Tuning LLM from PTE

examples/llm_pte_finetuning/TARGETS

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+load("@fbcode_macros//build_defs:python_binary.bzl", "python_binary")
+load("@fbcode_macros//build_defs:python_library.bzl", "python_library")
+
+oncall("papaya_oncall")
+
+python_library(
+    name = "model_loading_lib",
+    srcs = [
+        "model_loading_lib.py",
+    ],
+    deps = [
+        "fbcode//caffe2:torch",
+        "fbcode//executorch/examples/llm_pte_finetuning:training_lib",
+        "fbcode//executorch/exir:lib",
+        "fbcode//executorch/extension/pybindings:aten_lib",  # @manual For PTE loader
+        "fbcode//pytorch/torchtune:lib",
+        "fbsource//third-party/pypi/blobfile:blobfile",  # @manual For tokenizer
+        "fbsource//third-party/pypi/omegaconf:omegaconf",
+        "fbsource//third-party/pypi/tiktoken:tiktoken",  # @manual For tokenizer
+    ],
+)
+
+python_library(
+    name = "training_lib",
+    srcs = [
+        "training_lib.py",
+    ],
+    deps = [
+        "fbcode//caffe2:torch",
+        "fbcode//executorch/extension/pybindings:aten_lib",  # @manual For PTE loader
+        "fbcode//pytorch/torchtune:lib",
+        "fbsource//third-party/pypi/blobfile:blobfile",  # @manual For tokenizer
+        "fbsource//third-party/pypi/tiktoken:tiktoken",  # @manual For tokenizer
+        "fbsource//third-party/pypi/tqdm:tqdm",
+    ],
+)
+
+python_binary(
+    name = "runner",
+    srcs = [
+        "runner.py",
+    ],
+    main_function = "executorch.examples.llm_pte_finetuning.runner.main",
+    deps = [
+        "fbcode//caffe2:torch",
+        "fbcode//executorch/examples/llm_pte_finetuning:training_lib",
+        "fbcode//pytorch/torchtune:lib",
+        "fbsource//third-party/pypi/blobfile:blobfile",  # @manual For tokenizer
+        "fbsource//third-party/pypi/omegaconf:omegaconf",
+        "fbsource//third-party/pypi/tiktoken:tiktoken",  # @manual For tokenizer
+        "fbsource//third-party/pypi/tqdm:tqdm",
+    ],
+)
+
+python_binary(
+    name = "model_exporter",
+    srcs = [
+        "model_exporter.py",
+    ],
+    main_function = "executorch.examples.llm_pte_finetuning.model_exporter.main",
+    deps = [
+        "fbcode//caffe2:torch",
+        "fbcode//executorch/examples/llm_pte_finetuning:model_loading_lib",  # @manual for model loading
+        "fbcode//executorch/examples/llm_pte_finetuning:training_lib",  # @manual for model exporting
+        "fbcode//pytorch/torchtune:lib",
+        "fbsource//third-party/pypi/blobfile:blobfile",  # @manual For tokenizer
+        "fbsource//third-party/pypi/omegaconf:omegaconf",
+        "fbsource//third-party/pypi/tiktoken:tiktoken",  # @manual For tokenizer
+    ],
+)

examples/llm_pte_finetuning/model_exporter.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+import argparse
+
+import torch
+from executorch.examples.llm_pte_finetuning.model_loading_lib import (
+    export_model_lora_training,
+    load_checkpoint,
+    setup_model,
+)
+
+from executorch.examples.llm_pte_finetuning.training_lib import (
+    get_dataloader,
+    TrainingModule,
+)
+
+from omegaconf import OmegaConf
+from torch.nn import functional as F
+from torchtune import config
+
+from torchtune.training import MODEL_KEY
+
+parser = argparse.ArgumentParser(
+    prog="ModelExporter",
+    description="Export a LoRA model to ExecuTorch.",
+    epilog="Model exported to be used for fine-tuning.",
+)
+
+parser.add_argument("--cfg", type=str, help="Path to the config file.")
+parser.add_argument("--output_file", type=str, help="Path to the output ET model.")
+
+
+def main() -> None:
+    args = parser.parse_args()
+    config_file = args.cfg
+    output_file = args.output_file
+    cfg = OmegaConf.load(config_file)
+    tokenizer = config.instantiate(
+        cfg.tokenizer,
+    )
+
+    loss_fn = config.instantiate(cfg.loss)
+
+    ds = config.instantiate(cfg.dataset, tokenizer)
+    train_set, val_set = torch.utils.data.random_split(ds, [0.8, 0.2])
+    train_dataloader = get_dataloader(cfg, train_set, tokenizer, loss_fn)
+
+    max_seq_len = cfg.tokenizer.max_seq_len
+
+    # Example inputs, needed for ET export.
+    batch = next(iter(train_dataloader))
+    tokens, labels = batch["tokens"], batch["labels"]
+    token_size = tokens.shape[1]
+    labels_size = labels.shape[1]
+
+    if token_size > max_seq_len:
+        tokens = tokens[:, :max_seq_len]
+    else:
+        tokens = F.pad(tokens, (0, max_seq_len - token_size), value=0)
+
+    if labels_size > max_seq_len:
+        labels = labels[:, :max_seq_len]
+    else:
+        labels = F.pad(labels, (0, max_seq_len - labels_size), value=0)
+
+    # Load pre-trained checkpoint.
+    checkpoint_dict = load_checkpoint(cfg=cfg)
+    model = setup_model(
+        # pyre-ignore
+        cfg=cfg,
+        base_model_state_dict=checkpoint_dict[MODEL_KEY],
+    )
+
+    training_module = TrainingModule(model, loss_fn)
+
+    # Export the model to ExecuTorch for training.
+    export_model_lora_training(training_module, (tokens, labels), output_file)
+
+
+if __name__ == "__main__":
+    main()

examples/llm_pte_finetuning/model_loading_lib.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+from typing import Any, Dict, Tuple
+
+import torch
+from executorch.examples.llm_pte_finetuning.training_lib import TrainingModule
+from executorch.exir import to_edge
+
+from omegaconf import DictConfig
+from torch.export import export, ExportedProgram
+from torch.export.experimental import _export_forward_backward
+from torch.nn.attention import sdpa_kernel, SDPBackend
+from torchtune import config
+from torchtune.modules.peft import get_adapter_params, set_trainable_params
+from torchtune.training.precision import get_dtype, set_default_dtype
+from torchtune.utils._device import get_device
+
+
+def load_checkpoint(cfg: Any) -> Dict[str, Any]:  # pyre-ignore[2]
+    """
+    Extract the checkpoint state from file and validate. This includes the
+    base model weights. If resume_from_checkpoint is True, this also includes
+    the adapter weights and recipe state
+    """
+    checkpointer = config.instantiate(
+        cfg.checkpointer,
+        resume_from_checkpoint=cfg.resume_from_checkpoint,
+    )
+    checkpoint_dict = checkpointer.load_checkpoint()
+    return checkpoint_dict
+
+
+def setup_model(
+    cfg: DictConfig,
+    base_model_state_dict: Dict[str, Any],
+) -> torch.nn.Module:
+    device = get_device(device=cfg.device)
+    dtype = get_dtype(cfg.dtype, device=device)
+    with set_default_dtype(dtype), device:
+        model = config.instantiate(cfg.model)
+
+    adapter_params = get_adapter_params(model)
+    set_trainable_params(model, adapter_params)
+    model.load_state_dict(base_model_state_dict, strict=False)
+    return model
+
+
+def export_model_lora_training(
+    model: TrainingModule,
+    example_args: Tuple[Any, ...],  # pyre-ignore[2]
+    output_file: str,
+) -> None:
+    """
+    Export model with LoRA model to executorch for training, only.
+    """
+
+    # 0. Mark the LoRA layers as trainable (requires_grad = True) in order
+    # to just export the backwards pass for these layers later in the
+    # export process.
+    set_trainable_params(model, get_adapter_params(model))
+
+    print("Exporting model with LoRA for training")
+    # 1. torch.export: Defines the program with the ATen operator set.
+
+    with sdpa_kernel([SDPBackend.MATH]):
+        exported_graph: ExportedProgram = export(model, example_args, strict=False)
+        print("Creating a joint forward-backwards graph for training")
+        joint_graph = _export_forward_backward(exported_graph)
+
+        # 2. to_edge: Make optimizations for Edge devices.
+        print("Lowering to edge dialect")
+        edge_program = to_edge(joint_graph)
+
+        print(edge_program._edge_programs["forward"].graph_module)
+
+    # 3. to_executorch: Convert the graph to an ExecuTorch program.
+    print("Exporting to executorch")
+    executorch_program = edge_program.to_executorch()
+    print(executorch_program.exported_program().graph_signature)
+    print(f"Saving to {output_file}")
+    with open(output_file, "wb") as file:
+        file.write(executorch_program.buffer)

examples/llm_pte_finetuning/phi3_alpaca_code_config.yaml

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+tokenizer:
+  _component_: torchtune.models.phi3.phi3_mini_tokenizer
+  path: /tmp/Phi-3-mini-4k-instruct/tokenizer.model
+  max_seq_len: 1024
+
+dataset:
+  _component_: torchtune.datasets.instruct_dataset
+  template: papaya.toolkit.experimental.llm_pte_finetuning.utils.DatabricksDolly
+  source: iamtarun/python_code_instructions_18k_alpaca
+  split: train
+  column_map:
+    instruction: instruction
+    prompt: prompt
+    input: input
+    output: output
+seed: null
+shuffle: True
+batch_size: 1
+
+loss:
+  _component_: torch.nn.CrossEntropyLoss
+
+model:
+  _component_: torchtune.models.phi3.lora_phi3_mini
+  lora_attn_modules: ['q_proj', 'v_proj']
+  apply_lora_to_mlp: False
+  apply_lora_to_output: False
+  lora_rank: 8
+  lora_alpha: 16
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Phi-3-mini-4k-instruct
+  checkpoint_files: [
+    model-00001-of-00002.safetensors,
+    model-00002-of-00002.safetensors
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Phi-3-mini-4k-instruct/
+  model_type: PHI3_MINI
+
+resume_from_checkpoint: False
+save_adapter_weights_only: False
+
+device: cpu
+dtype: fp32
+
+enable_activation_checkpointing: True
+compile: False

examples/llm_pte_finetuning/phi3_config.yaml

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+tokenizer:
+  _component_: torchtune.models.phi3.phi3_mini_tokenizer
+  path: /tmp/Phi-3-mini-4k-instruct/tokenizer.model
+  max_seq_len: 512
+
+dataset:
+  _component_: torchtune.datasets.alpaca_cleaned_dataset
+seed: null
+shuffle: True
+batch_size: 1
+
+loss:
+  _component_: torch.nn.CrossEntropyLoss
+
+model:
+  _component_: torchtune.models.phi3.lora_phi3_mini
+  lora_attn_modules: ['q_proj', 'v_proj']
+  apply_lora_to_mlp: False
+  apply_lora_to_output: False
+  lora_rank: 8
+  lora_alpha: 16
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Phi-3-mini-4k-instruct
+  checkpoint_files: [
+    model-00001-of-00002.safetensors,
+    model-00002-of-00002.safetensors
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Phi-3-mini-4k-instruct/
+  model_type: PHI3_MINI
+resume_from_checkpoint: False
+save_adapter_weights_only: False
+
+device: cpu
+dtype: fp32
+
+enable_activation_checkpointing: True
+compile: False

examples/llm_pte_finetuning/qwen_05b_config.yaml

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+tokenizer:
+  _component_: torchtune.models.qwen2.qwen2_tokenizer
+  path: /tmp/Qwen2-0.5B-Instruct/vocab.json
+  merges_file: /tmp/Qwen2-0.5B-Instruct/merges.txt
+  max_seq_len: 512
+
+dataset:
+  _component_: torchtune.datasets.alpaca_cleaned_dataset
+seed: null
+shuffle: True
+batch_size: 1
+
+loss:
+  _component_: torch.nn.CrossEntropyLoss
+
+model:
+  _component_: torchtune.models.qwen2.lora_qwen2_0_5b
+  lora_attn_modules: ['q_proj', 'k_proj', 'v_proj']
+  apply_lora_to_mlp: False
+  lora_rank: 32
+  lora_alpha: 64
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Qwen2-0.5B-Instruct
+  checkpoint_files: [
+    model.safetensors
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Qwen2-0.5B-Instruct
+  model_type: QWEN2
+resume_from_checkpoint: False
+save_adapter_weights_only: False
+
+device: cpu
+dtype: fp32
+
+enable_activation_checkpointing: True
+compile: False