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python: embedding cancel callback for nomic client dynamic mode (#2214)

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Signed-off-by: Jared Van Bortel <jared@nomic.ai>
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Jared Van Bortel 2024-04-12 16:00:39 -04:00 committed by GitHub
parent 459289b94c
commit 46818e466e
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11 changed files with 95 additions and 28 deletions
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36
gpt4all-backend/llamamodel.cpp
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@ -158,7 +158,7 @@ static int32_t get_arch_key_u32(std::string const &modelPath, std::string const
struct LLamaPrivate {
const std::string modelPath;
bool modelLoaded;
bool modelLoaded = false;
int device = -1;
llama_model *model = nullptr;
llama_context *ctx = nullptr;
@ -166,12 +166,11 @@ struct LLamaPrivate {
llama_context_params ctx_params;
int64_t n_threads = 0;
std::vector<LLModel::Token> end_tokens;
const char *backend_name = nullptr;
};
LLamaModel::LLamaModel()
: d_ptr(new LLamaPrivate) {
d_ptr->modelLoaded = false;
}
: d_ptr(new LLamaPrivate) {}
// default hparams (LLaMA 7B)
struct llama_file_hparams {
@ -291,6 +290,8 @@ bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx, int ngl)
d_ptr->model_params.progress_callback = &LLModel::staticProgressCallback;
d_ptr->model_params.progress_callback_user_data = this;
d_ptr->backend_name = "cpu"; // default
#ifdef GGML_USE_KOMPUTE
if (d_ptr->device != -1) {
d_ptr->model_params.main_gpu = d_ptr->device;
@ -301,6 +302,7 @@ bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx, int ngl)
if (llama_verbose()) {
std::cerr << "llama.cpp: using Metal" << std::endl;
d_ptr->backend_name = "metal";
}
// always fully offload on Metal
@ -364,6 +366,7 @@ bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx, int ngl)
#ifdef GGML_USE_KOMPUTE
if (usingGPUDevice() && ggml_vk_has_device()) {
std::cerr << "llama.cpp: using Vulkan on " << ggml_vk_current_device().name << std::endl;
d_ptr->backend_name = "kompute";
}
#endif
@ -674,7 +677,7 @@ void LLamaModel::embed(
void LLamaModel::embed(
const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
size_t *tokenCount, bool doMean, bool atlas
size_t *tokenCount, bool doMean, bool atlas, LLModel::EmbedCancelCallback *cancelCb
) {
if (!d_ptr->model)
throw std::logic_error("no model is loaded");
@ -712,7 +715,7 @@ void LLamaModel::embed(
throw std::invalid_argument(ss.str());
}
embedInternal(texts, embeddings, *prefix, dimensionality, tokenCount, doMean, atlas, spec);
embedInternal(texts, embeddings, *prefix, dimensionality, tokenCount, doMean, atlas, cancelCb, spec);
}
// MD5 hash of "nomic empty"
@ -730,7 +733,7 @@ double getL2NormScale(T *start, T *end) {
void LLamaModel::embedInternal(
const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
size_t *tokenCount, bool doMean, bool atlas, const EmbModelSpec *spec
size_t *tokenCount, bool doMean, bool atlas, LLModel::EmbedCancelCallback *cancelCb, const EmbModelSpec *spec
) {
typedef std::vector<LLModel::Token> TokenString;
static constexpr int32_t atlasMaxLength = 8192;
@ -822,6 +825,23 @@ void LLamaModel::embedInternal(
}
inputs.clear();
if (cancelCb) {
// copy of batching code below, but just count tokens instead of running inference
unsigned nBatchTokens = 0;
std::vector<unsigned> batchSizes;
for (const auto &inp: batches) {
if (nBatchTokens + inp.batch.size() > n_batch) {
batchSizes.push_back(nBatchTokens);
nBatchTokens = 0;
}
nBatchTokens += inp.batch.size();
}
batchSizes.push_back(nBatchTokens);
if (cancelCb(batchSizes.data(), batchSizes.size(), d_ptr->backend_name)) {
throw std::runtime_error("operation was canceled");
}
}
// initialize batch
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
@ -871,7 +891,7 @@ void LLamaModel::embedInternal(
};
// break into batches
for (auto &inp: batches) {
for (const auto &inp: batches) {
// encode if at capacity
if (batch.n_tokens + inp.batch.size() > n_batch) {
decode();

6
gpt4all-backend/llamamodel_impl.h
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@ -39,7 +39,8 @@ public:
size_t embeddingSize() const override;
// user-specified prefix
void embed(const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix,
int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false) override;
int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false,
EmbedCancelCallback *cancelCb = nullptr) override;
// automatic prefix
void embed(const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality = -1,
size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false) override;
@ -61,7 +62,8 @@ protected:
int32_t layerCount(std::string const &modelPath) const override;
void embedInternal(const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
size_t *tokenCount, bool doMean, bool atlas, const EmbModelSpec *spec);
size_t *tokenCount, bool doMean, bool atlas, EmbedCancelCallback *cancelCb,
const EmbModelSpec *spec);
};
#endif // LLAMAMODEL_H

5
gpt4all-backend/llmodel.h
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@ -105,12 +105,15 @@ public:
bool special = false,
std::string *fakeReply = nullptr);
using EmbedCancelCallback = bool(unsigned *batchSizes, unsigned nBatch, const char *backend);
virtual size_t embeddingSize() const {
throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
}
// user-specified prefix
virtual void embed(const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix,
int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false);
int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false,
EmbedCancelCallback *cancelCb = nullptr);
// automatic prefix
virtual void embed(const std::vector<std::string> &texts, float *embeddings, bool isRetrieval,
int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false);

4
gpt4all-backend/llmodel_c.cpp
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@ -159,7 +159,7 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
float *llmodel_embed(
llmodel_model model, const char **texts, size_t *embedding_size, const char *prefix, int dimensionality,
size_t *token_count, bool do_mean, bool atlas, const char **error
size_t *token_count, bool do_mean, bool atlas, llmodel_emb_cancel_callback cancel_cb, const char **error
) {
auto *wrapper = static_cast<LLModelWrapper *>(model);
@ -185,7 +185,7 @@ float *llmodel_embed(
if (prefix) { prefixStr = prefix; }
embedding = new float[embd_size];
wrapper->llModel->embed(textsVec, embedding, prefixStr, dimensionality, token_count, do_mean, atlas);
wrapper->llModel->embed(textsVec, embedding, prefixStr, dimensionality, token_count, do_mean, atlas, cancel_cb);
} catch (std::exception const &e) {
llmodel_set_error(error, e.what());
return nullptr;

13
gpt4all-backend/llmodel_c.h
View File

@ -82,6 +82,15 @@ typedef bool (*llmodel_response_callback)(int32_t token_id, const char *response
*/
typedef bool (*llmodel_recalculate_callback)(bool is_recalculating);
/**
* Embedding cancellation callback for use with llmodel_embed.
* @param batch_sizes The number of tokens in each batch that will be embedded.
* @param n_batch The number of batches that will be embedded.
* @param backend The backend that will be used for embedding. One of "cpu", "kompute", or "metal".
* @return True to cancel llmodel_embed, false to continue.
*/
typedef bool (*llmodel_emb_cancel_callback)(unsigned *batch_sizes, unsigned n_batch, const char *backend);
/**
* Create a llmodel instance.
* Recognises correct model type from file at model_path
@ -198,12 +207,14 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
* truncate.
* @param atlas Try to be fully compatible with the Atlas API. Currently, this means texts longer than 8192 tokens with
* long_text_mode="mean" will raise an error. Disabled by default.
* @param cancel_cb Cancellation callback, or NULL. See the documentation of llmodel_emb_cancel_callback.
* @param error Return location for a malloc()ed string that will be set on error, or NULL.
* @return A pointer to an array of floating point values passed to the calling method which then will
* be responsible for lifetime of this memory. NULL if an error occurred.
*/
float *llmodel_embed(llmodel_model model, const char **texts, size_t *embedding_size, const char *prefix,
int dimensionality, size_t *token_count, bool do_mean, bool atlas, const char **error);
int dimensionality, size_t *token_count, bool do_mean, bool atlas,
llmodel_emb_cancel_callback cancel_cb, const char **error);
/**
* Frees the memory allocated by the llmodel_embedding function.

3
gpt4all-backend/llmodel_shared.cpp
View File

@ -270,7 +270,7 @@ void LLModel::generateResponse(std::function<bool(int32_t, const std::string&)>
void LLModel::embed(
const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
size_t *tokenCount, bool doMean, bool atlas
size_t *tokenCount, bool doMean, bool atlas, EmbedCancelCallback *cancelCb
) {
(void)texts;
(void)embeddings;
@ -279,6 +279,7 @@ void LLModel::embed(
(void)tokenCount;
(void)doMean;
(void)atlas;
(void)cancelCb;
throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
}

2
gpt4all-bindings/python/gpt4all/__init__.py
View File

@ -1 +1 @@
from .gpt4all import Embed4All as Embed4All, GPT4All as GPT4All
from .gpt4all import CancellationError as CancellationError, Embed4All as Embed4All, GPT4All as GPT4All

29
gpt4all-bindings/python/gpt4all/_pyllmodel.py
View File

@ -9,7 +9,7 @@ import sys
import threading
from enum import Enum
from queue import Queue
from typing import Any, Callable, Generic, Iterable, NoReturn, TypeVar, overload
from typing import TYPE_CHECKING, Any, Callable, Generic, Iterable, NoReturn, TypeVar, overload
if sys.version_info >= (3, 9):
import importlib.resources as importlib_resources
@ -22,6 +22,9 @@ if (3, 9) <= sys.version_info < (3, 11):
else:
from typing import TypedDict
if TYPE_CHECKING:
from typing_extensions import TypeAlias
EmbeddingsType = TypeVar('EmbeddingsType', bound='list[Any]')
@ -95,6 +98,7 @@ llmodel.llmodel_isModelLoaded.restype = ctypes.c_bool
PromptCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_int32)
ResponseCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_int32, ctypes.c_char_p)
RecalculateCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_bool)
EmbCancelCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.POINTER(ctypes.c_uint), ctypes.c_uint, ctypes.c_char_p)
llmodel.llmodel_prompt.argtypes = [
ctypes.c_void_p,
@ -119,6 +123,7 @@ llmodel.llmodel_embed.argtypes = [
ctypes.POINTER(ctypes.c_size_t),
ctypes.c_bool,
ctypes.c_bool,
EmbCancelCallback,
ctypes.POINTER(ctypes.c_char_p),
]
@ -155,6 +160,7 @@ llmodel.llmodel_has_gpu_device.restype = ctypes.c_bool
ResponseCallbackType = Callable[[int, str], bool]
RawResponseCallbackType = Callable[[int, bytes], bool]
EmbCancelCallbackType: TypeAlias = 'Callable[[list[int], str], bool]'
def empty_response_callback(token_id: int, response: str) -> bool:
@ -171,6 +177,10 @@ class EmbedResult(Generic[EmbeddingsType], TypedDict):
n_prompt_tokens: int
class CancellationError(Exception):
"""raised when embedding is canceled"""
class LLModel:
"""
Base class and universal wrapper for GPT4All language models
@ -323,19 +333,22 @@ class LLModel:
@overload
def generate_embeddings(
self, text: str, prefix: str, dimensionality: int, do_mean: bool, atlas: bool,
self, text: str, prefix: str, dimensionality: int, do_mean: bool, atlas: bool, cancel_cb: EmbCancelCallbackType,
) -> EmbedResult[list[float]]: ...
@overload
def generate_embeddings(
self, text: list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
cancel_cb: EmbCancelCallbackType,
) -> EmbedResult[list[list[float]]]: ...
@overload
def generate_embeddings(
self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
cancel_cb: EmbCancelCallbackType,
) -> EmbedResult[list[Any]]: ...
def generate_embeddings(
self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
cancel_cb: EmbCancelCallbackType,
) -> EmbedResult[list[Any]]:
if not text:
raise ValueError("text must not be None or empty")
@ -343,7 +356,7 @@ class LLModel:
if self.model is None:
self._raise_closed()
if (single_text := isinstance(text, str)):
if single_text := isinstance(text, str):
text = [text]
# prepare input
@ -355,14 +368,22 @@ class LLModel:
for i, t in enumerate(text):
c_texts[i] = t.encode()
def wrap_cancel_cb(batch_sizes: ctypes.POINTER(ctypes.c_uint), n_batch: int, backend: bytes) -> bool:
assert cancel_cb is not None
return cancel_cb(batch_sizes[:n_batch], backend.decode())
cancel_cb_wrapper = EmbCancelCallback(0x0 if cancel_cb is None else wrap_cancel_cb)
# generate the embeddings
embedding_ptr = llmodel.llmodel_embed(
self.model, c_texts, ctypes.byref(embedding_size), c_prefix, dimensionality, ctypes.byref(token_count),
do_mean, atlas, ctypes.byref(error),
do_mean, atlas, cancel_cb_wrapper, ctypes.byref(error),
)
if not embedding_ptr:
msg = "(unknown error)" if error.value is None else error.value.decode()
if msg == "operation was canceled":
raise CancellationError(msg)
raise RuntimeError(f'Failed to generate embeddings: {msg}')
# extract output

21
gpt4all-bindings/python/gpt4all/gpt4all.py
View File

@ -19,7 +19,8 @@ from requests.exceptions import ChunkedEncodingError
from tqdm import tqdm
from urllib3.exceptions import IncompleteRead, ProtocolError
from ._pyllmodel import EmbedResult as EmbedResult, LLModel, ResponseCallbackType, empty_response_callback
from ._pyllmodel import (CancellationError as CancellationError, EmbCancelCallbackType, EmbedResult as EmbedResult,
LLModel, ResponseCallbackType, empty_response_callback)
if TYPE_CHECKING:
from typing_extensions import Self, TypeAlias
@ -72,34 +73,36 @@ class Embed4All:
@overload
def embed(
self, text: str, *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
return_dict: Literal[False] = ..., atlas: bool = ...,
return_dict: Literal[False] = ..., atlas: bool = ..., cancel_cb: EmbCancelCallbackType | None = ...,
) -> list[float]: ...
@overload
def embed(
self, text: list[str], *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
return_dict: Literal[False] = ..., atlas: bool = ...,
return_dict: Literal[False] = ..., atlas: bool = ..., cancel_cb: EmbCancelCallbackType | None = ...,
) -> list[list[float]]: ...
@overload
def embed(
self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
long_text_mode: str = ..., return_dict: Literal[False] = ..., atlas: bool = ...,
cancel_cb: EmbCancelCallbackType | None = ...,
) -> list[Any]: ...
# return_dict=True
@overload
def embed(
self, text: str, *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
return_dict: Literal[True], atlas: bool = ...,
return_dict: Literal[True], atlas: bool = ..., cancel_cb: EmbCancelCallbackType | None = ...,
) -> EmbedResult[list[float]]: ...
@overload
def embed(
self, text: list[str], *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
return_dict: Literal[True], atlas: bool = ...,
return_dict: Literal[True], atlas: bool = ..., cancel_cb: EmbCancelCallbackType | None = ...,
) -> EmbedResult[list[list[float]]]: ...
@overload
def embed(
self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
long_text_mode: str = ..., return_dict: Literal[True], atlas: bool = ...,
cancel_cb: EmbCancelCallbackType | None = ...,
) -> EmbedResult[list[Any]]: ...
# return type unknown
@ -107,11 +110,13 @@ class Embed4All:
def embed(
self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
long_text_mode: str = ..., return_dict: bool = ..., atlas: bool = ...,
cancel_cb: EmbCancelCallbackType | None = ...,
) -> Any: ...
def embed(
self, text: str | list[str], *, prefix: str | None = None, dimensionality: int | None = None,
long_text_mode: str = "mean", return_dict: bool = False, atlas: bool = False,
cancel_cb: EmbCancelCallbackType | None = None,
) -> Any:
"""
Generate one or more embeddings.
@ -127,10 +132,14 @@ class Embed4All:
return_dict: Return the result as a dict that includes the number of prompt tokens processed.
atlas: Try to be fully compatible with the Atlas API. Currently, this means texts longer than 8192 tokens
with long_text_mode="mean" will raise an error. Disabled by default.
cancel_cb: Called with arguments (batch_sizes, backend_name). Return true to cancel embedding.
Returns:
With return_dict=False, an embedding or list of embeddings of your text(s).
With return_dict=True, a dict with keys 'embeddings' and 'n_prompt_tokens'.
Raises:
CancellationError: If cancel_cb returned True and embedding was canceled.
"""
if dimensionality is None:
dimensionality = -1
@ -146,7 +155,7 @@ class Embed4All:
do_mean = {"mean": True, "truncate": False}[long_text_mode]
except KeyError:
raise ValueError(f"Long text mode must be one of 'mean' or 'truncate', got {long_text_mode!r}")
result = self.gpt4all.model.generate_embeddings(text, prefix, dimensionality, do_mean, atlas)
result = self.gpt4all.model.generate_embeddings(text, prefix, dimensionality, do_mean, atlas, cancel_cb)
return result if return_dict else result['embeddings']

2
gpt4all-bindings/python/setup.py
View File

@ -68,7 +68,7 @@ def get_long_description():
setup(
name=package_name,
version="2.4.1",
version="2.5.0",
description="Python bindings for GPT4All",
long_description=get_long_description(),
long_description_content_type="text/markdown",

2
gpt4all-bindings/typescript/index.cc
View File

@ -258,7 +258,7 @@ Napi::Value NodeModelWrapper::GenerateEmbedding(const Napi::CallbackInfo &info)
const char *_err = nullptr;
float *embeds = llmodel_embed(GetInference(), str_ptrs.data(), &embedding_size,
prefix.IsUndefined() ? nullptr : prefix.As<Napi::String>().Utf8Value().c_str(),
dimensionality, &token_count, do_mean, atlas, &_err);
dimensionality, &token_count, do_mean, atlas, nullptr, &_err);
if (!embeds)
{
// i dont wanna deal with c strings lol