update documentation and config_utils

Signed-off-by: Mamta Singh <[email protected]>

QEfficient/finetune/configs/training.py

@@ -37,7 +37,7 @@ class train_config:
     save_model: bool = True
     save_metrics: bool = True  # saves training metrics to a json file for later plotting
     intermediate_step_save: int = 1000
-    batching_strategy: str = "padding"
+    batching_strategy: str = "packing"
 
     # TODO: vbaddi: Uncomment post adding qaic to Pytorch Profiler
     # flop_counter: bool = False # Enable flop counter to measure model throughput, can not be used with pytorch profiler at the same time.

QEfficient/finetune/utils/config_utils.py

@@ -21,7 +21,7 @@
 import QEfficient.finetune.configs.dataset_config as datasets
 from QEfficient.finetune.configs.peft_config import lora_config, prefix_config
 from QEfficient.finetune.configs.training import train_config
-from QEfficient.finetune.data.sampler import DistributedLengthBasedBatchSampler, LengthBasedBatchSampler
+from QEfficient.finetune.data.sampler import DistributedLengthBasedBatchSampler
 from QEfficient.finetune.dataset.dataset_config import DATASET_PREPROC
 
 
@@ -74,29 +74,25 @@ def generate_dataset_config(train_config, kwargs):
 def get_dataloader_kwargs(train_config, dataset, dataset_processer, mode):
     kwargs = {}
     batch_size = train_config.batch_size_training if mode == "train" else train_config.val_batch_size
-    if train_config.batching_strategy == "padding":
-        if train_config.enable_ddp:
-            kwargs["batch_sampler"] = DistributedLengthBasedBatchSampler(
-                dataset,
-                batch_size=batch_size,
-                rank=dist.get_rank(),
-                num_replicas=dist.get_world_size(),
+    if train_config.enable_ddp:
+        if train_config.context_length:
+            kwargs["sampler"] = data_utils.DistributedSampler(
+                dataset, num_replicas=dist.get_world_size(), rank=dist.get_rank(), shuffle=True
             )
+            kwargs["batch_size"] = batch_size
+            kwargs["drop_last"] = True
+            kwargs["collate_fn"] = default_data_collator
         else:
-            kwargs["batch_sampler"] = LengthBasedBatchSampler(dataset, batch_size, drop_last=True)
-        kwargs["collate_fn"] = DataCollatorForSeq2Seq(dataset_processer)
-    elif train_config.batching_strategy == "packing":
-        if train_config.enable_ddp:
-            kwargs["sampler"] = data_utils.DistributedSampler(
+            kwargs["batch_sampler"] = DistributedLengthBasedBatchSampler(
                 dataset,
+                batch_size=batch_size,
                 rank=dist.get_rank(),
                 num_replicas=dist.get_world_size(),
-                shuffle=mode == "train",
-                drop_last=True,
+                shuffle=False,
             )
+            kwargs["collate_fn"] = DataCollatorForSeq2Seq(dataset_processer)
+    else:
         kwargs["batch_size"] = batch_size
         kwargs["drop_last"] = True
         kwargs["collate_fn"] = default_data_collator
-    else:
-        raise ValueError(f"Unknown batching strategy: {train_config.batching_strategy}")
     return kwargs

docs/source/finetune.md

@@ -6,6 +6,9 @@ Same CLI can be used to run Finetuning on gpu by setting the device flag.(for fi
 ## Installation
 
 Same as QEfficient along with QAIC PyTorch Eager mode.
+
+For QEfficient Library : https://github.com/quic/efficient-transformers
+
 For torch_qaic, assuming QEfficient is already installed,
 ```bash
 pip install /opt/qti-aic/integrations/torch_qaic/py310/torch_qaic-0.1.0-cp310-cp310-linux_x86_64.whl
@@ -51,4 +54,13 @@ python -m QEfficient.cloud.finetune --device qaic:0 --use-peft --output_dir ./me
 ```python
 QAIC_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc-per-node 4 -m QEfficient.cloud.finetune --device qaic --enable_ddp --dist_backend qccl --num_epochs 2  --model_name "meta-llama/Llama-3.2-1B"
 ```
-**nproc-per-node is number of workers(gpus) running locally.
+**nproc-per-node is number of workers(gpus) running locally.
+
+## Visualization
+
+Tensorboard logs are generated inside runs/ directory with date and time stamp.
+to visualise the data,
+
+```python
+tensorboard --logdir runs/<file> --bind_all
+```