chore: wip torchtune, dataset merger

train_torchtune.yaml

@@ -0,0 +1,120 @@
+# Config for single device LoRA finetuning in lora_finetune_single_device.py
+# using a Qwen2.5 7B model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download Qwen/Qwen2.5-7B-Instruct --output-dir /tmp/Qwen2.5-7B-Instruct
+#
+# To launch on a single device, run the following command from root:
+#   tune run lora_finetune_single_device --config qwen2_5/7B_lora_single_device
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run lora_finetune_single_device --config qwen2_5/7B_lora_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config works only for training on single device.
+
+output_dir: /home/mira/models/qwen2_5_7B_tune/lora_single_device # /tmp may be deleted by your system. Change it to your preference.
+
+# Model Arguments
+model:
+  _component_: torchtune.models.qwen2_5.lora_qwen2_5_7b_base
+  lora_attn_modules: ["q_proj", "k_proj", "v_proj", "output_proj"]
+  apply_lora_to_mlp: True
+  apply_lora_to_output: True
+  lora_rank: 32 # higher increases accuracy and memory
+  lora_alpha: 64 # usually alpha=2*rank
+  lora_dropout: 0.05
+  quantize_base: True
+
+tokenizer:
+  _component_: torchtune.models.qwen2_5.qwen2_5_tokenizer
+  path: /home/mira/models/Qwen2.5-7B-Base/vocab.json
+  merges_file: /home/mira/models/Qwen2.5-7B-Base/merges.txt
+  max_seq_len: 16384
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /home/mira/models/Qwen2.5-7B-Base
+  checkpoint_files:
+    [
+      model-00001-of-00004.safetensors,
+      model-00002-of-00004.safetensors,
+      model-00003-of-00004.safetensors,
+      model-00004-of-00004.safetensors,
+    ]
+  recipe_checkpoint: null
+  output_dir: ${output_dir}
+  model_type: QWEN2
+save_every_n_steps: 100
+resume_from_checkpoint: False
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_cleaned_dataset
+  packed: True # True increases speed
+seed: 42
+shuffle: False
+batch_size: 1
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  fused: True
+  weight_decay: 0.01
+  lr: 1e-4
+lr_scheduler:
+  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 32 # Use to increase effective batch size
+clip_grad_norm: null
+compile: True # torch.compile the model + loss, True increases speed + decreases memory
+
+# Logging
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}/logs
+log_every_n_steps: 5
+log_peak_memory_stats: True
+
+# Environment
+device: cuda
+dtype: fp16
+
+# Activations Offloading
+enable_activation_checkpointing: True # True reduces memory
+enable_activation_offloading: True # True reduces memory
+
+# Show case the usage of pytorch profiler
+# Set enabled to False as it's only needed for debugging training
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 5
+  active_steps: 2
+  num_cycles: 1