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pytorch/torch/cpu
Jiong Gong 914d3ca2ba [inductor][cpp] BF16 AMX micro-gemm support (#127195)
This PR adds the intrinsics based micro-gemm for BF16 using Advanced Matrix eXtension (AMX) instructions available in Intel 4th and 5th Xeon processors. A compilation check is added to `codecache.py` to check the validity of the compiler support. Also, since AMX requires an initialization in the Linux kernel to extra register states, an initialization function is added to do that and triggered via `codecache.py`.

Performance speedups with >=10% on BF16 AMP, max_autotune vs. no autotune, measured on Intel(R) Xeon(R) Platinum 8488C:
Static shapes
Single-threaded
| Model Family | Model Name | Speedup |
|--------------|------------|---------|
| timm_models | mixer_b16_224 | 1.54 |
| timm_models | convit_base | 1.53 |
| huggingface | MobileBertForQuestionAnswering | 1.52 |
| torchbench | fastNLP_Bert | 1.44 |
| torchbench | llama | 1.33 |
| timm_models | swin_base_patch4_window7_224 | 1.31 |
| torchbench | dlrm | 1.28 |
| torchbench | timm_vision_transformer_large | 1.28 |
| huggingface | MobileBertForMaskedLM | 1.27 |
| timm_models | vit_base_patch16_224 | 1.26 |
| timm_models | beit_base_patch16_224 | 1.23 |
| timm_models | jx_nest_base | 1.21 |
| torchbench | pyhpc_equation_of_state | 1.18 |
| huggingface | Speech2Text2ForCausalLM | 1.15 |
| timm_models | pit_b_224 | 1.14 |
| timm_models | twins_pcpvt_base | 1.14 |
| torchbench | maml_omniglot | 1.1 |
| timm_models | botnet26t_256 | 1.1 |

Multi-threaded
| Model Family | Model Name | Speedup |
|--------------|------------|---------|
| torchbench | BERT_pytorch | 1.35 |
| torchbench | lennard_jones | 2.43 |
| torchbench | hf_Albert | 1.35 |
| torchbench | hf_T5 | 1.34 |
| torchbench | soft_actor_critic | 1.34 |
| torchbench | fastNLP_Bert | 1.28 |
| huggingface | LayoutLMForSequenceClassification | 1.26 |
| torchbench | llama | 1.24 |
| huggingface | GPT2ForSequenceClassification | 1.19 |
| torchbench | hf_Bart | 1.17 |
| torchbench | hf_Bert_large | 1.16 |
| torchbench | hf_GPT2 | 1.16 |
| timm_models | gmixer_24_224 | 1.16 |
| torchbench | hf_GPT2_large | 1.15 |
| torchbench | maml_omniglot | 1.14 |
| torchbench | hf_Bert | 1.13 |
| torchbench | hf_DistilBert | 1.13 |
| torchbench | hf_T5_large | 1.12 |
| huggingface | MT5ForConditionalGeneration | 1.11 |

Dynamic shapes
Single-threaded
| Model Family | Model Name | Speedup |
|--------------|------------|-------|
| timm_models | mixer_b16_224 | 1.52 |
| timm_models | convit_base | 1.5 |
| huggingface | MobileBertForQuestionAnswering | 1.49 |
| torchbench | fastNLP_Bert | 1.42 |
| torchbench | timm_vision_transformer_large | 1.28 |
| timm_models | swin_base_patch4_window7_224 | 1.27 |
| torchbench | llama | 1.26 |
| huggingface | MobileBertForMaskedLM | 1.25 |
| timm_models | vit_base_patch16_224 | 1.25 |
| timm_models | beit_base_patch16_224 | 1.24 |
| timm_models | jx_nest_base | 1.2 |
| torchbench | dlrm | 1.19 |
| timm_models | pit_b_224 | 1.13 |
| timm_models | twins_pcpvt_base | 1.13 |
| torchbench | hf_Bert_large | 1.12 |
| torchbench | hf_BigBird | 1.11 |
| huggingface | Speech2Text2ForCausalLM | 1.11 |
| timm_models | eca_botnext26ts_256 | 1.11 |
| timm_models | botnet26t_256 | 1.1 |

Multi-threaded
| Model Family | Model Name | Speedup |
|--------------|------------|-------|
| torchbench | BERT_pytorch | 1.18 |
| torchbench | lennard_jones | 2.18 |
| torchbench | hf_Albert | 1.37 |
| torchbench | soft_actor_critic | 1.31 |
| huggingface | GPT2ForSequenceClassification | 1.29 |
| torchbench | hf_T5 | 1.28 |
| torchbench | fastNLP_Bert | 1.27 |
| torchbench | hf_Bart | 1.21 |
| torchbench | hf_Bert_large | 1.19 |
| torchbench | hf_T5_large | 1.19 |
| torchbench | hf_Bert | 1.16 |
| torchbench | hf_GPT2 | 1.16 |
| huggingface | CamemBert | 1.16 |
| torchbench | hf_GPT2_large | 1.13 |
| torchbench | functorch_maml_omniglot | 1.12 |
| huggingface | BertForMaskedLM | 1.12 |
| huggingface | MT5ForConditionalGeneration | 1.12 |
| torchbench | hf_DistilBert | 1.11 |
| timm_models | mixnet_l | 1.11 |
| timm_models | tf_mixnet_l | 1.11 |

No perf regressions.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127195
Approved by: https://github.com/jansel
2024-06-21 07:21:47 +00:00
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