Comments (7)
fyi @blester125
from t5x.
can you paste in your config?
from t5x.
python3 -m t5x.eval \
--gin_search_paths="${T5X_DIR},${FLAXFORMER_DIR},${PROMPT_DIR}" \
--gin_file="prompt_tuning/configs/models/t5_1_1_xl_prompt.gin" \
--gin_file="prompt_tuning/configs/runs/prompt_eval.gin" \
--gin.MIXTURE_OR_TASK_NAME="'glue_rte_32_shot_32_seed'" \
--gin.MIXTURE_OR_TASK_MODULE="'prompt_tuning.data.few_glue'" \
--gin.TASK_FEATURE_LENGTHS="{'inputs': 512, 'targets': 8}" \
--gin.CHECKPOINT_PATH="'${PRETRAINED_MODEL}'" \
--gin.EVAL_OUTPUT_DIR="'${EVAL_DIR}'" \
--gin.utils.DatasetConfig.split="'validation'" \
--gin.utils.DatasetConfig.batch_size="128" \
--gin.USE_CACHED_TASKS="False" \
--gin.partitioning.ModelBasedPjitPartitioner.model_parallel_submesh="(4, 4, 1, 2)" \
--gin.PROMPT_FILE="'${PROMPT_FILE}'" \
--tfds_data_dir=${TFDS_DATA_DIR}
I also tried removing the gin.partitioning.ModelBasedPjitPartitioner.model_parallel_submesh value, but got the same error. Thanks
from t5x.
This looks like a bug in the prompt-tuning configs -- can you file a bug there?
A given model_parallel_submesh
work for every TPU size. I'd recommend using num_partitions
instead.
FYI @blester125 @nconstant-google
from t5x.
Yup, I can file a bug there and try num_partitions
I used that same model_parallel_submesh
value for training on the same machine and it worked. Should it work for inference if it worked for training?
from t5x.
Yes, that likely isn't the issue -- although I would recommend @blester125 update their example configs regardless.
Could you log the trainstate that is being passed to
File "/home/dptam/.local/lib/python3.8/site-packages/t5x/utils.py", line 365, in __init__
self.train_state_axes = partitioner.get_mesh_axes(
and share it?
from t5x.
This is the print for self.global_train_state_shape
on line 365 being passed into partitioner.get_mesh_axes
pre_cross_attention_layer_norm: {
scale: ShapeDtypeStruct(shape=(2048,), dtype=float32),
},
pre_mlp_layer_norm: {
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self_attention: {
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out: {
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prompt: {
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token_embedder: {
embedding_axes: AxisMetadata(names=('vocab', 'embed')),
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}), flax_mutables=FrozenDict({}))
from t5x.
Related Issues (20)
- Missing import in t5x/checkpoints.py
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from t5x.