[TRTLLM-8679][feat] Scenario UX bulk refactor #8925
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…ested) Enables generating optimized TensorRT-LLM configurations from scenario constraints using profile-based logic. Supports dsr1-fp4, dsr1-fp8, and gptoss-fp4 profiles with validated example recipes. Note: This implementation has not been tested yet. Signed-off-by: Venky Ganesh <[email protected]>
Signed-off-by: Venky Ganesh <[email protected]>
- Simplify configure.py by removing redundant recipe loading logic - Fix recipe database initialization in db/__init__.py - Update matcher and profiles for improved recipe handling - Integrate recipe system with performance test infrastructure Signed-off-by: Venky Ganesh <[email protected]>
The README documented a non-existent --recipe flag and used outdated examples showing config.yaml output. Updated to reflect actual CLI behavior: trtllm-configure generates recipe files (scenario + env + config) from scenario parameters only, not from existing recipes. Signed-off-by: Venky Ganesh <[email protected]>
…trtllm-configure This commit enforces a standardized recipe schema and simplifies trtllm-configure to perform exact matching only, removing dynamic recipe generation. Schema Changes: - Rename 'config' → 'llm_api_config' in all recipe YAML files - Update recipe detection in trtllm-serve and trtllm-bench to use 'llm_api_config' - Update README examples to use new key name trtllm-configure Simplification: - Remove dynamic recipe generation using profiles - Implement exact matching only against tensorrt_llm/recipes/db/ - Add find_all_matching_recipes() to detect multiple matches - Return clear errors for no match or ambiguous (multiple) matches - Remove --profile CLI option (no longer needed) - Update help text and examples to reflect exact matching behavior Validation Changes: - Remove validate_llm_api_config() calls from configure/serve/bench - Comment out validation function pending PR NVIDIA#8331 - PR NVIDIA#8331 standardizes LlmArgs with Pydantic, after which validation will happen automatically when LlmArgs(**kwargs) is instantiated - Add TODO comments referencing PR NVIDIA#8331 Documentation Updates: - Remove "Profiles" section from README (no longer auto-generated) - Remove "Adding Custom Profiles" section - Update "Quick Start" to reflect exact matching behavior - Clarify that trtllm-configure retrieves (not generates) recipes Signed-off-by: Venky Ganesh <[email protected]>
This commit enables trtllm-bench to parse and apply recipe YAML files
that contain scenario parameters, environment variables, and LLM API
configuration in a unified format.
Key changes:
- Add scenario.py utility to extract and merge scenario parameters
- Modify throughput.py to detect recipe format and create temp YAML
with only llm_api_config section to pass to LLM constructor
- Fix dataset field name from output_len to output_tokens in scenario.py
- Add tinyllama-simple.yaml test recipe demonstrating recipe usage
Recipe format structure:
- scenario: test parameters (ISL/OSL/concurrency/num_requests)
- env: environment variables to set
- llm_api_config: LLM constructor arguments (KV cache, CUDA graphs, etc)
With this change, users can now run:
trtllm-bench --model <model> throughput \
--extra_llm_api_options <recipe.yaml>
Instead of specifying multiple CLI flags for ISL/OSL/concurrency/etc.
The recipe format simplifies configuration and enables reusable test
configurations.
Signed-off-by: Venky Ganesh <[email protected]>
…cation Add recipe system support to low_latency benchmark command and extract common llm_api_config processing logic to reduce code duplication. Changes: - Add prepare_llm_api_config_for_recipe() utility to scenario.py that extracts llm_api_config section from recipe YAML and creates temp file - Update low_latency.py to use shared utility for recipe processing - Refactor throughput.py to use shared utility instead of inline tempfile logic - Eliminates ~30 lines of duplicated code between benchmark files Both throughput and latency commands now support recipe format with auto-generated datasets and unified behavior. Signed-off-by: Venky Ganesh <[email protected]>
- Updated `merge_params_with_priority` function to reflect new parameter names in examples. - Modified `generate_bench_command` to include model name and provide detailed command templates for throughput, latency, and build benchmarks. - Renamed validation exceptions for clarity, changing `ValidationError` to `ScenarioValidationError` and `ValidationWarning` to `ScenarioValidationWarning`. - Added a new TinyLlama test recipe for streamlined testing and dataset generation. - Removed outdated recipe files for DeepSeek and GPT-OSS to clean up the repository. These changes enhance usability and maintainability of the benchmarking and recipe systems. Signed-off-by: Venky Ganesh <[email protected]>
…commands Added process_recipe_scenario() helper in scenario.py to eliminate code duplication between throughput.py and low_latency.py. This consolidates recipe scenario extraction, parameter merging, and dataset auto-generation into a single reusable function. Changes: - Added process_recipe_scenario() to tensorrt_llm/bench/utils/scenario.py - Refactored throughput.py to use new helper (40 lines -> 15 lines) - Refactored low_latency.py to use new helper (40 lines -> 15 lines) - Eliminated ~80 lines of duplicated code - Maintained 100% backward compatibility Tested with e2e recipe perf tests - all passing. Signed-off-by: Venky Ganesh <[email protected]>
venkywonka
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Nov 4, 2025
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outdated code. will need refactor.
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