mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-12 23:01:41 +00:00
5339c2d783
* fix(vertex/files): stream OpenAI->Vertex batch JSONL uploads to fix OOM on large files
Large (1GB+) batch JSONL uploads to Vertex AI / GCS caused OOM or killed the worker
because the request body was buffered and multiplied 2-3x in size. The create-file
path is now streaming end-to-end: transform_create_file_request returns a
ResumableChunkedUploadConfig carrying a lazy _OpenAIToVertexBatchUploadStream, and the
HTTP handler opens a GCS resumable session and PUTs the body in bounded 8 MiB chunks
(Content-Range, 308 between chunks) so the transformed payload is never held in full.
The proxy /v1/files endpoint streams from Starlette's spooled upload handle instead of
reading the whole body, and batch rate limiting counts tokens and models in a single
streaming pass.
Only gcs_bucket_name is supported for the GCS target; the legacy bucket_name key is
intentionally not read.
Also removes the unreachable VertexAIFilesHandler create path and everything only it
kept alive (VertexAIJsonlFilesTransformation, _stream_openai_jsonl_to_vertex, the legacy
transform helpers), plus the orphaned batch_utils helpers the streaming rewrite replaced.
* fix(batches): return original JSONL on unparseable row to avoid silent batch truncation
The streaming rewrite of replace_model_in_jsonl accumulated physical lines and
skipped a row on JSONDecodeError to support multi-line objects, but a genuinely
malformed or truncated row never completes: it poisons the buffer, swallows every
following row, and the function still returned the partial rewrite (the rows before
the bad one, already model-rewritten) as if the batch were complete. That turned the
pre-rewrite behavior of returning the original file unchanged (so the provider rejects
the bad batch loudly) into a silent partial submission.
Restore the original-content fallback: when an unparseable remainder is left after the
loop, return the original file_content (rewinding a consumed seekable source) instead of
the truncated output. The multi-line happy path is unchanged.
* test(batches): mock resumable GCS upload in vertex batch prediction test
The vertex batch file-create path now streams to a GCS resumable session via
_aresumable_chunked_upload (httpx send) instead of AsyncHTTPHandler.post, so the
existing test's post mock no longer intercepted the upload and a real request hit
GCS (401). Mock _aresumable_chunked_upload to return the GCS object response; the
resumable protocol itself is covered in test_vertex_ai_files_streaming.py.
* fix(batches): resilient per-row token accounting; no hard-block on count failure
The batch input-file pass iterated a generator whose json.loads raised on a
malformed line; the outer except caught it and stopped the loop, so any body.model
on rows after a bad line was never collected and the model allowlist check ran
against a partial set. It also hard-blocked the batch with a 400 whenever token
counting raised, a backwards-incompatible change from the prior swallow-and-proceed
behavior that breaks legitimate rows the token counter cannot measure (e.g. some
multimodal content).
Iterate the JSONL line-by-line and account each row independently. A malformed line
is skipped (its request cannot run upstream anyway) and a row the counter cannot
measure falls back to a conservative size-based estimate. The loop never aborts, so
the allowlist check always sees every parseable model, and the token total is never
zeroed, so a crafted uncountable row still cannot evade the TPM limit, without
hard-rejecting a legitimate batch.
* perf(vertex/files): unblock async upload; drop empty finalize; widen batch MIME types
Three review follow-ups on the resumable batch upload:
- _aresumable_chunked_upload pulled chunks from a synchronous generator that runs
the per-row transform inline on the event loop thread, blocking other requests
between PUTs on large uploads. Each chunk is now produced via asyncio.to_thread.
- _iter_resumable_chunks no longer yields a trailing empty chunk, so an exactly
chunk-aligned upload finalizes on its last data chunk instead of an extra
zero-byte PUT; a 0-byte stream still finalizes via the caller's empty request.
- valid_content_type now accepts the MIME types clients label .jsonl batch uploads
with (text/plain, application/json, ndjson, ...), so such a batch file no longer
silently bypasses the streaming path into the buffered media upload.
* fix(vertex/files): keep legacy bucket_name as GCS bucket fallback
The rename to gcs_bucket_name dropped the legacy bucket_name key entirely, so an SDK caller passing bucket_name to a Vertex AI file create/retrieve/content call with GCS_BUCKET_NAME unset got ValueError("GCS bucket_name is required") where it previously resolved the bucket. _get_configured_bucket_name now reads gcs_bucket_name, then bucket_name, then the env var, and bucket_name is restored to OPTIONAL_KWARGS_KEYS so it survives get_litellm_params on the retrieve and content paths. gcs_bucket_name keeps precedence when both are present
* style: sort imports in llm_http_handler to satisfy I001 budget
---------
Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
(cherry picked from commit
|
||
|---|---|---|
| .. | ||
| _experimental/mcp_server | ||
| a2a | ||
| agent_endpoints | ||
| anthropic_endpoints | ||
| auth | ||
| client | ||
| common_utils | ||
| db | ||
| discovery_endpoints | ||
| experimental/mcp_server | ||
| google_endpoints | ||
| guardrails | ||
| health_endpoints | ||
| hooks | ||
| image_endpoints | ||
| management_endpoints | ||
| management_helpers | ||
| memory | ||
| middleware | ||
| openai_files_endpoint | ||
| pass_through_endpoints | ||
| policy_engine | ||
| prompts | ||
| proxy_server | ||
| public_endpoints | ||
| rag_endpoints | ||
| realtime_endpoints | ||
| response_api_endpoints | ||
| shutdown | ||
| spend_tracking | ||
| test_configs | ||
| types_utils | ||
| ui_crud_endpoints | ||
| utils | ||
| vector_store_endpoints | ||
| __init__.py | ||
| conftest.py | ||
| test_aiohttp_cleanup_closed.py | ||
| test_aiohttp_session_recovery.py | ||
| test_api_key_masking_in_errors.py | ||
| test_audio_speech_prometheus_hooks.py | ||
| test_batch_expiry.py | ||
| test_batch_metadata_none_fix.py | ||
| test_batch_retrieve_bedrock.py | ||
| test_budget_reservation.py | ||
| test_caching_routes.py | ||
| test_chat_completion_metadata.py | ||
| test_common_request_processing.py | ||
| test_component_allowlists.py | ||
| test_cors_config.py | ||
| test_custom_proxy.py | ||
| test_dynamic_mcp_route.py | ||
| test_empty_model_list.py | ||
| test_enforce_user_param.py | ||
| test_fallback_management_endpoints.py | ||
| test_fastapi_offline_routes.py | ||
| test_filter_models_by_team_access_group.py | ||
| test_health_check_functions.py | ||
| test_health_check_max_tokens.py | ||
| test_langfuse_passthrough_security.py | ||
| test_lazy_openapi_snapshot.py | ||
| test_litellm_pre_call_utils.py | ||
| test_max_budget_env_var.py | ||
| test_mcp_asgi_response.py | ||
| test_model_dump_with_preserved_fields.py | ||
| test_model_id_header_propagation.py | ||
| test_model_info_default_limits.py | ||
| test_model_level_guardrails.py | ||
| test_model_list_healthy_only.py | ||
| test_openapi_schema_validation.py | ||
| test_pricing_field_strip.py | ||
| test_prometheus_cleanup.py | ||
| test_provider_url_destination_guard.py | ||
| test_proxy_cli.py | ||
| test_proxy_logging_hook_detection.py | ||
| test_proxy_server.py | ||
| test_proxy_types.py | ||
| test_proxy_utils.py | ||
| test_pyroscope.py | ||
| test_redis_auth_cache_flag.py | ||
| test_response_model_sanitization.py | ||
| test_route_a2a_models.py | ||
| test_route_llm_request.py | ||
| test_sensitive_route_auth.py | ||
| test_shared_health_check.py | ||
| test_spend_log_cleanup.py | ||
| test_swagger_chat_completions.py | ||
| test_team_member_update.py | ||
| test_team_org_move.py | ||
| test_tools_allowlist_enforcement.py | ||
| test_update_llm_router_resilience.py | ||