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llama.cpp
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Merge branch 'ggml-org:master' into power-law-sampler

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ddh0 2025-12-28 20:12:16 -06:00 committed by GitHub
parent b95b0884dd 382808c14b
commit f0d3f13124
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34 changed files with 604 additions and 185 deletions
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3
.github/ISSUE_TEMPLATE/010-bug-compilation.yml vendored
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@ -8,7 +8,8 @@ body:
value: >
Thanks for taking the time to fill out this bug report!
This issue template is intended for bug reports where the compilation of llama.cpp fails.
Before opening an issue, please confirm that the compilation still fails with `-DGGML_CCACHE=OFF`.
Before opening an issue, please confirm that the compilation still fails
after recreating the CMake build directory and with `-DGGML_CCACHE=OFF`.
If the compilation succeeds with ccache disabled you should be able to permanently fix the issue
by clearing `~/.cache/ccache` (on Linux).
- type: textarea

15
.github/ISSUE_TEMPLATE/011-bug-results.yml vendored
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@ -98,7 +98,18 @@ body:
label: Relevant log output
description: >
Please copy and paste any relevant log output, including the command that you entered and any generated text.
This will be automatically formatted into code, so no need for backticks.
render: shell
For very long logs (thousands of lines), preferably upload them as files instead.
On Linux you can redirect console output into a file by appending ` > llama.log 2>&1` to your command.
value: |
<details>
<summary>Logs</summary>
<!-- Copy-pasted short logs go into the "console" area here -->
```console
```
</details>
<!-- Long logs that you upload as files go here, outside the "console" area -->
validations:
required: true

15
.github/ISSUE_TEMPLATE/019-bug-misc.yml vendored
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@ -85,8 +85,19 @@ body:
label: Relevant log output
description: >
If applicable, please copy and paste any relevant log output, including any generated text.
This will be automatically formatted into code, so no need for backticks.
If you are encountering problems specifically with the `llama_params_fit` module, always upload `--verbose` logs as well.
render: shell
For very long logs (thousands of lines), please upload them as files instead.
On Linux you can redirect console output into a file by appending ` > llama.log 2>&1` to your command.
value: |
<details>
<summary>Logs</summary>
<!-- Copy-pasted short logs go into the "console" area here -->
```console
```
</details>
<!-- Long logs that you upload as files go here, outside the "console" area -->
validations:
required: false

3
.github/workflows/docker.yml vendored
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@ -45,8 +45,7 @@ jobs:
- { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true, runs_on: "ubuntu-22.04" }
- { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
- { tag: "s390x", dockerfile: ".devops/s390x.Dockerfile", platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04-s390x" }
# Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
#- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: true }
- { tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true, runs_on: "ubuntu-22.04" }
steps:
- name: Check out the repo
uses: actions/checkout@v4

29
common/arg.cpp
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@ -2037,7 +2037,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
if (llama_supports_rpc()) {
add_opt(common_arg(
{"--rpc"}, "SERVERS",
"comma separated list of RPC servers",
"comma separated list of RPC servers (host:port)",
[](common_params & params, const std::string & value) {
add_rpc_devices(value);
GGML_UNUSED(params);
@ -2157,11 +2157,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
GGML_ASSERT(params.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
add_opt(common_arg(
{"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
string_format("max. number of layers to store in VRAM (default: %d)", params.n_gpu_layers),
[](common_params & params, int value) {
params.n_gpu_layers = value;
string_format("max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)", params.n_gpu_layers == -1 ? "auto" : "all"),
[](common_params & params, const std::string & value) {
if (value == "auto") {
params.n_gpu_layers = -1;
} else if (value == "all") {
params.n_gpu_layers = -2;
} else {
params.n_gpu_layers = std::stoi(value);
}
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n");
fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n");
@ -3195,11 +3202,19 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.speculative.devices = parse_device_list(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
GGML_ASSERT(params.speculative.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0
add_opt(common_arg(
{"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
"number of layers to store in VRAM for the draft model",
[](common_params & params, int value) {
params.speculative.n_gpu_layers = value;
string_format("max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)",
params.speculative.n_gpu_layers == -1 ? "auto" : "all"),
[](common_params & params, const std::string & value) {
if (value == "auto") {
params.speculative.n_gpu_layers = -1;
} else if (value == "all") {
params.speculative.n_gpu_layers = -2;
} else {
params.speculative.n_gpu_layers = std::stoi(value);
}
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n");

5
common/common.cpp
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@ -1341,10 +1341,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
mparams.devices = params.devices.data();
}
if (params.n_gpu_layers != -1) {
mparams.n_gpu_layers = params.n_gpu_layers;
}
mparams.n_gpu_layers = params.n_gpu_layers;
mparams.main_gpu = params.main_gpu;
mparams.split_mode = params.split_mode;
mparams.tensor_split = params.tensor_split;

2
common/common.h
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@ -332,7 +332,7 @@ struct common_params {
// offload params
std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers = -1; // number of layers to store in VRAM, -1 is auto, <= -2 is all
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
bool fit_params = true; // whether to fit unset model/context parameters to free device memory

210
convert_hf_to_gguf.py
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@ -1696,6 +1696,84 @@ class TextModel(ModelBase):
if template is not None:
self.gguf_writer.add_chat_template(template)
def _set_vocab_plamo(self):
# PLaMo models use a custom tokenizer with a .jsonl file
tokenizer_jsonl_path = self.dir_model / "tokenizer.jsonl"
tokenizer_config_path = self.dir_model / "tokenizer_config.json"
if not tokenizer_jsonl_path.is_file():
raise FileNotFoundError(f"PLaMo tokenizer file not found: {tokenizer_jsonl_path}")
# Load tokenizer config
with open(tokenizer_config_path, "r", encoding="utf-8") as f:
tokenizer_config = json.load(f)
# Load tokens from JSONL file (actually a list format)
tokens = []
scores = []
toktypes = []
with open(tokenizer_jsonl_path, "r", encoding="utf-8") as f:
for line_num, line in enumerate(f):
if line.strip():
token_data = json.loads(line)
# Format: [token, score, type, ?, ?, ?, ?]
token = token_data[0].encode("utf-8")
score = float(token_data[1])
token_type_str = token_data[2] if len(token_data) > 2 else "NORMAL"
tokens.append(token)
scores.append(score)
if token_type_str == "UNKNOWN":
toktypes.append(gguf.TokenType.UNKNOWN)
elif token_type_str == "CONTROL":
toktypes.append(gguf.TokenType.CONTROL)
elif token_type_str == "BYTE":
toktypes.append(gguf.TokenType.BYTE)
else:
token_str = token_data[0]
if token_str.startswith("<|plamo:") and token_str.endswith("|>"):
toktypes.append(gguf.TokenType.CONTROL)
else:
toktypes.append(gguf.TokenType.NORMAL)
vocab_size = self.hparams["vocab_size"]
if vocab_size > len(tokens):
pad_count = vocab_size - len(tokens)
logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
for i in range(1, pad_count + 1):
tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
scores.append(-1000.0)
toktypes.append(gguf.TokenType.UNUSED)
self.gguf_writer.add_tokenizer_model("plamo2")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] is not None:
token_id = tokens.index(tokenizer_config["bos_token"].encode("utf-8"))
self.gguf_writer.add_bos_token_id(token_id)
if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] is not None:
token_id = tokens.index(tokenizer_config["eos_token"].encode("utf-8"))
self.gguf_writer.add_eos_token_id(token_id)
if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] is not None:
token_id = tokens.index(tokenizer_config["pad_token"].encode("utf-8"))
self.gguf_writer.add_pad_token_id(token_id)
if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] is not None:
token_id = tokens.index(tokenizer_config["sep_token"].encode("utf-8"))
self.gguf_writer.add_sep_token_id(token_id)
if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] is not None:
token_id = tokens.index(tokenizer_config["unk_token"].encode("utf-8"))
self.gguf_writer.add_unk_token_id(token_id)
# Add <|plamo:op|> as EOT to ensure appropriate end of generation
self.gguf_writer.add_eot_token_id(4)
self.gguf_writer.add_add_space_prefix(False)
class MmprojModel(ModelBase):
model_type = ModelType.MMPROJ
@ -4798,87 +4876,7 @@ class Plamo2Model(TextModel):
model_arch = gguf.MODEL_ARCH.PLAMO2
def set_vocab(self):
# PLaMo 2 uses a custom tokenizer with a .jsonl file
# We need to handle this specially
tokenizer_jsonl_path = self.dir_model / "tokenizer.jsonl"
tokenizer_config_path = self.dir_model / "tokenizer_config.json"
if not tokenizer_jsonl_path.is_file():
raise FileNotFoundError(f"PLaMo 2 tokenizer file not found: {tokenizer_jsonl_path}")
# Load tokenizer config
with open(tokenizer_config_path, 'r', encoding='utf-8') as f:
tokenizer_config = json.load(f)
# Load tokens from JSONL file (actually a list format)
tokens = []
scores = []
toktypes = []
with open(tokenizer_jsonl_path, 'r', encoding='utf-8') as f:
for line_num, line in enumerate(f):
if line.strip():
token_data = json.loads(line)
# Format: [token, score, type, ?, ?, ?, ?]
token = token_data[0].encode("utf-8")
score = float(token_data[1])
token_type_str = token_data[2] if len(token_data) > 2 else "NORMAL"
tokens.append(token)
scores.append(score)
# Map token type strings to GGUF token types
if token_type_str == "UNKNOWN":
toktypes.append(gguf.TokenType.UNKNOWN)
elif token_type_str == "CONTROL":
toktypes.append(gguf.TokenType.CONTROL)
elif token_type_str == "BYTE":
toktypes.append(gguf.TokenType.BYTE)
else:
# Check for PLaMo-2 special tokens
token_str = token_data[0]
if token_str.startswith("<|plamo:") and token_str.endswith("|>"):
toktypes.append(gguf.TokenType.CONTROL)
else:
toktypes.append(gguf.TokenType.NORMAL)
vocab_size = self.hparams["vocab_size"]
if vocab_size > len(tokens):
pad_count = vocab_size - len(tokens)
logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
for i in range(1, pad_count + 1):
tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
scores.append(-1000.0)
toktypes.append(gguf.TokenType.UNUSED)
# Use "plamo2" tokenizer type for PLaMo-2's custom Aho-Corasick tokenizer
self.gguf_writer.add_tokenizer_model("plamo2")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
# Add special tokens from config
if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] is not None:
token_id = tokens.index(tokenizer_config["bos_token"].encode("utf-8"))
self.gguf_writer.add_bos_token_id(token_id)
if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] is not None:
token_id = tokens.index(tokenizer_config["eos_token"].encode("utf-8"))
self.gguf_writer.add_eos_token_id(token_id)
if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] is not None:
token_id = tokens.index(tokenizer_config["pad_token"].encode("utf-8"))
self.gguf_writer.add_pad_token_id(token_id)
if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] is not None:
token_id = tokens.index(tokenizer_config["sep_token"].encode("utf-8"))
self.gguf_writer.add_sep_token_id(token_id)
if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] is not None:
token_id = tokens.index(tokenizer_config["unk_token"].encode("utf-8"))
self.gguf_writer.add_unk_token_id(token_id)
# Add <|plamo:op|> as EOT to ensure appropriate end of generation
self.gguf_writer.add_eot_token_id(4)
self.gguf_writer.add_add_space_prefix(False)
self._set_vocab_plamo()
def set_gguf_parameters(self):
hparams = self.hparams
@ -4966,6 +4964,56 @@ class Plamo2Model(TextModel):
return [(new_name, data_torch)]
@ModelBase.register("Plamo3ForCausalLM", "PLaMo3ForCausalLM")
class Plamo3Model(TextModel):
model_arch = gguf.MODEL_ARCH.PLAMO3
def set_vocab(self):
self._set_vocab_plamo()
tokenizer_config_path = self.dir_model / "tokenizer_config.json"
tokenizer_config = {}
if tokenizer_config_path.is_file():
with open(tokenizer_config_path, encoding="utf-8") as f:
tokenizer_config = json.load(f)
chat_template = tokenizer_config.get("chat_template")
chat_template_jinja = self.dir_model / "chat_template.jinja"
if chat_template_jinja.is_file():
with open(chat_template_jinja, encoding="utf-8") as f:
chat_template = f.read()
if chat_template:
self.gguf_writer.add_chat_template(chat_template)
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
if (sliding_window := self.find_hparam(["window_size", "sliding_window"], optional=True)) is not None:
self.gguf_writer.add_sliding_window(sliding_window)
self.gguf_writer.add_sliding_window_pattern(self.hparams["sliding_window_pattern"])
self.gguf_writer.add_rope_freq_base_swa(self.rope_parameters.get("sliding_attention", {"rope_theta": self.hparams.get("rope_local_theta")})["rope_theta"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.endswith(".pre_mixer_norm.weight"):
data_torch = data_torch + 1.0
elif name.endswith(".post_mixer_norm.weight"):
data_torch = data_torch + 1.0 / 5
elif name.endswith(".pre_mlp_norm.weight"):
data_torch = data_torch + 1.0
elif name.endswith(".post_mlp_norm.weight"):
data_torch = data_torch + 1.0 / (5**1.5)
elif name.endswith((".mixer.q_norm.weight", ".mixer.k_norm.weight")):
data_torch = data_torch + 1.0
elif name.endswith(".norm.weight"):
data_torch = data_torch + 1.0
return [(self.map_tensor_name(name), data_torch)]
@ModelBase.register("CodeShellForCausalLM")
class CodeShellModel(TextModel):
model_arch = gguf.MODEL_ARCH.CODESHELL

12
ggml/CMakeLists.txt
View File

@ -430,10 +430,22 @@ if (MSVC)
configure_msvc_target(ggml-cpu-x64)
configure_msvc_target(ggml-cpu-sse42)
configure_msvc_target(ggml-cpu-sandybridge)
# __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
# skipping ggml-cpu-ivybridge
# skipping ggml-cpu-piledriver
configure_msvc_target(ggml-cpu-haswell)
configure_msvc_target(ggml-cpu-skylakex)
configure_msvc_target(ggml-cpu-cannonlake)
configure_msvc_target(ggml-cpu-cascadelake)
configure_msvc_target(ggml-cpu-icelake)
# MSVC 2022 doesn't support BF16 intrinsics without `/arch:AVX10.1` ?!
# https://learn.microsoft.com/en-us/cpp/intrinsics/x64-amd64-intrinsics-list?view=msvc-170
# https://learn.microsoft.com/en-us/cpp/build/reference/arch-x64?view=msvc-170
# skipping ggml-cpu-cooperlake
# skipping ggml-cpu-zen4
configure_msvc_target(ggml-cpu-alderlake)
# MSVC doesn't support AMX
# skipping ggml-cpu-sapphirerapids
if (GGML_BUILD_EXAMPLES)
configure_msvc_target(common-ggml)

28
ggml/src/CMakeLists.txt
View File

@ -357,15 +357,29 @@ if (GGML_CPU_ALL_VARIANTS)
endif()
if (GGML_SYSTEM_ARCH STREQUAL "x86")
ggml_add_cpu_backend_variant(x64)
ggml_add_cpu_backend_variant(sse42 SSE42)
ggml_add_cpu_backend_variant(sandybridge SSE42 AVX)
ggml_add_cpu_backend_variant(haswell SSE42 AVX F16C AVX2 BMI2 FMA)
ggml_add_cpu_backend_variant(skylakex SSE42 AVX F16C AVX2 BMI2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake SSE42 AVX F16C AVX2 BMI2 FMA AVX_VNNI)
ggml_add_cpu_backend_variant(sse42 SSE42)
ggml_add_cpu_backend_variant(sandybridge SSE42 AVX)
if (NOT MSVC)
# __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
ggml_add_cpu_backend_variant(ivybridge SSE42 AVX F16C)
ggml_add_cpu_backend_variant(piledriver SSE42 AVX F16C FMA)
endif()
ggml_add_cpu_backend_variant(haswell SSE42 AVX F16C FMA AVX2 BMI2)
ggml_add_cpu_backend_variant(skylakex SSE42 AVX F16C FMA AVX2 BMI2 AVX512)
ggml_add_cpu_backend_variant(cannonlake SSE42 AVX F16C FMA AVX2 BMI2 AVX512 AVX512_VBMI)
ggml_add_cpu_backend_variant(cascadelake SSE42 AVX F16C FMA AVX2 BMI2 AVX512 AVX512_VNNI)
ggml_add_cpu_backend_variant(icelake SSE42 AVX F16C FMA AVX2 BMI2 AVX512 AVX512_VBMI AVX512_VNNI)
if (NOT MSVC)
# MSVC 2022 doesn't support BF16 intrinsics without `/arch:AVX10.1` ?!
# https://learn.microsoft.com/en-us/cpp/intrinsics/x64-amd64-intrinsics-list?view=msvc-170
# https://learn.microsoft.com/en-us/cpp/build/reference/arch-x64?view=msvc-170
ggml_add_cpu_backend_variant(cooperlake SSE42 AVX F16C FMA AVX2 BMI2 AVX512 AVX512_VNNI AVX512_BF16)
ggml_add_cpu_backend_variant(zen4 SSE42 AVX F16C FMA AVX2 BMI2 AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16)
endif()
ggml_add_cpu_backend_variant(alderlake SSE42 AVX F16C FMA AVX2 BMI2 AVX_VNNI)
if (NOT MSVC)
# MSVC doesn't support AMX
ggml_add_cpu_backend_variant(sapphirerapids SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
ggml_add_cpu_backend_variant(sapphirerapids SSE42 AVX F16C FMA AVX2 BMI2 AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
endif()
elseif(GGML_SYSTEM_ARCH STREQUAL "ARM")
if (CMAKE_SYSTEM_NAME MATCHES "Linux")

2
ggml/src/ggml-cpu/ggml-cpu-impl.h
View File

@ -328,7 +328,7 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b)
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <intrin.h>
#elif defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__)
#elif defined(__SSE__) || defined(__SSE3__) || defined(__SSSE3__) || defined(__AVX__) || defined(__F16C__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX512BF16__)
#include <immintrin.h>
#endif

4
ggml/src/ggml-cpu/simd-mappings.h
View File

@ -14,10 +14,6 @@
#include <arm_neon.h>
#endif
#if defined(__F16C__)
#include <immintrin.h>
#endif
#if defined(__riscv_v_intrinsic)
#include <riscv_vector.h>
#endif

2
ggml/src/ggml-cuda/CMakeLists.txt
View File

@ -61,7 +61,7 @@ if (CUDAToolkit_FOUND)
set(CMAKE_CUDA_ARCHITECTURES ${PROCESSED_ARCHITECTURES})
else()
foreach(ARCH ${CMAKE_CUDA_ARCHITECTURES})
if(ARCH MATCHES "^12[0-9]$")
if(ARCH MATCHES "^12[0-9](-real|-virtual)?$")
message(FATAL_ERROR "Compute capability ${ARCH} used, use ${ARCH}a or ${ARCH}f for Blackwell specific optimizations")
endif()
endforeach()

6
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@ -2211,7 +2211,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
const int cc = ggml_cuda_info().devices[id].cc;
const int warp_size = ggml_cuda_info().devices[id].warp_size;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1], /*n_experts=*/0);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[1], /*mul_mat_id=*/false);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc);
@ -2219,7 +2219,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
} else {
const int cc = ggml_cuda_info().devices[ctx.device].cc;
const int warp_size = ggml_cuda_info().devices[ctx.device].warp_size;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1], /*n_experts=*/0);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[1], /*mul_mat_id=*/false);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc);
@ -2287,7 +2287,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
return;
}
if (ggml_cuda_should_use_mmq(src0->type, cc, ne12)) {
if (ggml_cuda_should_use_mmq(src0->type, cc, ne12, /*n_experts=*/ne02)) {
ggml_cuda_mul_mat_q(ctx, src0, src1, ids, dst);
return;
}

7
ggml/src/ggml-cuda/mmq.cu
View File

@ -259,7 +259,7 @@ void ggml_cuda_op_mul_mat_q(
GGML_UNUSED_VARS(src1, dst, src1_ddf_i, src1_padded_row_size);
}
bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t n_experts) {
#ifdef GGML_CUDA_FORCE_CUBLAS
return false;
#endif // GGML_CUDA_FORCE_CUBLAS
@ -320,7 +320,10 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
if (GGML_CUDA_CC_IS_CDNA3(cc)) {
return true;
}
if (ne11 <= 128 || type == GGML_TYPE_Q4_0 || type == GGML_TYPE_Q4_1 || type == GGML_TYPE_Q5_0 || type == GGML_TYPE_Q5_1) {
if (n_experts > 64 || ne11 <= 128) {
return true;
}
if (type == GGML_TYPE_Q4_0 || type == GGML_TYPE_Q4_1 || type == GGML_TYPE_Q5_0 || type == GGML_TYPE_Q5_1) {
return true;
}
if (ne11 <= 256 && (type == GGML_TYPE_Q4_K || type == GGML_TYPE_Q5_K)) {

2
ggml/src/ggml-cuda/mmq.cuh
View File

@ -4082,4 +4082,4 @@ void ggml_cuda_op_mul_mat_q(
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
const int64_t src1_padded_row_size, cudaStream_t stream);
bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11);
bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t n_experts);

4
ggml/src/ggml-impl.h
View File

@ -24,10 +24,6 @@
#include <arm_neon.h>
#endif
#if defined(__F16C__)
#include <immintrin.h>
#endif
#ifdef __cplusplus
extern "C" {
#endif

56
ggml/src/ggml-opencl/ggml-opencl.cpp
View File

@ -263,6 +263,32 @@ static ggml_cl_compiler_version get_adreno_cl_compiler_version(const char *drive
return { type, major, minor, patch };
}
// cl buffer wrapper
struct ggml_cl_buffer {
cl_mem buffer;
size_t size;
ggml_cl_buffer()
: buffer(nullptr), size(0) {}
~ggml_cl_buffer() {
if (buffer) {
CL_CHECK(clReleaseMemObject(buffer));
}
}
void allocate(cl_context context, size_t new_size) {
if (new_size > size) {
size = new_size;
if (buffer) {
CL_CHECK(clReleaseMemObject(buffer));
}
cl_int err;
CL_CHECK((buffer = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err));
}
}
};
// Profiling
struct ProfilingInfo {
std::string op_name;
@ -376,6 +402,11 @@ struct ggml_backend_opencl_context {
cl_context context;
cl_command_queue queue;
// prealloc buffers for transposing weights and activations
ggml_cl_buffer prealloc_quant_trans;
ggml_cl_buffer prealloc_scales_trans;
ggml_cl_buffer prealloc_act_trans;
cl_program program_add;
cl_program program_add_id;
cl_program program_clamp;
@ -638,10 +669,6 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_transpose_16_buf;
cl_kernel kernel_transpose_16_4x1;
cl_mem A_s_d_max; // max scale buffer size for transpose
cl_mem A_q_d_max; // max weight buffer size for transpose
cl_mem B_d_max; // max activation buffer size for transpose
// Gemm and Gemv related programs, kernels, etc
cl_program program_CL_gemm;
cl_program program_CL_gemv_general;
@ -2600,9 +2627,9 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
required_B_d_bytes, max_B_d_bytes);
}
CL_CHECK((backend_ctx->A_q_d_max = clCreateBuffer(context, 0, max_A_q_d_bytes, NULL, &err), err));
CL_CHECK((backend_ctx->A_s_d_max = clCreateBuffer(context, 0, max_A_s_d_bytes, NULL, &err), err));
CL_CHECK((backend_ctx->B_d_max = clCreateBuffer(context, 0, max_B_d_bytes, NULL, &err), err));
backend_ctx->prealloc_quant_trans.allocate(context, max_A_q_d_bytes);
backend_ctx->prealloc_scales_trans.allocate(context, max_A_s_d_bytes);
backend_ctx->prealloc_act_trans.allocate(context, max_B_d_bytes);
#endif // GGML_OPENCL_USE_ADRENO_KERNELS
backend_ctx->disable_fusion = getenv("GGML_OPENCL_DISABLE_FUSION") != nullptr;
@ -3607,32 +3634,35 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
// use sub_buffer of max buffer size instead
size_t q_size_bytes = K * M / 8 * sizeof(float);
backend_ctx->prealloc_quant_trans.allocate(context, q_size_bytes);
cl_buffer_region region;
region.origin = 0;
region.size = q_size_bytes;
cl_mem qT_d = clCreateSubBuffer(
backend_ctx->A_q_d_max,
backend_ctx->prealloc_quant_trans.buffer,
0,
CL_BUFFER_CREATE_TYPE_REGION,
&region,
&err);
// cl_mem qT_d = clCreateBuffer(context, CL_MEM_READ_WRITE, q_size_bytes, NULL, &err);
CL_CHECK(err);
bool K_tile_trans = true;
if ((K / 32) % 4 != 0){
K_tile_trans =false;
}
size_t d_size_bytes = M * (K / 32) * 2;
backend_ctx->prealloc_scales_trans.allocate(context, d_size_bytes);
region.origin = 0;
region.size = d_size_bytes;
cl_mem dT_d = clCreateSubBuffer(
backend_ctx->A_s_d_max,
backend_ctx->prealloc_scales_trans.buffer,
0,
CL_BUFFER_CREATE_TYPE_REGION,
&region,
&err);
// cl_mem dT_d = clCreateBuffer(context, CL_MEM_READ_WRITE, d_size_bytes, NULL, &err);
CL_CHECK(err);
// <----------------------------------------------------------------------------------> //
@ -7395,8 +7425,10 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
region.origin = 0;
// Specify the size of the sub-buffer (divide by 2 for FP16)
region.size = K * (N + padding) * sizeof(float)/2;
backend_ctx->prealloc_act_trans.allocate(context, region.size);
B_d = clCreateSubBuffer(
backend_ctx->B_d_max,
backend_ctx->prealloc_act_trans.buffer,
0,
CL_BUFFER_CREATE_TYPE_REGION,
&region,

5
ggml/src/ggml-rpc/ggml-rpc.cpp
View File

@ -524,6 +524,7 @@ static std::shared_ptr<socket_t> get_socket(const std::string & endpoint) {
std::string host;
int port;
if (!parse_endpoint(endpoint, host, port)) {
GGML_LOG_ERROR("Failed to parse endpoint: %s\n", endpoint.c_str());
return nullptr;
}
#ifdef _WIN32
@ -2053,6 +2054,10 @@ ggml_backend_reg_t ggml_backend_rpc_reg(void) {
static uint32_t ggml_backend_rpc_get_device_count(const char * endpoint) {
auto sock = get_socket(endpoint);
if (sock == nullptr) {
GGML_LOG_ERROR("Failed to connect to %s\n", endpoint);
return 0;
}
rpc_msg_device_count_rsp response;
bool status = send_rpc_cmd(sock, RPC_CMD_DEVICE_COUNT, nullptr, 0, &response, sizeof(response));
RPC_STATUS_ASSERT(status);

17
gguf-py/gguf/constants.py
View File

@ -377,6 +377,7 @@ class MODEL_ARCH(IntEnum):
PHIMOE = auto()
PLAMO = auto()
PLAMO2 = auto()
PLAMO3 = auto()
CODESHELL = auto()
ORION = auto()
INTERNLM2 = auto()
@ -773,6 +774,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.PHIMOE: "phimoe",
MODEL_ARCH.PLAMO: "plamo",
MODEL_ARCH.PLAMO2: "plamo2",
MODEL_ARCH.PLAMO3: "plamo3",
MODEL_ARCH.CODESHELL: "codeshell",
MODEL_ARCH.ORION: "orion",
MODEL_ARCH.INTERNLM2: "internlm2",
@ -1763,6 +1765,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.SSM_B_NORM,
MODEL_TENSOR.SSM_C_NORM,
],
MODEL_ARCH.PLAMO3: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_POST_NORM,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_POST_NORM,
],
MODEL_ARCH.GPT2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.POS_EMBD,

2
gguf-py/gguf/tensor_mapping.py
View File

@ -595,6 +595,7 @@ class TensorNameMap:
"encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2
"transformer.layers.{bid}.attn.q_norm", # openelm
"model.layers.layers.{bid}.mixer.q", # plamo2
"model.layers.layers.{bid}.mixer.q_norm", # plamo3
"layers.{bid}.self_attn.q_norm", # qwen3-embedding
"model.layers.{bid}.attention.query_layernorm", # apertus
),
@ -610,6 +611,7 @@ class TensorNameMap:
"encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2
"transformer.layers.{bid}.attn.k_norm", # openelm
"model.layers.layers.{bid}.mixer.k", # plamo2
"model.layers.layers.{bid}.mixer.k_norm", # plamo3
"layers.{bid}.self_attn.k_norm", # qwen3-embedding
"model.layers.{bid}.attention.key_layernorm", # apertus
),

15
include/llama.h
View File

@ -286,7 +286,7 @@ extern "C" {
// NULL-terminated list of buffer types to use for tensors that match a pattern
const struct llama_model_tensor_buft_override * tensor_buft_overrides;
int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t n_gpu_layers; // number of layers to store in VRAM, a negative value means all layers
enum llama_split_mode split_mode; // how to split the model across multiple GPUs
// the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
@ -467,10 +467,17 @@ extern "C" {
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
enum llama_params_fit_status {
LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, // found allocations that are projected to fit
LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, // could not find allocations that are projected to fit
LLAMA_PARAMS_FIT_STATUS_ERROR = 2, // a hard error occured, e.g. because no model could be found at the specified path
};
// fits mparams and cparams to free device memory (assumes system memory is unlimited)
// returns true if the parameters could be successfully modified to fit device memory
// this function is NOT thread safe because it modifies the global llama logger state
LLAMA_API bool llama_params_fit(
// - returns true if the parameters could be successfully modified to fit device memory
// - this function is NOT thread safe because it modifies the global llama logger state
// - only parameters that have the same value as in llama_default_model_params are modified
LLAMA_API enum llama_params_fit_status llama_params_fit(
const char * path_model,
struct llama_model_params * mparams,
struct llama_context_params * cparams,

1
src/CMakeLists.txt
View File

@ -107,6 +107,7 @@ add_library(llama
models/phi3.cpp
models/plamo.cpp
models/plamo2.cpp
models/plamo3.cpp
models/plm.cpp
models/qwen.cpp
models/qwen2.cpp

17
src/llama-arch.cpp
View File

@ -42,6 +42,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_PHIMOE, "phimoe" },
{ LLM_ARCH_PLAMO, "plamo" },
{ LLM_ARCH_PLAMO2, "plamo2" },
{ LLM_ARCH_PLAMO3, "plamo3" },
{ LLM_ARCH_CODESHELL, "codeshell" },
{ LLM_ARCH_ORION, "orion" },
{ LLM_ARCH_INTERNLM2, "internlm2" },
@ -1077,6 +1078,22 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_ATTN_POST_NORM,
LLM_TENSOR_FFN_POST_NORM,
};
case LLM_ARCH_PLAMO3:
return {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_QKV,
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_POST_NORM,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_POST_NORM,
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
};
case LLM_ARCH_CODESHELL:
return {
LLM_TENSOR_TOKEN_EMBD,

1
src/llama-arch.h
View File

@ -46,6 +46,7 @@ enum llm_arch {
LLM_ARCH_PHIMOE,
LLM_ARCH_PLAMO,
LLM_ARCH_PLAMO2,
LLM_ARCH_PLAMO3,
LLM_ARCH_CODESHELL,
LLM_ARCH_ORION,
LLM_ARCH_INTERNLM2,

6
src/llama-context.cpp
View File

@ -294,8 +294,8 @@ llama_context::llama_context(
// enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
bool pipeline_parallel =
model.n_devices() > 1 &&
model.params.n_gpu_layers > (int) model.hparams.n_layer &&
model.params.split_mode == LLAMA_SPLIT_MODE_LAYER &&
model.n_gpu_layers() > model.hparams.n_layer &&
model.split_mode() == LLAMA_SPLIT_MODE_LAYER &&
cparams.offload_kqv &&
!model.has_tensor_overrides();
@ -1570,7 +1570,7 @@ llm_graph_cb llama_context::graph_get_cb() const {
// norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
// FIXME: fix in ggml_backend_sched
const bool full_offload = model.params.n_gpu_layers > (int) model.hparams.n_layer;
const bool full_offload = model.n_gpu_layers() > model.hparams.n_layer;
if (ubatch.n_tokens < 32 || full_offload) {
if (il != -1 && strcmp(name, "norm") == 0) {
const auto & dev_layer = model.dev_layer(il);

81
src/llama-model.cpp
View File

@ -1227,6 +1227,26 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
} break;
case LLM_ARCH_PLAMO3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
if (found_swa && hparams.n_swa > 0) {
uint32_t swa_period = 8;
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.rope_freq_scale_train_swa = 1.0f;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa);
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
hparams.set_swa_pattern(swa_period);
} else {
hparams.swa_type = LLAMA_SWA_TYPE_NONE;
}
switch (hparams.n_layer) {
case 24: type = LLM_TYPE_2B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_GPT2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@ -2378,11 +2398,11 @@ void llama_model::load_vocab(llama_model_loader & ml) {
bool llama_model::load_tensors(llama_model_loader & ml) {
const auto & split_mode = params.split_mode;
const auto & n_gpu_layers = params.n_gpu_layers;
const auto & use_mlock = params.use_mlock;
const auto & tensor_split = params.tensor_split;
const int n_layer = hparams.n_layer;
const int n_layer = hparams.n_layer;
const int n_gpu_layers = this->n_gpu_layers();
const bool use_mmap_buffer = true;
@ -3828,6 +3848,44 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
}
} break;
case LLM_ARCH_PLAMO3:
{
const int64_t head_dim_q = hparams.n_embd_head_k;
const int64_t head_dim_v = hparams.n_embd_head_v;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
const int64_t num_attention_heads = hparams.n_head(i);
const int64_t num_key_value_heads = hparams.n_head_kv(i);
const int64_t q_proj_dim = num_attention_heads * head_dim_q;
const int64_t k_proj_dim = num_key_value_heads * head_dim_q;
const int64_t v_proj_dim = num_key_value_heads * head_dim_v;
const int64_t n_ff_cur = hparams.n_ff(i);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i),
{n_embd,q_proj_dim + k_proj_dim + v_proj_dim}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim_q}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim_q}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {num_attention_heads * head_dim_v, n_embd}, 0);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, i), {n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff_cur * 2}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_cur, n_embd}, 0);
}
} break;
case LLM_ARCH_GPT2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@ -6884,6 +6942,14 @@ size_t llama_model::n_devices() const {
return devices.size();
}
uint32_t llama_model::n_gpu_layers() const {
return params.n_gpu_layers >= 0 ? params.n_gpu_layers : hparams.n_layer + 1;
}
llama_split_mode llama_model::split_mode() const {
return params.split_mode;
}
std::map<ggml_backend_buffer_type_t, size_t> llama_model::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, size_t> ret;
for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) {
@ -7465,6 +7531,14 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_plamo2>(*this, params);
} break;
case LLM_ARCH_PLAMO3:
{
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
llm = std::make_unique<llm_build_plamo3<true>> (*this, params);
} else {
llm = std::make_unique<llm_build_plamo3<false>>(*this, params);
}
} break;
case LLM_ARCH_GPT2:
{
llm = std::make_unique<llm_build_gpt2>(*this, params);
@ -7794,7 +7868,7 @@ llama_model_params llama_model_default_params() {
llama_model_params result = {
/*.devices =*/ nullptr,
/*.tensor_buft_overrides =*/ nullptr,
/*.n_gpu_layers =*/ 999,
/*.n_gpu_layers =*/ -1,
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
@ -7969,6 +8043,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_PHIMOE:
case LLM_ARCH_PLAMO:
case LLM_ARCH_PLAMO2:
case LLM_ARCH_PLAMO3:
case LLM_ARCH_GEMMA:
case LLM_ARCH_GEMMA2:
case LLM_ARCH_GEMMA3:

7
src/llama-model.h
View File

@ -466,8 +466,6 @@ struct llama_model {
struct ggml_tensor * dense_2_out_layers = nullptr;
struct ggml_tensor * dense_3_out_layers = nullptr;
llama_model_params params;
// gguf metadata
std::unordered_map<std::string, std::string> gguf_kv;
@ -498,6 +496,9 @@ struct llama_model {
size_t n_tensors() const;
size_t n_devices() const;
uint32_t n_gpu_layers() const;
llama_split_mode split_mode() const;
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const;
// total number of parameters in the model
@ -526,6 +527,8 @@ struct llama_model {
ggml_cgraph * build_graph(const llm_graph_params & params) const;
private:
llama_model_params params;
struct impl;
std::unique_ptr<impl> pimpl;
};

87
src/llama.cpp
View File

@ -140,6 +140,10 @@ enum layer_fraction_t {
};
// this enum is only used in llama_params_fit_impl but needs to be defined outside of it to fix a Windows compilation issue
class llama_params_fit_exception : public std::runtime_error {
using std::runtime_error::runtime_error;
};
static void llama_params_fit_impl(
const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
@ -181,12 +185,11 @@ static void llama_params_fit_impl(
}
}
int64_t sum_total = 0;
int64_t sum_free = 0;
int64_t sum_projected_free = 0;
int64_t min_projected_free = INT64_MAX;
int64_t sum_projected_used = 0;
int64_t sum_projected_model = 0;
int64_t sum_projected_ctx = 0;
if (nd > 1) {
LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__);
@ -197,12 +200,11 @@ static void llama_params_fit_impl(
const int64_t projected_used = dmd.mb.total();
const int64_t projected_free = dmd.free - projected_used;
sum_total += dmd.total;
sum_free += dmd.free;
sum_projected_used += projected_used;
sum_projected_free += projected_free;
min_projected_free = std::min(min_projected_free, projected_free);
sum_projected_model += dmd.mb.model;
sum_projected_ctx += dmd.mb.context;
if (nd > 1) {
LLAMA_LOG_INFO("%s: - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " %s\n",
@ -210,10 +212,9 @@ static void llama_params_fit_impl(
projected_free >= 0 ? "surplus" : "deficit");
}
}
assert(sum_total >= 0 && sum_projected_used >= 0 && sum_projected_ctx >= 0);
assert(sum_projected_used >= sum_projected_ctx);
assert(sum_free >= 0 && sum_projected_used >= 0);
LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n",
__func__, sum_projected_used/MiB, sum_total/MiB);
__func__, sum_projected_used/MiB, sum_free/MiB);
if (min_projected_free >= margin) {
if (nd == 1) {
LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n",
@ -236,9 +237,7 @@ static void llama_params_fit_impl(
__func__, margin/MiB, -global_surplus/MiB);
if (cparams->n_ctx == 0) {
if (hp_nct > n_ctx_min) {
const int64_t bytes_per_ctx = sum_projected_ctx / hp_nct;
int64_t memory_reduction = -global_surplus;
int64_t sum_used_target = sum_free - nd*margin_s;
if (nd > 1) {
// for multiple devices we need to be more conservative in terms of how much context we think can fit:
// - for dense models only whole layers can be assigned to devices
@ -246,24 +245,34 @@ static void llama_params_fit_impl(
// - on average we expect a waste of 0.5 layers/tensors per device
// - use slightly more than the expected average for nd devices to be safe
const int64_t model_per_layer = sum_projected_model / std::min(uint32_t(mparams->n_gpu_layers), hp_ngl);
memory_reduction += (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6);
sum_used_target -= (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6);
}
uint32_t ctx_reduction = std::min(uint32_t((memory_reduction + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min);
cparams->n_ctx = hp_nct - ctx_reduction;
cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend
int64_t sum_projected_used_min_ctx = 0;
cparams->n_ctx = n_ctx_min;
const dmds_t dmds_min_ctx = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
for (const auto & dmd : dmds_min_ctx) {
sum_projected_used_min_ctx += dmd.mb.total();
}
if (sum_used_target > sum_projected_used_min_ctx) {
// linear interpolation between minimum and maximum context size:
cparams->n_ctx += (hp_nct - n_ctx_min) * (sum_used_target - sum_projected_used_min_ctx)
/ (sum_projected_used - sum_projected_used_min_ctx);
cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend
ctx_reduction = hp_nct - cparams->n_ctx;
memory_reduction = ctx_reduction * bytes_per_ctx;
global_surplus += memory_reduction;
LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
if (global_surplus >= 0) {
const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (hp_nct - n_ctx_min);
const int64_t memory_reduction = (hp_nct - cparams->n_ctx) * bytes_per_ctx;
LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
if (nd == 1) {
LLAMA_LOG_INFO("%s: entire model can be fit by reducing context\n", __func__);
return;
}
LLAMA_LOG_INFO("%s: entire model should be fit across devices by reducing context\n", __func__);
} else {
const int64_t memory_reduction = sum_projected_used - sum_projected_used_min_ctx;
LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
__func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
}
} else {
LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
@ -276,28 +285,28 @@ static void llama_params_fit_impl(
}
if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) {
throw std::runtime_error("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort");
throw llama_params_fit_exception("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort");
}
if (nd > 1) {
if (!tensor_split) {
throw std::runtime_error("did not provide a buffer to write the tensor_split to, abort");
throw llama_params_fit_exception("did not provide a buffer to write the tensor_split to, abort");
}
if (mparams->tensor_split) {
for (size_t id = 0; id < nd; id++) {
if (mparams->tensor_split[id] != 0.0f) {
throw std::runtime_error("model_params::tensor_split already set by user, abort");
throw llama_params_fit_exception("model_params::tensor_split already set by user, abort");
}
}
}
if (mparams->split_mode == LLAMA_SPLIT_MODE_ROW) {
throw std::runtime_error("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort");
throw llama_params_fit_exception("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort");
}
}
if (!tensor_buft_overrides) {
throw std::runtime_error("did not provide buffer to set tensor_buft_overrides, abort");
throw llama_params_fit_exception("did not provide buffer to set tensor_buft_overrides, abort");
}
if (mparams->tensor_buft_overrides && (mparams->tensor_buft_overrides->pattern || mparams->tensor_buft_overrides->buft)) {
throw std::runtime_error("model_params::tensor_buft_overrides already set by user, abort");
throw llama_params_fit_exception("model_params::tensor_buft_overrides already set by user, abort");
}
// step 3: iteratively fill the back to front with "dense" layers
@ -380,8 +389,8 @@ static void llama_params_fit_impl(
tensor_buft_overrides[itbo].buft = nullptr;
itbo++;
mparams.tensor_buft_overrides = tensor_buft_overrides;
throw std::runtime_error("llama_params_fit_n_tensor_buft_overrides() == "
+ std::to_string(ntbo) + " is insufficient for model\n");
throw llama_params_fit_exception("llama_max_tensor_buft_overrides() == "
+ std::to_string(ntbo) + " is insufficient for model");
}
tensor_buft_overrides[itbo].pattern = get_overflow_pattern(il, il == il0 ? ngl_per_device[id].overflow_type : LAYER_FRACTION_MOE);
tensor_buft_overrides[itbo].buft = overflow_bufts[id];
@ -503,6 +512,9 @@ static void llama_params_fit_impl(
if (mem_high[id] > targets[id]) {
assert(ngl_per_device_high[id].n_layer > ngl_per_device[id].n_layer);
uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
if (hp_nex > 0 && size_t(id) == nd - 1) {
delta--;
}
LLAMA_LOG_DEBUG("%s: start filling device %" PRIu32 ", delta=%" PRIu32 "\n", __func__, id, delta);
while (delta > 1) {
uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
@ -638,7 +650,7 @@ static void llama_params_fit_impl(
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_UP;
LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__);
std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
if (mem_test[id] < targets[id]) {
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
@ -648,7 +660,7 @@ static void llama_params_fit_impl(
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE;
LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__);
mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
if (mem_test[id] < targets[id]) {
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
@ -659,7 +671,7 @@ static void llama_params_fit_impl(
ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN;
LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__);
mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
if (mem_test[id] < targets[id]) {
if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
ngl_per_device = ngl_per_device_test;
mem = mem_test;
id_dense_start = id_dense_start_test;
@ -678,22 +690,25 @@ static void llama_params_fit_impl(
set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams);
}
bool llama_params_fit(
enum llama_params_fit_status llama_params_fit(
const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
const int64_t t0_us = llama_time_us();
bool ok = true;
llama_params_fit_status status = LLAMA_PARAMS_FIT_STATUS_SUCCESS;
try {
llama_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margin_s, n_ctx_min, log_level);
LLAMA_LOG_INFO("%s: successfully fit params to free device memory\n", __func__);
} catch (const std::runtime_error & e) {
} catch (const llama_params_fit_exception & e) {
LLAMA_LOG_WARN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
ok = false;
status = LLAMA_PARAMS_FIT_STATUS_FAILURE;
} catch (const std::runtime_error & e) {
LLAMA_LOG_ERROR("%s: encountered an error while trying to fit params to free device memory: %s\n", __func__, e.what());
status = LLAMA_PARAMS_FIT_STATUS_ERROR;
}
const int64_t t1_us = llama_time_us();
LLAMA_LOG_INFO("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6);
return ok;
return status;
}
struct llama_sampler_chain_params llama_sampler_chain_default_params() {

5
src/models/models.h
View File

@ -406,6 +406,11 @@ struct llm_build_plamo : public llm_graph_context {
llm_build_plamo(const llama_model & model, const llm_graph_params & params);
};
template <bool iswa>
struct llm_build_plamo3 : public llm_graph_context {
llm_build_plamo3(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_plm : public llm_graph_context {
llm_build_plm(const llama_model & model, const llm_graph_params & params);
};

128
src/models/plamo3.cpp Normal file
View File

@ -0,0 +1,128 @@
#include "models.h"
template <bool iswa>
llm_build_plamo3<iswa>::llm_build_plamo3(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
const int64_t head_dim_q = hparams.n_embd_head_k;
const int64_t head_dim_v = hparams.n_embd_head_v;
ggml_tensor * cur;
ggml_tensor * inpL = build_inp_embd(model.tok_embd);
ggml_tensor * inp_pos = build_inp_pos();
using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
inp_attn_type * inp_attn = nullptr;
if constexpr (iswa) {
inp_attn = build_attn_inp_kv_iswa();
} else {
inp_attn = build_attn_inp_kv();
}
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * residual = inpL;
float freq_base_l = 0.0f;
float freq_scale_l = 0.0f;
if constexpr (iswa) {
freq_base_l = model.get_rope_freq_base (cparams, il);
freq_scale_l = model.get_rope_freq_scale(cparams, il);
} else {
freq_base_l = freq_base;
freq_scale_l = freq_scale;
}
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
const int32_t n_head = hparams.n_head(il);
const int32_t n_head_kv = hparams.n_head_kv(il);
const int64_t q_offset = 0;
const int64_t k_offset = head_dim_q * n_head;
const int64_t v_offset = k_offset + head_dim_q * n_head_kv;
ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, head_dim_q, n_head, n_tokens,
head_dim_q * sizeof(float), qkv->nb[1], q_offset * ggml_element_size(qkv));
ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, head_dim_q, n_head_kv, n_tokens,
head_dim_q * sizeof(float), qkv->nb[1], k_offset * ggml_element_size(qkv));
ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, head_dim_v, n_head_kv, n_tokens,
head_dim_v * sizeof(float), qkv->nb[1], v_offset * ggml_element_size(qkv));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "attn_q_norm", il);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "attn_k_norm", il);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
const float attn_scale = 1.0f / sqrtf(float(head_dim_q));
cur = build_attn(inp_attn,
model.layers[il].wo, NULL,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, attn_scale, il);
cb(cur, "attn_out", il);
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
}
cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_post_norm", il);
cur = ggml_add(ctx0, cur, residual);
cb(cur, "attn_residual", il);
residual = cur;
cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
NULL, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
cb(cur, "ffn_out", il);
cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_post_norm", il);
cur = ggml_add(ctx0, cur, residual);
cb(cur, "ffn_residual", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
res->t_embd = cur;
cur = build_lora_mm(model.output, cur);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
// Explicit template instantiations
template struct llm_build_plamo3<false>;
template struct llm_build_plamo3<true>;

4
tools/fit-params/fit-params.cpp
View File

@ -26,10 +26,10 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
auto mparams = common_model_params_to_llama(params);
auto cparams = common_context_params_to_llama(params);
const bool success = llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
const llama_params_fit_status status = llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
if (!success) {
if (status != LLAMA_PARAMS_FIT_STATUS_SUCCESS) {
LOG_ERR("%s: failed to fit CLI arguments to free memory, exiting...\n", __func__);
exit(1);
}

5
tools/mtmd/models/models.h
View File

@ -2,6 +2,11 @@
#include "../clip-graph.h"
/*
* IMPORTANT: The mtmd module does NOT accept pull requests that are fully or predominantly AI-generated.
* We encourage human contributors to ensure the quality and reliability of the codebase.
*/
struct clip_graph_siglip : clip_graph {
clip_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;

3
tools/mtmd/mtmd.h
View File

@ -27,6 +27,9 @@
* - Make sure the C API is aligned with the libllama C API (as in llama.h)
* - Do not include model name (e.g., qwen, gemma) in the API, use generic terms instead
* - Keep the API minimal, do not expose internal details unless necessary
*
* IMPORTANT: The mtmd module does NOT accept pull requests that are fully or predominantly AI-generated.
* We encourage human contributors to ensure the quality and reliability of the codebase.
*/
#ifdef LLAMA_SHARED