v1.8.1

v1.8.1

Functionality

• Expose the interface of embedding lookup.

Performance

• Optimized the performance of grouped query attention (GQA).
• Enhanced the performance of creating keys for the oneDNN primitive cache.
• Set the [bs][nh][seq][hs] layout as the default for KV Cache, resulting in better performance.
• Improved the task split imbalance issue in self-attention.

v1.8.0 Continuous Batching on Single ARC GPU and AMX_FP16 Support.

Highlight

• Continuous Batching on Single ARC GPU is supported and can be integrated by vllm-xft.
• Introduce Intel AMX instructions support for float16 data type.

Models

• Support ChatGLM4 series models.
• Introduce BF16/FP16 full path support for Qwen series models.

BUG fix

• Fixed memory leak of oneDNN primitive cache.
• Fixed SPR-HBM flat QUAD mode detect issue in benchmark scripts.
• Fixed heads Split error for distributed Grouped-query attention(GQA).
• Fixed an issue with the invokeAttentionLLaMA API.

What's Changed

Generated release nots

What's Changed

• [Kernel] Enable continuous batching on single GPU. by @changqi1 in #452
• [Bugfix] fixed shm reduceAdd & rope error when batch size is large by @abenmao in #457
• [Feature] Enable AMX FP16 on next generation CPU by @wenhuanh in #456
• [Kernel] Cache oneDNN primitive when M < XFT_PRIMITIVE_CACHE_M, default 256. by @Duyi-Wang in #460
• [Denpendency] Pin python requirements.txt version. by @Duyi-Wang in #458
• [Dependency] Bump web_demo requirement. by @Duyi-Wang in #463
• [Layers] Enable AMX FP16 of FlashAttn by @abenmao in #459
• [Layers] Fix invokeAttentionLLaMA API by @wenhuanh in #464
• [Readme] Add accepted papers by @wenhuanh in #465
• [Kernel] Make SelfAttention prepared for AMX_FP16; More balanced task split in Cross Attention by @pujiang2018 in #466
• [Kernel] Upgrade xDNN to v1.5.2 and make AMX_FP16 work by @pujiang2018 in #468

Full Changelog: v1.7.3...v1.8.0

v1.7.3

BUG fix

• Fixed SHM reduceAdd & rope error when batch size is large.
• Fixed the issue of abnormal usage of oneDNN primitive cache.

v1.7.2 - Continuous batching feature supports Qwen 1.0 & hybrid data types.

v1.7.2 - Continuous batching feature supports Qwen 1.0 & hybrid data types.

Functionality

• Add continuous batching support of Qwen 1.0 models.
• Enable hybrid data types for continuous batching feature, including BF16_FP16, BF16_INT8, BF16_W8A8, BF16_INT4, BF16_NF4, W8A8_INT8, W8A8_int4, W8A8_NF4.

BUG fix

• Fixed the convert fault in Baichuan1 models.

What's Changed

Generated release nots

• [Doc] Add vllm benchmark docs. by @marvin-Yu in #448
• [Kernel] Add GPU kernels and enable LLaMA model. by @changqi1 in #372
• [Tools] Add Baichuan1/2 convert tool by @abenmao in #451
• [Layers] Add qwenRope support for Qwen1.0 in CB mode by @abenmao in #449
• [Framework] Remove duplicated code by @xiangzez in #450
• [Model] Support hybrid model in continuous batching. by @Duyi-Wang in #453
• [Version] v1.7.2. by @Duyi-Wang in #454

Full Changelog: v1.7.1...v1.7.2

v1.7.1 - Continuous batching feature supports ChatGLM2/3.

v1.7.1 - Continuous batching feature supports ChatGLM2/3.

Functionality

• Add continuous batching support of ChatGLM2/3 models.
• Qwen2Convert supports quantized Qwen2 models by GPTQ, such as GPTQ-Int8 and GPTQ-Int4, by param from_quantized_model="gptq".

BUG fix

• Fixed the segament fault error when running with more than 2 ranks in vllm-xft serving.

What's Changed

Generated release nots

• [README] Update README.md. by @Duyi-Wang in #434
• [README] Update README.md. by @Duyi-Wang in #435
• [Common]Add INT8/UINT4 to BF16 weight convert by @xiangzez in #436
• Add Continue Batching support for Chatglm2/3 by @a3213105 in #438
• [Model] Add Qwen2 GPTQ model support by @xiangzez in #439
• [Model] Fix array out of bounds when rank > 2. by @Duyi-Wang in #441
• Bump gradio from 4.19.2 to 4.36.0 in /examples/web_demo by @dependabot in #442
• [Version] v1.7.1. by @Duyi-Wang in #445

Full Changelog: v1.7.0...v1.7.1

v1.7.0 - Continuous batching feature supported.

v1.7.0 - Continuous batching feature supported.

Functionality

• Refactor framework to support continuous batching feature. vllm-xft, a fork of vllm, integrates the xFasterTransformer backend and maintains compatibility with most of the official vLLM's features.
• Remove FP32 data type option of KV Cache.
• Add get_env() python API to get recommended LD_PRELOAD set.
• Add GPU build option for Intel Arc GPU series.
• Exposed the interface of the LLaMA model, including Attention and decoder.

Performance

• Update xDNN to release v1.5.1
• Baichuan series models supports full FP16 pipline to improve performance.
• More FP16 data type kernel added, including MHA, MLP, YARN rotary_embedding, rmsnorm and rope.
• Kernel implementation of crossAttnByHead.

Dependency

• Bump torch to 2.3.0.

BUG fix

• Fixed the segament fault error when running with more than 4 ranks.
• Fixed the bugs of core dump && hang when running croos nodes.

What's Changed

Generated release nots

• [Fix] add utf-8 encoding. by @marvin-Yu in #354
• [Benchmark] Calculate throughput using avg latency. by @Duyi-Wang in #360
• [GPU] Add GPU build option. by @changqi1 in #359
• Fix Qwen prompt.json by @JunxiChhen in #368
• [Model] Fix ICX build issue. by @changqi1 in #370
• [CMake] Remove evaluation under XFT_BUILD_TESTS option. by @Duyi-Wang in #374
• [Kernel][UT] Kernel impl. of crossAttnByHead and unit test for cross attention. by @pujiang2018 in #348
• [API] Add LLaMA attention API. by @changqi1 in #378
• [Finetune] Scripts for Llama2-7b lora finetune example using stock pytorch by @ustcuna in #327
• [Demo] Add abbreviation for output length. by @Duyi-Wang in #385
• [API] Add LLaMA decoder API. by @changqi1 in #386
• [API] Optimize API Impl. by @changqi1 in #396
• [Framework] Continuous Batching Support by @pujiang2018 in #357
• [KVCache] Remove FP32 data type. by @Duyi-Wang in #399
• [Interface] Change return shape of forward_cb. by @Duyi-Wang in #400
• [Example] Add demo of offline continuous batching by @pujiang2018 in #401
• [Layers] Add alibiSlopes Attn && Flash Attn for CB. by @abenmao in #402
• [Interface] Support List[int] and List[List[int]] for set_input_sb. by @Duyi-Wang in #404
• [Bug] fix incorrect input offset computing by @pujiang2018 in #405
• [Example] Fix incorrect tensor dimension with latest interface by @pujiang2018 in #406
• [Models/Layers/Kernels] Add Baichuan1/2 full-link bf16 support & Fix next-tok gen bug by @abenmao in #407
• [xDNN] Release v1.5.0. by @changqi1 in #410
• [Kernel] Add FP16 rmsnorm and rope kernels. by @changqi1 in #408
• [Kenrel] Add FP16 LLaMA YARN rotary_embedding. by @changqi1 in #412
• [Benchmark] Add platform options. Support real model. by @JunxiChhen in #409
• [Dependency] Update torch to 2.3.0. by @Duyi-Wang in #416
• [COMM] Fix bugs of core dump && hang when running cross nodes by @abenmao in #423
• [xDNN] Release v1.5.1. by @changqi1 in #422
• [Kernel] Add FP16 MHA and MLP kernels. by @changqi1 in #415
• [Python] Add get_env() to get LD_PRELOAD set. by @Duyi-Wang in #427
• Add --padding and fix bug by @yangkunx in #418
• [Layers] Fixed the seg fault error when running with more than 4 ranks by @abenmao in #424
• [Kernel] Less compute for Self-Attention (Q * K) by @pujiang2018 in #420
• [Dependency] Update libiomp5.so to 5.0.20230815 contained in mkl. by @Duyi-Wang in #430
• [Distribute] Add distribute support for continuous batching api. by @Duyi-Wang in #421
• [Layers] Fixed error in yarn by @abenmao in #429
• [README] Update readme. by @Duyi-Wang in #431
• [Dependency] Fix wrong so path returned in get_env(). by @Duyi-Wang in #432
• [Version] v1.7.0. by @Duyi-Wang in #433

New Contributors

• @ustcuna made their first contribution in #327
• @yangkunx made their first contribution in #418

Full Changelog: v1.6.0...v1.7.0

v1.6.0 - Llama3 and Qwen2 series models supported.

v1.6.0 - Llama3 and Qwen2 series models supported.

Functionality

• Support Llama3 and Qwen2 series models.
• Add INT8 KV cache datatype, using kv_cache_dtype params to specify, including int8, fp16(default) and fp32.
• More models enable full BF16 pipline, includes Chatglm2/3 and yarn-llama.
• Add invokeMLPLLaMA FP16 API.
• Support logits output using forward() api.

Dependency

• Bump transformers to 4.40.0 to support Llama3 models.

Performance

• Update xDNN to release v1.4.6

BUG fix

• Fix numeric overflow when calculate softmax in sampling.
• fix assert bug when concat gate&up.

What's Changed

Generated release nots

• [Model] Expose KV cache data type in Llama model. by @pujiang2018 in #313
• [API] Format rotary_embedding api. by @changqi1 in #303
• [Kernel] Add kernel support for INT8 KV cache. by @pujiang2018 in #314
• [Convert] Fix Qwen convert issue. by @marvin-Yu in #315
• [API] Add invokeMLPLLaMA FP16 API. by @changqi1 in #302
• [Build] Fix build issue. by @changqi1 in #316
• Chatglm2/3 bf16 pipeline support by @a3213105 in #301
• [README] Add README_CN.md. by @Duyi-Wang in #317
• [Kernel] Bug fix for small_gemm_transb by @pujiang2018 in #318
• [Eval] Get logits output. by @marvin-Yu in #319
• [CMake] Add oneccl build depends for comm_helper. by @Duyi-Wang in #322
• [Layers] fix assert bug when concat gate&up by @abenmao in #323
• [Sample] Fix numeric overflow when calculate softmax. by @Duyi-Wang in #326
• [Models] Use factory class to create decoder. by @Duyi-Wang in #321
• [RAEDME] Update readme for the dependent lib. by @xwang98 in #331
• [KVCache] INT8 KV cache implementation and related changes by @pujiang2018 in #320
• [Model] Add Qwen2 model. by @marvin-Yu in #330
• [KVCache] Add inferface and register for kvcache. by @Duyi-Wang in #336
• [Demo] Add kvcache type option in web demo. by @Duyi-Wang in #338
• [Benchmark] Add KVCache data type option. by @Duyi-Wang in #337
• [model] Add llama3 model. by @marvin-Yu in #340
• [Kernel] Add 'acc' param in small_gemm, add lacked and remove unused small_gemm kernels. by @pujiang2018 in #346
• [xDNN] Release v1.4.6. by @changqi1 in #342
• [Evaluation] fix the model register bug in evaluation by @abenmao in #347
• [Models] YaRN-Llama full-link bf16 support by @abenmao in #344
• [UT] Remove beam search test temporarily. by @Duyi-Wang in #349
• [Version] v1.6.0. by @Duyi-Wang in #352

New Contributors

• @xwang98 made their first contribution in #331

Full Changelog: v1.5.0...v1.6.0

v1.5.0 - Gemma series models supported.

v1.5.0 - Gemma series models supported.

Functionality

• Support Gemma series medels, including Gemma and CodeGemma, and DeepSeek model.
• Llama Converter support convert quantized huggingface model by params from_quantized_model='gptq' into xFt format INT8/INT4 model files.
• Support loading INT4 data weights directly from local files.
• Optimize memory usage during QWen model conversion, particularly for QWen 72B.

Dependency

• Bump transformers to 4.38.1 to support Gemma models.
• Add protobuf to support new behavier in tokenzier.

Performance

• Update xDNN to release v1.4.5
• Add GPU kernel library gpuDNN v0.1 to support Intel Arc GPU series.
• Optimize ROPE perfermance by reducing repeated sin and cos embedding table data.
• Accelerate KVCache copy by increasing parallelism in self attention.
• Accelerate addreduce operation in long sequence case by transposing KVCache and tuned comm.

BUG fix

• Fix a incorrect computing which should be in float, but was in integer.
• Fix timeline is disordered.
• Fix runtime issue of Qwen when seq_length is bigger than 32768.

Generated release nots

What's Changed

• [Kernel] Fix the incorrect computing which should be in float, but was in integer by @pujiang2018 in #267
• [Layer] Reduce repeated sin and cos embedding table data to optimize ROPE perf. by @changqi1 in #266
• [Kernel] increase parallelism for KV cache copy in self attention by @pujiang2018 in #268
• [Include] Fix include not work. by @Duyi-Wang in #271
• Issue qwen72b seq length by @a3213105 in #273
• [Common] Unify memory allocation into xft::alloc by @pujiang2018 in #272
• [Timeline] Fix disordered timeline. by @changqi1 in #277
• [model] Add deepseek model. by @marvin-Yu in #274
• [Bug] Fix incorrect context parameter order. by @changqi1 in #280
• [CI] Check for UT status. by @marvin-Yu in #278
• [CMake] Check existence of MKL & oneDNN directory before installation. by @Duyi-Wang in #283
• Add KVCache trans for long sequence && tuned comm for faster Addreduce by @abenmao in #279
• [Dependency] Add protobuf in requirements.txt by @Duyi-Wang in #284
• [xDNN] Release v1.4.5. by @changqi1 in #285
• [CI] Add rls test case. by @marvin-Yu in #286
• [Bug] fix baichuan model test issue. by @marvin-Yu in #287
• [Fix] Fix baichuan2-13 without rope. by @marvin-Yu in #289
• [Tools] Add convert tool for Llama models quantized by AutoGPTQ by @xiangzez in #276
• [Common] Support loading int4 weights by @xiangzez in #275
• [KVCache] KV Cache refactor and related unit test case fix by @pujiang2018 in #290
• [Model] Update isMaster func. by @changqi1 in #292
• [Bug] Fix oneDNN GPU build issue. by @changqi1 in #293
• [UT] add unit test for selfAttention, and a small fix by @pujiang2018 in #294
• [gpuDNN] Add gpuDNN v0.1.0 library files. by @feng-intel in #291
• [UT] MLP unit test case fix by @abenmao in #296
• [Fix] Reduce convert memory usage. by @marvin-Yu in #297
• [ENV] Use Meyers' Singleton Env object. by @Duyi-Wang in #295
• [fix] fix compile issue. by @marvin-Yu in #299
• [Example] Add gemma model config and web demo. by @marvin-Yu in #304
• [Model] Add gemma model support. by @marvin-Yu in #259
• [example] add gemma model support with example. by @marvin-Yu in #307
• Bump transformers from 4.36.0 to 4.38.0 in /examples/web_demo by @dependabot in #308
• Fix timeline compile issue by @xiangzez in #309
• [Build] Fix build issues. by @changqi1 in #310
• [Version] v1.5.0. by @Duyi-Wang in #311

New Contributors

• @feng-intel made their first contribution in #291

Full Changelog: v1.4.0...v1.5.0

v1.4.0 - Fully BF16 support in Llama for better performance and serving framework support.

Functionality

• Introduce pure BF16 support to Llama series models, now can use fully BF16 data type to to utilize AMX more effectively when deploying Llama models.
• Add MLServer serving framework support and demo in serving directory.
• GCC for compiling release binary files has been updated from GCC 8.5 to GCC 12.
• Introduce pipeline parallel feature for distributing deployment. Enabled by cmake .. -DWITH_PIPELINE_PARALLEL=ON in compilation and use XFT_PIPELINE_STAGE Marco to define pipeline parallel stages num.
• Deprecate convert tool scripts in tools directory and it recommended to using Convert in xfastertransformer python wheel.
• Support loading int8 data weights directly from local files.

Performance

• Update xDNN to release v1.4.4.
• Accelerate model weights loading by optimizing cast operation after loading and gain up to 50% speed up.
• Optimize BF16 performance using AMX instruction when batchsize <= 8, and add XFT_USE_AMX_M to set threshold of M using AMX instead of AVX512, default 1.

Demo & Benchmark

• Update dependency transformers requirement from 4.30.0 to 4.36.0 for high risk CVE Vulnerabilities.
• Add distributed inference benchmark script which support deployment across platfrom.
• Add single node platform support in benchmark script.
• Add Yi model web demo.
• Enhance the command-line chat mode in pytorch demo.py, using --chat true to enable.

BUG fix

• Fix calculation issue in Qwen models and enhance LogN support for long token sequence.
• Fix unsync issue in multi-rank model when do_sample is enabled.
• Fix Baichuan models calculation and convert issue.
• Fix repetition penalties not taking effect on other batches.

What's Changed

• [Demo] Update web demo to adapt gradio 4.11.0. by @Duyi-Wang in #201
• Bump gradio from 3.40.1 to 4.11.0 in /examples/web_demo by @dependabot in #150
• [demo] Add Yi model demo. by @marvin-Yu in #200
• [Dependency] transformers version warning when error occurs. by @Duyi-Wang in #202
• [Tools] Deprecate convert tools in tools dir. by @Duyi-Wang in #203
• [benchmark] Add one node benchmark. by @marvin-Yu in #205
• Bump transformers from 4.30.0 to 4.36.0 by @dependabot in #145
• Bump transformers from 4.30.0 to 4.36.0 in /examples/web_demo by @dependabot in #144
• [CMake] Check if the compiler really supports avx512bf16 with try_compile by @pujiang2018 in #206
• [Layer] Fine grained data type definition for Attention and MLP by @pujiang2018 in #194
• Add recommend GCC version by @a3213105 in #207
• [TP] Make split dimension align with oneDNN packing by @pujiang2018 in #208
• Support loading int8 weights by @xiangzez in #157
• [benchmark] Add distributed benchmark. by @marvin-Yu in #211
• [ci] Fix python path issue. by @marvin-Yu in #214
• [Fix] Fix repetition penalties not taking effect on other batches. by @Duyi-Wang in #212
• [xDNN] Release v1.4.3. by @changqi1 in #213
• [ci] Add workflow permission. by @marvin-Yu in #218
• [Layer] Enable pipeline parallel feature. by @changqi1 in #221
• [Dockerfile] Remove dockerfile. by @Duyi-Wang in #219
• [CI] Align using benchmark tests. by @marvin-Yu in #216
• [xDNN] Release v1.4.4. by @changqi1 in #223
• [Layer] Support pure full-link BF16 LLaMa model. by @pujiang2018 in #222
• [Layers] Qwen LogN for query by @a3213105 in #215
• [Layer] Convert static MMHelper class to instance Class in DecoderContext. by @changqi1 in #225
• [models][layers/tools] Refine and bugfix for baichuan models by @abenmao in #226
• [Serving] Add MLServer serving support. by @Duyi-Wang in #217
• [Dependencies] Remove tokenizers requirement. by @Duyi-Wang in #227
• [kernel] Add ICX compiler. by @changqi1 in #228
• [Env] Add XFT_ENGINE env variable. by @changqi1 in #231
• [CMake] Open the pip-install information for MKL. by @marvin-Yu in #234
• [Fix] Add parameter check for logN and NTK rotary embedding of QWEN by @a3213105 in #232
• [CMake] Remvoe force reinstall for mkl dependencies. by @Duyi-Wang in #237
• [Example] Add seq_length in qwen fake config.ini by @Duyi-Wang in #238
• [Tools] Accelerate model loading. by @marvin-Yu in #224
• [Fix] Fix the wrong output of QWEN-14B. by @marvin-Yu in #240
• fix issue #220 by @a3213105 in #242
• Bump gradio from 4.11.0 to 4.19.2 in /examples/web_demo by @dependabot in #241
• [Example] Add llama2 chat support in Cli demo. by @Duyi-Wang in #243
• [Dependency] Update web demo requirement. by @Duyi-Wang in #246
• [Docs] Initial documents. by @Duyi-Wang in #248
• Fix Opt issue by @xiangzez in #251
• [Serving] Fix fail to set pad_token_id when it's not None in single mode. by @Duyi-Wang in #254
• [layers] Add bf16-type input/output support for flash attention by @abenmao in #252
• [Kernel] Set USE_AMX_M to 1. by @Duyi-Wang in #245
• [Benchmark] Fix typo in benchmark script. by @Duyi-Wang in #261
• [Attention Kernel/Layer] group attention support in full-link BF16 path; attention layer refactor by @pujiang2018 in #258
• [Search] Sync smaple result in multi-rank. by @Duyi-Wang in #260
• [Benchmark] Update model cfg for transformers>4.36. by @Duyi-Wang in #257
• [Layer] Use flash attention when larger than threshold ('>=' to '>') by @pujiang2018 in #265
• [Benchmark] Modify CPU affinity logic, add CI prompt output. by @marvin-Yu in #263
• [Version] v1.4.0. by @Duyi-Wang in #262

New Contributors

• @dependabot made their first contribution in #150

Full Changelog: v1.3.1...v1.4.0

v1.3.1

BUG fix

• Fix oneCCL environment is still needed when running in single-rank mode.

What's Changed

• [demo] Add qwen demo deps. by @marvin-Yu in #193
• Add base_initial to store the original base passed from config by @a3213105 in #196
• [Comm] Check mpirun and env before load helper. by @Duyi-Wang in #197
• [Version] v1.3.1. by @Duyi-Wang in #198

Full Changelog: v1.3.0...v1.3.1