Support activation quantization #2607
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Summary: X-link: pytorch/benchmark#2607 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba Differential Revision: D70522237
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Summary: X-link: pytorch/pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 Differential Revision: D70522237
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Summary: X-link: pytorch/pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 Differential Revision: D70522237
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Summary: X-link: pytorch/pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 Differential Revision: D70522237
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Summary: X-link: pytorch/pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 Differential Revision: D70522237
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Summary: X-link: pytorch/pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Differential Revision: D70522237
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…ng (#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: #148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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…ng (#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: #148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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…ng (#148380) Summary: X-link: pytorch/benchmark#2607 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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Summary: X-link: pytorch/pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
Summary: X-link: pytorch/pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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May 1, 2025
…ng (pytorch#148380) Summary: X-link: pytorch/benchmark#2607 Pull Request resolved: pytorch#148380 We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize. Test Plan: # unit test ``` buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization -- test_activation_quantization_aten ``` Buck UI: https://www.internalfb.com/buck2/9a53c909-d3ea-479a-874e-cc917999ca88 Test UI: https://www.internalfb.com/intern/testinfra/testrun/12384899050440719 Network: Up: 62KiB Down: 81KiB (reSessionID-913ca82d-c395-4492-818e-6e004df37f87) Executing actions. Remaining 0/4 6.1s exec time total Command: test. Finished 2 local Time elapsed: 3:22.9s Tests finished: Pass 2. Fail 0. Fatal 0. Skip 0. Build failure 0 # E2E ### how to enable ``` post_grad_fusion_options={ "activation_quantization_aten_pass": {"quant_type": "torch.float8_e5m2"}, }, ``` see D51860030 to check how to set the config under dynamo_config_map Note: you can change the quant_type, if nothing gives, then the default type torch.float8_e5m2 will be used to quantize #### If you use FSDP - You may also need to set inline_inbuilt_nn_modules to true for models that use FSDP (see D70023488 to check the config setting) - Remove UNSAFE_SKIP_FSDP_MODULE_GUARDS=1 (context: https://fb.workplace.com/groups/1075192433118967/permalink/1629608671010671/) ``` buck2 run mode/opt //aps_models/ads/icvr:icvr_launcher -- mode=mast_omnifm_v1-5_mwb launcher.max_retries=3 data_loader.dataset.batch_size=8 launcher.data_project=oncall_ads_model_platform launcher.fbl_entitlement=ads_global_tc_training_efficiency_qps max_ind_range=1 launcher.num_workers=8 data_loader.reading_service.num_remote_dpp_workers=30 data_loader.dataset.num_batches=100 trainer.gpu_tracer.wait=50 trainer.gpu_tracer.active=3 trainer.gpu_tracer.overhead_detection=10 launcher.tags=[ads_ranking_taxonomy_mc_qps_optimization] ``` aps-512_8_remove_fsdp_guards-92ae3972ba tlparse: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/aps-512_8_remove_fsdp_guards-92ae3972ba/attempt_0/version_0/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 baseline w/o fp8 quantization: aps-mengluy_remove_fsdp-ce75b306fa w/ fp8 quantization: aps-mengluy_remove_fsdp_fp8-96541deec4 ### QPS {F1977040587} ### memory baseline {F1977040640} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=1767027467197075&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot with fp8 {F1977040641} memory snapshot: https://www.internalfb.com/ai_infra/zoomer/profiling-run/insights?profilingRunID=639378375763157&tab=INSIGHTS&primarySubtab=Memory%20Analysis&secondarySubtab=Memory%20Snapshot ### conclusion: - ~9% qps improvement, reduces peak memory from 82.01 to 78.97. - for NE, we need have longer verification, WIP with scaling version. Differential Revision: D70522237
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Summary:
X-link: pytorch/pytorch#148380
We enable the activation quantization in the forward pass, and users can customize the dtype they want to quantize.
Differential Revision: D70522237