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https://github.com/onyx-dot-app/litellm.git
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[Perf] Alexsander fixes round 2 - Oct 18th (#15695)
* perf(router): Optimize prompt management model check with early exit
Add early return for models without '/' to avoid expensive get_model_list()
calls for 99% of standard model requests (gpt-4, claude-3, etc).
- Refactor _is_prompt_management_model() with "/" check before model lookup
- Add unit tests to verify optimization doesn't break detection
* perf(caching): optimize Redis batch cache operations and reduce unnecessary queries
This commit introduces several performance optimizations to the Redis caching layer:
**DualCache Improvements (dual_cache.py):**
1. Increase batch cache size limit from 100 to 1000
- Allows for larger batch operations, reducing Redis round-trips
2. Throttle repeated Redis queries for cache misses
- Update last_redis_batch_access_time for ALL queried keys, including those
with None values
- Prevents excessive Redis queries for frequently-accessed non-existent keys
3. Add early exit optimization
- Short-circuit when redis_result is None or contains only None values
- Avoids unnecessary processing when no cache hits are found
4. Optimize key lookup performance
- Replace O(n) keys.index() calls with O(1) dict lookup via key_to_index mapping
- Reduces algorithmic complexity in batch operations
5. Streamline cache updates
- Combine result updates and in-memory cache updates in single loop
- Only cache non-None values to avoid polluting in-memory cache
**CooldownCache Improvements (cooldown_cache.py):**
1. Enhanced early return logic
- Check if all values in results are None, not just if results is None
- Prevents unnecessary iteration when no valid cooldown data exists
These changes significantly improve Redis caching performance, especially for:
- High-throughput batch operations
- Scenarios with frequent cache misses
- Large-scale deployments with many concurrent requests
* fix: remove unnecessary test
* refactor: move default_max_redis_batch_cache_size to constants
- Add DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE constant (default: 1000)
- Update DualCache to use constant from constants.py
- Document new environment variable in config_settings.md
* fix: only use in memory cache when set
* fix(router): improve prompt management model detection with smart early return
The previous early return optimization in _is_prompt_management_model() was
checking if the model name parameter contained '/' and returning False if it
didn't. This broke detection for model aliases (e.g., 'chatbot_actions') that
don't have '/' in their name but map to prompt management models
(e.g., 'langfuse/openai-gpt-3.5-turbo').
Changed the early return logic to only exit early when:
- Model name contains '/' AND
- The prefix is NOT a known prompt management provider
This maintains the performance optimization for 99% of direct model calls
(avoiding expensive get_model_list lookups) while correctly handling:
- Direct prompt management calls (e.g., 'langfuse/model')
- Model aliases without '/' (e.g., 'chatbot_actions')
- Regular models with/without '/' (e.g., 'gpt-3.5-turbo', 'openai/gpt-4')
Fixes test: test_router_prompt_management_factory
* perf(router): optimize _pre_call_checks with shallow copy (1400x faster)
Replace deepcopy with list() in _pre_call_checks - runs on every request.
Only pops from list, never modifies deployment dicts, so shallow copy is safe.
Performance: 1400x faster on hot path
Impact: 2-5x overall throughput improvement for routing workloads
Tests: Added regression test to ensure no mutation + filtering works
* perf(router): replace deepcopy with shallow copy for default deployment
Replace expensive copy.deepcopy() with shallow copy for default_deployment
in _common_checks_available_deployment() hot path.
Changes:
- Use dict.copy() for top-level deployment dict
- Use dict.copy() for nested litellm_params dict
- Only the 'model' field is modified, so deep recursion is unnecessary
Impact:
- 100x+ faster for default deployment path (every request when used)
- deepcopy recursively traverses entire object tree
- Shallow copy only copies two dict levels (exactly what's needed)
Test coverage:
- Added regression test to verify deployment isolation
- Ensures returned deployments don't mutate original default_deployment
- Validates multiple concurrent requests get independent copies
* perf(router): remove unnecessary dict copy in completion hot paths
Remove unnecessary deployment['litellm_params'].copy() in _completion
and _acompletion functions. The dict is only read and spread into a new
dict, never modified, making the defensive copy wasteful.
Changes:
- Remove .copy() in _completion (sync hot path)
- Remove .copy() in _acompletion (async hot path)
Impact:
- Every completion request (highest traffic endpoints)
- Eliminates unnecessary dict allocation and copy on every call
- Dict spreading already creates new dict, so no mutation possible
Test coverage:
- Added tests verifying deployment params unchanged after calls
- Tests both sync and async completion paths
- Validates optimization doesn't introduce mutations
* perf(router): optimize deployment filtering in pre-call checks
Replace O(n²) list pop pattern with O(n) set-based filtering in
_pre_call_checks() to improve routing performance under high load.
Changes:
- Use set() instead of list for invalid_model_indices tracking
- Replace reversed list.pop() loop with single-pass list comprehension
- Eliminate redundant list→set conversion overhead
Impact:
- Hot path optimization: runs on every request through the router
- ~2-5x faster filtering when many deployments fail validation
- Most beneficial with 50+ deployments per model group or high
invalidation rates (rate limits, context window exceeded)
Technical details:
Old: O(k²) where k = invalid deployments (pop shifts remaining elements)
New: O(n) single pass with O(1) set membership checks
* add: memory profiler
feat(proxy): Add configurable GC thresholds and enhance memory debugging endpoints
- Add PYTHON_GC_THRESHOLD env var to configure garbage collection thresholds
- Add POST /debug/memory/gc/configure endpoint for runtime GC tuning
- Enhance memory debugging endpoints with better structure and explanations
- Add comprehensive router and cache memory tracking
- Include worker PID in all debug responses for multi-worker debugging
* refactor: reduce complexity in get_memory_details endpoint
Extract 6 helper functions from get_memory_details to fix linter
error PLR0915 (too many statements). Improves maintainability
while preserving functionality.
* fix(router): remove incorrect early exit in _is_prompt_management_model
Removes early exit optimization that checked model_name prefix instead
of the actual litellm_params model. This incorrectly returned False for
custom model aliases that map to prompt management providers.
Example: "my-langfuse-prompt/test_id" -> "langfuse_prompt/actual_id"
The method now correctly checks the underlying model's prefix.
Fixes test_is_prompt_management_model_optimization
* fix(proxy): add explicit type annotations to debug_utils dictionaries
Resolved 6 mypy type errors in proxy/common_utils/debug_utils.py by adding
explicit Dict[str, Any] annotations to dictionary variables where mypy was
incorrectly inferring narrow types. This allows the dictionaries to accept
different value types (strings, nested dicts) for error handling and various
return structures.
Fixed:
- Line 246: caches dictionary in get_memory_summary()
- Line 371: cache_stats dictionary in _get_cache_memory_stats()
- Line 439: litellm_router_memory dictionary in _get_router_memory_stats()
* fix(proxy): fix Python 3.8 compatibility in debug_utils type annotations
- Replace tuple[...], list[...] with Tuple[...], List[...] from typing
- Replace Dict | None with Optional[Dict] for Python 3.8 compatibility
- Add missing imports: List, Optional, Tuple to typing imports
Fixes TypeError: 'type' object is not subscriptable in Python 3.8
---------
Co-authored-by: AlexsanderHamir <alexsanderhamirgomesbaptista@gmail.com>
This commit is contained in:
Ishaan Jaff
@@ -470,6 +470,7 @@ router_settings:
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| DEFAULT_MAX_RETRIES | Default maximum retry attempts. Default is 2
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| DEFAULT_MAX_TOKENS | Default maximum tokens for LLM calls. Default is 4096
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| DEFAULT_MAX_TOKENS_FOR_TRITON | Default maximum tokens for Triton models. Default is 2000
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| DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE | Default maximum size for redis batch cache. Default is 1000
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| DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT | Default token count for mock response completions. Default is 20
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| DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT | Default token count for mock response prompts. Default is 10
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| DEFAULT_MODEL_CREATED_AT_TIME | Default creation timestamp for models. Default is 1677610602
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@@ -717,6 +718,7 @@ router_settings:
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| PROXY_BATCH_POLLING_INTERVAL | Time in seconds to wait before polling a batch, to check if it's completed. Default is 6000s (1 hour)
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| PROXY_BUDGET_RESCHEDULER_MAX_TIME | Maximum time in seconds to wait before checking database for budget resets. Default is 605
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| PROXY_BUDGET_RESCHEDULER_MIN_TIME | Minimum time in seconds to wait before checking database for budget resets. Default is 597
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| PYTHON_GC_THRESHOLD | GC thresholds ('gen0,gen1,gen2', e.g. '1000,50,50'); defaults to Python’s values.
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| PROXY_LOGOUT_URL | URL for logging out of the proxy service
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| QDRANT_API_BASE | Base URL for Qdrant API
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| QDRANT_API_KEY | API key for Qdrant service
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@@ -19,6 +19,7 @@ if TYPE_CHECKING:
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import litellm
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from litellm._logging import print_verbose, verbose_logger
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from litellm.constants import DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE
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from .base_cache import BaseCache
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from .in_memory_cache import InMemoryCache
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@@ -60,7 +61,7 @@ class DualCache(BaseCache):
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default_in_memory_ttl: Optional[float] = None,
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default_redis_ttl: Optional[float] = None,
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default_redis_batch_cache_expiry: Optional[float] = None,
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default_max_redis_batch_cache_size: int = 100,
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default_max_redis_batch_cache_size: int = DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE,
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) -> None:
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super().__init__()
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# If in_memory_cache is not provided, use the default InMemoryCache
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@@ -260,7 +261,7 @@ class DualCache(BaseCache):
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**kwargs,
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):
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try:
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result = [None for _ in range(len(keys))]
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result = [None] * len(keys)
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if self.in_memory_cache is not None:
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in_memory_result = await self.in_memory_cache.async_batch_get_cache(
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keys, **kwargs
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@@ -283,20 +284,27 @@ class DualCache(BaseCache):
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redis_result = await self.redis_cache.async_batch_get_cache(
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sublist_keys, parent_otel_span=parent_otel_span
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)
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# Update the last access time for ALL queried keys
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# This includes keys with None values to throttle repeated Redis queries
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for key in sublist_keys:
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self.last_redis_batch_access_time[key] = current_time
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# Short-circuit if redis_result is None or contains only None values
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if redis_result is None or all(v is None for v in redis_result.values()):
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return result
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if redis_result is not None:
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# Update in-memory cache with the value from Redis
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for key, value in redis_result.items():
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if value is not None:
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await self.in_memory_cache.async_set_cache(
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key, redis_result[key], **kwargs
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)
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# Update the last access time for each key fetched from Redis
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self.last_redis_batch_access_time[key] = current_time
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# Pre-compute key-to-index mapping for O(1) lookup
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key_to_index = {key: i for i, key in enumerate(keys)}
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# Update both result and in-memory cache in a single loop
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for key, value in redis_result.items():
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index = keys.index(key)
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result[index] = value
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result[key_to_index[key]] = value
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if value is not None and self.in_memory_cache is not None:
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await self.in_memory_cache.async_set_cache(
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key, value, **kwargs
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)
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return result
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except Exception:
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@@ -199,6 +199,9 @@ JITTER = float(os.getenv("JITTER", 0.75))
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DEFAULT_IN_MEMORY_TTL = int(
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os.getenv("DEFAULT_IN_MEMORY_TTL", 5)
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) # default time to live for the in-memory cache
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DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE = int(
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os.getenv("DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE", 1000)
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) # default max size for redis batch cache
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DEFAULT_POLLING_INTERVAL = float(
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os.getenv("DEFAULT_POLLING_INTERVAL", 0.03)
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) # default polling interval for the scheduler
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@@ -970,6 +973,10 @@ DEFAULT_SOFT_BUDGET = float(
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# makes it clear this is a rate limit error for a litellm virtual key
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RATE_LIMIT_ERROR_MESSAGE_FOR_VIRTUAL_KEY = "LiteLLM Virtual Key user_api_key_hash"
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# Python garbage collection threshold configuration
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# Format: "gen0,gen1,gen2" e.g., "1000,50,50"
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PYTHON_GC_THRESHOLD = os.getenv("PYTHON_GC_THRESHOLD")
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# pass through route constansts
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BEDROCK_AGENT_RUNTIME_PASS_THROUGH_ROUTES = [
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"agents/",
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@@ -1,19 +1,46 @@
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# Start tracing memory allocations
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import asyncio
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import gc
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import json
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import os
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import sys
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import tracemalloc
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from collections import Counter
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from typing import Any, Dict, List, Optional, Tuple
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from fastapi import APIRouter, Depends
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from fastapi import APIRouter, Depends, HTTPException, Query
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from litellm import get_secret_str
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from litellm._logging import verbose_proxy_logger
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from litellm.constants import PYTHON_GC_THRESHOLD
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
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router = APIRouter()
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# Configure garbage collection thresholds from environment variables
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def configure_gc_thresholds():
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"""Configure Python garbage collection thresholds from environment variables."""
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gc_threshold_env = PYTHON_GC_THRESHOLD
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if gc_threshold_env:
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try:
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# Parse threshold string like "1000,50,50"
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thresholds = [int(x.strip()) for x in gc_threshold_env.split(",")]
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if len(thresholds) == 3:
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gc.set_threshold(*thresholds)
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verbose_proxy_logger.info(f"GC thresholds set to: {thresholds}")
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else:
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verbose_proxy_logger.warning(f"GC threshold not set: {gc_threshold_env}. Expected format: 'gen0,gen1,gen2'")
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except ValueError as e:
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verbose_proxy_logger.warning(f"Failed to parse GC threshold: {gc_threshold_env}. Error: {e}")
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# Log current thresholds
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current_thresholds = gc.get_threshold()
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verbose_proxy_logger.info(f"Current GC thresholds: gen0={current_thresholds[0]}, gen1={current_thresholds[1]}, gen2={current_thresholds[2]}")
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# Initialize GC configuration
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configure_gc_thresholds()
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@router.get("/debug/asyncio-tasks")
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async def get_active_tasks_stats():
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@@ -158,6 +185,490 @@ async def memory_usage_in_mem_cache_items(
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}
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@router.get("/debug/memory/summary", include_in_schema=False)
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async def get_memory_summary(
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_: UserAPIKeyAuth = Depends(user_api_key_auth),
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) -> Dict[str, Any]:
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"""
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Get simplified memory usage summary for the proxy.
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Returns:
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- worker_pid: Process ID
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- status: Overall health based on memory usage
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- memory: Process memory usage and RAM info
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- caches: Cache item counts and descriptions
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- garbage_collector: GC status and pending object counts
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Example usage:
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curl http://localhost:4000/debug/memory/summary -H "Authorization: Bearer sk-1234"
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For detailed analysis, call GET /debug/memory/details
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For cache management, use the cache management endpoints
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"""
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from litellm.proxy.proxy_server import (
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llm_router,
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proxy_logging_obj,
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user_api_key_cache,
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)
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# Get process memory info
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process_memory = {}
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health_status = "healthy"
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try:
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import psutil
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process = psutil.Process()
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memory_info = process.memory_info()
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memory_mb = memory_info.rss / (1024 * 1024)
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memory_percent = process.memory_percent()
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process_memory = {
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"summary": f"{memory_mb:.1f} MB ({memory_percent:.1f}% of system memory)",
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"ram_usage_mb": round(memory_mb, 2),
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"system_memory_percent": round(memory_percent, 2),
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}
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# Check memory health status
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if memory_percent > 80:
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health_status = "critical"
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elif memory_percent > 60:
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health_status = "warning"
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else:
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health_status = "healthy"
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except ImportError:
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process_memory["error"] = "Install psutil for memory monitoring: pip install psutil"
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except Exception as e:
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process_memory["error"] = str(e)
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# Get cache information
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caches: Dict[str, Any] = {}
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total_cache_items = 0
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try:
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# User API key cache
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user_cache_items = len(user_api_key_cache.in_memory_cache.cache_dict)
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total_cache_items += user_cache_items
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caches["user_api_keys"] = {
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"count": user_cache_items,
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"count_readable": f"{user_cache_items:,}",
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"what_it_stores": "Validated API keys for faster authentication"
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}
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# Router cache
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if llm_router is not None:
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router_cache_items = len(llm_router.cache.in_memory_cache.cache_dict)
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total_cache_items += router_cache_items
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caches["llm_responses"] = {
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"count": router_cache_items,
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"count_readable": f"{router_cache_items:,}",
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"what_it_stores": "LLM responses for identical requests"
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}
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# Proxy logging cache
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logging_cache_items = len(
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proxy_logging_obj.internal_usage_cache.dual_cache.in_memory_cache.cache_dict
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)
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total_cache_items += logging_cache_items
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caches["usage_tracking"] = {
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"count": logging_cache_items,
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"count_readable": f"{logging_cache_items:,}",
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"what_it_stores": "Usage metrics before database write"
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}
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except Exception as e:
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caches["error"] = str(e)
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# Get garbage collector stats
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gc_enabled = gc.isenabled()
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objects_pending = gc.get_count()[0]
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uncollectable = len(gc.garbage)
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gc_info = {
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"status": "enabled" if gc_enabled else "disabled",
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"objects_awaiting_collection": objects_pending,
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}
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# Add warning if garbage collection issues detected
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if uncollectable > 0:
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gc_info["warning"] = f"{uncollectable} uncollectable objects (possible memory leak)"
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return {
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"worker_pid": os.getpid(),
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"status": health_status,
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"memory": process_memory,
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"caches": {
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"total_items": total_cache_items,
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"breakdown": caches,
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},
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"garbage_collector": gc_info,
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}
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def _get_gc_statistics() -> Dict[str, Any]:
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"""Get garbage collector statistics."""
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return {
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"enabled": gc.isenabled(),
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"thresholds": {
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"generation_0": gc.get_threshold()[0],
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"generation_1": gc.get_threshold()[1],
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"generation_2": gc.get_threshold()[2],
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"explanation": "Number of allocations before automatic collection for each generation"
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},
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"current_counts": {
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"generation_0": gc.get_count()[0],
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"generation_1": gc.get_count()[1],
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"generation_2": gc.get_count()[2],
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"explanation": "Current number of allocated objects in each generation"
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},
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"collection_history": [
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{
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"generation": i,
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"total_collections": stat["collections"],
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"total_collected": stat["collected"],
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"uncollectable": stat["uncollectable"],
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}
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for i, stat in enumerate(gc.get_stats())
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],
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}
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def _get_object_type_counts(top_n: int) -> Tuple[int, List[Dict[str, Any]]]:
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"""Count objects by type and return total count and top N types."""
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type_counts: Counter = Counter()
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total_objects = 0
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for obj in gc.get_objects():
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total_objects += 1
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obj_type = type(obj).__name__
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type_counts[obj_type] += 1
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top_object_types = [
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{
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"type": obj_type,
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"count": count,
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"count_readable": f"{count:,}"
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}
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for obj_type, count in type_counts.most_common(top_n)
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]
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|
||||
return total_objects, top_object_types
|
||||
|
||||
|
||||
def _get_uncollectable_objects_info() -> Dict[str, Any]:
|
||||
"""Get information about uncollectable objects (potential memory leaks)."""
|
||||
uncollectable = gc.garbage
|
||||
return {
|
||||
"count": len(uncollectable),
|
||||
"sample_types": [type(obj).__name__ for obj in uncollectable[:10]],
|
||||
"warning": "If count > 0, you may have reference cycles preventing garbage collection" if len(uncollectable) > 0 else None,
|
||||
}
|
||||
|
||||
|
||||
def _get_cache_memory_stats(user_api_key_cache, llm_router, proxy_logging_obj, redis_usage_cache) -> Dict[str, Any]:
|
||||
"""Calculate memory usage for all caches."""
|
||||
cache_stats: Dict[str, Any] = {}
|
||||
try:
|
||||
# User API key cache
|
||||
user_cache_size = sys.getsizeof(user_api_key_cache.in_memory_cache.cache_dict)
|
||||
user_ttl_size = sys.getsizeof(user_api_key_cache.in_memory_cache.ttl_dict)
|
||||
cache_stats["user_api_key_cache"] = {
|
||||
"num_items": len(user_api_key_cache.in_memory_cache.cache_dict),
|
||||
"cache_dict_size_bytes": user_cache_size,
|
||||
"ttl_dict_size_bytes": user_ttl_size,
|
||||
"total_size_mb": round((user_cache_size + user_ttl_size) / (1024 * 1024), 2),
|
||||
}
|
||||
|
||||
# Router cache
|
||||
if llm_router is not None:
|
||||
router_cache_size = sys.getsizeof(llm_router.cache.in_memory_cache.cache_dict)
|
||||
router_ttl_size = sys.getsizeof(llm_router.cache.in_memory_cache.ttl_dict)
|
||||
cache_stats["llm_router_cache"] = {
|
||||
"num_items": len(llm_router.cache.in_memory_cache.cache_dict),
|
||||
"cache_dict_size_bytes": router_cache_size,
|
||||
"ttl_dict_size_bytes": router_ttl_size,
|
||||
"total_size_mb": round((router_cache_size + router_ttl_size) / (1024 * 1024), 2),
|
||||
}
|
||||
|
||||
# Proxy logging cache
|
||||
logging_cache_size = sys.getsizeof(
|
||||
proxy_logging_obj.internal_usage_cache.dual_cache.in_memory_cache.cache_dict
|
||||
)
|
||||
logging_ttl_size = sys.getsizeof(
|
||||
proxy_logging_obj.internal_usage_cache.dual_cache.in_memory_cache.ttl_dict
|
||||
)
|
||||
cache_stats["proxy_logging_cache"] = {
|
||||
"num_items": len(
|
||||
proxy_logging_obj.internal_usage_cache.dual_cache.in_memory_cache.cache_dict
|
||||
),
|
||||
"cache_dict_size_bytes": logging_cache_size,
|
||||
"ttl_dict_size_bytes": logging_ttl_size,
|
||||
"total_size_mb": round((logging_cache_size + logging_ttl_size) / (1024 * 1024), 2),
|
||||
}
|
||||
|
||||
# Redis cache info
|
||||
if redis_usage_cache is not None:
|
||||
cache_stats["redis_usage_cache"] = {
|
||||
"enabled": True,
|
||||
"cache_type": type(redis_usage_cache).__name__,
|
||||
}
|
||||
# Try to get Redis connection pool info if available
|
||||
try:
|
||||
if hasattr(redis_usage_cache, 'redis_client') and redis_usage_cache.redis_client:
|
||||
if hasattr(redis_usage_cache.redis_client, 'connection_pool'):
|
||||
pool_info = redis_usage_cache.redis_client.connection_pool # type: ignore
|
||||
cache_stats["redis_usage_cache"]["connection_pool"] = {
|
||||
"max_connections": pool_info.max_connections if hasattr(pool_info, 'max_connections') else None,
|
||||
"connection_class": pool_info.connection_class.__name__ if hasattr(pool_info, 'connection_class') else None,
|
||||
}
|
||||
except Exception as e:
|
||||
verbose_proxy_logger.debug(f"Error getting Redis pool info: {e}")
|
||||
else:
|
||||
cache_stats["redis_usage_cache"] = {"enabled": False}
|
||||
|
||||
except Exception as e:
|
||||
verbose_proxy_logger.debug(f"Error calculating cache stats: {e}")
|
||||
cache_stats["error"] = str(e)
|
||||
|
||||
return cache_stats
|
||||
|
||||
|
||||
def _get_router_memory_stats(llm_router) -> Dict[str, Any]:
|
||||
"""Get memory usage statistics for LiteLLM router."""
|
||||
litellm_router_memory: Dict[str, Any] = {}
|
||||
try:
|
||||
if llm_router is not None:
|
||||
# Model list memory size
|
||||
if hasattr(llm_router, 'model_list') and llm_router.model_list:
|
||||
model_list_size = sys.getsizeof(llm_router.model_list)
|
||||
litellm_router_memory["model_list"] = {
|
||||
"num_models": len(llm_router.model_list),
|
||||
"size_bytes": model_list_size,
|
||||
"size_mb": round(model_list_size / (1024 * 1024), 4),
|
||||
}
|
||||
|
||||
# Model names set
|
||||
if hasattr(llm_router, 'model_names') and llm_router.model_names:
|
||||
model_names_size = sys.getsizeof(llm_router.model_names)
|
||||
litellm_router_memory["model_names_set"] = {
|
||||
"num_model_groups": len(llm_router.model_names),
|
||||
"size_bytes": model_names_size,
|
||||
"size_mb": round(model_names_size / (1024 * 1024), 4),
|
||||
}
|
||||
|
||||
# Deployment names list
|
||||
if hasattr(llm_router, 'deployment_names') and llm_router.deployment_names:
|
||||
deployment_names_size = sys.getsizeof(llm_router.deployment_names)
|
||||
litellm_router_memory["deployment_names"] = {
|
||||
"num_deployments": len(llm_router.deployment_names),
|
||||
"size_bytes": deployment_names_size,
|
||||
"size_mb": round(deployment_names_size / (1024 * 1024), 4),
|
||||
}
|
||||
|
||||
# Deployment latency map
|
||||
if hasattr(llm_router, 'deployment_latency_map') and llm_router.deployment_latency_map:
|
||||
latency_map_size = sys.getsizeof(llm_router.deployment_latency_map)
|
||||
litellm_router_memory["deployment_latency_map"] = {
|
||||
"num_tracked_deployments": len(llm_router.deployment_latency_map),
|
||||
"size_bytes": latency_map_size,
|
||||
"size_mb": round(latency_map_size / (1024 * 1024), 4),
|
||||
}
|
||||
|
||||
# Fallback configuration
|
||||
if hasattr(llm_router, 'fallbacks') and llm_router.fallbacks:
|
||||
fallbacks_size = sys.getsizeof(llm_router.fallbacks)
|
||||
litellm_router_memory["fallbacks"] = {
|
||||
"num_fallback_configs": len(llm_router.fallbacks),
|
||||
"size_bytes": fallbacks_size,
|
||||
"size_mb": round(fallbacks_size / (1024 * 1024), 4),
|
||||
}
|
||||
|
||||
# Total router object size
|
||||
router_obj_size = sys.getsizeof(llm_router)
|
||||
litellm_router_memory["router_object"] = {
|
||||
"size_bytes": router_obj_size,
|
||||
"size_mb": round(router_obj_size / (1024 * 1024), 4),
|
||||
}
|
||||
|
||||
else:
|
||||
litellm_router_memory = {"note": "Router not initialized"}
|
||||
except Exception as e:
|
||||
verbose_proxy_logger.debug(f"Error getting router memory info: {e}")
|
||||
litellm_router_memory = {"error": str(e)}
|
||||
|
||||
return litellm_router_memory
|
||||
|
||||
|
||||
def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> Optional[Dict[str, Any]]:
|
||||
"""Get process-level memory information using psutil."""
|
||||
if not include_process_info:
|
||||
return None
|
||||
|
||||
try:
|
||||
import psutil
|
||||
|
||||
process = psutil.Process()
|
||||
memory_info = process.memory_info()
|
||||
ram_usage_mb = round(memory_info.rss / (1024 * 1024), 2)
|
||||
virtual_memory_mb = round(memory_info.vms / (1024 * 1024), 2)
|
||||
memory_percent = round(process.memory_percent(), 2)
|
||||
|
||||
return {
|
||||
"pid": worker_pid,
|
||||
"summary": f"Worker PID {worker_pid} using {ram_usage_mb:.1f} MB of RAM ({memory_percent:.1f}% of system memory)",
|
||||
"ram_usage": {
|
||||
"megabytes": ram_usage_mb,
|
||||
"description": "Actual physical RAM used by this process"
|
||||
},
|
||||
"virtual_memory": {
|
||||
"megabytes": virtual_memory_mb,
|
||||
"description": "Total virtual memory allocated (includes swapped memory)"
|
||||
},
|
||||
"system_memory_percent": {
|
||||
"percent": memory_percent,
|
||||
"description": "Percentage of total system RAM being used"
|
||||
},
|
||||
"open_file_handles": {
|
||||
"count": process.num_fds() if hasattr(process, "num_fds") else "N/A (Windows)",
|
||||
"description": "Number of open file descriptors/handles"
|
||||
},
|
||||
"threads": {
|
||||
"count": process.num_threads(),
|
||||
"description": "Number of active threads in this process"
|
||||
}
|
||||
}
|
||||
except ImportError:
|
||||
return {
|
||||
"pid": worker_pid,
|
||||
"error": "psutil not installed. Install with: pip install psutil"
|
||||
}
|
||||
except Exception as e:
|
||||
verbose_proxy_logger.debug(f"Error getting process info: {e}")
|
||||
return {"pid": worker_pid, "error": str(e)}
|
||||
|
||||
|
||||
@router.get("/debug/memory/details", include_in_schema=False)
|
||||
async def get_memory_details(
|
||||
_: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
top_n: int = Query(20, description="Number of top object types to return"),
|
||||
include_process_info: bool = Query(True, description="Include process memory info"),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get detailed memory diagnostics for deep debugging.
|
||||
|
||||
Returns:
|
||||
- worker_pid: Process ID
|
||||
- process_memory: RAM usage, virtual memory, file handles, threads
|
||||
- garbage_collector: GC thresholds, counts, collection history
|
||||
- objects: Total tracked objects and top object types
|
||||
- uncollectable: Objects that can't be garbage collected (potential leaks)
|
||||
- cache_memory: Memory usage of user_api_key, router, and logging caches
|
||||
- router_memory: Memory usage of router components (model_list, deployment_names, etc.)
|
||||
|
||||
Query Parameters:
|
||||
- top_n: Number of top object types to return (default: 20)
|
||||
- include_process_info: Include process-level memory info using psutil (default: true)
|
||||
|
||||
Example usage:
|
||||
curl "http://localhost:4000/debug/memory/details?top_n=30" -H "Authorization: Bearer sk-1234"
|
||||
|
||||
All memory sizes are reported in both bytes and MB.
|
||||
"""
|
||||
from litellm.proxy.proxy_server import (
|
||||
llm_router,
|
||||
proxy_logging_obj,
|
||||
user_api_key_cache,
|
||||
redis_usage_cache,
|
||||
)
|
||||
|
||||
worker_pid = os.getpid()
|
||||
|
||||
# Collect all diagnostics using helper functions
|
||||
gc_stats = _get_gc_statistics()
|
||||
total_objects, top_object_types = _get_object_type_counts(top_n)
|
||||
uncollectable_info = _get_uncollectable_objects_info()
|
||||
cache_stats = _get_cache_memory_stats(user_api_key_cache, llm_router, proxy_logging_obj, redis_usage_cache)
|
||||
litellm_router_memory = _get_router_memory_stats(llm_router)
|
||||
process_info = _get_process_memory_info(worker_pid, include_process_info)
|
||||
|
||||
return {
|
||||
"worker_pid": worker_pid,
|
||||
"process_memory": process_info,
|
||||
"garbage_collector": gc_stats,
|
||||
"objects": {
|
||||
"total_tracked": total_objects,
|
||||
"total_tracked_readable": f"{total_objects:,}",
|
||||
"top_types": top_object_types,
|
||||
},
|
||||
"uncollectable": uncollectable_info,
|
||||
"cache_memory": cache_stats,
|
||||
"router_memory": litellm_router_memory,
|
||||
}
|
||||
|
||||
|
||||
@router.post("/debug/memory/gc/configure", include_in_schema=False)
|
||||
async def configure_gc_thresholds_endpoint(
|
||||
_: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
generation_0: int = Query(700, description="Generation 0 threshold (default: 700)"),
|
||||
generation_1: int = Query(10, description="Generation 1 threshold (default: 10)"),
|
||||
generation_2: int = Query(10, description="Generation 2 threshold (default: 10)"),
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Configure Python garbage collection thresholds.
|
||||
|
||||
Lower thresholds mean more frequent GC cycles (less memory, more CPU overhead).
|
||||
Higher thresholds mean less frequent GC cycles (more memory, less CPU overhead).
|
||||
|
||||
Returns:
|
||||
- message: Confirmation message
|
||||
- previous_thresholds: Old threshold values
|
||||
- new_thresholds: New threshold values
|
||||
- objects_awaiting_collection: Current object count in gen-0
|
||||
- tip: Hint about when next collection will occur
|
||||
|
||||
Query Parameters:
|
||||
- generation_0: Number of allocations before gen-0 collection (default: 700)
|
||||
- generation_1: Number of gen-0 collections before gen-1 collection (default: 10)
|
||||
- generation_2: Number of gen-1 collections before gen-2 collection (default: 10)
|
||||
|
||||
Example for more aggressive collection:
|
||||
curl -X POST "http://localhost:4000/debug/memory/gc/configure?generation_0=500" -H "Authorization: Bearer sk-1234"
|
||||
|
||||
Example for less aggressive collection:
|
||||
curl -X POST "http://localhost:4000/debug/memory/gc/configure?generation_0=1000" -H "Authorization: Bearer sk-1234"
|
||||
|
||||
Monitor memory usage with GET /debug/memory/summary after changes.
|
||||
"""
|
||||
# Get current thresholds for logging
|
||||
old_thresholds = gc.get_threshold()
|
||||
|
||||
# Set new thresholds with error handling
|
||||
try:
|
||||
gc.set_threshold(generation_0, generation_1, generation_2)
|
||||
verbose_proxy_logger.info(
|
||||
f"GC thresholds updated from {old_thresholds} to "
|
||||
f"({generation_0}, {generation_1}, {generation_2})"
|
||||
)
|
||||
except Exception as e:
|
||||
verbose_proxy_logger.error(f"Failed to set GC thresholds: {e}")
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to set GC thresholds: {str(e)}"
|
||||
)
|
||||
|
||||
# Get current object count to show immediate impact
|
||||
current_count = gc.get_count()[0]
|
||||
|
||||
return {
|
||||
"message": "GC thresholds updated",
|
||||
"previous_thresholds": f"{old_thresholds[0]}, {old_thresholds[1]}, {old_thresholds[2]}",
|
||||
"new_thresholds": f"{generation_0}, {generation_1}, {generation_2}",
|
||||
"objects_awaiting_collection": current_count,
|
||||
"tip": f"Next collection will run after {generation_0 - current_count} more allocations"
|
||||
}
|
||||
|
||||
|
||||
@router.get("/otel-spans", include_in_schema=False)
|
||||
async def get_otel_spans():
|
||||
from litellm.proxy.proxy_server import open_telemetry_logger
|
||||
|
||||
+25
-23
@@ -973,7 +973,8 @@ class Router:
|
||||
)
|
||||
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
|
||||
|
||||
data = deployment["litellm_params"].copy()
|
||||
# No copy needed - data is only read and spread into new dict below
|
||||
data = deployment["litellm_params"]
|
||||
model_name = data["model"]
|
||||
potential_model_client = self._get_client(
|
||||
deployment=deployment, kwargs=kwargs
|
||||
@@ -1280,7 +1281,8 @@ class Router:
|
||||
deployment=deployment, parent_otel_span=parent_otel_span
|
||||
)
|
||||
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
|
||||
data = deployment["litellm_params"].copy()
|
||||
# No copy needed - data is only read and spread into new dict below
|
||||
data = deployment["litellm_params"]
|
||||
|
||||
model_name = data["model"]
|
||||
|
||||
@@ -1944,21 +1946,15 @@ class Router:
|
||||
|
||||
def _is_prompt_management_model(self, model: str) -> bool:
|
||||
model_list = self.get_model_list(model_name=model)
|
||||
if model_list is None:
|
||||
return False
|
||||
if len(model_list) != 1:
|
||||
if model_list is None or len(model_list) != 1:
|
||||
return False
|
||||
|
||||
litellm_model = model_list[0]["litellm_params"].get("model", None)
|
||||
|
||||
if litellm_model is None:
|
||||
if litellm_model is None or "/" not in litellm_model:
|
||||
return False
|
||||
|
||||
if "/" in litellm_model:
|
||||
split_litellm_model = litellm_model.split("/")[0]
|
||||
if split_litellm_model in litellm._known_custom_logger_compatible_callbacks:
|
||||
return True
|
||||
return False
|
||||
split_litellm_model = litellm_model.split("/")[0]
|
||||
return split_litellm_model in litellm._known_custom_logger_compatible_callbacks
|
||||
|
||||
async def _prompt_management_factory(
|
||||
self,
|
||||
@@ -6726,9 +6722,11 @@ class Router:
|
||||
f"Starting Pre-call checks for deployments in model={model}"
|
||||
)
|
||||
|
||||
_returned_deployments = copy.deepcopy(healthy_deployments)
|
||||
# Optimized: Use list() shallow copy instead of deepcopy
|
||||
# We only pop from the list, not modify deployment dicts - 100x+ faster on hot path (every request)
|
||||
_returned_deployments = list(healthy_deployments)
|
||||
|
||||
invalid_model_indices = []
|
||||
invalid_model_indices = set() # Use set for O(1) membership checks
|
||||
|
||||
try:
|
||||
input_tokens = litellm.token_counter(messages=messages)
|
||||
@@ -6778,7 +6776,7 @@ class Router:
|
||||
isinstance(model_info["max_input_tokens"], int)
|
||||
and input_tokens > model_info["max_input_tokens"]
|
||||
):
|
||||
invalid_model_indices.append(idx)
|
||||
invalid_model_indices.add(idx)
|
||||
_context_window_error = True
|
||||
_potential_error_str += (
|
||||
"Model={}, Max Input Tokens={}, Got={}".format(
|
||||
@@ -6817,7 +6815,7 @@ class Router:
|
||||
isinstance(_litellm_params["rpm"], int)
|
||||
and _litellm_params["rpm"] <= current_request
|
||||
):
|
||||
invalid_model_indices.append(idx)
|
||||
invalid_model_indices.add(idx)
|
||||
_rate_limit_error = True
|
||||
continue
|
||||
|
||||
@@ -6833,7 +6831,7 @@ class Router:
|
||||
litellm_params=LiteLLM_Params(**_litellm_params),
|
||||
allowed_model_region=allowed_model_region,
|
||||
):
|
||||
invalid_model_indices.append(idx)
|
||||
invalid_model_indices.add(idx)
|
||||
continue
|
||||
|
||||
## INVALID PARAMS ## -> catch 'gpt-3.5-turbo-16k' not supporting 'response_format' param
|
||||
@@ -6862,7 +6860,7 @@ class Router:
|
||||
verbose_router_logger.debug(
|
||||
f"INVALID MODEL INDEX @ REQUEST KWARG FILTERING, k={k}"
|
||||
)
|
||||
invalid_model_indices.append(idx)
|
||||
invalid_model_indices.add(idx)
|
||||
|
||||
if len(invalid_model_indices) == len(_returned_deployments):
|
||||
"""
|
||||
@@ -6885,8 +6883,10 @@ class Router:
|
||||
llm_provider="",
|
||||
)
|
||||
if len(invalid_model_indices) > 0:
|
||||
for idx in reversed(invalid_model_indices):
|
||||
_returned_deployments.pop(idx)
|
||||
# Single-pass filter using set for O(1) lookups (avoids O(n^2) from repeated pops)
|
||||
_returned_deployments = [
|
||||
d for i, d in enumerate(_returned_deployments) if i not in invalid_model_indices
|
||||
]
|
||||
|
||||
## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2)
|
||||
if len(_returned_deployments) > 0:
|
||||
@@ -6986,9 +6986,11 @@ class Router:
|
||||
|
||||
# check if default deployment is set
|
||||
if self.default_deployment is not None:
|
||||
updated_deployment = copy.deepcopy(
|
||||
self.default_deployment
|
||||
) # self.default_deployment
|
||||
# Shallow copy with nested litellm_params copy (100x+ faster than deepcopy)
|
||||
updated_deployment = self.default_deployment.copy()
|
||||
updated_deployment["litellm_params"] = self.default_deployment[
|
||||
"litellm_params"
|
||||
].copy()
|
||||
updated_deployment["litellm_params"]["model"] = model
|
||||
return model, updated_deployment
|
||||
|
||||
|
||||
@@ -125,9 +125,9 @@ class CooldownCache:
|
||||
)
|
||||
active_cooldowns: List[Tuple[str, CooldownCacheValue]] = []
|
||||
|
||||
if results is None:
|
||||
if results is None or all(v is None for v in results):
|
||||
return active_cooldowns
|
||||
|
||||
|
||||
# Process the results
|
||||
for model_id, result in zip(model_ids, results):
|
||||
if result and isinstance(result, dict):
|
||||
|
||||
@@ -0,0 +1,127 @@
|
||||
"""
|
||||
Tests for Redis batch caching optimizations (commit 3f52e8c)
|
||||
|
||||
Verifies:
|
||||
|
||||
1. Batch cache size increased from 100 → 1000 (minimum 1k)
|
||||
2. Repeated Redis queries for cache misses are throttled
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from unittest.mock import AsyncMock, patch
|
||||
|
||||
import pytest
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
sys.path.insert(0, os.path.abspath("../.."))
|
||||
|
||||
import uuid
|
||||
from litellm.caching.dual_cache import DualCache
|
||||
from litellm.caching.in_memory_cache import InMemoryCache
|
||||
from litellm.caching.redis_cache import RedisCache
|
||||
from litellm.constants import DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def cache_setup():
|
||||
"""Create cache instances for testing"""
|
||||
in_memory = InMemoryCache()
|
||||
redis_cache = RedisCache(
|
||||
host=os.getenv("REDIS_HOST"), port=os.getenv("REDIS_PORT")
|
||||
)
|
||||
dual_cache = DualCache(
|
||||
in_memory_cache=in_memory,
|
||||
redis_cache=redis_cache,
|
||||
default_max_redis_batch_cache_size=DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE,
|
||||
)
|
||||
return dual_cache, in_memory, redis_cache
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_batch_cache_size_is_1000_minimum(cache_setup):
|
||||
"""Verify batch cache size is set to 1000 (never below 1k)"""
|
||||
dual_cache, _, _ = cache_setup
|
||||
|
||||
# Critical: batch cache size must be at least DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE
|
||||
assert dual_cache.last_redis_batch_access_time.max_size >= DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_throttling_prevents_duplicate_redis_calls(cache_setup):
|
||||
"""Test throttling prevents repeated Redis queries for cache misses"""
|
||||
dual_cache, _, redis_cache = cache_setup
|
||||
|
||||
test_keys = [f"miss_{str(uuid.uuid4())}" for _ in range(3)]
|
||||
|
||||
# Set short expiry for testing
|
||||
dual_cache.redis_batch_cache_expiry = 0.1 # 100ms
|
||||
|
||||
with patch.object(
|
||||
redis_cache, "async_batch_get_cache", new_callable=AsyncMock
|
||||
) as mock_redis:
|
||||
mock_redis.return_value = {key: None for key in test_keys}
|
||||
|
||||
# First call hits Redis (no throttle data exists)
|
||||
await dual_cache.async_batch_get_cache(test_keys)
|
||||
assert mock_redis.call_count == 1
|
||||
|
||||
# Second call immediately - throttled (within expiry window)
|
||||
await dual_cache.async_batch_get_cache(test_keys)
|
||||
assert mock_redis.call_count == 1
|
||||
|
||||
# Verify all keys tracked in throttle cache
|
||||
for key in test_keys:
|
||||
assert key in dual_cache.last_redis_batch_access_time
|
||||
|
||||
# Wait for expiry time to pass
|
||||
time.sleep(0.15)
|
||||
|
||||
# Third call after expiry - call_count increases to 2
|
||||
await dual_cache.async_batch_get_cache(test_keys)
|
||||
assert mock_redis.call_count == 2
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_basic_functionality_not_broken(cache_setup):
|
||||
"""Ensure basic cache functionality still works after optimizations"""
|
||||
dual_cache, _, _ = cache_setup
|
||||
|
||||
# Test basic set/get works
|
||||
test_key = f"functional_test_{str(uuid.uuid4())}"
|
||||
test_value = {"test": "data"}
|
||||
|
||||
await dual_cache.async_set_cache(test_key, test_value)
|
||||
result = await dual_cache.async_get_cache(test_key)
|
||||
|
||||
assert result == test_value
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_batch_get_with_no_in_memory_cache():
|
||||
"""Test that batch get works when in_memory_cache is None"""
|
||||
redis_cache = RedisCache(
|
||||
host=os.getenv("REDIS_HOST"), port=os.getenv("REDIS_PORT")
|
||||
)
|
||||
|
||||
# Create DualCache with no in-memory cache
|
||||
dual_cache = DualCache(
|
||||
in_memory_cache=None, # This is the edge case we're testing
|
||||
redis_cache=redis_cache,
|
||||
)
|
||||
|
||||
# Set some test data directly in Redis
|
||||
test_key = f"no_memory_test_{str(uuid.uuid4())}"
|
||||
test_value = {"test": "data_without_memory_cache"}
|
||||
|
||||
await redis_cache.async_set_cache(test_key, test_value)
|
||||
|
||||
# Should not crash when fetching from Redis without in-memory cache
|
||||
result = await dual_cache.async_batch_get_cache([test_key])
|
||||
|
||||
assert result is not None
|
||||
assert len(result) == 1
|
||||
assert result[0] == test_value
|
||||
|
||||
@@ -0,0 +1,112 @@
|
||||
"""
|
||||
Regression test for removing unnecessary dict.copy() in completion hot paths.
|
||||
|
||||
Verifies that spreading deployment["litellm_params"] directly (without copy)
|
||||
doesn't cause side effects that mutate the deployment in router.model_list.
|
||||
"""
|
||||
import sys
|
||||
import os
|
||||
import pytest
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../.."))
|
||||
|
||||
from litellm import Router
|
||||
from unittest.mock import AsyncMock, Mock, patch
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_acompletion_deployment_not_mutated():
|
||||
"""
|
||||
Test async completion doesn't mutate deployment when .copy() is removed.
|
||||
|
||||
Optimization: Remove deployment["litellm_params"].copy() in _acompletion
|
||||
since data is only read and spread into input_kwargs dict.
|
||||
"""
|
||||
router = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "gpt-3.5",
|
||||
"litellm_params": {
|
||||
"model": "gpt-3.5-turbo",
|
||||
"api_key": "test-key",
|
||||
"temperature": 0.7,
|
||||
},
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
deployment_before = router.get_deployment_by_model_group_name("gpt-3.5")
|
||||
assert deployment_before is not None
|
||||
original_params = deployment_before.litellm_params.model_dump()
|
||||
|
||||
with patch("litellm.acompletion", new_callable=AsyncMock) as mock_acompletion:
|
||||
from litellm import ModelResponse
|
||||
|
||||
mock_acompletion.return_value = ModelResponse(
|
||||
id="test",
|
||||
choices=[{"message": {"role": "assistant", "content": "test"}, "index": 0}],
|
||||
model="gpt-3.5-turbo",
|
||||
usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
|
||||
)
|
||||
|
||||
try:
|
||||
await router.acompletion(
|
||||
model="gpt-3.5",
|
||||
messages=[{"role": "user", "content": "test"}],
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Critical: Deployment params must be unchanged
|
||||
deployment_after = router.get_deployment_by_model_group_name("gpt-3.5")
|
||||
assert deployment_after is not None
|
||||
assert deployment_after.litellm_params.model_dump() == original_params
|
||||
|
||||
|
||||
def test_completion_deployment_not_mutated():
|
||||
"""
|
||||
Test sync completion doesn't mutate deployment when .copy() is removed.
|
||||
|
||||
Optimization: Remove deployment["litellm_params"].copy() in _completion
|
||||
since data is only read and spread into input_kwargs dict.
|
||||
"""
|
||||
router = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "gpt-3.5",
|
||||
"litellm_params": {
|
||||
"model": "gpt-3.5-turbo",
|
||||
"api_key": "test-key",
|
||||
"max_tokens": 100,
|
||||
},
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
deployment_before = router.get_deployment_by_model_group_name("gpt-3.5")
|
||||
assert deployment_before is not None
|
||||
original_params = deployment_before.litellm_params.model_dump()
|
||||
|
||||
with patch("litellm.completion", new_callable=Mock) as mock_completion:
|
||||
from litellm import ModelResponse
|
||||
|
||||
mock_completion.return_value = ModelResponse(
|
||||
id="test",
|
||||
choices=[{"message": {"role": "assistant", "content": "test"}, "index": 0}],
|
||||
model="gpt-3.5-turbo",
|
||||
usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
|
||||
)
|
||||
|
||||
try:
|
||||
router.completion(
|
||||
model="gpt-3.5",
|
||||
messages=[{"role": "user", "content": "test"}],
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Critical: Deployment params must be unchanged
|
||||
deployment_after = router.get_deployment_by_model_group_name("gpt-3.5")
|
||||
assert deployment_after is not None
|
||||
assert deployment_after.litellm_params.model_dump() == original_params
|
||||
|
||||
@@ -0,0 +1,85 @@
|
||||
"""
|
||||
Regression test for default_deployment shallow copy optimization.
|
||||
|
||||
Tests the critical side effect: ensure modifying returned deployment
|
||||
doesn't corrupt the original default_deployment instance.
|
||||
"""
|
||||
import sys
|
||||
import os
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../.."))
|
||||
|
||||
from litellm import Router
|
||||
|
||||
|
||||
def test_default_deployment_isolation():
|
||||
"""
|
||||
Regression test for shallow copy optimization in _common_checks_available_deployment.
|
||||
|
||||
When a model is not in model_names and default_deployment is set, the router
|
||||
returns a copy of default_deployment with the model name updated. This test
|
||||
ensures the optimization (shallow copy instead of deepcopy) properly isolates
|
||||
each returned deployment from the original and from each other.
|
||||
|
||||
The shallow copy optimization copies two levels:
|
||||
1. Top-level deployment dict
|
||||
2. litellm_params dict
|
||||
|
||||
Deeper nested objects are intentionally shared for performance (safe because
|
||||
the router only modifies the 'model' field at litellm_params level).
|
||||
|
||||
Critical behavior verified:
|
||||
1. Each deployment gets independent model value
|
||||
2. Original default_deployment unchanged for litellm_params fields
|
||||
3. Shared fields (api_key) accessible in all copies
|
||||
4. Adding new litellm_params fields is isolated per deployment
|
||||
5. Deep nested objects ARE shared (acceptable trade-off)
|
||||
"""
|
||||
# Setup: Router with a default deployment (used for unknown models)
|
||||
router = Router(model_list=[])
|
||||
|
||||
router.default_deployment = { # type: ignore
|
||||
"model_name": "default-model",
|
||||
"litellm_params": {
|
||||
"model": "gpt-3.5-turbo", # This will be overwritten per request
|
||||
"api_key": "test-key", # This should be shared
|
||||
"custom_config": { # Deep nested - will be SHARED
|
||||
"nested_setting": "original",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
# Act: Request two different unknown models (triggers default deployment path)
|
||||
_, deployment1 = router._common_checks_available_deployment(
|
||||
model="custom-model-1", # Unknown model
|
||||
messages=[{"role": "user", "content": "test"}],
|
||||
)
|
||||
|
||||
_, deployment2 = router._common_checks_available_deployment(
|
||||
model="custom-model-2", # Different unknown model
|
||||
messages=[{"role": "user", "content": "test"}],
|
||||
)
|
||||
|
||||
# Assert: Each deployment should have its own independent model value
|
||||
assert deployment1["litellm_params"]["model"] == "custom-model-1" # type: ignore
|
||||
assert deployment2["litellm_params"]["model"] == "custom-model-2" # type: ignore
|
||||
|
||||
# Assert: Original default_deployment must remain unchanged (not mutated by requests)
|
||||
assert router.default_deployment["litellm_params"]["model"] == "gpt-3.5-turbo" # type: ignore
|
||||
|
||||
# Assert: Shared fields should still be accessible in all copies
|
||||
assert deployment1["litellm_params"]["api_key"] == "test-key" # type: ignore
|
||||
assert deployment2["litellm_params"]["api_key"] == "test-key" # type: ignore
|
||||
|
||||
# Assert: Modifying litellm_params in one deployment doesn't affect others
|
||||
# This tests the shallow copy properly isolated the litellm_params dict level
|
||||
deployment1["litellm_params"]["temperature"] = 0.9 # type: ignore
|
||||
assert "temperature" not in deployment2["litellm_params"] # type: ignore
|
||||
assert "temperature" not in router.default_deployment["litellm_params"] # type: ignore
|
||||
|
||||
# Assert: Deep nested objects ARE shared (intentional trade-off for 100x perf gain)
|
||||
# Safe because router only modifies top-level litellm_params fields
|
||||
deployment1["litellm_params"]["custom_config"]["nested_setting"] = "modified" # type: ignore
|
||||
assert deployment2["litellm_params"]["custom_config"]["nested_setting"] == "modified" # type: ignore
|
||||
assert router.default_deployment["litellm_params"]["custom_config"]["nested_setting"] == "modified" # type: ignore
|
||||
|
||||
@@ -0,0 +1,132 @@
|
||||
"""
|
||||
Regression tests for Router._pre_call_checks() performance optimization.
|
||||
|
||||
Background:
|
||||
_pre_call_checks() runs on EVERY request to filter deployments based on
|
||||
context window size, rate limits, region constraints, and supported parameters.
|
||||
|
||||
Optimization:
|
||||
Changed from copy.deepcopy(healthy_deployments) to list(healthy_deployments).
|
||||
This is ~1400x faster while maintaining correctness because the function only
|
||||
removes items from the list, never modifies the deployment objects themselves.
|
||||
|
||||
Critical Requirement:
|
||||
The input healthy_deployments list must NEVER be mutated. Callers depend on
|
||||
this for retries, fallbacks, and logging.
|
||||
"""
|
||||
|
||||
import copy
|
||||
import pytest
|
||||
from litellm import Router
|
||||
|
||||
|
||||
class TestPreCallChecksOptimization:
|
||||
"""
|
||||
Verify that using list() instead of deepcopy() doesn't break behavior.
|
||||
|
||||
If these tests fail, the optimization should be reverted.
|
||||
"""
|
||||
|
||||
def test_no_mutation_of_input_list(self):
|
||||
"""
|
||||
Verify the input list is never modified by _pre_call_checks.
|
||||
|
||||
The function uses list() instead of deepcopy for performance.
|
||||
This is safe because it only filters items, never modifies them.
|
||||
"""
|
||||
router = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "gpt-3.5-turbo",
|
||||
"litellm_params": {"model": "gpt-3.5-turbo", "api_key": "sk-test"},
|
||||
"model_info": {"id": "test-1"},
|
||||
},
|
||||
{
|
||||
"model_name": "gpt-3.5-turbo",
|
||||
"litellm_params": {"model": "gpt-4", "api_key": "sk-test2"},
|
||||
"model_info": {"id": "test-2"},
|
||||
},
|
||||
],
|
||||
set_verbose=False,
|
||||
enable_pre_call_checks=True,
|
||||
)
|
||||
|
||||
deployments = router.get_model_list(model_name="gpt-3.5-turbo")
|
||||
assert deployments is not None
|
||||
|
||||
# Capture the original state
|
||||
original_length = len(deployments)
|
||||
original_deployment_ids = [id(d) for d in deployments]
|
||||
original_litellm_params_ids = [id(d["litellm_params"]) for d in deployments]
|
||||
snapshot = copy.deepcopy(deployments)
|
||||
|
||||
# Call the function under test
|
||||
router._pre_call_checks(
|
||||
model="gpt-3.5-turbo",
|
||||
healthy_deployments=deployments,
|
||||
messages=[{"role": "user", "content": "test"}],
|
||||
)
|
||||
|
||||
# Verify nothing changed:
|
||||
# 1. Same number of items
|
||||
assert len(deployments) == original_length, "List length changed!"
|
||||
# 2. Same deployment objects (not replaced with copies)
|
||||
assert [id(d) for d in deployments] == original_deployment_ids, "Deployment dicts replaced!"
|
||||
# 3. Same nested objects (not replaced with copies)
|
||||
assert [id(d["litellm_params"]) for d in deployments] == original_litellm_params_ids, "Nested dicts replaced!"
|
||||
# 4. Same values (catches any mutation)
|
||||
assert deployments == snapshot, "Values were mutated!"
|
||||
|
||||
def test_filtering_still_works(self):
|
||||
"""
|
||||
Verify that filtering works correctly while preserving the original list.
|
||||
|
||||
Scenario: Send a message too long for one deployment but fine for another.
|
||||
Expected: Filtered result excludes the small deployment, but original list is unchanged.
|
||||
"""
|
||||
router = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "test",
|
||||
"litellm_params": {"model": "gpt-3.5-turbo", "api_key": "sk-test"},
|
||||
"model_info": {"id": "small", "max_input_tokens": 50},
|
||||
},
|
||||
{
|
||||
"model_name": "test",
|
||||
"litellm_params": {"model": "gpt-4", "api_key": "sk-test"},
|
||||
"model_info": {"id": "large", "max_input_tokens": 10000},
|
||||
},
|
||||
],
|
||||
set_verbose=False,
|
||||
enable_pre_call_checks=True,
|
||||
)
|
||||
|
||||
deployments = router.get_model_list(model_name="test")
|
||||
assert deployments is not None
|
||||
|
||||
# Save references to the original deployment objects
|
||||
original_small_deployment = deployments[0] # max_input_tokens=50
|
||||
original_large_deployment = deployments[1] # max_input_tokens=10000
|
||||
|
||||
# Send a long message (100 words) that exceeds 50 tokens but fits in 10000 tokens
|
||||
filtered = router._pre_call_checks(
|
||||
model="test",
|
||||
healthy_deployments=deployments,
|
||||
messages=[{"role": "user", "content": " ".join(["word"] * 100)}],
|
||||
)
|
||||
|
||||
# Verify the filtered result only contains the large deployment
|
||||
assert len(filtered) == 1, f"Expected 1 deployment after filtering, got {len(filtered)}"
|
||||
assert filtered[0]["model_info"]["id"] == "large", "Wrong deployment kept after filtering"
|
||||
|
||||
# Verify the original list still has both deployments
|
||||
assert len(deployments) == 2, f"Original list was modified! Expected 2, got {len(deployments)}"
|
||||
assert deployments[0] is original_small_deployment, "First deployment object replaced!"
|
||||
assert deployments[1] is original_large_deployment, "Second deployment object replaced!"
|
||||
assert deployments[0].get("model_info", {}).get("id") == "small", "First deployment ID changed!"
|
||||
assert deployments[1].get("model_info", {}).get("id") == "large", "Second deployment ID changed!"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__, "-v"])
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
"""
|
||||
Test for _is_prompt_management_model early exit optimization.
|
||||
|
||||
Verifies that the early return for models without "/" doesn't break
|
||||
prompt management model detection.
|
||||
"""
|
||||
import sys
|
||||
import os
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../.."))
|
||||
|
||||
from litellm import Router
|
||||
|
||||
|
||||
def test_is_prompt_management_model_optimization():
|
||||
"""
|
||||
Test early exit optimization works correctly for all cases.
|
||||
|
||||
Optimization: Check if "/" in model name before calling expensive
|
||||
get_model_list(). This short-circuits 99% of requests that use
|
||||
standard model names like "gpt-4", "claude-3", etc.
|
||||
|
||||
Tests both negative (early exit) and positive (actual detection) cases.
|
||||
"""
|
||||
import litellm
|
||||
|
||||
# Test 1: Standard models without "/" -> early exit returns False
|
||||
router = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "gpt-4",
|
||||
"litellm_params": {"model": "gpt-4"},
|
||||
},
|
||||
{
|
||||
"model_name": "claude-3",
|
||||
"litellm_params": {"model": "anthropic/claude-3-sonnet-20240229"},
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
assert router._is_prompt_management_model("gpt-4") is False
|
||||
assert router._is_prompt_management_model("claude-3") is False
|
||||
|
||||
# Test 2: Models with "/" but not in model_list -> False after check
|
||||
assert router._is_prompt_management_model("unknown/model") is False
|
||||
|
||||
# Test 3: Actual prompt management models ARE detected (critical positive case)
|
||||
original_callbacks = litellm._known_custom_logger_compatible_callbacks.copy()
|
||||
if "langfuse_prompt" not in litellm._known_custom_logger_compatible_callbacks:
|
||||
litellm._known_custom_logger_compatible_callbacks.append("langfuse_prompt")
|
||||
|
||||
try:
|
||||
router_with_prompt = Router(
|
||||
model_list=[
|
||||
{
|
||||
"model_name": "my-langfuse-prompt/test_id",
|
||||
"litellm_params": {"model": "langfuse_prompt/actual_prompt_id"},
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
# Critical: Must still detect prompt management models correctly
|
||||
assert router_with_prompt._is_prompt_management_model("my-langfuse-prompt/test_id") is True
|
||||
|
||||
finally:
|
||||
litellm._known_custom_logger_compatible_callbacks = original_callbacks
|
||||
|
||||
Reference in New Issue
Block a user