{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adaptive-gradient-quantization-for-data","title":"Adaptive Gradient Quantization for Data-Parallel SGD","arxiv_id":"2010.12460","date":"2020-10-23","proceeding":"NeurIPS 2020 12","authors":["Fartash Faghri","Iman Tabrizian","Ilia Markov","Dan Alistarh","Daniel Roy","Ali Ramezani-Kebrya"],"abstract":"Many communication-efficient variants of SGD use gradient quantization schemes. These schemes are often heuristic and fixed over the course of training. We empirically observe that the statistics of gradients of deep models change during the training. Motivated by this observation, we introduce two adaptive quantization schemes, ALQ and AMQ. In both schemes, processors update their compression schemes in parallel by efficiently computing sufficient statistics of a parametric distribution. We improve the validation accuracy by almost 2% on CIFAR-10 and 1% on ImageNet in challenging low-cost communication setups. Our adaptive methods are also significantly more robust to the choice of hyperparameters.","url_abs":"https://arxiv.org/abs/2010.12460v1","url_pdf":"https://arxiv.org/pdf/2010.12460v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adaptive-gradient-quantization-for-data","repo_url":"https://github.com/tabrizian/learning-to-quantize","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"}],"methods":[{"method_slug":"alq-and-amq","method_name":"ALQ and AMQ"},{"method_slug":"sgd","method_name":"SGD"}],"datasets_introduced":[],"methods_introduced":[{"slug":"alq-and-amq","name":"ALQ and AMQ","full_name":"Gradient Quantization with Adaptive Levels/Multiplier"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2010.12460","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12460"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tabrizian/learning-to-quantize","reach":null}],"summary":{"ran_honours":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"82931da66cc18f58","entry":"get_quantile_levels","repo":"tabrizian/learning-to-quantize","repo_kind":"official","path":"nuq/quantize.py","file_url":"https://github.com/tabrizian/learning-to-quantize/blob/HEAD/nuq/quantize.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"82931da66cc18f58"}},{"code_sha256_prefix":"a214f5c5e4cbdc68","entry":"get_uniform_levels","repo":"tabrizian/learning-to-quantize","repo_kind":"official","path":"nuq/quantize.py","file_url":"https://github.com/tabrizian/learning-to-quantize/blob/HEAD/nuq/quantize.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a214f5c5e4cbdc68"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}