Files
axolotl/deepspeed_configs/zero2_torch_compile.json
Wing Lian ccc94da8ad KD fix w/ online distillation (#2700) [skip ci]
* kd fixes

* fix collator setup

* fix input args

* better handling to drop string fields for kd with raw dataset

* kd trainer has kd temp as part of the init

* drop top_k before softmax

* simplfy and remove zscore

* WIP chunked KD loss with autograd wrapper

* more fixes and liger-type chunked loss

* collator cls for plugins

* remove debugging

* additional plugin collator kwargs, don't scale up kd loss by t^2

* don't need temp arg to distill method

* online kd wip

* add close to comment block

* suport sampling params/max new tokens

* handle when no custom collator is used in plugins

* logsumexp trick:

* fix check

* shift off the first empty token

* fix length of padding

* use max not min

* temp scale kd loss at end

* support for dynamic plugin training args mixins and symmetric kl

* chore: lint

* fix trainer callback base class

* Fix decay

* accept compressed responses for smaller wire payload

* post-rebase lint

* more KD updates

* increase hyperparams_count for gradients for added normalize_topk

* fix to remove attention_mask

* rename vars for consistency

* fix rebase issues

* default to dropping last batch in multipack batch sampler

* improve handling of train len

* init collator_cls_and_kwargs

* explicit drop_last=False when checking for multipack completeness

* use separate v2 loader for kd

* fix kd tests to use subprocess so it picks up kd training args

* default value for kd_beta arg

* use updated dataset for ci

* longer timeout for e2e
2025-06-17 12:09:13 -04:00

32 lines
642 B
JSON

{
"compile": {
"disable": false,
"backend": "inductor"
},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu"
},
"contiguous_gradients": true,
"overlap_comm": true
},
"bf16": {
"enabled": "auto"
},
"fp16": {
"enabled": "auto",
"auto_cast": false,
"loss_scale": 0,
"initial_scale_power": 32,
"loss_scale_window": 1000,
"hysteresis": 2,
"min_loss_scale": 1
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}