Repository navigation
Expand file tree
/
Copy pathapp.py
More file actions
903 lines (798 loc) · 31.3 KB
/
Copy pathapp.py
File metadata and controls
903 lines (798 loc) · 31.3 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
import sys
import json
import traceback
import multiprocessing
import time
from re import compile, Match, Pattern
from threading import Lock
from functools import partial
from typing import Callable, Coroutine, Iterator, List, Optional, Tuple, Union, Dict
from typing_extensions import TypedDict, Literal
import llama_cpp
import anyio
from anyio.streams.memory import MemoryObjectSendStream
from starlette.concurrency import run_in_threadpool, iterate_in_threadpool
from fastapi import Depends, FastAPI, APIRouter, Request, Response
from fastapi.middleware import Middleware
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from fastapi.routing import APIRoute
from pydantic import BaseModel, Field
from pydantic_settings import BaseSettings
from sse_starlette.sse import EventSourceResponse
from starlette_context import plugins
from starlette_context.middleware import RawContextMiddleware
import numpy as np
import numpy.typing as npt
# Disable warning for model and model_alias settings
BaseSettings.model_config["protected_namespaces"] = ()
class Settings(BaseSettings):
model: str = Field(
description="The path to the model to use for generating completions."
)
model_alias: Optional[str] = Field(
default=None,
description="The alias of the model to use for generating completions.",
)
# Model Params
n_gpu_layers: int = Field(
default=0,
ge=-1,
description="The number of layers to put on the GPU. The rest will be on the CPU. Set -1 to move all to GPU.",
)
main_gpu: int = Field(
default=0,
ge=0,
description="Main GPU to use.",
)
tensor_split: Optional[List[float]] = Field(
default=None,
description="Split layers across multiple GPUs in proportion.",
)
vocab_only: bool = Field(
default=False, description="Whether to only return the vocabulary."
)
use_mmap: bool = Field(
default=llama_cpp.llama_mmap_supported(),
description="Use mmap.",
)
use_mlock: bool = Field(
default=llama_cpp.llama_mlock_supported(),
description="Use mlock.",
)
# Context Params
seed: int = Field(
default=llama_cpp.LLAMA_DEFAULT_SEED, description="Random seed. -1 for random."
)
n_ctx: int = Field(default=2048, ge=1, description="The context size.")
n_batch: int = Field(
default=512, ge=1, description="The batch size to use per eval."
)
n_threads: int = Field(
default=max(multiprocessing.cpu_count() // 2, 1),
ge=1,
description="The number of threads to use.",
)
n_threads_batch: int = Field(
default=max(multiprocessing.cpu_count() // 2, 1),
ge=0,
description="The number of threads to use when batch processing.",
)
rope_scaling_type: int = Field(default=llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED)
rope_freq_base: float = Field(default=0.0, description="RoPE base frequency")
rope_freq_scale: float = Field(
default=0.0, description="RoPE frequency scaling factor"
)
yarn_ext_factor: float = Field(default=-1.0)
yarn_attn_factor: float = Field(default=1.0)
yarn_beta_fast: float = Field(default=32.0)
yarn_beta_slow: float = Field(default=1.0)
yarn_orig_ctx: int = Field(default=0)
mul_mat_q: bool = Field(
default=True, description="if true, use experimental mul_mat_q kernels"
)
f16_kv: bool = Field(default=True, description="Whether to use f16 key/value.")
logits_all: bool = Field(default=True, description="Whether to return logits.")
embedding: bool = Field(default=True, description="Whether to use embeddings.")
# Sampling Params
last_n_tokens_size: int = Field(
default=64,
ge=0,
description="Last n tokens to keep for repeat penalty calculation.",
)
# LoRA Params
lora_base: Optional[str] = Field(
default=None,
description="Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.",
)
lora_path: Optional[str] = Field(
default=None,
description="Path to a LoRA file to apply to the model.",
)
# Backend Params
numa: bool = Field(
default=False,
description="Enable NUMA support.",
)
# Chat Format Params
chat_format: str = Field(
default="llama-2",
description="Chat format to use.",
)
clip_model_path: Optional[str] = Field(
default=None,
description="Path to a CLIP model to use for multi-modal chat completion.",
)
# Cache Params
cache: bool = Field(
default=False,
description="Use a cache to reduce processing times for evaluated prompts.",
)
cache_type: Literal["ram", "disk"] = Field(
default="ram",
description="The type of cache to use. Only used if cache is True.",
)
cache_size: int = Field(
default=2 << 30,
description="The size of the cache in bytes. Only used if cache is True.",
)
# Misc
verbose: bool = Field(
default=True, description="Whether to print debug information."
)
# Server Params
host: str = Field(default="localhost", description="Listen address")
port: int = Field(default=8000, description="Listen port")
interrupt_requests: bool = Field(
default=True,
description="Whether to interrupt requests when a new request is received.",
)
class ErrorResponse(TypedDict):
"""OpenAI style error response"""
message: str
type: str
param: Optional[str]
code: Optional[str]
class ErrorResponseFormatters:
"""Collection of formatters for error responses.
Args:
request (Union[CreateCompletionRequest, CreateChatCompletionRequest]):
Request body
match (Match[str]): Match object from regex pattern
Returns:
Tuple[int, ErrorResponse]: Status code and error response
"""
@staticmethod
def context_length_exceeded(
request: Union["CreateCompletionRequest", "CreateChatCompletionRequest"],
match, # type: Match[str] # type: ignore
) -> Tuple[int, ErrorResponse]:
"""Formatter for context length exceeded error"""
context_window = int(match.group(2))
prompt_tokens = int(match.group(1))
completion_tokens = request.max_tokens
if hasattr(request, "messages"):
# Chat completion
message = (
"This model's maximum context length is {} tokens. "
"However, you requested {} tokens "
"({} in the messages, {} in the completion). "
"Please reduce the length of the messages or completion."
)
else:
# Text completion
message = (
"This model's maximum context length is {} tokens, "
"however you requested {} tokens "
"({} in your prompt; {} for the completion). "
"Please reduce your prompt; or completion length."
)
return 400, ErrorResponse(
message=message.format(
context_window,
completion_tokens + prompt_tokens,
prompt_tokens,
completion_tokens,
),
type="invalid_request_error",
param="messages",
code="context_length_exceeded",
)
@staticmethod
def model_not_found(
request: Union["CreateCompletionRequest", "CreateChatCompletionRequest"],
match, # type: Match[str] # type: ignore
) -> Tuple[int, ErrorResponse]:
"""Formatter for model_not_found error"""
model_path = str(match.group(1))
message = f"The model `{model_path}` does not exist"
return 400, ErrorResponse(
message=message,
type="invalid_request_error",
param=None,
code="model_not_found",
)
class RouteErrorHandler(APIRoute):
"""Custom APIRoute that handles application errors and exceptions"""
# key: regex pattern for original error message from llama_cpp
# value: formatter function
pattern_and_formatters: Dict[
"Pattern",
Callable[
[
Union["CreateCompletionRequest", "CreateChatCompletionRequest"],
"Match[str]",
],
Tuple[int, ErrorResponse],
],
] = {
compile(
r"Requested tokens \((\d+)\) exceed context window of (\d+)"
): ErrorResponseFormatters.context_length_exceeded,
compile(
r"Model path does not exist: (.+)"
): ErrorResponseFormatters.model_not_found,
}
def error_message_wrapper(
self,
error: Exception,
body: Optional[
Union[
"CreateChatCompletionRequest",
"CreateCompletionRequest",
"CreateEmbeddingRequest",
]
] = None,
) -> Tuple[int, ErrorResponse]:
"""Wraps error message in OpenAI style error response"""
print(f"Exception: {str(error)}", file=sys.stderr)
traceback.print_exc(file=sys.stderr)
if body is not None and isinstance(
body,
(
CreateCompletionRequest,
CreateChatCompletionRequest,
),
):
# When text completion or chat completion
for pattern, callback in self.pattern_and_formatters.items():
match = pattern.search(str(error))
if match is not None:
return callback(body, match)
# Wrap other errors as internal server error
return 500, ErrorResponse(
message=str(error),
type="internal_server_error",
param=None,
code=None,
)
def get_route_handler(
self,
) -> Callable[[Request], Coroutine[None, None, Response]]:
"""Defines custom route handler that catches exceptions and formats
in OpenAI style error response"""
original_route_handler = super().get_route_handler()
async def custom_route_handler(request: Request) -> Response:
try:
start_sec = time.perf_counter()
response = await original_route_handler(request)
elapsed_time_ms = int((time.perf_counter() - start_sec) * 1000)
response.headers["openai-processing-ms"] = f"{elapsed_time_ms}"
return response
except Exception as exc:
json_body = await request.json()
try:
if "messages" in json_body:
# Chat completion
body: Optional[
Union[
CreateChatCompletionRequest,
CreateCompletionRequest,
CreateEmbeddingRequest,
]
] = CreateChatCompletionRequest(**json_body)
elif "prompt" in json_body:
# Text completion
body = CreateCompletionRequest(**json_body)
else:
# Embedding
body = CreateEmbeddingRequest(**json_body)
except Exception:
# Invalid request body
body = None
# Get proper error message from the exception
(
status_code,
error_message,
) = self.error_message_wrapper(error=exc, body=body)
return JSONResponse(
{"error": error_message},
status_code=status_code,
)
return custom_route_handler
router = APIRouter(route_class=RouteErrorHandler)
settings: Optional[Settings] = None
llama: Optional[llama_cpp.Llama] = None
def create_app(settings: Optional[Settings] = None):
if settings is None:
settings = Settings()
middleware = [
Middleware(RawContextMiddleware, plugins=(plugins.RequestIdPlugin(),))
]
app = FastAPI(
middleware=middleware,
title="🦙 llama.cpp Python API",
version="0.0.1",
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.include_router(router)
global llama
##
chat_handler = None
if settings.chat_format == "llava-1-5":
assert settings.clip_model_path is not None
chat_handler = llama_cpp.llama_chat_format.Llava15ChatHandler(
clip_model_path=settings.clip_model_path, verbose=settings.verbose
)
##
llama = llama_cpp.Llama(
model_path=settings.model,
# Model Params
n_gpu_layers=settings.n_gpu_layers,
main_gpu=settings.main_gpu,
tensor_split=settings.tensor_split,
vocab_only=settings.vocab_only,
use_mmap=settings.use_mmap,
use_mlock=settings.use_mlock,
# Context Params
seed=settings.seed,
n_ctx=settings.n_ctx,
n_batch=settings.n_batch,
n_threads=settings.n_threads,
n_threads_batch=settings.n_threads_batch,
rope_scaling_type=settings.rope_scaling_type,
rope_freq_base=settings.rope_freq_base,
rope_freq_scale=settings.rope_freq_scale,
yarn_ext_factor=settings.yarn_ext_factor,
yarn_attn_factor=settings.yarn_attn_factor,
yarn_beta_fast=settings.yarn_beta_fast,
yarn_beta_slow=settings.yarn_beta_slow,
yarn_orig_ctx=settings.yarn_orig_ctx,
mul_mat_q=settings.mul_mat_q,
f16_kv=settings.f16_kv,
logits_all=settings.logits_all,
embedding=settings.embedding,
# Sampling Params
last_n_tokens_size=settings.last_n_tokens_size,
# LoRA Params
lora_base=settings.lora_base,
lora_path=settings.lora_path,
# Backend Params
numa=settings.numa,
# Chat Format Params
chat_format=settings.chat_format,
chat_handler=chat_handler,
# Misc
verbose=settings.verbose,
)
if settings.cache:
if settings.cache_type == "disk":
if settings.verbose:
print(f"Using disk cache with size {settings.cache_size}")
cache = llama_cpp.LlamaDiskCache(capacity_bytes=settings.cache_size)
else:
if settings.verbose:
print(f"Using ram cache with size {settings.cache_size}")
cache = llama_cpp.LlamaRAMCache(capacity_bytes=settings.cache_size)
cache = llama_cpp.LlamaCache(capacity_bytes=settings.cache_size)
llama.set_cache(cache)
def set_settings(_settings: Settings):
global settings
settings = _settings
set_settings(settings)
return app
llama_outer_lock = Lock()
llama_inner_lock = Lock()
def get_llama():
# NOTE: This double lock allows the currently streaming llama model to
# check if any other requests are pending in the same thread and cancel
# the stream if so.
llama_outer_lock.acquire()
release_outer_lock = True
try:
llama_inner_lock.acquire()
try:
llama_outer_lock.release()
release_outer_lock = False
yield llama
finally:
llama_inner_lock.release()
finally:
if release_outer_lock:
llama_outer_lock.release()
def get_settings():
yield settings
async def get_event_publisher(
request: Request,
inner_send_chan: MemoryObjectSendStream,
iterator: Iterator,
):
async with inner_send_chan:
try:
async for chunk in iterate_in_threadpool(iterator):
await inner_send_chan.send(dict(data=json.dumps(chunk)))
if await request.is_disconnected():
raise anyio.get_cancelled_exc_class()()
if settings.interrupt_requests and llama_outer_lock.locked():
await inner_send_chan.send(dict(data="[DONE]"))
raise anyio.get_cancelled_exc_class()()
await inner_send_chan.send(dict(data="[DONE]"))
except anyio.get_cancelled_exc_class() as e:
print("disconnected")
with anyio.move_on_after(1, shield=True):
print(f"Disconnected from client (via refresh/close) {request.client}")
raise e
model_field = Field(
description="The model to use for generating completions.", default=None
)
max_tokens_field = Field(
default=16, ge=1, description="The maximum number of tokens to generate."
)
temperature_field = Field(
default=0.8,
ge=0.0,
le=2.0,
description="Adjust the randomness of the generated text.\n\n"
+ "Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.",
)
top_p_field = Field(
default=0.95,
ge=0.0,
le=1.0,
description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n"
+ "Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.",
)
min_p_field = Field(
default=0.05,
ge=0.0,
le=1.0,
description="Sets a minimum base probability threshold for token selection.\n\n"
+ "The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter min_p represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with min_p=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.",
)
stop_field = Field(
default=None,
description="A list of tokens at which to stop generation. If None, no stop tokens are used.",
)
stream_field = Field(
default=False,
description="Whether to stream the results as they are generated. Useful for chatbots.",
)
top_k_field = Field(
default=40,
ge=0,
description="Limit the next token selection to the K most probable tokens.\n\n"
+ "Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text.",
)
repeat_penalty_field = Field(
default=1.1,
ge=0.0,
description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\n"
+ "Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.",
)
presence_penalty_field = Field(
default=0.0,
ge=-2.0,
le=2.0,
description="Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.",
)
frequency_penalty_field = Field(
default=0.0,
ge=-2.0,
le=2.0,
description="Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.",
)
mirostat_mode_field = Field(
default=0,
ge=0,
le=2,
description="Enable Mirostat constant-perplexity algorithm of the specified version (1 or 2; 0 = disabled)",
)
mirostat_tau_field = Field(
default=5.0,
ge=0.0,
le=10.0,
description="Mirostat target entropy, i.e. the target perplexity - lower values produce focused and coherent text, larger values produce more diverse and less coherent text",
)
mirostat_eta_field = Field(
default=0.1, ge=0.001, le=1.0, description="Mirostat learning rate"
)
grammar = Field(
default=None,
description="A CBNF grammar (as string) to be used for formatting the model's output.",
)
class CreateCompletionRequest(BaseModel):
prompt: Union[str, List[str]] = Field(
default="", description="The prompt to generate completions for."
)
suffix: Optional[str] = Field(
default=None,
description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots.",
)
max_tokens: int = max_tokens_field
temperature: float = temperature_field
top_p: float = top_p_field
min_p: float = min_p_field
echo: bool = Field(
default=False,
description="Whether to echo the prompt in the generated text. Useful for chatbots.",
)
stop: Optional[Union[str, List[str]]] = stop_field
stream: bool = stream_field
logprobs: Optional[int] = Field(
default=None,
ge=0,
description="The number of logprobs to generate. If None, no logprobs are generated.",
)
presence_penalty: Optional[float] = presence_penalty_field
frequency_penalty: Optional[float] = frequency_penalty_field
logit_bias: Optional[Dict[str, float]] = Field(None)
logprobs: Optional[int] = Field(None)
seed: Optional[int] = Field(None)
# ignored or currently unsupported
model: Optional[str] = model_field
n: Optional[int] = 1
best_of: Optional[int] = 1
user: Optional[str] = Field(default=None)
# llama.cpp specific parameters
top_k: int = top_k_field
repeat_penalty: float = repeat_penalty_field
logit_bias_type: Optional[Literal["input_ids", "tokens"]] = Field(None)
mirostat_mode: int = mirostat_mode_field
mirostat_tau: float = mirostat_tau_field
mirostat_eta: float = mirostat_eta_field
grammar: Optional[str] = None
model_config = {
"json_schema_extra": {
"examples": [
{
"prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
"stop": ["\n", "###"],
}
]
}
}
def _logit_bias_tokens_to_input_ids(
llama: llama_cpp.Llama,
logit_bias: Dict[str, float],
) -> Dict[str, float]:
to_bias: Dict[str, float] = {}
for token, score in logit_bias.items():
token = token.encode("utf-8")
for input_id in llama.tokenize(token, add_bos=False, special=True):
to_bias[str(input_id)] = score
return to_bias
@router.post(
"/v1/completions",
summary="Completion"
)
@router.post("/v1/engines/copilot-codex/completions", include_in_schema=False)
async def create_completion(
request: Request,
body: CreateCompletionRequest,
llama: llama_cpp.Llama = Depends(get_llama),
) -> llama_cpp.Completion:
if isinstance(body.prompt, list):
assert len(body.prompt) <= 1
body.prompt = body.prompt[0] if len(body.prompt) > 0 else ""
exclude = {
"n",
"best_of",
"logit_bias_type",
"user",
}
kwargs = body.model_dump(exclude=exclude)
if body.logit_bias is not None:
kwargs["logit_bias"] = (
_logit_bias_tokens_to_input_ids(llama, body.logit_bias)
if body.logit_bias_type == "tokens"
else body.logit_bias
)
if body.grammar is not None:
kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar)
iterator_or_completion: Union[
llama_cpp.CreateCompletionResponse,
Iterator[llama_cpp.CreateCompletionStreamResponse],
] = await run_in_threadpool(llama, **kwargs)
if isinstance(iterator_or_completion, Iterator):
# EAFP: It's easier to ask for forgiveness than permission
first_response = await run_in_threadpool(next, iterator_or_completion)
# If no exception was raised from first_response, we can assume that
# the iterator is valid and we can use it to stream the response.
def iterator() -> Iterator[llama_cpp.CreateCompletionStreamResponse]:
yield first_response
yield from iterator_or_completion
send_chan, recv_chan = anyio.create_memory_object_stream(10)
return EventSourceResponse(
recv_chan,
data_sender_callable=partial( # type: ignore
get_event_publisher,
request=request,
inner_send_chan=send_chan,
iterator=iterator(),
),
)
else:
return iterator_or_completion
class CreateEmbeddingRequest(BaseModel):
model: Optional[str] = model_field
input: Union[str, List[str]] = Field(description="The input to embed.")
user: Optional[str] = Field(default=None)
model_config = {
"json_schema_extra": {
"examples": [
{
"input": "The food was delicious and the waiter...",
}
]
}
}
@router.post(
"/v1/embeddings",
summary="Embedding"
)
async def create_embedding(
request: CreateEmbeddingRequest, llama: llama_cpp.Llama = Depends(get_llama)
):
return await run_in_threadpool(
llama.create_embedding, **request.model_dump(exclude={"user"})
)
class ChatCompletionRequestMessage(BaseModel):
role: Literal["system", "user", "assistant", "function"] = Field(
default="user", description="The role of the message."
)
content: Optional[str] = Field(
default="", description="The content of the message."
)
class CreateChatCompletionRequest(BaseModel):
messages: List[llama_cpp.ChatCompletionRequestMessage] = Field(
default=[], description="A list of messages to generate completions for."
)
functions: Optional[List[llama_cpp.ChatCompletionFunction]] = Field(
default=None,
description="A list of functions to apply to the generated completions.",
)
function_call: Optional[llama_cpp.ChatCompletionRequestFunctionCall] = Field(
default=None,
description="A function to apply to the generated completions.",
)
tools: Optional[List[llama_cpp.ChatCompletionTool]] = Field(
default=None,
description="A list of tools to apply to the generated completions.",
)
tool_choice: Optional[llama_cpp.ChatCompletionToolChoiceOption] = Field(
default=None,
description="A tool to apply to the generated completions.",
) # TODO: verify
max_tokens: Optional[int] = Field(
default=None,
description="The maximum number of tokens to generate. Defaults to inf",
)
temperature: float = temperature_field
top_p: float = top_p_field
min_p: float = min_p_field
stop: Optional[Union[str, List[str]]] = stop_field
stream: bool = stream_field
presence_penalty: Optional[float] = presence_penalty_field
frequency_penalty: Optional[float] = frequency_penalty_field
logit_bias: Optional[Dict[str, float]] = Field(None)
seed: Optional[int] = Field(None)
response_format: Optional[llama_cpp.ChatCompletionRequestResponseFormat] = Field(
default=None,
)
# ignored or currently unsupported
model: Optional[str] = model_field
n: Optional[int] = 1
user: Optional[str] = Field(None)
# llama.cpp specific parameters
top_k: int = top_k_field
repeat_penalty: float = repeat_penalty_field
logit_bias_type: Optional[Literal["input_ids", "tokens"]] = Field(None)
mirostat_mode: int = mirostat_mode_field
mirostat_tau: float = mirostat_tau_field
mirostat_eta: float = mirostat_eta_field
grammar: Optional[str] = None
model_config = {
"json_schema_extra": {
"examples": [
{
"messages": [
ChatCompletionRequestMessage(
role="system", content="You are a helpful assistant."
).model_dump(),
ChatCompletionRequestMessage(
role="user", content="What is the capital of France?"
).model_dump(),
]
}
]
}
}
@router.post(
"/v1/chat/completions",
summary="Chat"
)
async def create_chat_completion(
request: Request,
body: CreateChatCompletionRequest,
llama: llama_cpp.Llama = Depends(get_llama),
settings: Settings = Depends(get_settings),
) -> llama_cpp.ChatCompletion:
exclude = {
"n",
"logit_bias_type",
"user",
}
kwargs = body.model_dump(exclude=exclude)
if body.logit_bias is not None:
kwargs["logit_bias"] = (
_logit_bias_tokens_to_input_ids(llama, body.logit_bias)
if body.logit_bias_type == "tokens"
else body.logit_bias
)
if body.grammar is not None:
kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar)
iterator_or_completion: Union[
llama_cpp.ChatCompletion, Iterator[llama_cpp.ChatCompletionChunk]
] = await run_in_threadpool(llama.create_chat_completion, **kwargs)
if isinstance(iterator_or_completion, Iterator):
# EAFP: It's easier to ask for forgiveness than permission
first_response = await run_in_threadpool(next, iterator_or_completion)
# If no exception was raised from first_response, we can assume that
# the iterator is valid and we can use it to stream the response.
def iterator() -> Iterator[llama_cpp.ChatCompletionChunk]:
yield first_response
yield from iterator_or_completion
send_chan, recv_chan = anyio.create_memory_object_stream(10)
return EventSourceResponse(
recv_chan,
data_sender_callable=partial( # type: ignore
get_event_publisher,
request=request,
inner_send_chan=send_chan,
iterator=iterator(),
),
)
else:
return iterator_or_completion
class ModelData(TypedDict):
id: str
object: Literal["model"]
owned_by: str
permissions: List[str]
class ModelList(TypedDict):
object: Literal["list"]
data: List[ModelData]
@router.get("/v1/models", summary="Models")
async def get_models(
settings: Settings = Depends(get_settings),
) -> ModelList:
assert llama is not None
return {
"object": "list",
"data": [
{
"id": settings.model_alias
if settings.model_alias is not None
else llama.model_path,
"object": "model",
"owned_by": "me",
"permissions": [],
}
],
}