{"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/qpytorch-a-low-precision-arithmetic","title":"QPyTorch: A Low-Precision Arithmetic Simulation Framework","arxiv_id":"1910.04540","date":"2019-10-09","proceeding":null,"authors":["Tianyi Zhang","Zhiqiu Lin","Guandao Yang","Christopher De Sa"],"abstract":"Low-precision training reduces computational cost and produces efficient models. Recent research in developing new low-precision training algorithms often relies on simulation to empirically evaluate the statistical effects of quantization while avoiding the substantial overhead of building specific hardware. To support this empirical research, we introduce QPyTorch, a low-precision arithmetic simulation framework. Built natively in PyTorch, QPyTorch provides a convenient interface that minimizes the efforts needed to reliably convert existing codes to study low-precision training. QPyTorch is general, and supports a variety of combinations of precisions, number formats, and rounding options. Additionally, it leverages an efficient fused-kernel approach to reduce simulator overhead, which enables simulation of large-scale, realistic problems. QPyTorch is publicly available at https://github.com/Tiiiger/QPyTorch.","url_abs":"https://arxiv.org/abs/1910.04540v1","url_pdf":"https://arxiv.org/pdf/1910.04540v1.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":"qpytorch-a-low-precision-arithmetic","repo_url":"https://github.com/Tiiiger/QPyTorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"qpytorch-a-low-precision-arithmetic","repo_url":"https://github.com/robust-systems-group/illusion_system","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.04540","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.04540"}},"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/robust-systems-group/illusion_system","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Tiiiger/QPyTorch","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":6},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1},"listed":{"samples":5,"ran":0,"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":"2dc1247ced8e633e","entry":"cost_loopblocking_gbuf_regf","repo":"robust-systems-group/illusion_system","repo_kind":"listed","path":"illusion_paper_sim/modules/nn_dataflow/LoopBlocking.py","file_url":"https://github.com/robust-systems-group/illusion_system/blob/HEAD/illusion_paper_sim/modules/nn_dataflow/LoopBlocking.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"2dc1247ced8e633e"}},{"code_sha256_prefix":"251461e3ef7c0ae7","entry":"ndarray_to_string","repo":"robust-systems-group/illusion_system","repo_kind":"listed","path":"illusion_testing/training/get_model_D2NN_MNIST.py","file_url":"https://github.com/robust-systems-group/illusion_system/blob/HEAD/illusion_testing/training/get_model_D2NN_MNIST.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"251461e3ef7c0ae7"}},{"code_sha256_prefix":"46f69e02f2721956","entry":"random_string","repo":"robust-systems-group/illusion_system","repo_kind":"listed","path":"distributed_endurer/endurer_tests/illusion_lifetime_feb.py","file_url":"https://github.com/robust-systems-group/illusion_system/blob/HEAD/distributed_endurer/endurer_tests/illusion_lifetime_feb.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"46f69e02f2721956"}},{"code_sha256_prefix":"d10217a422916a6e","entry":"run_cmd","repo":"robust-systems-group/illusion_system","repo_kind":"listed","path":"illusion_paper_sim/util.py","file_url":"https://github.com/robust-systems-group/illusion_system/blob/HEAD/illusion_paper_sim/util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"d10217a422916a6e"}},{"code_sha256_prefix":"1ecbbbe92957c81b","entry":"sequential_lower","repo":"Tiiiger/QPyTorch","repo_kind":"official","path":"qtorch/auto_low/auto_low.py","file_url":"https://github.com/Tiiiger/QPyTorch/blob/HEAD/qtorch/auto_low/auto_low.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1ecbbbe92957c81b"}},{"code_sha256_prefix":"397bdc14bd0b9498","entry":"size_to_string","repo":"robust-systems-group/illusion_system","repo_kind":"listed","path":"illusion_testing/training/get_model_D2NN_MNIST.py","file_url":"https://github.com/robust-systems-group/illusion_system/blob/HEAD/illusion_testing/training/get_model_D2NN_MNIST.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"397bdc14bd0b9498"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}