Add minimal FSDP example (#23)

README.md

@@ -88,3 +88,9 @@ This repository contains a variety of Determined examples that are not actively
 | Example                                                                  | Dataset | Framework  |
 |:------------------------------------------------------------------------:|:-------:|:----------:|
 | [asha\_search\_method](custom_search_method/asha_search_method)          | MNIST   | PyTorch    |
+
+## Fully Sharded Data Parallel
+
+| Example                                                                  | Framework  |
+|:------------------------------------------------------------------------:|:----------:|
+| [minimal\_fsdp](fsdp/minimal_fsdp)          | PyTorch    |

fsdp/minimal_fsdp/README.md

@@ -0,0 +1,39 @@
+# FSDP + Core API for LLM Training
+
+This example shows how to use Fully Sharded Data Parallel [(FSDP)](https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html) with Determined and Core API. (Relatively) simple transformer model adapted from [GPT-fast
+](https://github.com/pytorch-labs/gpt-fast) training on fake data.
+
+## Files
+* **fsdp.py**: Training setup and loop, including checkpointing, reporting, and profiling.
+* **model.py**: Model architecture.
+* **config.yaml**: Experiment configuration file.
+
+## Configuration
+Settings can be changed in `config.yaml` `hyperparameters` section.
+
+### Hyperparameters
+* `batch_size`: Per-device batch size.  Global batch size will be `batch_size * slots_per_trial`.
+* `lr`: Learning rate.
+* `d_model`, `max_seq_len`, `n_heads`, `n_layers`, `vocab_size`: Model architecture parameters.  Check code for more details.
+* `report_rate`: Number of training steps to take between metric reports.
+* `checkpoint_rate`: Number of training steps to take between checkpoint saves.
+* `amp_dtype`: Whether to use torch automatic mixed-precision, and which dtype to use.  Options are `'auto'`, `'bfloat16'`, `'float16'`, and `null`.
+* `validation_batches`: Number of batches to use when calculating validation metrics.
+* `core_api_profiler`: Set to true to enable Core API profiler.  Results visible in Web UI.
+* `torch_profiler`: Set to true to enable `torch` profiler.  Results visible in Tensorboard, which can be launched through the Web UI.
+
+### Other configuration
+Users might want to change `resources.slots_per_trial`, `workspace`, `project`, and `searcher.max_length` in `config.yaml`.
+
+## Data
+This example uses a synthetically generated random dataset for simplicity.
+
+## To Run
+If you have not yet installed Determined, installation instructions can be found at https://docs.determined.ai/latest/index.html
+
+Change any desired configuration variables as outlined in the **Configuration** section, then run the following command: `det -m <master-host:port> experiment create
+config.yaml .`.
+
+
+## Results
+Training loss should decrease from ~10.5 to ~8.5 with default settings run for 100 steps, while validation loss remains constant.  This is due to validation data being a separate random dataset.

fsdp/minimal_fsdp/config.yaml

@@ -0,0 +1,26 @@
+name: fsdp example
+entrypoint: python3 -m determined.launch.torch_distributed -- python3 fsdp.py
+searcher:
+  name: single
+  metric: loss
+  max_length: 100
+resources:
+  slots_per_trial: 2
+environment:
+  image:
+    gpu: determinedai/environments:cuda-11.8-pytorch-2.0-gpu-mpi-0.31.1
+hyperparameters:
+  batch_size: 1
+  lr: 1e-4
+  d_model: 512
+  max_seq_len: 2048
+  n_heads: 8
+  n_layers: 4
+  vocab_size: 32000
+  report_rate: 10
+  checkpoint_rate: 50
+  amp_dtype: float16
+  validation_batches: 10
+  core_api_profiler: false
+  torch_profiler: false
+max_restarts: 0