enable LoRA + FSDP2

pyproject.toml

@@ -41,12 +41,13 @@ tune = "torchtune._cli.tune:main"
 dev = [
     "bitsandbytes>=0.43.0",
     "pre-commit",
-    "pytest",
+    "pytest==7.4.0",
     "pytest-cov",
     "pytest-mock",
     "pytest-integration",
     "tensorboard",
     "wandb",
+    "expecttest",
 ]
 
 [tool.setuptools.dynamic]

recipes/configs/dev/llama2/13B_lora_fsdp2.yaml

@@ -0,0 +1,86 @@
+# Config for multi-device LoRA in lora_finetune_distributed.py
+# using a Llama2 13B model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Llama-2-13b-hf --output-dir /tmp/Llama-2-13b-hf --hf-token <HF_TOKEN>
+#
+# To launch on 4 devices, run the following command from root:
+#   tune run --nnodes 1 --nproc_per_node 4 lora_finetune_distributed --config llama2/13B_lora
+#
+# 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 lora_finetune_distributed --config llama2/13B_lora checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config works best when the model is being fine-tuned on 2+ GPUs.
+# For single device LoRA finetuning please use 7B_lora_single_device.yaml
+# or 7B_qlora_single_device.yaml and update the model and checkpoints to
+# the 13B model.
+
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama2.lora_llama2_13b
+  lora_attn_modules: ['q_proj', 'v_proj', 'k_proj']
+  apply_lora_to_mlp: True
+  apply_lora_to_output: True
+  lora_rank: 8
+  lora_alpha: 16
+
+checkpointer:
+  _component_: torchtune.utils.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Llama-2-13b-hf/
+  checkpoint_files: [
+    pytorch_model-00001-of-00003.bin,
+    pytorch_model-00002-of-00003.bin,
+    pytorch_model-00003-of-00003.bin
+  ]
+  adapter_checkpoint: null
+  recipe_checkpoint: null
+  output_dir: /tmp/Llama-2-13b-hf/
+  model_type: LLAMA2
+resume_from_checkpoint: False
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama2.llama2_tokenizer
+  path: /tmp/Llama-2-13b-hf/tokenizer.model
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_cleaned_dataset
+  train_on_input: True
+seed: null
+shuffle: True
+batch_size: 2
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  weight_decay: 0.01
+  lr: 2e-4
+lr_scheduler:
+  _component_: torchtune.modules.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torch.nn.CrossEntropyLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 16
+
+# Logging
+output_dir: /tmp/lora_finetune_output
+metric_logger:
+  _component_: torchtune.utils.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+log_every_n_steps: 1
+log_peak_memory_stats: False
+
+# Environment
+device: cuda
+dtype: bf16
+enable_activation_checkpointing: False

recipes/configs/dev/llama2/70B_lora_fsdp2.yaml

@@ -0,0 +1,86 @@
+# Config for multi-device LoRA in lora_finetune_distributed.py
+# using a Llama2 70B model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Llama-2-70b-hf --output-dir /tmp/Llama-2-70b-hf --hf-token <HF_TOKEN>
+#
+# This config needs 8 GPUs to run
+#   # tune run --nproc_per_node 8 lora_finetune_distributed --config llama2/70B_lora
+#
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama2.lora_llama2_70b
+  lora_attn_modules: ['q_proj', 'v_proj', 'k_proj']
+  apply_lora_to_mlp: False
+  apply_lora_to_output: False
+  lora_rank: 16
+  lora_alpha: 32
+
+tokenizer:
+  _component_: torchtune.models.llama2.llama2_tokenizer
+  path: /tmp/Llama-2-70b-hf/tokenizer.model
+
+checkpointer:
+  _component_: torchtune.utils.FullModelHFCheckpointer
+  checkpoint_dir:  /tmp/Llama-2-70b-hf
+  checkpoint_files: [
+    pytorch_model-00001-of-00015.bin,
+    pytorch_model-00002-of-00015.bin,
+    pytorch_model-00003-of-00015.bin,
+    pytorch_model-00004-of-00015.bin,
+    pytorch_model-00005-of-00015.bin,
+    pytorch_model-00006-of-00015.bin,
+    pytorch_model-00007-of-00015.bin,
+    pytorch_model-00008-of-00015.bin,
+    pytorch_model-00009-of-00015.bin,
+    pytorch_model-00010-of-00015.bin,
+    pytorch_model-00011-of-00015.bin,
+    pytorch_model-00012-of-00015.bin,
+    pytorch_model-00013-of-00015.bin,
+    pytorch_model-00014-of-00015.bin,
+    pytorch_model-00015-of-00015.bin,
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Llama-2-70b-hf
+  model_type: LLAMA2
+resume_from_checkpoint: False
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_dataset
+  train_on_input: True
+seed: null
+shuffle: True
+batch_size: 2
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  weight_decay: 0.01
+  lr: 3e-4
+lr_scheduler:
+  _component_: torchtune.modules.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torch.nn.CrossEntropyLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 1
+
+# Logging
+output_dir: /tmp/lora_finetune_output
+metric_logger:
+  _component_: torchtune.utils.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+log_every_n_steps: 1
+log_peak_memory_stats: False
+
+# Environment
+device: cuda
+dtype: bf16
+enable_activation_checkpointing: True

recipes/configs/dev/llama2/7B_lora_fsdp2.yaml

@@ -0,0 +1,83 @@
+# Config for multi-device LoRA finetuning in lora_finetune_distributed.py
+# using a Llama2 7B model
+#
+# 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 2 devices, run the following command from root:
+#   tune run --nnodes 1 --nproc_per_node 2 lora_finetune_distributed --config llama2/7B_lora
+#
+# 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 2 lora_finetune_distributed --config llama2/7B_lora checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config works best when the model is being fine-tuned on 2+ GPUs.
+# For single device LoRA finetuning please use 7B_lora_single_device.yaml
+# or 7B_qlora_single_device.yaml
+
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama2.lora_llama2_7b
+  lora_attn_modules: ['q_proj', 'v_proj']
+  apply_lora_to_mlp: False
+  apply_lora_to_output: False
+  lora_rank: 8
+  lora_alpha: 16
+
+tokenizer:
+  _component_: torchtune.models.llama2.llama2_tokenizer
+  path: /tmp/Llama-2-7b-hf/tokenizer.model
+
+checkpointer:
+  _component_: torchtune.utils.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Llama-2-7b-hf
+  checkpoint_files: [
+    pytorch_model-00001-of-00002.bin,
+    pytorch_model-00002-of-00002.bin
+  ]
+  adapter_checkpoint: null
+  recipe_checkpoint: null
+  output_dir: /tmp/Llama-2-7b-hf
+  model_type: LLAMA2
+resume_from_checkpoint: False
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_cleaned_dataset
+  train_on_input: True
+seed: null
+shuffle: True
+batch_size: 2
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  weight_decay: 0.01
+  lr: 3e-4
+lr_scheduler:
+  _component_: torchtune.modules.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torch.nn.CrossEntropyLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 32
+
+# Logging
+output_dir: /tmp/lora_finetune_output
+metric_logger:
+  _component_: torchtune.utils.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+log_every_n_steps: 1
+log_peak_memory_stats: False
+
+# Environment
+device: cuda
+dtype: bf16
+enable_activation_checkpointing: False