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Georgi Gerganov 2 years ago
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3
.gitignore vendored
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sync.sh
main
*.o

109
Makefile
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main: ggml.o main.o
g++ -o main ggml.o main.o
ggml.o: ggml.c ggml.h
gcc -O3 -mavx -mavx2 -mfma -mf16c -c ggml.c
main.o: main.cpp ggml.h
g++ -O3 -std=c++11 -c main.cpp
# clean up the directory
clean:
rm -f *.o main
# run the program
run: main
./main
# download the following audio samples into folder "./samples":
.PHONY: samples
samples:
@echo "Downloading samples..."
mkdir -p samples
@wget --quiet --show-progress -O samples/gb0.ogg https://upload.wikimedia.org/wikipedia/commons/2/22/George_W._Bush%27s_weekly_radio_address_%28November_1%2C_2008%29.oga
@wget --quiet --show-progress -O samples/gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
@wget --quiet --show-progress -O samples/hp0.ogg https://upload.wikimedia.org/wikipedia/en/d/d4/En.henryfphillips.ogg
@echo "Converting to 16-bit WAV ..."
@ffmpeg -loglevel -0 -y -i samples/gb0.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/gb0.wav
@ffmpeg -loglevel -0 -y -i samples/gb1.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/gb1.wav
@ffmpeg -loglevel -0 -y -i samples/hp0.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/hp0.wav
.PHONY: tiny.en
tiny.en: main
@echo "Downloading tiny.en (75 MB just once)"
mkdir -p models
@if [ ! -f models/ggml-tiny.en.bin ]; then \
wget --quiet --show-progress -O models/ggml-tiny.en.bin https://ggml.ggerganov.com/ggml-model-whisper-tiny.en.bin ; \
fi
@echo "==============================================="
@echo "Running tiny.en on all samples in ./samples ..."
@echo "==============================================="
@echo ""
@for f in samples/*.wav; do \
echo "----------------------------------------------" ; \
echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
echo "----------------------------------------------" ; \
echo "" ; \
./main -m models/ggml-tiny.en.bin -f $$f ; \
echo "" ; \
done
.PHONY: base.en
base.en: main
@echo "Downloading base.en (142 MB just once)"
mkdir -p models
@if [ ! -f models/ggml-base.en.bin ]; then \
wget --quiet --show-progress -O models/ggml-base.en.bin https://ggml.ggerganov.com/ggml-model-whisper-base.en.bin ; \
fi
@echo "==============================================="
@echo "Running base.en on all samples in ./samples ..."
@echo "==============================================="
@echo ""
@for f in samples/*.wav; do \
echo "----------------------------------------------" ; \
echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
echo "----------------------------------------------" ; \
echo "" ; \
./main -m models/ggml-base.en.bin -f $$f ; \
echo "" ; \
done
.PHONY: small.en
small.en: main
@echo "Downloading small.en (466 MB just once)"
mkdir -p models
@if [ ! -f models/ggml-small.en.bin ]; then \
wget --quiet --show-progress -O models/ggml-small.en.bin https://ggml.ggerganov.com/ggml-model-whisper-small.en.bin ; \
fi
@echo "==============================================="
@echo "Running small.en on all samples in ./samples ..."
@echo "==============================================="
@echo ""
@for f in samples/*.wav; do \
echo "----------------------------------------------" ; \
echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
echo "----------------------------------------------" ; \
echo "" ; \
./main -m models/ggml-small.en.bin -f $$f ; \
echo "" ; \
done
.PHONY: medium.en
medium.en: main
@echo "Downloading medium.en (1.5 GB just once)"
mkdir -p models
@if [ ! -f models/ggml-medium.en.bin ]; then \
wget --quiet --show-progress -O models/ggml-medium.en.bin https://ggml.ggerganov.com/ggml-model-whisper-medium.en.bin ; \
fi
@echo "==============================================="
@echo "Running medium.en on all samples in ./samples ..."
@echo "==============================================="
@echo ""
@for f in samples/*.wav; do \
echo "----------------------------------------------" ; \
echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
echo "----------------------------------------------" ; \
echo "" ; \
./main -m models/ggml-medium.en.bin -f $$f ; \
echo "" ; \
done

328
convert-pt-to-ggml.py
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# Convert Whisper transformer model from PyTorch to ggml format
#
# Usage: python convert-pt-to-ggml.py ~/.cache/whisper/medium.pt ~/path/to/repo/whisper/ ./models/whisper-medium
#
# You need to clone the original repo in ~/path/to/repo/whisper/
#
# git clone https://github.com/openai/whisper ~/path/to/repo/whisper/
#
# It is used to various assets needed by the algorithm:
#
# - tokenizer
# - mel filters
#
# Also, you need to have the original models in ~/.cache/whisper/
# See the original repo for more details.
#
# This script loads the specified model and whisper assets and saves them in ggml format.
# The output is a single binary file containing the following information:
#
# - hparams
# - mel filters
# - tokenizer vocab
# - model variables
#
# For each variable, write the following:
#
# - Number of dimensions (int)
# - Name length (int)
# - Dimensions (int[n_dims])
# - Name (char[name_length])
# - Data (float[n_dims])
#
import io
import os
import sys
import struct
import json
import code
import torch
import numpy as np
from transformers import GPTJForCausalLM
from transformers import GPT2TokenizerFast
# ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L10-L110
LANGUAGES = {
"en": "english",
"zh": "chinese",
"de": "german",
"es": "spanish",
"ru": "russian",
"ko": "korean",
"fr": "french",
"ja": "japanese",
"pt": "portuguese",
"tr": "turkish",
"pl": "polish",
"ca": "catalan",
"nl": "dutch",
"ar": "arabic",
"sv": "swedish",
"it": "italian",
"id": "indonesian",
"hi": "hindi",
"fi": "finnish",
"vi": "vietnamese",
"iw": "hebrew",
"uk": "ukrainian",
"el": "greek",
"ms": "malay",
"cs": "czech",
"ro": "romanian",
"da": "danish",
"hu": "hungarian",
"ta": "tamil",
"no": "norwegian",
"th": "thai",
"ur": "urdu",
"hr": "croatian",
"bg": "bulgarian",
"lt": "lithuanian",
"la": "latin",
"mi": "maori",
"ml": "malayalam",
"cy": "welsh",
"sk": "slovak",
"te": "telugu",
"fa": "persian",
"lv": "latvian",
"bn": "bengali",
"sr": "serbian",
"az": "azerbaijani",
"sl": "slovenian",
"kn": "kannada",
"et": "estonian",
"mk": "macedonian",
"br": "breton",
"eu": "basque",
"is": "icelandic",
"hy": "armenian",
"ne": "nepali",
"mn": "mongolian",
"bs": "bosnian",
"kk": "kazakh",
"sq": "albanian",
"sw": "swahili",
"gl": "galician",
"mr": "marathi",
"pa": "punjabi",
"si": "sinhala",
"km": "khmer",
"sn": "shona",
"yo": "yoruba",
"so": "somali",
"af": "afrikaans",
"oc": "occitan",
"ka": "georgian",
"be": "belarusian",
"tg": "tajik",
"sd": "sindhi",
"gu": "gujarati",
"am": "amharic",
"yi": "yiddish",
"lo": "lao",
"uz": "uzbek",
"fo": "faroese",
"ht": "haitian creole",
"ps": "pashto",
"tk": "turkmen",
"nn": "nynorsk",
"mt": "maltese",
"sa": "sanskrit",
"lb": "luxembourgish",
"my": "myanmar",
"bo": "tibetan",
"tl": "tagalog",
"mg": "malagasy",
"as": "assamese",
"tt": "tatar",
"haw": "hawaiian",
"ln": "lingala",
"ha": "hausa",
"ba": "bashkir",
"jw": "javanese",
"su": "sundanese",
}
# ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L273-L292
def build_tokenizer(path_to_whisper_repo: str, name: str = "gpt2"):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
path = os.path.join(path_to_whisper_repo, "whisper/assets", name)
tokenizer = GPT2TokenizerFast.from_pretrained(path)
specials = [
"<|startoftranscript|>",
*[f"<|{lang}|>" for lang in LANGUAGES.keys()],
"<|translate|>",
"<|transcribe|>",
"<|startoflm|>",
"<|startofprev|>",
"<|nocaptions|>",
"<|notimestamps|>",
]
tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
return tokenizer
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
if len(sys.argv) < 4:
print("Usage: convert-pt-to-ggml.py model.pt path-to-whisper-repo dir-output [use-f32]\n")
sys.exit(1)
fname_inp = sys.argv[1]
dir_whisper = sys.argv[2]
dir_out = sys.argv[3]
# try to load PyTorch binary data
try:
model_bytes = open(fname_inp, "rb").read()
with io.BytesIO(model_bytes) as fp:
checkpoint = torch.load(fp, map_location="cpu")
except:
print("Error: failed to load PyTorch model file: %s" % fname_inp)
sys.exit(1)
hparams = checkpoint["dims"]
print("hparams:", hparams)
list_vars = checkpoint["model_state_dict"]
#print(list_vars['encoder.positional_embedding'])
#print(list_vars['encoder.conv1.weight'])
#print(list_vars['encoder.conv1.weight'].shape)
# load mel filters
n_mels = hparams["n_mels"]
with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as f:
filters = torch.from_numpy(f[f"mel_{n_mels}"])
#print (filters)
#code.interact(local=locals())
multilingual = hparams["n_vocab"] == 51865
tokenizer = build_tokenizer(dir_whisper, multilingual and "multilingual" or "gpt2")
#print(tokenizer)
#print(tokenizer.name_or_path)
#print(len(tokenizer.additional_special_tokens))
dir_tokenizer = tokenizer.name_or_path
# output in the same directory as the model
fname_out = dir_out + "/ggml-model.bin"
with open(dir_tokenizer + "/vocab.json", "r") as f:
tokens = json.load(f)
# use 16-bit or 32-bit floats
use_f16 = True
if len(sys.argv) > 4:
use_f16 = False
fname_out = dir_out + "/ggml-model-f32.bin"
fout = open(fname_out, "wb")
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["n_vocab"]))
fout.write(struct.pack("i", hparams["n_audio_ctx"]))
fout.write(struct.pack("i", hparams["n_audio_state"]))
fout.write(struct.pack("i", hparams["n_audio_head"]))
fout.write(struct.pack("i", hparams["n_audio_layer"]))
fout.write(struct.pack("i", hparams["n_text_ctx"]))
fout.write(struct.pack("i", hparams["n_text_state"]))
fout.write(struct.pack("i", hparams["n_text_head"]))
fout.write(struct.pack("i", hparams["n_text_layer"]))
fout.write(struct.pack("i", hparams["n_mels"]))
fout.write(struct.pack("i", use_f16))
# write mel filters
fout.write(struct.pack("i", filters.shape[0]))
fout.write(struct.pack("i", filters.shape[1]))
for i in range(filters.shape[0]):
for j in range(filters.shape[1]):
fout.write(struct.pack("f", filters[i][j]))
byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}
fout.write(struct.pack("i", len(tokens)))
for key in tokens:
text = bytearray([byte_decoder[c] for c in key]).decode('utf-8', errors='replace').encode('utf-8')
fout.write(struct.pack("i", len(text)))
fout.write(text)
for name in list_vars.keys():
data = list_vars[name].squeeze().numpy()
print("Processing variable: " + name + " with shape: ", data.shape)
# reshape conv bias from [n] to [n, 1]
if name == "encoder.conv1.bias" or \
name == "encoder.conv2.bias":
data = data.reshape(data.shape[0], 1)
print(" Reshaped variable: " + name + " to shape: ", data.shape)
n_dims = len(data.shape);
# looks like the whisper models are in f16 by default
# so we need to convert the small tensors to f32 until we fully support f16 in ggml
# ftype == 0 -> float32, ftype == 1 -> float16
ftype = 1;
if use_f16:
if n_dims < 2 or \
name == "encoder.conv1.bias" or \
name == "encoder.conv2.bias" or \
name == "encoder.positional_embedding" or \
name == "decoder.positional_embedding":
ftype = 0
data = data.astype(np.float32)
print(" Converting to float32")
data = data.astype(np.float32)
ftype = 0
else:
data = data.astype(np.float32)
ftype = 0
#if name.startswith("encoder"):
# if name.endswith("mlp.0.weight") or \
# name.endswith("mlp.2.weight"):
# print(" Transposing")
# data = data.transpose()
# header
str = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(str), ftype))
for i in range(n_dims):
fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
fout.write(str);
# data
data.tofile(fout)
fout.close()
print("Done. Output file: " + fname_out)
print("")

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ggml.h
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#pragma once
#ifdef __cplusplus
extern "C" {
#endif
#include <stdint.h>
#include <stddef.h>
#include <stdbool.h>
#define GGML_MAX_DIMS 4
#define GGML_MAX_NODES 4096
#define GGML_MAX_PARAMS 16
#define GGML_MAX_CONTEXTS 16
#ifdef __ARM_NEON
// we use the built-in 16-bit float type
typedef __fp16 ggml_fp16_t;
#else
typedef uint16_t ggml_fp16_t;
#endif
float ggml_fp16_to_fp32(ggml_fp16_t x);
ggml_fp16_t ggml_fp32_to_fp16(float x);
struct ggml_object;
struct ggml_context;
enum ggml_type {
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
GGML_TYPE_F16,
GGML_TYPE_F32,
GGML_TYPE_COUNT,
};
enum ggml_op {
GGML_OP_NONE = 0,
GGML_OP_DUP,
GGML_OP_ADD,
GGML_OP_SUB,
GGML_OP_MUL,
GGML_OP_DIV,
GGML_OP_SQR,
GGML_OP_SQRT,
GGML_OP_SUM,
GGML_OP_MEAN,
GGML_OP_REPEAT,
GGML_OP_ABS,
GGML_OP_SGN,
GGML_OP_NEG,
GGML_OP_STEP,
GGML_OP_RELU,
GGML_OP_GELU,
GGML_OP_NORM, // normalize
GGML_OP_MUL_MAT,
GGML_OP_SCALE,
GGML_OP_CPY,
GGML_OP_RESHAPE,
GGML_OP_VIEW,
GGML_OP_PERMUTE,
GGML_OP_TRANSPOSE,
GGML_OP_GET_ROWS,
GGML_OP_DIAG_MASK_INF,
GGML_OP_SOFT_MAX,
GGML_OP_ROPE,
GGML_OP_CONV_1D_1S,
GGML_OP_CONV_1D_2S,
GGML_OP_COUNT,
};
// n-dimensional tensor
struct ggml_tensor {
enum ggml_type type;
int n_dims;
int ne[GGML_MAX_DIMS]; // number of elements
size_t nb[GGML_MAX_DIMS]; // stride in bytes:
// nb[0] = sizeof(type)
// nb[1] = nb[0] * ne[0] + padding
// nb[i] = nb[i-1] * ne[i-1]
// compute data
enum ggml_op op;
bool is_param;
struct ggml_tensor * grad;
struct ggml_tensor * src0;
struct ggml_tensor * src1;
// thread scheduling
int n_tasks;
// performance
int perf_runs;
int64_t perf_cycles;
int64_t perf_time_us;
void * data;
char pad[8];
};
// computation graph
struct ggml_cgraph {
int n_nodes;
int n_leafs;
int n_threads;
size_t work_size;
struct ggml_tensor * work;
struct ggml_tensor * nodes[GGML_MAX_NODES];
struct ggml_tensor * grads[GGML_MAX_NODES];
struct ggml_tensor * leafs[GGML_MAX_NODES];
// performance
int perf_runs;
int64_t perf_cycles;
int64_t perf_time_us;
};
struct ggml_init_params {
// memory pool
size_t mem_size; // bytes
void * mem_buffer; // if NULL, memory will be allocated internally
};
int64_t ggml_time_ms(void);
int64_t ggml_time_us(void);
int64_t ggml_cycles(void);
int64_t ggml_cycles_per_ms(void);
void ggml_print_object (const struct ggml_object * obj);
void ggml_print_objects(const struct ggml_context * ctx);
int ggml_nelements(const struct ggml_tensor * tensor);
size_t ggml_nbytes (const struct ggml_tensor * tensor);
size_t ggml_type_size (enum ggml_type type);
size_t ggml_element_size(const struct ggml_tensor * tensor);
struct ggml_context * ggml_init(struct ggml_init_params params);
void ggml_free(struct ggml_context * ctx);
size_t ggml_used_mem(const struct ggml_context * ctx);
struct ggml_tensor * ggml_new_tensor(
struct ggml_context * ctx,
enum ggml_type type,
int n_dims,
const int *ne);
struct ggml_tensor * ggml_new_tensor_1d(
struct ggml_context * ctx,
enum ggml_type type,
int ne0);
struct ggml_tensor * ggml_new_tensor_2d(
struct ggml_context * ctx,
enum ggml_type type,
int ne0,
int ne1);
struct ggml_tensor * ggml_new_tensor_3d(
struct ggml_context * ctx,
enum ggml_type type,
int ne0,
int ne1,
int ne2);
struct ggml_tensor * ggml_new_tensor_4d(
struct ggml_context * ctx,
enum ggml_type type,
int ne0,
int ne1,
int ne2,
int ne3);
struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src);
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
void * ggml_get_data (const struct ggml_tensor * tensor);
float * ggml_get_data_f32(const struct ggml_tensor * tensor);
//
// operations on tensors with backpropagation
//
struct ggml_tensor * ggml_dup(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_add(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * ggml_sub(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * ggml_mul(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * ggml_div(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * ggml_sqr(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_sqrt(
struct ggml_context * ctx,
struct ggml_tensor * a);
// return scalar
// TODO: compute sum along rows
struct ggml_tensor * ggml_sum(
struct ggml_context * ctx,
struct ggml_tensor * a);
// mean along rows
struct ggml_tensor * ggml_mean(
struct ggml_context * ctx,
struct ggml_tensor * a);
// if a is the same shape as b, and a is not parameter, return a
// otherwise, return a new tensor: repeat(a) to fit in b
struct ggml_tensor * ggml_repeat(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * ggml_abs(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_sgn(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_neg(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_step(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_relu(
struct ggml_context * ctx,
struct ggml_tensor * a);
// TODO: double-check this computation is correct
struct ggml_tensor * ggml_gelu(
struct ggml_context * ctx,
struct ggml_tensor * a);
// normalize along rows
// TODO: eps is hardcoded to 1e-5 for now
struct ggml_tensor * ggml_norm(
struct ggml_context * ctx,
struct ggml_tensor * a);
// A: m rows, n columns
// B: p rows, n columns (i.e. we transpose it internally)
// result is m columns, p rows
struct ggml_tensor * ggml_mul_mat(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
//
// operations on tensors without backpropagation
//
// in-place, returns view(a)
struct ggml_tensor * ggml_scale(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// a -> b, return view(b)
struct ggml_tensor * ggml_cpy(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// return view(a), b specifies the new shape
// TODO: when we start computing gradient, make a copy instead of view
struct ggml_tensor * ggml_reshape(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// return view(a)
// TODO: when we start computing gradient, make a copy instead of view
struct ggml_tensor * ggml_reshape_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1);
// return view(a)
// TODO: when we start computing gradient, make a copy instead of view
struct ggml_tensor * ggml_reshape_3d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1,
int ne2);
// offset in bytes
struct ggml_tensor * ggml_view_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
size_t offset);
struct ggml_tensor * ggml_view_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1,
size_t nb1, // row stride in bytes
size_t offset);
struct ggml_tensor * ggml_permute(
struct ggml_context * ctx,
struct ggml_tensor * a,
int axis0,
int axis1,
int axis2,
int axis3);
// alias for ggml_permute(ctx, a, 1, 0, 2, 3)
struct ggml_tensor * ggml_transpose(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * ggml_get_rows(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// set elements above the diagonal to -INF
// in-place, returns view(a)
struct ggml_tensor * ggml_diag_mask_inf(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past);
// in-place, returns view(a)
struct ggml_tensor * ggml_soft_max(
struct ggml_context * ctx,
struct ggml_tensor * a);
// rotary position embedding
// in-place, returns view(a)
// if mode == 1, skip n_past elements
// TODO: avoid creating a new tensor every time
struct ggml_tensor * ggml_rope(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode);
// padding = 1
// TODO: we don't support extra parameters for now
// that's why we are hard-coding the stride, padding, and dilation
// not great ..
struct ggml_tensor * ggml_conv_1d_1s(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * ggml_conv_1d_2s(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
//
// automatic differentiation
//
void ggml_set_param(
struct ggml_context * ctx,
struct ggml_tensor * tensor);
void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph);
void ggml_graph_reset (struct ggml_cgraph * cgraph);
// print info and performance information for the graph
void ggml_graph_print(const struct ggml_cgraph * cgraph);
// dump the graph into a file using the dot format
void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename);
//
// optimization
//
// optimization methods
enum ggml_opt_type {
GGML_OPT_ADAM,
GGML_OPT_LBFGS,
};
// linesearch methods
enum ggml_linesearch {
GGML_LINESEARCH_DEFAULT = 1,
GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0,
GGML_LINESEARCH_BACKTRACKING_WOLFE = 1,
GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2,
};
// optimization return values
enum ggml_opt_result {
GGML_OPT_OK = 0,
GGML_OPT_DID_NOT_CONVERGE,
GGML_OPT_NO_CONTEXT,
GGML_OPT_INVALID_WOLFE,
GGML_OPT_FAIL,
GGML_LINESEARCH_FAIL = -128,
GGML_LINESEARCH_MINIMUM_STEP,
GGML_LINESEARCH_MAXIMUM_STEP,
GGML_LINESEARCH_MAXIMUM_ITERATIONS,
GGML_LINESEARCH_INVALID_PARAMETERS,
};
// optimization parameters
//
// see ggml.c (ggml_opt_default_params) for default values
//
struct ggml_opt_params {
enum ggml_opt_type type;
int n_threads;
// delta-based convergence test
//
// if past == 0 - disabled
// if past > 0:
// stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|)
//
int past;
float delta;
// maximum number of iterations without improvement
//
// if 0 - disabled
// if > 0:
// assume convergence if no cost improvement in this number of iterations
//
int max_no_improvement;
bool print_forward_graph;
bool print_backward_graph;
union {
// ADAM parameters
struct {
int n_iter;
float alpha; // learning rate
float beta1;
float beta2;
float eps; // epsilon for numerical stability
float eps_f; // epsilon for convergence test
float eps_g; // epsilon for convergence test
} adam;
// LBFGS parameters
struct {
int m; // number of corrections to approximate the inv. Hessian
int n_iter;
int max_linesearch;
float eps; // convergence tolerance
float ftol; // line search tolerance
float wolfe;
float min_step;
float max_step;
enum ggml_linesearch linesearch;
} lbfgs;
};
};
struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
// optimize the function defined by the tensor f
enum ggml_opt_result ggml_opt(
struct ggml_context * ctx,
struct ggml_opt_params params,
struct ggml_tensor * f);
#ifdef __cplusplus
}
#endif

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