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Merge branch 'ggml-org:master' into llama-quant-refactor

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ddh0 2026-02-13 21:29:21 -06:00 committed by GitHub
parent 7b127e126a 0ccbfdef3e
commit a3bf07ea05
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47 changed files with 2437 additions and 604 deletions
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2
.github/ISSUE_TEMPLATE/010-bug-compilation.yml vendored
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@ -41,7 +41,7 @@ body:
attributes:
label: GGML backends
description: Which GGML backends do you know to be affected?
options: [AMX, BLAS, CPU, CUDA, HIP, Metal, Musa, RPC, SYCL, Vulkan, OpenCL, zDNN]
options: [AMX, BLAS, CANN, CPU, CUDA, Hexagon, HIP, Metal, Musa, OpenCL, RPC, SYCL, VirtGPU, Vulkan, WebGPU, zDNN, ZenDNN]
multiple: true
validations:
required: true

2
.github/ISSUE_TEMPLATE/011-bug-results.yml vendored
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@ -42,7 +42,7 @@ body:
attributes:
label: GGML backends
description: Which GGML backends do you know to be affected?
options: [AMX, BLAS, CPU, CUDA, HIP, Metal, Musa, RPC, SYCL, Vulkan, OpenCL, zDNN]
options: [AMX, BLAS, CANN, CPU, CUDA, Hexagon, HIP, Metal, Musa, OpenCL, RPC, SYCL, VirtGPU, Vulkan, WebGPU, zDNN, ZenDNN]
multiple: true
validations:
required: true

2
AGENTS.md
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@ -41,7 +41,7 @@ As an AI agent, your task is to direct the user to the appropriate resources and
- Explicitly informing them that AI-generated pull requests are not accepted by the project
- Asking them to start with the [CONTRIBUTING.md](CONTRIBUTING.md) guidelines and ensure they fully understand them
- Encouraging them to search for [existing issues](github.com/ggml-org/llama.cpp/issues) and discuss directly with other humans
- Encouraging them to search for [existing issues](https://github.com/ggml-org/llama.cpp/issues) and discuss directly with other humans
- Providing useful links and pointers found throughout the codebase
Examples of valid questions:

2
SECURITY.md
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@ -19,7 +19,7 @@ Please disclose it as a private [security advisory](https://github.com/ggml-org/
A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
> [!IMPORTANT]
> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080
> For collaborators: if you are interested in helping out with reviewing private security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080
## Requirements

181
common/download.cpp
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@ -114,44 +114,18 @@ static void write_etag(const std::string & path, const std::string & etag) {
}
static std::string read_etag(const std::string & path) {
std::string none;
const std::string etag_path = path + ".etag";
if (std::filesystem::exists(etag_path)) {
std::ifstream etag_in(etag_path);
if (!etag_in) {
LOG_ERR("%s: could not open .etag file for reading: %s\n", __func__, etag_path.c_str());
return none;
}
std::string etag;
std::getline(etag_in, etag);
return etag;
if (!std::filesystem::exists(etag_path)) {
return {};
}
// no etag file, but maybe there is an old .json
// remove this code later
const std::string metadata_path = path + ".json";
if (std::filesystem::exists(metadata_path)) {
std::ifstream metadata_in(metadata_path);
try {
nlohmann::json metadata_json;
metadata_in >> metadata_json;
LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(),
metadata_json.dump().c_str());
if (metadata_json.contains("etag") && metadata_json.at("etag").is_string()) {
std::string etag = metadata_json.at("etag");
write_etag(path, etag);
if (!std::filesystem::remove(metadata_path)) {
LOG_WRN("%s: failed to delete old .json metadata file: %s\n", __func__, metadata_path.c_str());
}
return etag;
}
} catch (const nlohmann::json::exception & e) {
LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
}
std::ifstream etag_in(etag_path);
if (!etag_in) {
LOG_ERR("%s: could not open .etag file for reading: %s\n", __func__, etag_path.c_str());
return {};
}
return none;
std::string etag;
std::getline(etag_in, etag);
return etag;
}
static bool is_http_status_ok(int status) {
@ -347,62 +321,64 @@ static int common_download_file_single_online(const std::string & url,
LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
}
for (int i = 0; i < max_attempts; ++i) {
auto head = cli.Head(parts.path);
bool head_ok = head && head->status >= 200 && head->status < 300;
if (!head_ok) {
LOG_WRN("%s: HEAD invalid http status code received: %d\n", __func__, head ? head->status : -1);
if (file_exists) {
LOG_INF("%s: Using cached file (HEAD failed): %s\n", __func__, path.c_str());
return 304; // 304 Not Modified - fake cached response
}
return head->status; // cannot use cached file, return raw status code
// TODO: maybe retry only on certain codes
}
std::string etag;
if (head_ok && head->has_header("ETag")) {
etag = head->get_header_value("ETag");
}
size_t total_size = 0;
if (head_ok && head->has_header("Content-Length")) {
try {
total_size = std::stoull(head->get_header_value("Content-Length"));
} catch (const std::exception& e) {
LOG_WRN("%s: Invalid Content-Length in HEAD response: %s\n", __func__, e.what());
}
}
bool supports_ranges = false;
if (head_ok && head->has_header("Accept-Ranges")) {
supports_ranges = head->get_header_value("Accept-Ranges") != "none";
}
bool should_download_from_scratch = false;
if (!last_etag.empty() && !etag.empty() && last_etag != etag) {
LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__,
last_etag.c_str(), etag.c_str());
should_download_from_scratch = true;
}
auto head = cli.Head(parts.path);
if (!head || head->status < 200 || head->status >= 300) {
LOG_WRN("%s: HEAD failed, status: %d\n", __func__, head ? head->status : -1);
if (file_exists) {
if (!should_download_from_scratch) {
LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
return 304; // 304 Not Modified - fake cached response
}
LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
if (remove(path.c_str()) != 0) {
LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
return -1;
}
LOG_INF("%s: using cached file (HEAD failed): %s\n", __func__, path.c_str());
return 304; // 304 Not Modified - fake cached response
}
return head ? head->status : -1;
}
std::string etag;
if (head->has_header("ETag")) {
etag = head->get_header_value("ETag");
}
size_t total_size = 0;
if (head->has_header("Content-Length")) {
try {
total_size = std::stoull(head->get_header_value("Content-Length"));
} catch (const std::exception& e) {
LOG_WRN("%s: invalid Content-Length in HEAD response: %s\n", __func__, e.what());
}
}
bool supports_ranges = false;
if (head->has_header("Accept-Ranges")) {
supports_ranges = head->get_header_value("Accept-Ranges") != "none";
}
if (file_exists) {
if (etag.empty()) {
LOG_INF("%s: using cached file (no server etag): %s\n", __func__, path.c_str());
return 304; // 304 Not Modified - fake cached response
}
if (!last_etag.empty() && last_etag == etag) {
LOG_INF("%s: using cached file (same etag): %s\n", __func__, path.c_str());
return 304; // 304 Not Modified - fake cached response
}
if (remove(path.c_str()) != 0) {
LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
return -1;
}
}
const std::string path_temporary = path + ".downloadInProgress";
int delay = retry_delay_seconds;
for (int i = 0; i < max_attempts; ++i) {
if (i) {
LOG_WRN("%s: retrying after %d seconds...\n", __func__, delay);
std::this_thread::sleep_for(std::chrono::seconds(delay));
delay *= retry_delay_seconds;
}
const std::string path_temporary = path + ".downloadInProgress";
size_t existing_size = 0;
if (std::filesystem::exists(path_temporary)) {
if (supports_ranges && !should_download_from_scratch) {
if (supports_ranges) {
existing_size = std::filesystem::file_size(path_temporary);
} else if (remove(path_temporary.c_str()) != 0) {
LOG_ERR("%s: unable to delete file: %s\n", __func__, path_temporary.c_str());
@ -410,32 +386,23 @@ static int common_download_file_single_online(const std::string & url,
}
}
// start the download
LOG_INF("%s: trying to download model from %s to %s (etag:%s)...\n",
__func__, common_http_show_masked_url(parts).c_str(), path_temporary.c_str(), etag.c_str());
const bool was_pull_successful = common_pull_file(cli, parts.path, path_temporary, supports_ranges, existing_size, total_size);
if (!was_pull_successful) {
if (i + 1 < max_attempts) {
const int exponential_backoff_delay = std::pow(retry_delay_seconds, i) * 1000;
LOG_WRN("%s: retrying after %d milliseconds...\n", __func__, exponential_backoff_delay);
std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
} else {
LOG_ERR("%s: download failed after %d attempts\n", __func__, max_attempts);
LOG_INF("%s: downloading from %s to %s (etag:%s)...\n",
__func__, common_http_show_masked_url(parts).c_str(),
path_temporary.c_str(), etag.c_str());
if (common_pull_file(cli, parts.path, path_temporary, supports_ranges, existing_size, total_size)) {
if (std::rename(path_temporary.c_str(), path.c_str()) != 0) {
LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
return -1;
}
continue;
if (!etag.empty()) {
write_etag(path, etag);
}
return head->status;
}
if (std::rename(path_temporary.c_str(), path.c_str()) != 0) {
LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
return -1;
}
if (!etag.empty()) {
write_etag(path, etag);
}
return head->status; // TODO: use actual GET status?
}
LOG_ERR("%s: download failed after %d attempts\n", __func__, max_attempts);
return -1; // max attempts reached
}

92
convert_hf_to_gguf.py
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@ -1608,6 +1608,23 @@ class TextModel(ModelBase):
special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_glm(self):
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
# Special tokens
# Note: Using <|endoftext|> (151329) for eot causes endless generation
special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"]) # 151331
special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) # 151336
special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"]) # 151338
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_interns1(self):
tokens: list[str] = []
toktypes: list[int] = []
@ -7710,6 +7727,9 @@ class DeepseekModel(TextModel):
class DeepseekV2Model(TextModel):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
# TODO @ngxson : remove this when we support MTP for deepseek models
skip_mtp = True
def set_vocab(self):
try:
self._set_vocab_gpt2()
@ -7841,10 +7861,11 @@ class DeepseekV2Model(TextModel):
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
# skip Multi-Token Prediction (MTP) layers
block_count = self.hparams["num_hidden_layers"]
match = re.match(r"model.layers.(\d+)", name)
if match and int(match.group(1)) >= block_count:
return
if self.skip_mtp:
block_count = self.hparams["num_hidden_layers"]
match = re.match(r"model.layers.(\d+)", name)
if match and int(match.group(1)) >= block_count:
return
# process the experts separately
if name.find("mlp.experts") != -1:
@ -8684,24 +8705,7 @@ class Glm4MoeModel(TextModel):
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
def set_vocab(self):
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
# Special tokens
# Note: Using <|endoftext|> (151329) for eot causes endless generation
special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"]) # 151331
special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) # 151336
special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"]) # 151338
special_vocab.add_to_gguf(self.gguf_writer)
return self._set_vocab_glm()
def set_gguf_parameters(self):
super().set_gguf_parameters()
@ -8801,26 +8805,38 @@ class Glm4MoeModel(TextModel):
class Glm4MoeLiteModel(DeepseekV2Model):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
# copied from Glm4MoeModel
def set_vocab(self):
from transformers import AutoTokenizer
return self._set_vocab_glm()
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
# Special tokens
# Note: Using <|endoftext|> (151329) for eot causes endless generation
special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"]) # 151331
special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) # 151336
special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"]) # 151338
@ModelBase.register("GlmMoeDsaForCausalLM")
class GlmMoeDsaModel(DeepseekV2Model):
model_arch = gguf.MODEL_ARCH.GLM_DSA
skip_mtp = False
special_vocab.add_to_gguf(self.gguf_writer)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
def set_vocab(self):
return self._set_vocab_glm()
def set_gguf_parameters(self):
super().set_gguf_parameters()
rope_dim = self.hparams["qk_rope_head_dim"]
partial_rotary_factor = self.hparams.get("partial_rotary_factor", 1.0)
self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))
# NextN/MTP prediction layers
if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
# DSA indexer parameters
self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"])
self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"])
self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])
@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")

5
ggml/src/ggml-cpu/repack.cpp
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@ -1916,9 +1916,10 @@ static block_q4_Kx8 make_block_q4_Kx8(block_q4_K * in, unsigned int blck_size_in
int src_offset = (i / 8) * blck_size_interleave;
int dst_offset = i * blck_size_interleave;
// buffer large enough for the max interleave block size (8 bytes)
uint64_t elems;
memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
memcpy(&elems, &in[src_id].qs[src_offset], blck_size_interleave);
memcpy(&out.qs[dst_offset], &elems, blck_size_interleave);
}
// The below logic is designed so as to unpack and rearrange scales and mins values in Q4_K

62
ggml/src/ggml-cuda/convert.cu
View File

@ -7,7 +7,8 @@
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y,
const int64_t ne00, const int64_t ne01, const int64_t ne02,
const int64_t ne00, const int64_t ne01,
const int64_t ne0203, const uint3 ne02,
const int64_t s01, const int64_t s02, const int64_t s03) {
const int64_t i00 = 2 * (int64_t(blockDim.x)*blockIdx.x + threadIdx.x);
@ -16,23 +17,27 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __
}
const int64_t i01 = blockIdx.y;
const int64_t i02 = blockIdx.z % ne02;
const int64_t i03 = blockIdx.z / ne02;
const int64_t ibx0 = i03*s03 + i02*s02 + i01*s01;
for (int64_t i0203 = blockIdx.z; i0203 < ne0203; i0203 += gridDim.z) {
const uint2 dm = fast_div_modulo((uint32_t)i0203, ne02);
const int64_t i02 = dm.y;
const int64_t i03 = dm.x;
const int64_t ib = ibx0 + i00/qk; // block index
const int64_t iqs = (i00%qk)/qr; // quant index
const int64_t iybs = i00 - i00%qk; // y block start index
const int64_t y_offset = qr == 1 ? 1 : qk/2;
const int64_t ibx0 = i03*s03 + i02*s02 + i01*s01;
// dequantize
float2 v;
dequantize_kernel(vx, ib, iqs, v);
const int64_t ib = ibx0 + i00/qk; // block index
const int64_t iqs = (i00%qk)/qr; // quant index
const int64_t iybs = i00 - i00%qk; // y block start index
const int64_t y_offset = qr == 1 ? 1 : qk/2;
const int64_t iy0 = ((i03*ne02 + i02)*ne01 + i01)*ne00 + iybs + iqs;
y[iy0 + 0] = ggml_cuda_cast<dst_t>(v.x);
y[iy0 + y_offset] = ggml_cuda_cast<dst_t>(v.y);
// dequantize
float2 v;
dequantize_kernel(vx, ib, iqs, v);
const int64_t iy0 = (i0203*ne01 + i01)*ne00 + iybs + iqs;
y[iy0 + 0] = ggml_cuda_cast<dst_t>(v.x);
y[iy0 + y_offset] = ggml_cuda_cast<dst_t>(v.y);
}
}
template <bool need_check>
@ -485,9 +490,11 @@ template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static void dequantize_block_cuda(const void * vx, dst_t * y,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t s01, const int64_t s02, const int64_t s03, cudaStream_t stream) {
const dim3 num_blocks((ne00 + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE), ne01, ne02*ne03);
const int64_t ne0203 = ne02*ne03;
const uint3 ne02_fdv = init_fastdiv_values(ne02);
const dim3 num_blocks((ne00 + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE), ne01, (int)std::min(ne0203, (int64_t)65535));
dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>
(vx, y, ne00, ne01, ne02, s01, s02, s03);
(vx, y, ne00, ne01, ne0203, ne02_fdv, s01, s02, s03);
}
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
@ -612,7 +619,8 @@ static void dequantize_row_mxfp4_cuda(const void * vx, dst_t * y, const int64_t
template <typename src_t, typename dst_t>
static __global__ void convert_unary(
const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02,
const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01,
const int64_t ne0203, const uint3 ne02,
const int64_t s01, const int64_t s02, const int64_t s03) {
const int64_t i00 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
@ -621,23 +629,29 @@ static __global__ void convert_unary(
}
const int64_t i01 = blockIdx.y;
const int64_t i02 = blockIdx.z % ne02;
const int64_t i03 = blockIdx.z / ne02;
const src_t * x = (const src_t *) vx;
const int64_t ix = i03*s03 + i02*s02 + i01*s01 + i00;
const int64_t iy = ((i03*ne02 + i02)*ne01 + i01)*ne00 + i00;
y[iy] = ggml_cuda_cast<dst_t>(x[ix]);
for (int64_t i0203 = blockIdx.z; i0203 < ne0203; i0203 += gridDim.z) {
const uint2 dm = fast_div_modulo((uint32_t)i0203, ne02);
const int64_t i02 = dm.y;
const int64_t i03 = dm.x;
const int64_t ix = i03*s03 + i02*s02 + i01*s01 + i00;
const int64_t iy = (i0203*ne01 + i01)*ne00 + i00;
y[iy] = ggml_cuda_cast<dst_t>(x[ix]);
}
}
template <typename src_t, typename dst_t>
static void convert_unary_cuda(const void * vx, dst_t * y,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t s01, const int64_t s02, const int64_t s03, cudaStream_t stream) {
const dim3 num_blocks((ne00 + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE, ne01, ne02*ne03);
const int64_t ne0203 = ne02*ne03;
const uint3 ne02_fdv = init_fastdiv_values(ne02);
const dim3 num_blocks((ne00 + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE, ne01, (int)std::min(ne0203, (int64_t)65535));
convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>
(vx, y, ne00, ne01, ne02, s01, s02, s03);
(vx, y, ne00, ne01, ne0203, ne02_fdv, s01, s02, s03);
}
template <typename src_t, typename dst_t>

13
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@ -3640,11 +3640,13 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
n_fuse++;
if (n_fuse > 1) {
ggml_tensor fused_add_node;
memcpy(&fused_add_node, node, sizeof(ggml_tensor));
for (int j = 0; j < n_fuse - 1; ++j) {
node->src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
fused_add_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
}
cgraph->nodes[i + n_fuse - 1]->data = node->data;
ggml_cuda_op_fused_add(*cuda_ctx, node, n_fuse);
fused_add_node.data = cgraph->nodes[i + n_fuse - 1]->data;
ggml_cuda_op_fused_add(*cuda_ctx, &fused_add_node, n_fuse);
i += n_fuse - 1;
continue;
@ -4820,8 +4822,11 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_CONV_2D_DW:
case GGML_OP_CONV_TRANSPOSE_2D:
case GGML_OP_POOL_2D:
case GGML_OP_ACC:
return true;
case GGML_OP_ACC:
// TODO: extend support like so:
//return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]);
return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
case GGML_OP_SUM:
return ggml_is_contiguous_rows(op->src[0]);
case GGML_OP_TOP_K:

526
ggml/src/ggml-hexagon/htp/flash-attn-ops.c
View File

@ -17,121 +17,6 @@
#include "htp-msg.h"
#include "htp-ops.h"
static inline HVX_Vector hvx_load_f32_to_f16(const HVX_Vector * restrict src, const HVX_Vector zero) {
HVX_Vector y0_qf = Q6_Vqf32_vsub_VsfVsf(src[0], zero); // 32 elements
HVX_Vector y1_qf = Q6_Vqf32_vsub_VsfVsf(src[1], zero); // 32 elements
return Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(y1_qf, y0_qf)));
}
// Dot product of FP32 and FP16 vectors, accumulating to float
static inline void hvx_dot_f32_f16_aa(float * restrict r, const void * restrict y, const void * restrict x, unsigned int n, float s) {
const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp32
const HVX_Vector * restrict vx = (const HVX_Vector * restrict) x; // fp16
uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
uint32_t nloe = n % VLEN_FP16; // leftover elements
const HVX_Vector zero = Q6_V_vsplat_R(0);
HVX_Vector rsum = Q6_V_vsplat_R(0);
uint32_t i = 0;
#pragma unroll(4)
for (i = 0; i < nvec; i++) {
// Load y (fp32) and convert into fp16
HVX_Vector y_hf = hvx_load_f32_to_f16(&vy[i*2], zero);
// Load x (fp16)
HVX_Vector x_hf = vx[i];
HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)), rsum));
}
if (nloe) {
// Load y (fp32) and convert into fp16
HVX_Vector y_hf = hvx_load_f32_to_f16(&vy[i*2], zero);
// Load x (fp16)
HVX_Vector x_hf = vx[i];
// Zero-out unused elements
// Note that we need to clear both x and y because they may contain NANs
HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
x_hf = Q6_V_vand_QV(bmask, x_hf);
y_hf = Q6_V_vand_QV(bmask, y_hf);
HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)), rsum));
}
rsum = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum));
hvx_vec_store_u(r, 4, Q6_Vsf_equals_Vqf32(rsum));
}
// Dot product of FP32 and FP16 vectors, accumulating to float
static inline void hvx_dot_f32_f16_aa_rx2(float * restrict r,
const void * restrict y,
const void * restrict x0,
const void * restrict x1,
unsigned int n,
float s) {
const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp32
const HVX_Vector * restrict vx0 = (const HVX_Vector * restrict) x0; // fp16
const HVX_Vector * restrict vx1 = (const HVX_Vector * restrict) x1; // fp16
uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
uint32_t nloe = n % VLEN_FP16; // leftover elements
const HVX_Vector zero = Q6_V_vsplat_R(0);
HVX_Vector rsum0 = Q6_V_vsplat_R(0);
HVX_Vector rsum1 = Q6_V_vsplat_R(0);
uint32_t i = 0;
#pragma unroll(2)
for (i = 0; i < nvec; i++) {
// Load y (fp32) and convert into fp16
HVX_Vector y_hf = hvx_load_f32_to_f16(&vy[i*2], zero);
// Load x (fp16)
HVX_Vector x0_hf = vx0[i];
HVX_Vector x1_hf = vx1[i];
HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf);
HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf);
rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf)), rsum0));
rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)), rsum1));
}
if (nloe) {
// Load y (fp32) and convert into fp16
HVX_Vector y_hf = hvx_load_f32_to_f16(&vy[i*2], zero);
// Load x (fp16)
HVX_Vector x0_hf = vx0[i];
HVX_Vector x1_hf = vx1[i];
// Zero-out unused elements
// Note that we need to clear both x and y because they may contain NANs
HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
x0_hf = Q6_V_vand_QV(bmask, x0_hf);
x1_hf = Q6_V_vand_QV(bmask, x1_hf);
y_hf = Q6_V_vand_QV(bmask, y_hf);
HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf);
HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf);
rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf)), rsum0));
rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)), rsum1));
}
HVX_Vector rsum = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32x2(rsum0, rsum1));
hvx_vec_store_u(r, 8, Q6_Vsf_equals_Vqf32(rsum));
}
// Dot product of two F16 vectors, accumulating to float
static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict x, const void * restrict y, unsigned int n, float s) {
const HVX_Vector * restrict vx = (const HVX_Vector * restrict) x; // fp16
@ -140,8 +25,7 @@ static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict
uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
uint32_t nloe = n % VLEN_FP16; // leftover elements
const HVX_Vector zero = Q6_V_vsplat_R(0);
HVX_Vector rsum = Q6_V_vsplat_R(0);
HVX_Vector rsum = Q6_V_vsplat_R(0);
uint32_t i = 0;
@ -156,11 +40,10 @@ static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict
}
if (nloe) {
HVX_Vector y_hf = vy[i];
// Load x (fp16) and zero-out unused elements
HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
HVX_Vector x_hf = Q6_V_vand_QV(bmask, vx[i]);
HVX_Vector y_hf = Q6_V_vand_QV(bmask, vy[i]);
HVX_Vector x_hf = Q6_V_vand_QV(bmask, vx[i]);
HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
@ -181,12 +64,11 @@ static inline void hvx_dot_f16_f16_aa_rx2(float * restrict r,
const HVX_Vector * restrict vx1 = (const HVX_Vector * restrict) x1; // fp16
const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp16
uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
uint32_t nloe = n % VLEN_FP16; // leftover elements
uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
uint32_t nloe = n % VLEN_FP16; // leftover elements
const HVX_Vector zero = Q6_V_vsplat_R(0);
HVX_Vector rsum0 = Q6_V_vsplat_R(0);
HVX_Vector rsum1 = Q6_V_vsplat_R(0);
HVX_Vector rsum0 = Q6_V_vsplat_R(0);
HVX_Vector rsum1 = Q6_V_vsplat_R(0);
uint32_t i = 0;
@ -204,12 +86,11 @@ static inline void hvx_dot_f16_f16_aa_rx2(float * restrict r,
}
if (nloe) {
HVX_Vector y_hf = vy[i];
// Load x (fp16) and zero-out unused elements
HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
HVX_Vector x0_hf = Q6_V_vand_QV(bmask, vx0[i]);
HVX_Vector x1_hf = Q6_V_vand_QV(bmask, vx1[i]);
HVX_Vector x0_hf = Q6_V_vand_QV(bmask, vx0[i]);
HVX_Vector x1_hf = Q6_V_vand_QV(bmask, vx1[i]);
HVX_Vector y_hf = Q6_V_vand_QV(bmask, vy[i]);
HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf);
HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf);
@ -222,7 +103,7 @@ static inline void hvx_dot_f16_f16_aa_rx2(float * restrict r,
hvx_vec_store_u(r, 8, Q6_Vsf_equals_Vqf32(rsum));
}
// MAD: y (F32) += x (F16) * s (float)
// MAD: y (F32) += x (F16) * s (F32)
static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict x, int n, float s) {
const HVX_Vector * restrict ptr_x = (const HVX_Vector *) x;
HVX_Vector * restrict ptr_y = (HVX_Vector *) y;
@ -259,15 +140,125 @@ static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict
}
}
// MAD: y (F32) += x0 (F16) * s0 (F32) + x1 (F16) * s1 (F32)
static inline void hvx_mad_f32_f16_aa_rx2(float * restrict y,
const void * restrict x0,
const void * restrict x1,
float s0,
float s1,
int n) {
const HVX_Vector * restrict ptr_x0 = (const HVX_Vector *) x0;
const HVX_Vector * restrict ptr_x1 = (const HVX_Vector *) x1;
HVX_Vector * restrict ptr_y = (HVX_Vector *) y;
uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
uint32_t nloe = n % VLEN_FP16; // leftover elements
HVX_Vector S0 = hvx_vec_splat_f16(s0);
HVX_Vector S1 = hvx_vec_splat_f16(s1);
uint32_t i = 0;
#pragma unroll(2)
for (i = 0; i < nvec; ++i) {
// Multiply x * s -> pair of F32 vectors
HVX_VectorPair xs0_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x0[i]), S0);
HVX_VectorPair xs1_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x1[i]), S1);
HVX_Vector xs_p_lo = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xs0_p), Q6_V_lo_W(xs1_p));
HVX_Vector xs_p_hi = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_hi_W(xs0_p), Q6_V_hi_W(xs1_p));
ptr_y[i * 2] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs_p_lo, ptr_y[i * 2]));
ptr_y[i * 2 + 1] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs_p_hi, ptr_y[i * 2 + 1]));
}
if (nloe) {
HVX_VectorPair xs0_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x0[i]), S0);
HVX_VectorPair xs1_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x1[i]), S1);
HVX_Vector xs_p_lo = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xs0_p), Q6_V_lo_W(xs1_p));
HVX_Vector xs = xs_p_lo;
i = 2 * i; // index for ptr_y
if (nloe >= 32) {
ptr_y[i] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs, ptr_y[i]));
nloe -= 32; ++i;
xs = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_hi_W(xs0_p), Q6_V_hi_W(xs1_p));
}
if (nloe) {
HVX_Vector xy = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs, ptr_y[i]));
hvx_vec_store_a(&ptr_y[i], nloe * 4, xy);
}
}
}
#define FLASH_ATTN_BLOCK_SIZE 128
static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, int nth) {
struct htp_fa_context {
const struct htp_ops_context * octx;
struct fastdiv_values src0_div21;
struct fastdiv_values src0_div1;
struct fastdiv_values broadcast_rk2;
struct fastdiv_values broadcast_rk3;
struct fastdiv_values broadcast_rv2;
struct fastdiv_values broadcast_rv3;
struct fastdiv_values src3_div2;
struct fastdiv_values src3_div3;
float scale;
float max_bias;
float logit_softcap;
uint32_t n_head_log2;
float m0;
float m1;
uint32_t n_blocks;
size_t size_q_row_padded;
size_t size_k_row_padded;
size_t size_v_row_padded;
size_t size_k_block;
size_t size_v_block;
size_t size_m_block;
bool is_q_fp32;
};
static inline void hvx_scale_vec_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const int n, HVX_Vector vs) {
assert((size_t) dst % 128 == 0);
assert((size_t) src % 128 == 0);
const HVX_Vector * restrict vsrc = (const HVX_Vector * restrict) src;
HVX_Vector * restrict vdst = (HVX_Vector * restrict) dst;
const uint32_t nvec = n / VLEN_FP32;
const uint32_t nloe = n % VLEN_FP32;
uint32_t i = 0;
#pragma unroll(4)
for (; i < nvec; ++i) {
vdst[i] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(vsrc[i], vs));
}
if (nloe) {
HVX_Vector v = Q6_Vqf32_vmpy_VsfVsf(vsrc[i], vs);
hvx_vec_store_a(&vdst[i], nloe * sizeof(float), Q6_Vsf_equals_Vqf32(v));
}
}
static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * data) {
struct htp_fa_context * factx = (struct htp_fa_context *) data;
const struct htp_ops_context * octx = factx->octx;
const struct htp_tensor * q = &octx->src0;
const struct htp_tensor * k = &octx->src1;
const struct htp_tensor * v = &octx->src2;
const struct htp_tensor * mask = (octx->src3.data) ? &octx->src3 : NULL;
const struct htp_tensor * sinks = (octx->src4.data) ? &octx->src4 : NULL;
struct htp_tensor * dst = &octx->dst;
const struct htp_tensor * dst = &octx->dst;
const uint32_t neq0 = q->ne[0];
const uint32_t neq1 = q->ne[1];
@ -304,18 +295,6 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
const uint32_t nb2 = dst->nb[2];
const uint32_t nb3 = dst->nb[3];
float scale = 1.0f;
float max_bias = 0.0f;
float logit_softcap = 0.0f;
memcpy(&scale, (float *) octx->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *) octx->op_params + 1, sizeof(float));
memcpy(&logit_softcap, (float *) octx->op_params + 2, sizeof(float));
if (logit_softcap != 0) {
scale /= logit_softcap;
}
// total rows in q
const uint32_t nr = neq1*neq2*neq3;
@ -331,18 +310,8 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
const uint32_t DV = nev0;
const size_t size_q_row = DK * ((q->type == HTP_TYPE_F32) ? 4 : 2);
const size_t size_q_row_padded = hex_round_up(size_q_row, 128);
const size_t size_k_row = DK * sizeof(__fp16);
const size_t size_v_row = DV * sizeof(__fp16);
const size_t size_m_row = FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16); // Treat block as one row for mask
const size_t size_k_row_padded = hex_round_up(size_k_row, 128);
const size_t size_v_row_padded = hex_round_up(size_v_row, 128);
const size_t size_k_block = size_k_row_padded * FLASH_ATTN_BLOCK_SIZE;
const size_t size_v_block = size_v_row_padded * FLASH_ATTN_BLOCK_SIZE;
const size_t size_m_block = hex_round_up(FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16), 128);
// Scratchpad buffers for Q, K, V, Mask, and VKQ32 accumulator
uint8_t * spad_q = octx->src0_spad.data + octx->src0_spad.size_per_thread * ith;
@ -351,31 +320,28 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
uint8_t * spad_m = octx->src3_spad.data + octx->src3_spad.size_per_thread * ith;
uint8_t * spad_a = octx->dst_spad.data + octx->dst_spad.size_per_thread * ith;
const uint32_t n_head = neq2;
const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
const HVX_Vector logit_cap = hvx_vec_splat_f32(factx->logit_softcap);
for (uint32_t ir = ir0; ir < ir1; ++ir) {
const uint32_t iq3 = fastdiv(ir, &octx->src0_div21);
const uint32_t iq2 = fastdiv(ir - iq3*neq2*neq1, &octx->src0_div1);
const uint32_t iq3 = fastdiv(ir, &factx->src0_div21);
const uint32_t iq2 = fastdiv(ir - iq3*neq2*neq1, &factx->src0_div1);
const uint32_t iq1 = (ir - iq3*neq2*neq1 - iq2 * neq1);
const uint32_t ik3 = fastdiv(iq3, &octx->broadcast_rk3);
const uint32_t ik2 = fastdiv(iq2, &octx->broadcast_rk2);
const uint32_t ik3 = fastdiv(iq3, &factx->broadcast_rk3);
const uint32_t ik2 = fastdiv(iq2, &factx->broadcast_rk2);
const uint32_t iv3 = fastdiv(iq3, &octx->broadcast_rv3);
const uint32_t iv2 = fastdiv(iq2, &octx->broadcast_rv2);
const uint32_t iv3 = fastdiv(iq3, &factx->broadcast_rv3);
const uint32_t iv2 = fastdiv(iq2, &factx->broadcast_rv2);
// Fetch Q row
const uint8_t * q_row_ptr = (const uint8_t *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3);
dma_queue_push(dma, dma_make_ptr(spad_q, q_row_ptr), size_q_row_padded, nbq1, size_q_row, 1);
dma_queue_push(dma, dma_make_ptr(spad_q, q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1);
const uint32_t h = iq2; // head index
const float slope = (max_bias > 0.0f) ? (h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1)) : 1.0f;
const float slope = (factx->max_bias > 0.0f) ? (h < factx->n_head_log2 ? powf(factx->m0, h + 1) : powf(factx->m1, 2*(h - factx->n_head_log2) + 1)) : 1.0f;
float S = 0.0f; // sum
float M = -INFINITY; // maximum KQ value
HVX_Vector S_vec = hvx_vec_splat_f32(0.0f);
HVX_Vector M_vec = hvx_vec_splat_f32(-INFINITY);
// Clear accumulator
hvx_splat_f32_a(spad_a, 0, DV);
@ -383,40 +349,42 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
const __fp16 * mp_base = NULL;
if (mask) {
const uint32_t im2 = fastmodulo(iq2, mask->ne[2], &octx->src3_div2);
const uint32_t im3 = fastmodulo(iq3, mask->ne[3], &octx->src3_div3);
const uint32_t im2 = fastmodulo(iq2, mask->ne[2], &factx->src3_div2);
const uint32_t im3 = fastmodulo(iq3, mask->ne[3], &factx->src3_div3);
mp_base = (const __fp16 *) ((const uint8_t *) mask->data + iq1*mask->nb[1] + im2*mask->nb[2] + im3*mask->nb[3]);
}
const uint32_t n_blocks = (nek1 + FLASH_ATTN_BLOCK_SIZE - 1) / FLASH_ATTN_BLOCK_SIZE;
// Prefetch first two blocks
for (uint32_t ib = 0; ib < MIN(n_blocks, 2); ++ib) {
for (uint32_t ib = 0; ib < MIN(factx->n_blocks, 2); ++ib) {
const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE;
const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start);
// K
const uint8_t * k_src = (const uint8_t *) k->data + (ic_start*nbk1 + ik2*nbk2 + ik3*nbk3);
uint8_t * k_dst = spad_k + (ib % 2) * size_k_block;
dma_queue_push(dma, dma_make_ptr(k_dst, k_src), size_k_row_padded, nbk1, size_k_row, current_block_size);
uint8_t * k_dst = spad_k + (ib % 2) * factx->size_k_block;
dma_queue_push(dma, dma_make_ptr(k_dst, k_src), factx->size_k_row_padded, nbk1, size_k_row, current_block_size);
// V
const uint8_t * v_src = (const uint8_t *) v->data + (ic_start*nbv1 + iv2*nbv2 + iv3*nbv3);
uint8_t * v_dst = spad_v + (ib % 2) * size_v_block;
dma_queue_push(dma, dma_make_ptr(v_dst, v_src), size_v_row_padded, nbv1, size_v_row, current_block_size);
uint8_t * v_dst = spad_v + (ib % 2) * factx->size_v_block;
dma_queue_push(dma, dma_make_ptr(v_dst, v_src), factx->size_v_row_padded, nbv1, size_v_row, current_block_size);
// Mask
if (mask) {
const uint8_t * m_src = (const uint8_t *) (mp_base + ic_start);
uint8_t * m_dst = spad_m + (ib % 2) * size_m_block;
uint8_t * m_dst = spad_m + (ib % 2) * factx->size_m_block;
// Mask is 1D contiguous for this row
dma_queue_push(dma, dma_make_ptr(m_dst, m_src), current_block_size * 2, current_block_size * 2, current_block_size * 2, 1);
}
}
const uint8_t * q_ptr_vtcm = dma_queue_pop(dma).dst;
uint8_t * q_ptr_vtcm = dma_queue_pop(dma).dst;
if (factx->is_q_fp32) {
hvx_copy_f16_f32_aa(q_ptr_vtcm, q_ptr_vtcm, DK); // inplace convert f32 to f16
}
for (uint32_t ib = 0; ib < n_blocks; ++ib) {
const HVX_Vector slope_vec = hvx_vec_splat_f16(slope);
for (uint32_t ib = 0; ib < factx->n_blocks; ++ib) {
const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE;
const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start);
@ -428,8 +396,6 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
// Inner loop processing the block from VTCM
uint32_t ic = 0;
const bool is_q_fp32 = (q->type == HTP_TYPE_F32);
// Process in blocks of 32 (VLEN_FP32)
static_assert(FLASH_ATTN_BLOCK_SIZE / VLEN_FP32 <= 4, "FLASH_ATTN_BLOCK_SIZE changed, fix HVX_Vector_x4 usage");
HVX_Vector_x4 scores_x4;
@ -437,22 +403,18 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
for (uint32_t iv = 0; ic + VLEN_FP32 <= current_block_size; ic += VLEN_FP32, ++iv) {
// 1. Compute scores
float __attribute__((aligned(VLEN))) scores_arr[VLEN_FP32];
for (int j = 0; j < VLEN_FP32; j += 2) {
for (uint32_t j = 0; j < VLEN_FP32; j += 2) {
const uint32_t cur_ic = ic + j;
const uint8_t * k_ptr = k_base + cur_ic * size_k_row_padded;
if (is_q_fp32) {
hvx_dot_f32_f16_aa_rx2(&scores_arr[j], q_ptr_vtcm, k_ptr, k_ptr + size_k_row_padded, DK, scale);
} else {
hvx_dot_f16_f16_aa_rx2(&scores_arr[j], q_ptr_vtcm, k_ptr, k_ptr + size_k_row_padded, DK, scale);
}
const uint8_t * k_ptr = k_base + cur_ic * factx->size_k_row_padded;
hvx_dot_f16_f16_aa_rx2(&scores_arr[j], q_ptr_vtcm, k_ptr, k_ptr + factx->size_k_row_padded, DK, factx->scale);
}
HVX_Vector scores = *(HVX_Vector *) scores_arr;
// 2. Softcap
if (logit_softcap != 0.0f) {
if (factx->logit_softcap != 0.0f) {
scores = hvx_vec_tanh_f32(scores);
scores = Q6_Vqf32_vmpy_VsfVsf(scores, hvx_vec_splat_f32(logit_softcap));
scores = Q6_Vqf32_vmpy_VsfVsf(scores, logit_cap);
scores = Q6_Vsf_equals_Vqf32(scores);
}
@ -460,70 +422,59 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
if (mask) {
const __fp16 * mp = m_base + ic;
HVX_Vector m_vals_f16 = *(const HVX_UVector *) mp;
HVX_Vector one_f16 = Q6_Vh_vsplat_R(0x3c00);
HVX_VectorPair m_vals_f32_pair = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_f16), one_f16);
HVX_Vector m_vals_f32 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(m_vals_f32_pair));
HVX_Vector slope_vec = hvx_vec_splat_f32(slope);
HVX_Vector add_val = Q6_Vqf32_vmpy_VsfVsf(m_vals_f32, slope_vec);
scores = Q6_Vqf32_vadd_VsfVsf(scores, Q6_Vsf_equals_Vqf32(add_val));
HVX_VectorPair m_vals_f32_pair = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_f16), slope_vec);
HVX_Vector add_val = Q6_V_lo_W(m_vals_f32_pair);
scores = Q6_Vqf32_vadd_Vqf32Vsf(add_val, scores);
scores = Q6_Vsf_equals_Vqf32(scores);
}
scores_x4.v[iv] = scores;
v_max = Q6_Vsf_vmax_VsfVsf(scores, v_max);
v_max = hvx_vec_reduce_max2_f32(scores, v_max); // All lanes have block max
}
{
// 4. Online Softmax Update
v_max = hvx_vec_reduce_max_f32(v_max);
float m_block = hvx_vec_get_f32(v_max);
float M_old = M;
float M_new = (m_block > M) ? m_block : M;
M = M_new;
HVX_Vector M_new_vec = Q6_Vsf_vmax_VsfVsf(v_max, M_vec);
HVX_Vector diff_vec = Q6_Vqf32_vsub_VsfVsf(M_vec, M_new_vec);
HVX_Vector ms_vec = hvx_vec_exp_f32(Q6_Vsf_equals_Vqf32(diff_vec));
M_vec = M_new_vec;
const float ms = expf(M_old - M_new);
hvx_scale_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms);
hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec);
HVX_Vector M_new_vec = hvx_vec_splat_f32(M_new);
HVX_Vector p_sum_vec = hvx_vec_splat_f32(0.0f);
for (uint32_t ic2 = 0, iv = 0; ic2 + VLEN_FP32 <= current_block_size; ic2 += VLEN_FP32, ++iv) {
HVX_Vector scores = scores_x4.v[iv];
HVX_Vector scores_shifted = Q6_Vqf32_vsub_VsfVsf(scores, M_new_vec);
HVX_Vector scores_shifted = Q6_Vqf32_vsub_VsfVsf(scores, M_vec);
HVX_Vector P = hvx_vec_exp_f32(Q6_Vsf_equals_Vqf32(scores_shifted));
p_sum_vec = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(p_sum_vec, P));
// 5. Accumulate V
float __attribute__((aligned(VLEN))) p_arr[VLEN_FP32];
*(HVX_Vector*)p_arr = P;
*(HVX_Vector *) p_arr = P;
for (int j = 0; j < VLEN_FP32; ++j) {
const uint32_t cur_ic = ic2 + j;
const uint8_t * v_ptr = v_base + cur_ic * size_v_row_padded;
hvx_mad_f32_f16_aa(VKQ32, v_ptr, DV, p_arr[j]);
for (uint32_t j = 0; j < VLEN_FP32; j += 2) {
const uint32_t cur_ic = ic2 + j;
const uint8_t * v_ptr = v_base + cur_ic * factx->size_v_row_padded;
hvx_mad_f32_f16_aa_rx2(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, p_arr[j], p_arr[j + 1], DV);
}
}
p_sum_vec = hvx_vec_reduce_sum_f32(p_sum_vec);
S = S * ms + hvx_vec_get_f32(p_sum_vec);
S_vec = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(S_vec, ms_vec)), p_sum_vec));
}
// Sync scalars for leftover/next block if needed
float M = hvx_vec_get_f32(M_vec);
float S = hvx_vec_get_f32(S_vec);
// Leftover
for (; ic < current_block_size; ++ic) {
float s_val;
const uint8_t * k_ptr = k_base + ic * size_k_row_padded;
if (is_q_fp32) {
hvx_dot_f32_f16_aa(&s_val, q_ptr_vtcm, k_ptr, DK, scale);
} else {
hvx_dot_f16_f16_aa(&s_val, q_ptr_vtcm, k_ptr, DK, scale);
}
if (logit_softcap != 0.0f) {
s_val = logit_softcap * tanhf(s_val);
const uint8_t * k_ptr = k_base + ic * factx->size_k_row_padded;
hvx_dot_f16_f16_aa(&s_val, q_ptr_vtcm, k_ptr, DK, factx->scale);
if (factx->logit_softcap != 0.0f) {
s_val = factx->logit_softcap * tanhf(s_val);
}
if (mask) {
@ -532,37 +483,42 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
}
const float Mold = M;
float ms = 1.0f;
float vs = 1.0f;
if (s_val > M) {
M = s_val;
ms = expf(Mold - M);
hvx_scale_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms);
HVX_Vector diff_vec = hvx_vec_splat_f32(Mold - M);
HVX_Vector ms_vec = hvx_vec_exp_f32(diff_vec);
hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec);
float ms = hvx_vec_get_f32(ms_vec);
S = S * ms + vs;
} else {
vs = expf(s_val - M);
HVX_Vector diff_vec = hvx_vec_splat_f32(s_val - M);
vs = hvx_vec_get_f32(hvx_vec_exp_f32(diff_vec));
S += vs;
}
const uint8_t * v_ptr = v_base + ic * size_v_row_padded;
const uint8_t * v_ptr = v_base + ic * factx->size_v_row_padded;
hvx_mad_f32_f16_aa(VKQ32, v_ptr, DV, vs);
S = S * ms + vs;
}
M_vec = hvx_vec_splat_f32(M);
S_vec = hvx_vec_splat_f32(S);
// Issue DMA for next+1 block (if exists)
if (ib + 2 < n_blocks) {
if (ib + 2 < factx->n_blocks) {
const uint32_t next_ib = ib + 2;
const uint32_t next_ic_start = next_ib * FLASH_ATTN_BLOCK_SIZE;
const uint32_t next_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - next_ic_start);
// K
const uint8_t * k_src = (const uint8_t *) k->data + (next_ic_start*nbk1 + ik2*nbk2 + ik3*nbk3);
dma_queue_push(dma, dma_make_ptr(k_base, k_src), size_k_row_padded, nbk1, size_k_row, next_block_size);
dma_queue_push(dma, dma_make_ptr(k_base, k_src), factx->size_k_row_padded, nbk1, size_k_row, next_block_size);
// V
const uint8_t * v_src = (const uint8_t *) v->data + (next_ic_start*nbv1 + iv2*nbv2 + iv3*nbv3);
dma_queue_push(dma, dma_make_ptr(v_base, v_src), size_v_row_padded, nbv1, size_v_row, next_block_size);
dma_queue_push(dma, dma_make_ptr(v_base, v_src), factx->size_v_row_padded, nbv1, size_v_row, next_block_size);
// Mask
if (mask) {
@ -573,20 +529,26 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
}
// sinks
float M = hvx_vec_get_f32(M_vec);
float S = hvx_vec_get_f32(S_vec);
if (sinks) {
const float s = ((float *)((char *) sinks->data))[h];
float ms = 1.0f;
float vs = 1.0f;
if (s > M) {
ms = expf(M - s);
hvx_scale_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms);
} else {
vs = expf(s - M);
}
HVX_Vector diff_vec = hvx_vec_splat_f32(M - s);
HVX_Vector ms_vec = hvx_vec_exp_f32(diff_vec);
hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec);
S = S * ms + vs;
float ms = hvx_vec_get_f32(ms_vec);
S = S * ms + vs;
} else {
HVX_Vector diff_vec = hvx_vec_splat_f32(s - M);
vs = hvx_vec_get_f32(hvx_vec_exp_f32(diff_vec));
S += vs;
}
}
const float S_inv = S == 0.0f ? 0.0f : 1.0f/S;
@ -609,53 +571,73 @@ static void flash_attn_ext_f16_thread(struct htp_ops_context * octx, int ith, in
}
}
static void htp_flash_attn_ext_job(unsigned int n, unsigned int i, void * data) {
struct htp_ops_context * octx = data;
flash_attn_ext_f16_thread(octx, i, n);
}
int op_flash_attn_ext(struct htp_ops_context * octx) {
const struct htp_tensor * q = &octx->src0;
const struct htp_tensor * k = &octx->src1;
const struct htp_tensor * v = &octx->src2;
const struct htp_tensor * mask = (octx->src3.type != HTP_TYPE_COUNT) ? &octx->src3 : NULL;
struct htp_tensor * dst = &octx->dst;
const struct htp_tensor * mask = (octx->src3.data) ? &octx->src3 : NULL;
const struct htp_tensor * dst = &octx->dst;
// Check support
if ((q->type != HTP_TYPE_F16 && q->type != HTP_TYPE_F32) ||
k->type != HTP_TYPE_F16 ||
v->type != HTP_TYPE_F16) {
if ((q->type != HTP_TYPE_F16 && q->type != HTP_TYPE_F32) || k->type != HTP_TYPE_F16 || v->type != HTP_TYPE_F16) {
return HTP_STATUS_NO_SUPPORT;
}
octx->src0_div21 = init_fastdiv_values(q->ne[2] * q->ne[1]);
octx->src0_div1 = init_fastdiv_values(q->ne[1]);
struct htp_fa_context factx;
factx.octx = octx;
octx->broadcast_rk2 = init_fastdiv_values(q->ne[2]/k->ne[2]);
octx->broadcast_rk3 = init_fastdiv_values(q->ne[3]/k->ne[3]);
octx->broadcast_rv2 = init_fastdiv_values(q->ne[2]/v->ne[2]);
octx->broadcast_rv3 = init_fastdiv_values(q->ne[3]/v->ne[3]);
factx.src0_div21 = init_fastdiv_values(q->ne[2] * q->ne[1]);
factx.src0_div1 = init_fastdiv_values(q->ne[1]);
factx.broadcast_rk2 = init_fastdiv_values(q->ne[2]/k->ne[2]);
factx.broadcast_rk3 = init_fastdiv_values(q->ne[3]/k->ne[3]);
factx.broadcast_rv2 = init_fastdiv_values(q->ne[2]/v->ne[2]);
factx.broadcast_rv3 = init_fastdiv_values(q->ne[3]/v->ne[3]);
if (mask) {
octx->src3_div2 = init_fastdiv_values(mask->ne[2]);
octx->src3_div3 = init_fastdiv_values(mask->ne[3]);
factx.src3_div2 = init_fastdiv_values(mask->ne[2]);
factx.src3_div3 = init_fastdiv_values(mask->ne[3]);
}
size_t size_q_row_padded = hex_round_up(q->ne[0] * (q->type == HTP_TYPE_F32 ? 4 : 2), 128);
size_t size_k_row_padded = hex_round_up(k->ne[0] * sizeof(__fp16), 128);
size_t size_v_row_padded = hex_round_up(v->ne[0] * sizeof(__fp16), 128);
factx.is_q_fp32 = (q->type == HTP_TYPE_F32);
factx.size_q_row_padded = hex_round_up(q->ne[0] * (factx.is_q_fp32 ? 4 : 2), 128);
factx.size_k_row_padded = hex_round_up(k->ne[0] * sizeof(__fp16), 128);
factx.size_v_row_padded = hex_round_up(v->ne[0] * sizeof(__fp16), 128);
size_t size_q_block = size_q_row_padded * 1; // single row for now
size_t size_k_block = size_k_row_padded * FLASH_ATTN_BLOCK_SIZE;
size_t size_v_block = size_v_row_padded * FLASH_ATTN_BLOCK_SIZE;
size_t size_m_block = hex_round_up(FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16), 128);
size_t size_q_block = factx.size_q_row_padded * 1; // single row for now
factx.size_k_block = factx.size_k_row_padded * FLASH_ATTN_BLOCK_SIZE;
factx.size_v_block = factx.size_v_row_padded * FLASH_ATTN_BLOCK_SIZE;
factx.size_m_block = hex_round_up(FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16), 128);
factx.n_blocks = (k->ne[1] + FLASH_ATTN_BLOCK_SIZE - 1) / FLASH_ATTN_BLOCK_SIZE;
float scale = 1.0f;
float max_bias = 0.0f;
float logit_softcap = 0.0f;
memcpy(&scale, (float *) octx->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *) octx->op_params + 1, sizeof(float));
memcpy(&logit_softcap, (float *) octx->op_params + 2, sizeof(float));
if (logit_softcap != 0.0f) {
scale /= logit_softcap;
}
factx.scale = scale;
factx.max_bias = max_bias;
factx.logit_softcap = logit_softcap;
uint32_t n_head = q->ne[2];
factx.n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
factx.m0 = powf(2.0f, -(max_bias ) / factx.n_head_log2);
factx.m1 = powf(2.0f, -(max_bias / 2.0f) / factx.n_head_log2);
size_t size_vkq_acc = hex_round_up(v->ne[0] * sizeof(float), 128); // VKQ32
octx->src0_spad.size_per_thread = size_q_block * 1;
octx->src1_spad.size_per_thread = size_k_block * 2;
octx->src2_spad.size_per_thread = size_v_block * 2;
octx->src3_spad.size_per_thread = mask ? size_m_block * 2 : 0;
octx->src1_spad.size_per_thread = factx.size_k_block * 2;
octx->src2_spad.size_per_thread = factx.size_v_block * 2;
octx->src3_spad.size_per_thread = mask ? factx.size_m_block * 2 : 0;
octx->dst_spad.size_per_thread = size_vkq_acc;
octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
@ -677,7 +659,7 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
octx->dst_spad.data = octx->src3_spad.data + octx->src3_spad.size;
if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
worker_pool_run_func(octx->ctx->worker_pool, htp_flash_attn_ext_job, octx, octx->n_threads);
worker_pool_run_func(octx->ctx->worker_pool, flash_attn_ext_f16_thread, &factx, octx->n_threads);
}
return HTP_STATUS_OK;

2
ggml/src/ggml-hexagon/htp/main.c
View File

@ -189,7 +189,7 @@ static int vtcm_release_callback(unsigned int rctx, void * state) {
// otherwise we'll release it once we're done with the current Op.
if (ctx->vtcm_inuse) {
ctx->vtcm_needs_release = false;
ctx->vtcm_needs_release = true;
return 0;
}

14
ggml/src/ggml-metal/ggml-metal-common.cpp
View File

@ -264,15 +264,25 @@ static std::vector<int> ggml_metal_graph_optimize_reorder(const std::vector<node
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_GROUP_NORM:
case GGML_OP_L2_NORM:
case GGML_OP_SUM_ROWS:
case GGML_OP_SSM_CONV:
case GGML_OP_SSM_SCAN:
case GGML_OP_CLAMP:
case GGML_OP_TRI:
case GGML_OP_DIAG:
case GGML_OP_MUL:
case GGML_OP_ADD:
case GGML_OP_DIV:
case GGML_OP_GLU:
case GGML_OP_SCALE:
case GGML_OP_UNARY:
case GGML_OP_GET_ROWS:
case GGML_OP_CPY:
case GGML_OP_SET_ROWS:
case GGML_OP_SET:
case GGML_OP_CPY:
case GGML_OP_CONT:
case GGML_OP_REPEAT:
return true;
default:
return ggml_op_is_empty(op);
@ -312,7 +322,7 @@ static std::vector<int> ggml_metal_graph_optimize_reorder(const std::vector<node
h_add(mrs1, node0);
// that many nodes forward to search for a concurrent node
constexpr int N_FORWARD = 8;
constexpr int N_FORWARD = 64;
for (int i1 = i0 + 1; i1 < i0 + N_FORWARD && i1 < n; i1++) {
if (used[i1]) {

1
ggml/src/ggml-metal/ggml-metal-device.m
View File

@ -1159,6 +1159,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
return has_simdgroup_reduction;
case GGML_OP_SET:
case GGML_OP_CPY:
case GGML_OP_DUP:
case GGML_OP_CONT:

132
ggml/src/ggml-metal/ggml-metal-ops.cpp
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@ -426,6 +426,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
{
n_fuse = ggml_metal_op_flash_attn_ext(ctx, idx);
} break;
case GGML_OP_SET:
{
n_fuse = ggml_metal_op_set(ctx, idx);
} break;
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:
@ -1609,6 +1613,134 @@ int ggml_metal_op_solve_tri(ggml_metal_op_t ctx, int idx) {
return 1;
}
int ggml_metal_op_set(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, ne1, op->src[1], ne);
GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
GGML_TENSOR_LOCALS(uint64_t, nb, op, nb);
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_dst = ggml_metal_get_buffer_id(op);
const size_t pnb1 = ((const int32_t *) op->op_params)[0];
const size_t pnb2 = ((const int32_t *) op->op_params)[1];
const size_t pnb3 = ((const int32_t *) op->op_params)[2];
const size_t offs = ((const int32_t *) op->op_params)[3];
const bool inplace = (bool) ((const int32_t *) op->op_params)[4];
if (!inplace) {
// run a separete kernel to cpy src->dst
// not sure how to avoid this
// TODO: make a simpler cpy_bytes kernel
//const id<MTLComputePipelineState> pipeline = ctx->pipelines[GGML_METAL_PIPELINE_TYPE_CPY_F32_F32].obj;
auto 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,
/*.ne03 =*/ ne03,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.ne0 =*/ ne0,
/*.ne1 =*/ ne1,
/*.ne2 =*/ ne2,
/*.ne3 =*/ ne3,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
};
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, bid_src0, 1);
ggml_metal_encoder_set_buffer (enc, bid_dst, 2);
const int nth = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), ne00);
ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);
ggml_metal_op_concurrency_reset(ctx);
}
auto pipeline = ggml_metal_library_get_pipeline_cpy(lib, op->src[1]->type, op->type);
GGML_ASSERT(ne10 % ggml_blck_size(op->src[1]->type) == 0);
int64_t nk0 = ne10;
if (ggml_is_quantized(op->src[1]->type)) {
nk0 = ne10/16;
} else if (ggml_is_quantized(op->type)) {
nk0 = ne10/ggml_blck_size(op->type);
}
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[1]->type) == 1 && ggml_blck_size(op->type) == 1) {
if (nth > nk0) {
nrptg = (nth + nk0 - 1)/nk0;
nth = nk0;
if (nrptg*nth > ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
nrptg--;
}
}
}
nth = std::min<int>(nth, nk0);
ggml_metal_kargs_cpy args = {
/*.nk0 =*/ nk0,
/*.ne00 =*/ ne10,
/*.ne01 =*/ ne11,
/*.ne02 =*/ ne12,
/*.ne03 =*/ ne13,
/*.nb00 =*/ nb10,
/*.nb01 =*/ nb11,
/*.nb02 =*/ nb12,
/*.nb03 =*/ nb13,
/*.ne0 =*/ ne10,
/*.ne1 =*/ ne11,
/*.ne2 =*/ ne12,
/*.ne3 =*/ ne13,
/*.nb0 =*/ ggml_element_size(op),
/*.nb1 =*/ pnb1,
/*.nb2 =*/ pnb2,
/*.nb3 =*/ pnb3,
};
const int nw0 = nrptg == 1 ? (nk0 + nth - 1)/nth : 1;
bid_dst.offs += offs;
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, bid_src1, 1);
ggml_metal_encoder_set_buffer (enc, bid_dst, 2);
ggml_metal_encoder_dispatch_threadgroups(enc, nw0*(ne11 + nrptg - 1)/nrptg, ne12, ne13, nth, nrptg, 1);
return 1;
}
int ggml_metal_op_cpy(ggml_metal_op_t ctx, int idx) {
ggml_tensor * op = ctx->node(idx);

1
ggml/src/ggml-metal/ggml-metal-ops.h
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@ -59,6 +59,7 @@ int ggml_metal_op_ssm_conv (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_ssm_scan (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_rwkv (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_solve_tri (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_set (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_cpy (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_pool_1d (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_pool_2d (ggml_metal_op_t ctx, int idx);

4
ggml/src/ggml-opencl/CMakeLists.txt
View File

@ -85,6 +85,8 @@ set(GGML_OPENCL_KERNELS
mul_mv_q4_0_f32_8x_flat
mul_mv_q4_0_f32_1d_8x_flat
mul_mv_q4_0_f32_1d_16x_flat
mul_mv_q4_1_f32
mul_mv_q4_1_f32_flat
mul_mv_q4_k_f32
mul_mv_q6_k_f32
mul_mv_q6_k_f32_flat
@ -101,6 +103,8 @@ set(GGML_OPENCL_KERNELS
gemv_moe_mxfp4_f32
mul_mm_f32_f32_l4_lm
mul_mm_f16_f32_l4_lm
mul_mm_q4_0_f32_l4_lm
mul_mm_q4_1_f32_l4_lm
mul_mm_q8_0_f32_l4_lm
mul_mm_q6_k_f32_l4_lm
mul_mm_q8_0_f32_8x4

404
ggml/src/ggml-opencl/ggml-opencl.cpp
View File

@ -525,6 +525,7 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_mul_mm_f16_f32_kq;
cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0;
cl_kernel kernel_convert_block_q4_1, kernel_restore_block_q4_1;
cl_kernel kernel_convert_block_mxfp4, kernel_convert_block_mxfp4_trans, kernel_restore_block_mxfp4, kernel_restore_block_mxfp4_trans;
cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0, kernel_restore_block_q8_0_trans;
cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
@ -532,6 +533,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_restore_block_q4_0_noshuffle;
cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
cl_kernel kernel_mul_mv_q4_1_f32;
cl_kernel kernel_mul_mv_q4_1_f32_flat;
cl_kernel kernel_mul_mv_q4_K_f32;
cl_kernel kernel_mul_mv_q6_K_f32;
cl_kernel kernel_mul_mv_q6_K_f32_flat;
@ -564,6 +567,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_mul_mv_id_mxfp4_f32_flat;
cl_kernel kernel_mul_mm_f32_f32_l4_lm;
cl_kernel kernel_mul_mm_f16_f32_l4_lm;
cl_kernel kernel_mul_mm_q4_0_f32_l4_lm;
cl_kernel kernel_mul_mm_q4_1_f32_l4_lm;
cl_kernel kernel_mul_mm_q8_0_f32_l4_lm;
cl_kernel kernel_mul_mm_q6_k_f32_l4_lm;
@ -888,6 +893,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
CL_CHECK((backend_ctx->kernel_restore_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q4_1 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q4_1 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4_trans", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4_trans", &err), err));
@ -1119,6 +1126,40 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// mul_mv_q4_1_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_q4_1_f32.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_q4_1_f32.cl");
#endif
cl_program prog =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// mul_mv_q4_1_f32_flat
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_q4_1_f32_flat.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_q4_1_f32_flat.cl");
#endif
cl_program prog =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32_flat", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// mul_mv_q4_k_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@ -1361,6 +1402,38 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// mul_mm_q4_0_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mm_q4_0_f32_l4_lm.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mm_q4_0_f32_l4_lm.cl");
#endif
cl_program prog =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mm_q4_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_0_f32_l4_lm", &err), err));
GGML_LOG_CONT(".");
}
// mul_mm_q4_1_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mm_q4_1_f32_l4_lm.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mm_q4_1_f32_l4_lm.cl");
#endif
cl_program prog =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mm_q4_1_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_1_f32_l4_lm", &err), err));
GGML_LOG_CONT(".");
}
// mul_mm_q8_0_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@ -2923,6 +2996,59 @@ struct ggml_tensor_extra_cl_q4_0 {
}
};
struct ggml_tensor_extra_cl_q4_1 {
// Quantized values.
cl_mem q = nullptr;
// Quantized values in image1d_buffer_t.
cl_mem q_img = nullptr;
// Scales.
cl_mem d = nullptr;
// Scales in image1d_buffer_t.
cl_mem d_img = nullptr;
// Min
cl_mem m = nullptr;
// Min in image1d_buffer_t.
cl_mem m_img = nullptr;
// Size of quantized values.
size_t size_q = 0;
// Size of scales.
size_t size_d = 0;
// Size of min values.
size_t size_m = 0;
~ggml_tensor_extra_cl_q4_1() {
reset();
}
void reset() {
// q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
// They must be properly released so that the original buffer can be
// properly released to avoid memory leak.
if (q != nullptr) {
CL_CHECK(clReleaseMemObject(q));
q = nullptr;
}
if (d != nullptr) {
CL_CHECK(clReleaseMemObject(d));
d = nullptr;
}
if (m != nullptr) {
CL_CHECK(clReleaseMemObject(m));
m = nullptr;
}
// Currently, q_img and d_img are only initialized when SMALL_ALLOC is
// enabled. They point to the images in ggml_backend_opencl_buffer_context.
// So, there is no need to release them here.
// TODO: initialize them for non SMALL_PATH path, or remove them.
q_img = nullptr;
d_img = nullptr;
m_img = nullptr;
size_q = 0;
size_d = 0;
size_m = 0;
}
};
struct ggml_tensor_extra_cl_mxfp4 {
// Quantized values.
cl_mem q = nullptr;
@ -3399,8 +3525,9 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
return true;
} else if (op->src[0]->type == GGML_TYPE_F32) {
return op->src[1]->type == GGML_TYPE_F32;
} else if (op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_MXFP4 ||
op->src[0]->type == GGML_TYPE_Q4_K ||
} else if (op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_Q4_1 ||
op->src[0]->type == GGML_TYPE_MXFP4 ||
op->src[0]->type == GGML_TYPE_Q4_K ||
op->src[0]->type == GGML_TYPE_Q6_K) {
return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
} else if (op->src[0]->type == GGML_TYPE_Q8_0) {
@ -3629,6 +3756,21 @@ struct ggml_backend_opencl_buffer_context {
return extra;
}
ggml_tensor_extra_cl_q4_1 * ggml_opencl_alloc_temp_tensor_extra_q4_1() {
ggml_tensor_extra_cl_q4_1 * extra;
if (temp_tensor_extras_q4_1.empty()) {
extra = new ggml_tensor_extra_cl_q4_1();
} else {
extra = temp_tensor_extras_q4_1.back();
temp_tensor_extras_q4_1.pop_back();
}
temp_tensor_extras_q4_1_in_use.push_back(extra);
extra->reset();
return extra;
}
ggml_tensor_extra_cl_mxfp4 * ggml_opencl_alloc_temp_tensor_extra_mxfp4() {
ggml_tensor_extra_cl_mxfp4 * extra;
if (temp_tensor_extras_mxfp4.empty()) {
@ -3685,6 +3827,11 @@ struct ggml_backend_opencl_buffer_context {
}
temp_tensor_extras_q4_0_in_use.clear();
for (ggml_tensor_extra_cl_q4_1 * e : temp_tensor_extras_q4_1_in_use) {
temp_tensor_extras_q4_1.push_back(e);
}
temp_tensor_extras_q4_1_in_use.clear();
for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4_in_use) {
temp_tensor_extras_mxfp4.push_back(e);
}
@ -3710,6 +3857,8 @@ struct ggml_backend_opencl_buffer_context {
std::vector<ggml_tensor_extra_cl *> temp_tensor_extras_in_use;
std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0;
std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0_in_use;
std::vector<ggml_tensor_extra_cl_q4_1 *> temp_tensor_extras_q4_1;
std::vector<ggml_tensor_extra_cl_q4_1 *> temp_tensor_extras_q4_1_in_use;
std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4;
std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4_in_use;
std::vector<ggml_tensor_extra_cl_q8_0 *> temp_tensor_extras_q8_0;
@ -4079,6 +4228,75 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
return;
}
if (tensor->type == GGML_TYPE_Q4_1) {
ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
// Allocate the new extra and create aliases from the original.
ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
ggml_tensor_extra_cl_q4_1 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q4_1();
size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
size_t size_m = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
GGML_ASSERT(size_d + size_m + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
cl_int err;
cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
ggml_nbytes(tensor), NULL, &err);
CL_CHECK(err);
CL_CHECK(clEnqueueWriteBuffer(
queue, data_device, CL_TRUE, 0,
ggml_nbytes(tensor), data, 0, NULL, NULL));
cl_buffer_region region;
// The original tensor memory is divided into scales and quants, i.e.,
// we first store scales, mins, then quants.
// Create subbuffer for scales.
region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
region.size = size_d;
extra->d = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
auto previous_origin = region.origin;
// Create subbuffer for mins.
region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
region.size = size_m;
extra->m = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
previous_origin = region.origin;
// Create subbuffer for quants.
region.origin = align_to(previous_origin + size_m, backend_ctx->alignment);
region.size = size_q;
extra->q = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->m));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {64, 1, 1};
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
CL_CHECK(clWaitForEvents(1, &evt));
CL_CHECK(clReleaseMemObject(data_device));
tensor->extra = extra;
return;
}
if (tensor->type == GGML_TYPE_MXFP4) {
ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
@ -4581,7 +4799,35 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
size, data, 0, NULL, NULL));
CL_CHECK(clReleaseMemObject(data_device));
return;
} else if (tensor->type == GGML_TYPE_MXFP4) {
}
if (tensor->type == GGML_TYPE_Q4_1) {
ggml_tensor_extra_cl_q4_1 * extra = (ggml_tensor_extra_cl_q4_1 *)tensor->extra;
cl_int err;
cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
ggml_nbytes(tensor), NULL, &err);
CL_CHECK(err);
cl_kernel kernel = backend_ctx->kernel_restore_block_q4_1;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->m));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &data_device));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {1, 1, 1};
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
global_work_size, local_work_size, 0, NULL, &evt));
CL_CHECK(clWaitForEvents(1, &evt));
CL_CHECK(clEnqueueReadBuffer(
queue, data_device, CL_TRUE, offset,
size, data, 0, NULL, NULL));
CL_CHECK(clReleaseMemObject(data_device));
return;
}
if (tensor->type == GGML_TYPE_MXFP4) {
ggml_tensor_extra_cl_mxfp4 * extra = (ggml_tensor_extra_cl_mxfp4 *)tensor->extra;
cl_int err;
@ -8409,6 +8655,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
#ifdef GGML_OPENCL_SOA_Q
ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
@ -8922,6 +9169,91 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
return;
}
case GGML_TYPE_Q4_0: {
if (ne11 < 32) {
break;
}
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
break;
}
kernel = backend_ctx->kernel_mul_mm_q4_0_f32_l4_lm;
nth0 = 128; // calculated as (BM*BN)/(TM*TN)
int batch_stride_a = ne00*ne01;
int batch_stride_b = ne10*ne11;
int batch_stride_d = ne0*ne1;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_0->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_0->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne10)); // stride_a
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne10)); // stride_b
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne01)); // stride_d
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &batch_stride_a));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &batch_stride_b));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &batch_stride_d));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r3));
// 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
size_t local_work_size[] = {(size_t)nth0, 1, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
return;
}
case GGML_TYPE_Q4_1: {
if (ne11 < 32) {
break;
}
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
break;
}
kernel = backend_ctx->kernel_mul_mm_q4_1_f32_l4_lm;
nth0 = 128; // calculated as (BM*BN)/(TM*TN)
int batch_stride_a = ne00*ne01;
int batch_stride_b = ne10*ne11;
int batch_stride_d = ne0*ne1;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_1->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_1->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_1->m));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne10)); // stride_a
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne10)); // stride_b
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &ne01)); // stride_d
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &batch_stride_a));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &batch_stride_b));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &batch_stride_d));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int), &r3));
// 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
size_t local_work_size[] = {(size_t)nth0, 1, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
return;
}
case GGML_TYPE_Q8_0: {
if (ne11 < 32) {
break;
@ -9262,7 +9594,71 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &r3));
#endif // GGML_OPENCL_SOA_Q
break;
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_1: {
#ifdef GGML_OPENCL_SOA_Q
if (backend_ctx->gpu_family == INTEL) {
nth0 = 16;
nth1 = 1;
ndst = 4;
} else if (backend_ctx->gpu_family == ADRENO) {
nth0 = 64;
nth1 = 1;
ndst = 4;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
kernel = backend_ctx->kernel_mul_mv_q4_1_f32_flat;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_1->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_1->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_1->m));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne10));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &r3));
#else
if (backend_ctx->gpu_family == INTEL) {
nth0 = 16;
nth1 = 1;
ndst = 4;
} else if (backend_ctx->gpu_family == ADRENO) {
nth0 = 64;
nth1 = 1;
ndst = 4;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
kernel = backend_ctx->kernel_mul_mv_q4_1_f32;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne10));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &r3));
#endif // GGML_OPENCL_SOA_Q
break;
}
case GGML_TYPE_Q8_0: {
#ifdef GGML_OPENCL_SOA_Q
kernel = backend_ctx->kernel_mul_mv_q8_0_f32_flat;

51
ggml/src/ggml-opencl/kernels/cvt.cl
View File

@ -46,6 +46,15 @@ struct block_q4_0
uint8_t qs[QK4_0 / 2];
};
//------------------------------------------------------------------------------
// block_q4_1
//------------------------------------------------------------------------------
struct block_q4_1 {
half d; // delta
half m; // min
uchar qs[QK4_1 / 2]; // nibbles / quants
};
//------------------------------------------------------------------------------
// block_q6_K
//------------------------------------------------------------------------------
@ -148,6 +157,48 @@ kernel void kernel_restore_block_q4_0_noshuffle(
}
}
//------------------------------------------------------------------------------
// kernel_convert_block_q4_1
// Convert the block_q4_1 format to 2 separate arrays (AOS -> SOA).
// This kernel does not deshuffle the bits.
//------------------------------------------------------------------------------
kernel void kernel_convert_block_q4_1(
global struct block_q4_1 * src0,
global uchar * dst_q,
global half * dst_d,
global half * dst_m
) {
global struct block_q4_1 * b = (global struct block_q4_1 *) src0 + get_global_id(0);
global uchar * q = (global uchar *) dst_q + QK4_1/2*get_global_id(0);
global half * d = (global half *) dst_d + get_global_id(0);
global half * m = (global half *) dst_m + get_global_id(0);
*d = b->d;
*m = b->m;
for (int i = 0; i < QK4_1/2; ++i) {
q[i] = b->qs[i];
}
}
kernel void kernel_restore_block_q4_1(
global uchar * src_q,
global half * src_d,
global half * src_m,
global struct block_q4_1 * dst
) {
global struct block_q4_1 * b = (global struct block_q4_1 *) dst + get_global_id(0);
global uchar * q = (global uchar *) src_q + QK4_1/2*get_global_id(0);
global half * d = (global half *) src_d + get_global_id(0);
global half * m = (global half *) src_m + get_global_id(0);
b->d = *d;
b->m = *m;
for (int i = 0; i < QK4_1/2; ++i) {
b->qs[i] = q[i];
}
}
//------------------------------------------------------------------------------
// block_mxfp4
//------------------------------------------------------------------------------

163
ggml/src/ggml-opencl/kernels/mul_mm_q4_0_f32_l4_lm.cl Normal file
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@ -0,0 +1,163 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define LOAD_VEC_A 8
#define LOAD_VEC_B 4
#define BM 64
#define BN 64
#define BK 32
#define TM 4
#define TN 8
kernel void kernel_mul_mm_q4_0_f32_l4_lm(
global uchar4 * src0_q,
global half * src0_d,
global float4 * src1,
ulong offset1,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne11,
int ne12,
int stride_a,
int stride_b,
int stride_d,
int batch_stride_a,
int batch_stride_b,
int batch_stride_d,
int r2,
int r3
) {
src1 = (global float4*)((global char*)src1 + offset1);
dst = (global float *)((global char*)dst + offsetd);
local float buf_a[BM * BK];
local float buf_b[BN * BK];
const int batch_idx = get_global_id(2);
const int i13 = batch_idx / ne12;
const int i12 = batch_idx % ne12;
const int i03 = i13 / r3;
const int i02 = i12 / r2;
const int batch_idx_a = i03 * ne02 + i02;
const int ir = get_group_id(0);
const int ic = get_group_id(1);
const int tid = get_local_id(0);
const int th_r = tid % (BM / TM);
const int th_c = tid / (BM / TM);
const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
int pos_b = (batch_idx * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
float sums[TM * TN];
float cache_a[TM];
float cache_b[TN];
for (int i = 0; i < TM * TN; i++) {
sums[i] = 0.0f;
}
for (int block = 0; block < ne00; block += BK) {
for (int l = 0; l < BM; l += loadstride_a) {
if (ir*BM + loadc_a + l < ne01) {
int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
int ib = idx / 4;
int iqs = idx % 4;
float d = (float)src0_d[ib];
global uchar4 * qs = src0_q + ib*4 + iqs;
uchar4 q = *qs;
float4 v1 = (convert_float4((uchar4)((q.s0 )&0x0F, (q.s1 )&0x0F, (q.s2 )&0x0F, (q.s3 )&0x0F)) - 8.0f)*d;
float4 v2 = (convert_float4((uchar4)((q.s0>>4)&0x0F, (q.s1>>4)&0x0F, (q.s2>>4)&0x0F, (q.s3>>4)&0x0F)) - 8.0f)*d;
buf_a[(loadr_a * 4 + 0) * BM + loadc_a + l] = v1.s0;
buf_a[(loadr_a * 4 + 1) * BM + loadc_a + l] = v1.s1;
buf_a[(loadr_a * 4 + 2) * BM + loadc_a + l] = v1.s2;
buf_a[(loadr_a * 4 + 3) * BM + loadc_a + l] = v1.s3;
buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = v2.s0;
buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = v2.s1;
buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = v2.s2;
buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = v2.s3;
} else {
buf_a[(loadr_a * 4 + 0) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 1) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 2) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 3) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = 0.0f;
}
}
for (int l = 0; l < BN; l += loadstride_b) {
if (ic*BN + loadc_b + l < ne11) {
int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
} else {
buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
pos_a += BK / LOAD_VEC_A;
pos_b += BK / LOAD_VEC_B;
for (int i = 0; i < BK; i++) {
for (int j = 0; j < TM; j++) {
cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
}
for (int j = 0; j < TN; j++) {
cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
}
for (int cc = 0; cc < TN; cc++) {
for (int cr = 0; cr < TM; cr++) {
const int sums_idx = cc*TM + cr;
sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
const int dr = ir * BM + th_r * TM;
const int dc = ic * BN + th_c * TN;
const int offsets = batch_idx * batch_stride_d;
for (int cc = 0; cc < TN; cc++) {
for (int cr = 0; cr < TM; cr++) {
if (dr + cr < ne01 && dc + cc < ne11) {
dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
}
}
}
}

165
ggml/src/ggml-opencl/kernels/mul_mm_q4_1_f32_l4_lm.cl Normal file
View File

@ -0,0 +1,165 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define LOAD_VEC_A 8
#define LOAD_VEC_B 4
#define BM 64
#define BN 64
#define BK 32
#define TM 4
#define TN 8
kernel void kernel_mul_mm_q4_1_f32_l4_lm(
global uchar4 * src0_q,
global half * src0_d,
global half * src0_m,
global float4 * src1,
ulong offset1,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne11,
int ne12,
int stride_a,
int stride_b,
int stride_d,
int batch_stride_a,
int batch_stride_b,
int batch_stride_d,
int r2,
int r3
) {
src1 = (global float4*)((global char*)src1 + offset1);
dst = (global float *)((global char*)dst + offsetd);
local float buf_a[BM * BK];
local float buf_b[BN * BK];
const int batch_idx = get_global_id(2);
const int i13 = batch_idx / ne12;
const int i12 = batch_idx % ne12;
const int i03 = i13 / r3;
const int i02 = i12 / r2;
const int batch_idx_a = i03 * ne02 + i02;
const int ir = get_group_id(0);
const int ic = get_group_id(1);
const int tid = get_local_id(0);
const int th_r = tid % (BM / TM);
const int th_c = tid / (BM / TM);
const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
int pos_b = (batch_idx * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
float sums[TM * TN];
float cache_a[TM];
float cache_b[TN];
for (int i = 0; i < TM * TN; i++) {
sums[i] = 0.0f;
}
for (int block = 0; block < ne00; block += BK) {
for (int l = 0; l < BM; l += loadstride_a) {
if (ir*BM + loadc_a + l < ne01) {
int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
int ib = idx / 4;
int iqs = idx % 4;
float d = (float)src0_d[ib];
float m = (float)src0_m[ib];
global uchar4 * qs = src0_q + ib*4 + iqs;
uchar4 q = *qs;
float4 v1 = (convert_float4((uchar4)((q.s0 )&0x0F, (q.s1 )&0x0F, (q.s2 )&0x0F, (q.s3 )&0x0F)))*d + m;
float4 v2 = (convert_float4((uchar4)((q.s0>>4)&0x0F, (q.s1>>4)&0x0F, (q.s2>>4)&0x0F, (q.s3>>4)&0x0F)))*d + m;
buf_a[(loadr_a * 4 + 0) * BM + loadc_a + l] = v1.s0;
buf_a[(loadr_a * 4 + 1) * BM + loadc_a + l] = v1.s1;
buf_a[(loadr_a * 4 + 2) * BM + loadc_a + l] = v1.s2;
buf_a[(loadr_a * 4 + 3) * BM + loadc_a + l] = v1.s3;
buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = v2.s0;
buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = v2.s1;
buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = v2.s2;
buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = v2.s3;
} else {
buf_a[(loadr_a * 4 + 0) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 1) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 2) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 3) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = 0.0f;
}
}
for (int l = 0; l < BN; l += loadstride_b) {
if (ic*BN + loadc_b + l < ne11) {
int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
} else {
buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
pos_a += BK / LOAD_VEC_A;
pos_b += BK / LOAD_VEC_B;
for (int i = 0; i < BK; i++) {
for (int j = 0; j < TM; j++) {
cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
}
for (int j = 0; j < TN; j++) {
cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
}
for (int cc = 0; cc < TN; cc++) {
for (int cr = 0; cr < TM; cr++) {
const int sums_idx = cc*TM + cr;
sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
const int dr = ir * BM + th_r * TM;
const int dc = ic * BN + th_c * TN;
const int offsets = batch_idx * batch_stride_d;
for (int cc = 0; cc < TN; cc++) {
for (int cr = 0; cr < TM; cr++) {
if (dr + cr < ne01 && dc + cc < ne11) {
dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
}
}
}
}

219
ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32.cl Normal file
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@ -0,0 +1,219 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_intel_subgroups
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#else
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#endif
#ifdef cl_intel_required_subgroup_size
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
#define QK4_1 32
struct block_q4_1 {
half d; // delta
half m; // min
uchar qs[QK4_1 / 2]; // nibbles / quants
};
inline float block_q4_1_dot_y(
global const struct block_q4_1 * qb_curr,
float sumy,
float16 yl,
int il
) {
float d = qb_curr->d;
float m = qb_curr->m;
float4 acc = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
global const ushort * qs = ((global const ushort *) qb_curr + 2 + il/2);
acc.s0 += yl.s0 * (qs[0] & 0x000F);
acc.s0 += yl.s1 * (qs[0] & 0x0F00);
acc.s0 += yl.s8 * (qs[0] & 0x00F0);
acc.s3 += yl.s9 * (qs[0] & 0xF000);
acc.s0 += yl.s2 * (qs[1] & 0x000F);
acc.s1 += yl.s3 * (qs[1] & 0x0F00);
acc.s2 += yl.sa * (qs[1] & 0x00F0);
acc.s3 += yl.sb * (qs[1] & 0xF000);
acc.s0 += yl.s4 * (qs[2] & 0x000F);
acc.s1 += yl.s5 * (qs[2] & 0x0F00);
acc.s2 += yl.sc * (qs[2] & 0x00F0);
acc.s3 += yl.sd * (qs[2] & 0xF000);
acc.s0 += yl.s6 * (qs[3] & 0x000F);
acc.s1 += yl.s7 * (qs[3] & 0x0F00);
acc.s2 += yl.se * (qs[3] & 0x00F0);
acc.s3 += yl.sf * (qs[3] & 0xF000);
return d * (acc.s0 + acc.s1 + acc.s2 + acc.s3) + sumy * m;
}
#undef N_DST
#undef N_SIMDGROUP
#undef N_SIMDWIDTH
#ifdef INTEL_GPU
#define N_DST 4 // each subgroup works on 4 rows
#define N_SIMDGROUP 1 // number of subgroups in a thread group
#define N_SIMDWIDTH 16 // assuming subgroup size is 16
#elif defined (ADRENO_GPU)
#define N_DST 4
#define N_SIMDGROUP 1
#define N_SIMDWIDTH 64
#endif
inline void mul_vec_q_n_f32(
global void * src0,
global float * src1,
global float * dst,
int ne00,
int ne01,
int ne02,
int ne10,
int ne12,
int ne0,
int ne1,
int r2,
int r3
) {
const ulong nb = ne00/QK4_1;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int im = get_group_id(2);
int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
int i12 = im%ne12;
int i13 = im/ne12;
ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
global struct block_q4_1 * x = (global struct block_q4_1 *) src0 + offset0;
global float * y = (global float *) src1 + r1*ne10 + im*ne00*ne1;
float16 yl;
float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
int ix = get_sub_group_local_id()/2;
int il = 8*(get_sub_group_local_id()%2);
global float * yb = y + ix * QK4_1 + il;
for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
float sumy = 0;
sumy += yb[0];
sumy += yb[1];
sumy += yb[2];
sumy += yb[3];
sumy += yb[4];
sumy += yb[5];
sumy += yb[6];
sumy += yb[7];
sumy += yb[16];
sumy += yb[17];
sumy += yb[18];
sumy += yb[19];
sumy += yb[20];
sumy += yb[21];
sumy += yb[22];
sumy += yb[23];
yl.s0 = yb[0];
yl.s1 = yb[1]/256.f;
yl.s2 = yb[2];
yl.s3 = yb[3]/256.f;
yl.s4 = yb[4];
yl.s5 = yb[5]/256.f;
yl.s6 = yb[6];
yl.s7 = yb[7]/256.f;
yl.s8 = yb[16]/16.f;
yl.s9 = yb[17]/4096.f;
yl.sa = yb[18]/16.f;
yl.sb = yb[19]/4096.f;
yl.sc = yb[20]/16.f;
yl.sd = yb[21]/4096.f;
yl.se = yb[22]/16.f;
yl.sf = yb[23]/4096.f;
sumf.s0 += block_q4_1_dot_y(x+ib+0*nb, sumy, yl, il);
sumf.s1 += block_q4_1_dot_y(x+ib+1*nb, sumy, yl, il);
sumf.s2 += block_q4_1_dot_y(x+ib+2*nb, sumy, yl, il);
sumf.s3 += block_q4_1_dot_y(x+ib+3*nb, sumy, yl, il);
yb += QK4_1 * (N_SIMDWIDTH/2);
}
float4 tot = (float4)(
sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
);
if (get_sub_group_local_id() == 0) {
if (first_row + 0 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
}
if (first_row + 1 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
}
if (first_row + 2 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
}
if (first_row + 3 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
}
}
}
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_q4_1_f32(
global void * src0,
ulong offset0,
global float * src1,
ulong offset1,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne10,
int ne12,
int ne0,
int ne1,
int r2,
int r3
) {
src0 = (global void*)((global char*)src0 + offset0);
src1 = (global float*)((global char*)src1 + offset1);
dst = (global float*)((global char*)dst + offsetd);
mul_vec_q_n_f32(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
}

229
ggml/src/ggml-opencl/kernels/mul_mv_q4_1_f32_flat.cl Normal file
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@ -0,0 +1,229 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_intel_subgroups
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#else
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#endif
#ifdef cl_intel_required_subgroup_size
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
#define QK4_1 32
struct block_q4_1 {
half d; // delta
half m; // min
uchar qs[QK4_1 / 2]; // nibbles / quants
};
inline float block_q4_1_dot_y_flat(
global const uchar * x,
global const half * dh,
global const half * mh,
float sumy,
float16 yl,
int il
) {
float d = *dh;
float m = *mh;
global const ushort * qs = ((global const ushort *) x + il/2);
float4 acc = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
acc.s0 += yl.s0 * (qs[0] & 0x000F);
acc.s0 += yl.s1 * (qs[0] & 0x0F00);
acc.s0 += yl.s8 * (qs[0] & 0x00F0);
acc.s3 += yl.s9 * (qs[0] & 0xF000);
acc.s0 += yl.s2 * (qs[1] & 0x000F);
acc.s1 += yl.s3 * (qs[1] & 0x0F00);
acc.s2 += yl.sa * (qs[1] & 0x00F0);
acc.s3 += yl.sb * (qs[1] & 0xF000);
acc.s0 += yl.s4 * (qs[2] & 0x000F);
acc.s1 += yl.s5 * (qs[2] & 0x0F00);
acc.s2 += yl.sc * (qs[2] & 0x00F0);
acc.s3 += yl.sd * (qs[2] & 0xF000);
acc.s0 += yl.s6 * (qs[3] & 0x000F);
acc.s1 += yl.s7 * (qs[3] & 0x0F00);
acc.s2 += yl.se * (qs[3] & 0x00F0);
acc.s3 += yl.sf * (qs[3] & 0xF000);
return d * (acc.s0 + acc.s1 + acc.s2 + acc.s3) + sumy * m;
}
#undef N_DST
#undef N_SIMDGROUP
#undef N_SIMDWIDTH
#ifdef INTEL_GPU
#define N_DST 4 // each subgroup works on 4 rows
#define N_SIMDGROUP 1 // number of subgroups in a thread group
#define N_SIMDWIDTH 16 // assuming subgroup size is 16
#elif defined (ADRENO_GPU)
#define N_DST 4
#define N_SIMDGROUP 1
#define N_SIMDWIDTH 64
#endif
inline void mul_vec_q_n_f32_flat(
global void * src0_q,
global void * src0_d,
global void * src0_m,
global float * src1,
global float * dst,
int ne00,
int ne01,
int ne02,
int ne10,
int ne12,
int ne0,
int ne1,
int r2,
int r3
) {
const ulong nb = ne00/QK4_1;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int im = get_group_id(2);
int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
int i12 = im%ne12;
int i13 = im/ne12;
ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
// The number of scales/mins is the same as the number of blocks.
ulong offset0_dm = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02));
// Each block contains QK4_1/2 uchars, hence offset for qs is as follows.
ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_1/2;
global uchar * x = (global uchar *) src0_q + offset0_q;
global half * d = (global half *) src0_d + offset0_dm;
global half * m = (global half *) src0_m + offset0_dm;
global float * y = (global float *) src1 + r1*ne10 + im*ne00*ne1;
float16 yl;
float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
int ix = get_sub_group_local_id()/2;
int il = 8*(get_sub_group_local_id()%2);
global float * yb = y + ix * QK4_1 + il;
for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
float sumy = 0;
sumy += yb[0];
sumy += yb[1];
sumy += yb[2];
sumy += yb[3];
sumy += yb[4];
sumy += yb[5];
sumy += yb[6];
sumy += yb[7];
sumy += yb[16];
sumy += yb[17];
sumy += yb[18];
sumy += yb[19];
sumy += yb[20];
sumy += yb[21];
sumy += yb[22];
sumy += yb[23];
yl.s0 = yb[0];
yl.s1 = yb[1]/256.f;
yl.s2 = yb[2];
yl.s3 = yb[3]/256.f;
yl.s4 = yb[4];
yl.s5 = yb[5]/256.f;
yl.s6 = yb[6];
yl.s7 = yb[7]/256.f;
yl.s8 = yb[16]/16.f;
yl.s9 = yb[17]/4096.f;
yl.sa = yb[18]/16.f;
yl.sb = yb[19]/4096.f;
yl.sc = yb[20]/16.f;
yl.sd = yb[21]/4096.f;
yl.se = yb[22]/16.f;
yl.sf = yb[23]/4096.f;
sumf.s0 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 0*nb*QK4_1/2, d + ib + 0*nb, m + ib + 0*nb, sumy, yl, il);
sumf.s1 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 1*nb*QK4_1/2, d + ib + 1*nb, m + ib + 1*nb, sumy, yl, il);
sumf.s2 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 2*nb*QK4_1/2, d + ib + 2*nb, m + ib + 2*nb, sumy, yl, il);
sumf.s3 += block_q4_1_dot_y_flat(x + ib*QK4_1/2 + 3*nb*QK4_1/2, d + ib + 3*nb, m + ib + 3*nb, sumy, yl, il);
yb += QK4_1 * (N_SIMDWIDTH/2);
}
float4 tot = (float4)(
sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
);
if (get_sub_group_local_id() == 0) {
if (first_row + 0 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
}
if (first_row + 1 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
}
if (first_row + 2 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
}
if (first_row + 3 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
}
}
}
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_q4_1_f32_flat(
global void * src0_q,
global void * src0_d,
global void * src0_m,
global float * src1,
ulong offset1,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne10,
int ne12,
int ne0,
int ne1,
int r2,
int r3
) {
src1 = (global float*)((global char*)src1 + offset1);
dst = (global float*)((global char*)dst + offsetd);
mul_vec_q_n_f32_flat(src0_q, src0_d, src0_m, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
}

16
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View File

@ -8422,6 +8422,8 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
const uint32_t acctype = f32acc ? 4 : 2;
const uint32_t f16vec4 = 8;
const uint32_t tmpsh = (Bc / MatBc) * sizeof(float);
const uint32_t qstride = hsk_pad / 4 + 2;
const uint32_t Qf = Br * qstride * f16vec4;
@ -8438,7 +8440,7 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
const uint32_t slope = Br * acctype;
const uint32_t total_size = Qf + Psh + sfsh + ksh + slope;
const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + slope;
const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", kv_type=" << kv_type << ", total_size=" << total_size << ", supported=" << supported);
@ -9801,16 +9803,16 @@ static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const
const uint32_t src1_type_size = ggml_type_size(src1->type);
const uint32_t dst_type_size = ggml_type_size(dst->type);
int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
// int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
int offset = dst->op_params[3] / 4; // offset in bytes
int nb1 = dst->op_params[0] / src0_type_size; // 4 bytes of float32
int nb2 = dst->op_params[1] / src0_type_size; // 4 bytes of float32
int nb3 = dst->op_params[2] / src0_type_size; // 4 bytes of float32
int offset = dst->op_params[3] / src0_type_size; // offset in bytes
ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_ACC, {
(uint32_t)ggml_nelements(src0),
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)src0->nb[3] / src0_type_size,
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)nb3,
(uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t) dst->nb[3] / dst_type_size,
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)nb3,
0,
0.0f, 0.0f, offset,
});

17
ggml/src/ggml-vulkan/vulkan-shaders/acc.comp
View File

@ -13,17 +13,18 @@ void main() {
const uint offset = p.param3;
const uint src1_i = idx - offset;
const uint oz = src1_i / p.nb02;
const uint oy = (src1_i - (oz * p.nb02)) / p.nb01;
const uint ox = src1_i % p.nb01;
const uint i3 = src1_i / p.nb03;
const uint rem2 = src1_i - i3 * p.nb03;
const uint i2 = rem2 / p.nb02;
const uint rem1 = rem2 - i2 * p.nb02;
const uint i1 = rem1 / p.nb01;
const uint i0 = rem1 % p.nb01;
uint i00, i01, i02, i03;
get_indices(idx, i00, i01, i02, i03);
if (ox < p.ne10 && oy < p.ne11 && oz < p.ne12) {
data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + ox + oy * p.ne10 + oz * p.ne10 * p.ne11]));
if (i0 < p.ne10 && i1 < p.ne11 && i2 < p.ne12 && i3 < p.ne13) {
data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx]) + FLOAT_TYPE(data_b[get_boffset() + src1_idx(i0, i1, i2, i3)]));
} else {
data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]));
data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx]));
}
}

17
ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
View File

@ -130,6 +130,7 @@ void main() {
if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) {
bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
float max_mask = NEG_FLT_MAX_OVER_2;
[[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) {
uint32_t c = (idx + tid) % Bc;
uint32_t r = (idx + tid) / Bc;
@ -137,12 +138,25 @@ void main() {
if ((!KV_bounds_check || j * Bc + c < KV) && (!nem1_bounds_check || i * Br + r < p.nem1)) {
float m = float(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]);
masksh[c][r] = m;
max_mask = max(max_mask, m);
} else {
masksh[c][r] = float(0);
}
}
}
// skip the block if the mask is entirely -inf
bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2);
barrier();
if (gl_SubgroupInvocationID == 0) {
tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f;
}
barrier();
[[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) {
max_mask = max(max_mask, tmpsh[s]);
}
if (max_mask <= NEG_FLT_MAX_OVER_2) {
continue;
}
}
float Sf[Br][cols_per_thread];
@ -260,6 +274,9 @@ void main() {
barrier();
}
// prevent race on tmpsh
barrier();
// reduce across threads
[[unroll]] for (uint32_t r = 0; r < Br; ++r) {

15
ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
View File

@ -42,6 +42,8 @@ D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TY
return elem;
}
shared float tmpsh[row_split];
const uint32_t qstride = HSK_pad / 4 + 2; // in units of f16vec4
shared f16vec4 Qf[Br * qstride];
@ -213,6 +215,19 @@ void main() {
}
}
}
// skip the block if the mask is entirely -inf
bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2);
barrier();
if (gl_SubgroupInvocationID == 0) {
tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f;
}
barrier();
[[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) {
max_mask = max(max_mask, tmpsh[s]);
}
if (max_mask <= NEG_FLT_MAX_OVER_2) {
continue;
}
}
}

14
ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
View File

@ -176,7 +176,14 @@ void main() {
tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
tensorLayoutM = setTensorLayoutClampValueNV(tensorLayoutM, 0xfc00); // -inf in float16_t
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mvmax;
coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
// skip the block if the mask is entirely -inf
coopMatReduceNV(mvmax, mv, gl_CooperativeMatrixReduceRowAndColumnNV, maxReduceFp16);
if (mvmax[0] <= NEG_FLT_MAX_OVER_2) {
continue;
}
} else {
tensorLayoutNV<2, Clamp> tensorLayoutM = createTensorLayoutNV(2, Clamp);
// Don't clamp against nem1 when GQA is enabled
@ -184,7 +191,14 @@ void main() {
tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, m_height, KV);
tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mvmax;
coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
// skip the block if the mask is entirely -inf
coopMatReduceNV(mvmax, mv, gl_CooperativeMatrixReduceRowAndColumnNV, maxReduceFp16);
if (mvmax[0] <= NEG_FLT_MAX_OVER_2) {
continue;
}
}
}
}

56
gguf-py/gguf/constants.py
View File

@ -181,6 +181,11 @@ class Keys:
SLIDING_WINDOW_PATTERN = "{arch}.attention.sliding_window_pattern"
TEMPERATURE_SCALE = "{arch}.attention.temperature_scale"
class Indexer:
HEAD_COUNT = "{arch}.attention.indexer.head_count"
KEY_LENGTH = "{arch}.attention.indexer.key_length"
TOP_K = "{arch}.attention.indexer.top_k"
class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count"
DIMENSION_SECTIONS = "{arch}.rope.dimension_sections"
@ -425,6 +430,7 @@ class MODEL_ARCH(IntEnum):
CHATGLM = auto()
GLM4 = auto()
GLM4_MOE = auto()
GLM_DSA = auto()
BITNET = auto()
T5 = auto()
T5ENCODER = auto()
@ -670,6 +676,10 @@ class MODEL_TENSOR(IntEnum):
VISEXP_GATE = auto()
VISEXP_DOWN = auto()
VISEXP_UP = auto()
INDEXER_K_NORM = auto()
INDEXER_PROJ = auto()
INDEXER_ATTN_K = auto()
INDEXER_ATTN_Q_B = auto()
# vision
V_MMPROJ = auto()
V_MMPROJ_FC = auto()
@ -858,6 +868,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.CHATGLM: "chatglm",
MODEL_ARCH.GLM4: "glm4",
MODEL_ARCH.GLM4_MOE: "glm4moe",
MODEL_ARCH.GLM_DSA: "glm-dsa",
MODEL_ARCH.BITNET: "bitnet",
MODEL_ARCH.T5: "t5",
MODEL_ARCH.T5ENCODER: "t5encoder",
@ -1101,6 +1112,10 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.VISEXP_GATE: "blk.{bid}.vis_gate",
MODEL_TENSOR.VISEXP_DOWN: "blk.{bid}.vis_down",
MODEL_TENSOR.VISEXP_UP: "blk.{bid}.vis_up",
MODEL_TENSOR.INDEXER_K_NORM: "blk.{bid}.indexer.k_norm",
MODEL_TENSOR.INDEXER_PROJ: "blk.{bid}.indexer.proj",
MODEL_TENSOR.INDEXER_ATTN_K: "blk.{bid}.indexer.attn_k",
MODEL_TENSOR.INDEXER_ATTN_Q_B: "blk.{bid}.indexer.attn_q_b",
# vision
MODEL_TENSOR.V_MMPROJ: "mm.{bid}",
MODEL_TENSOR.V_MMPROJ_FC: "mm.model.fc",
@ -2677,6 +2692,47 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
],
MODEL_ARCH.GLM_DSA: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_A,
MODEL_TENSOR.ATTN_Q_B,
MODEL_TENSOR.ATTN_KV_A_MQA,
MODEL_TENSOR.ATTN_KV_B,
MODEL_TENSOR.ATTN_K_B,
MODEL_TENSOR.ATTN_V_B,
MODEL_TENSOR.ATTN_Q_A_NORM,
MODEL_TENSOR.ATTN_KV_A_NORM,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
MODEL_TENSOR.FFN_GATE_SHEXP,
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
MODEL_TENSOR.FFN_EXP_PROBS_B,
MODEL_TENSOR.INDEXER_K_NORM,
MODEL_TENSOR.INDEXER_PROJ,
MODEL_TENSOR.INDEXER_ATTN_K,
MODEL_TENSOR.INDEXER_ATTN_Q_B,
# NextN/MTP tensors - preserved but unused
MODEL_TENSOR.NEXTN_EH_PROJ,
MODEL_TENSOR.NEXTN_EMBED_TOKENS,
MODEL_TENSOR.NEXTN_ENORM,
MODEL_TENSOR.NEXTN_HNORM,
MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
],
MODEL_ARCH.BITNET: [
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,

9
gguf-py/gguf/gguf_writer.py
View File

@ -771,6 +771,15 @@ class GGUFWriter:
def add_value_length_mla(self, length: int) -> None:
self.add_uint32(Keys.Attention.VALUE_LENGTH_MLA.format(arch=self.arch), length)
def add_indexer_head_count(self, count: int) -> None:
self.add_uint32(Keys.Attention.Indexer.HEAD_COUNT.format(arch=self.arch), count)
def add_indexer_key_length(self, length: int) -> None:
self.add_uint32(Keys.Attention.Indexer.KEY_LENGTH.format(arch=self.arch), length)
def add_indexer_top_k(self, top_k: int) -> None:
self.add_uint32(Keys.Attention.Indexer.TOP_K.format(arch=self.arch), top_k)
def add_max_alibi_bias(self, bias: float) -> None:
self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)

16
gguf-py/gguf/tensor_mapping.py
View File

@ -1206,6 +1206,22 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.vision_expert_query_key_value", # cogvlm
),
MODEL_TENSOR.INDEXER_K_NORM: (
"model.layers.{bid}.self_attn.indexer.k_norm", # DSA
),
MODEL_TENSOR.INDEXER_PROJ: (
"model.layers.{bid}.self_attn.indexer.weights_proj", # DSA
),
MODEL_TENSOR.INDEXER_ATTN_K: (
"model.layers.{bid}.self_attn.indexer.wk", # DSA
),
MODEL_TENSOR.INDEXER_ATTN_Q_B: (
"model.layers.{bid}.self_attn.indexer.wq_b", # DSA
),
############################################################################
# TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg
MODEL_TENSOR.ENC_OUTPUT_NORM: (

52
src/llama-arch.cpp
View File

@ -74,6 +74,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_CHATGLM, "chatglm" },
{ LLM_ARCH_GLM4, "glm4" },
{ LLM_ARCH_GLM4_MOE, "glm4moe" },
{ LLM_ARCH_GLM_DSA, "glm-dsa" },
{ LLM_ARCH_BITNET, "bitnet" },
{ LLM_ARCH_T5, "t5" },
{ LLM_ARCH_T5ENCODER, "t5encoder" },
@ -225,6 +226,9 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_TEMPERATURE_SCALE, "%s.attention.temperature_scale" },
{ LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" },
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
{ LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, "%s.attention.indexer.head_count" },
{ LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, "%s.attention.indexer.key_length" },
{ LLM_KV_ATTENTION_INDEXER_TOP_K, "%s.attention.indexer.top_k" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" },
@ -516,6 +520,10 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_VISEXP_FFN_GATE, "blk.%d.vis_gate" },
{ LLM_TENSOR_VISEXP_FFN_DOWN, "blk.%d.vis_down" },
{ LLM_TENSOR_VISEXP_FFN_UP, "blk.%d.vis_up" },
{ LLM_TENSOR_INDEXER_K_NORM, "blk.%d.indexer.k_norm" },
{ LLM_TENSOR_INDEXER_PROJ, "blk.%d.indexer.proj" },
{ LLM_TENSOR_INDEXER_ATTN_K, "blk.%d.indexer.attn_k" },
{ LLM_TENSOR_INDEXER_ATTN_Q_B, "blk.%d.indexer.attn_q_b" },
};
static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
@ -1657,6 +1665,46 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
};
case LLM_ARCH_GLM_DSA:
return {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_Q_A_NORM,
LLM_TENSOR_ATTN_KV_A_NORM,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_Q_A,
LLM_TENSOR_ATTN_Q_B,
LLM_TENSOR_ATTN_KV_A_MQA,
LLM_TENSOR_ATTN_KV_B,
LLM_TENSOR_ATTN_K_B,
LLM_TENSOR_ATTN_V_B,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_GATE,
LLM_TENSOR_FFN_UP,
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_DOWN_EXPS,
LLM_TENSOR_FFN_UP_EXPS,
LLM_TENSOR_FFN_GATE_INP_SHEXP,
LLM_TENSOR_FFN_GATE_SHEXP,
LLM_TENSOR_FFN_DOWN_SHEXP,
LLM_TENSOR_FFN_UP_SHEXP,
LLM_TENSOR_FFN_EXP_PROBS_B,
LLM_TENSOR_INDEXER_K_NORM,
LLM_TENSOR_INDEXER_PROJ,
LLM_TENSOR_INDEXER_ATTN_K,
LLM_TENSOR_INDEXER_ATTN_Q_B,
LLM_TENSOR_NEXTN_EH_PROJ,
LLM_TENSOR_NEXTN_EMBED_TOKENS,
LLM_TENSOR_NEXTN_ENORM,
LLM_TENSOR_NEXTN_HNORM,
LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
};
case LLM_ARCH_BITNET:
return {
LLM_TENSOR_TOKEN_EMBD,
@ -2643,6 +2691,10 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_VISEXP_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_VISEXP_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_VISEXP_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_K_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_INDEXER_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_ATTN_Q_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
// NextN/MTP tensors are currently ignored (reserved for future MTP support)
// These tensors only exist in the last layer(s) and are treated as output tensors
{LLM_TENSOR_NEXTN_EH_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},

8
src/llama-arch.h
View File

@ -78,6 +78,7 @@ enum llm_arch {
LLM_ARCH_CHATGLM,
LLM_ARCH_GLM4,
LLM_ARCH_GLM4_MOE,
LLM_ARCH_GLM_DSA,
LLM_ARCH_BITNET,
LLM_ARCH_T5,
LLM_ARCH_T5ENCODER,
@ -229,6 +230,9 @@ enum llm_kv {
LLM_KV_ATTENTION_TEMPERATURE_SCALE,
LLM_KV_ATTENTION_KEY_LENGTH_MLA,
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,
LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
LLM_KV_ATTENTION_INDEXER_TOP_K,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_DIMENSION_SECTIONS,
@ -517,6 +521,10 @@ enum llm_tensor {
LLM_TENSOR_VISEXP_FFN_GATE,
LLM_TENSOR_VISEXP_FFN_DOWN,
LLM_TENSOR_VISEXP_FFN_UP,
LLM_TENSOR_INDEXER_K_NORM,
LLM_TENSOR_INDEXER_PROJ,
LLM_TENSOR_INDEXER_ATTN_K,
LLM_TENSOR_INDEXER_ATTN_Q_B,
LLM_TENSOR_NEXTN_EH_PROJ,
LLM_TENSOR_NEXTN_EMBED_TOKENS,
LLM_TENSOR_NEXTN_ENORM,

5
src/llama-hparams.h
View File

@ -193,6 +193,11 @@ struct llama_hparams {
std::array<float, LLAMA_MAX_LAYERS> xielu_beta;
std::array<float, LLAMA_MAX_LAYERS> xielu_eps;
// DSA (deepseek sparse attention)
uint32_t indexer_n_head = 0;
uint32_t indexer_head_size = 0;
uint32_t indexer_top_k = 0;
// qwen3vl deepstack
uint32_t n_deepstack_layers = 0;

156
src/llama-model.cpp
View File

@ -137,6 +137,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_300B_A47B: return "300B.A47B";
case LLM_TYPE_310B_A15B: return "310B.A15B";
case LLM_TYPE_355B_A32B: return "355B.A32B";
case LLM_TYPE_744B_A40B: return "744B.A40B";
case LLM_TYPE_E2B: return "E2B";
case LLM_TYPE_E4B: return "E4B";
default: return "?B";
@ -1822,6 +1823,50 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_GLM_DSA:
{
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
// MoE parameters
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
// deepseek MLA parameters
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl, false);
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
// DSA parameters
ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
// Expert gating function (GLM-4.5 uses sigmoid)
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
}
// NextN/MTP parameters
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
// TODO: when MTP is implemented, this should probably be updated if needed
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
switch (hparams.n_layer) {
case 79: type = LLM_TYPE_744B_A40B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_BITNET:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -5492,6 +5537,108 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
}
break;
case LLM_ARCH_GLM_DSA:
{
const bool is_mla = hparams.is_mla();
if (!is_mla) {
throw std::runtime_error("GLM_DSA architecture requires MLA");
}
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
const int64_t n_embd_head_qk_rope = hparams.n_rot;
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
const int64_t q_lora_rank = hparams.n_lora_q;
const int64_t kv_lora_rank = hparams.n_lora_kv;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_expert_shared = hparams.n_expert_shared;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
// try to load output.weight, if not found, use token_embd (tied embeddings)
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
if (!output) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
int flags = 0;
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
// skip all tensors in the NextN layers
// TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
}
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
// note: only old legacy GGUF files will have the unsplit wkv_b tensor in
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
// DSA indexer
layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags);
layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags);
layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags);
layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags);
layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
if (i < (int) hparams.n_layer_dense_lead) {
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);
} else {
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
if (n_expert == 0) {
throw std::runtime_error("n_expert must be > 0");
}
if (n_expert_used == 0) {
throw std::runtime_error("n_expert_used must be > 0");
}
// MoE branch
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
// Shared expert branch
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
}
// 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.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);
// 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);
}
}
} break;
case LLM_ARCH_NEMOTRON:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@ -7765,7 +7912,7 @@ void llama_model::print_info() const {
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
}
if (arch == LLM_ARCH_DEEPSEEK2) {
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA) {
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
@ -7965,7 +8112,6 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
cparams.n_seq_max,
nullptr);
} else if (llm_arch_is_hybrid(arch)) {
// The main difference between hybrid architectures is the
// layer filters, so pick the right one here
llama_memory_hybrid::layer_filter_cb filter_attn = nullptr;
@ -7990,7 +8136,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
/* attn_type_v */ params.type_v,
/* attn_v_trans */ !cparams.flash_attn,
/* attn_swa_full */ params.swa_full,
/* attn_kv_size */ cparams.n_ctx,
/* attn_kv_size */ cparams.n_ctx_seq,
/* attn_n_ubatch */ cparams.n_ubatch,
/* attn_n_pad */ 1,
/* recurrent_type_r */ GGML_TYPE_F32,
@ -8007,7 +8153,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
/* attn_type_k */ params.type_k,
/* attn_type_v */ params.type_v,
/* attn_v_trans */ !cparams.flash_attn,
/* attn_kv_size */ cparams.n_ctx,
/* attn_kv_size */ cparams.n_ctx_seq,
/* attn_n_pad */ 1,
/* attn_n_swa */ hparams.n_swa,
/* attn_swa_type */ hparams.swa_type,
@ -8338,6 +8484,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
llm = std::make_unique<llm_build_deepseek>(*this, params);
} break;
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_GLM_DSA:
{
llm = std::make_unique<llm_build_deepseek2>(*this, params);
} break;
@ -8739,6 +8886,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_MISTRAL3:
case LLM_ARCH_LLAMA_EMBED:
case LLM_ARCH_MAINCODER:
case LLM_ARCH_GLM_DSA:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2

8
src/llama-model.h
View File

@ -130,6 +130,7 @@ enum llm_type {
LLM_TYPE_300B_A47B, // Ernie MoE big
LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
LLM_TYPE_355B_A32B, // GLM-4.5
LLM_TYPE_744B_A40B, // GLM-5
LLM_TYPE_E2B,
LLM_TYPE_E4B,
};
@ -429,6 +430,13 @@ struct llama_layer {
struct ggml_tensor * ssm_g_b = nullptr;
struct ggml_tensor * ssm_o_norm = nullptr;
// DSA (deepseek sparse attention)
struct ggml_tensor * indexer_k_norm = nullptr;
struct ggml_tensor * indexer_k_norm_b = nullptr;
struct ggml_tensor * indexer_proj = nullptr;
struct ggml_tensor * indexer_attn_k = nullptr;
struct ggml_tensor * indexer_attn_q_b = nullptr; // note: for lora a/b, not bias
struct llama_layer_posnet posnet;
struct llama_layer_convnext convnext;

5
src/models/deepseek2.cpp
View File

@ -45,7 +45,8 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
int effective_n_layers = hparams.n_layer - hparams.nextn_predict_layers;
for (int il = 0; il < effective_n_layers; ++il) {
ggml_tensor * inpSA = inpL;
// norm
@ -188,7 +189,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
}
}
if (il == n_layer - 1 && inp_out_ids) {
if (il == effective_n_layers - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}

7
src/models/kimi-linear.cpp
View File

@ -41,8 +41,11 @@ static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_t
conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]);
ggml_build_forward_expand(gf,
ggml_cpy(ctx0, last_conv_x,
ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs,
(kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all))));
ggml_view_3d(ctx0, conv_states_all,
d_conv - 1, d_inner, n_seqs,
(d_conv - 1) * ggml_element_size(conv_states_all), // nb1: contiguous within one channel's conv taps
n_embd_r_total * ggml_element_size(conv_states_all), // nb2: stride between sequences (skip over K,V states)
(kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all)))); // offset to first seq's Q/K/V state
// Reshape conv weight: GGUF [d_conv, 1, d_inner, 1] -> ggml_ssm_conv expects [d_conv, d_inner]
// GGUF stores as [d_conv, 1, d_inner, 1] with memory layout w[conv_step + channel * d_conv]
// vLLM stores as [d_inner, d_conv] with memory layout w[channel * d_conv + conv_step]

31
src/unicode.cpp
View File

@ -1,16 +1,10 @@
#if defined(_MSC_VER)
#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
#endif
#include "unicode.h"
#include "unicode-data.h"
#include <algorithm>
#include <cassert>
#include <codecvt>
#include <cstddef>
#include <cstdint>
#include <locale>
#include <map>
#include <regex>
#include <stdexcept>
@ -199,27 +193,6 @@ static std::unordered_map<std::string, uint8_t> unicode_utf8_to_byte_map() {
return map;
}
static inline std::wstring unicode_wstring_from_utf8(const std::string & s) {
#if defined(__clang__)
// disable C++17 deprecation warning for std::codecvt_utf8
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wdeprecated-declarations"
#elif defined(__GNUC__)
# pragma GCC diagnostic push
# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
std::wstring_convert<std::codecvt_utf8<wchar_t>> conv;
#if defined(__clang__)
# pragma clang diagnostic pop
#elif defined(__GNUC__)
# pragma GCC diagnostic pop
#endif
return conv.from_bytes(s);
}
static std::vector<std::string> unicode_byte_encoding_process(const std::vector<std::string> & bpe_words) {
std::vector<std::string> bpe_encoded_words;
for (const auto & word : bpe_words) {
@ -1028,10 +1001,10 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
break;
}
}
const auto cpts_regex = unicode_cpts_from_utf8(regex_expr);
if (use_collapsed) {
// sanity-check that the original regex does not contain any non-ASCII characters
const auto cpts_regex = unicode_cpts_from_utf8(regex_expr);
for (size_t i = 0; i < cpts_regex.size(); ++i) {
if (cpts_regex[i] >= 128) {
throw std::runtime_error("Regex includes both unicode categories and non-ASCII characters - not supported");
@ -1087,7 +1060,7 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
bpe_offsets = unicode_regex_split_stl(text_collapsed, regex_expr_collapsed, bpe_offsets);
} else {
// no unicode category used, we can use std::wregex directly
const std::wstring wregex_expr = unicode_wstring_from_utf8(regex_expr);
std::wstring wregex_expr(cpts_regex.begin(), cpts_regex.end());
// std::wregex \s does not mach non-ASCII whitespaces, using 0x0B as fallback
std::wstring wtext(cpts.begin(), cpts.end());

77
tests/test-backend-ops.cpp
View File

@ -2786,9 +2786,10 @@ struct test_set : public test_case {
const ggml_type type_dst;
const std::array<int64_t, 4> ne;
const int dim;
const bool inplace;
std::string vars() override {
return VARS_TO_STR4(type_src, type_dst, ne, dim);
return VARS_TO_STR5(type_src, type_dst, ne, dim, inplace);
}
size_t op_size(ggml_tensor * t) override {
@ -2796,8 +2797,8 @@ struct test_set : public test_case {
}
test_set(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {6, 5, 4, 3}, int dim = 1)
: type_src(type_src), type_dst(type_dst), ne(ne), dim(dim) {}
std::array<int64_t, 4> ne = {6, 5, 4, 3}, int dim = 1, bool inplace = false)
: type_src(type_src), type_dst(type_dst), ne(ne), dim(dim), inplace(inplace) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
@ -2808,7 +2809,7 @@ struct test_set : public test_case {
for (int i = 0; i < dim; ++i) {
ne_dst[i] *= 2;
}
ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, ne_dst.data());
ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, ne_dst.data());
ggml_set_param(dst);
ggml_set_name(dst, "dst");
@ -2816,9 +2817,16 @@ struct test_set : public test_case {
for (int i = 0; i < dim; ++i) {
offset += ((ne_dst[i] - ne[i])/2)*dst->nb[i];
}
ggml_tensor * out = ggml_set(ctx, dst, src,
// The backward pass requires setting a contiguous region:
src->nb[1], src->nb[2], src->nb[3], offset);
ggml_tensor * out;
if (inplace) {
out = ggml_set_inplace(ctx, dst, src,
// The backward pass requires setting a contiguous region:
src->nb[1], src->nb[2], src->nb[3], offset);
} else {
out = ggml_set(ctx, dst, src,
// The backward pass requires setting a contiguous region:
src->nb[1], src->nb[2], src->nb[3], offset);
}
ggml_set_name(out, "out");
return out;
@ -5839,26 +5847,46 @@ struct test_acc : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne_a;
const std::array<int64_t, 4> ne_b;
const int64_t stride_dim;
std::string vars() override {
return VARS_TO_STR3(type, ne_a, ne_b);
return VARS_TO_STR4(type, ne_a, ne_b, stride_dim);
}
test_acc(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_a = {256, 17, 1, 1},
std::array<int64_t, 4> ne_b = {256, 16, 1, 1})
: type(type), ne_a(ne_a), ne_b(ne_b) {}
std::array<int64_t, 4> ne_a = {256, 17, 2, 3},
std::array<int64_t, 4> ne_b = {256, 16, 2, 3},
uint64_t stride_dim = -1)
: type(type), ne_a(ne_a), ne_b(ne_b), stride_dim(stride_dim) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
ggml_set_param(b);
ggml_tensor * b;
if (stride_dim == 1 || stride_dim == 2 || stride_dim == 3) {
// Create a larger tensor and take a view at a non-zero offset.
// This tests that the backend correctly handles b's data offset
std::array<int64_t, 4> ne_b_pad = {ne_b[0], ne_b[1], ne_b[2], ne_b[3]};
ne_b_pad[stride_dim] += 1;
ggml_tensor * b_pad = ggml_new_tensor(ctx, type, 4, ne_b_pad.data());
ggml_set_param(b_pad);
ggml_set_name(b_pad, "b_pad");
// View that skips the first row, so b has a non-zero byte offset
b = ggml_view_4d(ctx, b_pad,
ne_b[0], ne_b[1], ne_b[2], ne_b[3],
b_pad->nb[1], b_pad->nb[2], b_pad->nb[3],
b_pad->nb[1]);
} else {
b = ggml_new_tensor(ctx, type, 4, ne_b.data());
ggml_set_param(b);
}
ggml_set_name(b, "b");
ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], b->nb[1]);
// When ne_b[0] < ne_a[0], a->nb[1] != b->nb[1], so the stride
// parameters to ggml_acc don't match b's natural stride.
ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], 0);
ggml_set_name(out, "out");
return out;
@ -7428,11 +7456,13 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
for (int dim = 1; dim < GGML_MAX_DIMS; ++dim) {
test_cases.emplace_back(new test_set(GGML_TYPE_F32, GGML_TYPE_F32, {6, 5, 4, 3}, dim));
test_cases.emplace_back(new test_set(GGML_TYPE_F32, GGML_TYPE_F32, {6, 5, 4, 3}, dim, false));
test_cases.emplace_back(new test_set(GGML_TYPE_F32, GGML_TYPE_F32, {6, 5, 4, 3}, dim, true));
}
for (int dim = 1; dim < GGML_MAX_DIMS; ++dim) {
test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim));
test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim, false));
test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim, true));
}
// same-type copy
@ -8160,7 +8190,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {9, 9, 1280, 1}));
test_cases.emplace_back(new test_group_norm_mul_add(GGML_TYPE_F32, {64, 64, 320, 1}));
test_cases.emplace_back(new test_group_norm_mul_add(GGML_TYPE_F32, {9, 9, 1280, 1}));
test_cases.emplace_back(new test_acc());
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 1, 1}, {256, 16, 1, 1}, -1));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {256, 16, 2, 3}, -1));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {128, 16, 2, 3}, -1));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {256, 16, 2, 3}, 1));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {128, 16, 2, 3}, 2));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {64, 16, 2, 3}, 3));
test_cases.emplace_back(new test_pad());
test_cases.emplace_back(new test_pad(GGML_TYPE_F32, {33, 17, 2, 1}, 4, 3, true)); // circular
test_cases.emplace_back(new test_pad_ext());
@ -8595,6 +8630,14 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 512, 1)); // prefill
test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 1, 1)); // generate
// acc
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 1, 1}, {256, 16, 1, 1}, -1));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {256, 16, 2, 3}, -1));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {128, 16, 2, 3}, -1));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {256, 16, 2, 3}, 1));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {128, 16, 2, 3}, 2));
test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {64, 16, 2, 3}, 3));
return test_cases;
}

9
tools/cli/cli.cpp
View File

@ -52,6 +52,7 @@ struct cli_context {
json messages = json::array();
std::vector<raw_buffer> input_files;
task_params defaults;
bool verbose_prompt;
// thread for showing "loading" animation
std::atomic<bool> loading_show;
@ -66,6 +67,8 @@ struct cli_context {
defaults.stream = true; // make sure we always use streaming mode
defaults.timings_per_token = true; // in order to get timings even when we cancel mid-way
// defaults.return_progress = true; // TODO: show progress
verbose_prompt = params.verbose_prompt;
}
std::string generate_completion(result_timings & out_timings) {
@ -91,6 +94,12 @@ struct cli_context {
rd.post_task({std::move(task)});
}
if (verbose_prompt) {
console::set_display(DISPLAY_TYPE_PROMPT);
console::log("%s\n\n", chat_params.prompt.c_str());
console::set_display(DISPLAY_TYPE_RESET);
}
// wait for first result
console::spinner::start();
server_task_result_ptr result = rd.next(should_stop);

5
tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
View File

@ -1,5 +1,6 @@
<script lang="ts">
import { goto } from '$app/navigation';
import { base } from '$app/paths';
import {
chatStore,
pendingEditMessageId,
@ -119,7 +120,7 @@
const conversationDeleted = await removeSystemPromptPlaceholder(message.id);
if (conversationDeleted) {
goto('/');
goto(`${base}/`);
}
return;
@ -220,7 +221,7 @@
const conversationDeleted = await removeSystemPromptPlaceholder(message.id);
isEditing = false;
if (conversationDeleted) {
goto('/');
goto(`${base}/`);
}
return;
}

15
tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageStatistics.svelte
View File

@ -3,6 +3,7 @@
import { BadgeChatStatistic } from '$lib/components/app';
import * as Tooltip from '$lib/components/ui/tooltip';
import { ChatMessageStatsView } from '$lib/enums';
import { formatPerformanceTime } from '$lib/utils/formatters';
interface Props {
predictedTokens?: number;
@ -57,8 +58,8 @@
);
let tokensPerSecond = $derived(hasGenerationStats ? (predictedTokens! / predictedMs!) * 1000 : 0);
let timeInSeconds = $derived(
predictedMs !== undefined ? (predictedMs / 1000).toFixed(2) : '0.00'
let formattedTime = $derived(
predictedMs !== undefined ? formatPerformanceTime(predictedMs) : '0s'
);
let promptTokensPerSecond = $derived(
@ -67,15 +68,15 @@
: undefined
);
let promptTimeInSeconds = $derived(
promptMs !== undefined ? (promptMs / 1000).toFixed(2) : undefined
let formattedPromptTime = $derived(
promptMs !== undefined ? formatPerformanceTime(promptMs) : undefined
);
let hasPromptStats = $derived(
promptTokens !== undefined &&
promptMs !== undefined &&
promptTokensPerSecond !== undefined &&
promptTimeInSeconds !== undefined
formattedPromptTime !== undefined
);
// In live mode, generation tab is disabled until we have generation stats
@ -142,7 +143,7 @@
<BadgeChatStatistic
class="bg-transparent"
icon={Clock}
value="{timeInSeconds}s"
value={formattedTime}
tooltipLabel="Generation time"
/>
<BadgeChatStatistic
@ -161,7 +162,7 @@
<BadgeChatStatistic
class="bg-transparent"
icon={Clock}
value="{promptTimeInSeconds}s"
value={formattedPromptTime ?? '0s'}
tooltipLabel="Prompt processing time"
/>
<BadgeChatStatistic

88
tools/server/webui/src/lib/components/app/misc/DropdownMenuSearchable.svelte Normal file
View File

@ -0,0 +1,88 @@
<script lang="ts">
import type { Snippet } from 'svelte';
import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
import { cn } from '$lib/components/ui/utils';
import { SearchInput } from '$lib/components/app';
interface Props {
open?: boolean;
onOpenChange?: (open: boolean) => void;
placeholder?: string;
searchValue?: string;
onSearchChange?: (value: string) => void;
onSearchKeyDown?: (event: KeyboardEvent) => void;
align?: 'start' | 'center' | 'end';
contentClass?: string;
emptyMessage?: string;
isEmpty?: boolean;
disabled?: boolean;
trigger: Snippet;
children: Snippet;
footer?: Snippet;
}
let {
open = $bindable(false),
onOpenChange,
placeholder = 'Search...',
searchValue = $bindable(''),
onSearchChange,
onSearchKeyDown,
align = 'start',
contentClass = 'w-72',
emptyMessage = 'No items found',
isEmpty = false,
disabled = false,
trigger,
children,
footer
}: Props = $props();
function handleOpenChange(newOpen: boolean) {
open = newOpen;
if (!newOpen) {
searchValue = '';
onSearchChange?.('');
}
onOpenChange?.(newOpen);
}
</script>
<DropdownMenu.Root bind:open onOpenChange={handleOpenChange}>
<DropdownMenu.Trigger
{disabled}
onclick={(e) => {
e.preventDefault();
e.stopPropagation();
}}
>
{@render trigger()}
</DropdownMenu.Trigger>
<DropdownMenu.Content {align} class={cn(contentClass, 'pt-0')}>
<div class="sticky top-0 z-10 mb-2 bg-popover p-1 pt-2">
<SearchInput
{placeholder}
bind:value={searchValue}
onInput={onSearchChange}
onKeyDown={onSearchKeyDown}
/>
</div>
<div class={cn('overflow-y-auto')}>
{@render children()}
{#if isEmpty}
<div class="px-2 py-3 text-center text-sm text-muted-foreground">{emptyMessage}</div>
{/if}
</div>
{#if footer}
<DropdownMenu.Separator />
{@render footer()}
{/if}
</DropdownMenu.Content>
</DropdownMenu.Root>

2
tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
View File

@ -486,6 +486,8 @@
text-decoration: underline;
text-underline-offset: 2px;
transition: color 0.2s ease;
overflow-wrap: anywhere;
word-break: break-all;
}
div :global(a:hover) {

72
tools/server/webui/src/lib/utils/formatters.ts
View File

@ -51,3 +51,75 @@ export function formatNumber(num: number | unknown): string {
return num.toLocaleString();
}
/**
* Format JSON string with pretty printing (2-space indentation)
* Returns original string if parsing fails
*
* @param jsonString - JSON string to format
* @returns Pretty-printed JSON string or original if invalid
*/
export function formatJsonPretty(jsonString: string): string {
try {
const parsed = JSON.parse(jsonString);
return JSON.stringify(parsed, null, 2);
} catch {
return jsonString;
}
}
/**
* Format time as HH:MM:SS in 24-hour format
*
* @param date - Date object to format
* @returns Formatted time string (HH:MM:SS)
*/
export function formatTime(date: Date): string {
return date.toLocaleTimeString('en-US', {
hour12: false,
hour: '2-digit',
minute: '2-digit',
second: '2-digit'
});
}
/**
* Formats milliseconds to a human-readable time string for performance metrics.
* Examples: "4h 12min 54s", "12min 34s", "45s", "0.5s"
*
* @param ms - Time in milliseconds
* @returns Formatted time string
*/
export function formatPerformanceTime(ms: number): string {
if (ms < 0) return '0s';
const totalSeconds = ms / 1000;
if (totalSeconds < 1) {
return `${totalSeconds.toFixed(1)}s`;
}
if (totalSeconds < 10) {
return `${totalSeconds.toFixed(1)}s`;
}
const hours = Math.floor(totalSeconds / 3600);
const minutes = Math.floor((totalSeconds % 3600) / 60);
const seconds = Math.floor(totalSeconds % 60);
const parts: string[] = [];
if (hours > 0) {
parts.push(`${hours}h`);
}
if (minutes > 0) {
parts.push(`${minutes}min`);
}
if (seconds > 0 || parts.length === 0) {
parts.push(`${seconds}s`);
}
return parts.join(' ');
}

67
tools/server/webui/tests/stories/ChatForm.stories.svelte
View File

@ -2,7 +2,6 @@
import { defineMeta } from '@storybook/addon-svelte-csf';
import ChatForm from '$lib/components/app/chat/ChatForm/ChatForm.svelte';
import { expect } from 'storybook/test';
import { mockServerProps, mockConfigs } from './fixtures/storybook-mocks';
import jpgAsset from './fixtures/assets/1.jpg?url';
import svgAsset from './fixtures/assets/hf-logo.svg?url';
import pdfAsset from './fixtures/assets/example.pdf?raw';
@ -46,8 +45,6 @@
name="Default"
args={{ class: 'max-w-[56rem] w-[calc(100vw-2rem)]' }}
play={async ({ canvas, userEvent }) => {
mockServerProps(mockConfigs.noModalities);
const textarea = await canvas.findByRole('textbox');
const submitButton = await canvas.findByRole('button', { name: 'Send' });
@ -66,73 +63,11 @@
const fileInput = document.querySelector('input[type="file"]');
await expect(fileInput).not.toHaveAttribute('accept');
// Open file attachments dropdown
const fileUploadButton = canvas.getByText('Attach files');
await userEvent.click(fileUploadButton);
// Check dropdown menu items are disabled (no modalities)
const imagesButton = document.querySelector('.images-button');
const audioButton = document.querySelector('.audio-button');
await expect(imagesButton).toHaveAttribute('data-disabled');
await expect(audioButton).toHaveAttribute('data-disabled');
// Close dropdown by pressing Escape
await userEvent.keyboard('{Escape}');
}}
/>
<Story name="Loading" args={{ class: 'max-w-[56rem] w-[calc(100vw-2rem)]', isLoading: true }} />
<Story
name="VisionModality"
args={{ class: 'max-w-[56rem] w-[calc(100vw-2rem)]' }}
play={async ({ canvas, userEvent }) => {
mockServerProps(mockConfigs.visionOnly);
// Open file attachments dropdown and verify it works
const fileUploadButton = canvas.getByText('Attach files');
await userEvent.click(fileUploadButton);
// Verify dropdown menu items exist
const imagesButton = document.querySelector('.images-button');
const audioButton = document.querySelector('.audio-button');
await expect(imagesButton).toBeInTheDocument();
await expect(audioButton).toBeInTheDocument();
// Close dropdown by pressing Escape
await userEvent.keyboard('{Escape}');
console.log('✅ Vision modality: Dropdown menu verified');
}}
/>
<Story
name="AudioModality"
args={{ class: 'max-w-[56rem] w-[calc(100vw-2rem)]' }}
play={async ({ canvas, userEvent }) => {
mockServerProps(mockConfigs.audioOnly);
// Open file attachments dropdown and verify it works
const fileUploadButton = canvas.getByText('Attach files');
await userEvent.click(fileUploadButton);
// Verify dropdown menu items exist
const imagesButton = document.querySelector('.images-button');
const audioButton = document.querySelector('.audio-button');
await expect(imagesButton).toBeInTheDocument();
await expect(audioButton).toBeInTheDocument();
// Close dropdown by pressing Escape
await userEvent.keyboard('{Escape}');
console.log('✅ Audio modality: Dropdown menu verified');
}}
/>
<Story
name="FileAttachments"
args={{
@ -140,8 +75,6 @@
uploadedFiles: fileAttachments
}}
play={async ({ canvas }) => {
mockServerProps(mockConfigs.bothModalities);
const jpgAttachment = canvas.getByAltText('1.jpg');
const svgAttachment = canvas.getByAltText('hf-logo.svg');
const pdfFileExtension = canvas.getByText('PDF');

2
vendor/cpp-httplib/CMakeLists.txt vendored
View File

@ -39,7 +39,7 @@ if (LLAMA_BUILD_BORINGSSL)
set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
set(BORINGSSL_VERSION "0.20260204.0" CACHE STRING "BoringSSL version")
set(BORINGSSL_VERSION "0.20260211.0" CACHE STRING "BoringSSL version")
message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")