added hugging face example (#81)

Co-authored-by: John Calderon <[email protected]>

Author: johncalesp

examples/huggingface/entry_point.py

@@ -0,0 +1,55 @@
+from transformers import (
+    get_linear_schedule_with_warmup,
+    AutoModelForCausalLM,
+    Trainer,
+)
+import torch
+import torch.optim as optim
+
+model_id = "roberta-base"
+
+
+def deepview_model_provider():
+    return AutoModelForCausalLM.from_pretrained(model_id, is_decoder=True).cuda()
+
+
+def deepview_input_provider(batch_size=2):
+    vocab_size = 30522
+    src_seq_len = 512
+    tgt_seq_len = 512 
+
+    device = torch.device("cuda")
+
+    source = torch.randint(
+        low=0,
+        high=vocab_size,
+        size=(batch_size, src_seq_len),
+        dtype=torch.int64,
+        device=device,
+    )
+    target = torch.randint(
+        low=0,
+        high=vocab_size,
+        size=(batch_size, tgt_seq_len),
+        dtype=torch.int64,
+        device=device,
+    )
+    return (source, target)
+
+
+def deepview_iteration_provider(model):
+    model.parameters()
+    optimizer = optim.AdamW(
+        params=model.parameters(),
+        betas=(0.9, 0.999),
+        eps=1e-6,
+        weight_decay=0.01,
+        lr=1e-4,
+    )
+    scheduler = get_linear_schedule_with_warmup(optimizer, 10000, 500000)
+    trainer = Trainer(model=model, optimizers=(optimizer, scheduler))
+
+    def iteration(source, label):
+        trainer.training_step(model, {"input_ids": source, "labels": label})
+
+    return iteration