Convert formatting to use ruff instead of yapf + isort (#26247)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
@@ -6,7 +6,9 @@ import json
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import pytest
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from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
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MistralToolCall, MistralToolParser)
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MistralToolCall,
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MistralToolParser,
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)
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from vllm.sampling_params import SamplingParams
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from vllm.transformers_utils.tokenizer import MistralTokenizer
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@@ -33,136 +35,114 @@ SYMBOLIC_LANG_PROMPTS = [
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]
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# for function calling
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TOOLS = [{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type":
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"string",
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"description":
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"The city to find the weather for, e.g. 'San Francisco'"
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TOOLS = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "The city to find the weather for, e.g. 'San Francisco'",
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},
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"state": {
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"type": "string",
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"description": "the two-letter abbreviation for the state that the city is"
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" in, e.g. 'CA' which would mean 'California'",
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},
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"unit": {
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"type": "string",
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"description": "The unit to fetch the temperature in",
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"enum": ["celsius", "fahrenheit"],
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},
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},
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"state": {
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"type":
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"string",
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"description":
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"the two-letter abbreviation for the state that the city is"
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" in, e.g. 'CA' which would mean 'California'"
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},
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"unit": {
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"type": "string",
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"description": "The unit to fetch the temperature in",
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"enum": ["celsius", "fahrenheit"]
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}
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"required": ["city", "state", "unit"],
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},
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"required": ["city", "state", "unit"]
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}
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},
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},
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}, {
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"type": "function",
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"function": {
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"name": "rewrite",
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"description": "Rewrites text",
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"parameters": {
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"type": "object",
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"required": [],
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"properties": {
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"text": {
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"type": "string",
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"description": "The input text to rewrite."
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}
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}
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}
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}
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}]
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{
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"type": "function",
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"function": {
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"name": "rewrite",
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"description": "Rewrites text",
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"parameters": {
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"type": "object",
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"required": [],
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"properties": {
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"text": {
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"type": "string",
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"description": "The input text to rewrite.",
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}
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},
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},
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},
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},
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]
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MSGS = [
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{"role": "system", "content": "You are an assistant."},
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{
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"role": "system",
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"content": "You are an assistant."
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},
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{
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"role":
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"user",
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"content":
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"Could you please rewrite the below article? \n\n My English needs improvving, maybe I make errors." # noqa
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},
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{
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"role":
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"assistant",
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"content":
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"",
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"tool_calls": [{
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"id": "bbc5b7ede",
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"type": "function",
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"function": {
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"name":
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"rewrite",
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"arguments":
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'{\"text\":\"My English needs improvving, maybe I make errors.\"}' # noqa
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}
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}]
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},
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{
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"role": "tool",
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"content":
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"{\"action\":\"rewrite\",\"outcome\":\"My English needs improving, maybe I make errors.\"}", # noqa
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"tool_call_id": "bbc5b7ede",
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"name": "rewrite"
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"role": "user",
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"content": "Could you please rewrite the below article? \n\n My English needs improvving, maybe I make errors.", # noqa
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},
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{
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"role": "assistant",
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"content": "---\n\nMy English needs improving, maybe I make errors"
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"content": "",
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"tool_calls": [
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{
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"id": "bbc5b7ede",
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"type": "function",
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"function": {
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"name": "rewrite",
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"arguments": '{"text":"My English needs improvving, maybe I make errors."}', # noqa
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},
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}
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],
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},
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{
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"role":
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"user",
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"content": ("Can you tell me what the temperate"
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" will be in Dallas, in fahrenheit?")
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}
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"role": "tool",
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"content": '{"action":"rewrite","outcome":"My English needs improving, maybe I make errors."}', # noqa
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"tool_call_id": "bbc5b7ede",
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"name": "rewrite",
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},
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{
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"role": "assistant",
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"content": "---\n\nMy English needs improving, maybe I make errors",
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},
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{
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"role": "user",
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"content": (
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"Can you tell me what the temperate will be in Dallas, in fahrenheit?"
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),
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},
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]
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SAMPLE_JSON_SCHEMA = {
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"type": "object",
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"properties": {
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"name": {
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"type": "string"
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},
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"age": {
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"type": "integer"
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},
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"name": {"type": "string"},
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"age": {"type": "integer"},
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"skills": {
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"type": "array",
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"items": {
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"type": "string",
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"maxLength": 10
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},
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"minItems": 3
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"items": {"type": "string", "maxLength": 10},
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"minItems": 3,
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},
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"work_history": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"company": {
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"type": "string"
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},
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"duration": {
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"type": "number"
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},
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"position": {
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"type": "string"
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}
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"company": {"type": "string"},
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"duration": {"type": "number"},
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"position": {"type": "string"},
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},
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"required": ["company", "position"]
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}
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}
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"required": ["company", "position"],
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},
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},
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},
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"required": ["name", "age", "skills", "work_history"]
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"required": ["name", "age", "skills", "work_history"],
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}
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@@ -170,17 +150,25 @@ SAMPLE_JSON_SCHEMA = {
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.parametrize("max_tokens", [64])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_models(hf_runner, vllm_runner, example_prompts, model: str,
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dtype: str, max_tokens: int, num_logprobs: int) -> None:
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def test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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# TODO(sang): Sliding window should be tested separately.
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with hf_runner(model, dtype=dtype) as hf_model:
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hf_outputs = hf_model.generate_greedy_logprobs_limit(
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example_prompts, max_tokens, num_logprobs)
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example_prompts, max_tokens, num_logprobs
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)
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with vllm_runner(model, dtype=dtype,
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tokenizer_mode="mistral") as vllm_model:
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with vllm_runner(model, dtype=dtype, tokenizer_mode="mistral") as vllm_model:
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vllm_outputs = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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example_prompts, max_tokens, num_logprobs
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)
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check_logprobs_close(
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outputs_0_lst=hf_outputs,
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@@ -194,27 +182,35 @@ def test_models(hf_runner, vllm_runner, example_prompts, model: str,
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.parametrize("max_tokens", [64])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_mistral_format(vllm_runner, example_prompts, model: str, dtype: str,
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max_tokens: int, num_logprobs: int) -> None:
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def test_mistral_format(
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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with vllm_runner(
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model,
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dtype=dtype,
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tokenizer_mode="mistral",
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load_format="mistral",
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config_format="mistral",
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model,
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dtype=dtype,
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tokenizer_mode="mistral",
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load_format="mistral",
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config_format="mistral",
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) as mistral_format_model:
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mistral_format_outputs = mistral_format_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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example_prompts, max_tokens, num_logprobs
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)
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with vllm_runner(
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model,
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dtype=dtype,
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tokenizer_mode="auto",
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load_format="safetensors",
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config_format="hf",
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model,
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dtype=dtype,
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tokenizer_mode="auto",
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load_format="safetensors",
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config_format="hf",
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) as hf_format_model:
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hf_format_outputs = hf_format_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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example_prompts, max_tokens, num_logprobs
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)
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check_logprobs_close(
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outputs_0_lst=hf_format_outputs,
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@@ -226,34 +222,35 @@ def test_mistral_format(vllm_runner, example_prompts, model: str, dtype: str,
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@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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def test_mistral_symbolic_languages(vllm_runner, model: str,
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dtype: str) -> None:
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with vllm_runner(model,
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dtype=dtype,
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max_model_len=8192,
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tokenizer_mode="mistral",
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config_format="mistral",
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load_format="mistral") as vllm_model:
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def test_mistral_symbolic_languages(vllm_runner, model: str, dtype: str) -> None:
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with vllm_runner(
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model,
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dtype=dtype,
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max_model_len=8192,
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tokenizer_mode="mistral",
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config_format="mistral",
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load_format="mistral",
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) as vllm_model:
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for prompt in SYMBOLIC_LANG_PROMPTS:
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msg = {"role": "user", "content": prompt}
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outputs = vllm_model.llm.chat([msg],
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sampling_params=SAMPLING_PARAMS)
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outputs = vllm_model.llm.chat([msg], sampling_params=SAMPLING_PARAMS)
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assert "<EFBFBD>" not in outputs[0].outputs[0].text.strip()
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@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
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with vllm_runner(model,
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dtype=dtype,
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tokenizer_mode="mistral",
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config_format="mistral",
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load_format="mistral") as vllm_model:
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with vllm_runner(
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model,
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dtype=dtype,
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tokenizer_mode="mistral",
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config_format="mistral",
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load_format="mistral",
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) as vllm_model:
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msgs = copy.deepcopy(MSGS)
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outputs = vllm_model.llm.chat(msgs,
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tools=TOOLS,
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sampling_params=SAMPLING_PARAMS)
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outputs = vllm_model.llm.chat(
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msgs, tools=TOOLS, sampling_params=SAMPLING_PARAMS
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)
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tokenizer = vllm_model.llm.get_tokenizer()
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tool_parser = MistralToolParser(tokenizer)
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@@ -265,10 +262,11 @@ def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
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assert parsed_message.tools_called
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assert MistralToolCall.is_valid_id(parsed_message.tool_calls[0].id)
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assert parsed_message.tool_calls[
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0].function.name == "get_current_weather"
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assert parsed_message.tool_calls[
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0].function.arguments == '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}' # noqa
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assert parsed_message.tool_calls[0].function.name == "get_current_weather"
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assert (
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parsed_message.tool_calls[0].function.arguments
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== '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'
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) # noqa
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assert parsed_message.content is None
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@@ -297,17 +295,10 @@ def test_mistral_function_call_nested_json():
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"city": "Dallas",
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"state": "TX",
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"unit": "fahrenheit",
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"sub_dict": {
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"foo": "bar",
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"inner": {
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"x": 1,
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"y": 2
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}
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},
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"sub_dict": {"foo": "bar", "inner": {"x": 1, "y": 2}},
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}
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model_output = (
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f"{parser.bot_token}get_current_weather{json.dumps(args_dict)}")
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model_output = f"{parser.bot_token}get_current_weather{json.dumps(args_dict)}"
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parsed = parser.extract_tool_calls(model_output, None)
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Reference in New Issue
Block a user