{"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":"/census/concat-all-gather","entry":"concat_all_gather","source":"Syntology differential census (groundwork/55, run v2_2026-09-22), per sample; not an archive number","census_date":"2026-09-22","battery_sha256":["b986f7e04d794a0d88ad4c5f32cf63ec3590b5deff0192150737bc5f1c0b4677"],"runner_sha256":["5a452d0e7c0da5b80771d1be2afe3572e253568cf5c5d59a0d08ebd663d00808"],"bucket_key":"positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name","bucket_fields":["rank","kind","dtype"],"claim":"Implementations sharing this entry name were each run on one shared input fixed by the positional (rank, kind, dtype) of their array arguments (the bucket). A cluster is the set whose recorded output digest (sha256 of the output rounded to 6 decimals) is identical. Identical values to six decimals on the shared input are agreement on those inputs, not a statement about the whole domain and not a substitution claim.","n_implementations_compared":7,"n_papers":7,"n_buckets":1,"n_distinct_outputs":2,"n_class_bearing":0,"not_compared":{"not_run_on_shared_input":{"n":0,"by_error":{}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":0,"n_papers":0,"recorded_shared_digest_equals_own_fixture_digest":0},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/concat-all-gather","buckets":[{"bucket":[[2,"float","float32"]],"n_implementations_compared":7,"n_papers":7,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"d7bd5ceec8a69dcd","size":4,"n_papers":4,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":4,"torch_reference_conventions_with_this_digest":[],"values":[1.8026286363601685,-1.5337762832641602,0.47605323791503906,-0.6908870339393616,-0.9028494954109192,-1.0304712057113647,-0.6627609133720398,0.5728892087936401,0.5188759565353394,0.9591456651687622,-0.3971995711326599,-1.5683481693267822,-0.28853508830070496,2.322108745574951,-0.5147839784622192,0.5505285859107971,0.06639543920755386,-1.6228755712509155,0.4321114122867584,-0.4061858654022217,-0.02259422466158867,-1.180782437324524,-0.5368682742118835,0.983674168586731,-0.7956128120422363,0.43327441811561584,0.9449777603149414,1.0175182819366455,-1.225785732269287,1.274217963218689,-1.3222218751907349,-0.5941091179847717],"values_recorded":32,"members":[{"code_sha256_prefix":"090af570926107cc","path":"all-seeing/utils/model.py","papers":["2402.19474"],"paper_pages":[{"arxiv_id":"2402.19474","page":"/paper/the-all-seeing-project-v2-towards-general"}],"arg_sig_recorded":[["tensor",2,"float","float32"]],"scalar_args":{"gather_with_grad":"False"},"class_bearing":false},{"code_sha256_prefix":"a2ad46fbfc2d7b20","path":"model/csg_builder.py","papers":["2104.02290"],"paper_pages":[{"arxiv_id":"2104.02290","page":"/paper/contrastive-syn-to-real-generalization-1"}],"arg_sig_recorded":[["tensor",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"c64b9f8d154957e3","path":"network/deepv3.py","papers":["2204.01446"],"paper_pages":[{"arxiv_id":"2204.01446","page":"/paper/wildnet-learning-domain-generalized-semantic"}],"arg_sig_recorded":[["tensor",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"f723ada1d6eea3ab","path":"criterions/ssrl_audiopeak_aotv2.py","papers":["2209.13583"],"paper_pages":[{"arxiv_id":"2209.13583","page":"/paper/learning-state-aware-visual-representations"}],"arg_sig_recorded":[["tensor",2,"float","float32"]],"scalar_args":{},"class_bearing":false}]},{"output_sha":"b0057f7d211aeff8","size":3,"n_papers":3,"shape":[8,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":3,"torch_reference_conventions_with_this_digest":[],"values":[1.8026286363601685,-1.5337762832641602,0.47605323791503906,-0.6908870339393616,-0.9028494954109192,-1.0304712057113647,-0.6627609133720398,0.5728892087936401,0.5188759565353394,0.9591456651687622,-0.3971995711326599,-1.5683481693267822,-0.28853508830070496,2.322108745574951,-0.5147839784622192,0.5505285859107971,0.06639543920755386,-1.6228755712509155,0.4321114122867584,-0.4061858654022217,-0.02259422466158867,-1.180782437324524,-0.5368682742118835,0.983674168586731,-0.7956128120422363,0.43327441811561584,0.9449777603149414,1.0175182819366455,-1.225785732269287,1.274217963218689,-1.3222218751907349,-0.5941091179847717,1.8026286363601685,-1.5337762832641602,0.47605323791503906,-0.6908870339393616,-0.9028494954109192,-1.0304712057113647,-0.6627609133720398,0.5728892087936401,0.5188759565353394,0.9591456651687622,-0.3971995711326599,-1.5683481693267822,-0.28853508830070496,2.322108745574951,-0.5147839784622192,0.5505285859107971,0.06639543920755386,-1.6228755712509155,0.4321114122867584,-0.4061858654022217,-0.02259422466158867,-1.180782437324524,-0.5368682742118835,0.983674168586731,-0.7956128120422363,0.43327441811561584,0.9449777603149414,1.0175182819366455,-1.225785732269287,1.274217963218689,-1.3222218751907349,-0.5941091179847717],"values_recorded":64,"members":[{"code_sha256_prefix":"209eec589964bb56","path":"util/pointdata_process.py","papers":["2110.08188"],"paper_pages":[{"arxiv_id":"2110.08188","page":"/paper/guided-point-contrastive-learning-for-semi-1"}],"arg_sig_recorded":[["tensor",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"b363135da9a62f61","path":"trainer/trainer.py","papers":["2407.18244"],"paper_pages":[{"arxiv_id":"2407.18244","page":"/paper/refmask3d-language-guided-transformer-for-3d"}],"arg_sig_recorded":[["tensor",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"fa052d95a1f73aa9","path":"openmixup/models/augments/mixup.py","papers":["1710.09412"],"paper_pages":[{"arxiv_id":"1710.09412","page":"/paper/mixup-beyond-empirical-risk-minimization"}],"arg_sig_recorded":[["tensor",2,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]}]}