tinygrad

mirror of https://github.com/tinygrad/tinygrad.git synced 2026-06-13 08:28:55 +08:00

Files

qazal 845814f396 revert buffer_view change (#9311 )

* Revert "BUFFER_VIEW is a node in the kernel graph + delete ViewOp (#9298)"

This reverts commit 3210b656b6.

* Revert "substitute ast from kernel op [pr] (#9293)"

This reverts commit 5a9c788ae6.

2025-03-01 11:00:12 +01:00

scripts

UNet3D MLPerf (#3470 )

2024-09-10 04:37:28 -04:00

training_submission_v4.0/tinycorp

copy mlperf 4.0 to mlperf 4.1 (#5614 )

2024-07-20 16:12:00 -04:00

training_submission_v4.1/tinycorp

update mlperf systems and copy 4.1 to 5.0 (#7004 )

2024-10-11 16:20:34 -04:00

training_submission_v5.0/tinycorp

RESET_STEP in bert setup and beam (#9248 )

2025-02-25 19:15:10 -05:00

dataloader.py

revert buffer_view change (#9311 )

2025-03-01 11:00:12 +01:00

helpers.py

CLANG -> CPU (#9189 )

2025-02-20 18:03:09 -05:00

initializers.py

Tuple -> tuple, List -> list [pr] (#8936 )

2025-02-06 14:21:19 -05:00

losses.py

[MLPerf][UNet3D] Add DICE loss + metrics (#4204 )

2024-04-17 20:09:33 -04:00

lr_schedulers.py

fp16 resnet (without expand backwards sum in float, doesn't work) (#3816 )

2024-03-28 01:25:37 -04:00

metrics.py

[MLPerf][UNet3D] Add DICE loss + metrics (#4204 )

2024-04-17 20:09:33 -04:00

model_eval.py

[MLPerf] Prepare openimages dataset script (#6747 )

2024-09-27 11:13:56 -04:00

model_spec.py

move globalcounters to ops (#2960 )

2024-01-01 14:21:02 -08:00

model_train.py

test for device mismatch [pr] (#9250 )

2025-02-26 13:06:33 +08:00

README

start on mlperf models

2023-05-10 16:30:49 -07:00

README

Each model should be a clean single file.
They are imported from the top level `models` directory

It should be capable of loading weights from the reference imp.

We will focus on these 5 models:

# Resnet50-v1.5 (classic) -- 8.2 GOPS/input
# Retinanet
# 3D UNET (upconvs)
# RNNT
# BERT-large (transformer)

They are used in both the training and inference benchmark:
https://mlcommons.org/en/training-normal-21/
https://mlcommons.org/en/inference-edge-30/
And we will submit to both.

NOTE: we are Edge since we don't have ECC RAM