Support Early Exit Loss and/or Layer Dropout

recipes/dev/7B_full_early_exit.yaml

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+# Config for multi-device full finetuning with early exit loss and/or layer dropout
+# in dev/early_exit_finetune_distributed.py using a Llama2 7B model on a small TOPv2
+# instruction set.
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Llama-2-7b-hf --output-dir /tmp/Llama-2-7b-hf --hf-token <HF_TOKEN>
+#
+# To launch on 4 devices, run the following command from root:
+#   tune run --nnodes 1 --nproc_per_node 4 dev/early_exit_finetune_distributed --config recipes/dev/7B_full_early_exit.yaml
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nnodes 1 --nproc_per_node 4 dev/early_exit_finetune_distributed --config recipes/dev/7B_full_early_exit.yaml checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# To reproduce experiments of various papers that use early exit loss and/or layer dropout:
+# - LayerSkip (https://arxiv.org/abs/2404.16710) on TOPv2:
+#   tune run --nnodes 1 --nproc_per_node 4 dev/early_exit_finetune_distributed --config recipes/dev/7B_full_early_exit.yaml early_exit_loss.scale=1.0 early_exit_loss.curriculum=gradual early_exit_loss.scale_type=l layer_dropout.prob=0.2 layer_dropout.scale=exp
+#
+# - LITE (https://arxiv.org/abs/2310.18581):
+#   tune run --nnodes 1 --nproc_per_node 4 dev/early_exit_finetune_distributed --config recipes/dev/7B_full_early_exit.yaml layer_dropout=null early_exit_loss.layers=8,12,16,20,24,28 early_exit_loss.scale_type=one early_exit_loss.curriculum=null epochs=5
+#
+# - LayerDrop (https://arxiv.org/abs/1909.11556):
+#   tune run --nnodes 1 --nproc_per_node 4 dev/early_exit_finetune_distributed --config recipes/dev/7B_full_early_exit.yaml early_exit_loss=null layer_dropout.prob=0.2 layer_dropout.layers=1::2
+#
+# - Progressive Layer Dropping (https://arxiv.org/abs/2010.13369) (The paper also implements a curriculum for layer drop probability which is not yet implemented.):
+#   tune run --nnodes 1 --nproc_per_node 4 dev/early_exit_finetune_distributed --config recipes/dev/7B_full_early_exit.yaml early_exit_loss=null layer_dropout.prob=0.5 layer_dropout.scale=exp
+#
+# This config works best for distributed training, hence when the model is being fine-tuned on 2+ GPUs.
+#
+
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama2.llama2_tokenizer
+  path: /tmp/Llama-2-7b-hf/tokenizer.model
+  max_seq_len: null
+
+# Dataset
+dataset:
+  _component_: torchtune.datasets.instruct_dataset
+  source: WillHeld/top_v2
+  split: train
+  column_map:
+    input: utterance
+    output: semantic_parse
+
+seed: null
+shuffle: True
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama2.llama2_7b
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Llama-2-7b-hf
+  checkpoint_files: [
+    pytorch_model-00001-of-00002.bin,
+    pytorch_model-00002-of-00002.bin
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Llama-2-7b-hf
+  model_type: LLAMA2
+resume_from_checkpoint: False
+
+# Fine-tuning arguments
+batch_size: 8
+epochs: 1
+optimizer:
+  _component_: torch.optim.AdamW
+  fused: True
+  lr: 2e-5
+loss:
+  _component_: torch.nn.CrossEntropyLoss
+max_steps_per_epoch: null
+gradient_accumulation_steps: 1  # Use to increase virtual batch size
+compile: False  # pytorch compile, set to true for better perf/memory
+optimizer_in_bwd: False  # True saves memory. Requires gradient_accumulation_steps=1
+
+# Training env
+device: cuda
+
+# Memory management
+enable_activation_checkpointing: True  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+
+# Reduced precision
+dtype: bf16
+
+# Logging
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+output_dir: /tmp/topv2-llama2-finetune
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+# Profiler (disabled)
+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: 3
+  active_steps: 2
+  num_cycles: 1
+
+# Early Exit Loss
+early_exit_loss:
+  layers: ":"
+  curriculum: "gradual"
+  scale_type: "sum_l"
+  scale: 1.0
+
+# Layer Dropout
+layer_dropout:
+  prob: 0.2
+  layers: ":"
+  layers_scale: "exp"
+  disable_on_eval: True