mirror of
https://github.com/meta-llama/llama-stack.git
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score normalization
This commit is contained in:
Ashwin Bharambe
parent
e0b32b8bdf
commit
aeb91f67ac
24 changed files with 34629 additions and 3 deletions
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@ -9,6 +9,7 @@ from __future__ import annotations # for forward references
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import hashlib
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import json
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import os
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import re
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from collections.abc import Callable, Generator
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from contextlib import contextmanager
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from enum import StrEnum
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@ -61,6 +62,35 @@ _ID_KIND_PREFIXES: dict[str, str] = {
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}
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_FLOAT_IN_STRING_PATTERN = re.compile(r"(-?\d+\.\d{4,})")
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def _normalize_numeric_literal_strings(value: str) -> str:
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"""Round any long decimal literals embedded in strings for stable hashing."""
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def _replace(match: re.Match[str]) -> str:
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number = float(match.group(0))
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return f"{number:.6f}"
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return _FLOAT_IN_STRING_PATTERN.sub(_replace, value)
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def _normalize_body_for_hash(value: Any) -> Any:
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"""Recursively normalize a JSON-like value to improve hash stability."""
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if isinstance(value, dict):
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return {key: _normalize_body_for_hash(item) for key, item in value.items()}
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if isinstance(value, list):
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return [_normalize_body_for_hash(item) for item in value]
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if isinstance(value, tuple):
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return tuple(_normalize_body_for_hash(item) for item in value)
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if isinstance(value, float):
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return round(value, 6)
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if isinstance(value, str):
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return _normalize_numeric_literal_strings(value)
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return value
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def _allocate_test_scoped_id(kind: str) -> str | None:
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"""Return the next deterministic ID for the given kind within the current test."""
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@ -108,22 +138,24 @@ def normalize_inference_request(method: str, url: str, headers: dict[str, Any],
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Exception: Model list endpoints (/v1/models, /api/tags) exclude test_id since
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they are infrastructure/shared and need to work across session setup and tests.
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"""
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# Extract just the endpoint path
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from urllib.parse import urlparse
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parsed = urlparse(url)
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body_for_hash = _normalize_body_for_hash(body)
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normalized: dict[str, Any] = {
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"method": method.upper(),
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"endpoint": parsed.path,
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"body": body,
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"body": body_for_hash,
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}
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# Include test_id for isolation, except for shared infrastructure endpoints
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if parsed.path not in ("/api/tags", "/v1/models"):
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normalized["test_id"] = get_test_context()
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# Create hash - sort_keys=True ensures deterministic ordering
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normalized_json = json.dumps(normalized, sort_keys=True)
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return hashlib.sha256(normalized_json.encode()).hexdigest()
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@ -0,0 +1,894 @@
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{
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"test_id": "tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[client_with_models-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536]",
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"request": {
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"method": "POST",
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"url": "https://api.openai.com/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "gpt-4o",
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"messages": [
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{
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"role": "user",
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"content": "How many experts does the Llama 4 Maverick model have?"
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},
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"index": 0,
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"id": "call_zS2WxgXWetjnlPt2MzH9Asrc",
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
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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": "tool",
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"tool_call_id": "call_zS2WxgXWetjnlPt2MzH9Asrc",
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"content": [
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{
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"type": "text",
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"text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
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},
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{
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"type": "text",
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"text": "[1] document_id: file-5217982280, score: 2.57802841833685, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-5217982280', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-5217982280|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n"
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},
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{
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"type": "text",
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"text": "END of knowledge_search tool results.\n"
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},
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{
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"type": "text",
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"text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
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}
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]
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}
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],
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"stream": true,
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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": "knowledge_search",
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"description": "Search for information in a database.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The query to search for. Can be a natural language sentence or keywords."
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}
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},
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"required": [
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"query"
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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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"endpoint": "/v1/chat/completions",
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"model": "gpt-4o"
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},
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"response": {
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"is_streaming": true
|
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"id_normalization_mapping": {}
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}
|
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|
|
@ -122,7 +122,30 @@ class TestInferenceRecording:
|
|||
# Test that different float precision produces different hashes (no rounding)
|
||||
hash3 = normalize_inference_request("POST", "http://test/v1/chat/completions", {}, {"temperature": 0.7000001})
|
||||
hash4 = normalize_inference_request("POST", "http://test/v1/chat/completions", {}, {"temperature": 0.7})
|
||||
assert hash3 != hash4 # Different precision should produce different hashes
|
||||
assert hash3 == hash4 # Small float precision differences should normalize to the same hash
|
||||
|
||||
# String-embedded decimals with excessive precision should also normalize.
|
||||
body_with_precise_scores = {
|
||||
"messages": [
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "score: 0.7472640164649847",
|
||||
}
|
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]
|
||||
}
|
||||
body_with_precise_scores_variation = {
|
||||
"messages": [
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "score: 0.74726414959878",
|
||||
}
|
||||
]
|
||||
}
|
||||
hash5 = normalize_inference_request("POST", "http://test/v1/chat/completions", {}, body_with_precise_scores)
|
||||
hash6 = normalize_inference_request(
|
||||
"POST", "http://test/v1/chat/completions", {}, body_with_precise_scores_variation
|
||||
)
|
||||
assert hash5 == hash6
|
||||
|
||||
def test_response_storage(self, temp_storage_dir):
|
||||
"""Test the ResponseStorage class."""
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue