Open arjunsuresh opened 6 months ago
@nv-ananjappa Can you please help?
As discussed in the WG meeting today. Please reach out to Ashwin.
the same auc to you, let me konw what is the problem?
@kkkparty are you running on 4090 as well? We got no reply from Nvidia yet.
No,i ran on 3090,and i cut piece of day23 for auc varify.get 63%,further,i can see any dense weights loaded.can u?
---Original--- From: "Arjun @.> Date: Tue, Apr 23, 2024 21:16 PM To: @.>; Cc: @.**@.>; Subject: Re: [mlcommons/inference] Getting a lower than expected accuracy withDLRMv2 using Nvidia code for v3.1 (Issue #1619)
@kkkparty are you running on 4090 as well? We got no reply from Nvidia yet.
— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>
We originally had an issue with the preprocessed data which is now fixed and we get the expected accuracy with Intel submissions using the same dataset. But we still have the issue with Nvidia code.
[2024-07-10 09:56:32,793 run_harness.py:166 INFO] Result: Accuracy run detected.
[2024-07-10 09:56:32,806 __init__.py:46 INFO] Running command: PYTHONPATH=/work:/usr/lib/python38.zip:/usr/lib/python3.8:/usr/lib/python3.8/lib-dynload:/usr/local/lib/python3.8/dist-packages:/usr/lib/python3/dist-packages:/usr/lib/python3.8/dist-packages python3 -S /work/build/inference/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py --mlperf-accuracy-file /work/build/logs/2024.07.10-09.46.34/spr_TRT/dlrm-v2-99/Offline/mlperf_log_accuracy.json --day-23-file /home/mlperf_inf_dlrmv2/criteo/day23/raw_data --aggregation-trace-file /home/mlperf_inf_dlrmv2/criteo/day23/sample_partition.txt --dtype float32
Assuming loadgen accuracy log does not contain ground truth labels.
Parsing loadgen accuracy log...
Parsing aggregation trace file...
Parsing ground truth labels from day_23 file...
Re-ordering ground truth labels...
Calculating AUC metric...
AUC=67.515%, accuracy=96.586%, good=86093981, total=89137319, queries=330067
======================== Result summaries: ========================
spr_TRT-custom_k_99_MaxP-Offline:
dlrm-v2-99:
accuracy: [FAILED] AUC: 67.515 (Threshold=79.507)
[2024-07-10 00:31:07,727 run_harness.py:170 INFO] Result: Accuracy run detected.
[2024-07-10 00:31:07,784 __init__.py:46 INFO] Running command: PYTHONPATH=/work:/usr/lib/python38.zip:/usr/lib/python3.8:/usr/lib/python3.8/lib-dynload:/home/arjun/.local/lib/python3.8/site-packages:/usr/local/lib/python3.8/dist-packages:/usr/lib/python3/dist-packages:/usr/lib/python3.8/dist-packages python3 -S /work/build/inference/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py --mlperf-accuracy-file /work/build/logs/2024.07.09-23.00.04/phoenix_TRT/dlrm-v2-99/Offline/mlperf_log_accuracy.json --day-23-file /home/mlperf_inf_dlrmv2/criteo/day23/raw_data --aggregation-trace-file /home/mlperf_inf_dlrmv2/criteo/day23/sample_partition.txt --dtype float32
Assuming loadgen accuracy log does not contain ground truth labels.
Parsing loadgen accuracy log...
Parsing aggregation trace file...
Parsing ground truth labels from day_23 file...
Re-ordering ground truth labels...
Calculating AUC metric...
AUC=71.568%, accuracy=96.587%, good=86094780, total=89137319, queries=330067
======================== Result summaries: ========================
phoenix_TRT-custom_k_99_MaxP-Offline:
dlrm-v2-99:
accuracy: [FAILED] AUC: 71.568 (Threshold=79.507)
Nvidia code actually requires running this script to generate the frequency data. But this step is not documented anywhere and not sure if this script is the actual expected one.
We tried the same on a 8xH100 system and getting AUC around ~63%.
@viraatc Please look into this if you have spare time before the v4.1 submission deadline. Else we can investigate after the deadline.
Nvidia code actually requires running this script to generate the frequency data. But this step is not documented anywhere and not sure if this script is the actual expected one.
@arjunsuresh you are correct, this script needs to be run. We are missing this in docs for v4.0. My apologies for this. please let us know if u still encounter issues after running it within container as such before your run:
$ python -m "code.dlrm-v2.tensorrt.scripts.gen_frequency_data"
for v4.1 we have inlined this step as part of engine build, so there will be no additional steps / documentation.
Thank you @viraatc for checking. We also ran that script inside the container and that script produced the required npy file. But accuracy was still low.
Are we required to do "make calibrate" for Dlrmv2? We tried that but that was giving error with v4.0 code.
Are we required to do "make calibrate" for Dlrmv2? We tried that but that was giving error with v4.0 code.
Yes, that is required after running gen frequency data script.
Please share the error you face when you run make calibrate
here, I will investigate this after v4.1 submission deadline.
Thank you @viraatc for confirming. The error during calibration is added below.
[V] Input tensor: sparse_input (dtype=DataType.INT32, shape=(256, 214)) | Setting input tensor shapes to: (min=[256, 214], opt=[256, 214], max=[256, 214])
[I] Using calibration profile: {numerical_input [min=[256, 13, 1, 1], opt=[256, 13, 1, 1], max=[256, 13, 1, 1]],
sparse_input [min=[256, 214], opt=[256, 214], max=[256, 214]]}
[V] Input tensor: numerical_input (dtype=DataType.FLOAT, shape=(256, 13, 1, 1)) | Setting input tensor shapes to: (min=[256, 13, 1, 1], opt=[256, 13, 1, 1], max=[256, 13, 1, 1])
[V] Input tensor: sparse_input (dtype=DataType.INT32, shape=(256, 214)) | Setting input tensor shapes to: (min=[256, 214], opt=[256, 214], max=[256, 214])
[V] Loaded Module: numpy | Version: 1.23.5 | Path: ['/home/cmuser/.local/lib/python3.8/site-packages/numpy']
[V] Loaded Module: onnx | Version: 1.16.1 | Path: ['/home/cmuser/.local/lib/python3.8/site-packages/onnx']
[I] Building engine with configuration:
Flags | [INT8]
Engine Capability | EngineCapability.DEFAULT
Memory Pools | [WORKSPACE: 80994.94 MiB, TACTIC_DRAM: 80994.94 MiB]
Tactic Sources | [CUBLAS, CUDNN, EDGE_MASK_CONVOLUTIONS, JIT_CONVOLUTIONS]
Profiling Verbosity | ProfilingVerbosity.DETAILED
Preview Features | [FASTER_DYNAMIC_SHAPES_0805, DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
Calibrator | Calibrator(<generator object DLRMv2.calibrate.<locals>.data_loader at 0x7f0b69ed1f20>, cache=PosixPath('code/dlrm-v2/tensorrt/calibrator.cache'), BaseClass=<class 'tensorrt.tensorrt.IInt8EntropyCalibrator2'>, batch_size=256)
[I] Loading calibration cache from code/dlrm-v2/tensorrt/calibrator.cache
[V] Reading Calibration Cache for calibrator: EntropyCalibration2
[V] Generated calibration scales using calibration cache. Make sure that calibration cache has latest scales.
[V] To regenerate calibration cache, please delete the existing one. TensorRT will generate a new calibration cache.
[W] Missing scale and zero-point for tensor sparse_input, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[V] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +6, GPU +66, now: CPU 159813, GPU 1761 (MiB)
[V] [MemUsageChange] Init cuDNN: CPU +4, GPU +72, now: CPU 159817, GPU 1833 (MiB)
[V] Global timing cache in use. Profiling results in this builder pass will be stored.
[V] Detected 2 inputs and 1 output network tensors.
[V] Total Host Persistent Memory: 85440
[V] Total Device Persistent Memory: 0
[V] Total Scratch Memory: 219136
[V] [BlockAssignment] Started assigning block shifts. This will take 39 steps to complete.
[V] [BlockAssignment] Algorithm ShiftNTopDown took 0.632123ms to assign 7 blocks to 39 nodes requiring 6194688 bytes.
[V] Total Activation Memory: 6193152
[V] Total Weights Memory: 16327680
[V] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +64, now: CPU 159956, GPU 1959 (MiB)
[V] [MemUsageChange] Init cuDNN: CPU +1, GPU +72, now: CPU 159957, GPU 2031 (MiB)
Starting plugin init...
IO Config:
Dense Input: FLOAT
Sparse Input: INT32
Embedding Table: FLOAT
Output: FLOAT
Loading embedding weights...
WARNING: Logging before InitGoogleLogging() is written to STDERR
F0712 02:55:56.512785 15401 numpy.hpp:52] Check failed: m_FStream Unable to parse: /home/mlperf_inf_dlrmv2/model/embedding_weights/mega_table.npy
*** Check failure stack trace: ***
make: *** [Makefile:123: calibrate] Aborted (core dumped)
We created a softlink to mega_table_fp32.npy to get around the missing file error and regenerated the calibration.cache. But that too didn't help the accuracy.
[2024-07-12 04:18:44,188 run_harness.py:166 INFO] Result: Accuracy run detected.
[2024-07-12 04:18:44,189 __init__.py:46 INFO] Running command: python3 /home/cmuser/CM/repos/local/cache/28c3eeb6237c4e93/repo/closed/NVIDIA/build/inference/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py --mlperf-accuracy-file /home/cmuser/CM/repos/local/cache/6c8bb6e9615a4933/test_results/4709e5ccb133-nvidia_original-gpu-tensorrt-vdefault-default_config/dlrm-v2-99/offline/accuracy/mlperf_log_accuracy.json --day-23-file /home/mlperf_inf_dlrmv2/criteo/day23/raw_data --aggregation-trace-file /home/mlperf_inf_dlrmv2/criteo/day23/sample_partition.txt --dtype float32
Assuming loadgen accuracy log does not contain ground truth labels.
Parsing loadgen accuracy log...
Parsing aggregation trace file...
Parsing ground truth labels from day_23 file...
Re-ordering ground truth labels...
Calculating AUC metric...
AUC=62.914%, accuracy=96.586%, good=86094219, total=89137319, queries=330067
thanks @arjunsuresh for all the details, I will be now looking at this.
let us first focus on v4.0-H100x1. assuming default config for v4.0-H100x1-Offline.
can you please follow the following steps and share respective files:
ensure clean environment
make clean
make build
share md5 of all dlrm data folder:
/home/mlperf_inf_dlrmv2
, run find . -type f | xargs -P $(nproc) -I {} md5sum {} | tee md5_tree.log
md5_tree.log
share generated calibrator.cache file:
rm /work/code/dlrm-v2/tensorrt/calibrator.cache
make calibrate RUN_ARGS="--benchmarks=dlrmv2"
/work/code/dlrm-v2/tensorrt/calibrator.cache
share fresh logs which use the generated calibrator.cache:
make run RUN_ARGS="--benchmarks=dlrmv2 --scenarios=offline --config_ver=default --test_mode=AccuracyOnly"
Thank you @viraatc for looking into this. Below are the md5sum values of the dlrm data folder. I'll share the calibrator.cache file and logs soon. Meanwhile this is the MLCommons preprocessed criteo dataset which is used by Intel and others including us. Can you please confirm if this is the same one Nvidia is using?
a145bf8ba38d71137f8601876896d8d9 ./model/.snapshot_metadata
f1e1d545f964fb24d7e65fec86ca4020 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_9194304_0
ee2f4e147ae72e98baeb4c683876ecdd ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_39194304_0
80633f51d26f7aeed777c2f77d3c76c0 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_10.weight_2097152_0
548ef6312dbe5daeec9574d2b8e8b2fb ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_25000000_0
78477f306525dc7227eb9f438b24a5e7 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_21048576_0
e54c6d2993b511ad9eb7aedc70da931d ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_7097152_0
7b80ccfa3c056be76c3bde7141710b16 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_30000000_0
08bc8409e38cb92d433eb9aaabeaef1f ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_37097152_0
33c471827515f1101c20800ab22f3dd8 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_38145728_0
74535d7d559a463a90d17e02de5e2c79 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_15000000_0
392e7cfbeb3fe5c59a2fb52c340cd4ce ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_2097152_0
28c667f303e8c6da85a5a5614847170f ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_26048576_0
f7bb6ccbc4d6454b90f4d3d4f476d8ab ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_25000000_0
7dacf3a85889c6384b3a3b85efc25a95 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_1048576_0
fe211034447c9063d4b5141f2dfc6904 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_20000000_0
6b9e38777dcb03b3c379a88fb92bc2da ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_10000000_0
642dd6c9445d5d3fcb6fcc4670d9e395 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_7097152_0
a3fbc6af26a6dccea0b8d06d7f829ab7 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_32097152_0
949419855a540b778c186fa7e241ceda ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_1048576_0
a0f8ef9b4a4f14fe4211c07819f2fcf1 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/18.npy
41610d7810e5552ab5ecf0332e47a55e ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/7.npy
2e841abb6f8f7cd30e3e5ef5df855bf1 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/2.npy
a7650873137dc3518fa06d296d47df2b ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/8.npy
ced0a164f926f97a7501b047d3d05fad ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/17.npy
da0245108f14131171ac3d43418a100c ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/6.npy
cc7daf94cf81e89360f1273750b0a78a ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/24.npy
7bd8c842b7504c97e1078654d2e3a5c0 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/25.npy
ef346cd1ce26c7c85b6c4c108bdafaf0 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/5.npy
c3ae2edfb9c2279ec5e10e452226f661 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/16.npy
e9b71259c97546df1e9c82841f9c3d03 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/12.npy
dd68f93301812026ed6f58dfb0757fa7 ./criteo/day23/fp32/day_23_labels.npy
82adff98b58345f168b9ac5dd581eb4f ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_9194304_0
41a547605fb3247df77102ba2f7c7b2d ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_34194304_0
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fcb38bb3766fe4ccb80dcedeb9a07dc7 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_18145728_0
30e1de64b8a384d2ccc8291d0935e7e7 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_14194304_0
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4c7f7170a6fdc20ac16668d6cf979cb4 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_2097152_0
3f624f10f2b8f8ec5f12f01aa2e52cb2 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_31048576_0
8b3a5356d633da8f0cad37cd41d24420 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_36048576_0
a7e49ca0f6ef4ece5d030ac7da550f54 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_31048576_0
e10540b2e8f1113bddceb9a6ea1a6743 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_20000000_0
669f05242472963bd614317046273efe ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_26048576_0
6fedfb7e6c12cb54fbdfd1a8653b03e5 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_0_0
b660c54389d1bd86afe26ca830d6ccbe ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_38145728_0
d369d35b7c9db97cc2d299d15ffcf399 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_28145728_0
49a78383f3b4f748fb98e4addfdbf414 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_25000000_0
4f774c3c4f90ff007353e0892708c7d9 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_3145728_0
e7e697a7fe4319274fde1e20f5a2caaf ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_37097152_0
2ca471d6813a0970d8ac10518bbd2b8f ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_9194304_0
3bc32c339b56b7d670572ef19d9c6e56 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_19194304_0
830e9aa0c756f1c8f81bcf39bc9f2e4e ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_19194304_0
ce01efbacf831f423546d0024b69ebc3 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_14194304_0
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c72983df6a02717aeb0208681fe5e9b8 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_2097152_0
55b7eee36dc3a711adb6ce165c05961c ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_18145728_0
01b35f69f6fe5497db425b0d63c4842c ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_13145728_0
31656faa5c8292694ccc5a60af9e8b5a ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_20000000_0
0586fb4f3ebeaf146cbf830f07d7d139 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_32097152_0
e67a63066a1ac73646ffbfb7e01ef0c1 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_13145728_0
a684ab288fe2bcc76374be0b0744fa2f ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/1.npy
e020aa1563b8b1a411405e420b322f49 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/3.npy
b424cd23564a5ae90167cdec5310a699 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_1048576_0
757441a8dbadcd597bf321343406245c ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_12097152_0
673d528813f90d96351f3be6ac60a376 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_17097152_0
80a328d0c168f910019b96e19b0f2670 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_33145728_0
14a13d00245609b987c492a1a2c9734b ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_4194304_0
67ffe3b29b331f57f7bde4c64e38584c ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_39194304_0
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a18e5255e34d2bb7157000e5e4bb5d03 ./model/model_weights/batched/a96647f1-5f9d-4269-a513-951187353c89
a11e6f3cc0806d6265f9f2b943a6c62e ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_13145728_0
66f753d18943b8965c68e3833fe0494a ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_13145728_0
67e81be3c4821e837e4f8c5b9241283a ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_12097152_0
5c9b0f77920b41373cbb69dfd8b7da79 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_0.weight_22097152_0
3b1cfc9654f42ce4f491c7295d41ac5e ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_37097152_0
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ddba8f13390d15b75e183edf9db99966 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_27097152_0
db5c23cabb631fed54149df34f69d518 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_38145728_0
faeb52ac2524dd748aebc1e0ad3d920c ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_27097152_0
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67c36c043cc318a7cd75801f575b4318 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_16048576_0
26e24e807b654f89ebbcb9dcdf1ff53a ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_3145728_0
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4d3a38f17b00272fac92e73e14cc4f4d ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_3145728_0
b83080f49b43d74fabee20bf629738e2 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_38145728_0
e37470532b01f46e213eecb023f2c075 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_4194304_0
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29b6319884c87eac2dc10e4670576bc5 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/15.npy
12b036c64fdd762fb72478ad4c5b76f2 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_20.weight_35000000_0
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2ef9a3cc86ef7009208d13d17d14b072 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_15000000_0
662ae59409f54e143738aeb6b26c02ab ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_33145728_0
33515f75e36ad4deb25269084b397ac6 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_0_0
9748c2a81d508b92834c7c25d1cdcefb ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_30000000_0
d4a709eb503e524461e7b94de5a12263 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_19.weight_5000000_0
be0e93562e96a66a6718026616c50a3a ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_21048576_0
806dfb03fa65e6a9e7e154fdc934b925 ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_9.weight_16048576_0
e2143d80b2a7860c80ab6ebc770daa2b ./model/model_weights/sharded/model/model.sparse_arch.embedding_bag_collection.embedding_bags.t_cat_21.weight_30000000_0
cabb6b0d784a9d74192ef029f53309d4 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/4.npy
4b8e79310e06168422e6aa7f1b66f6ae ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/13.npy
49acd882a1b742af1743922f9409fc1e ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/9.npy
b1ce2de05b791c1ddb36e0e573a75d93 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/11.npy
f9acdc32bd6b766358be846d34b7dd19 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/14.npy
45026929433aa879157e9b4f033c75b2 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/22.npy
0ab3a06e2b648cf574d1235d71ebb006 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/19.npy
cdf7af87cbc7e9b468c0be46b1767601 ./criteo/day23/fp32/day_23_dense.npy
3f8626a163420fc26c35c82b5b42e7ee ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/21.npy
08e251af4f3d1e8771ea15e405f39600 ./criteo/day23/raw_data
7c753b13d54ad9e3e6c5e73719622201 ./criteo/day23/fp32/day_23_sparse_multi_hot_unpacked/20.npy
@viraatc The below tar file has the logs and also the calibration.cache file https://drive.google.com/file/d/1wJG5wqzH3IP7pLE-jGTmKptLEwgtfxzN/view?usp=sharing
@viraatc Can you please confirm if the checksums are as expected?
While running the Nvidia code for DLRMv2 on a 4090 GPU with batch size 1400, we are seeing the below accuracy which is lower than expected. Can someone help us if we are missing something? We have tried on 2 systems (both with 4090) and using the same as well as modified Nvidia docker containers and getting the same result everywhere. Will try to run on a different GPU.
Full console log is here:
Preprocessed dataset looks fine as the checksum is matching the required