Minimal transformer examples

Author: lantiga

examples/fabric/fp8_fsdp2_compile/README.md

@@ -0,0 +1,106 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torch.distributed._composable.fsdp.fully_shard import fully_shard
+from torch.distributed.device_mesh import DeviceMesh
+
+from torchao.float8 import convert_to_float8_training, Float8LinearConfig
+
+import lightning as L
+from lightning.fabric.strategies import ModelParallelStrategy
+from lightning.pytorch.demos import Transformer, WikiText2
+
+from tqdm import tqdm
+
+
+def configure_model(model: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+    float8_config = Float8LinearConfig(
+        # pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly
+        pad_inner_dim=True,
+    )
+
+    def module_filter_fn(mod: torch.nn.Module, fqn: str):
+        # we skip the decoder because it typically vocabulary size
+        # is not divisible by 16 as required by float8
+        if fqn == "decoder":
+            return False
+        return True
+
+    convert_to_float8_training(model, config=float8_config, module_filter_fn=module_filter_fn)
+
+    for module in model.modules():
+        if isinstance(module, (torch.nn.TransformerEncoderLayer, torch.nn.TransformerDecoderLayer)):
+            fully_shard(module, mesh=device_mesh)
+
+    fully_shard(model, mesh=device_mesh)
+
+    model = torch.compile(model)
+
+    return model
+
+
+def train():
+    L.seed_everything(42)
+
+    batch_size = 8
+    micro_batch_size = 1
+
+    dataset = WikiText2()
+    dataloader = DataLoader(dataset, num_workers=8, batch_size=micro_batch_size)
+
+    with torch.device("meta"):
+        model = Transformer(
+            vocab_size=dataset.vocab_size,
+            nlayers=16,
+            nhid=4096,
+            ninp=1024,
+            nhead=32,
+        )
+
+    strategy = ModelParallelStrategy(
+        data_parallel_size=4,
+        tensor_parallel_size=1,
+        parallelize_fn=configure_model
+    )
+
+    fabric = L.Fabric(precision="bf16-true", strategy=strategy)
+    fabric.launch()
+
+    model = fabric.setup(model)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+    optimizer = fabric.setup_optimizers(optimizer)
+
+    dataloader = fabric.setup_dataloaders(dataloader)
+
+    iterable = tqdm(enumerate(dataloader), total=len(dataloader)) if fabric.is_global_zero else enumerate(dataloader)
+
+    for i, batch in iterable:
+        input, target = batch
+
+        is_accumulating = i % (batch_size // micro_batch_size) != 0
+
+        with fabric.no_backward_sync(model, enabled=is_accumulating):
+            output = model(input, target)
+            loss = F.nll_loss(output, target.view(-1))
+            fabric.backward(loss)
+
+        if not is_accumulating:
+            fabric.clip_gradients(model, optimizer, max_norm=1.0)
+            optimizer.step()
+            optimizer.zero_grad()
+
+        if fabric.is_global_zero:
+            iterable.set_postfix_str(f"train_loss={loss.item():.2f}")
+
+        if i // (batch_size // micro_batch_size) > 100:
+            break
+
+    fabric.print(torch.cuda.memory_summary())
+
+
+if __name__ == "__main__":
+    torch.set_float32_matmul_precision('high')
+
+    train()

examples/fabric/fp8_fsdp2_compile/train.py

@@ -0,0 +1,94 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torch.distributed._composable.fsdp.fully_shard import fully_shard
+
+from torchao.float8 import convert_to_float8_training, Float8LinearConfig
+
+import lightning as L
+from lightning.pytorch.strategies import ModelParallelStrategy
+from lightning.pytorch.demos import Transformer, WikiText2
+
+
+class LanguageModel(L.LightningModule):
+    def __init__(self, vocab_size):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.model = None
+
+    def configure_model(self):
+        if self.model is not None:
+            return
+
+        with torch.device("meta"):
+            model = Transformer(
+                vocab_size=self.vocab_size,
+                nlayers=16,
+                nhid=4096,
+                ninp=1024,
+                nhead=32,
+            )
+
+        float8_config = Float8LinearConfig(
+            # pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly
+            pad_inner_dim=True,
+        )
+
+        def module_filter_fn(mod: torch.nn.Module, fqn: str):
+            # we skip the decoder because it typically vocabulary size
+            # is not divisible by 16 as required by float8
+            if fqn == "decoder":
+                return False
+            return True
+
+        convert_to_float8_training(model, config=float8_config, module_filter_fn=module_filter_fn)
+
+        for module in model.modules():
+            if isinstance(module, (nn.TransformerEncoderLayer, nn.TransformerDecoderLayer)):
+                fully_shard(module, mesh=self.device_mesh)
+
+        fully_shard(model, mesh=self.device_mesh)
+
+        self.model = torch.compile(model)
+
+    def training_step(self, batch):
+        input, target = batch
+        output = self.model(input, target)
+        loss = F.nll_loss(output, target.view(-1))
+        self.log("train_loss", loss, prog_bar=True)
+        return loss
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=1e-4)
+
+
+def train():
+    L.seed_everything(42)
+
+    dataset = WikiText2()
+    train_dataloader = DataLoader(dataset, num_workers=8, batch_size=1)
+
+    model = LanguageModel(vocab_size=dataset.vocab_size)
+
+    mp_strategy = ModelParallelStrategy(
+        data_parallel_size=4,
+        tensor_parallel_size=1,
+    )
+
+    trainer = L.Trainer(
+        strategy=mp_strategy,
+        max_steps=100,
+        precision="bf16-true",
+        accumulate_grad_batches=8
+    )
+
+    trainer.fit(model, train_dataloader)
+
+    trainer.print(torch.cuda.memory_summary())
+
+
+if __name__ == "__main__":
+    torch.set_float32_matmul_precision('high')
+
+    train()