Create ft-config.yaml

end-to-end-use-cases/data-tool/scripts/finetuning/ft-config.yaml

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+# Config for multi-device full finetuning in full_finetune_distributed.py
+# using a Llama3.1 70B Instruct model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Meta-Llama-3.1-70B-Instruct --output-dir /tmp/Meta-Llama-3.1-70B-Instruct --ignore-patterns "original/consolidated*"
+#
+# To launch on 8 devices, run the following command from root:
+#   tune run --nproc_per_node 8 full_finetune_distributed --config llama3_1/70B_full
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nproc_per_node 8 full_finetune_distributed --config llama3_1/70B_full checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config is only tested on an 8xA100 machine.
+#
+
+output_dir: /tmp/torchtune/llama3_1_70B/full # /tmp may be deleted by your system. Change it to your preference.
+seed: 69
+shuffle: True
+# Parallelism
+tensor_parallel_dim: 1
+tensor_parallel_plan:
+  _component_: torchtune.models.llama3.base_llama_tp_plan
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /tmp/Meta-Llama-3.1-70B-Instruct/original/tokenizer.model
+  max_seq_len: 16384
+
+dataset:
+  _component_: toolcall.custom_dataset
+  #data_files: "train_data.json"
+  #split: "train"
+
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_1.llama3_1_70b
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Meta-Llama-3.1-70B-Instruct/
+  checkpoint_files:
+    filename_format: model-{}-of-{}.safetensors
+    max_filename: "00030"
+  recipe_checkpoint: null
+  output_dir: ${output_dir}
+  model_type: LLAMA3
+resume_from_checkpoint: False
+
+# Fine-tuning arguments
+batch_size: 2
+epochs: 1
+
+optimizer:
+  _component_: torch.optim.AdamW
+  lr: 2e-5
+  # Note: highly recommended to use fused=True optimizer flag
+  # with CPU offload for faster optimizer step.
+  fused: False
+
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+max_steps_per_epoch: null
+gradient_accumulation_steps: 1  # Use to increase effective batch size
+
+
+# Training env
+device: cuda
+
+# Memory management
+enable_activation_checkpointing: True  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+custom_sharded_layers: ['tok_embeddings', 'output']  # Layers to shard separately (useful for large vocab size models). Lower Memory, but lower speed.
+fsdp_cpu_offload: True
+clip_grad_norm: null
+compile: False  # torch.compile the model + loss, True increases speed + decreases memory
+optimizer_in_bwd: False  # True saves memory. Requires gradient_accumulation_steps=1
+
+# Reduced precision
+dtype: bf16
+
+# Logging
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}/logs
+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