Signed-off-by: Shijie Wang <jaywan@nvidia.com>
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
Signed-off-by: Shijie <jaywan@nvidia.com>
Co-authored-by: Yukun He <23156053+hyukn@users.noreply.github.com>
The performance results of some kernels could be easily affected by the warm/cold L2 cache status. To achieve more precise profiling results, the L2 cache is cleared for every execution by the circular buffer method for better benchmarking during autotuning.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Skip the shape profile generating process if the profile has already been found in the cache under tuning mode. This is a prerequisite for nested autotuning because host overhead might be included during the profiling of the high-level op.
* Enable the profiling with CUDA graph as the default profiling method.
* Apply a heuristic method to cut off the number of repeat times of profiling according to a few-run time measurement.
Some tunable ops require a more realistic data distribution, for instance, a shape-associated tensor. Thus, a customizable pre-hook function can be declared in the tuning config to modify the input tensor before the tuning process.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
To achieve determinism for the AutoTuner profiling cache, serialization and deserialization are introduced to store the cache on disk in JSON format. Use TLLM_AUTOTUNER_CACHE_PATH to indicate the path where the cache file should be stored:
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Generalize the definition of tactics so that users can implement more customizable tactic types, making the configurations clearer for each kernel run.
* Allow the user not to specify the `gen_tuning_buckets` or the `map_to_tuning_buckets` function.
* Other code refactoring.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Change the fallback alert from DEBUG to WARNING level and only do it once.
* Add debug information for profiling cache right after the warmup phase.
* Change the level of exception message during tactic profiling from ERROR to WARNING level. All exception details are pushed to the DEBUG level.
* Other trivial refinements and cleanups.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
- Adds a new Python custom op (fp8_block_scale_moe_runner) and a FP8BlockScaleMoERunner class for autotuning.
- Updates C++ MoE and batched GEMM kernels to accept a configIndex for workspace sizing and execution.
- Extends the unit test to run both autotuned and non-autotuned code paths.
Signed-off-by: Dom Brown <3886319+DomBrown@users.noreply.github.com>
Fix AutoTuner warmup request generating.
* The current warmup phase creates one request, which is insufficient for the warmup to cover the max_num_tokens. Revise the warmup phase to a batch of requests to cover the max_num_tokens to eliminate potential fallback cases.
Refactor AutoTuner API and reduce host overhead.
Refine (min, opt, max) values of optimization profile setup for get_valid_tactics to achieve the correct canImplement definition.
* Refine cache key assembly process to reduce host overhead and simplify API.
* Fix lru_cache usage to reduce host overhead.
* Move tuning config initialization as a one-time object in tunable runner to reduce host overhead.
Improve tuning config readability.
* Use dataclass to define tuning config.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Several optimizations and fixings on the Autotuner.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Apply the new Python side Autotuner on current linear for nvFP4 data type.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Apply the new Python side Autotuner on MoE op
* Remove routers from cache key to improve inference perf
* Prevent unnecessary code profiling. Use do_preparation keyword to select which part should be executed during before evaluating any tactic.
* Remove try-catch inside moe profiling process.
* Move default tactic -1 to 0 transforms in cpp runner.
* Revise relavant tests.
* Predefined the bucketizing strategy for fused_moe
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Add specific_profile support for AutoTuner to bypass the standard cache search process for perf optimization
* Add specific_profile for moe
* Add specific profile for linear
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Fixing and revising according to reviewer's suggestions.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Use lru_cache for inference pref optimization.
* Revert gen_custom_cache_key feature
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Replace runner with runner id to achieve a serializable cache.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Code clean up and minor fixings.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Move all tunable runners and custom ops into torch_custom_ops.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
* Treat min_latency_mode as a independent dynamic tensor. Modify get_valid_tactics to suit for it.
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
---------
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
