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Use FlashAttention for multi_query_kv_attention
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Support for optimum-intel models
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…o-model-executor Adapt OpenVINO CPU plugin implementation
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BA-78760: Jamba * Add support for n concat and splitting * change naming * input_metadata is a dict list now in order to pass "n" * clean up code from unecessary changes and prints * Remove kv cache allocation in case of mamba layer * Add the considerations of mamba layer cache into the num of blocks calculation * Delete mamba cache after profile * Remove prints * Cleaning * - and not _ for requirements Approved-by: Tomer Asida
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patching for having type su
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…ect#4 magic_wand semi_structured_sparse_tensor_linear branch integrates 2:4 semi-structured sparsity into SparseTensor. This PR adds a new sparsity config for 2:4 sparsity to neuralmagic-vllm, using the SparseTensor 2:4 support. This PR also refactors the sparse linear method into a separate file, vllm/model_executor/layers/sparsity/sparse_w16a16_linear_method.py, which supports all sparsity formats.
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…ect#4 magic_wand semi_structured_sparse_tensor_linear branch integrates 2:4 semi-structured sparsity into SparseTensor. This PR adds a new sparsity config for 2:4 sparsity to neuralmagic-vllm, using the SparseTensor 2:4 support. This PR also refactors the sparse linear method into a separate file, vllm/model_executor/layers/sparsity/sparse_w16a16_linear_method.py, which supports all sparsity formats.
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Lookup buffer implementation
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This PR is to use FlashAttention kernels for
multi_query_kv_attention
, which performs masked attention for the prompt inputs.Pros
Cons
Besides, note that FlashAttention does not support cached KV, which is required for interactive generation.
Tested models:
Tested GPUs: