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This commit is contained in:
manikbhandari 2023-12-20 06:28:20 -05:00
commit 5d5b6088b5
24 changed files with 1684 additions and 762 deletions
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3
.editorconfig
View file

@ -23,3 +23,6 @@ insert_final_newline = unset
[examples/server/public/*]
indent_size = 2
[examples/llama.swiftui/llama.swiftui.xcodeproj/*]
indent_style = tab

7
CMakeLists.txt
View file

@ -291,7 +291,12 @@ if (LLAMA_CUBLAS)
add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE})
if (LLAMA_STATIC)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
if (WIN32)
# As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
else ()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
endif()
else()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
endif()

12
Makefile
View file

@ -439,9 +439,15 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
endif # LLAMA_CLBLAST
ifdef LLAMA_HIPBLAS
ROCM_PATH ?= /opt/rocm
HIPCC ?= $(ROCM_PATH)/bin/hipcc
GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
ifeq ($(wildcard /opt/rocm),)
ROCM_PATH ?= /usr
GPU_TARGETS ?= $(shell $(shell which amdgpu-arch))
else
ROCM_PATH ?= /opt/rocm
GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
endif
HIPCC ?= $(ROCM_PATH)/bin/hipcc
LLAMA_CUDA_DMMV_X ?= 32
LLAMA_CUDA_MMV_Y ?= 1
LLAMA_CUDA_KQUANTS_ITER ?= 2

6
README.md
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@ -10,11 +10,11 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
### Hot topics
- Collecting Apple Silicon performance stats:
- M-series: https://github.com/ggerganov/llama.cpp/discussions/4167
- A-series: https://github.com/ggerganov/llama.cpp/discussions/4508
- Added Mixtral support: https://github.com/ggerganov/llama.cpp/pull/4406
- **llama.h API change for handling KV cache offloading and data type: https://github.com/ggerganov/llama.cpp/pull/4309**
- Using `llama.cpp` with AWS instances: https://github.com/ggerganov/llama.cpp/discussions/4225
- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
- Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167
----

21
convert-hf-to-gguf.py
View file

@ -185,6 +185,8 @@ class Model:
return MixtralModel
if model_architecture == "GPT2LMHeadModel":
return GPT2Model
if model_architecture == "PhiForCausalLM":
return Phi2Model
return Model
def _is_model_safetensors(self) -> bool:
@ -226,6 +228,8 @@ class Model:
return gguf.MODEL_ARCH.LLAMA
if arch == "GPT2LMHeadModel":
return gguf.MODEL_ARCH.GPT2
if arch == "PhiForCausalLM":
return gguf.MODEL_ARCH.PHI2
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -1046,6 +1050,23 @@ class GPT2Model(Model):
print(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor("output.weight", data)
class Phi2Model(Model):
def set_gguf_parameters(self):
block_count = self.hparams["n_layer"]
self.gguf_writer.add_name("Phi2")
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_head_count_kv(self.hparams["n_head"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_add_bos_token(False)
###### CONVERSION LOGIC ######

86
convert-lora-to-ggml.py
View file

@ -3,7 +3,6 @@ from __future__ import annotations
import json
import os
import re
import struct
import sys
from typing import Any, BinaryIO, Sequence
@ -11,43 +10,15 @@ from typing import Any, BinaryIO, Sequence
import numpy as np
import torch
from pathlib import Path
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
import gguf
NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
HF_SUBLAYER_TO_GGML = {
"self_attn.q_proj": "attn_q",
"self_attn.k_proj": "attn_k",
"self_attn.v_proj": "attn_v",
"self_attn.o_proj": "attn_output",
"mlp.gate_proj": "ffn_gate",
"mlp.down_proj": "ffn_down",
"mlp.up_proj": "ffn_up",
"input_layernorm": "attn_norm",
"post_attention_layernorm": "ffn_norm",
}
def translate_tensor_name(t: str) -> str:
match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
if match:
nn = match.group(1)
sub_layer = match.group(2)
lora_type = match.group(3)
sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
if sub_layer_renamed is None:
print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
sys.exit(1)
output_string = (
f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
)
return output_string
else:
print(f"Error: unrecognized tensor {t}")
sys.exit(1)
def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
fout.write(b"ggla"[::-1]) # magic (ggml lora)
fout.write(struct.pack("i", 1)) # file version
@ -61,9 +32,7 @@ def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
fout.write(struct.pack("i", int(params["lora_alpha"])))
def write_tensor_header(
self, name: str, shape: Sequence[int], data_type: np.dtype[Any]
) -> None:
def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
sname = name.encode("utf-8")
fout.write(
struct.pack(
@ -78,11 +47,12 @@ def write_tensor_header(
fout.seek((fout.tell() + 31) & -32)
if len(sys.argv) != 2:
print(f"Usage: python {sys.argv[0]} <path>")
if len(sys.argv) < 2:
print(f"Usage: python {sys.argv[0]} <path> [arch]")
print(
"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
)
print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
sys.exit(1)
input_json = os.path.join(sys.argv[1], "adapter_config.json")
@ -90,6 +60,14 @@ input_model = os.path.join(sys.argv[1], "adapter_model.bin")
output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
model = torch.load(input_model, map_location="cpu")
arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
if arch_name not in gguf.MODEL_ARCH_NAMES.values():
print(f"Error: unsupported architecture {arch_name}")
sys.exit(1)
arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
with open(input_json, "r") as f:
params = json.load(f)
@ -117,6 +95,7 @@ with open(output_path, "wb") as fout:
write_file_header(fout, params)
for k, v in model.items():
orig_k = k
if k.endswith(".default.weight"):
k = k.replace(".default.weight", ".weight")
if k in ["llama_proj.weight", "llama_proj.bias"]:
@ -129,7 +108,32 @@ with open(output_path, "wb") as fout:
v = v.float()
t = v.detach().numpy()
tname = translate_tensor_name(k)
prefix = "base_model.model."
if k.startswith(prefix):
k = k[len(prefix) :]
lora_suffixes = (".lora_A.weight", ".lora_B.weight")
if k.endswith(lora_suffixes):
suffix = k[-len(lora_suffixes[0]):]
k = k[: -len(lora_suffixes[0])]
else:
print(f"Error: unrecognized tensor name {orig_k}")
sys.exit(1)
tname = name_map.get_name(k)
if tname is None:
print(f"Error: could not map tensor name {orig_k}")
print(" Note: the arch parameter must be specified if the model is not llama")
sys.exit(1)
if suffix == ".lora_A.weight":
tname += ".weight.loraA"
elif suffix == ".lora_B.weight":
tname += ".weight.loraB"
else:
assert False
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
write_tensor_header(fout, tname, t.shape, t.dtype)
t.tofile(fout)

15
examples/finetune/finetune.cpp
View file

@ -1620,8 +1620,6 @@ int main(int argc, char ** argv) {
opt->params.adam.gclip = params.common.adam_gclip;
opt->params.adam.eps_f = params.common.adam_eps_f;
ggml_allocr * alloc = NULL;
printf("%s: init model\n", __func__);
bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train);
@ -1725,10 +1723,9 @@ int main(int argc, char ** argv) {
// allocate input tensors
mem_input_data.resize(max_input_size);
alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
ggml_allocr_alloc(alloc, tokens_input);
ggml_allocr_alloc(alloc, target_probs);
ggml_allocr_free(alloc);
ggml_allocr_t alloc_inps = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
ggml_allocr_alloc(alloc_inps, tokens_input);
ggml_allocr_alloc(alloc_inps, target_probs);
// context for compute tensors without their data
const size_t estimated_compute_size_wo_data = (
@ -1755,7 +1752,7 @@ int main(int argc, char ** argv) {
// find best evaluation order
for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
ctx_compute = ggml_init(ctx_compute_params);
alloc = ggml_allocr_new_measure(tensor_alignment);
ggml_allocr_t alloc = ggml_allocr_new_measure(tensor_alignment);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = (enum ggml_cgraph_eval_order) order;
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@ -1788,7 +1785,7 @@ int main(int argc, char ** argv) {
// allocate compute tensors
mem_compute_data.resize(max_compute_size);
ctx_compute = ggml_init(ctx_compute_params);
alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
ggml_allocr_t alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = best_order;
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@ -1804,6 +1801,8 @@ int main(int argc, char ** argv) {
params.common.use_checkpointing
);
ggml_allocr_free(alloc);
ggml_allocr_free(alloc_inps);
// tokenize data
std::vector<llama_token> train_tokens;

1
examples/llama.swiftui/.gitignore vendored
View file

@ -1 +1,2 @@
xcuserdata
xcshareddata

182
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -6,16 +6,34 @@ enum LlamaError: Error {
case couldNotInitializeContext
}
func llama_batch_clear(_ batch: inout llama_batch) {
batch.n_tokens = 0
}
func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama_pos, _ seq_ids: [llama_seq_id], _ logits: Bool) {
batch.token [Int(batch.n_tokens)] = id
batch.pos [Int(batch.n_tokens)] = pos
batch.n_seq_id[Int(batch.n_tokens)] = Int32(seq_ids.count)
for i in 0..<seq_ids.count {
batch.seq_id[Int(batch.n_tokens)]![Int(i)] = seq_ids[i]
}
batch.logits [Int(batch.n_tokens)] = logits ? 1 : 0
batch.n_tokens += 1
}
actor LlamaContext {
private var model: OpaquePointer
private var context: OpaquePointer
private var batch: llama_batch
private var tokens_list: [llama_token]
/// This variable is used to store temporarily invalid cchars
private var temporary_invalid_cchars: [CChar]
var n_len: Int32 = 512
var n_len: Int32 = 64
var n_cur: Int32 = 0
var n_decode: Int32 = 0
init(model: OpaquePointer, context: OpaquePointer) {
@ -27,25 +45,34 @@ actor LlamaContext {
}
deinit {
llama_batch_free(batch)
llama_free(context)
llama_free_model(model)
llama_backend_free()
}
static func createContext(path: String) throws -> LlamaContext {
static func create_context(path: String) throws -> LlamaContext {
llama_backend_init(false)
let model_params = llama_model_default_params()
var model_params = llama_model_default_params()
#if targetEnvironment(simulator)
model_params.n_gpu_layers = 0
print("Running on simulator, force use n_gpu_layers = 0")
#endif
let model = llama_load_model_from_file(path, model_params)
guard let model else {
print("Could not load model at \(path)")
throw LlamaError.couldNotInitializeContext
}
let n_threads = max(1, min(8, ProcessInfo.processInfo.processorCount - 2))
print("Using \(n_threads) threads")
var ctx_params = llama_context_default_params()
ctx_params.seed = 1234
ctx_params.seed = 1234
ctx_params.n_ctx = 2048
ctx_params.n_threads = 8
ctx_params.n_threads_batch = 8
ctx_params.n_threads = UInt32(n_threads)
ctx_params.n_threads_batch = UInt32(n_threads)
let context = llama_new_context_with_model(model, ctx_params)
guard let context else {
@ -56,6 +83,26 @@ actor LlamaContext {
return LlamaContext(model: model, context: context)
}
func model_info() -> String {
let result = UnsafeMutablePointer<Int8>.allocate(capacity: 256)
result.initialize(repeating: Int8(0), count: 256)
defer {
result.deallocate()
}
// TODO: this is probably very stupid way to get the string from C
let nChars = llama_model_desc(model, result, 256)
let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nChars))
var SwiftString = ""
for char in bufferPointer {
SwiftString.append(Character(UnicodeScalar(UInt8(char))))
}
return SwiftString
}
func get_n_tokens() -> Int32 {
return batch.n_tokens;
}
@ -79,16 +126,11 @@ actor LlamaContext {
print(String(cString: token_to_piece(token: id) + [0]))
}
// batch = llama_batch_init(512, 0) // done in init()
batch.n_tokens = Int32(tokens_list.count)
llama_batch_clear(&batch)
for i1 in 0..<batch.n_tokens {
for i1 in 0..<tokens_list.count {
let i = Int(i1)
batch.token[i] = tokens_list[i]
batch.pos[i] = i1
batch.n_seq_id[Int(i)] = 1
batch.seq_id[Int(i)]![0] = 0
batch.logits[i] = 0
llama_batch_add(&batch, tokens_list[i], Int32(i), [0], false)
}
batch.logits[Int(batch.n_tokens) - 1] = 1 // true
@ -141,18 +183,11 @@ actor LlamaContext {
print(new_token_str)
// tokens_list.append(new_token_id)
batch.n_tokens = 0
batch.token[Int(batch.n_tokens)] = new_token_id
batch.pos[Int(batch.n_tokens)] = n_cur
batch.n_seq_id[Int(batch.n_tokens)] = 1
batch.seq_id[Int(batch.n_tokens)]![0] = 0
batch.logits[Int(batch.n_tokens)] = 1 // true
batch.n_tokens += 1
llama_batch_clear(&batch)
llama_batch_add(&batch, new_token_id, n_cur, [0], true)
n_decode += 1
n_cur += 1
n_cur += 1
if llama_decode(context, batch) != 0 {
print("failed to evaluate llama!")
@ -161,14 +196,111 @@ actor LlamaContext {
return new_token_str
}
func bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) -> String {
var pp_avg: Double = 0
var tg_avg: Double = 0
var pp_std: Double = 0
var tg_std: Double = 0
for _ in 0..<nr {
// bench prompt processing
llama_batch_clear(&batch)
let n_tokens = pp
for i in 0..<n_tokens {
llama_batch_add(&batch, 0, Int32(i), [0], false)
}
batch.logits[Int(batch.n_tokens) - 1] = 1 // true
llama_kv_cache_clear(context)
let t_pp_start = ggml_time_us()
if llama_decode(context, batch) != 0 {
print("llama_decode() failed during prompt")
}
let t_pp_end = ggml_time_us()
// bench text generation
llama_kv_cache_clear(context)
let t_tg_start = ggml_time_us()
for i in 0..<tg {
llama_batch_clear(&batch)
for j in 0..<pl {
llama_batch_add(&batch, 0, Int32(i), [Int32(j)], true)
}
if llama_decode(context, batch) != 0 {
print("llama_decode() failed during text generation")
}
}
let t_tg_end = ggml_time_us()
llama_kv_cache_clear(context)
let t_pp = Double(t_pp_end - t_pp_start) / 1000000.0
let t_tg = Double(t_tg_end - t_tg_start) / 1000000.0
let speed_pp = Double(pp) / t_pp
let speed_tg = Double(pl*tg) / t_tg
pp_avg += speed_pp
tg_avg += speed_tg
pp_std += speed_pp * speed_pp
tg_std += speed_tg * speed_tg
print("pp \(speed_pp) t/s, tg \(speed_tg) t/s")
}
pp_avg /= Double(nr)
tg_avg /= Double(nr)
if nr > 1 {
pp_std = sqrt(pp_std / Double(nr - 1) - pp_avg * pp_avg * Double(nr) / Double(nr - 1))
tg_std = sqrt(tg_std / Double(nr - 1) - tg_avg * tg_avg * Double(nr) / Double(nr - 1))
} else {
pp_std = 0
tg_std = 0
}
let model_desc = model_info();
let model_size = String(format: "%.2f GiB", Double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0);
let model_n_params = String(format: "%.2f B", Double(llama_model_n_params(model)) / 1e9);
let backend = "Metal";
let pp_avg_str = String(format: "%.2f", pp_avg);
let tg_avg_str = String(format: "%.2f", tg_avg);
let pp_std_str = String(format: "%.2f", pp_std);
let tg_std_str = String(format: "%.2f", tg_std);
var result = ""
result += String("| model | size | params | backend | test | t/s |\n")
result += String("| --- | --- | --- | --- | --- | --- |\n")
result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | pp \(pp) | \(pp_avg_str) ± \(pp_std_str) |\n")
result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | tg \(tg) | \(tg_avg_str) ± \(tg_std_str) |\n")
return result;
}
func clear() {
tokens_list.removeAll()
temporary_invalid_cchars.removeAll()
llama_kv_cache_clear(context)
}
private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
let utf8Count = text.utf8.count
let n_tokens = utf8Count + (add_bos ? 1 : 0)
let n_tokens = utf8Count + (add_bos ? 1 : 0) + 1
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)

898
examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
View file

@ -1,481 +1,483 @@
// !$*UTF8*$!
{
archiveVersion = 1;
classes = {
};
objectVersion = 56;
objects = {
archiveVersion = 1;
classes = {
};
objectVersion = 56;
objects = {
/* Begin PBXBuildFile section */
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5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; };
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defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
};
8A1C83812AC328BE0096AF73 /* Build configuration list for PBXNativeTarget "llama.swiftui" */ = {
isa = XCConfigurationList;
buildConfigurations = (
8A1C83822AC328BE0096AF73 /* Debug */,
8A1C83832AC328BE0096AF73 /* Release */,
);
defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
};
8A1C836E2AC328BD0096AF73 /* Build configuration list for PBXProject "llama.swiftui" */ = {
isa = XCConfigurationList;
buildConfigurations = (
8A1C837F2AC328BE0096AF73 /* Debug */,
8A1C83802AC328BE0096AF73 /* Release */,
);
defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
};
8A1C83812AC328BE0096AF73 /* Build configuration list for PBXNativeTarget "llama.swiftui" */ = {
isa = XCConfigurationList;
buildConfigurations = (
8A1C83822AC328BE0096AF73 /* Debug */,
8A1C83832AC328BE0096AF73 /* Release */,
);
defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
};
/* End XCConfigurationList section */
};
rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
};
rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
}

52
examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
View file

@ -3,24 +3,26 @@ import Foundation
@MainActor
class LlamaState: ObservableObject {
@Published var messageLog = ""
@Published var cacheCleared = false
private var llamaContext: LlamaContext?
private var modelUrl: URL? {
Bundle.main.url(forResource: "q8_0", withExtension: "gguf", subdirectory: "models")
private var defaultModelUrl: URL? {
Bundle.main.url(forResource: "ggml-model", withExtension: "gguf", subdirectory: "models")
// Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models")
}
init() {
do {
try loadModel()
try loadModel(modelUrl: defaultModelUrl)
} catch {
messageLog += "Error!\n"
}
}
private func loadModel() throws {
func loadModel(modelUrl: URL?) throws {
messageLog += "Loading model...\n"
if let modelUrl {
llamaContext = try LlamaContext.createContext(path: modelUrl.path())
llamaContext = try LlamaContext.create_context(path: modelUrl.path())
messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
} else {
messageLog += "Could not locate model\n"
@ -31,7 +33,7 @@ class LlamaState: ObservableObject {
guard let llamaContext else {
return
}
messageLog += "Attempting to complete text...\n"
await llamaContext.completion_init(text: text)
messageLog += "\(text)"
@ -42,4 +44,42 @@ class LlamaState: ObservableObject {
await llamaContext.clear()
messageLog += "\n\ndone\n"
}
func bench() async {
guard let llamaContext else {
return
}
messageLog += "\n"
messageLog += "Running benchmark...\n"
messageLog += "Model info: "
messageLog += await llamaContext.model_info() + "\n"
let t_start = DispatchTime.now().uptimeNanoseconds
await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up
let t_end = DispatchTime.now().uptimeNanoseconds
let t_heat = Double(t_end - t_start) / 1_000_000_000.0
messageLog += "Heat up time: \(t_heat) seconds, please wait...\n"
// if more than 5 seconds, then we're probably running on a slow device
if t_heat > 5.0 {
messageLog += "Heat up time is too long, aborting benchmark\n"
return
}
let result = await llamaContext.bench(pp: 512, tg: 128, pl: 1, nr: 3)
messageLog += "\(result)"
messageLog += "\n"
}
func clear() async {
guard let llamaContext else {
return
}
await llamaContext.clear()
messageLog = ""
}
}

149
examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
View file

@ -5,24 +5,132 @@ struct ContentView: View {
@State private var multiLineText = ""
private static func cleanupModelCaches() {
// Delete all models (*.gguf)
let fileManager = FileManager.default
let documentsUrl = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0]
do {
let fileURLs = try fileManager.contentsOfDirectory(at: documentsUrl, includingPropertiesForKeys: nil)
for fileURL in fileURLs {
if fileURL.pathExtension == "gguf" {
try fileManager.removeItem(at: fileURL)
}
}
} catch {
print("Error while enumerating files \(documentsUrl.path): \(error.localizedDescription)")
}
}
var body: some View {
VStack {
ScrollView(.vertical) {
ScrollView(.vertical, showsIndicators: true) {
Text(llamaState.messageLog)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
.padding()
.onTapGesture {
UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil)
}
}
TextEditor(text: $multiLineText)
.frame(height: 200)
.frame(height: 80)
.padding()
.border(Color.gray, width: 0.5)
Button(action: {
sendText()
}) {
Text("Send")
.padding()
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
HStack {
Button("Send") {
sendText()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Bench") {
bench()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Clear") {
clear()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Copy") {
UIPasteboard.general.string = llamaState.messageLog
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
}
VStack {
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)",
modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
)
.font(.system(size: 12))
.padding(.top, 4)
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (Q8_0, 1.1 GiB)",
modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true",
filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf"
)
.font(.system(size: 12))
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (F16, 2.2 GiB)",
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true",
filename: "tinyllama-1.1b-f16.gguf"
)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
modelName: "Phi-2.7B (Q4_0, 1.6 GiB)",
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true",
filename: "phi-2-q4_0.gguf"
)
.font(.system(size: 12))
DownloadButton(
llamaState: llamaState,
modelName: "Phi-2.7B (Q8_0, 2.8 GiB)",
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true",
filename: "phi-2-q8_0.gguf"
)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
modelName: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)",
modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true",
filename: "mistral-7b-v0.1.Q4_0.gguf"
)
.font(.system(size: 12))
Button("Clear downloaded models") {
ContentView.cleanupModelCaches()
llamaState.cacheCleared = true
}
.padding(8)
.font(.system(size: 12))
}
}
.padding()
@ -34,9 +142,20 @@ struct ContentView: View {
multiLineText = ""
}
}
func bench() {
Task {
await llamaState.bench()
}
}
func clear() {
Task {
await llamaState.clear()
}
}
}
/*
#Preview {
ContentView()
}
*/
//#Preview {
// ContentView()
//}

122
examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift Normal file
View file

@ -0,0 +1,122 @@
import SwiftUI
struct DownloadButton: View {
@ObservedObject private var llamaState: LlamaState
private var modelName: String
private var modelUrl: String
private var filename: String
@State private var status: String
@State private var downloadTask: URLSessionDownloadTask?
@State private var progress = 0.0
@State private var observation: NSKeyValueObservation?
private static func getFileURL(filename: String) -> URL {
FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0].appendingPathComponent(filename)
}
private func checkFileExistenceAndUpdateStatus() {
}
init(llamaState: LlamaState, modelName: String, modelUrl: String, filename: String) {
self.llamaState = llamaState
self.modelName = modelName
self.modelUrl = modelUrl
self.filename = filename
let fileURL = DownloadButton.getFileURL(filename: filename)
status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
}
private func download() {
status = "downloading"
print("Downloading model \(modelName) from \(modelUrl)")
guard let url = URL(string: modelUrl) else { return }
let fileURL = DownloadButton.getFileURL(filename: filename)
downloadTask = URLSession.shared.downloadTask(with: url) { temporaryURL, response, error in
if let error = error {
print("Error: \(error.localizedDescription)")
return
}
guard let response = response as? HTTPURLResponse, (200...299).contains(response.statusCode) else {
print("Server error!")
return
}
do {
if let temporaryURL = temporaryURL {
try FileManager.default.copyItem(at: temporaryURL, to: fileURL)
print("Writing to \(filename) completed")
llamaState.cacheCleared = false
status = "downloaded"
}
} catch let err {
print("Error: \(err.localizedDescription)")
}
}
observation = downloadTask?.progress.observe(\.fractionCompleted) { progress, _ in
self.progress = progress.fractionCompleted
}
downloadTask?.resume()
}
var body: some View {
VStack {
if status == "download" {
Button(action: download) {
Text("Download " + modelName)
}
} else if status == "downloading" {
Button(action: {
downloadTask?.cancel()
status = "download"
}) {
Text("\(modelName) (Downloading \(Int(progress * 100))%)")
}
} else if status == "downloaded" {
Button(action: {
let fileURL = DownloadButton.getFileURL(filename: filename)
if !FileManager.default.fileExists(atPath: fileURL.path) {
download()
return
}
do {
try llamaState.loadModel(modelUrl: fileURL)
} catch let err {
print("Error: \(err.localizedDescription)")
}
}) {
Text("\(modelName) (Downloaded)")
}
} else {
Text("Unknown status")
}
}
.onDisappear() {
downloadTask?.cancel()
}
.onChange(of: llamaState.cacheCleared) { newValue in
if newValue {
downloadTask?.cancel()
let fileURL = DownloadButton.getFileURL(filename: filename)
status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
}
}
}
}
// #Preview {
// DownloadButton(
// llamaState: LlamaState(),
// modelName: "TheBloke / TinyLlama-1.1B-1T-OpenOrca-GGUF (Q4_0)",
// modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
// filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
// )
// }

52
examples/server/server.cpp
View file

@ -10,7 +10,8 @@
// crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1
#endif
// increase max payload length to allow use of larger context size
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
#include "httplib.h"
#include "json.hpp"
@ -2413,7 +2414,7 @@ json oaicompat_completion_params_parse(
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0);
if (llama_params.count("grammar") != 0) {
if (body.count("grammar") != 0) {
llama_params["grammar"] = json_value(body, "grammar", json::object());
}
@ -2644,6 +2645,9 @@ static void append_to_generated_text_from_generated_token_probs(llama_server_con
int main(int argc, char **argv)
{
#if SERVER_VERBOSE != 1
log_disable();
#endif
// own arguments required by this example
gpt_params params;
server_params sparams;
@ -2698,7 +2702,7 @@ int main(int argc, char **argv)
}
// API key is invalid or not provided
res.set_content("Unauthorized: Invalid API Key", "text/plain");
res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8");
res.status = 401; // Unauthorized
LOG_WARNING("Unauthorized: Invalid API Key", {});
@ -2713,28 +2717,28 @@ int main(int argc, char **argv)
// this is only called if no index.html is found in the public --path
svr.Get("/", [](const httplib::Request &, httplib::Response &res)
{
res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html; charset=utf-8");
return false;
});
// this is only called if no index.js is found in the public --path
svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res)
{
res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript; charset=utf-8");
return false;
});
// this is only called if no index.html is found in the public --path
svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res)
{
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript; charset=utf-8");
return false;
});
// this is only called if no index.html is found in the public --path
svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res)
{
res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript");
res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
return false;
});
@ -2745,7 +2749,7 @@ int main(int argc, char **argv)
{ "user_name", llama.name_user.c_str() },
{ "assistant_name", llama.name_assistant.c_str() }
};
res.set_content(data.dump(), "application/json");
res.set_content(data.dump(), "application/json; charset=utf-8");
});
svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
@ -2759,12 +2763,12 @@ int main(int argc, char **argv)
std::string completion_text;
task_result result = llama.next_result(task_id);
if (!result.error && result.stop) {
res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json");
res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
}
else
{
res.status = 404;
res.set_content(result.result_json["content"], "text/plain");
res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
return;
}
} else {
@ -2835,7 +2839,7 @@ int main(int argc, char **argv)
}}
};
res.set_content(models.dump(), "application/json");
res.set_content(models.dump(), "application/json; charset=utf-8");
});
// TODO: add mount point without "/v1" prefix -- how?
@ -2857,10 +2861,10 @@ int main(int argc, char **argv)
res.set_content(oaicompat_result.dump(-1, ' ', false,
json::error_handler_t::replace),
"application/json");
"application/json; charset=utf-8");
} else {
res.status = 500;
res.set_content(result.result_json["content"], "text/plain");
res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
return;
}
} else {
@ -2924,12 +2928,12 @@ int main(int argc, char **argv)
task_result result = llama.next_result(task_id);
if (!result.error && result.stop)
{
res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json");
res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
}
else
{
res.status = 404;
res.set_content(result.result_json["content"], "text/plain");
res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
return;
}
} else {
@ -2978,11 +2982,11 @@ int main(int argc, char **argv)
svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
{
const json data = llama.get_model_props();
return res.set_content(data.dump(), "application/json");
return res.set_content(data.dump(), "application/json; charset=utf-8");
});
svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
{ return res.set_content("", "application/json"); });
{ return res.set_content("", "application/json; charset=utf-8"); });
svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res)
{
@ -2993,7 +2997,7 @@ int main(int argc, char **argv)
tokens = llama.tokenize(body["content"], false);
}
const json data = format_tokenizer_response(tokens);
return res.set_content(data.dump(), "application/json");
return res.set_content(data.dump(), "application/json; charset=utf-8");
});
svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res)
@ -3007,7 +3011,7 @@ int main(int argc, char **argv)
}
const json data = format_detokenized_response(content);
return res.set_content(data.dump(), "application/json");
return res.set_content(data.dump(), "application/json; charset=utf-8");
});
svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res)
@ -3024,7 +3028,7 @@ int main(int argc, char **argv)
}
const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1);
task_result result = llama.next_result(task_id);
return res.set_content(result.result_json.dump(), "application/json");
return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
});
svr.set_logger(log_server_request);
@ -3045,7 +3049,7 @@ int main(int argc, char **argv)
{
snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
}
res.set_content(buf, "text/plain");
res.set_content(buf, "text/plain; charset=utf-8");
res.status = 500;
});
@ -3053,15 +3057,15 @@ int main(int argc, char **argv)
{
if (res.status == 401)
{
res.set_content("Unauthorized", "text/plain");
res.set_content("Unauthorized", "text/plain; charset=utf-8");
}
if (res.status == 400)
{
res.set_content("Invalid request", "text/plain");
res.set_content("Invalid request", "text/plain; charset=utf-8");
}
else if (res.status == 404)
{
res.set_content("File Not Found", "text/plain");
res.set_content("File Not Found", "text/plain; charset=utf-8");
res.status = 404;
}
});

237
ggml-cuda.cu
View file

@ -31,6 +31,7 @@
#define CUDA_R_16F HIPBLAS_R_16F
#define CUDA_R_32F HIPBLAS_R_32F
#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
#define cublasCreate hipblasCreate
#define cublasGemmEx hipblasGemmEx
#define cublasGemmBatchedEx hipblasGemmBatchedEx
@ -40,6 +41,7 @@
#define cublasSetStream hipblasSetStream
#define cublasSgemm hipblasSgemm
#define cublasStatus_t hipblasStatus_t
#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6
#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
@ -4998,7 +5000,16 @@ static __global__ void rope_neox(
const int ib = col / n_dims;
const int ic = col % n_dims;
const int i = row*ncols + ib*n_dims + ic/2;
if (ib > 0) {
const int i = row*ncols + ib*n_dims + ic;
dst[i + 0] = x[i + 0];
dst[i + 1] = x[i + 1];
return;
}
const int i = row*ncols + ib*n_dims + ic/2;
const int i2 = row/p_delta_rows;
float cur_rot = inv_ndims * ic - ib;
@ -7057,6 +7068,7 @@ inline void ggml_cuda_op_upscale(
(void) src1;
(void) dst;
(void) src1_dd;
}
inline void ggml_cuda_op_pad(
@ -7073,6 +7085,7 @@ inline void ggml_cuda_op_pad(
(void) src1;
(void) dst;
(void) src1_dd;
}
inline void ggml_cuda_op_rms_norm(
@ -7376,7 +7389,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
const int compute_capability = g_compute_capabilities[id];
if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1]) {
if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
// convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
half * src0_as_f16 = nullptr;
size_t src0_as = 0;
@ -7817,6 +7830,11 @@ static void ggml_cuda_set_peer_access(const int n_tokens) {
}
#ifdef NDEBUG
for (int id = 0; id < g_device_count; ++id) {
CUDA_CHECK(ggml_cuda_set_device(id));
CUDA_CHECK(cudaDeviceSynchronize());
}
for (int id = 0; id < g_device_count; ++id) {
CUDA_CHECK(ggml_cuda_set_device(id));
@ -7868,8 +7886,6 @@ static void ggml_cuda_op_mul_mat(
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
ggml_cuda_set_peer_access(ne11);
GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
@ -8300,27 +8316,27 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
}
static __global__ void k_compute_batched_ptrs(
const half * src0_as_f16, const half * src1_as_f16, half * dst_f16,
const half * src0_as_f16, const half * src1_as_f16, char * dst,
const void ** ptrs_src, void ** ptrs_dst,
int ne12, int ne13,
int ne23,
int nb02, int nb03,
int nb12, int nb13,
int nb2, int nb3,
int r2, int r3) {
int i13 = blockIdx.x * blockDim.x + threadIdx.x;
int i12 = blockIdx.y * blockDim.y + threadIdx.y;
int64_t ne12, int64_t ne13,
int64_t ne23,
size_t nb02, size_t nb03,
size_t nb12, size_t nb13,
size_t nbd2, size_t nbd3,
int64_t r2, int64_t r3) {
int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x;
int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y;
if (i13 >= ne13 || i12 >= ne12) {
return;
}
int i03 = i13 / r3;
int i02 = i12 / r2;
int64_t i03 = i13 / r3;
int64_t i02 = i12 / r2;
ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2;
ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3;
}
static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@ -8376,7 +8392,41 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
size_t dst_as = 0;
half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
half * dst_f16 = nullptr;
char * dst_t = nullptr;
cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
cudaDataType_t cu_data_type = CUDA_R_16F;
// dst strides
size_t nbd2 = dst->nb[2];
size_t nbd3 = dst->nb[3];
const half alpha_f16 = 1.0f;
const half beta_f16 = 0.0f;
const float alpha_f32 = 1.0f;
const float beta_f32 = 0.0f;
const void * alpha = &alpha_f16;
const void * beta = &beta_f16;
if (dst->op_params[0] == GGML_PREC_DEFAULT) {
dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
dst_t = (char *) dst_f16;
nbd2 /= sizeof(float) / sizeof(half);
nbd3 /= sizeof(float) / sizeof(half);
} else {
dst_t = (char *) dst_ddf;
cu_compute_type = CUBLAS_COMPUTE_32F;
cu_data_type = CUDA_R_32F;
alpha = &alpha_f32;
beta = &beta_f32;
}
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);
@ -8385,9 +8435,6 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
const half alpha_f16 = 1.0f;
const half beta_f16 = 0.0f;
#if 0
// use cublasGemmEx
{
@ -8397,12 +8444,12 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
int i02 = i12 / r2;
CUBLAS_CHECK(
cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
cublasGemmEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha_f16, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half),
(const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float),
&beta_f16, ( char *) dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2, CUDA_R_16F, ne01,
CUBLAS_COMPUTE_16F,
alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half),
(const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float),
beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01,
cu_compute_type,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
}
}
@ -8414,11 +8461,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
CUBLAS_CHECK(
cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha_f16, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA
(const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
&beta_f16, ( char *) dst_f16, CUDA_R_16F, ne01, dst->nb[2]/sizeof(float), // strideC
alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA
(const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
beta, ( char *) dst_t, cu_data_type, ne01, dst->nb[2]/sizeof(float), // strideC
ne12*ne13,
CUBLAS_COMPUTE_16F,
cu_compute_type,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
} else {
// use cublasGemmBatchedEx
@ -8435,24 +8482,24 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
dim3 block_dims(ne13, ne12);
k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
src0_as_f16, src1_as_f16, dst_f16,
src0_as_f16, src1_as_f16, dst_t,
ptrs_src, ptrs_dst,
ne12, ne13,
ne23,
nb02, nb03,
nb12, nb13,
dst->nb[2], dst->nb[3],
nbd2, nbd3,
r2, r3);
CUDA_CHECK(cudaGetLastError());
CUBLAS_CHECK(
cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
(const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
&beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
alpha, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
(const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
beta, ( void **) (ptrs_dst + 0*ne23), cu_data_type, ne01,
ne23,
CUBLAS_COMPUTE_16F,
cu_compute_type,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
if (ptrs_src_s != 0) {
@ -8464,11 +8511,14 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
}
#endif
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
if (dst->op_params[0] == GGML_PREC_DEFAULT) {
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
ggml_cuda_pool_free(dst_f16, dst_as);
}
ggml_cuda_pool_free(src1_as_f16, src1_as);
ggml_cuda_pool_free(dst_f16, dst_as);
}
static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@ -8734,16 +8784,21 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
GGML_ASSERT(dst->backend == GGML_BACKEND_GPU);
const int64_t nb11 = src1->nb[1];
const int64_t nb1 = dst->nb[1];
const struct ggml_tensor * ids = src0;
const int32_t id = ((int32_t *) dst->op_params)[0];
const int32_t n_as = ((int32_t *) dst->op_params)[1];
std::vector<char> ids_host(ggml_nbytes(ids));
const cudaStream_t stream = g_cudaStreams[g_main_device][0];
if (ids->backend == GGML_BACKEND_GPU) {
const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
CUDA_CHECK(cudaStreamSynchronize(stream));
} else {
memcpy(ids_host.data(), ids->data, ggml_nbytes(ids));
}
@ -8757,37 +8812,93 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
ggml_tensor src1_row = *src1;
ggml_tensor dst_row = *dst;
src1_row.ne[1] = 1;
dst_row.ne[1] = 1;
src1_row.nb[2] = src1_row.nb[1];
dst_row.nb[2] = dst_row.nb[1];
src1_row.nb[3] = src1_row.nb[1];
dst_row.nb[3] = dst_row.nb[1];
src1_row.extra = &src1_row_extra;
dst_row.extra = &dst_row_extra;
char * src1_original = (char *) src1_extra->data_device[g_main_device];
char * dst_original = (char *) dst_extra->data_device[g_main_device];
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
//int32_t row_id;
//CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
//CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
if (src1->ne[1] == 1) {
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
//int32_t row_id;
//CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
//CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
GGML_ASSERT(row_id >= 0 && row_id < n_as);
GGML_ASSERT(row_id >= 0 && row_id < n_as);
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
src1_row_extra.data_device[g_main_device] = (char *) src1_extra->data_device[g_main_device] + i01*src1->nb[1];
src1_row.data = (char *) src1->data + i01*src1->nb[1];
src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1];
src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set?
dst_row_extra.data_device[g_main_device] = (char *) dst_extra->data_device[g_main_device] + i01*dst->nb[1];
dst_row.data = (char *) dst->data + i01*dst->nb[1];
dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1];
dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set?
ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
}
} else {
size_t as_src1, as_dst;
char * src1_contiguous = (char *) ggml_cuda_pool_malloc(sizeof(float)*ggml_nelements(src1), &as_src1);
char * dst_contiguous = (char *) ggml_cuda_pool_malloc(sizeof(float)*ggml_nelements(dst), &as_dst);
src1_row_extra.data_device[g_main_device] = src1_contiguous;
dst_row_extra.data_device[g_main_device] = dst_contiguous;
for (int32_t row_id = 0; row_id < n_as; ++row_id) {
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
int64_t num_src1_rows = 0;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
if (row_id_i != row_id) {
continue;
}
GGML_ASSERT(row_id >= 0 && row_id < n_as);
CUDA_CHECK(cudaMemcpyAsync(src1_contiguous + num_src1_rows*nb11, src1_original + i01*nb11,
nb11, cudaMemcpyDeviceToDevice, stream));
num_src1_rows++;
}
if (num_src1_rows == 0) {
continue;
}
src1_row.ne[1] = num_src1_rows;
dst_row.ne[1] = num_src1_rows;
src1_row.nb[1] = nb11;
src1_row.nb[2] = num_src1_rows*nb11;
src1_row.nb[3] = num_src1_rows*nb11;
dst_row.nb[1] = nb1;
dst_row.nb[2] = num_src1_rows*nb1;
dst_row.nb[3] = num_src1_rows*nb1;
ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
num_src1_rows = 0;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
if (row_id_i != row_id) {
continue;
}
GGML_ASSERT(row_id >= 0 && row_id < n_as);
CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous + num_src1_rows*nb1,
nb1, cudaMemcpyDeviceToDevice, stream));
num_src1_rows++;
}
}
ggml_cuda_pool_free(src1_contiguous, as_src1);
ggml_cuda_pool_free(dst_contiguous, as_dst);
}
}
@ -9323,6 +9434,10 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
return false;
}
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
ggml_cuda_set_peer_access(tensor->src[1]->ne[1]);
}
if (params->ith != 0) {
return true;
}

13
ggml-metal.metal
View file

@ -1702,8 +1702,9 @@ kernel void kernel_rope(
dst_data[1] = x0*sin_theta + x1*cos_theta;
}
} else {
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 2*tiitg; ic < n_dims; ic += 2*tptg.x) {
for (int64_t ic = 2*tiitg; ic < ne0; ic += 2*tptg.x) {
if (ic < n_dims) {
const int64_t ib = 0;
// simplified from `(ib * n_dims + ic) * inv_ndims`
const float cur_rot = inv_ndims*ic - ib;
@ -1722,6 +1723,14 @@ kernel void kernel_rope(
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
} else {
const int64_t i0 = ic;
device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}

46
ggml.c
View file

@ -4098,6 +4098,14 @@ struct ggml_tensor * ggml_mul_mat(
return result;
}
void ggml_mul_mat_set_prec(
struct ggml_tensor * a,
enum ggml_prec prec) {
const int32_t prec_i32 = (int32_t) prec;
ggml_set_op_params_i32(a, 0, prec_i32);
}
// ggml_mul_mat_id
struct ggml_tensor * ggml_mul_mat_id(
@ -9168,6 +9176,8 @@ static void ggml_compute_forward_norm_f32(
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
GGML_ASSERT(eps > 0.0f);
// TODO: optimize
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
@ -9237,6 +9247,8 @@ static void ggml_compute_forward_rms_norm_f32(
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
GGML_ASSERT(eps > 0.0f);
// TODO: optimize
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
@ -11562,10 +11574,13 @@ static void ggml_compute_forward_rope_f32(
}
} else {
// TODO: this might be wrong for ne0 != n_dims - need double check
// ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
// it seems we have to rope just the first n_dims elements and do nothing with the rest
// ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26
theta_base *= freq_scale;
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {
for (int64_t ic = 0; ic < ne0; ic += 2) {
if (ic < n_dims) {
const int64_t ib = 0;
// simplified from `(ib * n_dims + ic) * inv_ndims`
float cur_rot = inv_ndims * ic - ib;
@ -11588,6 +11603,14 @@ static void ggml_compute_forward_rope_f32(
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
} else {
const int64_t i0 = ic;
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
@ -11715,10 +11738,13 @@ static void ggml_compute_forward_rope_f16(
}
} else {
// TODO: this might be wrong for ne0 != n_dims - need double check
// ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
// it seems we have to rope just the first n_dims elements and do nothing with the rest
// ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26
theta_base *= freq_scale;
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {
for (int64_t ic = 0; ic < ne0; ic += 2) {
if (ic < n_dims) {
const int64_t ib = 0;
// simplified from `(ib * n_dims + ic) * inv_ndims`
float cur_rot = inv_ndims * ic - ib;
@ -11741,6 +11767,14 @@ static void ggml_compute_forward_rope_f16(
dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
} else {
const int64_t i0 = ic;
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}

14
ggml.h
View file

@ -303,7 +303,7 @@ extern "C" {
#if defined(__ARM_NEON) && defined(__CUDACC__)
typedef half ggml_fp16_t;
#elif defined(__ARM_NEON)
#elif defined(__ARM_NEON) && !defined(_MSC_VER)
typedef __fp16 ggml_fp16_t;
#else
typedef uint16_t ggml_fp16_t;
@ -343,6 +343,12 @@ extern "C" {
GGML_TYPE_COUNT,
};
// precision
enum ggml_prec {
GGML_PREC_DEFAULT,
GGML_PREC_F32,
};
enum ggml_backend_type {
GGML_BACKEND_CPU = 0,
GGML_BACKEND_GPU = 10,
@ -1057,6 +1063,12 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// change the precision of a matrix multiplication
// set to GGML_PREC_F32 for higher precision (useful for phi-2)
GGML_API void ggml_mul_mat_set_prec(
struct ggml_tensor * a,
enum ggml_prec prec);
// indirect matrix multiplication
// ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
GGML_API struct ggml_tensor * ggml_mul_mat_id(

13
gguf-py/gguf/constants.py
View file

@ -95,6 +95,7 @@ class MODEL_ARCH(IntEnum):
BLOOM = auto()
STABLELM = auto()
QWEN = auto()
PHI2 = auto()
class MODEL_TENSOR(IntEnum):
@ -140,6 +141,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm",
MODEL_ARCH.QWEN: "qwen",
MODEL_ARCH.PHI2: "phi2",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -359,6 +361,17 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.PHI2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
]
# TODO
}

8
gguf-py/gguf/tensor_mapping.py
View file

@ -18,6 +18,7 @@ class TensorNameMap:
"embeddings.word_embeddings", # bert
"language_model.embedding.word_embeddings", # persimmon
"wte", # gpt2
"transformer.embd.wte", # phi2
),
# Token type embeddings
@ -43,6 +44,7 @@ class TensorNameMap:
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen
"output", # llama-pth bloom
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
),
# Output norm
@ -55,6 +57,7 @@ class TensorNameMap:
"transformer.norm_f", # mpt
"ln_f", # refact bloom qwen gpt2
"language_model.encoder.final_layernorm", # persimmon
"lm_head.ln", # phi2
),
# Rope frequencies
@ -78,6 +81,7 @@ class TensorNameMap:
"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
"model.layers.{bid}.ln1", # yi
"h.{bid}.ln_1", # gpt2
"transformer.h.{bid}.ln", # phi2
),
# Attention norm 2
@ -94,6 +98,7 @@ class TensorNameMap:
"h.{bid}.self_attention.query_key_value", # bloom
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
"h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.mixer.Wqkv", # phi2
),
# Attention query
@ -133,6 +138,7 @@ class TensorNameMap:
"transformer.h.{bid}.attn.out_proj", # gpt-j
"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
"h.{bid}.attn.c_proj", # gpt2
"transformer.h.{bid}.mixer.out_proj", # phi2
),
# Rotary embeddings
@ -174,6 +180,7 @@ class TensorNameMap:
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
"transformer.h.{bid}.mlp.w1", # qwen
"h.{bid}.mlp.c_fc", # gpt2
"transformer.h.{bid}.mlp.fc1", # phi2
),
MODEL_TENSOR.FFN_UP_EXP: (
@ -206,6 +213,7 @@ class TensorNameMap:
"transformer.h.{bid}.mlp.fc_out", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
"h.{bid}.mlp.c_proj", # gpt2
"transformer.h.{bid}.mlp.fc2", # phi2
),
MODEL_TENSOR.FFN_DOWN_EXP: (

4
gguf-py/gguf/vocab.py
View file

@ -109,8 +109,10 @@ class SpecialVocab:
return True
def _set_special_token(self, typ: str, tid: Any) -> None:
if not isinstance(tid, int) or tid < 0:
if not isinstance(tid, int):
return
if tid < 0:
raise ValueError(f'invalid value for special token type {typ}: {tid}')
if self.n_vocab is None or tid < self.n_vocab:
if typ in self.special_token_ids:
return

503
llama.cpp
View file

@ -195,6 +195,7 @@ enum llm_arch {
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_QWEN,
LLM_ARCH_PHI2,
LLM_ARCH_UNKNOWN,
};
@ -212,6 +213,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
{ LLM_ARCH_QWEN, "qwen" },
{ LLM_ARCH_PHI2, "phi2" },
};
enum llm_kv {
@ -559,6 +561,19 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_PHI2,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_UNKNOWN,
@ -1430,6 +1445,7 @@ struct llama_model {
struct ggml_tensor * output_norm;
struct ggml_tensor * output_norm_b;
struct ggml_tensor * output;
struct ggml_tensor * output_b;
std::vector<llama_layer> layers;
@ -1515,6 +1531,10 @@ struct llama_context {
// decode output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
#ifndef NDEBUG
// guard against access to unset logits
std::vector<bool> logits_valid;
#endif
bool logits_all = false;
// input embedding (1-dimensional array: [n_embd])
@ -1943,7 +1963,7 @@ namespace GGUFMeta {
target = override->bool_value;
return true;
}
return true;
return false;
}
template<typename OT>
@ -2403,25 +2423,25 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
case LLAMA_FTYPE_MOSTLY_F16: return "F16";
case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0";
case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
return "mostly Q4_1, some F16";
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
return "Q4_1, some F16";
case LLAMA_FTYPE_MOSTLY_Q5_0: return "Q5_0";
case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1";
case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K";
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small";
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K";
default: return "unknown, may not work";
}
@ -2540,6 +2560,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 22: model.type = e_model::MODEL_1B; break;
case 26: model.type = e_model::MODEL_3B; break;
case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = e_model::MODEL_13B; break;
@ -2641,6 +2662,15 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_PHI2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_3B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_GPT2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@ -3001,7 +3031,7 @@ static void llm_load_tensors(
(void) main_gpu;
enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU;
enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU;
enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU;
#ifdef GGML_USE_CUBLAS
@ -3644,6 +3674,73 @@ static void llm_load_tensors(
}
}
} break;
case LLM_ARCH_PHI2:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
backend_norm = llama_backend_offload;
backend_output = llama_backend_offload;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
model.output_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, backend_output);
if (backend_norm == GGML_BACKEND_GPU) {
vram_weights += ggml_nbytes(model.output_norm);
vram_weights += ggml_nbytes(model.output_norm_b);
vram_weights += ggml_nbytes(model.output);
vram_weights += ggml_nbytes(model.output_b);
}
}
const uint32_t n_ff = hparams.n_ff;
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
if (backend == GGML_BACKEND_GPU) {
vram_weights +=
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) +
ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) +
ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_up_b) +
ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_down_b);
}
}
} break;
case LLM_ARCH_GPT2:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
@ -3716,7 +3813,6 @@ static void llm_load_tensors(
}
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@ -4077,6 +4173,7 @@ static struct ggml_tensor * llm_build_ffn(
// if max_alibi_bias > 0 then apply ALiBi
static struct ggml_tensor * llm_build_kqv(
struct ggml_context * ctx,
const llama_model & model,
const llama_hparams & hparams,
const llama_kv_cache & kv,
struct ggml_tensor * wo,
@ -4088,6 +4185,7 @@ static struct ggml_tensor * llm_build_kqv(
int32_t n_tokens,
int32_t n_kv,
float max_alibi_bias,
float scale,
const llm_build_cb & cb,
int il) {
const int64_t n_embd = hparams.n_embd;
@ -4110,6 +4208,12 @@ static struct ggml_tensor * llm_build_kqv(
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
cb(kq, "kq", il);
if (model.arch == LLM_ARCH_PHI2) {
// for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
// ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
}
if (max_alibi_bias > 0.0f) {
// temporary branch until we figure out how to handle ggml_alibi through ggml_add
kq = ggml_scale(ctx, kq, kq_scale);
@ -4129,7 +4233,7 @@ static struct ggml_tensor * llm_build_kqv(
kq = ggml_soft_max(ctx, kq);
cb(kq, "kq_soft_max", il);
} else {
kq = ggml_soft_max_ext(ctx, kq, kq_mask, 1.0f/sqrtf(float(n_embd_head)));
kq = ggml_soft_max_ext(ctx, kq, kq_mask, scale);
cb(kq, "kq_soft_max_ext", il);
}
@ -4336,9 +4440,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -4519,9 +4623,9 @@ struct llm_build_context {
// apply ALiBi for 13B model
const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f;
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -4643,9 +4747,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -4743,9 +4847,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -4952,9 +5056,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
// TODO: not tested, could be broken
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -5043,9 +5147,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -5140,9 +5244,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -5234,9 +5338,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -5347,9 +5451,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -5406,15 +5510,15 @@ struct llm_build_context {
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos= ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_scale
struct ggml_tensor * KQ_scale= ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
cb(KQ_scale, "KQ_scale", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask= ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
@ -5464,9 +5568,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, NULL,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -5510,6 +5614,122 @@ struct llm_build_context {
return gf;
}
struct ggml_cgraph * build_phi2() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
struct ggml_tensor * cur;
struct ggml_tensor * attn_norm_output;
struct ggml_tensor * ffn_output;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// Q_scale
struct ggml_tensor * Q_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
cb(Q_scale, "Q_scale", -1);
// KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
cb(KQ_scale, "KQ_scale", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(attn_norm_output, "attn_norm", il);
// self-attention
{
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
cb(cur, "wqkv", il);
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Qcur = ggml_rope_custom(
ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Qcur = ggml_scale(ctx0, Qcur, Q_scale);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f, cb, il);
cb(cur, "kqv_out", il);
}
// FF
{
ffn_output = llm_build_ffn(ctx0, attn_norm_output,
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
cb(ffn_output, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_output);
cb(cur, "l_out", il);
cur = ggml_add(ctx0, cur, inpL);
cb(cur, "l_out", il);
inpL = cur;
}
cur = llm_build_norm(ctx0, inpL, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output_no_bias", -1);
cur = ggml_add(ctx0, cur, model.output_b);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_gpt2() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
@ -5565,9 +5785,9 @@ struct llm_build_context {
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
model.layers[il].wo, model.layers[il].bo,
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
@ -5621,7 +5841,7 @@ enum llm_offload_func_e {
OFFLOAD_FUNC_FRC, // force offload
OFFLOAD_FUNC_KQV,
OFFLOAD_FUNC_NR,
OFFLOAD_FUNC_EMB,
OFFLOAD_FUNC_EMB, // embeddings
OFFLOAD_FUNC_OUT,
};
@ -5706,6 +5926,7 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
{ "pos_embd", OFFLOAD_FUNC_NR },
{ "inp_pos", OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope)
{ "Q_scale", OFFLOAD_FUNC_FRC },
{ "KQ_scale", OFFLOAD_FUNC_FRC },
{ "KQ_mask", OFFLOAD_FUNC_FRC },
{ "K_shift", OFFLOAD_FUNC_FRC },
@ -5790,6 +6011,7 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
{ "l_out", OFFLOAD_FUNC },
{ "result_norm", OFFLOAD_FUNC_EMB },
{ "result_output_no_bias", OFFLOAD_FUNC_EMB },
{ "result_output", OFFLOAD_FUNC_OUT },
};
@ -5807,6 +6029,7 @@ static struct ggml_cgraph * llama_build_graph(
bool alloc_inp_tokens = false;
bool alloc_inp_embd = false;
bool alloc_inp_pos = false;
bool alloc_inp_Q_scale = false;
bool alloc_inp_KQ_scale = false;
bool alloc_inp_KQ_mask = false;
bool alloc_inp_K_shift = false;
@ -5874,7 +6097,7 @@ static struct ggml_cgraph * llama_build_graph(
alloc_inp_pos = true;
}
if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) {
if (!alloc_inp_Q_scale && strcmp(name, "Q_scale") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
@ -5882,6 +6105,23 @@ static struct ggml_cgraph * llama_build_graph(
ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head)));
}
alloc_inp_Q_scale = true;
}
if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) {
ggml_allocr_alloc(lctx.alloc, cur);
if (!ggml_allocr_is_measure(lctx.alloc)) {
const int64_t n_embd_head = model.hparams.n_embd_head();
if (model.arch == LLM_ARCH_PHI2) {
// with phi2, we scale the Q to avoid precision issues
// ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66
ggml_set_f32(cur, 1.0f);
} else {
ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head)));
}
}
alloc_inp_KQ_scale = true;
}
@ -6106,6 +6346,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_qwen();
} break;
case LLM_ARCH_PHI2:
{
result = llm.build_phi2();
} break;
case LLM_ARCH_GPT2:
{
result = llm.build_gpt2();
@ -6243,12 +6487,16 @@ static int llama_decode_internal(
ggml_allocr_alloc_graph(lctx.alloc, gf);
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
// the output is always the last tensor in the graph
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
GGML_ASSERT(strcmp(res->name, "result_output") == 0);
// the embeddings could be the second to last tensor, or the third to last tensor
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
GGML_ASSERT(strcmp(res->name, "result_output") == 0);
GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
if (strcmp(embeddings->name, "result_norm") != 0) {
embeddings = gf->nodes[gf->n_nodes - 3];
GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
}
#ifdef GGML_USE_CUBLAS
for (int i = 0; i < gf->n_leafs; i++) {
@ -6343,6 +6591,14 @@ static int llama_decode_internal(
{
auto & logits_out = lctx.logits;
#ifndef NDEBUG
auto & logits_valid = lctx.logits_valid;
logits_valid.clear();
logits_valid.resize(n_tokens);
logits_out.clear();
#endif
if (batch.logits) {
logits_out.resize(n_vocab * n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
@ -6350,13 +6606,22 @@ static int llama_decode_internal(
continue;
}
memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab);
#ifndef NDEBUG
logits_valid[i] = true;
#endif
}
} else if (lctx.logits_all) {
logits_out.resize(n_vocab * n_tokens);
memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens);
#ifndef NDEBUG
std::fill(logits_valid.begin(), logits_valid.end(), true);
#endif
} else {
logits_out.resize(n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab);
#ifndef NDEBUG
logits_valid[0] = true;
#endif
}
}
@ -8819,53 +9084,60 @@ static int llama_apply_lora_from_file_internal(
const int64_t t_start_lora_us = ggml_time_us();
auto fin = std::ifstream(path_lora, std::ios::binary);
if (!fin) {
LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
return 1;
}
llama_file fin(path_lora, "rb");
// verify magic and version
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
uint32_t format_version;
fin.read((char *) &format_version, sizeof(format_version));
uint32_t magic = fin.read_u32();
if (magic != LLAMA_FILE_MAGIC_GGLA) {
LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
return 1;
}
uint32_t format_version = fin.read_u32();
if (format_version != 1) {
LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
return 1;
}
}
int32_t lora_r;
int32_t lora_alpha;
fin.read((char *) &lora_r, sizeof(lora_r));
fin.read((char *) &lora_alpha, sizeof(lora_alpha));
int32_t lora_r = fin.read_u32();
int32_t lora_alpha = fin.read_u32();
float scaling = scale * (float)lora_alpha / (float)lora_r;
LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
// create a name -> tensor map of the model to accelerate lookups
// find the max tensor size to estimate the required temporary buffer size
size_t max_tensor_size = 0;
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
for (const auto & kv : model.tensors_by_name) {
model_tensors.insert(kv);
size_t f32_size = ggml_nelements(kv.second) * sizeof(float);
max_tensor_size = std::max(max_tensor_size, f32_size);
}
// create a temporary ggml context to store the lora tensors
// todo: calculate size from biggest possible tensor
std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
// TODO: use ggml-alloc
size_t lora_ctx_size = max_tensor_size * 3;
LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0);
std::vector<uint8_t> lora_buf(lora_ctx_size);
struct ggml_init_params params;
params.mem_size = lora_buf.size();
params.mem_buffer = lora_buf.data();
params.no_alloc = false;
ggml_context * lora_ctx = ggml_init(params);
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
using unique_context = std::unique_ptr<ggml_context, decltype(&ggml_free)>;
// create a name -> tensor map of the model to accelerate lookups
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
for (const auto & kv : model.tensors_by_name) {
model_tensors.insert(kv);
}
unique_context lora_ctx(nullptr, ggml_free);
lora_ctx.reset(ggml_init(params));
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
// load base model
std::unique_ptr<llama_model_loader> ml;
ggml_context * base_ctx = NULL;
unique_context base_ctx(nullptr, ggml_free);
std::vector<uint8_t> base_buf;
if (path_base_model) {
LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
@ -8874,6 +9146,7 @@ static int llama_apply_lora_from_file_internal(
size_t ctx_size;
size_t mmapped_size;
ml->calc_sizes(ctx_size, mmapped_size);
base_buf.resize(ctx_size);
ggml_init_params base_params;
@ -8881,9 +9154,9 @@ static int llama_apply_lora_from_file_internal(
base_params.mem_buffer = base_buf.data();
base_params.no_alloc = ml->use_mmap;
base_ctx = ggml_init(base_params);
base_ctx.reset(ggml_init(base_params));
// maybe this should in llama_model_loader
// maybe this should be in llama_model_loader
if (ml->use_mmap) {
ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa()));
}
@ -8896,27 +9169,35 @@ static int llama_apply_lora_from_file_internal(
std::vector<uint8_t> work_buffer;
while (true) {
if (fin.tell() == fin.size) {
// eof
break;
}
int32_t n_dims;
int32_t length;
int32_t name_len;
int32_t ftype;
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
if (fin.eof()) {
break;
fin.read_raw(&n_dims, sizeof(n_dims));
fin.read_raw(&name_len, sizeof(name_len));
fin.read_raw(&ftype, sizeof(ftype));
if (n_dims != 1 && n_dims != 2) {
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
fin.read_raw(&ne[i], sizeof(ne[i]));
}
std::string name;
{
GGML_ASSERT(name_len <= 1024);
char buf[1024];
fin.read(buf, length);
name = std::string(buf, length);
fin.read_raw(buf, name_len);
name = std::string(buf, name_len);
}
// check for lora suffix and get the type of tensor
@ -8930,7 +9211,7 @@ static int llama_apply_lora_from_file_internal(
std::string lora_type = name.substr(pos + lora_suffix.length());
std::string base_name = name;
base_name.erase(pos);
// LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
// LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str());
if (model_tensors.find(base_name) == model_tensors.end()) {
LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
@ -8949,22 +9230,15 @@ static int llama_apply_lora_from_file_internal(
return false;
}
}
ggml_tensor * lora_tensor;
if (n_dims == 2) {
lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
}
else {
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
ggml_set_name(lora_tensor, "lora_tensor");
ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]);
ggml_set_name(lora_tensor, name.c_str());
// load tensor data
size_t offset = fin.tellg();
size_t offset = fin.tell();
size_t tensor_data_size = ggml_nbytes(lora_tensor);
offset = (offset + 31) & -32;
fin.seekg(offset);
fin.read((char*)lora_tensor->data, tensor_data_size);
fin.seek(offset, SEEK_SET);
fin.read_raw(lora_tensor->data, tensor_data_size);
lora_tensors[name] = lora_tensor;
@ -8994,13 +9268,11 @@ static int llama_apply_lora_from_file_internal(
// load from base model
if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
// TODO: throw
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
return 1;
}
// TODO: not tested!! maybe not working!
base_t = ml->create_tensor(base_ctx, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
base_t = ml->create_tensor(base_ctx.get(), base_name, { dest_t->ne[0], dest_t->ne[1] }, GGML_BACKEND_CPU);
ml->load_data_for(base_t);
} else {
base_t = dest_t;
@ -9029,43 +9301,45 @@ static int llama_apply_lora_from_file_internal(
}
// w = w + BA*s
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB);
offload_func(BA);
ggml_set_name(BA, "BA");
if (scaling != 1.0f) {
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx.get(), scaling);
ggml_set_name(scale_tensor, "scale_tensor");
BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
BA = ggml_scale_inplace(lora_ctx.get(), BA, scale_tensor);
offload_func(BA);
ggml_set_name(BA, "BA_scaled");
}
ggml_tensor * r;
if (base_t == dest_t) {
r = ggml_add_inplace(lora_ctx, dest_t, BA);
r = ggml_add_inplace(lora_ctx.get(), dest_t, BA);
offload_func_force_inplace(r);
ggml_set_name(r, "r_add_inplace");
}
else {
r = ggml_add(lora_ctx, base_t, BA);
r = ggml_add(lora_ctx.get(), base_t, BA);
offload_func(r);
ggml_set_name(r, "r_add");
r = ggml_cpy(lora_ctx, r, dest_t);
r = ggml_cpy(lora_ctx.get(), r, dest_t);
offload_func(r);
ggml_set_name(r, "r_cpy");
}
struct ggml_cgraph * gf = ggml_new_graph(lora_ctx);
struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get());
ggml_build_forward_expand(gf, r);
ggml_graph_compute_helper(work_buffer, gf, n_threads);
// the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other
GGML_ASSERT(lora_tensors.size() == 2);
// we won't need these tensors again, reset the context to save memory
ggml_free(lora_ctx);
lora_ctx = ggml_init(params);
lora_ctx.reset(ggml_init(params));
lora_tensors.clear();
n_tensors++;
@ -9075,12 +9349,6 @@ static int llama_apply_lora_from_file_internal(
}
}
// TODO: this should be in a destructor, it will leak on failure
ggml_free(lora_ctx);
if (base_ctx) {
ggml_free(base_ctx);
}
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
@ -10245,6 +10513,7 @@ float * llama_get_logits(struct llama_context * ctx) {
}
float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
assert(ctx->logits_valid.at(i));
return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
}

1
llama.h
View file

@ -39,6 +39,7 @@
#define LLAMA_MAX_RNG_STATE (64*1024)
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN

1
tests/test-backend-ops.cpp
View file

@ -1555,6 +1555,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512)); // neox (falcon 40B)
test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512)); // neox (falcon 40B)
test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512)); // neox (stablelm)
test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512)); // neox (phi-2)
}
test_cases.emplace_back(new test_alibi());