{"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/microadam-accurate-adaptive-optimization-with","title":"MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence","arxiv_id":"2405.15593","date":"2024-05-24","proceeding":null,"authors":["Ionut-Vlad Modoranu","Mher Safaryan","Grigory Malinovsky","Eldar Kurtic","Thomas Robert","Peter Richtarik","Dan Alistarh"],"abstract":"We propose a new variant of the Adam optimizer called MicroAdam that specifically minimizes memory overheads, while maintaining theoretical convergence guarantees. We achieve this by compressing the gradient information before it is fed into the optimizer state, thereby reducing its memory footprint significantly. We control the resulting compression error via a novel instance of the classical \\emph{error feedback} mechanism from distributed optimization in which *the error correction information is itself compressed* to allow for practical memory gains. We prove that the resulting approach maintains theoretical convergence guarantees competitive to those of AMSGrad, while providing good practical performance. Specifically, we show that MicroAdam can be implemented efficiently on GPUs: on both million-scale (BERT) and billion-scale (LLaMA) models, MicroAdam provides practical convergence competitive to that of the uncompressed Adam baseline, with lower memory usage and similar running time. Our code is available at https://github.com/IST-DASLab/MicroAdam.","url_abs":"https://arxiv.org/abs/2405.15593v2","url_pdf":"https://arxiv.org/pdf/2405.15593v2.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":"microadam-accurate-adaptive-optimization-with","repo_url":"https://github.com/ist-daslab/microadam","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"distributed-optimization","task_name":"Distributed Optimization"}],"methods":[{"method_slug":"amsgrad","method_name":"AMSGrad"},{"method_slug":"adam","method_name":"Adam"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.15593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15593"}},"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/ist-daslab/microadam","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":7,"unverified":3},"by_repo_kind":{"official":{"samples":10,"ran":7,"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":"62d5375f1e75a0a4","entry":"calculate_named_averages","repo":"ist-daslab/microadam","repo_kind":"official","path":"llm-foundry/llmfoundry/callbacks/eval_gauntlet_callback.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/llm-foundry/llmfoundry/callbacks/eval_gauntlet_callback.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"62d5375f1e75a0a4"}},{"code_sha256_prefix":"bd9be9a296403c57","entry":"get_eval_parameters","repo":"ist-daslab/microadam","repo_kind":"official","path":"llm-foundry/llmfoundry/callbacks/async_eval_callback.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/llm-foundry/llmfoundry/callbacks/async_eval_callback.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"bd9be9a296403c57"}},{"code_sha256_prefix":"37b437ca0eb633ca","entry":"get_run_name","repo":"ist-daslab/microadam","repo_kind":"official","path":"llm-foundry/llmfoundry/callbacks/async_eval_callback.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/llm-foundry/llmfoundry/callbacks/async_eval_callback.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"37b437ca0eb633ca"}},{"code_sha256_prefix":"eda9ea6f0622874c","entry":"import_file","repo":"ist-daslab/microadam","repo_kind":"official","path":"llm-foundry/llmfoundry/registry.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/llm-foundry/llmfoundry/registry.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"eda9ea6f0622874c"}},{"code_sha256_prefix":"dbc7da64de6882d1","entry":"is_excluded_layer","repo":"ist-daslab/microadam","repo_kind":"official","path":"huggingface_glue_mnli/glue.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/huggingface_glue_mnli/glue.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"dbc7da64de6882d1"}},{"code_sha256_prefix":"83a7912b3e0b32f4","entry":"param_groups_weight_decay","repo":"ist-daslab/microadam","repo_kind":"official","path":"huggingface_glue_mnli/glue.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/huggingface_glue_mnli/glue.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"83a7912b3e0b32f4"}},{"code_sha256_prefix":"e25b382678ba9e73","entry":"param_groups_weight_decay_natural_order","repo":"ist-daslab/microadam","repo_kind":"official","path":"huggingface_glue_mnli/glue.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/huggingface_glue_mnli/glue.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"e25b382678ba9e73"}},{"code_sha256_prefix":"2904bff0953c2943","entry":"download_from_oras","repo":"ist-daslab/microadam","repo_kind":"official","path":"llm-foundry/llmfoundry/utils/model_download_utils.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/llm-foundry/llmfoundry/utils/model_download_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2904bff0953c2943"}},{"code_sha256_prefix":"97bfc07ce3b89c71","entry":"get_cuda_capability","repo":"ist-daslab/microadam","repo_kind":"official","path":"huggingface_glue_mnli/helpers.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/huggingface_glue_mnli/helpers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"97bfc07ce3b89c71"}},{"code_sha256_prefix":"a5159521471b0e58","entry":"setup_wandb","repo":"ist-daslab/microadam","repo_kind":"official","path":"huggingface_glue_mnli/helpers.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/huggingface_glue_mnli/helpers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a5159521471b0e58"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}