12920205472

Committed 23 Jan 2025 01:12AM UTC coverage: 95.993% (+15.4%) from 80.557%

Build # 12920205472

Build Type

Pull #74

github

Specific Base 9af6ef

Committed by

tito

Commit Message

fix: try fixing test again

Pull Request Pull Request #74: feat(metadata): include many metadata accessible at global or tracer level

Run Details

49 of 50 new or added lines in 4 files covered. (98.0%)

53 existing lines in 10 files now uncovered.

2204 of 2296 relevant lines covered (95.99%)

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98.84

/scope3ai/tracers/huggingface/chat.py

import time
from collections.abc import AsyncIterable, Iterable
from dataclasses import asdict, dataclass
from typing import Any, Callable, Optional, Union

import tiktoken
from huggingface_hub import (  # type: ignore[import-untyped]
    AsyncInferenceClient,
    InferenceClient,
)
from huggingface_hub import ChatCompletionOutput as _ChatCompletionOutput
from huggingface_hub import ChatCompletionStreamOutput as _ChatCompletionStreamOutput
from requests import Response

from scope3ai.api.types import ImpactRow, Scope3AIContext
from scope3ai.constants import PROVIDERS
from scope3ai.lib import Scope3AI
from scope3ai.response_interceptor.requests_interceptor import requests_response_capture

PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value
HUGGING_FACE_CHAT_TASK = "chat"


@dataclass
class ChatCompletionOutput(_ChatCompletionOutput):
    scope3ai: Optional[Scope3AIContext] = None


@dataclass
class ChatCompletionStreamOutput(_ChatCompletionStreamOutput):
    scope3ai: Optional[Scope3AIContext] = None


def huggingface_chat_wrapper(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> Union[ChatCompletionOutput, Iterable[ChatCompletionStreamOutput]]:
    if kwargs.get("stream", False):
        return huggingface_chat_wrapper_stream(wrapped, instance, args, kwargs)
    else:
        return huggingface_chat_wrapper_non_stream(wrapped, instance, args, kwargs)


def huggingface_chat_wrapper_non_stream(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> ChatCompletionOutput:
    timer_start = time.perf_counter()
    http_response: Response | None = None
    with requests_response_capture() as responses:
        response = wrapped(*args, **kwargs)
        http_responses = responses.get()
        if http_responses:
            http_response = http_responses[0]
    model = (
        instance.model
        or kwargs.get("model")
        or instance.get_recommended_model(HUGGING_FACE_CHAT_TASK)
    )
    if http_response:
        compute_time = http_response.headers.get("x-compute-time")
    else:
        compute_time = time.perf_counter() - timer_start
    scope3_row = ImpactRow(
        model_id=model,
        input_tokens=response.usage.prompt_tokens,
        output_tokens=response.usage.completion_tokens,
        request_duration_ms=float(compute_time) * 1000,
        managed_service_id=PROVIDER,
    )
    scope3ai_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
    chat = ChatCompletionOutput(**asdict(response))
    chat.scope3ai = scope3ai_ctx
    return chat


def huggingface_chat_wrapper_stream(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> Iterable[ChatCompletionStreamOutput]:
    timer_start = time.perf_counter()
    stream = wrapped(*args, **kwargs)
    token_count = 0
    model = (
        instance.model
        or kwargs.get("model")
        or instance.get_recommended_model(HUGGING_FACE_CHAT_TASK)
    )
    for chunk in stream:
        token_count += 1
        request_latency = time.perf_counter() - timer_start
        scope3_row = ImpactRow(
            model_id=model,
            output_tokens=token_count,
            request_duration_ms=request_latency * 1000,
            managed_service_id=PROVIDER,
        )
        chunk_data = ChatCompletionStreamOutput(**asdict(chunk))
        scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
        if scope3_ctx is not None:
            chunk_data.scope3ai = scope3_ctx
        yield chunk_data


async def huggingface_async_chat_wrapper(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> Union[ChatCompletionOutput, AsyncIterable[ChatCompletionStreamOutput]]:
    if kwargs.get("stream", False):
        return huggingface_async_chat_wrapper_stream(wrapped, instance, args, kwargs)
    else:
        return await huggingface_async_chat_wrapper_non_stream(
            wrapped, instance, args, kwargs
        )


async def huggingface_async_chat_wrapper_non_stream(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> ChatCompletionOutput:
    timer_start = time.perf_counter()

    response = await wrapped(*args, **kwargs)
    request_latency = time.perf_counter() - timer_start
    model = (
        instance.model or kwargs.get("model") or instance.get_recommended_model("chat")
    )
    encoder = tiktoken.get_encoding("cl100k_base")
    output_tokens = len(encoder.encode(response.choices[0].message.content))
    scope3_row = ImpactRow(
        model_id=model,
        input_tokens=response.usage.prompt_tokens,
        output_tokens=output_tokens,
        request_duration_ms=request_latency * 1000,
        managed_service_id=PROVIDER,
    )

    scope3ai_ctx = await Scope3AI.get_instance().asubmit_impact(scope3_row)
    chat = ChatCompletionOutput(**asdict(response))
    chat.scope3ai = scope3ai_ctx
    return chat


# Todo: How headers works for stream
async def huggingface_async_chat_wrapper_stream(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> AsyncIterable[ChatCompletionStreamOutput]:
    timer_start = time.perf_counter()
    stream = await wrapped(*args, **kwargs)
    token_count = 0
    model = instance.model or kwargs["model"]
    async for chunk in stream:
        token_count += 1
        request_latency = time.perf_counter() - timer_start
        scope3_row = ImpactRow(
            model_id=model,
            output_tokens=token_count,
            request_duration_ms=request_latency
            * 1000,  # TODO: can we get the header that has the processing time
            managed_service_id=PROVIDER,
        )
        scope3_ctx = await Scope3AI.get_instance().asubmit_impact(scope3_row)
        chunk_data = ChatCompletionStreamOutput(**asdict(chunk))
        if scope3_ctx is not None:
            chunk_data.scope3ai = scope3_ctx
        yield chunk_data

1	import time	4✔
2	from collections.abc import AsyncIterable, Iterable	4✔
3	from dataclasses import asdict, dataclass	4✔
4	from typing import Any, Callable, Optional, Union	4✔
5
6	import tiktoken	4✔
7	from huggingface_hub import ( # type: ignore[import-untyped]	4✔
8	AsyncInferenceClient,
9	InferenceClient,
10	)
11	from huggingface_hub import ChatCompletionOutput as _ChatCompletionOutput	4✔
12	from huggingface_hub import ChatCompletionStreamOutput as _ChatCompletionStreamOutput	4✔
13	from requests import Response	4✔
14
15	from scope3ai.api.types import ImpactRow, Scope3AIContext	4✔
16	from scope3ai.constants import PROVIDERS	4✔
17	from scope3ai.lib import Scope3AI	4✔
18	from scope3ai.response_interceptor.requests_interceptor import requests_response_capture	4✔
19
20	PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value	4✔
21	HUGGING_FACE_CHAT_TASK = "chat"	4✔
22
23
24	@dataclass	4✔
25	class ChatCompletionOutput(_ChatCompletionOutput):	4✔
26	scope3ai: Optional[Scope3AIContext] = None	4✔
27
28
29	@dataclass	4✔
30	class ChatCompletionStreamOutput(_ChatCompletionStreamOutput):	4✔
31	scope3ai: Optional[Scope3AIContext] = None	4✔
32
33
34	def huggingface_chat_wrapper(	4✔
35	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
36	) -> Union[ChatCompletionOutput, Iterable[ChatCompletionStreamOutput]]:
37	if kwargs.get("stream", False):	4✔
38	return huggingface_chat_wrapper_stream(wrapped, instance, args, kwargs)	4✔
39	else:
40	return huggingface_chat_wrapper_non_stream(wrapped, instance, args, kwargs)	4✔
41
42
43	def huggingface_chat_wrapper_non_stream(	4✔
44	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
45	) -> ChatCompletionOutput:
46	timer_start = time.perf_counter()	4✔
47	http_response: Response \| None = None	4✔
48	with requests_response_capture() as responses:	4✔
49	response = wrapped(args, *kwargs)	4✔
50	http_responses = responses.get()	4✔
51	if http_responses:	4✔
52	http_response = http_responses[0]	4✔
53	model = (	4✔
54	instance.model
55	or kwargs.get("model")
56	or instance.get_recommended_model(HUGGING_FACE_CHAT_TASK)
57	)
58	if http_response:	4✔
59	compute_time = http_response.headers.get("x-compute-time")	4✔
60	else:
UNCOV 61	compute_time = time.perf_counter() - timer_start	×
62	scope3_row = ImpactRow(	4✔
63	model_id=model,
64	input_tokens=response.usage.prompt_tokens,
65	output_tokens=response.usage.completion_tokens,
66	request_duration_ms=float(compute_time) * 1000,
67	managed_service_id=PROVIDER,
68	)
69	scope3ai_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
70	chat = ChatCompletionOutput(**asdict(response))	4✔
71	chat.scope3ai = scope3ai_ctx	4✔
72	return chat	4✔
73
74
75	def huggingface_chat_wrapper_stream(	4✔
76	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
77	) -> Iterable[ChatCompletionStreamOutput]:
78	timer_start = time.perf_counter()	4✔
79	stream = wrapped(args, *kwargs)	4✔
80	token_count = 0	4✔
81	model = (	4✔
82	instance.model
83	or kwargs.get("model")
84	or instance.get_recommended_model(HUGGING_FACE_CHAT_TASK)
85	)
86	for chunk in stream:	4✔
87	token_count += 1	4✔
88	request_latency = time.perf_counter() - timer_start	4✔
89	scope3_row = ImpactRow(	4✔
90	model_id=model,
91	output_tokens=token_count,
92	request_duration_ms=request_latency * 1000,
93	managed_service_id=PROVIDER,
94	)
95	chunk_data = ChatCompletionStreamOutput(**asdict(chunk))	4✔
96	scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
97	if scope3_ctx is not None:	4✔
98	chunk_data.scope3ai = scope3_ctx	4✔
99	yield chunk_data	4✔
100
101
102	async def huggingface_async_chat_wrapper(	4✔
103	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
104	) -> Union[ChatCompletionOutput, AsyncIterable[ChatCompletionStreamOutput]]:
105	if kwargs.get("stream", False):	4✔
106	return huggingface_async_chat_wrapper_stream(wrapped, instance, args, kwargs)	4✔
107	else:
108	return await huggingface_async_chat_wrapper_non_stream(	4✔
109	wrapped, instance, args, kwargs
110	)
111
112
113	async def huggingface_async_chat_wrapper_non_stream(	4✔
114	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
115	) -> ChatCompletionOutput:
116	timer_start = time.perf_counter()	4✔
117
118	response = await wrapped(args, *kwargs)	4✔
119	request_latency = time.perf_counter() - timer_start	4✔
120	model = (	4✔
121	instance.model or kwargs.get("model") or instance.get_recommended_model("chat")
122	)
123	encoder = tiktoken.get_encoding("cl100k_base")	4✔
124	output_tokens = len(encoder.encode(response.choices[0].message.content))	4✔
125	scope3_row = ImpactRow(	4✔
126	model_id=model,
127	input_tokens=response.usage.prompt_tokens,
128	output_tokens=output_tokens,
129	request_duration_ms=request_latency * 1000,
130	managed_service_id=PROVIDER,
131	)
132
133	scope3ai_ctx = await Scope3AI.get_instance().asubmit_impact(scope3_row)	4✔
134	chat = ChatCompletionOutput(**asdict(response))	4✔
135	chat.scope3ai = scope3ai_ctx	4✔
136	return chat	4✔
137
138
139	# Todo: How headers works for stream
140	async def huggingface_async_chat_wrapper_stream(	4✔
141	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
142	) -> AsyncIterable[ChatCompletionStreamOutput]:
143	timer_start = time.perf_counter()	4✔
144	stream = await wrapped(args, *kwargs)	4✔
145	token_count = 0	4✔
146	model = instance.model or kwargs["model"]	4✔
147	async for chunk in stream:	4✔
148	token_count += 1	4✔
149	request_latency = time.perf_counter() - timer_start	4✔
150	scope3_row = ImpactRow(	4✔
151	model_id=model,
152	output_tokens=token_count,
153	request_duration_ms=request_latency
154	* 1000, # TODO: can we get the header that has the processing time
155	managed_service_id=PROVIDER,
156	)
157	scope3_ctx = await Scope3AI.get_instance().asubmit_impact(scope3_row)	4✔
158	chunk_data = ChatCompletionStreamOutput(**asdict(chunk))	4✔
159	if scope3_ctx is not None:	4✔
160	chunk_data.scope3ai = scope3_ctx	4✔
161	yield chunk_data	4✔

scope3data / scope3ai-py / 12920205472

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