Datasets:

---

LunaNguyenX

/

Pretrain_language_model-1BL3-competesmoe7

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
profile: null
name: string
save_dir: string
reset: int64
log: string
save_interval: string
wandb_save_interval: string
seed: string
gpu: string
keep_alive: int64
sweep_id_for_grid_search: int64
restore: string
wandb_bug_workaround: int64
wandb_sync_checkpoints: int64
batch_size: int64
lr: double
min_lr_multiplier: double
wd: double
lr_warmup: int64
test_interval: int64
n_microbatch: string
per_device_batch_size: string
lr_sched.steps: string
lr_sched.gamma: double
lr_sched.type: string
length_bucketed_sampling: int64
grad_clip: string
test_batch_size: string
val_log_details: int64
reg_scales: string
reg_lin_decay: string
reg: double
optimizer: string
adam.betas: string
adam.eps: double
stop_after: string
amp: int64
bfloat16: int64
nan_detect: int64
max_length_per_batch: string
log_grad_norms: int64
speedtest: string
dump_logs: int64
debug_plot_interval: string
lm.trafo.context_blocks: int64
lm.trafo.test_context_blocks: string
lm.trafo.same_length_eval: int64
lm.trafo.same_length: int64
lm.trafo.last_layer_context: int64
lm.trafo.xl_init: int64
lm.trafo.norm_input: int64
rope.rotate_fraction: double
rope.base: double
pkm.n_heads: int64
moe.n_experts: int64
moe.expert_size: int64
moe_name: string
moe.selection_mode: string
moe.perplexity_reg: double
moe.perplexity_reg_mode: string
moe.att.perplexity_reg_mode: string
moe.activation_after_topk: int64
moe.att.expert_size: int64
moe.topk: int64
moe.bias: int64
moe.sel_bias: int64
moe.dropout_factor: double
moe.drop_expert: double
moe.sync_distributed: int64
moe.init_scale: double
moe.att.n_experts: int64
moe.att.enable: int64
moe.att.q_expert: int64
moe.att.k_expert: int64
moe.att.v_expert: int64
moe.att.o_expert: int64
moe.att.k: int64
moe.att.v_size: string
moe.att.same_sel: int64
moe.att.expert_dropout: string
moe.att.selection_mode: string
moe.att.perplexity_reg: string
moe.att.drop_expert: string
moe.att.separate_kq_sel: int64
moe.att.norm_init: int64
moe.att.dropout: double
moe.att.selection_dropout: double
moe.nonorm: int64
in_topk: int64
balance_affinity: int64
is_cosine: int64
is_norm_weight: int64
norm_softmax: int64
norm_sigmoid: int64
moa.cvloss: double
moa.switchloss: double
moa.zloss: double
balance_loss_coef: double
balance_loss_coef_comp: double
router_z_loss_coef: double
router_loss_coef: double
max_compete_in_iter: int64
warm_up: double
rate_flip: double
router_theta: double
scale_weight: double
hybrid: int64
tribrid: int64
moa.miloss: double
sut.sample_topk: int64
sut.max_relative_positions: int64
sut.drop_gate: double
moe.selection_dropout: double
moe.layer_std_constant: double
transformer.universal.group_size: int64
transformer.universal.group_type: string
transformer.embedding_scale: string
transformer.topk_value: int64
transformer.activation: string
transformer.p_drop_layer: double
transformer.head_projection_size: string
transformer.act_loss: double
transformer.plot_head_details: int64
lm.trafo.force_out_norm: int64
plot.n_steps: int64
dump_validation_plots: string
details_log_interval: string
lm.state_drop_probability: double
lm.unroll: int64
lm.unroll_eval: string
lm.example_context: int64
lm.example_window: int64
lm.eval.blimp.batch_mul: int64
lm.eval.enabled: int64
lm.eval.lambada.enabled: int64
lm.eval.cbt.batch_mul: int64
lm.eval.cbt.length_limit: string
lm.eval.cbt.enabled: int64
lm.eval.cbt.end_only: int64
lm.eval.blimp.enabled: int64
lm.eval.hellaswag.enabled: int64
lm.eval.hellaswag.batch_mul: int64
lm.eval.piqa.enabled: int64
lm.eval.piqa.batch_mul: int64
lm.eval.ai2arc.enabled: int64
lm.eval.ai2arc.batch_mul: int64
lm.eval.mmlu.enabled: int64
lm.eval.openbookqa.enabled: int64
lm.eval.race.enabled: int64
lm.eval.siqa.enabled: int64
lm.eval.winogrande.enabled: int64
lm.eval.commonsenseqa.enabled: int64
sentencepiece.n_pieces: int64
lmds.valid_ratio: double
thestack.languages: string
state_size: int64
task: string
dropout: double
embedding_size: string
transformer.n_heads: int64
transformer.variant: string
transformer.ff_multiplier: double
transformer.encoder_n_layers: int64
transformer.attention_dropout: double
load_pretrained_model: null
test_pretrained: int64
train_baseline: int64
test_only: int64
save_name_logs: string
fs_cache_pattern: string
vs
args: struct<profile: null, name: string, save_dir: string, reset: int64, log: string, save_interval: string, wandb_save_interval: string, seed: string, gpu: string, keep_alive: int64, sweep_id_for_grid_search: int64, restore: string, wandb_bug_workaround: int64, wandb_sync_checkpoints: int64, batch_size: int64, lr: double, min_lr_multiplier: double, wd: double, lr_warmup: int64, test_interval: int64, n_microbatch: string, per_device_batch_size: string, lr_sched.steps: string, lr_sched.gamma: double, lr_sched.type: string, length_bucketed_sampling: int64, grad_clip: string, test_batch_size: string, val_log_details: int64, reg_scales: string, reg_lin_decay: string, reg: double, optimizer: string, adam.betas: string, adam.eps: double, stop_after: string, amp: int64, bfloat16: int64, nan_detect: int64, max_length_per_batch: string, log_grad_norms: int64, speedtest: string, dump_logs: int64, debug_plot_interval: string, lm.trafo.context_blocks: int64, lm.trafo.test_context_blocks: string, lm.trafo.same_length_eval: int64, lm.trafo.same_length: int64, lm.trafo.last_layer_context: int64, lm.trafo.xl_init: int64, lm.trafo.norm_input: int64, rope.rotate_fraction: double, rope.base: double, pkm.n_heads: int64, moe.n_experts: int64, moe.expert_size: int64, moe_name: string, moe.selection_mode: string, moe.perplexity_reg: double, moe.perplexity_reg_mode: string, moe.att.perplexity_reg_mode: string, moe.activation_after_topk: int64, moe.att.expert_size: int64, moe.topk: int64, moe.bias: int64, moe.sel_bias: int64, moe.dropout_factor: double, moe.drop_expert: double, moe.sync_distributed: int64, moe.init_scale: double, moe.att.n_experts: int64, moe.att.enable: int64, moe.att.q_expert: int64, moe.att.k_expert: int64, moe.att.v_expert: int64, moe.att.o_expert: int64, moe.att.k: int64, moe.att.v_size: string, moe.att.same_sel: int64, moe.att.expert_dropout: string, moe.att.selection_mode: string, moe.att.perplexity_reg: string, moe.att.drop_expert: string, moe.att.separate_kq_sel: int64, moe.att.norm_init: int64, moe.att.dropout: double, moe.att.selection_dropout: double, moe.nonorm: int64, in_topk: int64, balance_affinity: int64, is_cosine: int64, is_norm_weight: int64, norm_softmax: int64, norm_sigmoid: int64, moa.cvloss: double, moa.switchloss: double, moa.zloss: double, balance_loss_coef: double, balance_loss_coef_comp: double, router_z_loss_coef: double, router_loss_coef: double, max_compete_in_iter: int64, warm_up: double, rate_flip: double, router_theta: double, scale_weight: double, hybrid: int64, tribrid: int64, moa.miloss: double, sut.sample_topk: int64, sut.max_relative_positions: int64, sut.drop_gate: double, moe.selection_dropout: double, moe.layer_std_constant: double, transformer.universal.group_size: int64, transformer.universal.group_type: string, transformer.embedding_scale: string, transformer.topk_value: int64, transformer.activation: string, transformer.p_drop_layer: double, transformer.head_projection_size: string, transformer.act_loss: double, transformer.plot_head_details: int64, lm.trafo.force_out_norm: int64, plot.n_steps: int64, dump_validation_plots: string, details_log_interval: string, lm.state_drop_probability: double, lm.unroll: int64, lm.unroll_eval: string, lm.example_context: int64, lm.example_window: int64, lm.eval.blimp.batch_mul: int64, lm.eval.enabled: int64, lm.eval.lambada.enabled: int64, lm.eval.cbt.batch_mul: int64, lm.eval.cbt.length_limit: string, lm.eval.cbt.enabled: int64, lm.eval.cbt.end_only: int64, lm.eval.blimp.enabled: int64, lm.eval.hellaswag.enabled: int64, lm.eval.hellaswag.batch_mul: int64, lm.eval.piqa.enabled: int64, lm.eval.piqa.batch_mul: int64, lm.eval.ai2arc.enabled: int64, lm.eval.ai2arc.batch_mul: int64, lm.eval.mmlu.enabled: int64, lm.eval.openbookqa.enabled: int64, lm.eval.race.enabled: int64, lm.eval.siqa.enabled: int64, lm.eval.winogrande.enabled: int64, lm.eval.commonsenseqa.enabled: int64, sentencepiece.n_pieces: int64, lmds.valid_ratio: double, thestack.languages: string, state_size: int64, task: string, dropout: double, embedding_size: string, transformer.n_heads: int64, transformer.variant: string, transformer.ff_multiplier: double, transformer.encoder_n_layers: int64, transformer.attention_dropout: double, load_pretrained_model: null, test_pretrained: int64, train_baseline: int64, test_only: int64, save_name_logs: string, fs_cache_pattern: string>
state: struct<iter: int64, best_losses: struct<val: struct<iter: int64, loss: double, accuracy: double>, lambada: struct<iter: int64, loss: double, accuracy: double>>, best_accuracies: struct<val: struct<iter: int64, loss: double, accuracy: double>, lambada: struct<iter: int64, loss: double, accuracy: double>>, epoch: int64>
log_history: list<item: null>
Total time: double
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 559, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              profile: null
              name: string
              save_dir: string
              reset: int64
              log: string
              save_interval: string
              wandb_save_interval: string
              seed: string
              gpu: string
              keep_alive: int64
              sweep_id_for_grid_search: int64
              restore: string
              wandb_bug_workaround: int64
              wandb_sync_checkpoints: int64
              batch_size: int64
              lr: double
              min_lr_multiplier: double
              wd: double
              lr_warmup: int64
              test_interval: int64
              n_microbatch: string
              per_device_batch_size: string
              lr_sched.steps: string
              lr_sched.gamma: double
              lr_sched.type: string
              length_bucketed_sampling: int64
              grad_clip: string
              test_batch_size: string
              val_log_details: int64
              reg_scales: string
              reg_lin_decay: string
              reg: double
              optimizer: string
              adam.betas: string
              adam.eps: double
              stop_after: string
              amp: int64
              bfloat16: int64
              nan_detect: int64
              max_length_per_batch: string
              log_grad_norms: int64
              speedtest: string
              dump_logs: int64
              debug_plot_interval: string
              lm.trafo.context_blocks: int64
              lm.trafo.test_context_blocks: string
              lm.trafo.same_length_eval: int64
              lm.trafo.same_length: int64
              lm.trafo.last_layer_context: int64
              lm.trafo.xl_init: int64
              lm.trafo.norm_input: int64
              rope.rotate_fraction: double
              rope.base: double
              pkm.n_heads: int64
              moe.n_experts: int64
              moe.expert_size: int64
              moe_name: string
              moe.selection_mode: string
              moe.perplexity_reg: double
              moe.perplexity_reg_mode: string
              moe.att.perplexity_reg_mode: string
              moe.activation_after_topk: int64
              moe.att.expert_size: int64
              moe.topk: int64
              moe.bias: int64
              moe.sel_bias: int64
              moe.dropout_factor: double
              moe.drop_expert: double
              moe.sync_distributed: int64
              moe.init_scale: double
              moe.att.n_experts: int64
              moe.att.enable: int64
              moe.att.q_expert: int64
              moe.att.k_expert: int64
              moe.att.v_expert: int64
              moe.att.o_expert: int64
              moe.att.k: int64
              moe.att.v_size: string
              moe.att.same_sel: int64
              moe.att.expert_dropout: string
              moe.att.selection_mode: string
              moe.att.perplexity_reg: string
              moe.att.drop_expert: string
              moe.att.separate_kq_sel: int64
              moe.att.norm_init: int64
              moe.att.dropout: double
              moe.att.selection_dropout: double
              moe.nonorm: int64
              in_topk: int64
              balance_affinity: int64
              is_cosine: int64
              is_norm_weight: int64
              norm_softmax: int64
              norm_sigmoid: int64
              moa.cvloss: double
              moa.switchloss: double
              moa.zloss: double
              balance_loss_coef: double
              balance_loss_coef_comp: double
              router_z_loss_coef: double
              router_loss_coef: double
              max_compete_in_iter: int64
              warm_up: double
              rate_flip: double
              router_theta: double
              scale_weight: double
              hybrid: int64
              tribrid: int64
              moa.miloss: double
              sut.sample_topk: int64
              sut.max_relative_positions: int64
              sut.drop_gate: double
              moe.selection_dropout: double
              moe.layer_std_constant: double
              transformer.universal.group_size: int64
              transformer.universal.group_type: string
              transformer.embedding_scale: string
              transformer.topk_value: int64
              transformer.activation: string
              transformer.p_drop_layer: double
              transformer.head_projection_size: string
              transformer.act_loss: double
              transformer.plot_head_details: int64
              lm.trafo.force_out_norm: int64
              plot.n_steps: int64
              dump_validation_plots: string
              details_log_interval: string
              lm.state_drop_probability: double
              lm.unroll: int64
              lm.unroll_eval: string
              lm.example_context: int64
              lm.example_window: int64
              lm.eval.blimp.batch_mul: int64
              lm.eval.enabled: int64
              lm.eval.lambada.enabled: int64
              lm.eval.cbt.batch_mul: int64
              lm.eval.cbt.length_limit: string
              lm.eval.cbt.enabled: int64
              lm.eval.cbt.end_only: int64
              lm.eval.blimp.enabled: int64
              lm.eval.hellaswag.enabled: int64
              lm.eval.hellaswag.batch_mul: int64
              lm.eval.piqa.enabled: int64
              lm.eval.piqa.batch_mul: int64
              lm.eval.ai2arc.enabled: int64
              lm.eval.ai2arc.batch_mul: int64
              lm.eval.mmlu.enabled: int64
              lm.eval.openbookqa.enabled: int64
              lm.eval.race.enabled: int64
              lm.eval.siqa.enabled: int64
              lm.eval.winogrande.enabled: int64
              lm.eval.commonsenseqa.enabled: int64
              sentencepiece.n_pieces: int64
              lmds.valid_ratio: double
              thestack.languages: string
              state_size: int64
              task: string
              dropout: double
              embedding_size: string
              transformer.n_heads: int64
              transformer.variant: string
              transformer.ff_multiplier: double
              transformer.encoder_n_layers: int64
              transformer.attention_dropout: double
              load_pretrained_model: null
              test_pretrained: int64
              train_baseline: int64
              test_only: int64
              save_name_logs: string
              fs_cache_pattern: string
              vs
              args: struct<profile: null, name: string, save_dir: string, reset: int64, log: string, save_interval: string, wandb_save_interval: string, seed: string, gpu: string, keep_alive: int64, sweep_id_for_grid_search: int64, restore: string, wandb_bug_workaround: int64, wandb_sync_checkpoints: int64, batch_size: int64, lr: double, min_lr_multiplier: double, wd: double, lr_warmup: int64, test_interval: int64, n_microbatch: string, per_device_batch_size: string, lr_sched.steps: string, lr_sched.gamma: double, lr_sched.type: string, length_bucketed_sampling: int64, grad_clip: string, test_batch_size: string, val_log_details: int64, reg_scales: string, reg_lin_decay: string, reg: double, optimizer: string, adam.betas: string, adam.eps: double, stop_after: string, amp: int64, bfloat16: int64, nan_detect: int64, max_length_per_batch: string, log_grad_norms: int64, speedtest: string, dump_logs: int64, debug_plot_interval: string, lm.trafo.context_blocks: int64, lm.trafo.test_context_blocks: string, lm.trafo.same_length_eval: int64, lm.trafo.same_length: int64, lm.trafo.last_layer_context: int64, lm.trafo.xl_init: int64, lm.trafo.norm_input: int64, rope.rotate_fraction: double, rope.base: double, pkm.n_heads: int64, moe.n_experts: int64, moe.expert_size: int64, moe_name: string, moe.selection_mode: string, moe.perplexity_reg: double, moe.perplexity_reg_mode: string, moe.att.perplexity_reg_mode: string, moe.activation_after_topk: int64, moe.att.expert_size: int64, moe.topk: int64, moe.bias: int64, moe.sel_bias: int64, moe.dropout_factor: double, moe.drop_expert: double, moe.sync_distributed: int64, moe.init_scale: double, moe.att.n_experts: int64, moe.att.enable: int64, moe.att.q_expert: int64, moe.att.k_expert: int64, moe.att.v_expert: int64, moe.att.o_expert: int64, moe.att.k: int64, moe.att.v_size: string, moe.att.same_sel: int64, moe.att.expert_dropout: string, moe.att.selection_mode: string, moe.att.perplexity_reg: string, moe.att.drop_expert: string, moe.att.separate_kq_sel: int64, moe.att.norm_init: int64, moe.att.dropout: double, moe.att.selection_dropout: double, moe.nonorm: int64, in_topk: int64, balance_affinity: int64, is_cosine: int64, is_norm_weight: int64, norm_softmax: int64, norm_sigmoid: int64, moa.cvloss: double, moa.switchloss: double, moa.zloss: double, balance_loss_coef: double, balance_loss_coef_comp: double, router_z_loss_coef: double, router_loss_coef: double, max_compete_in_iter: int64, warm_up: double, rate_flip: double, router_theta: double, scale_weight: double, hybrid: int64, tribrid: int64, moa.miloss: double, sut.sample_topk: int64, sut.max_relative_positions: int64, sut.drop_gate: double, moe.selection_dropout: double, moe.layer_std_constant: double, transformer.universal.group_size: int64, transformer.universal.group_type: string, transformer.embedding_scale: string, transformer.topk_value: int64, transformer.activation: string, transformer.p_drop_layer: double, transformer.head_projection_size: string, transformer.act_loss: double, transformer.plot_head_details: int64, lm.trafo.force_out_norm: int64, plot.n_steps: int64, dump_validation_plots: string, details_log_interval: string, lm.state_drop_probability: double, lm.unroll: int64, lm.unroll_eval: string, lm.example_context: int64, lm.example_window: int64, lm.eval.blimp.batch_mul: int64, lm.eval.enabled: int64, lm.eval.lambada.enabled: int64, lm.eval.cbt.batch_mul: int64, lm.eval.cbt.length_limit: string, lm.eval.cbt.enabled: int64, lm.eval.cbt.end_only: int64, lm.eval.blimp.enabled: int64, lm.eval.hellaswag.enabled: int64, lm.eval.hellaswag.batch_mul: int64, lm.eval.piqa.enabled: int64, lm.eval.piqa.batch_mul: int64, lm.eval.ai2arc.enabled: int64, lm.eval.ai2arc.batch_mul: int64, lm.eval.mmlu.enabled: int64, lm.eval.openbookqa.enabled: int64, lm.eval.race.enabled: int64, lm.eval.siqa.enabled: int64, lm.eval.winogrande.enabled: int64, lm.eval.commonsenseqa.enabled: int64, sentencepiece.n_pieces: int64, lmds.valid_ratio: double, thestack.languages: string, state_size: int64, task: string, dropout: double, embedding_size: string, transformer.n_heads: int64, transformer.variant: string, transformer.ff_multiplier: double, transformer.encoder_n_layers: int64, transformer.attention_dropout: double, load_pretrained_model: null, test_pretrained: int64, train_baseline: int64, test_only: int64, save_name_logs: string, fs_cache_pattern: string>
              state: struct<iter: int64, best_losses: struct<val: struct<iter: int64, loss: double, accuracy: double>, lambada: struct<iter: int64, loss: double, accuracy: double>>, best_accuracies: struct<val: struct<iter: int64, loss: double, accuracy: double>, lambada: struct<iter: int64, loss: double, accuracy: double>>, epoch: int64>
              log_history: list<item: null>
              Total time: double