM_KV_XIELU_ALPHA_N, "xielu.alpha_n" },
{ LLM_KV_XIELU_ALPHA_P, "xielu.alpha_p" },
{ LLM_KV_XIELU_BETA, "xielu.beta" },
{ LLM_KV_XIELU_EPS, "xielu.eps" },
// deprecated
{ LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
{ LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
@ -1066,6 +1078,8 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_DENSE_2_OUT, "dense_2" },
{ LLM_TENSOR_DENSE_3_OUT, "dense_3" },
{ 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" },
@ -2118,6 +2132,32 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_OUTPUT, "output" },
}
},
{
LLM_ARCH_LFM2MOE,
{
{ 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_K_NORM, "blk.%d.attn_k_norm" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_SHORTCONV_CONV, "blk.%d.shortconv.conv" },
{ LLM_TENSOR_SHORTCONV_INPROJ, "blk.%d.shortconv.in_proj" },
{ LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ 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_EXP_PROBS_B, "blk.%d.exp_probs_b" },
}
},
{
LLM_ARCH_SMALLTHINKER,
{
@ -2139,6 +2179,25 @@ 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_APERTUS,
{
{ 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_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
{ 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_DREAM,
{
@ -2249,6 +2308,8 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_OUTPUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CLS, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CLS_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_DENSE_2_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
{LLM_TENSOR_DENSE_3_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
{LLM_TENSOR_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_DEC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_ENC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
@ -2489,6 +2550,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
case LLM_ARCH_PLAMO2:
case LLM_ARCH_GRANITE_HYBRID:
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
case LLM_ARCH_NEMOTRON_H:
return true;
default:

15
llama/llama.cpp/src/llama-arch.h vendored
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@ -98,12 +98,14 @@ enum llm_arch {
LLM_ARCH_SMOLLM3,
LLM_ARCH_OPENAI_MOE,
LLM_ARCH_LFM2,
LLM_ARCH_LFM2MOE,
LLM_ARCH_DREAM,
LLM_ARCH_SMALLTHINKER,
LLM_ARCH_LLADA,
LLM_ARCH_LLADA_MOE,
LLM_ARCH_SEED_OSS,
LLM_ARCH_GROVEMOE,
LLM_ARCH_APERTUS,
LLM_ARCH_UNKNOWN,
};
@ -262,10 +264,21 @@ enum llm_kv {
LLM_KV_SHORTCONV_L_CACHE,
LLM_KV_XIELU_ALPHA_N,
LLM_KV_XIELU_ALPHA_P,
LLM_KV_XIELU_BETA,
LLM_KV_XIELU_EPS,
// deprecated:
LLM_KV_TOKENIZER_PREFIX_ID,
LLM_KV_TOKENIZER_SUFFIX_ID,
LLM_KV_TOKENIZER_MIDDLE_ID,
// sentence-transformers dense layers in and out features
LLM_KV_DENSE_2_FEAT_IN,
LLM_KV_DENSE_2_FEAT_OUT,
LLM_KV_DENSE_3_FEAT_IN,
LLM_KV_DENSE_3_FEAT_OUT,
};
enum llm_tensor {
@ -273,6 +286,8 @@ enum llm_tensor {
LLM_TENSOR_TOKEN_EMBD_NORM,
LLM_TENSOR_TOKEN_TYPES,
LLM_TENSOR_POS_EMBD,
LLM_TENSOR_DENSE_2_OUT,
LLM_TENSOR_DENSE_3_OUT,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_ROPE_FREQS,

2
llama/llama.cpp/src/llama-chat.cpp vendored
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@ -590,7 +590,7 @@ int32_t llm_chat_apply_template(
ss << message->content << "<|end_of_text|>\n";
}
if (add_ass) {
ss << "<|start_of_role|>assistant<|end_of_role|>\n";
ss << "<|start_of_role|>assistant<|end_of_role|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
// GigaChat template

6
llama/llama.cpp/src/llama-context.cpp vendored
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@ -2345,6 +2345,12 @@ llama_context * llama_init_from_model(
return nullptr;
}
if (params.pooling_type != model->hparams.pooling_type) {
//user-specified pooling-type is different from the model default
LLAMA_LOG_WARN("%s: model default pooling_type is [%d], but [%d] was specified\n", __func__,
model->hparams.pooling_type, params.pooling_type);
}
try {
auto * ctx = new llama_context(*model, params);
return ctx;

17
llama/llama.cpp/src/llama-graph.cpp vendored
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@ -1853,6 +1853,23 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
}
void llm_graph_context::build_dense_out(
ggml_tensor * dense_2,
ggml_tensor * dense_3) const {
if (!cparams.embeddings || dense_2 == nullptr || dense_3 == nullptr) {
return;
}
ggml_tensor * cur = res->t_embd_pooled != nullptr ? res->t_embd_pooled : res->t_embd;
GGML_ASSERT(cur != nullptr && "missing t_embd_pooled/t_embd");
cur = ggml_mul_mat(ctx0, dense_2, cur);
cur = ggml_mul_mat(ctx0, dense_3, cur);
cb(cur, "result_embd_pooled", -1);
res->t_embd_pooled = cur;
ggml_build_forward_expand(gf, cur);
}
void llm_graph_context::build_pooling(
ggml_tensor * cls,
ggml_tensor * cls_b,

8
llama/llama.cpp/src/llama-graph.h vendored
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@ -814,6 +814,14 @@ struct llm_graph_context {
ggml_tensor * cls_b,
ggml_tensor * cls_out,
ggml_tensor * cls_out_b) const;
//
// dense (out)
//
void build_dense_out(
ggml_tensor * dense_2,
ggml_tensor * dense_3) const;
};
// TODO: better name

4
llama/llama.cpp/src/llama-hparams.cpp vendored
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@ -140,9 +140,13 @@ uint32_t llama_hparams::n_embd_s() const {
}
bool llama_hparams::is_recurrent(uint32_t il) const {
if (il < n_layer) {
return recurrent_layer_arr[il];
}
GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer);
}
uint32_t llama_hparams::n_pos_per_embd() const {
return rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
}

12
llama/llama.cpp/src/llama-hparams.h vendored
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@ -171,6 +171,18 @@ struct llama_hparams {
uint32_t laurel_rank = 64;
uint32_t n_embd_altup = 256;
// needed for sentence-transformers dense layers
uint32_t dense_2_feat_in = 0; // in_features of the 2_Dense
uint32_t dense_2_feat_out = 0; // out_features of the 2_Dense
uint32_t dense_3_feat_in = 0; // in_features of the 3_Dense
uint32_t dense_3_feat_out = 0; // out_features of the 3_Dense
// xIELU
std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_n;
std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_p;
std::array<float, LLAMA_MAX_LAYERS> xielu_beta;
std::array<float, LLAMA_MAX_LAYERS> xielu_eps;
// needed by encoder-decoder models (e.g. T5, FLAN-T5)
// ref: https://github.com/ggerganov/llama.cpp/pull/8141
llama_token dec_start_token_id = LLAMA_TOKEN_NULL;

4
llama/llama.cpp/src/llama-kv-cache-iswa.cpp vendored
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@ -220,7 +220,7 @@ bool llama_kv_cache_iswa::get_can_shift() const {
}
void llama_kv_cache_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
if ((flags & LLAMA_STATE_SEQ_FLAGS_SWA_ONLY) == 0) {
if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
kv_base->state_write(io, seq_id, flags);
}
@ -228,7 +228,7 @@ void llama_kv_cache_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id
}
void llama_kv_cache_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
if ((flags & LLAMA_STATE_SEQ_FLAGS_SWA_ONLY) == 0) {
if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
kv_base->state_read(io, seq_id, flags);
}

7
llama/llama.cpp/src/llama-kv-cache.cpp vendored
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@ -123,11 +123,8 @@ llama_kv_cache::llama_kv_cache(
throw std::runtime_error("failed to create ggml context for kv cache");
}
ggml_tensor * k;
ggml_tensor * v;
k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream);
v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream);
ggml_tensor * k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream);
ggml_tensor * v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream);
ggml_format_name(k, "cache_k_l%d", il);
ggml_format_name(v, "cache_v_l%d", il);

20
llama/llama.cpp/src/llama-memory-hybrid.cpp vendored
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@ -73,7 +73,9 @@ llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & ba
// if all tokens are output, split by sequence
ubatch = balloc.split_seq(n_ubatch);
} else {
ubatch = balloc.split_equal(n_ubatch, false);
// TODO: non-sequential equal split can be done if using unified KV cache
// for simplicity, we always use sequential equal split for now
ubatch = balloc.split_equal(n_ubatch, true);
}
if (ubatch.n_tokens == 0) {
@ -175,17 +177,17 @@ std::map<ggml_backend_buffer_type_t, size_t> llama_memory_hybrid::memory_breakdo
}
void llama_memory_hybrid::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
GGML_UNUSED(flags);
mem_attn->state_write(io, seq_id);
mem_recr->state_write(io, seq_id);
if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
mem_attn->state_write(io, seq_id, flags);
}
mem_recr->state_write(io, seq_id, flags);
}
void llama_memory_hybrid::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
GGML_UNUSED(flags);
mem_attn->state_read(io, seq_id);
mem_recr->state_read(io, seq_id);
if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
mem_attn->state_read(io, seq_id, flags);
}
mem_recr->state_read(io, seq_id, flags);
}
llama_kv_cache * llama_memory_hybrid::get_mem_attn() const {

14
llama/llama.cpp/src/llama-memory-recurrent.cpp vendored
View File

@ -136,6 +136,7 @@ void llama_memory_recurrent::clear(bool data) {
}
bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
//printf("[DEBUG] calling llama_memory_recurrent::seq_rm` with `seq_id=%d, p0=%d, p1=%d`\n", seq_id, p0, p1);
uint32_t new_head = size;
if (p0 < 0) {
@ -156,7 +157,8 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
if (tail_id >= 0) {
const auto & cell = cells[tail_id];
// partial intersection is invalid
if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) {
if ((0 < p0 && p0 < cell.pos) || (0 < p1 && p1 <= cell.pos)) {
//printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: partial intersection is invalid, so returning false\n");
return false;
}
// invalidate tails which will be cleared
@ -167,6 +169,7 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
} else {
// seq_id is negative, then the range should include everything or nothing
if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
//printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: `seq_id` is negative, so returning false\n");
return false;
}
}
@ -379,7 +382,9 @@ llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr &
// if all tokens are output, split by sequence
ubatch = balloc.split_seq(n_ubatch);
} else {
ubatch = balloc.split_equal(n_ubatch, false);
// TODO: non-sequential equal split can be done if using unified KV cache
// for simplicity, we always use sequential equal split for now
ubatch = balloc.split_equal(n_ubatch, true);
}
if (ubatch.n_tokens == 0) {
@ -856,9 +861,12 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id) {
if (dest_seq_id != -1) {
// single sequence
seq_rm(dest_seq_id, -1, -1);
if (cell_count == 0) {
return true;
}
llama_batch_allocr balloc(hparams.n_pos_per_embd());
llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1);

1
llama/llama.cpp/src/llama-model-loader.cpp vendored
View File

@ -465,6 +465,7 @@ namespace GGUFMeta {
// TODO: this is not very clever - figure out something better
template bool llama_model_loader::get_key_or_arr<std::array<int, 4>>(enum llm_kv kid, std::array<int, 4> & result, uint32_t n, bool required);
template bool llama_model_loader::get_key_or_arr<std::array<uint32_t, 512>>(enum llm_kv kid, std::array<uint32_t, 512> & result, uint32_t n, bool required);
template bool llama_model_loader::get_key_or_arr<std::array<float, 512>>(enum llm_kv kid, std::array<float, 512> & result, uint32_t n, bool required);
template bool llama_model_loader::get_key_or_arr<uint32_t>(const std::string & key, std::array<uint32_t, 512> & result, uint32_t n, bool required);
llama_model_loader::llama_model_loader(

339
llama/llama.cpp/src/llama-model.cpp vendored
View File

@ -114,6 +114,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
case LLM_TYPE_A13B: return "A13B";
case LLM_TYPE_8B_A1B: return "8B.A1B";
case LLM_TYPE_21B_A3B: return "21B.A3B";
case LLM_TYPE_30B_A3B: return "30B.A3B";
case LLM_TYPE_106B_A12B: return "106B.A12B";
@ -310,7 +311,7 @@ static ggml_backend_buffer_type_t select_weight_buft(const llama_hparams & hpara
}
// CPU: ACCEL -> GPU host -> CPU extra -> CPU
static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & devices, bool use_extra_bufts) {
static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & devices, bool use_extra_bufts, bool no_host) {
buft_list_t buft_list;
// add ACCEL buffer types
@ -331,6 +332,7 @@ static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & de
// generally, this will be done using the first device in the list
// a better approach would be to handle this on a weight-by-weight basis using the offload_op
// function of the device to determine if it would benefit from being stored in a host buffer
if (!no_host) {
for (auto * dev : devices) {
ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev);
if (buft) {
@ -338,6 +340,7 @@ static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & de
break;
}
}
}
// add extra buffer types
if (use_extra_bufts) {
@ -512,9 +515,13 @@ void llama_model::load_hparams(llama_model_loader & ml) {
llm_arch_is_recurrent(ml.get_arch()));
std::fill(hparams.rope_sections.begin(), hparams.rope_sections.end(), 0);
std::fill(hparams.swa_layers.begin(), hparams.swa_layers.end(), 0);
std::fill(hparams.xielu_alpha_n.begin(), hparams.xielu_alpha_n.end(), 0.0f);
std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
std::fill(hparams.xielu_beta.begin(), hparams.xielu_beta.end(), 0.0f);
std::fill(hparams.xielu_eps.begin(), hparams.xielu_eps.end(), 0.0f);
ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer, false);
ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
@ -1085,6 +1092,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
break;
default: type = LLM_TYPE_UNKNOWN;
}
// Load attention parameters
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
} break;
case LLM_ARCH_GPT2:
{
@ -1214,6 +1225,15 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
//applied only if model converted with --sentence-transformers-dense-modules
ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
ml.get_key(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out, false);
ml.get_key(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in, false);
ml.get_key(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out, false);
GGML_ASSERT((hparams.dense_2_feat_in == 0 || hparams.dense_2_feat_in == hparams.n_embd) && "dense_2_feat_in must be equal to n_embd");
GGML_ASSERT((hparams.dense_3_feat_out == 0 || hparams.dense_3_feat_out == hparams.n_embd) && "dense_3_feat_out must be equal to n_embd");
switch (hparams.n_layer) {
case 24: type = LLM_TYPE_0_3B; break;
default: type = LLM_TYPE_UNKNOWN;
@ -2000,6 +2020,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
for (uint32_t il = 0; il < hparams.n_layer; ++il) {
hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
}
hparams.n_layer_dense_lead = hparams.n_layer;
switch (hparams.n_ff()) {
case 4608: type = LLM_TYPE_350M; break;
case 6912: type = LLM_TYPE_700M; break;
@ -2008,6 +2029,20 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_LFM2MOE:
{
ml.get_key(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache);
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_GATING_FUNC, hparams.expert_gating_func);
for (uint32_t il = 0; il < hparams.n_layer; ++il) {
hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
}
type = LLM_TYPE_8B_A1B;
} break;
case LLM_ARCH_SMALLTHINKER:
{
const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
@ -2044,6 +2079,19 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_APERTUS:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key_or_arr(LLM_KV_XIELU_ALPHA_N, hparams.xielu_alpha_n, hparams.n_layer);
ml.get_key_or_arr(LLM_KV_XIELU_ALPHA_P, hparams.xielu_alpha_p, hparams.n_layer);
ml.get_key_or_arr(LLM_KV_XIELU_BETA, hparams.xielu_beta, hparams.n_layer);
ml.get_key_or_arr(LLM_KV_XIELU_EPS, hparams.xielu_eps, hparams.n_layer);
switch (hparams.n_layer) {
case 32: type = LLM_TYPE_8B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
default: throw std::runtime_error("unsupported model architecture");
}
@ -2077,7 +2125,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s)\n", __func__, ml.use_mmap ? "true" : "false");
// build a list of buffer types for the CPU and GPU devices
pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts);
pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host);
for (auto * dev : devices) {
buft_list_t buft_list = make_gpu_buft_list(dev, split_mode, tensor_split);
// add CPU buffer types as a fallback
@ -3407,17 +3455,17 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
} break;
case LLM_ARCH_PLAMO2:
{
// mamba parameters
const uint32_t d_conv = hparams.ssm_d_conv;
const uint32_t d_state = hparams.ssm_d_state;
const uint32_t num_heads = hparams.ssm_dt_rank;
const uint32_t intermediate_size = hparams.ssm_d_inner;
const uint32_t head_dim = intermediate_size / num_heads;
const uint32_t qk_dim = head_dim;
const uint32_t v_dim = head_dim;
const int64_t num_attention_heads = hparams.n_head();
const int64_t q_num_heads = num_attention_heads;
const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16));
// attention parameters
const uint32_t qk_dim = hparams.n_embd_head_k;
const uint32_t v_dim = hparams.n_embd_head_v;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
@ -3451,6 +3499,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, i), {d_state}, 0);
layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, i), {d_state}, 0);
} else {
const int64_t num_attention_heads = hparams.n_head(i);
const int64_t q_num_heads = num_attention_heads;
const int64_t num_key_value_heads = hparams.n_head_kv(i);
const int64_t k_num_heads = num_key_value_heads;
const int64_t v_num_heads = num_key_value_heads;
@ -3459,8 +3509,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
const int64_t v_proj_dim = v_num_heads * v_dim;
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, q_proj_dim + k_proj_dim + v_proj_dim}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim, num_attention_heads}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim, k_num_heads}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {qk_dim, num_attention_heads}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {qk_dim, k_num_heads}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {q_num_heads * v_dim, n_embd}, 0);
}
@ -3660,6 +3710,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
// Dense linear weights
dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);
dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
@ -4840,11 +4895,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
// NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags);
// Optional tensors
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
}
}
}
@ -5830,6 +5887,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
} break;
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
@ -5841,11 +5899,23 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
// ffn is same for transformer and conv layers
const bool is_moe_layer = i >= static_cast<int>(hparams.n_layer_dense_lead);
// ffn/moe is same for transformer and conv layers
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
if (is_moe_layer) {
GGML_ASSERT(n_expert && n_expert_used);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
} else { // dense
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);
}
// for operator_norm
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
@ -5950,6 +6020,48 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_up_chexps = create_tensor(tn(LLM_TENSOR_FFN_UP_CHEXPS, "weight", i), { n_embd, n_ff_chexp, n_chunk_expert}, 0);
}
} break;
case LLM_ARCH_APERTUS:
{
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 }, 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);
if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
} else {
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 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_gqa }, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
// optional bias tensors
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_gqa }, TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_gqa }, TENSOR_NOT_REQUIRED);
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 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);
// Q and K layernorms for Apertus
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@ -6284,7 +6396,7 @@ void llama_model::print_info() const {
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
}
if (arch == LLM_ARCH_SMALLTHINKER) {
if (arch == LLM_ARCH_SMALLTHINKER || arch == LLM_ARCH_LFM2MOE) {
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
}
@ -7819,6 +7931,8 @@ struct llm_build_bert : public llm_graph_context {
}
if (model.layers[il].attn_q_norm) {
Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head*n_head, n_tokens);
Qcur = build_norm(Qcur,
model.layers[il].attn_q_norm,
model.layers[il].attn_q_norm_b,
@ -7828,6 +7942,8 @@ struct llm_build_bert : public llm_graph_context {
}
if (model.layers[il].attn_k_norm) {
Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head*n_head_kv, n_tokens);
Kcur = build_norm(Kcur,
model.layers[il].attn_k_norm,
model.layers[il].attn_k_norm_b,
@ -8210,6 +8326,9 @@ struct llm_build_mpt : public llm_graph_context {
// Q/K Layernorm
if (model.layers[il].attn_q_norm) {
Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head*n_head, n_tokens);
Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head*n_head_kv, n_tokens);
Qcur = build_norm(Qcur,
model.layers[il].attn_q_norm,
model.layers[il].attn_q_norm_b,
@ -17811,6 +17930,7 @@ private:
const int64_t n_embd_head_q = hparams.n_embd_head_k;
const int64_t n_embd_head_k = hparams.n_embd_head_k;
const int64_t n_embd_head_v = hparams.n_embd_head_v;
int32_t n_head = hparams.n_head(il);
int32_t n_head_kv = hparams.n_head_kv(il);
const int64_t q_offset = 0;
@ -18727,6 +18847,8 @@ struct llm_build_lfm2 : public llm_graph_context {
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
const bool is_moe_layer = il >= static_cast<int>(hparams.n_layer_dense_lead);
auto * prev_cur = cur;
cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "model.layers.{}.operator_norm", il);
@ -18741,7 +18863,16 @@ struct llm_build_lfm2 : public llm_graph_context {
}
cur = ggml_add(ctx0, prev_cur, cur);
cur = ggml_add(ctx0, cur, build_feed_forward(cur, il));
auto * ffn_norm_out = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(ffn_norm_out, "model.layers.{}.ffn_norm", il);
ggml_tensor * ffn_out = is_moe_layer ?
build_moe_feed_forward(ffn_norm_out, il) :
build_dense_feed_forward(ffn_norm_out, il);
cb(ffn_norm_out, "model.layers.{}.ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_out);
}
cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
@ -18756,23 +18887,32 @@ struct llm_build_lfm2 : public llm_graph_context {
ggml_build_forward_expand(gf, cur);
}
ggml_tensor * build_feed_forward(ggml_tensor * cur,
ggml_tensor * build_moe_feed_forward(ggml_tensor * cur,
int il) const {
cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "model.layers.{}.ffn_norm", il);
return 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, true,
false, 0.0,
static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
il);
}
ggml_tensor * build_dense_feed_forward(ggml_tensor * cur,
int il) const {
GGML_ASSERT(!model.layers[il].ffn_up_b);
GGML_ASSERT(!model.layers[il].ffn_gate_b);
GGML_ASSERT(!model.layers[il].ffn_down_b);
cur = build_ffn(cur,
return 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, "model.layers.{}.feed_forward.w2", il);
return cur;
}
ggml_tensor * build_attn_block(ggml_tensor * cur,
@ -19292,6 +19432,141 @@ struct llm_build_grovemoe : public llm_graph_context {
}
};
struct llm_build_apertus : public llm_graph_context {
llm_build_apertus(const llama_model & model, const llm_graph_params & params) : 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);
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv();
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
cur = build_norm(inpL,
model.layers[il].attn_norm, nullptr,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
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);
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);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);
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_pos", il);
cb(Kcur, "Kcur_pos", il);
cb(Vcur, "Vcur_pos", il);
cur = build_attn(inp_attn,
model.layers[il].wo, model.layers[il].bo,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
}
if (il == n_layer - 1 && 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 with xIELU activation
{
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, nullptr,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
// Up projection
ggml_tensor * up = build_lora_mm(model.layers[il].ffn_up, cur);
cb(up, "ffn_up", il);
float alpha_n_val = hparams.xielu_alpha_n[il];
float alpha_p_val = hparams.xielu_alpha_p[il];
float beta_val = hparams.xielu_beta[il];
float eps_val = hparams.xielu_eps[il];
// Apply xIELU activation
ggml_tensor * activated = ggml_xielu(ctx0, up, alpha_n_val, alpha_p_val, beta_val, eps_val);
cb(activated, "ffn_xielu", il);
// Down projection
cur = build_lora_mm(model.layers[il].ffn_down, activated);
cb(cur, "ffn_down", 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, nullptr,
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;
@ -19811,6 +20086,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
llm = std::make_unique<llm_build_falcon_h1>(*this, params);
} break;
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
{
llm = std::make_unique<llm_build_lfm2>(*this, params);
} break;
@ -19826,6 +20102,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_grovemoe>(*this, params);
} break;
case LLM_ARCH_APERTUS:
{
llm = std::make_unique<llm_build_apertus>(*this, params);
} break;
default:
GGML_ABORT("fatal error");
}
@ -19833,6 +20113,12 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
// add on pooling layer
llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
// if the gguf model was converted with --sentence-transformers-dense-modules
// there will be two additional dense projection layers
// dense linear projections are applied after pooling
// TODO: move reranking logic here and generalize
llm->build_dense_out(dense_2_out_layers, dense_3_out_layers);
return llm->res->get_gf();
}
@ -19857,6 +20143,7 @@ llama_model_params llama_model_default_params() {
/*.use_mlock =*/ false,
/*.check_tensors =*/ false,
/*.use_extra_bufts =*/ true,
/*.no_host =*/ false,
};
return result;
@ -20029,10 +20316,12 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_OPENAI_MOE:
case LLM_ARCH_HUNYUAN_DENSE:
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
case LLM_ARCH_SMALLTHINKER:
case LLM_ARCH_GLM4_MOE:
case LLM_ARCH_SEED_OSS:
case LLM_ARCH_GROVEMOE:
case LLM_ARCH_APERTUS:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:
@ -20143,6 +20432,10 @@ bool llama_model_is_recurrent(const llama_model * model) {
return llm_arch_is_recurrent(model->arch);
}
bool llama_model_is_hybrid(const llama_model * model) {
return llm_arch_is_hybrid(model->arch);
}
bool llama_model_is_diffusion(const llama_model * model) {
return llm_arch_is_diffusion(model->arch);
}

13
llama/llama.cpp/src/llama-model.h vendored
View File

@ -108,6 +108,7 @@ enum llm_type {
LLM_TYPE_17B_16E, // llama4 Scout
LLM_TYPE_17B_128E, // llama4 Maverick
LLM_TYPE_A13B,
LLM_TYPE_8B_A1B, // lfm2moe
LLM_TYPE_21B_A3B, // Ernie MoE small
LLM_TYPE_30B_A3B,
LLM_TYPE_106B_A12B, // GLM-4.5-Air
@ -381,6 +382,12 @@ struct llama_layer {
// openai-moe
struct ggml_tensor * attn_sinks = nullptr;
// xIELU activation parameters for Apertus
struct ggml_tensor * ffn_act_alpha_n = nullptr;
struct ggml_tensor * ffn_act_alpha_p = nullptr;
struct ggml_tensor * ffn_act_beta = nullptr;
struct ggml_tensor * ffn_act_eps = nullptr;
struct ggml_tensor * bskcn_tv = nullptr;
struct llama_layer_posnet posnet;
@ -434,6 +441,12 @@ struct llama_model {
std::vector<llama_layer> layers;
//Dense linear projections for SentenceTransformers models like embeddinggemma
// For Sentence Transformers models structure see
// https://sbert.net/docs/sentence_transformer/usage/custom_models.html#structure-of-sentence-transformer-models
struct ggml_tensor * dense_2_out_layers = nullptr;
struct ggml_tensor * dense_3_out_layers = nullptr;
llama_model_params params;
// gguf metadata

5
llama/llama.cpp/src/llama-sampling.cpp vendored
View File

@ -2541,8 +2541,13 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
if (n_non_eog == 0) {
cur_p->size = 1;
cur_p->data[0].id = ctx->vocab->token_eot();
if (cur_p->data[0].id == LLAMA_TOKEN_NULL) {
cur_p->data[0].id = ctx->vocab->token_eos();
}
cur_p->data[0].logit = 1.0f;
GGML_ASSERT(cur_p->data[0].id != LLAMA_TOKEN_NULL);
return;
}

6
llama/llama.cpp/src/llama-vocab.cpp vendored
View File

@ -347,6 +347,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
case LLAMA_VOCAB_PRE_TYPE_OLMO:
case LLAMA_VOCAB_PRE_TYPE_JAIS:
case LLAMA_VOCAB_PRE_TYPE_TRILLION:
case LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING:
regex_exprs = {
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
};
@ -1950,6 +1951,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
tokenizer_pre == "trillion") {
pre_type = LLAMA_VOCAB_PRE_TYPE_TRILLION;
clean_spaces = false;
} else if (
tokenizer_pre == "granite-docling") {
pre_type = LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING;
clean_spaces = false;
} else if (
tokenizer_pre == "bailingmoe" ||
tokenizer_pre == "llada-moe") {
@ -2156,6 +2161,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|| t.first == "<|end|>"
|| t.first == "<end_of_turn>"
|| t.first == "<|endoftext|>"
|| t.first == "<|end_of_text|>" // granite
|| t.first == "<EOT>"
|| t.first == "_<EOT>"
|| t.first == "<｜end▁of▁sentence｜>" // DeepSeek

1
llama/llama.cpp/src/llama-vocab.h vendored
View File

@ -48,6 +48,7 @@ enum llama_vocab_pre_type {
LLAMA_VOCAB_PRE_TYPE_KIMI_K2 = 37,
LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE = 38,
LLAMA_VOCAB_PRE_TYPE_GROK_2 = 39,
LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
};
struct LLM_KV;

1
llama/llama.cpp/tools/mtmd/clip-impl.h vendored
View File

@ -31,6 +31,7 @@
// vision-specific
#define KEY_IMAGE_SIZE "clip.vision.image_size"
#define KEY_PREPROC_IMAGE_SIZE "clip.vision.preproc_image_size"
#define KEY_PATCH_SIZE "clip.vision.patch_size"
#define KEY_IMAGE_MEAN "clip.vision.image_mean"
#define KEY_IMAGE_STD "clip.vision.image_std"

50
llama/llama.cpp/tools/mtmd/clip.cpp vendored
View File

@ -183,7 +183,9 @@ struct clip_hparams {
int32_t projection_dim;
int32_t n_head;
int32_t n_layer;
int32_t proj_scale_factor = 0; // idefics3
// idefics3
int32_t preproc_image_size = 0;
int32_t proj_scale_factor = 0;
float image_mean[3];
float image_std[3];
@ -2263,6 +2265,7 @@ struct clip_model_loader {
if (is_vision) {
get_u32(KEY_IMAGE_SIZE, hparams.image_size);
get_u32(KEY_PREPROC_IMAGE_SIZE, hparams.preproc_image_size, false);
get_u32(KEY_PATCH_SIZE, hparams.patch_size);
get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
get_i32(KEY_MINICPMV_VERSION, hparams.minicpmv_version, false); // legacy
@ -3590,10 +3593,51 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
// res_imgs->data[0] = *res;
res_imgs->entries.push_back(std::move(img_f32));
return true;
} else if (ctx->proj_type() == PROJECTOR_TYPE_IDEFICS3) {
// The refined size has two steps:
// 1. Resize w/ aspect-ratio preserving such that the longer side is
// the preprocessor longest size
// 2. Resize w/out preserving aspect ratio such that both sides are
// multiples of image_size (always rounding up)
//
// CITE: https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics3/image_processing_idefics3.py#L737
const clip_image_size refined_size = image_manipulation::calc_size_preserved_ratio(
original_size, params.image_size, params.preproc_image_size);
llava_uhd::slice_instructions instructions;
instructions.overview_size = clip_image_size{params.image_size, params.image_size};
instructions.refined_size = refined_size;
instructions.grid_size = clip_image_size{
static_cast<int>(std::ceil(static_cast<float>(refined_size.width) / params.image_size)),
static_cast<int>(std::ceil(static_cast<float>(refined_size.height) / params.image_size)),
};
for (int y = 0; y < refined_size.height; y += params.image_size) {
for (int x = 0; x < refined_size.width; x += params.image_size) {
instructions.slices.push_back(llava_uhd::slice_coordinates{
/* x */x,
/* y */y,
/* size */clip_image_size{
std::min(params.image_size, refined_size.width - x),
std::min(params.image_size, refined_size.height - y)
}
else if (ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE
});
}
}
auto imgs = llava_uhd::slice_image(img, instructions);
// cast and normalize to f32
for (size_t i = 0; i < imgs.size(); ++i) {
// clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
clip_image_f32_ptr res(clip_image_f32_init());
normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
res_imgs->entries.push_back(std::move(res));
}
res_imgs->grid_x = instructions.grid_size.width;
res_imgs->grid_y = instructions.grid_size.height;
return true;
} else if (ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE
|| ctx->proj_type() == PROJECTOR_TYPE_GEMMA3
|| ctx->proj_type() == PROJECTOR_TYPE_IDEFICS3
|| ctx->proj_type() == PROJECTOR_TYPE_INTERNVL // TODO @ngxson : support dynamic resolution
) {
clip_image_u8 resized_image;

105
llama/llama.cpp/tools/mtmd/mtmd.cpp vendored
View File

@ -76,7 +76,7 @@ enum mtmd_slice_tmpl {
MTMD_SLICE_TMPL_MINICPMV_2_5,
MTMD_SLICE_TMPL_MINICPMV_2_6,
MTMD_SLICE_TMPL_LLAMA4,
// TODO @ngxson : add support for idefics (SmolVLM)
MTMD_SLICE_TMPL_IDEFICS3,
};
mtmd_input_text* mtmd_input_text_init(const char * text, bool add_special, bool parse_special) {
@ -124,19 +124,22 @@ struct mtmd_context {
// for llava-uhd style models, we need special tokens in-between slices
// minicpmv calls them "slices", llama 4 calls them "tiles"
mtmd_slice_tmpl slice_tmpl = MTMD_SLICE_TMPL_NONE;
llama_token tok_ov_img_start = LLAMA_TOKEN_NULL; // overview image
llama_token tok_ov_img_end = LLAMA_TOKEN_NULL; // overview image
llama_token tok_slices_start = LLAMA_TOKEN_NULL; // start of all slices
llama_token tok_slices_end = LLAMA_TOKEN_NULL; // end of all slices
llama_token tok_sli_img_start = LLAMA_TOKEN_NULL; // single slice start
llama_token tok_sli_img_end = LLAMA_TOKEN_NULL; // single slice end
llama_token tok_sli_img_mid = LLAMA_TOKEN_NULL; // between 2 slices
llama_token tok_row_end = LLAMA_TOKEN_NULL; // end of row
std::vector<llama_token> tok_ov_img_start; // overview image
std::vector<llama_token> tok_ov_img_end; // overview image
std::vector<llama_token> tok_slices_start; // start of all slices
std::vector<llama_token> tok_slices_end; // end of all slices
std::vector<llama_token> tok_sli_img_start; // single slice start
std::vector<llama_token> tok_sli_img_end; // single slice end
std::vector<llama_token> tok_sli_img_mid; // between 2 slices
std::vector<llama_token> tok_row_end; // end of row
bool tok_row_end_trail = false;
bool ov_img_first = false;
bool use_mrope = false; // for Qwen2VL, we need to use M-RoPE
// string template for slice image delimiters with row/col (idefics3)
std::string sli_img_start_tmpl;
// for whisper, we pre-calculate the mel filter bank
whisper_preprocessor::whisper_filters w_filters;
@ -207,13 +210,13 @@ struct mtmd_context {
// minicpmv 2.5 format:
// <image> (overview) </image><slice><image> (slice) </image><image> (slice) </image>\n ... </slice>
slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_5;
tok_ov_img_start = lookup_token("<image>");
tok_ov_img_end = lookup_token("</image>");
tok_slices_start = lookup_token("<slice>");
tok_slices_end = lookup_token("</slice>");
tok_ov_img_start = {lookup_token("<image>")};
tok_ov_img_end = {lookup_token("</image>")};
tok_slices_start = {lookup_token("<slice>")};
tok_slices_end = {lookup_token("</slice>")};
tok_sli_img_start = tok_ov_img_start;
tok_sli_img_end = tok_ov_img_end;
tok_row_end = lookup_token("\n");
tok_row_end = {lookup_token("\n")};
tok_row_end_trail = false; // no trailing end-of-row token
ov_img_first = true;
@ -221,11 +224,11 @@ struct mtmd_context {
// minicpmv 2.6 format:
// <image> (overview) </image><slice> (slice) </slice><slice> (slice) </slice>\n ...
slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_6;
tok_ov_img_start = lookup_token("<image>");
tok_ov_img_end = lookup_token("</image>");
tok_sli_img_start = lookup_token("<slice>");
tok_sli_img_end = lookup_token("</slice>");
tok_row_end = lookup_token("\n");
tok_ov_img_start = {lookup_token("<image>")};
tok_ov_img_end = {lookup_token("</image>")};
tok_sli_img_start = {lookup_token("<slice>")};
tok_sli_img_end = {lookup_token("</slice>")};
tok_row_end = {lookup_token("\n")};
tok_row_end_trail = false; // no trailing end-of-row token
ov_img_first = true;
@ -240,9 +243,9 @@ struct mtmd_context {
// <|image|> (overview) <-- overview image is last
// <|image_end|>
slice_tmpl = MTMD_SLICE_TMPL_LLAMA4;
tok_ov_img_start = lookup_token("<|image|>");
tok_sli_img_mid = lookup_token("<|tile_x_separator|>");
tok_row_end = lookup_token("<|tile_y_separator|>");
tok_ov_img_start = {lookup_token("<|image|>")};
tok_sli_img_mid = {lookup_token("<|tile_x_separator|>")};
tok_row_end = {lookup_token("<|tile_y_separator|>")};
tok_row_end_trail = true; // add trailing end-of-row token
ov_img_first = false; // overview image is last
}
@ -255,8 +258,11 @@ struct mtmd_context {
} else if (proj == PROJECTOR_TYPE_IDEFICS3) {
// https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215
img_beg = "<fake_token_around_image><global-img>";
img_end = "<fake_token_around_image>";
slice_tmpl = MTMD_SLICE_TMPL_IDEFICS3;
tok_ov_img_start = {lookup_token("\n\n"), lookup_token("<fake_token_around_image>"), lookup_token("<global-img>")};
tok_ov_img_end = {lookup_token("<fake_token_around_image>")};
tok_row_end = {lookup_token("\n")};
sli_img_start_tmpl = "<fake_token_around_image><row_%d_col_%d>";
} else if (proj == PROJECTOR_TYPE_PIXTRAL) {
// https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md
@ -514,6 +520,7 @@ struct mtmd_tokenizer {
ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5
|| ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
|| ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
|| ctx->slice_tmpl == MTMD_SLICE_TMPL_IDEFICS3
) {
const int n_col = batch_f32.grid_x;
const int n_row = batch_f32.grid_y;
@ -527,53 +534,45 @@ struct mtmd_tokenizer {
// add overview image (first)
if (ctx->ov_img_first) {
if (ctx->tok_ov_img_start != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_ov_img_start});
}
add_text(ctx->tok_ov_img_start);
cur.entries.emplace_back(std::move(ov_chunk));
if (ctx->tok_ov_img_end != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_ov_img_end});
}
add_text(ctx->tok_ov_img_end);
}
// add slices (or tiles)
if (!chunks.empty()) {
GGML_ASSERT((int)chunks.size() == n_row * n_col);
if (ctx->tok_slices_start != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_slices_start});
}
add_text(ctx->tok_slices_start);
for (int y = 0; y < n_row; y++) {
for (int x = 0; x < n_col; x++) {
const bool is_last_in_row = (x == n_col - 1);
if (ctx->tok_sli_img_start != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_sli_img_start});
if (!ctx->tok_sli_img_start.empty()) {
add_text(ctx->tok_sli_img_start);
} else if (!ctx->sli_img_start_tmpl.empty()) {
// If using a template to preceed a slice image
const size_t sz = std::snprintf(nullptr, 0, ctx->sli_img_start_tmpl.c_str(), y+1, x+1) + 1;
std::unique_ptr<char[]> buf(new char[sz]);
std::snprintf(buf.get(), sz, ctx->sli_img_start_tmpl.c_str(), y+1, x+1);
add_text(std::string(buf.get(), buf.get() + sz - 1), true);
}
cur.entries.emplace_back(std::move(chunks[y * n_col + x]));
if (ctx->tok_sli_img_end != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_sli_img_end});
}
if (!is_last_in_row && ctx->tok_sli_img_mid != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_sli_img_mid});
add_text(ctx->tok_sli_img_end);
if (!is_last_in_row) {
add_text(ctx->tok_sli_img_mid);
}
}
if ((y != n_row - 1 || ctx->tok_row_end_trail) && ctx->tok_row_end != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_row_end});
if ((y != n_row - 1 || ctx->tok_row_end_trail)) {
add_text(ctx->tok_row_end);
}
}
if (ctx->tok_slices_end != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_slices_end});
}
add_text(ctx->tok_slices_end);
}
// add overview image (last)
if (!ctx->ov_img_first) {
if (ctx->tok_ov_img_start != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_ov_img_start});
}
add_text(ctx->tok_ov_img_start);
cur.entries.emplace_back(std::move(ov_chunk));
if (ctx->tok_ov_img_end != LLAMA_TOKEN_NULL) {
add_text({ctx->tok_ov_img_end});
}
add_text(ctx->tok_ov_img_end);
}
} else {
@ -790,7 +789,9 @@ int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens)
ctx->image_embd_v.resize(image_tokens->n_tokens() * n_mmproj_embd);
bool ok = false;
if (clip_is_llava(ctx_clip) || clip_is_minicpmv(ctx_clip) || clip_is_glm(ctx_clip)) {
if (clip_is_llava(ctx_clip)
|| clip_is_minicpmv(ctx_clip)
|| clip_is_glm(ctx_clip)) {
// TODO @ngxson : llava does not support batched encoding ; this should be fixed inside clip_image_batch_encode()
const auto & entries = image_tokens->batch_f32.entries;
for (size_t i = 0; i < entries.size(); i++) {

33
llama/llama.go
View File

@ -504,7 +504,12 @@ func (c *MtmdContext) Free() {
C.mtmd_free(c.c)
}
func (c *MtmdContext) NewEmbed(llamaContext *Context, data []byte) ([][]float32, error) {
type MtmdChunk struct {
Embed []float32
Tokens []int
}
func (c *MtmdContext) MultimodalTokenize(llamaContext *Context, data []byte) ([]MtmdChunk, error) {
// Initialize the input chunks pointer
ic := C.mtmd_input_chunks_init()
defer C.mtmd_input_chunks_free(ic)
@ -523,12 +528,25 @@ func (c *MtmdContext) NewEmbed(llamaContext *Context, data []byte) ([][]float32,
}
nChunks := C.mtmd_input_chunks_size(ic)
numEmbed := llamaContext.Model().NEmbd()
embed := make([][]float32, 0)
outChunks := make([]MtmdChunk, 0)
for i := range int(nChunks) {
chunk := C.mtmd_input_chunks_get(ic, C.size_t(i))
numTokens := int(C.mtmd_input_chunk_get_n_tokens(chunk))
slog.Debug("chunk tokens", "index", i, "numTokens", numTokens)
if C.mtmd_input_chunk_get_type(chunk) == C.MTMD_INPUT_CHUNK_TYPE_TEXT {
// If this is a text chunk, add the tokens
cNumTokens := C.size_t(0)
cTokens := C.mtmd_input_chunk_get_tokens_text(chunk, &cNumTokens)
cTokensArr := unsafe.Slice(cTokens, int(cNumTokens))
tokens := make([]int, int(cNumTokens))
for j := range int(cNumTokens) {
tokens[j] = int(cTokensArr[j])
}
outChunks = append(outChunks, MtmdChunk{Tokens: tokens})
} else {
// Otherwise, encode the image chunk to embeddings
// Encode the chunk
if C.int32_t(0) != C.mtmd_encode_chunk(c.c, chunk) {
return nil, errors.New("unable to encode mtmd image chunk")
@ -538,7 +556,7 @@ func (c *MtmdContext) NewEmbed(llamaContext *Context, data []byte) ([][]float32,
chunkEmbed := make([][]float32, numTokens)
chunkEmbd := C.mtmd_get_output_embd(c.c)
if nil == chunkEmbd {
continue
return nil, errors.New("no mtmd image embedding")
}
// Extend the embedding array for each token
@ -548,10 +566,13 @@ func (c *MtmdContext) NewEmbed(llamaContext *Context, data []byte) ([][]float32,
for i := range numTokens {
chunkEmbed[i] = rows[i*numEmbed : (i+1)*numEmbed]
}
embed = append(embed, chunkEmbed...)
for _, e := range chunkEmbed {
outChunks = append(outChunks, MtmdChunk{Embed: e})
}
slog.Debug("image embeddings", "totalEmbeddings", len(embed))
return embed, nil
}
}
slog.Debug("image tokenization chunks", "totalChunks", len(outChunks))
return outChunks, nil
}
func (c *Context) Synchronize() {

28
llama/patches/0001-ggml-backend-malloc-and-free-using-the-same-compiler.patch
View File

@ -64,7 +64,7 @@ index ff9135fe..8ba86f82 100644
/* .init_tensor = */ NULL, // no initialization required
/* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor,
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index b51b554e..3ba0f5a6 100755
index ad1adba6..7d44f74f 100755
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -843,6 +843,7 @@ static void ggml_backend_cann_buffer_free_buffer(
@ -84,7 +84,7 @@ index b51b554e..3ba0f5a6 100755
/**
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index b7e81b21..fdf8c63d 100644
index 856e9de2..c0b1e4c1 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -567,6 +567,7 @@ struct ggml_backend_cuda_buffer_context {
@ -112,7 +112,7 @@ index b7e81b21..fdf8c63d 100644
static void * ggml_cuda_host_malloc(size_t size) {
diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp
index e11555a7..909e17de 100644
index 7afc881f..bf096227 100644
--- a/ggml/src/ggml-metal/ggml-metal.cpp
+++ b/ggml/src/ggml-metal/ggml-metal.cpp
@@ -25,6 +25,7 @@ static void ggml_backend_metal_buffer_shared_free_buffer(ggml_backend_buffer_t b
@ -132,10 +132,10 @@ index e11555a7..909e17de 100644
static void * ggml_backend_metal_buffer_private_get_base(ggml_backend_buffer_t buffer) {
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 0cf3b924..09d706b5 100644
index 79d21487..38c75018 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -3215,6 +3215,7 @@ struct ggml_backend_opencl_buffer_context {
@@ -3212,6 +3212,7 @@ struct ggml_backend_opencl_buffer_context {
static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
delete ctx;
@ -144,10 +144,10 @@ index 0cf3b924..09d706b5 100644
static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) {
diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index f99681c8..59591770 100644
index aad48d62..a46c0f52 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -505,6 +505,7 @@ static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) {
@@ -528,6 +528,7 @@ static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) {
bool status = send_rpc_cmd(ctx->sock, RPC_CMD_FREE_BUFFER, &request, sizeof(request), nullptr, 0);
RPC_STATUS_ASSERT(status);
delete ctx;
@ -156,10 +156,10 @@ index f99681c8..59591770 100644
static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) {
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 4ac919ea..447ea3c4 100644
index 45b8c216..4ec9a592 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -331,6 +331,7 @@ ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try {
@@ -334,6 +334,7 @@ ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try {
ggml_sycl_set_device(ctx->device);
delete ctx;
@ -167,7 +167,7 @@ index 4ac919ea..447ea3c4 100644
}
catch (sycl::exception const &exc) {
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -792,6 +793,7 @@ struct ggml_backend_sycl_split_buffer_context {
@@ -795,6 +796,7 @@ struct ggml_backend_sycl_split_buffer_context {
static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
delete ctx;
@ -175,7 +175,7 @@ index 4ac919ea..447ea3c4 100644
}
static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) {
@@ -1134,6 +1136,7 @@ static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_
@@ -1137,6 +1139,7 @@ static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_
static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_sycl_host_free(buffer->context);
@ -184,10 +184,10 @@ index 4ac919ea..447ea3c4 100644
static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 2608cbd0..061cd078 100644
index 3cd89c71..ed83236f 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -11603,6 +11603,7 @@ static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
@@ -11600,6 +11600,7 @@ static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
ggml_vk_destroy_buffer(ctx->dev_buffer);
delete ctx;
@ -195,7 +195,7 @@ index 2608cbd0..061cd078 100644
}
static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) {
@@ -11746,6 +11747,7 @@ static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffe
@@ -11743,6 +11744,7 @@ static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffe
static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
VK_LOG_MEMORY("ggml_backend_vk_host_buffer_free_buffer()");
ggml_vk_host_free(vk_instance.devices[0], buffer->context);

6
llama/patches/0002-pretokenizer.patch
View File

@ -10,10 +10,10 @@ logs instead of throwing an error
1 file changed, 3 insertions(+), 11 deletions(-)
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index da938af0..2a38abf4 100644
index 7fffd171..0b6edaf4 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1811,16 +1811,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
@@ -1812,16 +1812,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
if (type == LLAMA_VOCAB_TYPE_BPE) {
add_space_prefix = false;
clean_spaces = true;
@ -31,7 +31,7 @@ index da938af0..2a38abf4 100644
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
} else if (
tokenizer_pre == "llama3" ||
@@ -1987,7 +1978,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
@@ -1992,7 +1983,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
pre_type = LLAMA_VOCAB_PRE_TYPE_GROK_2;
clean_spaces = false;
} else {

6
llama/patches/0003-clip-unicode.patch
View File

@ -10,7 +10,7 @@ filesystems for paths that include wide characters
1 file changed, 39 insertions(+)
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 210ecc88..355219a9 100644
index 98e68af2..6699b75a 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -28,6 +28,19 @@
@ -33,7 +33,7 @@ index 210ecc88..355219a9 100644
struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callback_default, NULL};
enum ffn_op_type {
@@ -2759,7 +2772,29 @@ struct clip_model_loader {
@@ -2762,7 +2775,29 @@ struct clip_model_loader {
{
std::vector<uint8_t> read_buf;
@ -63,7 +63,7 @@ index 210ecc88..355219a9 100644
if (!fin) {
throw std::runtime_error(string_format("%s: failed to open %s\n", __func__, fname.c_str()));
}
@@ -2786,7 +2821,11 @@ struct clip_model_loader {
@@ -2789,7 +2824,11 @@ struct clip_model_loader {
ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
}
}

53
llama/patches/0004-solar-pro.patch
View File

@ -9,13 +9,13 @@ adds support for the Solar Pro architecture
src/llama-arch.h | 3 +
src/llama-hparams.cpp | 8 ++
src/llama-hparams.h | 5 +
src/llama-model-loader.cpp | 1 +
src/llama-model-loader.cpp | 2 +-
src/llama-model.cpp | 207 +++++++++++++++++++++++++++++++++++++
src/llama-model.h | 3 +
7 files changed, 248 insertions(+)
7 files changed, 248 insertions(+), 1 deletion(-)
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 4e8d54c4..f98a3574 100644
index 869e4dcc..9f6b6ad2 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -81,6 +81,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
@ -26,7 +26,7 @@ index 4e8d54c4..f98a3574 100644
{ LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
{ LLM_ARCH_PLM, "plm" },
{ LLM_ARCH_BAILINGMOE, "bailingmoe" },
@@ -177,6 +178,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
@@ -179,6 +180,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
{ LLM_KV_ATTENTION_OUTPUT_SCALE, "%s.attention.output_scale" },
{ LLM_KV_ATTENTION_TEMPERATURE_LENGTH, "%s.attention.temperature_length" },
@ -34,7 +34,7 @@ index 4e8d54c4..f98a3574 100644
{ LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" },
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
@@ -1879,6 +1881,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
@@ -1893,6 +1895,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
@ -59,7 +59,7 @@ index 4e8d54c4..f98a3574 100644
{
LLM_ARCH_WAVTOKENIZER_DEC,
{
@@ -2368,6 +2388,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
@@ -2429,6 +2449,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_LAUREL_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
// this tensor is loaded for T5, but never used
{LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
@ -68,7 +68,7 @@ index 4e8d54c4..f98a3574 100644
{LLM_TENSOR_POS_NET_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_POS_NET_NORM1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index b5c6f3d7..aa8e0e7b 100644
index c3ae7165..dc7a362a 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -85,6 +85,7 @@ enum llm_arch {
@ -79,7 +79,7 @@ index b5c6f3d7..aa8e0e7b 100644
LLM_ARCH_WAVTOKENIZER_DEC,
LLM_ARCH_PLM,
LLM_ARCH_BAILINGMOE,
@@ -181,6 +182,7 @@ enum llm_kv {
@@ -183,6 +184,7 @@ enum llm_kv {
LLM_KV_ATTENTION_SCALE,
LLM_KV_ATTENTION_OUTPUT_SCALE,
LLM_KV_ATTENTION_TEMPERATURE_LENGTH,
@ -87,7 +87,7 @@ index b5c6f3d7..aa8e0e7b 100644
LLM_KV_ATTENTION_KEY_LENGTH_MLA,
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
@@ -417,6 +419,7 @@ enum llm_tensor {
@@ -432,6 +434,7 @@ enum llm_tensor {
LLM_TENSOR_ENC_OUTPUT_NORM,
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
@ -96,10 +96,10 @@ index b5c6f3d7..aa8e0e7b 100644
LLM_TENSOR_CONVNEXT_DW,
LLM_TENSOR_CONVNEXT_NORM,
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index c04ac58f..24a515a0 100644
index db65d69e..b6bf6bbf 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -147,6 +147,14 @@ uint32_t llama_hparams::n_pos_per_embd() const {
@@ -151,6 +151,14 @@ uint32_t llama_hparams::n_pos_per_embd() const {
return rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
}
@ -115,7 +115,7 @@ index c04ac58f..24a515a0 100644
if (il < n_layer) {
return swa_layers[il];
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 0fe4b569..eb13709f 100644
index 4e7f73ec..80582728 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -64,6 +64,8 @@ struct llama_hparams {
@ -127,7 +127,7 @@ index 0fe4b569..eb13709f 100644
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
@@ -236,6 +238,9 @@ struct llama_hparams {
@@ -248,6 +250,9 @@ struct llama_hparams {
uint32_t n_pos_per_embd() const;
@ -138,22 +138,23 @@ index 0fe4b569..eb13709f 100644
bool has_kv(uint32_t il) const;
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 8182a9ad..daef900c 100644
index aa3a65f8..ee303bd5 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -465,6 +465,7 @@ namespace GGUFMeta {
// TODO: this is not very clever - figure out something better
@@ -466,7 +466,7 @@ namespace GGUFMeta {
template bool llama_model_loader::get_key_or_arr<std::array<int, 4>>(enum llm_kv kid, std::array<int, 4> & result, uint32_t n, bool required);
template bool llama_model_loader::get_key_or_arr<std::array<uint32_t, 512>>(enum llm_kv kid, std::array<uint32_t, 512> & result, uint32_t n, bool required);
template bool llama_model_loader::get_key_or_arr<std::array<float, 512>>(enum llm_kv kid, std::array<float, 512> & result, uint32_t n, bool required);
-
+ template bool llama_model_loader::get_key_or_arr<uint32_t>(const std::string & key, std::array<uint32_t, 512> & result, uint32_t n, bool required);
llama_model_loader::llama_model_loader(
const std::string & fname,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 2470f878..0398b553 100644
index 36d495d6..74e1d162 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1845,6 +1845,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
@@ -1865,6 +1865,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
@ -175,7 +176,7 @@ index 2470f878..0398b553 100644
case LLM_ARCH_WAVTOKENIZER_DEC:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -5113,6 +5128,34 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
@@ -5170,6 +5185,34 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
@ -210,7 +211,7 @@ index 2470f878..0398b553 100644
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);
@@ -16273,6 +16316,165 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba {
@@ -16392,6 +16435,165 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba {
}
};
@ -376,7 +377,7 @@ index 2470f878..0398b553 100644
// ref: https://github.com/facebookresearch/chameleon
// based on the original build_llama() function, changes:
// * qk-norm
@@ -19552,6 +19754,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
@@ -19827,6 +20029,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_chameleon>(*this, params);
} break;
@ -387,7 +388,7 @@ index 2470f878..0398b553 100644
case LLM_ARCH_WAVTOKENIZER_DEC:
{
llm = std::make_unique<llm_build_wavtokenizer_dec>(*this, params);
@@ -19770,6 +19976,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
@@ -20057,6 +20263,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_GRANITE_HYBRID:
case LLM_ARCH_CHAMELEON:
@ -396,7 +397,7 @@ index 2470f878..0398b553 100644
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_SMOLLM3:
diff --git a/src/llama-model.h b/src/llama-model.h
index d73ce969..c086f94e 100644
index 7f48662f..ec3fbd33 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -76,6 +76,7 @@ enum llm_type {
@ -407,9 +408,9 @@ index d73ce969..c086f94e 100644
LLM_TYPE_27B,
LLM_TYPE_30B,
LLM_TYPE_32B,
@@ -380,6 +381,8 @@ struct llama_layer {
// openai-moe
struct ggml_tensor * attn_sinks = nullptr;
@@ -387,6 +388,8 @@ struct llama_layer {
struct ggml_tensor * ffn_act_beta = nullptr;
struct ggml_tensor * ffn_act_eps = nullptr;
+ struct ggml_tensor * bskcn_tv = nullptr;
+

2
llama/patches/0005-fix-deepseek-deseret-regex.patch
View File

@ -12,7 +12,7 @@ regex
2 files changed, 22 insertions(+), 1 deletion(-)
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 2a38abf4..26fa9fad 100644
index 0b6edaf4..3de95c67 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -299,7 +299,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {

6
llama/patches/0008-add-phony-target-ggml-cpu-for-all-cpu-variants.patch
View File

@ -8,10 +8,10 @@ Subject: [PATCH] add phony target ggml-cpu for all cpu variants
1 file changed, 2 insertions(+)
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index c8f3d859..ff6229a0 100644
index 892c2331..09fdf5fc 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -307,6 +307,7 @@ function(ggml_add_cpu_backend_variant tag_name)
@@ -310,6 +310,7 @@ function(ggml_add_cpu_backend_variant tag_name)
endif()
ggml_add_cpu_backend_variant_impl(${tag_name})
@ -19,7 +19,7 @@ index c8f3d859..ff6229a0 100644
endfunction()
ggml_add_backend(CPU)
@@ -317,6 +318,7 @@ if (GGML_CPU_ALL_VARIANTS)
@@ -320,6 +321,7 @@ if (GGML_CPU_ALL_VARIANTS)
elseif (GGML_CPU_ARM_ARCH)
message(FATAL_ERROR "Cannot use both GGML_CPU_ARM_ARCH and GGML_CPU_ALL_VARIANTS")
endif()

4
llama/patches/0009-remove-amx.patch
View File

@ -9,10 +9,10 @@ disable amx as it reduces performance on some systems
1 file changed, 4 deletions(-)
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index ff6229a0..33b3a15f 100644
index 09fdf5fc..0609c650 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -327,10 +327,6 @@ if (GGML_CPU_ALL_VARIANTS)
@@ -330,10 +330,6 @@ if (GGML_CPU_ALL_VARIANTS)
ggml_add_cpu_backend_variant(skylakex SSE42 AVX F16C AVX2 BMI2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake SSE42 AVX F16C AVX2 BMI2 FMA AVX_VNNI)

4
llama/patches/0010-fix-string-arr-kv-loading.patch
View File

@ -53,10 +53,10 @@ index 8cc4ef1c..d950dbdf 100644
}
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 26fa9fad..64c78a16 100644
index 3de95c67..217ede47 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1767,9 +1767,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
@@ -1768,9 +1768,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
if (precompiled_charsmap_keyidx != -1) {
const gguf_type pc_type = gguf_get_arr_type(ctx, precompiled_charsmap_keyidx);

4
llama/patches/0011-ollama-debug-tensor.patch
View File

@ -8,7 +8,7 @@ Subject: [PATCH] ollama debug tensor
1 file changed, 6 insertions(+)
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index dbc07301..f8574d01 100644
index ba2a36d9..99509b0c 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -15,6 +15,8 @@
@ -20,7 +20,7 @@ index dbc07301..f8574d01 100644
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
@@ -2881,6 +2883,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
@@ -2887,6 +2889,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
ggml_compute_forward(&params, node);

2
llama/patches/0012-add-ollama-vocab-for-grammar-support.patch
View File

@ -184,7 +184,7 @@ index f8c291de..2a3a62db 100644
const char * grammar_root,
bool lazy,
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 2186f827..8fb86009 100644
index 55d2e355..da34526b 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1563,7 +1563,7 @@ static void llama_sampler_grammar_reset(struct llama_sampler * smpl) {

10
llama/patches/0013-add-argsort-and-cuda-copy-for-i32.patch
View File

@ -12,10 +12,10 @@ Subject: [PATCH] add argsort and cuda copy for i32
5 files changed, 256 insertions(+), 2 deletions(-)
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 14f7dcf4..f7f8da35 100644
index 1c43865f..31478dd8 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -7893,6 +7893,45 @@ static void ggml_compute_forward_argsort_f32(
@@ -7889,6 +7889,45 @@ static void ggml_compute_forward_argsort_f32(
}
}
@ -61,7 +61,7 @@ index 14f7dcf4..f7f8da35 100644
void ggml_compute_forward_argsort(
const ggml_compute_params * params,
ggml_tensor * dst) {
@@ -7904,6 +7943,10 @@ void ggml_compute_forward_argsort(
@@ -7900,6 +7939,10 @@ void ggml_compute_forward_argsort(
{
ggml_compute_forward_argsort_f32(params, dst);
} break;
@ -272,10 +272,10 @@ index 746f4396..911220e9 100644
ggml_cpy_flt_cuda<nv_bfloat16, nv_bfloat16> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
} else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F16) {
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 96df6f0c..44dc31c0 100644
index 74a9aa99..375a0c7f 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -4428,8 +4428,72 @@ kernel void kernel_argsort_f32_i32(
@@ -4346,8 +4346,72 @@ kernel void kernel_argsort_f32_i32(
}
}

18
llama/patches/0014-graph-memory-reporting-on-failure.patch
View File

@ -23,10 +23,10 @@ index 2cb150fd..7ab3f019 100644
// Utils
// Create a buffer and allocate all the tensors in a ggml_context
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index 62b6d65e..fe20dca3 100644
index f1b74078..c54ff98b 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -316,6 +316,7 @@ extern "C" {
@@ -318,6 +318,7 @@ extern "C" {
GGML_API ggml_backend_buffer_type_t ggml_backend_sched_get_buffer_type(ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
@ -35,10 +35,10 @@ index 62b6d65e..fe20dca3 100644
GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index fa46f3b4..421ff7c7 100644
index 929bc448..eee9d3b1 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -492,6 +492,7 @@ struct node_alloc {
@@ -486,6 +486,7 @@ struct node_alloc {
struct ggml_gallocr {
ggml_backend_buffer_type_t * bufts; // [n_buffers]
struct vbuffer ** buffers; // [n_buffers]
@ -46,7 +46,7 @@ index fa46f3b4..421ff7c7 100644
struct ggml_dyn_tallocr ** buf_tallocs; // [n_buffers]
int n_buffers;
@@ -515,6 +516,9 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
@@ -509,6 +510,9 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
galloc->buffers = calloc(n_bufs, sizeof(struct vbuffer *));
GGML_ASSERT(galloc->buffers != NULL);
@ -56,7 +56,7 @@ index fa46f3b4..421ff7c7 100644
galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
GGML_ASSERT(galloc->buf_tallocs != NULL);
@@ -582,6 +586,7 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) {
@@ -576,6 +580,7 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) {
ggml_hash_set_free(&galloc->hash_set);
free(galloc->hash_values);
free(galloc->bufts);
@ -64,7 +64,7 @@ index fa46f3b4..421ff7c7 100644
free(galloc->buffers);
free(galloc->buf_tallocs);
free(galloc->node_allocs);
@@ -875,6 +880,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
@@ -869,6 +874,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
}
}
@ -73,7 +73,7 @@ index fa46f3b4..421ff7c7 100644
// reallocate buffers if needed
for (int i = 0; i < galloc->n_buffers; i++) {
// if the buffer type is used multiple times, we reuse the same buffer
@@ -896,14 +903,19 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
@@ -898,14 +905,19 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
ggml_vbuffer_free(galloc->buffers[i]);
galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
@ -96,7 +96,7 @@ index fa46f3b4..421ff7c7 100644
}
bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
@@ -1058,6 +1070,22 @@ size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
@@ -1060,6 +1072,22 @@ size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
return ggml_vbuffer_size(galloc->buffers[buffer_id]);
}

14
llama/patches/0015-ggml-Export-GPU-UUIDs.patch
View File

@ -12,7 +12,7 @@ with tools (e.g. nvidia-smi) and system management libraries (e.g. nvml).
3 files changed, 63 insertions(+), 6 deletions(-)
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index fe20dca3..48777212 100644
index c54ff98b..229bf387 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -158,6 +158,7 @@ extern "C" {
@ -24,7 +24,7 @@ index fe20dca3..48777212 100644
size_t memory_total;
// device type
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index fdf8c63d..ad389ece 100644
index c0b1e4c1..5b852f69 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -183,6 +183,51 @@ static int ggml_cuda_parse_id(char devName[]) {
@ -110,7 +110,7 @@ index fdf8c63d..ad389ece 100644
std::string device_name(prop.name);
if (device_name == "NVIDIA GeForce MX450") {
turing_devices_without_mma.push_back({ id, device_name });
@@ -3273,6 +3320,7 @@ struct ggml_backend_cuda_device_context {
@@ -3276,6 +3323,7 @@ struct ggml_backend_cuda_device_context {
std::string name;
std::string description;
std::string pci_bus_id;
@ -118,7 +118,7 @@ index fdf8c63d..ad389ece 100644
};
static const char * ggml_backend_cuda_device_get_name(ggml_backend_dev_t dev) {
@@ -3285,6 +3333,11 @@ static const char * ggml_backend_cuda_device_get_description(ggml_backend_dev_t
@@ -3288,6 +3336,11 @@ static const char * ggml_backend_cuda_device_get_description(ggml_backend_dev_t
return ctx->description.c_str();
}
@ -130,7 +130,7 @@ index fdf8c63d..ad389ece 100644
static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
ggml_cuda_set_device(ctx->device);
@@ -3301,6 +3354,7 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
@@ -3304,6 +3357,7 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
props->name = ggml_backend_cuda_device_get_name(dev);
props->description = ggml_backend_cuda_device_get_description(dev);
@ -138,7 +138,7 @@ index fdf8c63d..ad389ece 100644
props->type = ggml_backend_cuda_device_get_type(dev);
props->device_id = ctx->pci_bus_id.empty() ? nullptr : ctx->pci_bus_id.c_str();
ggml_backend_cuda_device_get_memory(dev, &props->memory_free, &props->memory_total);
@@ -3871,6 +3925,7 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
@@ -3873,6 +3927,7 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
cudaDeviceProp prop;
CUDA_CHECK(cudaGetDeviceProperties(&prop, i));
dev_ctx->description = prop.name;
@ -147,7 +147,7 @@ index fdf8c63d..ad389ece 100644
char pci_bus_id[16] = {};
snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.0", prop.pciDomainID, prop.pciBusID, prop.pciDeviceID);
diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp
index 909e17de..08ab4fc9 100644
index bf096227..f2ff9f32 100644
--- a/ggml/src/ggml-metal/ggml-metal.cpp
+++ b/ggml/src/ggml-metal/ggml-metal.cpp
@@ -538,6 +538,7 @@ static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backen

4
llama/patches/0016-add-C-API-for-mtmd_input_text.patch
View File

@ -10,11 +10,11 @@ Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
2 files changed, 13 insertions(+)
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index cd022c5e..3d680945 100644
index 4d487581..35a0d25e 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -79,6 +79,16 @@ enum mtmd_slice_tmpl {
// TODO @ngxson : add support for idefics (SmolVLM)
MTMD_SLICE_TMPL_IDEFICS3,
};
+mtmd_input_text* mtmd_input_text_init(const char * text, bool add_special, bool parse_special) {

4
llama/patches/0017-no-power-throttling-win32-with-gnuc.patch
View File

@ -8,10 +8,10 @@ Subject: [PATCH] no power throttling win32 with gnuc
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index f8574d01..530efce0 100644
index 99509b0c..b13a491d 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -2431,7 +2431,7 @@ static bool ggml_thread_apply_priority(int32_t prio) {
@@ -2437,7 +2437,7 @@ static bool ggml_thread_apply_priority(int32_t prio) {
// Newer Windows 11 versions aggresively park (offline) CPU cores and often place
// all our threads onto the first 4 cores which results in terrible performance with
// n_threads > 4

6
llama/patches/0019-Enable-CUDA-Graphs-for-gemma3n.patch
View File

@ -13,10 +13,10 @@ checks.
1 file changed, 18 insertions(+)
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index ad389ece..e51c5035 100644
index 5b852f69..827e3205 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2686,14 +2686,26 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
@@ -2689,14 +2689,26 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
// Loop over nodes in GGML graph to obtain info needed for CUDA graph
cuda_ctx->cuda_graph->cpy_dest_ptrs.clear();
@ -43,7 +43,7 @@ index ad389ece..e51c5035 100644
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
@@ -2717,6 +2729,12 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
@@ -2720,6 +2732,12 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
if (node->op == GGML_OP_ADD &&
node->src[1] && node->src[1]->ne[1] > 1 &&

26
llama/patches/0022-ggml-No-alloc-mode.patch
View File

@ -16,10 +16,10 @@ must be recreated with no-alloc set to false before loading data.
5 files changed, 310 insertions(+), 44 deletions(-)
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index 48777212..d4352663 100644
index 229bf387..1ff53ed0 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -303,6 +303,7 @@ extern "C" {
@@ -305,6 +305,7 @@ extern "C" {
// Initialize a backend scheduler, backends with low index are given priority over backends with high index
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel, bool op_offload);
@ -28,7 +28,7 @@ index 48777212..d4352663 100644
// Initialize backend buffers from a measure graph
diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h
index 07784d6f..869dc07d 100644
index 6792ba98..3c3f22fc 100644
--- a/ggml/src/ggml-backend-impl.h
+++ b/ggml/src/ggml-backend-impl.h
@@ -26,12 +26,17 @@ extern "C" {
@ -218,7 +218,7 @@ index cb2b9956..6ef5eeaf 100644
void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index c4246b65..448badf0 100644
index e0abde54..28d6bcd7 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -35,6 +35,31 @@
@ -253,7 +253,7 @@ index c4246b65..448badf0 100644
#define STRINGIZE_IMPL(...) #__VA_ARGS__
#define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__)
@@ -880,6 +905,9 @@ struct ggml_cuda_pool {
@@ -856,6 +881,9 @@ struct ggml_cuda_pool {
virtual void * alloc(size_t size, size_t * actual_size) = 0;
virtual void free(void * ptr, size_t size) = 0;
@ -263,7 +263,7 @@ index c4246b65..448badf0 100644
};
template<typename T>
@@ -1023,11 +1051,11 @@ struct ggml_backend_cuda_context {
@@ -999,11 +1027,11 @@ struct ggml_backend_cuda_context {
// pool
std::unique_ptr<ggml_cuda_pool> pools[GGML_CUDA_MAX_DEVICES];
@ -277,7 +277,7 @@ index c4246b65..448badf0 100644
}
return *pools[device];
}
@@ -1035,4 +1063,20 @@ struct ggml_backend_cuda_context {
@@ -1011,4 +1039,20 @@ struct ggml_backend_cuda_context {
ggml_cuda_pool & pool() {
return pool(device);
}
@ -299,7 +299,7 @@ index c4246b65..448badf0 100644
+ }
};
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index e51c5035..d324bc68 100644
index 827e3205..811462c7 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -350,6 +350,8 @@ const ggml_cuda_device_info & ggml_cuda_info() {
@ -540,7 +540,7 @@ index e51c5035..d324bc68 100644
};
ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
@@ -3008,6 +3070,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
@@ -3011,6 +3073,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
bool & graph_evaluated_or_captured, bool & use_cuda_graph, bool & cuda_graph_update_required) {
@ -548,7 +548,7 @@ index e51c5035..d324bc68 100644
// flag used to determine whether it is an integrated_gpu
const bool integrated = ggml_cuda_info().devices[cuda_ctx->device].integrated;
@@ -3023,6 +3086,11 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
@@ -3026,6 +3089,11 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
continue;
}
@ -560,7 +560,7 @@ index e51c5035..d324bc68 100644
static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
if (!disable_fusion) {
@@ -3149,6 +3217,7 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
@@ -3152,6 +3220,7 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
@ -568,7 +568,7 @@ index e51c5035..d324bc68 100644
ggml_cuda_set_device(cuda_ctx->device);
@@ -3228,6 +3297,71 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
@@ -3231,6 +3300,71 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
return GGML_STATUS_SUCCESS;
}
@ -640,7 +640,7 @@ index e51c5035..d324bc68 100644
static void ggml_backend_cuda_event_record(ggml_backend_t backend, ggml_backend_event_t event) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
@@ -3268,6 +3402,9 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
@@ -3271,6 +3405,9 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
/* .event_record = */ ggml_backend_cuda_event_record,
/* .event_wait = */ ggml_backend_cuda_event_wait,
/* .graph_optimize = */ NULL,

2
llama/patches/0023-decode-disable-output_all.patch
View File

@ -8,7 +8,7 @@ Subject: [PATCH] decode: disable output_all
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index d8a8b5e6..09247cef 100644
index e7526e7d..53a5e3a9 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -974,8 +974,7 @@ int llama_context::decode(const llama_batch & batch_inp) {

28
llama/patches/0024-ggml-Enable-resetting-backend-devices.patch
View File

@ -10,12 +10,12 @@ unused then it can be reset to free these data structures.
ggml/include/ggml-backend.h | 1 +
ggml/src/ggml-backend-impl.h | 4 ++++
ggml/src/ggml-backend.cpp | 8 ++++++++
ggml/src/ggml-cuda/ggml-cuda.cu | 17 +++++++++++++++--
ggml/src/ggml-cuda/ggml-cuda.cu | 16 +++++++++++++++-
ggml/src/ggml-cuda/vendors/hip.h | 1 +
5 files changed, 29 insertions(+), 2 deletions(-)
5 files changed, 29 insertions(+), 1 deletion(-)
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index d4352663..0a2dae26 100644
index 1ff53ed0..ba181d09 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -178,6 +178,7 @@ extern "C" {
@ -27,7 +27,7 @@ index d4352663..0a2dae26 100644
GGML_API ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device);
GGML_API ggml_backend_buffer_t ggml_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device, void * ptr, size_t size, size_t max_tensor_size);
diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h
index 869dc07d..4889df79 100644
index 3c3f22fc..43c91d9f 100644
--- a/ggml/src/ggml-backend-impl.h
+++ b/ggml/src/ggml-backend-impl.h
@@ -195,6 +195,10 @@ extern "C" {
@ -61,7 +61,7 @@ index 6ef5eeaf..0b757af5 100644
GGML_ASSERT(device);
return device->iface.get_buffer_type(device);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index d324bc68..531d6e27 100644
index 811462c7..87c6c34a 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -107,6 +107,11 @@ int ggml_cuda_get_device() {
@ -76,7 +76,7 @@ index d324bc68..531d6e27 100644
static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) {
ggml_cuda_set_device(device);
cudaError_t err;
@@ -3512,7 +3517,10 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
@@ -3515,7 +3520,10 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
props->id = ggml_backend_cuda_device_get_id(dev);
props->type = ggml_backend_cuda_device_get_type(dev);
props->device_id = ctx->pci_bus_id.empty() ? nullptr : ctx->pci_bus_id.c_str();
@ -88,7 +88,7 @@ index d324bc68..531d6e27 100644
bool host_buffer = getenv("GGML_CUDA_NO_PINNED") == nullptr;
#ifdef GGML_CUDA_NO_PEER_COPY
@@ -3945,6 +3953,11 @@ static void ggml_backend_cuda_device_event_synchronize(ggml_backend_dev_t dev, g
@@ -3948,6 +3956,11 @@ static void ggml_backend_cuda_device_event_synchronize(ggml_backend_dev_t dev, g
CUDA_CHECK(cudaEventSynchronize((cudaEvent_t)event->context));
}
@ -100,7 +100,7 @@ index d324bc68..531d6e27 100644
static const ggml_backend_device_i ggml_backend_cuda_device_interface = {
/* .get_name = */ ggml_backend_cuda_device_get_name,
/* .get_description = */ ggml_backend_cuda_device_get_description,
@@ -3961,6 +3974,7 @@ static const ggml_backend_device_i ggml_backend_cuda_device_interface = {
@@ -3964,6 +3977,7 @@ static const ggml_backend_device_i ggml_backend_cuda_device_interface = {
/* .event_new = */ ggml_backend_cuda_device_event_new,
/* .event_free = */ ggml_backend_cuda_device_event_free,
/* .event_synchronize = */ ggml_backend_cuda_device_event_synchronize,
@ -108,19 +108,11 @@ index d324bc68..531d6e27 100644
};
// backend reg
@@ -4076,7 +4090,6 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
dev_ctx->device = i;
dev_ctx->name = GGML_CUDA_NAME + std::to_string(i);
- ggml_cuda_set_device(i);
cudaDeviceProp prop;
CUDA_CHECK(cudaGetDeviceProperties(&prop, i));
dev_ctx->description = prop.name;
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 37386afc..06f9e7c1 100644
index 890c1036..1f06be80 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -41,6 +41,7 @@
@@ -45,6 +45,7 @@
#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
#define cudaDeviceProp hipDeviceProp_t

52
llama/patches/0026-GPU-discovery-enhancements.patch
View File

@ -9,17 +9,17 @@ management libraries for more accurate VRAM usage reporting if available.
ggml/include/ggml-backend.h | 9 +
ggml/src/CMakeLists.txt | 2 +
ggml/src/ggml-cuda/ggml-cuda.cu | 72 +++++
ggml/src/ggml-cuda/vendors/hip.h | 4 +
ggml/src/ggml-cuda/vendors/hip.h | 3 +
ggml/src/ggml-impl.h | 8 +
ggml/src/ggml-metal/ggml-metal.cpp | 3 +-
ggml/src/ggml-metal/ggml-metal.cpp | 2 +
ggml/src/mem_hip.cpp | 449 +++++++++++++++++++++++++++++
ggml/src/mem_nvml.cpp | 209 ++++++++++++++
8 files changed, 755 insertions(+), 1 deletion(-)
8 files changed, 754 insertions(+)
create mode 100644 ggml/src/mem_hip.cpp
create mode 100644 ggml/src/mem_nvml.cpp
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index 0a2dae26a..a6bf33785 100644
index ba181d09..09ff75f9 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -169,6 +169,15 @@ extern "C" {
@ -39,10 +39,10 @@ index 0a2dae26a..a6bf33785 100644
GGML_API const char * ggml_backend_dev_name(ggml_backend_dev_t device);
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 33b3a15f0..86191ef2c 100644
index 0609c650..aefe43bd 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -206,6 +206,8 @@ add_library(ggml-base
@@ -209,6 +209,8 @@ add_library(ggml-base
ggml-threading.h
ggml-quants.c
ggml-quants.h
@ -52,7 +52,7 @@ index 33b3a15f0..86191ef2c 100644
target_include_directories(ggml-base PRIVATE .)
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 531d6e272..3fa3a0575 100644
index 87c6c34a..6a278b5e 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -261,6 +261,16 @@ static ggml_cuda_device_info ggml_cuda_init() {
@ -84,7 +84,7 @@ index 531d6e272..3fa3a0575 100644
GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, ID: %s\n",
id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no",
ggml_cuda_parse_uuid(prop, id).c_str());
@@ -3481,6 +3496,14 @@ struct ggml_backend_cuda_device_context {
@@ -3484,6 +3499,14 @@ struct ggml_backend_cuda_device_context {
std::string description;
std::string pci_bus_id;
std::string id;
@ -99,7 +99,7 @@ index 531d6e272..3fa3a0575 100644
};
static const char * ggml_backend_cuda_device_get_name(ggml_backend_dev_t dev) {
@@ -3501,6 +3524,28 @@ static const char * ggml_backend_cuda_device_get_id(ggml_backend_dev_t dev) {
@@ -3504,6 +3527,28 @@ static const char * ggml_backend_cuda_device_get_id(ggml_backend_dev_t dev) {
static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
ggml_cuda_set_device(ctx->device);
@ -128,7 +128,7 @@ index 531d6e272..3fa3a0575 100644
CUDA_CHECK(cudaMemGetInfo(free, total));
}
@@ -3509,6 +3554,7 @@ static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend
@@ -3512,6 +3557,7 @@ static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend
return GGML_BACKEND_DEVICE_TYPE_GPU;
}
@ -136,7 +136,7 @@ index 531d6e272..3fa3a0575 100644
static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
@@ -3522,6 +3568,22 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
@@ -3525,6 +3571,22 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
// If you need the memory data, call ggml_backend_dev_memory() explicitly.
props->memory_total = props->memory_free = 0;
@ -159,7 +159,7 @@ index 531d6e272..3fa3a0575 100644
bool host_buffer = getenv("GGML_CUDA_NO_PINNED") == nullptr;
#ifdef GGML_CUDA_NO_PEER_COPY
bool events = false;
@@ -4084,6 +4146,8 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
@@ -4087,6 +4149,8 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
std::lock_guard<std::mutex> lock(mutex);
if (!initialized) {
ggml_backend_cuda_reg_context * ctx = new ggml_backend_cuda_reg_context;
@ -168,7 +168,7 @@ index 531d6e272..3fa3a0575 100644
for (int i = 0; i < ggml_cuda_info().device_count; i++) {
ggml_backend_cuda_device_context * dev_ctx = new ggml_backend_cuda_device_context;
@@ -4099,6 +4163,14 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
@@ -4102,6 +4166,14 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.0", prop.pciDomainID, prop.pciBusID, prop.pciDeviceID);
dev_ctx->pci_bus_id = pci_bus_id;
@ -184,20 +184,19 @@ index 531d6e272..3fa3a0575 100644
/* .iface = */ ggml_backend_cuda_device_interface,
/* .reg = */ &reg,
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index 06f9e7c1e..eb8f66cb0 100644
index 1f06be80..2f9ef2dc 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -5,6 +5,9 @@
@@ -5,6 +5,8 @@
#include <hipblas/hipblas.h>
#include <hip/hip_fp16.h>
#include <hip/hip_bf16.h>
+// for rocblas_initialize()
+#include "rocblas/rocblas.h"
+
#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
@@ -43,6 +46,7 @@
#if defined(GGML_HIP_ROCWMMA_FATTN)
#include <rocwmma/rocwmma-version.hpp>
@@ -47,6 +49,7 @@
#define cudaDeviceProp hipDeviceProp_t
#define cudaDeviceReset hipDeviceReset
#define cudaDeviceSynchronize hipDeviceSynchronize
@ -206,10 +205,10 @@ index 06f9e7c1e..eb8f66cb0 100644
#define cudaErrorPeerAccessAlreadyEnabled hipErrorPeerAccessAlreadyEnabled
#define cudaErrorPeerAccessNotEnabled hipErrorPeerAccessNotEnabled
diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h
index 86a1ebf62..9fc9fbfcf 100644
index d0fb3bcc..80597b6e 100644
--- a/ggml/src/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -635,6 +635,14 @@ static inline bool ggml_can_fuse(const struct ggml_cgraph * cgraph, int node_idx
@@ -638,6 +638,14 @@ static inline bool ggml_can_fuse(const struct ggml_cgraph * cgraph, int node_idx
return ggml_can_fuse_ext(cgraph, idxs, ops, num_ops);
}
@ -225,7 +224,7 @@ index 86a1ebf62..9fc9fbfcf 100644
}
#endif
diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp
index 08ab4fc91..17999a616 100644
index f2ff9f32..f356e4a0 100644
--- a/ggml/src/ggml-metal/ggml-metal.cpp
+++ b/ggml/src/ggml-metal/ggml-metal.cpp
@@ -535,6 +535,7 @@ static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backen
@ -236,18 +235,17 @@ index 08ab4fc91..17999a616 100644
static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
props->name = ggml_backend_metal_device_get_name(dev);
props->description = ggml_backend_metal_device_get_description(dev);
@@ -542,7 +543,7 @@ static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_bac
props->type = ggml_backend_metal_device_get_type(dev);
@@ -543,6 +544,7 @@ static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_bac
ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total);
-
+ props->library = GGML_METAL_NAME;
props->caps = {
/* .async = */ true,
/* .host_buffer = */ false,
diff --git a/ggml/src/mem_hip.cpp b/ggml/src/mem_hip.cpp
new file mode 100644
index 000000000..8ef19b8cf
index 00000000..8ef19b8c
--- /dev/null
+++ b/ggml/src/mem_hip.cpp
@@ -0,0 +1,449 @@
@ -703,7 +701,7 @@ index 000000000..8ef19b8cf
\ No newline at end of file
diff --git a/ggml/src/mem_nvml.cpp b/ggml/src/mem_nvml.cpp
new file mode 100644
index 000000000..c9073cef0
index 00000000..c9073cef
--- /dev/null
+++ b/ggml/src/mem_nvml.cpp
@@ -0,0 +1,209 @@

2
ml/backend/ggml/ggml/include/ggml-backend.h vendored
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@ -226,6 +226,8 @@ extern "C" {
// Backend registry
//
GGML_API void ggml_backend_register(ggml_backend_reg_t reg);
GGML_API void ggml_backend_device_register(ggml_backend_dev_t device);
// Backend (reg) enumeration

17
ml/backend/ggml/ggml/include/ggml-rpc.h vendored
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@ -7,26 +7,25 @@
extern "C" {
#endif
#define RPC_PROTO_MAJOR_VERSION 2
#define RPC_PROTO_MAJOR_VERSION 3
#define RPC_PROTO_MINOR_VERSION 0
#define RPC_PROTO_PATCH_VERSION 0
#define GGML_RPC_MAX_SERVERS 16
// backend API
GGML_BACKEND_API ggml_backend_t ggml_backend_rpc_init(const char * endpoint);
GGML_BACKEND_API ggml_backend_t ggml_backend_rpc_init(const char * endpoint, uint32_t device);
GGML_BACKEND_API bool ggml_backend_is_rpc(ggml_backend_t backend);
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint);
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint, uint32_t device);
GGML_BACKEND_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total);
GGML_BACKEND_API void ggml_backend_rpc_get_device_memory(const char * endpoint, uint32_t device, size_t * free, size_t * total);
GGML_BACKEND_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint,
const char * cache_dir,
size_t free_mem, size_t total_mem);
GGML_BACKEND_API void ggml_backend_rpc_start_server(const char * endpoint, const char * cache_dir,
size_t n_threads, size_t n_devices,
ggml_backend_dev_t * devices, size_t * free_mem, size_t * total_mem);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_reg(void);
GGML_BACKEND_API ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_add_server(const char * endpoint);
#ifdef __cplusplus
}

22
ml/backend/ggml/ggml/include/ggml.h vendored
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@ -237,6 +237,8 @@
#define GGML_EXIT_SUCCESS 0
#define GGML_EXIT_ABORTED 1
// TODO: convert to enum https://github.com/ggml-org/llama.cpp/pull/16187#discussion_r2388538726
#define GGML_ROPE_TYPE_NORMAL 0
#define GGML_ROPE_TYPE_NEOX 2
#define GGML_ROPE_TYPE_MROPE 8
#define GGML_ROPE_TYPE_VISION 24
@ -574,6 +576,7 @@ extern "C" {
GGML_UNARY_OP_HARDSIGMOID,
GGML_UNARY_OP_EXP,
GGML_UNARY_OP_GELU_ERF,
GGML_UNARY_OP_XIELU,
GGML_UNARY_OP_COUNT,
};
@ -1148,6 +1151,18 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// xIELU activation function
// x = x * (c_a(alpha_n) + c_b(alpha_p, beta) * sigmoid(beta * x)) + eps * (x > 0)
// where c_a = softplus and c_b(a, b) = softplus(a) + b are constraining functions
// that constrain the positive and negative source alpha values respectively
GGML_API struct ggml_tensor * ggml_xielu(
struct ggml_context * ctx,
struct ggml_tensor * a,
float alpha_n,
float alpha_p,
float beta,
float eps);
// gated linear unit ops
// A: n columns, r rows,
// result is n / 2 columns, r rows,
@ -1615,6 +1630,13 @@ extern "C" {
float scale,
float max_bias);
GGML_API struct ggml_tensor * ggml_soft_max_ext_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * mask,
float scale,
float max_bias);
GGML_API void ggml_soft_max_add_sinks(
struct ggml_tensor * a,
struct ggml_tensor * sinks);

3
ml/backend/ggml/ggml/src/CMakeLists.txt vendored
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@ -145,6 +145,9 @@ endif()
# which was introduced in POSIX.1-2008, forcing us to go higher
if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
add_compile_definitions(_XOPEN_SOURCE=700)
elseif (CMAKE_SYSTEM_NAME MATCHES "AIX")
# Don't define _XOPEN_SOURCE. We need _ALL_SOURCE, which is the default,
# in order to define _SC_PHYS_PAGES.
else()
add_compile_definitions(_XOPEN_SOURCE=600)
endif()

30
ml/backend/ggml/ggml/src/ggml-alloc.c vendored
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@ -392,12 +392,8 @@ static void ggml_dyn_tallocr_free(struct ggml_dyn_tallocr * alloc) {
free(alloc);
}
static size_t ggml_dyn_tallocr_max_size(struct ggml_dyn_tallocr * alloc) {
size_t max_size = 0;
for (int i = 0; i < alloc->n_chunks; i++) {
max_size += alloc->chunks[i]->max_size;
}
return max_size;
static size_t ggml_dyn_tallocr_max_size(struct ggml_dyn_tallocr * alloc, int chunk) {
return chunk < alloc->n_chunks ? alloc->chunks[chunk]->max_size : 0;
}
@ -417,10 +413,8 @@ static void ggml_vbuffer_free(struct vbuffer * buf) {
free(buf);
}
static int ggml_vbuffer_n_chunks(struct vbuffer * buf) {
int n = 0;
while (n < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[n]) n++;
return n;
static size_t ggml_vbuffer_chunk_size(struct vbuffer * buf, int chunk) {
return buf->chunks[chunk] ? ggml_backend_buffer_get_size(buf->chunks[chunk]) : 0;
}
static size_t ggml_vbuffer_size(struct vbuffer * buf) {
@ -892,12 +886,20 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
}
}
size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0;
size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
// even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
if (new_size > cur_size || galloc->buffers[i] == NULL) {
bool realloc = galloc->buffers[i] == NULL;
size_t new_size = 0;
for (int c = 0; c < galloc->buf_tallocs[i]->n_chunks; c++) {
size_t cur_chunk_size = galloc->buffers[i] ? ggml_vbuffer_chunk_size(galloc->buffers[i], c) : 0;
size_t new_chunk_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i], c);
new_size += new_chunk_size;
if (new_chunk_size > cur_chunk_size) {
realloc = true;
}
}
if (realloc) {
#ifndef NDEBUG
size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0;
GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif

3
ml/backend/ggml/ggml/src/ggml-backend-impl.h vendored
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@ -229,9 +229,6 @@ extern "C" {
void * context;
};
// Internal backend registry API
GGML_API void ggml_backend_register(ggml_backend_reg_t reg);
// Add backend dynamic loading support to the backend
// Initialize the backend

1
ml/backend/ggml/ggml/src/ggml-cpu/amx/amx.cpp vendored
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@ -149,6 +149,7 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
if (op->op == GGML_OP_MUL_MAT && is_contiguous_2d(op->src[0]) && // src0 must be contiguous
is_contiguous_2d(op->src[1]) && // src1 must be contiguous
op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_amx_buffer_type() &&
op->src[0]->ne[0] % (TILE_K * 2 * 32) == 0 && // TODO: not sure if correct (https://github.com/ggml-org/llama.cpp/pull/16315)
op->ne[0] % (TILE_N * 2) == 0 && // out_features is 32x
(qtype_has_amx_kernels(op->src[0]->type) || (op->src[0]->type == GGML_TYPE_F16))) {
// src1 must be host buffer

2
ml/backend/ggml/ggml/src/ggml-cpu/ggml-cpu-impl.h vendored
View File

@ -68,7 +68,7 @@ struct ggml_compute_params {
#endif // __VXE2__
#endif // __s390x__ && __VEC__
#if defined(__ARM_FEATURE_SVE)
#if defined(__ARM_FEATURE_SVE) && defined(__linux__)
#include <sys/prctl.h>
#endif

8
ml/backend/ggml/ggml/src/ggml-cpu/ggml-cpu.c vendored
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@ -691,8 +691,13 @@ bool ggml_is_numa(void) {
#endif
static void ggml_init_arm_arch_features(void) {
#if defined(__linux__) && defined(__aarch64__) && defined(__ARM_FEATURE_SVE)
#if defined(__aarch64__) && defined(__ARM_FEATURE_SVE)
#if defined(__linux__)
ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL);
#else
// TODO: add support of SVE for non-linux systems
#error "TODO: SVE is not supported on this platform. To use SVE, sve_cnt needs to be initialized here."
#endif
#endif
}
@ -2189,6 +2194,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
case GGML_UNARY_OP_GELU_ERF:
case GGML_UNARY_OP_GELU_QUICK:
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_XIELU:
{
n_tasks = n_threads;
} break;

34
ml/backend/ggml/ggml/src/ggml-cpu/ops.cpp vendored
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@ -3467,31 +3467,27 @@ static void ggml_compute_forward_norm_f32(
GGML_ASSERT(eps >= 0.0f);
// TODO: optimize
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
ggml_float sum = 0.0;
for (int64_t i00 = 0; i00 < ne00; i00++) {
sum += (ggml_float)x[i00];
}
float sum = 0.0;
ggml_vec_sum_f32(ne00, &sum, x);
float mean = sum/ne00;
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
float variance = 0;
ggml_float sum2 = 0.0;
for (int64_t i00 = 0; i00 < ne00; i00++) {
float v = x[i00] - mean;
y[i00] = v;
sum2 += (ggml_float)(v*v);
}
#ifdef GGML_USE_ACCELERATE
mean = -mean;
vDSP_vsadd(x, 1, &mean, y, 1, ne00);
vDSP_measqv(y, 1, &variance, ne00);
#else
variance = ggml_vec_cvar_f32(ne00, y, x, mean);
#endif //GGML_USE_ACCELERATE
float variance = sum2/ne00;
const float scale = 1.0f/sqrtf(variance + eps);
ggml_vec_scale_f32(ne00, y, scale);
}
}
@ -8178,7 +8174,7 @@ static void ggml_compute_forward_flash_attn_ext_f16(
}
// V /= S
const float S_inv = 1.0f/S;
const float S_inv = S == 0.0f ? 0.0f : 1.0f/S;
ggml_vec_scale_f32(DV, VKQ32, S_inv);
// dst indices
@ -8680,7 +8676,7 @@ static void ggml_compute_forward_ssm_scan_f32(
// n_head
for (int h = ih0; h < ih1; ++h) {
// ref: https://github.com/state-spaces/mamba/blob/62db608da60f6fc790b8ed9f4b3225e95ca15fde/mamba_ssm/ops/triton/softplus.py#L16
const float dt_soft_plus = dt[h] <= 20.0f ? log1pf(expf(dt[h])) : dt[h];
const float dt_soft_plus = ggml_softplus(dt[h]);
const float dA = expf(dt_soft_plus * A[h]);
const int g = h / (nh / ng); // repeat_interleave
@ -8777,7 +8773,7 @@ static void ggml_compute_forward_ssm_scan_f32(
// n_head
for (int h = ih0; h < ih1; ++h) {
// ref: https://github.com/state-spaces/mamba/blob/62db608da60f6fc790b8ed9f4b3225e95ca15fde/mamba_ssm/ops/triton/softplus.py#L16
const float dt_soft_plus = dt[h] <= 20.0f ? log1pf(expf(dt[h])) : dt[h];
const float dt_soft_plus = ggml_softplus(dt[h]);
const int g = h / (nh / ng); // repeat_interleave
// dim
@ -9040,6 +9036,10 @@ void ggml_compute_forward_unary(
{
ggml_compute_forward_exp(params, dst);
} break;
case GGML_UNARY_OP_XIELU:
{
ggml_compute_forward_xielu(params, dst);
} break;
default:
{
GGML_ABORT("fatal error");

103
ml/backend/ggml/ggml/src/ggml-cpu/unary-ops.cpp vendored
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@ -52,6 +52,15 @@ static inline float op_sqrt(float x) {
return sqrtf(x);
}
static inline float op_xielu(float x, float alpha_n, float alpha_p, float beta, float eps) {
if (x > 0.0f) {
return alpha_p * x * x + beta * x;
} else {
const float min_x_eps = fminf(x, eps);
return (expm1f(min_x_eps) - x) * alpha_n + beta * x;
}
}
static inline float op_sin(float x) {
return sinf(x);
}
@ -121,6 +130,86 @@ static void unary_op(const ggml_compute_params * params, ggml_tensor * dst) {
}
}
template <float (*op)(float, ggml_tensor *)>
static void unary_op_params(const ggml_compute_params * params, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
/* */ if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { // all f32
apply_unary_op<op, float, float>(params, dst);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { // all f16
apply_unary_op<op, ggml_fp16_t, ggml_fp16_t>(params, dst);
} else if (src0->type == GGML_TYPE_BF16 && dst->type == GGML_TYPE_BF16) { // all bf16
apply_unary_op<op, ggml_bf16_t, ggml_bf16_t>(params, dst);
} else if (src0->type == GGML_TYPE_BF16 && dst->type == GGML_TYPE_F32) {
apply_unary_op<op, ggml_bf16_t, float>(params, dst);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
apply_unary_op<op, ggml_fp16_t, float>(params, dst);
} else {
fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s\n", __func__,
ggml_type_name(dst->type), ggml_type_name(src0->type));
GGML_ABORT("fatal error");
}
}
// Extend vec_unary_op to support functors
template <typename Op, typename src0_t, typename dst_t>
static inline void vec_unary_op_functor(int64_t n, dst_t * y, const src0_t * x, Op op) {
constexpr auto src0_to_f32 = type_conversion_table<src0_t>::to_f32;
constexpr auto f32_to_dst = type_conversion_table<dst_t >::from_f32;
for (int i = 0; i < n; i++) {
y[i] = f32_to_dst(op(src0_to_f32(x[i])));
}
}
// Extend apply_unary_op to support functors
template <typename Op, typename src0_t, typename dst_t>
static void apply_unary_op_functor(const ggml_compute_params * params, ggml_tensor * dst, Op op) {
const ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(ggml_is_contiguous_1(src0) && ggml_is_contiguous_1(dst) && ggml_are_same_shape(src0, dst));
GGML_TENSOR_UNARY_OP_LOCALS
GGML_ASSERT( nb0 == sizeof(dst_t));
GGML_ASSERT(nb00 == sizeof(src0_t));
const auto [ir0, ir1] = get_thread_range(params, src0);
for (int64_t ir = ir0; ir < ir1; ++ir) {
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
dst_t * dst_ptr = (dst_t *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
const src0_t * src0_ptr = (const src0_t *) ((const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
vec_unary_op_functor(ne0, dst_ptr, src0_ptr, op);
}
}
// Generic dispatcher for functors
template <typename Op>
static void unary_op_functor(const ggml_compute_params * params, ggml_tensor * dst, Op op) {
const ggml_tensor * src0 = dst->src[0];
/* */ if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { // all f32
apply_unary_op_functor<Op, float, float>(params, dst, op);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { // all f16
apply_unary_op_functor<Op, ggml_fp16_t, ggml_fp16_t>(params, dst, op);
} else if (src0->type == GGML_TYPE_BF16 && dst->type == GGML_TYPE_BF16) { // all bf16
apply_unary_op_functor<Op, ggml_bf16_t, ggml_bf16_t>(params, dst, op);
} else if (src0->type == GGML_TYPE_BF16 && dst->type == GGML_TYPE_F32) {
apply_unary_op_functor<Op, ggml_bf16_t, float>(params, dst, op);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
apply_unary_op_functor<Op, ggml_fp16_t, float>(params, dst, op);
} else {
fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s\n", __func__,
ggml_type_name(dst->type), ggml_type_name(src0->type));
GGML_ABORT("fatal error");
}
}
void ggml_compute_forward_abs(const ggml_compute_params * params, ggml_tensor * dst) {
unary_op<op_abs>(params, dst);
}
@ -184,3 +273,17 @@ void ggml_compute_forward_cos(const ggml_compute_params * params, ggml_tensor *
void ggml_compute_forward_log(const ggml_compute_params * params, ggml_tensor * dst) {
unary_op<op_log>(params, dst);
}
void ggml_compute_forward_xielu(const ggml_compute_params * params, ggml_tensor * dst) {
const float alpha_n = ggml_get_op_params_f32(dst, 1);
const float alpha_p = ggml_get_op_params_f32(dst, 2);
const float beta = ggml_get_op_params_f32(dst, 3);
const float eps = ggml_get_op_params_f32(dst, 4);
const auto xielu_op_params = [alpha_n, alpha_p, beta, eps](float f) {
return op_xielu(f, alpha_n, alpha_p, beta, eps);
};
unary_op_functor(params, dst, xielu_op_params);
}

1
ml/backend/ggml/ggml/src/ggml-cpu/unary-ops.h vendored
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@ -22,6 +22,7 @@ void ggml_compute_forward_sqrt(const struct ggml_compute_params * params, struct
void ggml_compute_forward_sin(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_cos(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_log(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_xielu(const struct ggml_compute_params * params, struct ggml_tensor * dst);
#ifdef __cplusplus
}

66
ml/backend/ggml/ggml/src/ggml-cpu/vec.cpp vendored
View File

@ -404,6 +404,72 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *
}
}
ggml_float ggml_vec_cvar_f32(const int n, float * y, const float * x, const float mean) {
int i = 0;
ggml_float sum = 0;
// TODO: optimize to process the remaining elements in groups using the smaller vector sizes from AVX2 and SSE
// ref: https://github.com/ggml-org/llama.cpp/pull/15953#pullrequestreview-3310928344
#if defined(__AVX512F__) && defined(__AVX512DQ__)
for (; i + 15 < n; i += 16) {
__m512 val = _mm512_sub_ps(_mm512_loadu_ps(x + i),
_mm512_set1_ps(mean));
_mm512_storeu_ps(y + i, val);
sum += (ggml_float)_mm512_reduce_add_ps(_mm512_mul_ps(val, val));
}
#elif defined(__AVX2__) && defined(__FMA__)
for (; i + 7 < n; i += 8) {
__m256 val = _mm256_sub_ps(_mm256_loadu_ps(x + i),
_mm256_set1_ps(mean));
_mm256_storeu_ps(y + i, val);
val = _mm256_mul_ps(val,val);
__m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1),
_mm256_castps256_ps128(val));
val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2));
val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2));
sum += (ggml_float)_mm_cvtss_f32(val2);
}
#elif defined(__SSE2__)
for (; i + 3 < n; i += 4) {
__m128 val = _mm_sub_ps(_mm_loadu_ps(x + i),
_mm_set1_ps(mean));
_mm_storeu_ps(y + i, val);
val = _mm_mul_ps(val, val);
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
val = _mm_add_ps(val, _mm_movehl_ps(val, val));
val = _mm_add_ss(val, _mm_movehdup_ps(val));
#else
__m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1));
val = _mm_add_ps(val, tmp);
tmp = _mm_movehl_ps(tmp, val);
val = _mm_add_ss(val, tmp);
#endif // __AVX__ || __AVX2__ || __AVX512F__
sum += (ggml_float)_mm_cvtss_f32(val);
}
#elif defined(__ARM_NEON) && defined(__aarch64__)
for (; i + 3 < n; i += 4) {
float32x4_t val = vsubq_f32(vld1q_f32(x + i),
vdupq_n_f32(mean));
vst1q_f32(y + i, val);
val = vmulq_f32(val, val);
sum += (ggml_float)vaddvq_f32(val);
}
#elif defined(__VXE__) || defined(__VXE2__)
for (; i + 3 < n; i += 4) {
float32x4_t val = vec_sub(vec_xl(0, x + i), vec_splats(mean));
vec_xst(val, 0, y + i);
val = vec_mul(val, val);
sum += (ggml_float)vec_hsum_f32x4(val);
}
#endif
for (; i < n; ++i) {
float val = x[i] - mean;
y[i] = val;
val *= val;
sum += (ggml_float)val;
}
return sum/n;
}
ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) {
int i = 0;
ggml_float sum = 0;

18
ml/backend/ggml/ggml/src/ggml-cpu/vec.h vendored
View File

@ -44,6 +44,7 @@ void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t *
void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * GGML_RESTRICT x, size_t bx, ggml_fp16_t * GGML_RESTRICT y, size_t by, int nrc);
void ggml_vec_silu_f32(const int n, float * y, const float * x);
ggml_float ggml_vec_cvar_f32(const int n, float * y, const float * x, const float mean); //it will also center y ( y = y - mean )
ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max);
ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max);
@ -143,14 +144,14 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
for (int i = 0; i < np; i += ggml_f16_step) {
ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0); // 8 elements
ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elemnst
ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elements
sum_00 = GGML_F16x_VEC_FMA(sum_00, ax1, ay1); // sum_00 = sum_00+ax1*ay1
ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 0*ggml_f16_epr, 0); // 8 elements
sum_10 = GGML_F16x_VEC_FMA(sum_10, ax1, ay1);
ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1); // next 8 elements
ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 ekements
ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 elements
sum_01 = GGML_F16x_VEC_FMA(sum_01, ax2, ay2);
ax2 = GGML_F16x_VEC_LOAD(x[1] + i + 1*ggml_f16_epr, 1);
sum_11 = GGML_F16x_VEC_FMA(sum_11, ax2, ay2);
@ -159,7 +160,7 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
ax3 = GGML_F16x_VEC_LOAD(x[0] + i + 2*ggml_f16_epr, 2);
sum_02 = GGML_F16x_VEC_FMA(sum_02, ax3, ay3);
ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2);
ax3 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2);
sum_12 = GGML_F16x_VEC_FMA(sum_12, ax3, ay3);
ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
@ -654,11 +655,11 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
}
// leftovers
// maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
if (np < n) {
svbool_t pg = svwhilelt_b32(np, n);
ay1 = svld1_f32(pg, y + np);
for (int i = np; i < n; i += ggml_f32_epr) {
svbool_t pg = svwhilelt_b32(i, n);
ay1 = svld1_f32(pg, y + i);
ay1 = svmul_f32_m(pg, ay1, vx);
svst1_f32(pg, y + np, ay1);
svst1_f32(pg, y + i, ay1);
}
#elif defined(__riscv_v_intrinsic)
for (int i = 0, avl; i < n; i += avl) {
@ -819,7 +820,8 @@ inline static void ggml_vec_tanh_f16 (const int n, ggml_fp16_t * y, const ggml_f
inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); }
inline static void ggml_vec_elu_f16 (const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
for (int i = 0; i < n; ++i) {
y[i] = GGML_CPU_FP32_TO_FP16(expm1f(GGML_CPU_FP16_TO_FP32(x[i])));
const float v = GGML_CPU_FP16_TO_FP32(x[i]);
y[i] = GGML_CPU_FP32_TO_FP16((v > 0.f) ? v : expm1f(v));
}
}
inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }

2
ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt vendored
View File

@ -44,6 +44,8 @@ if (CUDAToolkit_FOUND)
list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h")
file(GLOB GGML_SOURCES_CUDA "*.cu")
file(GLOB SRCS "template-instances/fattn-tile*.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
file(GLOB SRCS "template-instances/fattn-mma*.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
file(GLOB SRCS "template-instances/mmq*.cu")

36
ml/backend/ggml/ggml/src/ggml-cuda/common.cuh vendored
View File

@ -245,14 +245,6 @@ static const char * cu_get_error_str(CUresult err) {
#define FAST_FP16_AVAILABLE
#endif // defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610
#if (!defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA) || defined(GGML_USE_MUSA)
#define FP16_MMA_AVAILABLE
#endif // (!defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA) || defined(GGML_USE_MUSA)
#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(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
#if defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
#define AMD_MFMA_AVAILABLE
#endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
@ -278,7 +270,8 @@ static bool fp16_available(const int cc) {
}
static bool fast_fp16_available(const int cc) {
return (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && cc != 610) || GGML_CUDA_CC_IS_AMD(cc);
return GGML_CUDA_CC_IS_AMD(cc) ||
(GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && ggml_cuda_highest_compiled_arch(cc) != 610);
}
// To be used for feature selection of external libraries, e.g. cuBLAS.
@ -287,27 +280,6 @@ static bool fast_fp16_hardware_available(const int cc) {
(GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_QY2);
}
// Any FP16 tensor core instructions are available for ggml code.
static bool fp16_mma_available(const int cc) {
#if defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
return false;
#else
if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) ||
GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) ||
GGML_CUDA_CC_IS_MTHREADS(cc)) {
return true;
} else if (GGML_CUDA_CC_IS_RDNA4(cc)) {
#if defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_HIP_ROCWMMA_FATTN_GFX12)
return true;
#else
return false;
#endif // defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_HIP_ROCWMMA_FATTN_GFX12)
} else {
return false;
}
#endif // defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
}
// To be used for feature selection of external libraries, e.g. cuBLAS.
static bool fp16_mma_hardware_available(const int cc) {
return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_VOLTA) ||
@ -625,6 +597,10 @@ static __device__ __forceinline__ void ggml_cuda_mad(half2 & acc, const half2 v,
}
// Aligned memory transfers of 8/16 bytes can be faster than 2 transfers with 4 bytes, especially on AMD.
// Important: do not use this function if dst and src both point at registers.
// Due to the strict aliasing rule the compiler can do incorrect optimizations if src and dst have different types.
// The function is intended for copies between registers and SRAM/VRAM to make the compiler emit the right instructions.
// If dst and src point at different address spaces then they are guaranteed to not be aliased.
template <int nbytes, int alignment = 0>
static __device__ __forceinline__ void ggml_cuda_memcpy_1(void * __restrict__ dst, const void * __restrict__ src) {
if constexpr (alignment != 0) {

9
ml/backend/ggml/ggml/src/ggml-cuda/fattn-common.cuh vendored
View File

@ -793,8 +793,6 @@ void launch_fattn(
GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
"the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");
GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
ggml_cuda_pool & pool = ctx.pool();
cudaStream_t main_stream = ctx.stream();
const int id = ggml_cuda_get_device();
@ -878,7 +876,7 @@ void launch_fattn(
// Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped.
// Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or
// multiple sequences of possibly different lengths.
if (mask && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) {
if (mask && K->ne[1] % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) {
const int s31 = mask->nb[1] / sizeof(half2);
const int s33 = mask->nb[3] / sizeof(half2);
@ -916,8 +914,7 @@ void launch_fattn(
dst_tmp_meta.alloc(blocks_num.x*ncols * (2*2 + DV) * sizeof(float));
} else {
GGML_ASSERT(K->ne[1] % KQ_row_granularity == 0);
const int ntiles_KQ = K->ne[1] / KQ_row_granularity; // Max. number of parallel blocks limited by tensor size.
const int ntiles_KQ = (K->ne[1] + KQ_row_granularity - 1) / KQ_row_granularity; // Max. number of parallel blocks limited by tensor size.
// parallel_blocks must not be larger than what the tensor size allows:
parallel_blocks = std::min(parallel_blocks, ntiles_KQ);
@ -946,7 +943,7 @@ void launch_fattn(
blocks_num.x = ntiles_x;
blocks_num.y = parallel_blocks;
blocks_num.z = Q->ne[2]*Q->ne[3];
blocks_num.z = (Q->ne[2]/ncols2)*Q->ne[3];
if (parallel_blocks > 1) {
dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));

774
ml/backend/ggml/ggml/src/ggml-cuda/fattn-tile.cu vendored
View File

@ -1,755 +1,45 @@
#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-tile.cuh"
// kq_stride == number of KQ rows to process per iteration
// kq_nbatch == number of K columns to load in parallel for KQ calculation
static int fattn_tile_get_kq_stride_host(const int D, const int ncols, const int cc, const int warp_size) {
if (GGML_CUDA_CC_IS_AMD(cc)) {
if (GGML_CUDA_CC_IS_RDNA(cc)) {
switch (D) {
case 64:
return 128;
case 128:
case 256:
return ncols <= 16 ? 128 : 64;
default:
GGML_ABORT("fatal error");
return -1;
}
}
switch (D) {
case 64:
return ncols == 32 ? 128 : 64;
case 128:
return ncols == 32 ? 64 : 32;
case 256:
return 32;
default:
GGML_ABORT("fatal error");
return -1;
}
}
if (fast_fp16_available(cc)) {
switch (D) {
case 64:
case 128:
case 256:
return ncols <= 16 ? 128 : 64;
default:
GGML_ABORT("fatal error");
return -1;
}
}
switch (D) {
case 64:
return ncols <= 16 ? 128 : 64;
case 128:
return ncols <= 16 ? 64 : 32;
case 256:
return 32;
default:
GGML_ABORT("fatal error");
return -1;
}
GGML_UNUSED(warp_size);
}
static constexpr __device__ int fattn_tile_get_kq_stride_device(int D, int ncols, int warp_size) {
#ifdef GGML_USE_HIP
#ifdef RDNA
switch (D) {
case 64:
return 128;
case 128:
case 256:
return ncols <= 16 ? 128 : 64;
default:
return -1;
}
#else
switch (D) {
case 64:
return ncols == 32 ? 128 : 64;
case 128:
return ncols == 32 ? 64 : 32;
case 256:
return 32;
default:
return -1;
}
#endif // RDNA
#else
#ifdef FAST_FP16_AVAILABLE
switch (D) {
case 64:
case 128:
case 256:
return ncols <= 16 ? 128 : 64;
default:
return -1;
}
#else
switch (D) {
case 64:
return ncols <= 16 ? 128 : 64;
case 128:
return ncols <= 16 ? 64 : 32;
case 256:
return 32;
default:
return -1;
}
#endif // FAST_FP16_AVAILABLE
#endif // GGML_USE_HIP
GGML_UNUSED_VARS(ncols, warp_size);
}
static constexpr __device__ int fattn_tile_get_kq_nbatch_device(int D, int ncols, int warp_size) {
#ifdef GGML_USE_HIP
switch (D) {
case 64:
return 64;
case 128:
case 256:
return 128;
default:
return -1;
}
#else
#ifdef FAST_FP16_AVAILABLE
switch (D) {
case 64:
return 64;
case 128:
case 256:
return 128;
default:
return -1;
}
#else
switch (D) {
case 64:
return 64;
case 128:
return 128;
case 256:
return ncols <= 16 ? 128 : 64;
default:
return -1;
}
#endif // FAST_FP16_AVAILABLE
#endif // GGML_USE_HIP
GGML_UNUSED_VARS(ncols, warp_size);
}
static int fattn_tile_get_nthreads_host(const int cc, const int ncols) {
return 256;
GGML_UNUSED_VARS(cc, ncols);
}
static constexpr __device__ int fattn_tile_get_nthreads_device(int ncols) {
return 256;
GGML_UNUSED(ncols);
}
static constexpr __device__ int fattn_tile_get_occupancy_device(int ncols) {
#ifdef RDNA
return 3;
#else
return ncols <= 16 ? 3 : 2;
#endif // RDNA
GGML_UNUSED(ncols);
}
template<int D, int ncols, bool use_logit_softcap> // D == head size
__launch_bounds__(fattn_tile_get_nthreads_device(ncols), fattn_tile_get_occupancy_device(ncols))
static __global__ void flash_attn_tile(
const char * __restrict__ Q,
const char * __restrict__ K,
const char * __restrict__ V,
const char * __restrict__ mask,
const char * __restrict__ sinks,
const int * __restrict__ KV_max,
float * __restrict__ dst,
float2 * __restrict__ dst_meta,
const float scale,
const float max_bias,
const float m0,
const float m1,
const uint32_t n_head_log2,
const float logit_softcap,
const int32_t ne00, const int32_t ne01, const int32_t ne02, const int32_t ne03,
const int32_t nb01, const int32_t nb02, const int32_t nb03,
const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13,
const int32_t nb11, const int32_t nb12, const int64_t nb13,
const int32_t nb21, const int32_t nb22, const int64_t nb23,
const int32_t ne31, const int32_t ne32, const int32_t ne33,
const int32_t nb31, const int32_t nb32, const int64_t nb33) {
#ifdef FLASH_ATTN_AVAILABLE
// Skip unused kernel variants for faster compilation:
#ifdef FP16_MMA_AVAILABLE
NO_DEVICE_CODE;
return;
#endif // FP16_MMA_AVAILABLE
if (use_logit_softcap && !(D == 128 || D == 256)) {
GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
max_bias, m0, m1, n_head_log2, logit_softcap,
ne00, ne01, ne02, ne03,
nb01, nb02, nb03,
ne10, ne11, ne12, ne13,
nb11, nb12, nb13,
nb21, nb22, nb23,
ne31, ne32, ne33,
nb31, nb32, nb33);
NO_DEVICE_CODE;
return;
}
constexpr int warp_size = 32;
constexpr int nwarps = fattn_tile_get_nthreads_device(ncols) / warp_size;
constexpr int kq_stride = fattn_tile_get_kq_stride_device(D, ncols, warp_size);
static_assert(kq_stride % warp_size == 0, "kq_stride not divisable by warp_size.");
constexpr int kq_nbatch = fattn_tile_get_kq_nbatch_device(D, ncols, warp_size);
static_assert(kq_nbatch % (2*warp_size) == 0, "bad kq_nbatch");
// In this kernel Q, K, V are matrices while i, j, k are matrix indices.
const int ic0 = blockIdx.x * ncols; // Index of the Q/QKV column to work on.
const int sequence = blockIdx.z / ne02;
const int head = blockIdx.z - sequence*ne02;
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
const float * Q_f = (const float *) (Q + nb03* sequence + nb02* head + nb01*ic0);
const half2 * K_h2 = (const half2 *) (K + nb13* sequence + nb12*(head / gqa_ratio));
const half2 * V_h2 = (const half2 *) (V + nb13* sequence + nb12*(head / gqa_ratio)); // K and V have same shape
const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0);
const float * sinksf = (const float *) (sinks);
const int stride_KV2 = nb11 / sizeof(half2);
const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes();
constexpr int cpy_ne = cpy_nb / 4;
constexpr int cpw = ncols/nwarps; // cols per warp
// softmax_iter_j == number of KQ columns for which to calculate softmax in parallel.
// KQ is originall 2D but uses a Z-shaped memory pattern for larger reads/writes.
#ifdef FAST_FP16_AVAILABLE
constexpr int softmax_iter_j = cpw < 2*cpy_ne ? cpw : 2*cpy_ne;
__shared__ half KQ[ncols/softmax_iter_j][kq_stride][softmax_iter_j];
__shared__ half2 Q_tmp[ncols][D/2];
__shared__ half2 KV_tmp[kq_stride * (kq_nbatch/2 + cpy_ne)]; // Padded to avoid memory bank conflicts.
half2 VKQ[cpw][D/(2*warp_size)] = {{{0.0f, 0.0f}}};
#else
constexpr int softmax_iter_j = cpw < 1*cpy_ne ? cpw : 1*cpy_ne;
__shared__ float KQ[ncols/softmax_iter_j][kq_stride][softmax_iter_j];
__shared__ float Q_tmp[ncols][D];
__shared__ float KV_tmp[kq_stride * (kq_nbatch + cpy_ne)]; // Padded to avoid memory bank conflicts.
float2 VKQ[cpw][D/(2*warp_size)] = {{{0.0f, 0.0f}}};
#endif // FAST_FP16_AVAILABLE
static_assert(cpw % softmax_iter_j == 0, "bad softmax_iter_j");
float KQ_max[cpw];
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
KQ_max[j0/nwarps] = -FLT_MAX/2.0f;
}
float KQ_sum[cpw] = {0.0f};
// Load Q data, convert to FP16 if fast.
#pragma unroll
for (int j0 = 0; j0 < cpw; ++j0) {
const int j = j0 + threadIdx.y*cpw;
constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size;
#pragma unroll
for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) {
float tmp_f[cpy_ne_D] = {0.0f};
if (ic0 + j < ne01) {
ggml_cuda_memcpy_1<sizeof(tmp_f)>(tmp_f, &Q_f[j*(nb01/sizeof(float)) + i0 + threadIdx.x*cpy_ne_D]);
}
#pragma unroll
for (int i1 = 0; i1 < cpy_ne_D; ++i1) {
tmp_f[i1] *= scale;
}
#ifdef FAST_FP16_AVAILABLE
half2 tmp_h2[cpy_ne_D/2];
#pragma unroll
for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) {
tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]);
}
ggml_cuda_memcpy_1<sizeof(tmp_h2)>(&Q_tmp[j][i0/2 + threadIdx.x*(cpy_ne_D/2)], tmp_h2);
#else
ggml_cuda_memcpy_1<sizeof(tmp_f)> (&Q_tmp[j][i0 + threadIdx.x* cpy_ne_D], tmp_f);
#endif // FAST_FP16_AVAILABLE
}
}
__syncthreads();
// Main loop over KV cache:
const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
for (int k_VKQ_0 = blockIdx.y*kq_stride; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*kq_stride) {
// Calculate KQ tile and keep track of new maximum KQ values:
float KQ_max_new[cpw];
#pragma unroll
for (int j = 0; j < cpw; ++j) {
KQ_max_new[j] = KQ_max[j];
}
float KQ_acc[kq_stride/warp_size][cpw] = {{0.0f}}; // Accumulators for KQ matrix multiplication.
// KQ = K @ Q matrix multiplication:
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += kq_nbatch) {
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += nwarps) {
const int i_KQ = i_KQ_0 + threadIdx.y;
#ifdef FAST_FP16_AVAILABLE
constexpr int cpy_ne_kqnb = cpy_ne < kq_nbatch/(2*warp_size) ? cpy_ne : kq_nbatch/(2*warp_size);
#pragma unroll
for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += warp_size*cpy_ne_kqnb) {
ggml_cuda_memcpy_1<cpy_ne_kqnb*4>(
&KV_tmp[i_KQ*(kq_nbatch/2 + cpy_ne) + k_KQ_1 + threadIdx.x*cpy_ne_kqnb],
&K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1 + threadIdx.x*cpy_ne_kqnb]);
}
#else
constexpr int cpy_ne_kqnb = cpy_ne < kq_nbatch/warp_size ? cpy_ne : kq_nbatch/warp_size;
#pragma unroll
for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; k_KQ_1 += warp_size*cpy_ne_kqnb) {
half2 tmp_h2[cpy_ne_kqnb/2];
ggml_cuda_memcpy_1<sizeof(tmp_h2)>(
tmp_h2, &K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1/2 + threadIdx.x*(cpy_ne_kqnb/2)]);
float2 tmp_f2[cpy_ne_kqnb/2];
#pragma unroll
for (int k_KQ_2 = 0; k_KQ_2 < cpy_ne_kqnb/2; ++k_KQ_2) {
tmp_f2[k_KQ_2] = __half22float2(tmp_h2[k_KQ_2]);
}
ggml_cuda_memcpy_1<sizeof(tmp_f2)>(
&KV_tmp[i_KQ*(kq_nbatch + cpy_ne) + k_KQ_1 + threadIdx.x*cpy_ne_kqnb], tmp_f2);
}
#endif // FAST_FP16_AVAILABLE
}
__syncthreads();
#ifdef FAST_FP16_AVAILABLE
#pragma unroll
for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += cpy_ne) {
half2 K_k[kq_stride/warp_size][cpy_ne];
half2 Q_k[cpw][cpy_ne];
#else
#pragma unroll
for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; k_KQ_1 += cpy_ne) {
float K_k[kq_stride/warp_size][cpy_ne];
float Q_k[cpw][cpy_ne];
#endif // FAST_FP16_AVAILABLE
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) {
const int i_KQ = i_KQ_0 + threadIdx.x;
#ifdef FAST_FP16_AVAILABLE
ggml_cuda_memcpy_1<cpy_nb>(&K_k[i_KQ_0/warp_size], &KV_tmp[i_KQ*(kq_nbatch/2 + cpy_ne) + k_KQ_1]);
#else
ggml_cuda_memcpy_1<cpy_nb>(&K_k[i_KQ_0/warp_size], &KV_tmp[i_KQ*(kq_nbatch + cpy_ne) + k_KQ_1]);
#endif // FAST_FP16_AVAILABLE
}
#pragma unroll
for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) {
const int j_KQ = j_KQ_0 + threadIdx.y*cpw;
#ifdef FAST_FP16_AVAILABLE
ggml_cuda_memcpy_1<cpy_nb>(&Q_k[j_KQ_0], &Q_tmp[j_KQ][k_KQ_0/2 + k_KQ_1]);
#else
ggml_cuda_memcpy_1<cpy_nb>(&Q_k[j_KQ_0], &Q_tmp[j_KQ][k_KQ_0 + k_KQ_1]);
#endif // FAST_FP16_AVAILABLE
}
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) {
#pragma unroll
for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) {
#pragma unroll
for (int k = 0; k < cpy_ne; ++k) {
ggml_cuda_mad(KQ_acc[i_KQ_0/warp_size][j_KQ_0], K_k[i_KQ_0/warp_size][k], Q_k[j_KQ_0][k]);
}
}
}
}
if (k_KQ_0 + kq_nbatch < D) {
__syncthreads(); // Sync not needed on last iteration.
}
}
// Apply logit softcap, mask, update KQ_max:
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) {
const int i_KQ = i_KQ_0 + threadIdx.x;
#pragma unroll
for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) {
const int j_KQ = j_KQ_0 + threadIdx.y*cpw;
if (use_logit_softcap) {
KQ_acc[i_KQ_0/warp_size][j_KQ_0] = logit_softcap * tanhf(KQ_acc[i_KQ_0/warp_size][j_KQ_0]);
}
KQ_acc[i_KQ_0/warp_size][j_KQ_0] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
KQ_max_new[j_KQ_0] = fmaxf(KQ_max_new[j_KQ_0], KQ_acc[i_KQ_0/warp_size][j_KQ_0]);
}
}
__syncthreads();
// Calculate KQ softmax, write to shared KQ buffer, re-scale VKQ accumulators:
#pragma unroll
for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) {
#ifdef FAST_FP16_AVAILABLE
half tmp[kq_stride/warp_size][softmax_iter_j];
#else
float tmp[kq_stride/warp_size][softmax_iter_j];
#endif // FAST_FP16_AVAILABLE
#pragma unroll
for (int j1 = 0; j1 < softmax_iter_j; ++j1) {
KQ_max_new[j0+j1] = warp_reduce_max<warp_size>(KQ_max_new[j0+j1]);
const float KQ_max_scale = expf(KQ_max[j0+j1] - KQ_max_new[j0+j1]);
KQ_max[j0+j1] = KQ_max_new[j0+j1];
float KQ_sum_add = 0.0f;
#pragma unroll
for (int i0 = 0; i0 < kq_stride; i0 += warp_size) {
const float val = expf(KQ_acc[i0/warp_size][j0+j1] - KQ_max[j0+j1]);
KQ_sum_add += val;
tmp[i0/warp_size][j1] = val;
}
KQ_sum[j0+j1] = KQ_sum[j0+j1]*KQ_max_scale + KQ_sum_add;
#ifdef FAST_FP16_AVAILABLE
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale);
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size) {
VKQ[j0+j1][i0/warp_size] *= KQ_max_scale_h2;
}
#else
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size) {
VKQ[j0+j1][i0/warp_size].x *= KQ_max_scale;
VKQ[j0+j1][i0/warp_size].y *= KQ_max_scale;
}
#endif // FAST_FP16_AVAILABLE
}
#pragma unroll
for (int i0 = 0; i0 < kq_stride; i0 += warp_size) {
const int i = i0 + threadIdx.x;
ggml_cuda_memcpy_1<sizeof(tmp[0])>(
KQ[j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j)][i], tmp[i0/warp_size]);
}
}
// VKQ = V @ KQ matrix multiplication:
constexpr int V_cols_per_iter = kq_stride*kq_nbatch / D; // Number of V columns that fit in SRAM for K.
static_assert(kq_stride % V_cols_per_iter == 0, "bad V_cols_per_iter");
#pragma unroll
for (int k0 = 0; k0 < kq_stride; k0 += V_cols_per_iter) {
#pragma unroll
for (int k1 = 0; k1 < V_cols_per_iter; k1 += nwarps) {
const int k_tile = k1 + threadIdx.y;
#ifdef FAST_FP16_AVAILABLE
constexpr int cpy_ne_D = cpy_ne < D/(2*warp_size) ? cpy_ne : D/(2*warp_size);
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) {
ggml_cuda_memcpy_1<cpy_ne_D*4>(
&KV_tmp[k_tile*(D/2) + i0 + threadIdx.x*cpy_ne_D],
&V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i0 + threadIdx.x*cpy_ne_D]);
}
#else
constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size;
#pragma unroll
for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) {
half2 tmp_h2[cpy_ne_D/2];
ggml_cuda_memcpy_1<sizeof(tmp_h2)>(
tmp_h2, &V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i0/2 + threadIdx.x*(cpy_ne_D/2)]);
float2 tmp_f2[cpy_ne_D/2];
#pragma unroll
for (int i1 = 0; i1 < cpy_ne_D/2; ++i1) {
tmp_f2[i1] = __half22float2(tmp_h2[i1]);
}
ggml_cuda_memcpy_1<sizeof(tmp_f2)>(
&KV_tmp[k_tile*D + i0 + threadIdx.x*cpy_ne_D], tmp_f2);
}
#endif // FAST_FP16_AVAILABLE
}
__syncthreads();
#ifdef FAST_FP16_AVAILABLE
#pragma unroll
for (int k1 = 0; k1 < V_cols_per_iter; ++k1) {
half2 V_k[(D/2)/warp_size];
half2 KQ_k[cpw];
constexpr int cpy_ne_D = cpy_ne/2 < (D/2)/warp_size ? cpy_ne/2 : (D/2)/warp_size;
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) {
ggml_cuda_memcpy_1<cpy_ne_D*4>(&V_k[i0/warp_size], &KV_tmp[k1*(D/2) + i0 + threadIdx.x*cpy_ne_D]);
}
#pragma unroll
for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) {
const int j = j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j);
half tmp[softmax_iter_j];
ggml_cuda_memcpy_1<softmax_iter_j*sizeof(half)>(
&tmp, KQ[j][k0 + k1]);
#pragma unroll
for (int j1 = 0; j1 < softmax_iter_j; ++j1) {
KQ_k[j0+j1] = __half2half2(tmp[j1]);
}
}
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size) {
#pragma unroll
for (int j0 = 0; j0 < cpw; ++j0) {
VKQ[j0][i0/warp_size] += V_k[i0/warp_size]*KQ_k[j0];
}
}
}
#else
#pragma unroll
for (int k1 = 0; k1 < V_cols_per_iter; ++k1) {
float2 V_k[(D/2)/warp_size];
float KQ_k[cpw];
constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size;
#pragma unroll
for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) {
ggml_cuda_memcpy_1<cpy_ne_D*4>(&V_k[i0/(2*warp_size)], &KV_tmp[k1*D + i0 + threadIdx.x*cpy_ne_D]);
}
#pragma unroll
for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) {
const int j = j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j);
ggml_cuda_memcpy_1<softmax_iter_j*sizeof(float)>(
&KQ_k[j0], KQ[j][k0 + k1]);
}
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size) {
#pragma unroll
for (int j0 = 0; j0 < cpw; ++j0) {
VKQ[j0][i0/warp_size].x += V_k[i0/warp_size].x*KQ_k[j0];
VKQ[j0][i0/warp_size].y += V_k[i0/warp_size].y*KQ_k[j0];
}
}
}
#endif // FAST_FP16_AVAILABLE
__syncthreads();
}
}
// Attention sink: adjust running max and sum once per head
if (sinksf && blockIdx.y == 0) {
const float sink = sinksf[head];
#pragma unroll
for (int j0 = 0; j0 < cpw; ++j0) {
float KQ_max_new_j = fmaxf(KQ_max[j0], sink);
KQ_max_new_j = warp_reduce_max<warp_size>(KQ_max_new_j);
const float KQ_max_scale = expf(KQ_max[j0] - KQ_max_new_j);
KQ_max[j0] = KQ_max_new_j;
const float val = expf(sink - KQ_max[j0]);
KQ_sum[j0] = KQ_sum[j0] * KQ_max_scale;
if (threadIdx.x == 0) {
KQ_sum[j0] += val;
}
#ifdef FAST_FP16_AVAILABLE
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale);
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size) {
VKQ[j0][i0/warp_size] *= KQ_max_scale_h2;
}
#else
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size) {
VKQ[j0][i0/warp_size].x *= KQ_max_scale;
VKQ[j0][i0/warp_size].y *= KQ_max_scale;
}
#endif // FAST_FP16_AVAILABLE
}
}
#pragma unroll
for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) {
KQ_sum[j_VKQ_0] = warp_reduce_sum<warp_size>(KQ_sum[j_VKQ_0]);
}
if (gridDim.y == 1) {
#pragma unroll
for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) {
#ifdef FAST_FP16_AVAILABLE
const half2 KQ_sum_j_inv = make_half2(1.0f/KQ_sum[j_VKQ_0], 1.0f/KQ_sum[j_VKQ_0]);
#pragma unroll
for (int i = 0; i < (D/2)/warp_size; ++i) {
VKQ[j_VKQ_0][i] *= KQ_sum_j_inv;
}
#else
const float KQ_sum_j_inv = 1.0f/KQ_sum[j_VKQ_0];
#pragma unroll
for (int i = 0; i < (D/2)/warp_size; ++i) {
VKQ[j_VKQ_0][i].x *= KQ_sum_j_inv;
VKQ[j_VKQ_0][i].y *= KQ_sum_j_inv;
}
#endif // FAST_FP16_AVAILABLE
}
}
// Write back results:
#pragma unroll
for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) {
const int j_VKQ = j_VKQ_0 + threadIdx.y*cpw;
if (ic0 + j_VKQ >= ne01) {
return;
}
const int j_dst_unrolled = ((sequence*ne01 + ic0 + j_VKQ)*ne02 + head)*gridDim.y + blockIdx.y;
#ifdef FAST_FP16_AVAILABLE
constexpr int cpy_ne_D = cpy_ne/2 < (D/2)/warp_size ? cpy_ne/2 : (D/2)/warp_size;
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) {
float2 tmp[cpy_ne_D];
#pragma unroll
for (int i1 = 0; i1 < cpy_ne_D; ++i1) {
tmp[i1] = __half22float2(VKQ[j_VKQ_0][i0/warp_size + i1]);
}
ggml_cuda_memcpy_1<sizeof(tmp)>(&dst[j_dst_unrolled*D + 2*i0 + threadIdx.x*(2*cpy_ne_D)], tmp);
}
#else
constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size;
#pragma unroll
for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) {
ggml_cuda_memcpy_1<cpy_ne_D*4>(
&dst[j_dst_unrolled*D + i0 + threadIdx.x*cpy_ne_D], &VKQ[j_VKQ_0][i0/(2*warp_size)]);
}
#endif // FAST_FP16_AVAILABLE
if (gridDim.y != 1 && threadIdx.x == 0) {
dst_meta[j_dst_unrolled] = make_float2(KQ_max[j_VKQ_0], KQ_sum[j_VKQ_0]);
}
}
#else
GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
max_bias, m0, m1, n_head_log2, logit_softcap,
ne00, ne01, ne02, ne03,
nb01, nb02, nb03,
ne10, ne11, ne12, ne13,
nb11, nb12, nb13,
nb21, nb22, nb23,
ne31, ne32, ne33,
nb31, nb32, nb33);
NO_DEVICE_CODE;
#endif // FLASH_ATTN_AVAILABLE
}
template <int D, bool use_logit_softcap>
static void launch_fattn_tile_switch_ncols(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
const int id = ggml_cuda_get_device();
const int cc = ggml_cuda_info().devices[id].cc;
const int warp_size = 32;
constexpr size_t nbytes_shared = 0;
#ifdef GGML_USE_HIP
if constexpr (D <= 128) {
if (Q->ne[1] > 32) {
constexpr int cols_per_block = 64;
const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size;
fattn_kernel_t fattn_kernel = flash_attn_tile<D, cols_per_block, use_logit_softcap>;
const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size);
launch_fattn<D, cols_per_block, 1>
(ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size);
return;
}
}
#endif // GGML_USE_HIP
if (Q->ne[1] > 16) {
constexpr int cols_per_block = 32;
const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size;
fattn_kernel_t fattn_kernel = flash_attn_tile<D, cols_per_block, use_logit_softcap>;
const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size);
launch_fattn<D, cols_per_block, 1>
(ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size);
return;
}
constexpr int cols_per_block = 16;
const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size;
fattn_kernel_t fattn_kernel = flash_attn_tile<D, cols_per_block, use_logit_softcap>;
const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size);
launch_fattn<D, cols_per_block, 1>
(ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size);
}
template <bool use_logit_softcap>
static void launch_fattn_tile_switch_head_size(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
switch (Q->ne[0]) {
#include "fattn-wmma-f16.cuh"
void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
switch (K->ne[0]) {
case 40: {
GGML_ASSERT(V->ne[0] == K->ne[0]);
ggml_cuda_flash_attn_ext_tile_case< 40, 40>(ctx, dst);
} break;
case 64: {
launch_fattn_tile_switch_ncols< 64, use_logit_softcap>(ctx, dst);
GGML_ASSERT(V->ne[0] == K->ne[0]);
ggml_cuda_flash_attn_ext_tile_case< 64, 64>(ctx, dst);
} break;
case 80: {
GGML_ASSERT(V->ne[0] == K->ne[0]);
ggml_cuda_flash_attn_ext_tile_case< 80, 80>(ctx, dst);
} break;
case 96: {
GGML_ASSERT(V->ne[0] == K->ne[0]);
ggml_cuda_flash_attn_ext_tile_case< 96, 96>(ctx, dst);
} break;
case 112: {
GGML_ASSERT(V->ne[0] == K->ne[0]);
ggml_cuda_flash_attn_ext_tile_case<112, 112>(ctx, dst);
} break;
case 128: {
launch_fattn_tile_switch_ncols<128, use_logit_softcap>(ctx, dst);
GGML_ASSERT(V->ne[0] == K->ne[0]);
ggml_cuda_flash_attn_ext_tile_case<128, 128>(ctx, dst);
} break;
case 256: {
launch_fattn_tile_switch_ncols<256, use_logit_softcap>(ctx, dst);
GGML_ASSERT(V->ne[0] == K->ne[0]);
ggml_cuda_flash_attn_ext_tile_case<256, 256>(ctx, dst);
} break;
case 576: {
GGML_ASSERT(V->ne[0] == 512);
ggml_cuda_flash_attn_ext_tile_case<576, 512>(ctx, dst);
} break;
default: {
GGML_ABORT("Unsupported head size");
} break;
}
}
void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * KQV = dst;
float logit_softcap;
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
launch_fattn_tile_switch_head_size<use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
launch_fattn_tile_switch_head_size<use_logit_softcap>(ctx, dst);
}
}

1203
ml/backend/ggml/ggml/src/ggml-cuda/fattn-tile.cuh vendored
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File diff suppressed because it is too large Load Diff

2
ml/backend/ggml/ggml/src/ggml-cuda/fattn-vec.cuh vendored
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@ -535,8 +535,6 @@ void ggml_cuda_flash_attn_ext_vec_case(ggml_backend_cuda_context & ctx, ggml_ten
float logit_softcap;
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
if (logit_softcap == 0.0f) {

12
ml/backend/ggml/ggml/src/ggml-cuda/fattn-wmma-f16.cu vendored
View File

@ -6,19 +6,19 @@
#include "fattn-common.cuh"
#include "fattn-wmma-f16.cuh"
#ifdef FP16_MMA_AVAILABLE
#ifdef GGML_USE_WMMA_FATTN
#if !defined(GGML_USE_HIP)
#include <mma.h>
#ifdef GGML_USE_MUSA
#if defined(GGML_USE_MUSA)
namespace wmma = mtmusa::wmma;
#else // GGML_USE_MUSA
namespace wmma = nvcuda::wmma;
#endif // GGML_USE_MUSA
#elif defined(GGML_HIP_ROCWMMA_FATTN) && defined(FP16_MMA_AVAILABLE)
#elif defined(GGML_USE_HIP)
#include <rocwmma/rocwmma.hpp>
namespace wmma = rocwmma;
#endif // !defined(GGML_USE_HIP)
#endif // FP16_MMA_AVAILABLE
#endif // GGML_USE_WMMA_FATTN
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
template<int D, int ncols, int nwarps, int VKQ_stride, typename KQ_acc_t, bool use_logit_softcap>
@ -45,7 +45,7 @@ static __global__ void flash_attn_ext_f16(
const int32_t nb21, const int32_t nb22, const int64_t nb23,
const int32_t ne31, const int32_t ne32, const int32_t ne33,
const int32_t nb31, const int32_t nb32, const int64_t nb33) {
#if defined(FLASH_ATTN_AVAILABLE) && (__CUDA_ARCH__ == GGML_CUDA_CC_VOLTA || (defined(GGML_HIP_ROCWMMA_FATTN) && defined(FP16_MMA_AVAILABLE)))
#if defined(FLASH_ATTN_AVAILABLE) && (__CUDA_ARCH__ == GGML_CUDA_CC_VOLTA || (defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_USE_WMMA_FATTN)))
// Skip unused kernel variants for faster compilation:
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
@ -481,7 +481,7 @@ static __global__ void flash_attn_ext_f16(
ne31, ne32, ne33,
nb31, nb32, nb33);
NO_DEVICE_CODE;
#endif // defined(FLASH_ATTN_AVAILABLE) && (__CUDA_ARCH__ == GGML_CUDA_CC_VOLTA || (defined(GGML_HIP_ROCWMMA_FATTN) && defined(FP16_MMA_AVAILABLE)))
#endif // defined(FLASH_ATTN_AVAILABLE) && (__CUDA_ARCH__ == GGML_CUDA_CC_VOLTA || (defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_USE_WMMA_FATTN)))
}
constexpr int get_max_power_of_2(int x) {

48
ml/backend/ggml/ggml/src/ggml-cuda/fattn-wmma-f16.cuh vendored
View File

@ -1,3 +1,51 @@
#pragma once
#include "common.cuh"
#if (!defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA) || defined(GGML_USE_MUSA)
#define GGML_USE_WMMA_FATTN
#endif // (!defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA) || defined(GGML_USE_MUSA)
#if defined(GGML_HIP_ROCWMMA_FATTN)
#if defined(CDNA) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0)
#define GGML_USE_WMMA_FATTN
#elif defined(CDNA)
#warning "rocwmma fattn on CDNA is broken on rocwmma v2.0.0, expect degraded performance"
#endif // defined(CDNA) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0)
#if defined(RDNA3)
#define GGML_USE_WMMA_FATTN
#endif // defined(RDNA3)
#if defined(RDNA4) && ROCWMMA_VERSION_MAJOR > 1
#define GGML_USE_WMMA_FATTN
#elif defined(RDNA4)
#warning "rocwmma fattn is not suported on RDNA4 on rocwmma < v2.0.0, expect degraded performance"
#endif // defined(RDNA4) && ROCWMMA_VERSION_MAJOR > 1
#endif // defined(GGML_HIP_ROCWMMA_FATTN)
// WMMA flash attention requires FP16 matrix instructions to be available for ggml code.
static bool ggml_cuda_should_use_wmma_fattn(const int cc) {
#if defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
return false;
#else
if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_VOLTA) ||
GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_MTHREADS(cc)) {
return true;
} else if (GGML_CUDA_CC_IS_CDNA(cc)){
#if defined(GGML_HIP_ROCWMMA_FATTN) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0)
return true;
#else
return false;
#endif // defined(GGML_HIP_ROCWMMA_FATTN) (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0)
} else if (GGML_CUDA_CC_IS_RDNA4(cc)) {
#if defined(GGML_HIP_ROCWMMA_FATTN) && ROCWMMA_VERSION_MAJOR > 1
return true;
#else
return false;
#endif // defined(GGML_HIP_ROCWMMA_FATTN) && ROCWMMA_VERSION_MAJOR > 1
} else {
return false;
}
#endif // defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
}
void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

80
ml/backend/ggml/ggml/src/ggml-cuda/fattn.cu vendored
View File

@ -198,6 +198,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
return BEST_FATTN_KERNEL_NONE;
#endif// FLASH_ATTN_AVAILABLE
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
@ -206,31 +207,32 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
const int gqa_ratio = Q->ne[2] / K->ne[2];
GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
float max_bias = 0.0f;
memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
// The effective batch size for the kernel can be increased by gqa_ratio.
// The kernel versions without this optimization are also used for ALiBi, if there is no mask, or if the KV cache is not padded,
const bool gqa_opt_applies = gqa_ratio % 2 == 0 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
const int cc = ggml_cuda_info().devices[device].cc;
switch (K->ne[0]) {
case 40:
case 64:
case 128:
case 256:
if (V->ne[0] != K->ne[0]) {
return BEST_FATTN_KERNEL_NONE;
}
break;
case 80:
case 96:
case 128:
case 112:
case 256:
if (V->ne[0] != K->ne[0]) {
return BEST_FATTN_KERNEL_NONE;
}
if (!fp16_mma_available(cc) && !turing_mma_available(cc)) {
return BEST_FATTN_KERNEL_NONE;
}
break;
case 576:
if (V->ne[0] != 512) {
return BEST_FATTN_KERNEL_NONE;
}
if (!turing_mma_available(cc) || gqa_ratio % 16 != 0) {
if (!gqa_opt_applies || gqa_ratio % 16 != 0) {
return BEST_FATTN_KERNEL_NONE;
}
break;
@ -264,47 +266,57 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
return BEST_FATTN_KERNEL_NONE;
}
const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0;
// If Turing tensor cores available, use them except for some cases with batch size 1:
if (turing_mma_available(cc)) {
best_fattn_kernel best = BEST_FATTN_KERNEL_MMA_F16;
// For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes:
const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0;
// If Turing tensor cores available, use them:
if (turing_mma_available(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40) {
if (can_use_vector_kernel) {
if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) {
if (cc >= GGML_CUDA_CC_ADA_LOVELACE && Q->ne[1] == 1 && Q->ne[3] == 1 && !(gqa_ratio > 4 && K->ne[1] >= 8192)) {
best = BEST_FATTN_KERNEL_VEC;
return BEST_FATTN_KERNEL_VEC;
}
} else {
if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
if (Q->ne[1] <= 2) {
best = BEST_FATTN_KERNEL_VEC;
return BEST_FATTN_KERNEL_VEC;
}
} else {
if (Q->ne[1] == 1) {
best = BEST_FATTN_KERNEL_VEC;
}
}
}
if ((gqa_ratio % 2 != 0 || !mask) && Q->ne[1] == 1) {
best = BEST_FATTN_KERNEL_VEC; // GQA-specific optimizations in the mma kernel do not apply.
}
}
return best;
}
// Use kernels specialized for small batch sizes if possible:
if (Q->ne[1] <= 8 && can_use_vector_kernel) {
return BEST_FATTN_KERNEL_VEC;
}
}
}
if (!gqa_opt_applies && Q->ne[1] == 1) {
return BEST_FATTN_KERNEL_VEC;
}
}
// For large batch sizes, use the WMMA kernel if possible:
if (fp16_mma_available(cc)) {
return BEST_FATTN_KERNEL_MMA_F16;
}
// Use the WMMA kernel if possible:
if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 576) {
if (can_use_vector_kernel && Q->ne[1] <= 2) {
return BEST_FATTN_KERNEL_VEC;
}
return BEST_FATTN_KERNEL_WMMA_F16;
}
// If there is no suitable kernel for tensor cores or small batch sizes, use the generic kernel for large batch sizes:
// If there are no tensor cores available, use the generic tile kernel:
if (can_use_vector_kernel) {
if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) {
if (Q->ne[1] == 1) {
if (!gqa_opt_applies) {
return BEST_FATTN_KERNEL_VEC;
}
}
} else {
if (Q->ne[1] <= 2) {
return BEST_FATTN_KERNEL_VEC;
}
}
}
return BEST_FATTN_KERNEL_TILE;
}

5
ml/backend/ggml/ggml/src/ggml-cuda/ggml-cuda.cu vendored
View File

@ -291,7 +291,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
info.default_tensor_split[id] = total_vram;
total_vram += prop.totalGlobalMem;
info.devices[id].integrated = prop.integrated;
info.devices[id].integrated = false; // Temporarily disabled due to issues with corrupted output (e.g. #15034)
info.devices[id].nsm = prop.multiProcessorCount;
info.devices[id].smpb = prop.sharedMemPerBlock;
info.devices[id].warp_size = prop.warpSize;
@ -2466,6 +2466,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_UNARY_OP_ELU:
ggml_cuda_op_elu(ctx, dst);
break;
case GGML_UNARY_OP_XIELU:
ggml_cuda_op_xielu(ctx, dst);
break;
default:
return false;
}

5
ml/backend/ggml/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq112-dv112.cu vendored Normal file
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@ -0,0 +1,5 @@
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
#include "../fattn-tile.cuh"
DECL_FATTN_TILE_CASE(112, 112);

5
ml/backend/ggml/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq128-dv128.cu vendored Normal file
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@ -0,0 +1,5 @@
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
#include "../fattn-tile.cuh"
DECL_FATTN_TILE_CASE(128, 128);

5
ml/backend/ggml/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq256-dv256.cu vendored Normal file
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@ -0,0 +1,5 @@
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
#include "../fattn-tile.cuh"
DECL_FATTN_TILE_CASE(256, 256);

5
ml/backend/ggml/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq40-dv40.cu vendored Normal file
View File

@ -0,0 +1,5 @@
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
#include "../fattn-tile.cuh"
DECL_FATTN_TILE_CASE(40, 40);

5
ml/backend/ggml/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq576-dv512.cu vendored Normal file
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@ -0,0 +1,5 @@
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
#include "../fattn-tile.cuh"
DECL_FATTN_TILE_CASE(576, 512);

5
ml/backend/ggml/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq64-dv64.cu vendored Normal file
View File

@ -0,0 +1,5 @@
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
#include "../fattn-tile.cuh"
DECL_FATTN_TILE_CASE(64, 64);

5
ml/backend/ggml/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq80-dv80.cu vendored Normal file
View File

@ -0,0 +1,5 @@
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
#include "../fattn-tile.cuh"
DECL_FATTN_TILE_CASE(80, 80);

5
ml/backend/ggml/ggml/src/ggml-cuda/template-instances/fattn-tile-instance-dkq96-dv96.cu vendored Normal file
View File

@ -0,0 +1,5 @@
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
#include "../fattn-tile.cuh"
DECL_FATTN_TILE_CASE(96, 96);

4
ml/backend/ggml/ggml/src/ggml-cuda/topk-moe.cu vendored
View File

@ -13,7 +13,7 @@
It is intended as fusion of softmax->top-k->get_rows pipeline for MoE models
*/
template <size_t n_experts, bool with_norm>
template <int n_experts, bool with_norm>
__launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * logits,
float * weights,
int32_t * ids,
@ -204,8 +204,6 @@ void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx,
GGML_ASSERT(ids->nb[1] / ggml_type_size(ids->type) == (size_t) n_experts);
cudaStream_t stream = ctx.stream();
const int n_expert_used = weights->ne[1];
if (with_norm) {

54
ml/backend/ggml/ggml/src/ggml-cuda/unary.cu vendored
View File

@ -1,4 +1,5 @@
#include "unary.cuh"
#include "convert.cuh"
static __device__ __forceinline__ float op_abs(float x) {
return fabsf(x);
@ -375,6 +376,59 @@ void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
swiglu_oai_cuda(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream);
}
/* CUDA kernel + launcher for xIELU */
template <typename T>
static __global__ void xielu_kernel(const T * x, T * dst, const int k, float alpha_n, float alpha_p, float beta, float eps) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
const float xi = ggml_cuda_cast<float>(x[i]);
const float gate_pos = (xi > 0.0f);
const float y_pos = alpha_p * xi * xi + beta * xi;
const float min_v_eps = fminf(xi, eps);
const float y_neg = (expm1f(min_v_eps) - xi) * alpha_n + beta * xi;
const float out = gate_pos * y_pos + (1.0f - gate_pos) * y_neg;
dst[i] = ggml_cuda_cast<T>(out);
}
template <typename T>
static void xielu_cuda(const T * x, T * dst, const int k, float alpha_n, float alpha_p, float beta, float eps, cudaStream_t stream) {
const int num_blocks = (k + CUDA_XIELU_BLOCK_SIZE) / CUDA_XIELU_BLOCK_SIZE;
xielu_kernel<<<num_blocks, CUDA_XIELU_BLOCK_SIZE, 0, stream>>>(x, dst, k, alpha_n, alpha_p, beta, eps);
}
void ggml_cuda_op_xielu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const void * src0_d = src0->data;
void * dst_d = dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
GGML_ASSERT(src0->type == dst->type);
const float alpha_n = ggml_get_op_params_f32(dst, 1);
const float alpha_p = ggml_get_op_params_f32(dst, 2);
const float beta = ggml_get_op_params_f32(dst, 3);
const float eps = ggml_get_op_params_f32(dst, 4);
if (src0->type == GGML_TYPE_F16) {
xielu_cuda((const half *)src0_d, (half *)dst_d, ggml_nelements(src0), alpha_n, alpha_p, beta, eps, stream);
} else {
xielu_cuda((const float *)src0_d, (float *)dst_d, ggml_nelements(src0), alpha_n, alpha_p, beta, eps, stream);
}
}
/* silu_back */
static __device__ __forceinline__ float op_silu_back(float grad, float x) {

3
ml/backend/ggml/ggml/src/ggml-cuda/unary.cuh vendored
View File

@ -16,6 +16,7 @@
#define CUDA_SIN_BLOCK_SIZE 256
#define CUDA_COS_BLOCK_SIZE 256
#define CUDA_GLU_BLOCK_SIZE 256
#define CUDA_XIELU_BLOCK_SIZE 256
void ggml_cuda_op_abs(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
@ -72,3 +73,5 @@ void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
void ggml_cuda_op_geglu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_xielu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

3
ml/backend/ggml/ggml/src/ggml-cuda/vendors/hip.h vendored
View File

@ -8,6 +8,9 @@
// for rocblas_initialize()
#include "rocblas/rocblas.h"
#if defined(GGML_HIP_ROCWMMA_FATTN)
#include <rocwmma/rocwmma-version.hpp>
#endif // defined(GGML_HIP_ROCWMMA_FATTN)
#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT

12
ml/backend/ggml/ggml/src/ggml-hip/CMakeLists.txt vendored
View File

@ -39,12 +39,6 @@ endif()
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
if (GGML_HIP_ROCWMMA_FATTN)
CHECK_INCLUDE_FILE_CXX("rocwmma/rocwmma.hpp" FOUND_ROCWMMA)
if (NOT ${FOUND_ROCWMMA})
message(FATAL_ERROR "rocwmma has not been found")
endif()
endif()
if (${hip_VERSION} VERSION_LESS 6.1)
message(FATAL_ERROR "At least ROCM/HIP V6.1 is required")
@ -59,6 +53,8 @@ file(GLOB GGML_HEADERS_ROCM "../ggml-cuda/*.cuh")
list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h")
file(GLOB GGML_SOURCES_ROCM "../ggml-cuda/*.cu")
file(GLOB SRCS "../ggml-cuda/template-instances/fattn-tile*.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu")
@ -117,10 +113,6 @@ if (NOT GGML_HIP_MMQ_MFMA)
add_compile_definitions(GGML_HIP_NO_MMQ_MFMA)
endif()
if (GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12 OR ${hip_VERSION} VERSION_GREATER_EQUAL 7.0)
add_compile_definitions(GGML_HIP_ROCWMMA_FATTN_GFX12)
endif()
if (GGML_HIP_EXPORT_METRICS)
set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -Rpass-analysis=kernel-resource-usage --save-temps")
endif()

3
ml/backend/ggml/ggml/src/ggml-impl.h vendored
View File

@ -102,6 +102,9 @@ static bool ggml_op_is_empty(enum ggml_op op) {
}
}
static inline float ggml_softplus(float input) {
return (input > 20.0f) ? input : logf(1 + expf(input));
}
//
// logging
//

4
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-common.cpp vendored
View File

@ -112,7 +112,7 @@ static bool ggml_mem_ranges_add_dst(ggml_mem_ranges_t mrs, const ggml_tensor * t
}
bool ggml_mem_ranges_add(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
for (int i = 0; i < GGML_MAX_DIMS; i++) {
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (tensor->src[i]) {
ggml_mem_ranges_add_src(mrs, tensor->src[i]);
}
@ -173,7 +173,7 @@ static bool ggml_mem_ranges_check_dst(ggml_mem_ranges_t mrs, const ggml_tensor *
}
bool ggml_mem_ranges_check(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
for (int i = 0; i < GGML_MAX_DIMS; i++) {
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (tensor->src[i]) {
if (!ggml_mem_ranges_check_src(mrs, tensor->src[i])) {
return false;

182
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-device.cpp vendored
View File

@ -268,6 +268,25 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_glu(ggml_metal_library_t l
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum(ggml_metal_library_t lib, const ggml_tensor * op) {
assert(op->op == GGML_OP_SUM);
char base[256];
char name[256];
snprintf(base, 256, "kernel_op_sum_%s", ggml_type_name(op->src[0]->type));
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
if (res) {
return res;
}
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum_rows(ggml_metal_library_t lib, const ggml_tensor * op) {
GGML_ASSERT(op->src[0]->nb[0] == ggml_type_size(op->src[0]->type));
@ -338,7 +357,13 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_ssm_conv(ggml_metal_librar
char base[256];
char name[256];
snprintf(base, 256, "kernel_ssm_conv_%s_%s", ggml_type_name(op->src[0]->type), ggml_type_name(op->src[1]->type));
const char * suffix = "";
if (op->src[1]->ne[0] % 4 == 0) {
suffix = "_4";
}
snprintf(base, 256, "kernel_ssm_conv_%s_%s%s", ggml_type_name(op->src[0]->type), ggml_type_name(op->src[1]->type), suffix);
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
@ -352,15 +377,15 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_ssm_conv(ggml_metal_librar
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_ssm_scan(ggml_metal_library_t lib, const ggml_tensor * op) {
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
char base[256];
char name[256];
if (op->src[3]->ne[0] == 1) {
snprintf(base, 256, "kernel_ssm_scan_group_%s", ggml_type_name(op->src[0]->type));
} else {
const int nsg = (ne00 + 31)/32;
snprintf(base, 256, "kernel_ssm_scan_%s", ggml_type_name(op->src[0]->type));
}
snprintf(name, 256, "%s", base);
snprintf(name, 256, "%s_nsg=%d", base, nsg);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
if (res) {
@ -369,7 +394,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_ssm_scan(ggml_metal_librar
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
ggml_metal_pipeline_set_smem(res, 32*sizeof(float));
ggml_metal_pipeline_set_smem(res, 32*sizeof(float)*nsg);
return res;
}
@ -918,6 +943,96 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argsort(ggml_metal_library
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_pad(
ggml_metal_library_t lib,
const struct ggml_tensor * op,
bool has_mask,
int32_t ncpsg) {
assert(op->op == GGML_OP_FLASH_ATTN_EXT);
GGML_UNUSED(op);
char base[256];
char name[256];
snprintf(base, 256, "kernel_%s",
"flash_attn_ext_pad");
snprintf(name, 256, "%s_mask=%d_ncpsg=%d",
base,
has_mask,
ncpsg);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
if (res) {
return res;
}
ggml_metal_cv_t cv = ggml_metal_cv_init();
ggml_metal_cv_set_bool(cv, has_mask, FC_FLASH_ATTN_EXT_PAD + 0);
//ggml_metal_cv_set_bool(cv, has_sinks, FC_FLASH_ATTN_EXT_PAD + 1);
//ggml_metal_cv_set_bool(cv, has_bias, FC_FLASH_ATTN_EXT_PAD + 2);
//ggml_metal_cv_set_bool(cv, has_scap, FC_FLASH_ATTN_EXT_PAD + 3);
//ggml_metal_cv_set_int32(cv, ns10, FC_FLASH_ATTN_EXT_PAD + 20);
//ggml_metal_cv_set_int32(cv, ns20, FC_FLASH_ATTN_EXT_PAD + 21);
//ggml_metal_cv_set_int32(cv, nsg, FC_FLASH_ATTN_EXT_PAD + 22);
//ggml_metal_cv_set_int32(cv, nwg, FC_FLASH_ATTN_EXT_PAD + 23);
//ggml_metal_cv_set_int32(cv, nqptg, FC_FLASH_ATTN_EXT_PAD + 24);
ggml_metal_cv_set_int32(cv, ncpsg, FC_FLASH_ATTN_EXT_PAD + 25);
res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
ggml_metal_cv_free(cv);
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_blk(
ggml_metal_library_t lib,
const struct ggml_tensor * op,
int32_t nqptg,
int32_t ncpsg) {
assert(op->op == GGML_OP_FLASH_ATTN_EXT);
GGML_UNUSED(op);
char base[256];
char name[256];
snprintf(base, 256, "kernel_%s",
"flash_attn_ext_blk");
snprintf(name, 256, "%s_nqptg=%d_ncpsg=%d",
base,
nqptg,
ncpsg);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
if (res) {
return res;
}
ggml_metal_cv_t cv = ggml_metal_cv_init();
//ggml_metal_cv_set_bool(cv, has_mask, FC_FLASH_ATTN_EXT_BLK + 0);
//ggml_metal_cv_set_bool(cv, has_sinks, FC_FLASH_ATTN_EXT_BLK + 1);
//ggml_metal_cv_set_bool(cv, has_bias, FC_FLASH_ATTN_EXT_BLK + 2);
//ggml_metal_cv_set_bool(cv, has_scap, FC_FLASH_ATTN_EXT_BLK + 3);
//ggml_metal_cv_set_int32(cv, ns10, FC_FLASH_ATTN_EXT_BLK + 20);
//ggml_metal_cv_set_int32(cv, ns20, FC_FLASH_ATTN_EXT_BLK + 21);
//ggml_metal_cv_set_int32(cv, nsg, FC_FLASH_ATTN_EXT_BLK + 22);
//ggml_metal_cv_set_int32(cv, nwg, FC_FLASH_ATTN_EXT_BLK + 23);
ggml_metal_cv_set_int32(cv, nqptg, FC_FLASH_ATTN_EXT_BLK + 24);
ggml_metal_cv_set_int32(cv, ncpsg, FC_FLASH_ATTN_EXT_BLK + 25);
res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
ggml_metal_cv_free(cv);
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext(
ggml_metal_library_t lib,
const ggml_tensor * op,
@ -925,6 +1040,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext(
bool has_sinks,
bool has_bias,
bool has_scap,
bool has_kvpad,
int32_t nsg) {
assert(op->op == GGML_OP_FLASH_ATTN_EXT);
@ -937,18 +1053,23 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext(
const int32_t ns10 = op->src[1]->nb[1]/op->src[1]->nb[0];
const int32_t ns20 = op->src[2]->nb[1]/op->src[2]->nb[0];
// do bounds checks for the mask?
const bool bc_mask = op->src[3] && (op->src[3]->ne[1] % 8 != 0);
snprintf(base, 256, "kernel_%s_%s_dk%d_dv%d",
"flash_attn_ext",
ggml_type_name(op->src[1]->type),
dk,
dv);
snprintf(name, 256, "%s_mask=%d_sinks=%d_bias=%d_scap=%d_ns10=%d_ns20=%d_nsg=%d",
snprintf(name, 256, "%s_mask=%d_sinks=%d_bias=%d_scap=%d_kvpad=%d_bcm=%d_ns10=%d_ns20=%d_nsg=%d",
base,
has_mask,
has_sinks,
has_bias,
has_scap,
has_kvpad,
bc_mask,
ns10,
ns20,
nsg);
@ -964,6 +1085,9 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext(
ggml_metal_cv_set_bool(cv, has_sinks, FC_FLASH_ATTN_EXT + 1);
ggml_metal_cv_set_bool(cv, has_bias, FC_FLASH_ATTN_EXT + 2);
ggml_metal_cv_set_bool(cv, has_scap, FC_FLASH_ATTN_EXT + 3);
ggml_metal_cv_set_bool(cv, has_kvpad, FC_FLASH_ATTN_EXT + 4);
ggml_metal_cv_set_bool(cv, bc_mask, FC_FLASH_ATTN_EXT + 10);
ggml_metal_cv_set_int32(cv, ns10, FC_FLASH_ATTN_EXT + 20);
ggml_metal_cv_set_int32(cv, ns20, FC_FLASH_ATTN_EXT + 21);
@ -983,6 +1107,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_vec(
bool has_sinks,
bool has_bias,
bool has_scap,
bool has_kvpad,
int32_t nsg,
int32_t nwg) {
assert(op->op == GGML_OP_FLASH_ATTN_EXT);
@ -1002,12 +1127,13 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_vec(
dk,
dv);
snprintf(name, 256, "%s_mask=%d_sink=%d_bias=%d_softcap=%d_ns10=%d_ns20=%d_nsg=%d_nwg=%d",
snprintf(name, 256, "%s_mask=%d_sink=%d_bias=%d_scap=%d_kvpad=%d_ns10=%d_ns20=%d_nsg=%d_nwg=%d",
base,
has_mask,
has_sinks,
has_bias,
has_scap,
has_kvpad,
ns10,
ns20,
nsg, nwg);
@ -1023,6 +1149,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_vec(
ggml_metal_cv_set_bool(cv, has_sinks, FC_FLASH_ATTN_EXT_VEC + 1);
ggml_metal_cv_set_bool(cv, has_bias, FC_FLASH_ATTN_EXT_VEC + 2);
ggml_metal_cv_set_bool(cv, has_scap, FC_FLASH_ATTN_EXT_VEC + 3);
ggml_metal_cv_set_bool(cv, has_kvpad, FC_FLASH_ATTN_EXT_VEC + 4);
ggml_metal_cv_set_int32(cv, ns10, FC_FLASH_ATTN_EXT_VEC + 20);
ggml_metal_cv_set_int32(cv, ns20, FC_FLASH_ATTN_EXT_VEC + 21);
@ -1374,3 +1501,40 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_timestep_embedding(ggml_me
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_opt_step_adamw(ggml_metal_library_t lib, const ggml_tensor * op) {
assert(op->op == GGML_OP_OPT_STEP_ADAMW);
char base[256];
char name[256];
snprintf(base, 256, "kernel_opt_step_adamw_%s", ggml_type_name(op->src[0]->type));
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
if (res) {
return res;
}
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_opt_step_sgd(ggml_metal_library_t lib, const ggml_tensor * op) {
assert(op->op == GGML_OP_OPT_STEP_SGD);
char base[256];
char name[256];
snprintf(base, 256, "kernel_opt_step_sgd_%s", ggml_type_name(op->src[0]->type));
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
if (res) {
return res;
}
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
return res;
}

17
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-device.h vendored
View File

@ -109,6 +109,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_set_rows (ggml_me
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_repeat (ggml_metal_library_t lib, enum ggml_type tsrc);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_unary (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_glu (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum_rows (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_soft_max (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_ssm_conv (ggml_metal_library_t lib, const struct ggml_tensor * op);
@ -134,6 +135,20 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad (ggml_me
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad_reflect_1d (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_arange (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_timestep_embedding(ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_opt_step_adamw (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_opt_step_sgd (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_pad(
ggml_metal_library_t lib,
const struct ggml_tensor * op,
bool has_mask,
int32_t ncpsg);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_blk(
ggml_metal_library_t lib,
const struct ggml_tensor * op,
int32_t nqptg,
int32_t ncpsg);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext(
ggml_metal_library_t lib,
@ -142,6 +157,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext(
bool has_sinks,
bool has_bias,
bool has_scap,
bool has_kvpad,
int32_t nsg);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_vec(
@ -151,6 +167,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_vec(
bool has_sinks,
bool has_bias,
bool has_scap,
bool has_kvpad,
int32_t nsg,
int32_t nwg);

8
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-device.m vendored
View File

@ -656,6 +656,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
case GGML_OP_COS:
case GGML_OP_LOG:
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
case GGML_OP_MEAN:
case GGML_OP_SOFT_MAX:
@ -776,9 +777,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
};
}
case GGML_OP_GET_ROWS:
{
return op->ne[3] == 1;
}
return true;
case GGML_OP_SET_ROWS:
{
if (op->src[0]->type != GGML_TYPE_F32) {
@ -800,6 +799,9 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
return false;
};
}
case GGML_OP_OPT_STEP_ADAMW:
case GGML_OP_OPT_STEP_SGD:
return has_simdgroup_reduction;
default:
return false;
}

1110
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal vendored
View File

File diff suppressed because it is too large Load Diff

80
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-impl.h vendored
View File

@ -69,11 +69,20 @@
#define N_SG_IQ4_XS 2
// function constants offsets
#define FC_FLASH_ATTN_EXT 100
#define FC_FLASH_ATTN_EXT_VEC 200
#define FC_FLASH_ATTN_EXT_VEC_REDUCE 300
#define FC_MUL_MV 400
#define FC_MUL_MM 500
#define FC_FLASH_ATTN_EXT_PAD 100
#define FC_FLASH_ATTN_EXT_BLK 200
#define FC_FLASH_ATTN_EXT 300
#define FC_FLASH_ATTN_EXT_VEC 400
#define FC_FLASH_ATTN_EXT_VEC_REDUCE 500
#define FC_MUL_MV 600
#define FC_MUL_MM 700
// op-specific constants
#define OP_FLASH_ATTN_EXT_NQPTG 8
#define OP_FLASH_ATTN_EXT_NCPSG 64
#define OP_FLASH_ATTN_EXT_VEC_NQPTG 1
#define OP_FLASH_ATTN_EXT_VEC_NCPSG 32
// kernel argument structs
//
@ -178,6 +187,7 @@ typedef struct {
} ggml_metal_kargs_clamp;
typedef struct {
int64_t nk0;
int64_t ne00;
int64_t ne01;
int64_t ne02;
@ -243,6 +253,35 @@ typedef struct {
int32_t sect_3;
} ggml_metal_kargs_rope;
typedef struct {
int32_t ne11;
int32_t ne_12_2; // assume K and V are same shape
int32_t ne_12_3;
uint64_t nb11;
uint64_t nb12;
uint64_t nb13;
uint64_t nb21;
uint64_t nb22;
uint64_t nb23;
int32_t ne31;
int32_t ne32;
int32_t ne33;
uint64_t nb31;
uint64_t nb32;
uint64_t nb33;
} ggml_metal_kargs_flash_attn_ext_pad;
typedef struct {
int32_t ne01;
int32_t ne30;
int32_t ne31;
int32_t ne32;
int32_t ne33;
uint64_t nb31;
uint64_t nb32;
uint64_t nb33;
} ggml_metal_kargs_flash_attn_ext_blk;
typedef struct {
int32_t ne01;
int32_t ne02;
@ -261,6 +300,7 @@ typedef struct {
uint64_t nb21;
uint64_t nb22;
uint64_t nb23;
int32_t ne31;
int32_t ne32;
int32_t ne33;
uint64_t nb31;
@ -295,6 +335,7 @@ typedef struct {
uint64_t nb21;
uint64_t nb22;
uint64_t nb23;
int32_t ne31;
int32_t ne32;
int32_t ne33;
uint64_t nb31;
@ -503,6 +544,10 @@ typedef struct{
float limit;
} ggml_metal_kargs_glu;
typedef struct {
uint64_t np;
} ggml_metal_kargs_sum;
typedef struct {
int64_t ne00;
int64_t ne01;
@ -572,32 +617,45 @@ typedef struct {
int64_t n_seq_tokens;
int64_t n_seqs;
uint64_t s_off;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
uint64_t nb03;
uint64_t nb10;
uint64_t nb11;
uint64_t nb12;
uint64_t ns12;
uint64_t nb13;
uint64_t nb20;
uint64_t nb21;
uint64_t ns21;
uint64_t nb22;
int64_t ne30;
uint64_t nb31;
uint64_t nb41;
uint64_t nb42;
uint64_t ns42;
uint64_t nb43;
uint64_t nb51;
uint64_t nb52;
uint64_t ns52;
uint64_t nb53;
uint64_t nb0;
} ggml_metal_kargs_ssm_scan;
typedef struct {
int64_t ne00;
int32_t ne00t;
int32_t ne00;
uint64_t nb01;
uint64_t nb02;
int64_t ne10;
uint64_t nb03;
int32_t ne10;
uint64_t nb10;
uint64_t nb11;
uint64_t nb12;
uint64_t nb1;
uint64_t nb2;
uint64_t nb3;
} ggml_metal_kargs_get_rows;
typedef struct {
@ -719,4 +777,12 @@ typedef struct {
uint64_t nb01;
} ggml_metal_kargs_argmax;
typedef struct {
int64_t np;
} ggml_metal_kargs_opt_step_adamw;
typedef struct {
int64_t np;
} ggml_metal_kargs_opt_step_sgd;
#endif // GGML_METAL_IMPL

499
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-ops.cpp vendored
View File

@ -226,6 +226,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
GGML_TENSOR_LOCALS(uint64_t, nb0, node->src[0], nb);
GGML_TENSOR_LOCALS( int64_t, ne1, node->src[1], ne);
GGML_TENSOR_LOCALS(uint64_t, nb1, node->src[1], nb);
GGML_TENSOR_LOCALS( int64_t, ne2, node->src[2], ne);
GGML_TENSOR_LOCALS(uint64_t, nb2, node->src[2], nb);
GGML_TENSOR_LOCALS( int64_t, ne3, node->src[3], ne);
GGML_TENSOR_LOCALS(uint64_t, nb3, node->src[3], nb);
GGML_TENSOR_LOCALS( int64_t, ne, node, ne);
GGML_TENSOR_LOCALS(uint64_t, nb, node, nb);
@ -237,6 +241,14 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
GGML_LOG_DEBUG("%s: src1 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(node->src[1]->type), ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13,
ggml_is_contiguous(node->src[1]), node->src[1]->name);
}
if (node->src[2]) {
GGML_LOG_DEBUG("%s: src2 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(node->src[2]->type), ne20, ne21, ne22, ne23, nb20, nb21, nb22, nb23,
ggml_is_contiguous(node->src[2]), node->src[2]->name);
}
if (node->src[3]) {
GGML_LOG_DEBUG("%s: src3 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(node->src[3]->type), ne30, ne31, ne32, ne33, nb30, nb31, nb32, nb33,
ggml_is_contiguous(node->src[3]), node->src[3]->name);
}
if (node) {
GGML_LOG_DEBUG("%s: node - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(node->type), ne0, ne1, ne2, ne3, nb0, nb1, nb2, nb3,
node->name);
@ -289,6 +301,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
{
n_fuse = ggml_metal_op_glu(ctx, idx);
} break;
case GGML_OP_SUM:
{
n_fuse = ggml_metal_op_sum(ctx, idx);
} break;
case GGML_OP_SUM_ROWS:
case GGML_OP_MEAN:
{
@ -398,6 +414,14 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
{
n_fuse = ggml_metal_op_argmax(ctx, idx);
} break;
case GGML_OP_OPT_STEP_ADAMW:
{
n_fuse = ggml_metal_op_opt_step_adamw(ctx, idx);
} break;
case GGML_OP_OPT_STEP_SGD:
{
n_fuse = ggml_metal_op_opt_step_sgd(ctx, idx);
} break;
default:
{
GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(node->op));
@ -577,6 +601,7 @@ int ggml_metal_op_acc(ggml_metal_op_t ctx, int idx) {
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_cpy(lib, op->src[0]->type, op->type);
ggml_metal_kargs_cpy args = {
/*.nk0 =*/ ne00,
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
@ -827,6 +852,30 @@ int ggml_metal_op_glu(ggml_metal_op_t ctx, int idx) {
return 1;
}
int ggml_metal_op_sum(ggml_metal_op_t ctx, int idx) {
ggml_tensor * op = ctx->node(idx);
ggml_metal_library_t lib = ctx->lib;
ggml_metal_encoder_t enc = ctx->enc;
const uint64_t n = (uint64_t) ggml_nelements(op->src[0]);
ggml_metal_kargs_sum args = {
/*.np =*/ n,
};
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_sum(lib, op);
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2);
ggml_metal_encoder_dispatch_threadgroups(enc, 1, 1, 1, 1, 1, 1);
return 1;
}
int ggml_metal_op_sum_rows(ggml_metal_op_t ctx, int idx) {
ggml_tensor * op = ctx->node(idx);
@ -906,23 +955,31 @@ int ggml_metal_op_get_rows(ggml_metal_op_t ctx, int idx) {
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_get_rows(lib, op->src[0]->type);
ggml_metal_kargs_get_rows args = {
/*.ne00t =*/ ggml_is_quantized(op->src[0]->type) ? ne00/16 : ne00,
/*.ne00 =*/ ne00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.ne10 =*/ ne10,
/*.nb10 =*/ nb10,
/*.nb11 =*/ nb11,
/*.nb12 =*/ nb12,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
};
const int nth = std::min(args.ne00t, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
const int nw0 = (args.ne00t + nth - 1)/nth;
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), 2);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 3);
ggml_metal_encoder_dispatch_threadgroups(enc, ne10, ne11, ne12, 32, 1, 1);
ggml_metal_encoder_dispatch_threadgroups(enc, nw0*ne10, ne11, ne12, nth, 1, 1);
return 1;
}
@ -1172,25 +1229,36 @@ int ggml_metal_op_ssm_scan(ggml_metal_op_t ctx, int idx) {
/*.n_seq_tokens =*/ n_seq_tokens,
/*.n_seqs =*/ n_seqs,
/*.s_off =*/ ggml_nelements(op->src[1]) * sizeof(float),
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.nb10 =*/ nb10,
/*.nb11 =*/ nb11,
/*.nb12 =*/ nb12,
/*.ns12 =*/ nb12/nb10,
/*.nb13 =*/ nb13,
/*.nb20 =*/ nb20,
/*.nb21 =*/ nb21,
/*.ns21 =*/ nb21/nb20,
/*.nb22 =*/ nb22,
/*.ne30 =*/ ne30,
/*.nb31 =*/ nb31,
/*.nb41 =*/ nb41,
/*.nb42 =*/ nb42,
/*.ns42 =*/ nb42/nb40,
/*.nb43 =*/ nb43,
/*.nb51 =*/ nb51,
/*.nb52 =*/ nb52,
/*.ns52 =*/ nb52/nb50,
/*.nb53 =*/ nb53,
/*.nb0 =*/ nb0,
};
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_ssm_scan(lib, op);
GGML_ASSERT(d_state <= ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
const size_t sms = ggml_metal_pipeline_get_smem(pipeline);
ggml_metal_encoder_set_pipeline(enc, pipeline);
@ -1206,13 +1274,7 @@ int ggml_metal_op_ssm_scan(ggml_metal_op_t ctx, int idx) {
ggml_metal_encoder_set_threadgroup_memory_size(enc, sms, 0);
if (ne30 == 1) {
// Mamba-2
ggml_metal_encoder_dispatch_threadgroups(enc, d_inner, n_head, n_seqs, d_state, 1, 1);
} else {
GGML_ASSERT(d_inner == 1);
ggml_metal_encoder_dispatch_threadgroups(enc, n_head, n_seqs, 1, d_state, 1, 1);
}
return 1;
}
@ -1273,26 +1335,23 @@ int ggml_metal_op_cpy(ggml_metal_op_t ctx, int idx) {
GGML_ASSERT(ne00 % ggml_blck_size(op->src[0]->type) == 0);
// TODO: support
//const int32_t nk00 = ne00/ggml_blck_size(op->type);
const int32_t nk00 = ne00;
int nth = 32; // SIMD width
while (nth < nk00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
nth *= 2;
int64_t nk0 = ne00;
if (ggml_is_quantized(op->src[0]->type)) {
nk0 = ne00/16;
} else if (ggml_is_quantized(op->type)) {
nk0 = ne00/ggml_blck_size(op->type);
}
nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
int nth = std::min<int>(nk0, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
// when rows are small, we can batch them together in a single threadgroup
int nrptg = 1;
// TODO: relax this constraint in the future
if (ggml_blck_size(op->src[0]->type) == 1 && ggml_blck_size(op->type) == 1) {
if (nth > nk00) {
nrptg = (nth + nk00 - 1)/nk00;
nth = nk00;
if (nth > nk0) {
nrptg = (nth + nk0 - 1)/nk0;
nth = nk0;
if (nrptg*nth > ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
nrptg--;
@ -1300,10 +1359,11 @@ int ggml_metal_op_cpy(ggml_metal_op_t ctx, int idx) {
}
}
nth = std::min(nth, nk00);
nth = std::min<int>(nth, nk0);
ggml_metal_kargs_cpy args = {
/*.ne00 =*/ nk00,
/*.nk0 =*/ nk0,
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.ne03 =*/ ne03,
@ -1321,12 +1381,14 @@ int ggml_metal_op_cpy(ggml_metal_op_t ctx, int idx) {
/*.nb3 =*/ nb3,
};
const int nw0 = nrptg == 1 ? (nk0 + nth - 1)/nth : 1;
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2);
ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, nrptg, 1);
ggml_metal_encoder_dispatch_threadgroups(enc, nw0*(ne01 + nrptg - 1)/nrptg, ne02, ne03, nth, nrptg, 1);
return 1;
}
@ -1520,9 +1582,8 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) {
!ggml_is_transposed(op->src[1]) &&
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
props_dev->has_simdgroup_mm && ne00 >= 64 &&
(ne11 > ne11_mm_min || (ggml_is_quantized(op->src[0]->type) && ne12 > 1))) {
//printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
props_dev->has_simdgroup_mm && ne00 >= 64 && ne11 > ne11_mm_min) {
//GGML_LOG_INFO("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
// some Metal matrix data types require aligned pointers
// ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
@ -1875,20 +1936,107 @@ bool ggml_metal_op_flash_attn_ext_use_vec(const ggml_tensor * op) {
return (ne01 < 20) && (ne00 % 32 == 0);
}
size_t ggml_metal_op_flash_attn_ext_extra_pad(const ggml_tensor * op) {
assert(op->op == GGML_OP_FLASH_ATTN_EXT);
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne);
GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
GGML_TENSOR_LOCALS( int32_t, ne2, op->src[2], ne);
GGML_TENSOR_LOCALS(uint64_t, nb2, op->src[2], nb);
GGML_TENSOR_LOCALS( int32_t, ne3, op->src[3], ne);
GGML_TENSOR_LOCALS(uint64_t, nb3, op->src[3], nb);
size_t res = 0;
const bool has_mask = op->src[3] != nullptr;
if (ggml_metal_op_flash_attn_ext_use_vec(op)) {
const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_VEC_NCPSG != 0;
if (has_kvpad) {
res += OP_FLASH_ATTN_EXT_VEC_NCPSG*(
nb11*ne12*ne13 +
nb21*ne22*ne23 +
(has_mask ? ggml_type_size(GGML_TYPE_F16)*ne31*ne32*ne33 : 0));
}
} else {
const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_NCPSG != 0;
if (has_kvpad) {
res += OP_FLASH_ATTN_EXT_NCPSG*(
nb11*ne12*ne13 +
nb21*ne22*ne23 +
(has_mask ? ggml_type_size(GGML_TYPE_F16)*ne31*ne32*ne33 : 0));
}
}
return res;
}
size_t ggml_metal_op_flash_attn_ext_extra_blk(const ggml_tensor * op) {
assert(op->op == GGML_OP_FLASH_ATTN_EXT);
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
//GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
//GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne);
//GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
//GGML_TENSOR_LOCALS( int32_t, ne2, op->src[2], ne);
//GGML_TENSOR_LOCALS(uint64_t, nb2, op->src[2], nb);
GGML_TENSOR_LOCALS( int32_t, ne3, op->src[3], ne);
GGML_TENSOR_LOCALS(uint64_t, nb3, op->src[3], nb);
size_t res = 0;
const bool has_mask = op->src[3] != nullptr;
if (!has_mask) {
return res;
}
const bool is_vec = ggml_metal_op_flash_attn_ext_use_vec(op);
// this optimization is not useful for the vector kernels
if (is_vec) {
return res;
}
const int nqptg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NQPTG : OP_FLASH_ATTN_EXT_NQPTG;
const int ncpsg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NCPSG : OP_FLASH_ATTN_EXT_NCPSG;
const int64_t ne1 = (ne01 + nqptg - 1)/nqptg;
const int64_t ne0 = (ne30 + ncpsg - 1)/ncpsg;
res += GGML_PAD(ggml_type_size(GGML_TYPE_I8)*ne0*ne1*ne32*ne33, 32);
return res;
}
size_t ggml_metal_op_flash_attn_ext_extra_tmp(const ggml_tensor * op) {
assert(op->op == GGML_OP_FLASH_ATTN_EXT);
const int64_t nwg = 32;
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
//GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne);
//GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
GGML_TENSOR_LOCALS( int32_t, ne2, op->src[2], ne);
GGML_TENSOR_LOCALS(uint64_t, nb2, op->src[2], nb);
//GGML_TENSOR_LOCALS( int32_t, ne3, op->src[3], ne);
//GGML_TENSOR_LOCALS(uint64_t, nb3, op->src[3], nb);
const int64_t ne01 = op->src[0]->ne[1];
const int64_t ne02 = op->src[0]->ne[2];
const int64_t ne03 = op->src[0]->ne[3];
const int64_t ne20 = op->src[2]->ne[0];
size_t res = 0;
if (ggml_metal_op_flash_attn_ext_use_vec(op)) {
const int64_t nwg = 32;
// temp buffer for writing the results from each workgroup
// - ne20: the size of the Value head
// - + 2: the S and M values for each intermediate result
return ggml_type_size(GGML_TYPE_F32)*(ne01*ne02*ne03*nwg*(ne20 + 2));
res += ggml_type_size(GGML_TYPE_F32)*(ne01*ne02*ne03*nwg*(ne20 + 2));
}
return res;
}
int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
@ -1911,7 +2059,6 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
GGML_TENSOR_LOCALS( int32_t, nb, op, nb);
GGML_ASSERT(ne00 % 4 == 0);
GGML_ASSERT(ne11 % 32 == 0);
GGML_ASSERT(op->src[0]->type == GGML_TYPE_F32);
GGML_ASSERT(op->src[1]->type == op->src[2]->type);
@ -1921,8 +2068,8 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
GGML_ASSERT(ne12 == ne22);
GGML_ASSERT(!op->src[3] || op->src[3]->type == GGML_TYPE_F16);
GGML_ASSERT(!op->src[3] || op->src[3]->ne[1] >= GGML_PAD(op->src[0]->ne[1], 8) &&
"the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big");
GGML_ASSERT(!op->src[3] || op->src[3]->ne[1] >= op->src[0]->ne[1] &&
"the Flash-Attention Metal kernel requires the mask to be at least n_queries big");
float scale;
float max_bias;
@ -1949,15 +2096,111 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
GGML_ASSERT(ne01 < 65536);
ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
ggml_metal_buffer_id bid_src1 = ggml_metal_get_buffer_id(op->src[1]);
ggml_metal_buffer_id bid_src2 = ggml_metal_get_buffer_id(op->src[2]);
ggml_metal_buffer_id bid_src3 = has_mask ? ggml_metal_get_buffer_id(op->src[3]) : bid_src0;
ggml_metal_buffer_id bid_src4 = has_sinks ? ggml_metal_get_buffer_id(op->src[4]) : bid_src0;
ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(op);
ggml_metal_buffer_id bid_pad = bid_dst;
bid_pad.offs += ggml_nbytes(op);
ggml_metal_buffer_id bid_blk = bid_pad;
bid_blk.offs += ggml_metal_op_flash_attn_ext_extra_pad(op);
ggml_metal_buffer_id bid_tmp = bid_blk;
bid_tmp.offs += ggml_metal_op_flash_attn_ext_extra_blk(op);
if (!ggml_metal_op_flash_attn_ext_use_vec(op)) {
// half8x8 kernel
const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !!
const int64_t ncpsg = 64; // cache values per simdgroup !! sync with kernel template arguments !!
const int nqptg = OP_FLASH_ATTN_EXT_NQPTG; // queries per threadgroup
const int ncpsg = OP_FLASH_ATTN_EXT_NCPSG; // cache values per simdgroup
GGML_ASSERT(nqptg <= 32);
GGML_ASSERT(nqptg % 8 == 0);
GGML_ASSERT(ncpsg % 32 == 0);
bool need_sync = false;
const bool has_kvpad = ne11 % ncpsg != 0;
if (has_kvpad) {
assert(ggml_metal_op_flash_attn_ext_extra_pad(op) != 0);
ggml_metal_kargs_flash_attn_ext_pad args0 = {
/*.ne11 =*/ne11,
/*.ne_12_2 =*/ne12,
/*.ne_12_3 =*/ne13,
/*.nb11 =*/nb11,
/*.nb12 =*/nb12,
/*.nb13 =*/nb13,
/*.nb21 =*/nb21,
/*.nb22 =*/nb22,
/*.nb23 =*/nb23,
/*.ne31 =*/ne31,
/*.ne32 =*/ne32,
/*.ne33 =*/ne33,
/*.nb31 =*/nb31,
/*.nb32 =*/nb32,
/*.nb33 =*/nb33,
};
ggml_metal_pipeline_t pipeline0 = ggml_metal_library_get_pipeline_flash_attn_ext_pad(lib, op, has_mask, ncpsg);
ggml_metal_encoder_set_pipeline(enc, pipeline0);
ggml_metal_encoder_set_bytes (enc, &args0, sizeof(args0), 0);
ggml_metal_encoder_set_buffer (enc, bid_src1, 1);
ggml_metal_encoder_set_buffer (enc, bid_src2, 2);
ggml_metal_encoder_set_buffer (enc, bid_src3, 3);
ggml_metal_encoder_set_buffer (enc, bid_pad, 4);
assert(ne12 == ne22);
assert(ne13 == ne23);
ggml_metal_encoder_dispatch_threadgroups(enc, ncpsg, std::max(ne12, ne32), std::max(ne13, ne33), 32, 1, 1);
need_sync = true;
} else {
assert(ggml_metal_op_flash_attn_ext_extra_pad(op) == 0);
}
if (has_mask) {
assert(ggml_metal_op_flash_attn_ext_extra_blk(op) != 0);
ggml_metal_kargs_flash_attn_ext_blk args0 = {
/*.ne01 =*/ ne01,
/*.ne30 =*/ ne30,
/*.ne31 =*/ ne31,
/*.ne32 =*/ ne32,
/*.ne33 =*/ ne33,
/*.nb31 =*/ nb31,
/*.nb32 =*/ nb32,
/*.nb33 =*/ nb33,
};
ggml_metal_pipeline_t pipeline0 = ggml_metal_library_get_pipeline_flash_attn_ext_blk(lib, op, nqptg, ncpsg);
ggml_metal_encoder_set_pipeline(enc, pipeline0);
ggml_metal_encoder_set_bytes (enc, &args0, sizeof(args0), 0);
ggml_metal_encoder_set_buffer (enc, bid_src3, 1);
ggml_metal_encoder_set_buffer (enc, bid_blk, 2);
const int32_t nblk1 = ((ne01 + nqptg - 1)/nqptg);
const int32_t nblk0 = ((ne30 + ncpsg - 1)/ncpsg);
ggml_metal_encoder_dispatch_threadgroups(enc, nblk0, nblk1, ne32*ne33, 32, 1, 1);
need_sync = true;
} else {
assert(ggml_metal_op_flash_attn_ext_extra_blk(op) == 0);
}
if (need_sync) {
ggml_metal_op_concurrency_reset(ctx);
}
const int is_q = ggml_is_quantized(op->src[1]->type) ? 1 : 0;
// 2*(2*ncpsg)
@ -2007,6 +2250,7 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
/*.nb21 =*/ nb21,
/*.nb22 =*/ nb22,
/*.nb23 =*/ nb23,
/*.ne31 =*/ ne31,
/*.ne32 =*/ ne32,
/*.ne33 =*/ ne33,
/*.nb31 =*/ nb31,
@ -2023,24 +2267,18 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
/*.logit_softcap =*/ logit_softcap,
};
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_flash_attn_ext(lib, op, has_mask, has_sinks, has_bias, has_scap, nsg);
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_flash_attn_ext(lib, op, has_mask, has_sinks, has_bias, has_scap, has_kvpad, nsg);
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), 2);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[2]), 3);
if (op->src[3]) {
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[3]), 4);
} else {
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 4);
}
if (op->src[4]) {
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[4]), 5);
} else {
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 5);
}
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 6);
ggml_metal_encoder_set_buffer (enc, bid_src0, 1);
ggml_metal_encoder_set_buffer (enc, bid_src1, 2);
ggml_metal_encoder_set_buffer (enc, bid_src2, 3);
ggml_metal_encoder_set_buffer (enc, bid_src3, 4);
ggml_metal_encoder_set_buffer (enc, bid_src4, 5);
ggml_metal_encoder_set_buffer (enc, bid_pad, 6);
ggml_metal_encoder_set_buffer (enc, bid_blk, 7);
ggml_metal_encoder_set_buffer (enc, bid_dst, 8);
ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
@ -2048,14 +2286,62 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
#undef FATTN_SMEM
} else {
// half4x4 kernel
const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !!
const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
const int64_t nkpsg = 1*ncpsg;
const int nqptg = OP_FLASH_ATTN_EXT_VEC_NQPTG; // queries per threadgroup
const int ncpsg = OP_FLASH_ATTN_EXT_VEC_NCPSG; // cache values per simdgroup !! sync with kernel template arguments !!
const int nkpsg = 1*ncpsg;
GGML_ASSERT(nqptg <= 32);
GGML_ASSERT(nqptg % 1 == 0);
GGML_ASSERT(ncpsg % 32 == 0);
bool need_sync = false;
const bool has_kvpad = ne11 % ncpsg != 0;
if (has_kvpad) {
assert(ggml_metal_op_flash_attn_ext_extra_pad(op) != 0);
ggml_metal_kargs_flash_attn_ext_pad args0 = {
/*.ne11 =*/ne11,
/*.ne_12_2 =*/ne12,
/*.ne_12_3 =*/ne13,
/*.nb11 =*/nb11,
/*.nb12 =*/nb12,
/*.nb13 =*/nb13,
/*.nb21 =*/nb21,
/*.nb22 =*/nb22,
/*.nb23 =*/nb23,
/*.ne31 =*/ne31,
/*.ne32 =*/ne32,
/*.ne33 =*/ne33,
/*.nb31 =*/nb31,
/*.nb32 =*/nb32,
/*.nb33 =*/nb33,
};
ggml_metal_pipeline_t pipeline0 = ggml_metal_library_get_pipeline_flash_attn_ext_pad(lib, op, has_mask, ncpsg);
ggml_metal_encoder_set_pipeline(enc, pipeline0);
ggml_metal_encoder_set_bytes (enc, &args0, sizeof(args0), 0);
ggml_metal_encoder_set_buffer (enc, bid_src1, 1);
ggml_metal_encoder_set_buffer (enc, bid_src2, 2);
ggml_metal_encoder_set_buffer (enc, bid_src3, 3);
ggml_metal_encoder_set_buffer (enc, bid_pad, 4);
assert(ne12 == ne22);
assert(ne13 == ne23);
ggml_metal_encoder_dispatch_threadgroups(enc, ncpsg, std::max(ne12, ne32), std::max(ne13, ne33), 32, 1, 1);
need_sync = true;
} else {
assert(ggml_metal_op_flash_attn_ext_extra_pad(op) == 0);
}
if (need_sync) {
ggml_metal_op_concurrency_reset(ctx);
}
// ne00 + 2*ncpsg*(nsg)
// for each query, we load it as f16 in shared memory (ne00)
// and store the soft_max values and the mask
@ -2120,6 +2406,7 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
/*.nb21 =*/ nb21,
/*.nb22 =*/ nb22,
/*.nb23 =*/ nb23,
/*.ne31 =*/ ne31,
/*.ne32 =*/ ne32,
/*.ne33 =*/ ne33,
/*.nb31 =*/ nb31,
@ -2136,25 +2423,17 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
/*.logit_softcap =*/ logit_softcap,
};
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_flash_attn_ext_vec(lib, op, has_mask, has_sinks, has_bias, has_scap, nsg, nwg);
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_flash_attn_ext_vec(lib, op, has_mask, has_sinks, has_bias, has_scap, has_kvpad, nsg, nwg);
GGML_ASSERT(nsg*32 <= ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), 2);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[2]), 3);
if (op->src[3]) {
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[3]), 4);
} else {
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 4);
}
if (op->src[4]) {
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[4]), 5);
} else {
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 5);
}
ggml_metal_encoder_set_buffer (enc, bid_src0, 1);
ggml_metal_encoder_set_buffer (enc, bid_src1, 2);
ggml_metal_encoder_set_buffer (enc, bid_src2, 3);
ggml_metal_encoder_set_buffer (enc, bid_src3, 4);
ggml_metal_encoder_set_buffer (enc, bid_src4, 5);
const size_t smem = FATTN_SMEM(nsg);
@ -2162,23 +2441,25 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) {
GGML_ASSERT(smem <= props_dev->max_theadgroup_memory_size);
if (nwg == 1) {
assert(ggml_metal_op_flash_attn_ext_extra_tmp(op) == 0);
// using 1 workgroup -> write the result directly into dst
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 6);
ggml_metal_encoder_set_buffer(enc, bid_pad, 6);
ggml_metal_encoder_set_buffer(enc, bid_dst, 7);
ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
ggml_metal_encoder_dispatch_threadgroups(enc, (ne01 + nqptg - 1)/nqptg, ne02, ne03*nwg, 32, nsg, 1);
} else {
// sanity checks
assert(ggml_metal_op_flash_attn_ext_extra_tmp(op) != 0);
GGML_ASSERT(ne01*ne02*ne03 == ne1*ne2*ne3);
GGML_ASSERT((uint64_t)ne1*ne2*ne3 <= (1u << 31));
ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(op);
// write the results from each workgroup into a temp buffer
ggml_metal_buffer_id bid_tmp = bid_dst;
bid_tmp.offs += ggml_nbytes(op);
ggml_metal_encoder_set_buffer(enc, bid_tmp, 6);
ggml_metal_encoder_set_buffer(enc, bid_pad, 6);
ggml_metal_encoder_set_buffer(enc, bid_tmp, 7);
ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
ggml_metal_encoder_dispatch_threadgroups(enc, (ne01 + nqptg - 1)/nqptg, ne02, ne03*nwg, 32, nsg, 1);
@ -3156,3 +3437,73 @@ int ggml_metal_op_leaky_relu(ggml_metal_op_t ctx, int idx) {
return 1;
}
int ggml_metal_op_opt_step_adamw(ggml_metal_op_t ctx, int idx) {
ggml_tensor * op = ctx->node(idx);
ggml_metal_library_t lib = ctx->lib;
ggml_metal_encoder_t enc = ctx->enc;
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
GGML_TENSOR_LOCALS(uint32_t, nb, op, nb);
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_opt_step_adamw(lib, op);
const int64_t np = ggml_nelements(op->src[0]);
ggml_metal_kargs_opt_step_adamw args = {
/*.np =*/ np,
};
int ida = 0;
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), ida++);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), ida++);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), ida++);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[2]), ida++);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[3]), ida++);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[4]), ida++);
const int nth = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), ne0);
const int64_t n = (np + nth - 1) / nth;
ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, nth, 1, 1);
return 1;
}
int ggml_metal_op_opt_step_sgd(ggml_metal_op_t ctx, int idx) {
ggml_tensor * op = ctx->node(idx);
ggml_metal_library_t lib = ctx->lib;
ggml_metal_encoder_t enc = ctx->enc;
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
GGML_TENSOR_LOCALS(uint32_t, nb, op, nb);
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_opt_step_sgd(lib, op);
const int64_t np = ggml_nelements(op->src[0]);
ggml_metal_kargs_opt_step_sgd args = {
/*.np =*/ np,
};
int ida = 0;
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), ida++);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), ida++);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), ida++);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[2]), ida++);
const int nth = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), ne0);
const int64_t n = (np + nth - 1) / nth;
ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, nth, 1, 1);
return 1;
}

5
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-ops.h vendored
View File

@ -39,6 +39,8 @@ size_t ggml_metal_op_mul_mat_id_extra_ids(const struct ggml_tensor * op);
// return true if we should use the FA vector kernel for this op
bool ggml_metal_op_flash_attn_ext_use_vec(const struct ggml_tensor * op);
size_t ggml_metal_op_flash_attn_ext_extra_pad(const struct ggml_tensor * op);
size_t ggml_metal_op_flash_attn_ext_extra_blk(const struct ggml_tensor * op);
size_t ggml_metal_op_flash_attn_ext_extra_tmp(const struct ggml_tensor * op);
int ggml_metal_op_concat (ggml_metal_op_t ctx, int idx);
@ -48,6 +50,7 @@ int ggml_metal_op_scale (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_clamp (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_unary (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_glu (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_sum (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_sum_rows (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_get_rows (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_set_rows (ggml_metal_op_t ctx, int idx);
@ -76,6 +79,8 @@ int ggml_metal_op_timestep_embedding(ggml_metal_op_t ctx, int idx);
int ggml_metal_op_argmax (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_argsort (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_leaky_relu (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_opt_step_adamw (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_opt_step_sgd (ggml_metal_op_t ctx, int idx);
#ifdef __cplusplus
}

5
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal.cpp vendored
View File

@ -195,9 +195,9 @@ static size_t ggml_backend_metal_buffer_type_get_alloc_size(ggml_backend_buffer_
} break;
case GGML_OP_FLASH_ATTN_EXT:
{
if (ggml_metal_op_flash_attn_ext_use_vec(tensor)) {
res += ggml_metal_op_flash_attn_ext_extra_pad(tensor);
res += ggml_metal_op_flash_attn_ext_extra_blk(tensor);
res += ggml_metal_op_flash_attn_ext_extra_tmp(tensor);
}
} break;
default:
break;
@ -543,6 +543,7 @@ static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_bac
props->type = ggml_backend_metal_device_get_type(dev);
ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->library = GGML_METAL_NAME;
props->caps = {
/* .async = */ true,

1030
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal.metal vendored
View File

File diff suppressed because it is too large Load Diff

36
ml/backend/ggml/ggml/src/ggml.c vendored
View File

@ -1143,10 +1143,10 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
"HARDSIGMOID",
"EXP",
"GELU_ERF",
"XIELU",
};
static_assert(GGML_UNARY_OP_COUNT == 15, "GGML_UNARY_OP_COUNT != 15");
static_assert(GGML_UNARY_OP_COUNT == 16, "GGML_UNARY_OP_COUNT != 16");
static const char * GGML_GLU_OP_NAME[GGML_GLU_OP_COUNT] = {
"REGLU",
@ -2652,6 +2652,29 @@ struct ggml_tensor * ggml_silu_inplace(
return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SILU);
}
// ggml_xielu
struct ggml_tensor * ggml_xielu(
struct ggml_context * ctx,
struct ggml_tensor * a,
float alpha_n,
float alpha_p,
float beta,
float eps) {
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
ggml_set_op_params_i32(result, 0, (int32_t) GGML_UNARY_OP_XIELU);
ggml_set_op_params_f32(result, 1, beta + ggml_softplus(alpha_n));
ggml_set_op_params_f32(result, 2, ggml_softplus(alpha_p));
ggml_set_op_params_f32(result, 3, beta);
ggml_set_op_params_f32(result, 4, eps);
result->op = GGML_OP_UNARY;
result->src[0] = a;
return result;
}
// ggml_silu_back
struct ggml_tensor * ggml_silu_back(
@ -3829,6 +3852,15 @@ struct ggml_tensor * ggml_soft_max_ext(
return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false);
}
struct ggml_tensor * ggml_soft_max_ext_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * mask,
float scale,
float max_bias) {
return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, true);
}
void ggml_soft_max_add_sinks(
struct ggml_tensor * a,
struct ggml_tensor * sinks) {

16
runner/llamarunner/image.go
View File

@ -56,7 +56,7 @@ func (c *ImageContext) Free(modelPath string) {
}
}
func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte) ([][]float32, error) {
func (c *ImageContext) MultimodalTokenize(llamaContext *llama.Context, data []byte) ([]llama.MtmdChunk, error) {
if c == nil {
return nil, nil
}
@ -70,10 +70,10 @@ func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte) ([][]f
c.mu.Lock()
defer c.mu.Unlock()
embed, err := c.findImage(hash)
chunks, err := c.findImage(hash)
if err != nil {
if c.mtmd != nil {
embed, err = c.mtmd.NewEmbed(llamaContext, data)
chunks, err = c.mtmd.MultimodalTokenize(llamaContext, data)
if err != nil {
return nil, err
}
@ -81,10 +81,10 @@ func (c *ImageContext) NewEmbed(llamaContext *llama.Context, data []byte) ([][]f
return nil, errors.New("received image but vision model not loaded")
}
c.addImage(hash, embed)
c.addImage(hash, chunks)
}
return embed, nil
return chunks, nil
}
func (c *ImageContext) BatchSize(configuredBatchSize int) int {
@ -102,7 +102,7 @@ func (c *ImageContext) EmbedSize(llamaContext *llama.Context) int {
type imageCache struct {
key uint64
val [][]float32
val []llama.MtmdChunk
lastUsed time.Time
}
@ -114,7 +114,7 @@ func (c *ImageContext) hashImage(image []byte) uint64 {
var errImageNotFound = errors.New("image not found in cache")
func (c *ImageContext) findImage(hash uint64) ([][]float32, error) {
func (c *ImageContext) findImage(hash uint64) ([]llama.MtmdChunk, error) {
for i := range c.images {
if c.images[i].key == hash {
slog.Debug("loading image embeddings from cache", "entry", i)
@ -126,7 +126,7 @@ func (c *ImageContext) findImage(hash uint64) ([][]float32, error) {
return nil, errImageNotFound
}
func (c *ImageContext) addImage(hash uint64, embed [][]float32) {
func (c *ImageContext) addImage(hash uint64, embed []llama.MtmdChunk) {
best := time.Now()
var bestImage int

12
runner/llamarunner/image_test.go
View File

@ -3,16 +3,18 @@ package llamarunner
import (
"reflect"
"testing"
"github.com/ollama/ollama/llama"
)
func TestImageCache(t *testing.T) {
cache := ImageContext{images: make([]imageCache, 4)}
valA := [][]float32{{0.1, 0.2}, {0.3}}
valB := [][]float32{{0.4}, {0.5}, {0.6}}
valC := [][]float32{{0.7}}
valD := [][]float32{{0.8}}
valE := [][]float32{{0.9}}
valA := []llama.MtmdChunk{{Embed: []float32{0.1, 0.2}}, {Embed: []float32{0.3}}}
valB := []llama.MtmdChunk{{Embed: []float32{0.4}}, {Embed: []float32{0.5}}, {Embed: []float32{0.6}}}
valC := []llama.MtmdChunk{{Embed: []float32{0.7}}}
valD := []llama.MtmdChunk{{Embed: []float32{0.8}}}
valE := []llama.MtmdChunk{{Embed: []float32{0.9}}}
// Empty cache
result, err := cache.findImage(0x5adb61d31933a946)

12
runner/llamarunner/runner.go
View File

@ -209,13 +209,19 @@ func (s *Server) inputs(prompt string, images []llm.ImageData) ([]input, error)
return nil, fmt.Errorf("invalid image index: %d", n)
}
embed, err := s.image.NewEmbed(s.lc, images[imageIndex].Data)
chunks, err := s.image.MultimodalTokenize(s.lc, images[imageIndex].Data)
if err != nil {
return nil, err
}
for _, e := range embed {
inputs = append(inputs, input{embed: e})
for _, c := range chunks {
if len(c.Embed) != 0 {
inputs = append(inputs, input{embed: c.Embed})
} else {
for _, t := range c.Tokens {
inputs = append(inputs, input{token: t})
}
}
}
}
}