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handle batch size correchtly when using split and dispatch batches #3076

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handle batch size correchtly when using split and dispatch batches #3076

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7fd3d8a
handle batch size correchtly when using split and dispatch batches
winglian Aug 17, 2025
bb65157
fix conditional for None values
winglian Aug 17, 2025
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2 changes: 1 addition & 1 deletion src/axolotl/core/builders/causal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -424,7 +424,7 @@ def build_collator(
):
if training_args.pretraining:
if (
self.cfg.pretraining_sample_concatenation is False
not self.cfg.pretraining_sample_concatenation
or self.cfg.micro_batch_size > 1
):
return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
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14 changes: 14 additions & 0 deletions src/axolotl/core/trainers/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -272,6 +272,20 @@ def _get_dataloader(
num_workers=self.args.dataloader_num_workers,
rank=self.args.process_index,
)

if (
self.args.accelerator_config is not None
and self.args.accelerator_config.split_batches
and self.args.accelerator_config.dispatch_batches
):
if self.args.sample_packing and self.args.pretraining:
if not self.args.eval_sample_packing and not is_training:
dataloader_params["batch_size"] *= self.accelerator.num_processes
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Should this only be scaling by the total dp size?

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accelerator handles the actual dp sharding, so I don't think so? I'll test.

else:
dataloader_params["batch_size"] = self.accelerator.num_processes
elif not self.args.sample_packing and self.args.pretraining:
dataloader_params["batch_size"] *= self.accelerator.num_processes

Comment on lines +276 to +288
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⚠️ Potential issue

Critical: Reintroducing batch_size alongside a BatchSampler causes DataLoader errors

This block can set or mutate dataloader_params["batch_size"] even when a BatchSampler is already configured (e.g., eval with sample packing returns a MultipackBatchSampler). PyTorch DataLoader forbids passing both batch_sampler and batch_size. In the eval pretraining + sample_packing + eval_sample_packing=True path, we delete batch_size earlier then re-add it here, leading to a runtime error and/or undefined behavior.

Guard against the presence of batch_sampler before touching batch_size:

         if (
             self.args.accelerator_config is not None
             and self.args.accelerator_config.split_batches
             and self.args.accelerator_config.dispatch_batches
         ):
-            if self.args.sample_packing and self.args.pretraining:
-                if not self.args.eval_sample_packing and not is_training:
-                    dataloader_params["batch_size"] *= self.accelerator.num_processes
-                else:
-                    dataloader_params["batch_size"] = self.accelerator.num_processes
-            elif not self.args.sample_packing and self.args.pretraining:
-                dataloader_params["batch_size"] *= self.accelerator.num_processes
+            # Avoid conflicting with BatchSampler: DataLoader forbids both batch_size and batch_sampler.
+            if "batch_sampler" not in dataloader_params:
+                if self.args.sample_packing and self.args.pretraining:
+                    if not self.args.eval_sample_packing and not is_training:
+                        dataloader_params["batch_size"] *= self.accelerator.num_processes
+                    else:
+                        dataloader_params["batch_size"] = self.accelerator.num_processes
+                elif not self.args.sample_packing and self.args.pretraining:
+                    dataloader_params["batch_size"] *= self.accelerator.num_processes

Follow-up: If you actually need different behavior for the multipack case under split/dispatch, the adjustment must be applied inside the MultipackBatchSampler construction (e.g., its batch_size or batch_max_len), not by reintroducing batch_size here.

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
if (
self.args.accelerator_config is not None
and self.args.accelerator_config.split_batches
and self.args.accelerator_config.dispatch_batches
):
if self.args.sample_packing and self.args.pretraining:
if not self.args.eval_sample_packing and not is_training:
dataloader_params["batch_size"] *= self.accelerator.num_processes
else:
dataloader_params["batch_size"] = self.accelerator.num_processes
elif not self.args.sample_packing and self.args.pretraining:
dataloader_params["batch_size"] *= self.accelerator.num_processes
if (
self.args.accelerator_config is not None
and self.args.accelerator_config.split_batches
and self.args.accelerator_config.dispatch_batches
):
# Avoid conflicting with BatchSampler: DataLoader forbids both batch_size and batch_sampler.
if "batch_sampler" not in dataloader_params:
if self.args.sample_packing and self.args.pretraining:
if not self.args.eval_sample_packing and not is_training:
dataloader_params["batch_size"] *= self.accelerator.num_processes
else:
dataloader_params["batch_size"] = self.accelerator.num_processes
elif not self.args.sample_packing and self.args.pretraining:
dataloader_params["batch_size"] *= self.accelerator.num_processes

if self.args.sample_packing and (
(is_training and not self.args.pretraining)
or (not is_training and self.args.eval_sample_packing is not False)
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Empty file added src/axolotl/exception_handling.py
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