* add dgx_h200 tests
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
* test
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
* fix pre-commit
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
* fix
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
* fix
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
* change bsl branch
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
* fix
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
* change multi gpu related file list
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
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Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
* Rename nvsmall to nemotron NAS
* Revert nvsmall to nemotron_nas rename in paths in tests that access llm_models_root/nvsmall/tests
* Add NemotronNAS to pytorch supported models table
Signed-off-by: Amit Zuker <203509407+amitz-nv@users.noreply.github.com>
* test: Add single gpu disaggregated tests
Signed-off-by: Iman Tabrizian <itabrizian@nvidia.com>
* Add deepseek with overlap tests
Signed-off-by: Iman Tabrizian <itabrizian@nvidia.com>
* Use updated prompt
Signed-off-by: Iman Tabrizian <itabrizian@nvidia.com>
* Move test to disaggregated folder
Signed-off-by: Iman Tabrizian <itabrizian@nvidia.com>
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Signed-off-by: Iman Tabrizian <itabrizian@nvidia.com>
* fix: Fix p-tuning test bug
* A change in the vocab_size calculation for T5Tokenizer,
introduced in transformers version 4.34, caused addition of incorrect vtokens for ptuning.
In general, instead of adding tokens which are outside the vocabulary, tokens inside the vocabulary were added.
Signed-off-by: Amir Klein <203507526+amirkl94@users.noreply.github.com>
* init trtllm attn no cache
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* fix: fix the seq_len issue and attn metadata prepare for qwen reward model test
fix: fix minor bugs after rebase
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* refactor: remove unnecessary debug logs and clean up commented code
refactor: update max_seq_len documentation and remove max_seq_len for decoder model contructor in PyTorchModelEngine
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* refactor: update calculate_ref_result function to accept tensor inputs and mask type, enhance test_attention_no_cache to support FULL and CAUSAL masks
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* refactor: remove unused BERT attention metadata conversion method and add type assertion for no cache attention in PyTorchModelEngine
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* refactor: remove use_kv_cache parameter from attention function and related classes, update documentation for KV cache handling
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* refactor: implement setAttentionMaskType method for better mask type handling and remove unused conversion function
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* refactor: streamline KV cache handling by replacing direct member access with useKVCache method and simplify token per block assignment
remove Debug code.
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* refactor: Resolve comments for Python code
Simplify no cache attention metadata preparation and streamline related attributes in TrtllmAttentionMetadata
Removed the private method for converting to no cache attention metadata and integrated its logic into the prepare method. Updated the test for BERT sequence classification to reflect these changes and ensure proper handling of attention metadata.
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* docs: Add is_dummy_attention field to attention metadata for simulation operations
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* refactor: add KVCacheParams to attention backend interface and import relevant metadata classes
Updated the attention backend interface to include KVCacheParams and imported TrtllmAttentionMetadata and VanillaAttentionMetadata in model_engine.py for enhanced functionality.
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* fix: fix rebase format issue
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* fix: extend attention mask type handling in MHARunnerFixedParams
Added support for additional attention mask types (BIDIRECTIONAL, BIDIRECTIONALGLM, BLOCKSPARSE) in the MHARunnerFixedParams structure to fix the mapping issue between ContextAttentionMaskType and AttentionMaskType
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
* fix: enhance attention mask type handling in TllmGenFmhaRunnerParams
Updated the setAttentionMaskType method to include a switch-case structure for better handling of attention mask types, ensuring proper mapping and error handling for invalid types.
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
---------
Signed-off-by: Qixiang Lin <qixiangl@nvidia.com>
