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609b5e8..f95e33d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,11 +20,10 @@ toml = "^0.10.2" gitpython = "^3.1.31" peft = {git = "https://github.com/huggingface/peft.git", rev = "v0.2.0"} duckdb = "^0.7.1" - +torch = "^2.0.0" [tool.poetry.group.dev.dependencies] pre-commit = "^3.1.1" -torch = "^2.0.0" [build-system] requires = ["poetry-core"] diff --git a/tabby/server/__init__.py b/tabby/server/__init__.py index 91c1ceb..e203c50 100644 --- a/tabby/server/__init__.py +++ b/tabby/server/__init__.py @@ -6,9 +6,8 @@ from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse from . import events +from .backend import PythonModelService, TritonService from .models import CompletionRequest, CompletionResponse -from .python import PythonModelService -from .triton import TritonService app = FastAPI( title="TabbyServer", diff --git a/tabby/server/backend/__init__.py b/tabby/server/backend/__init__.py new file mode 100644 index 0000000..94768ef --- /dev/null +++ b/tabby/server/backend/__init__.py @@ -0,0 +1,2 @@ +from .python import PythonModelService +from .triton import TritonService diff --git a/tabby/server/backend/language_presets.py b/tabby/server/backend/language_presets.py new file mode 100644 index 0000000..65f2c00 --- /dev/null +++ b/tabby/server/backend/language_presets.py @@ -0,0 +1,19 @@ +from typing import List + +from pydantic import BaseModel, Field + + +class LanguagePreset(BaseModel): + max_length: int + stop_words: List[str] + + +PythonPreset = LanguagePreset( + max_length=128, stop_words=["\n\n", "\ndef", "\n#", "\nimport", "\nfrom", "\nclass"] +) + +JavascriptPreset = LanguagePreset( + max_length=128, stop_words=["\n\n", "\nfunction", "\n//", "\nimport", "\nclass"] +) + +LanguagePresets = {"python": PythonPreset, "javascript": JavascriptPreset} diff --git a/tabby/server/backend/python.py b/tabby/server/backend/python.py new file mode 100644 index 0000000..5d8a24d --- /dev/null +++ b/tabby/server/backend/python.py @@ -0,0 +1,105 @@ +import time +from typing import List + +import torch +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + StoppingCriteria, + StoppingCriteriaList, +) + +from ..models import Choice, CompletionRequest, CompletionResponse +from .language_presets import LanguagePresets +from .utils import random_completion_id, trim_with_stopwords + + +class PythonModelService: + def __init__( + self, + model_name, + ): + if torch.cuda.is_available(): + device = torch.device("cuda") + dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32 + else: + device = torch.device("cpu") + dtype = torch.float32 + + self.device = device + self.tokenizer = AutoTokenizer.from_pretrained( + model_name, local_files_only=True + ) + self.model = ( + AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=dtype, + device_map="auto" if torch.cuda.is_available() else None, + local_files_only=True, + ) + .to(device) + .eval() + ) + self.stopping_criteria_mappings = {} + + def generate(self, request: CompletionRequest) -> List[Choice]: + # FIXME(meng): read preset from request. + preset_name = "python" + preset = LanguagePresets[preset_name] + + stopping_criteria_list = self.stopping_criteria_for_preset(preset_name) + + input_ids = self.tokenizer.encode(request.prompt, return_tensors="pt").to( + self.device + ) + res = self.model.generate( + input_ids, + max_length=preset.max_length, + stopping_criteria=stopping_criteria_list, + ) + output_ids = res[0][len(input_ids[0]) :] + text = trim_with_stopwords(self.tokenizer.decode(output_ids), preset.stop_words) + return [Choice(index=0, text=text)] + + def stopping_criteria_for_preset(self, name: str) -> StoppingCriteriaList: + return StoppingCriteriaList( + [ + StopWordsIdsCriteria( + [self.tokenizer.encode(x) for x in LanguagePresets[name].stop_words] + ) + ] + ) + + def __call__(self, request: CompletionRequest) -> CompletionResponse: + choices = self.generate(request) + return CompletionResponse( + id=random_completion_id(), created=int(time.time()), choices=choices + ) + + +class StopWordsIdsCriteria(StoppingCriteria): + def __init__(self, stop_words_ids: List[str]): + self.stop_words_ids = stop_words_ids + + def __call__( + self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs + ) -> bool: + if len(input_ids) != 1: + raise ValueError("Only 1-length list is handled") + + # FIXME(meng): trie based lookup. + tokens = input_ids[0] + for stop_word in self.stop_words_ids: + if len(tokens) < len(stop_word): + continue + + matched = True + for i in range(len(stop_word)): + if tokens[i - len(stop_word)] != stop_word[i]: + matched = False + break + + if matched: + return True + + return False diff --git a/tabby/server/triton.py b/tabby/server/backend/triton.py similarity index 81% rename from tabby/server/triton.py rename to tabby/server/backend/triton.py index 494ab0a..748318a 100644 --- a/tabby/server/triton.py +++ b/tabby/server/backend/triton.py @@ -1,5 +1,3 @@ -import random -import string import time from typing import List @@ -8,7 +6,9 @@ import tritonclient.grpc as client_util from transformers import AutoTokenizer from tritonclient.utils import InferenceServerException, np_to_triton_dtype -from .models import Choice, CompletionRequest, CompletionResponse +from ..models import Choice, CompletionRequest, CompletionResponse +from .language_presets import LanguagePresets +from .utils import random_completion_id, trim_with_stopwords class TritonService: @@ -25,12 +25,13 @@ class TritonService: ) def generate(self, data: CompletionRequest) -> List[Choice]: - # FIXME(meng): Make following vars configurable n = 1 np_type = np.uint32 - max_tokens = 128 model_name = "fastertransformer" - stop_words = ["\n\n"] + + # FIXME(meng): read preset from request. + preset_name = "python" + preset = LanguagePresets[preset_name] prompt = data.prompt input_start_ids = np.expand_dims(self.tokenizer.encode(prompt), 0) @@ -39,10 +40,10 @@ class TritonService: input_len = prompt_len * np.ones([input_start_ids.shape[0], 1]).astype(np_type) prompt_tokens: int = input_len[0][0] - output_len = np.ones_like(input_len).astype(np_type) * max_tokens + output_len = np.ones_like(input_len).astype(np_type) * preset.max_length stop_word_list = np.repeat( - to_word_list_format([stop_words], self.tokenizer), + to_word_list_format([preset.stop_words], self.tokenizer), input_start_ids.shape[0], axis=0, ) @@ -68,7 +69,7 @@ class TritonService: self.tokenizer.decode(out[prompt_len : prompt_len + g]) for g, out in zip(gen_len, output_data) ] - trimmed = [trim_with_stopwords(d, stop_words) for d in decoded] + trimmed = [trim_with_stopwords(d, preset.stop_words) for d in decoded] return [Choice(index=i, text=text) for i, text in enumerate(trimmed)] def __call__(self, data: CompletionRequest) -> CompletionResponse: @@ -86,20 +87,6 @@ def prepare_tensor(name: str, tensor_input): return t -def random_completion_id(): - return "cmpl-" + "".join( - random.choice(string.ascii_letters + string.digits) for _ in range(29) - ) - - -def trim_with_stopwords(output: str, stopwords: list) -> str: - for w in sorted(stopwords, key=len, reverse=True): - if output.endswith(w): - output = output[: -len(w)] - break - return output - - def to_word_list_format(word_dict, tokenizer): flat_ids = [] offsets = [] diff --git a/tabby/server/backend/utils.py b/tabby/server/backend/utils.py new file mode 100644 index 0000000..78fff92 --- /dev/null +++ b/tabby/server/backend/utils.py @@ -0,0 +1,16 @@ +import random +import string + + +def random_completion_id(): + return "cmpl-" + "".join( + random.choice(string.ascii_letters + string.digits) for _ in range(29) + ) + + +def trim_with_stopwords(output: str, stopwords: list) -> str: + for w in sorted(stopwords, key=len, reverse=True): + if output.endswith(w): + output = output[: -len(w)] + break + return output diff --git a/tabby/server/python.py b/tabby/server/python.py deleted file mode 100644 index 67b452b..0000000 --- a/tabby/server/python.py +++ /dev/null @@ -1,40 +0,0 @@ -import random -import string -import time -from typing import List - -from transformers import AutoModelForCausalLM, AutoTokenizer - -from .models import Choice, CompletionRequest, CompletionResponse - - -class PythonModelService: - def __init__( - self, - model_name, - ): - self.tokenizer = AutoTokenizer.from_pretrained( - model_name, local_files_only=True - ) - self.model = AutoModelForCausalLM.from_pretrained( - model_name, local_files_only=True - ) - - def generate(self, request: CompletionRequest) -> List[Choice]: - input_ids = self.tokenizer.encode(request.prompt, return_tensors="pt") - res = self.model.generate(input_ids, max_length=64) - output_ids = res[0][len(input_ids[0]) :] - text = self.tokenizer.decode(output_ids) - return [Choice(index=0, text=text)] - - def __call__(self, request: CompletionRequest) -> CompletionResponse: - choices = self.generate(request) - return CompletionResponse( - id=random_completion_id(), created=int(time.time()), choices=choices - ) - - -def random_completion_id(): - return "cmpl-" + "".join( - random.choice(string.ascii_letters + string.digits) for _ in range(29) - )