[auto-parallel] add auto-offload feature (#3154)

* add auto-offload feature

* polish code

* fix syn offload runtime pass bug

* add offload example

* fix offload testing bug

* fix example testing bug

examples/language/gpt/experiments/auto_offload/model_zoo.py

@@ -0,0 +1,65 @@
+import torch
+import torch.nn as nn
+from transformers import GPT2Config, GPT2LMHeadModel
+
+class GPTLMModel(nn.Module):
+
+    def __init__(self,
+                 hidden_size=768,
+                 num_layers=12,
+                 num_attention_heads=12,
+                 max_seq_len=1024,
+                 vocab_size=50257):
+        super().__init__()
+        self.model = GPT2LMHeadModel(
+            GPT2Config(n_embd=hidden_size,
+                       n_layer=num_layers,
+                       n_head=num_attention_heads,
+                       n_positions=max_seq_len,
+                       n_ctx=max_seq_len,
+                       vocab_size=vocab_size))
+
+    def forward(self, input_ids, attention_mask):
+        # Only return lm_logits
+        return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=True)[0]
+
+
+class GPTLMLoss(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.loss_fn = nn.CrossEntropyLoss()
+
+    def forward(self, logits, labels):
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+        # Flatten the tokens
+        return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+def get_gpt2_components(model_type: str, batch_size: int):
+    vocab_size = 1024
+    seq_len = 8
+
+    def gpt2_model_builder():
+        if model_type == "gpt2_medium":
+            return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16)
+        elif model_type == "gpt2_xl":
+            return GPTLMModel(hidden_size=1600, num_layers=48, num_attention_heads=32)
+        elif model_type == "gpt2_10b":
+            return GPTLMModel(hidden_size=4096, num_layers=50, num_attention_heads=16)
+        elif model_type == "gpt2_14b":
+            return GPTLMModel(hidden_size=4096, num_layers=70, num_attention_heads=16)
+        elif model_type == "gpt2_20b":
+            return GPTLMModel(hidden_size=8192, num_layers=25, num_attention_heads=16)
+        elif model_type == "gpt2_24b":
+            return GPTLMModel(hidden_size=8192, num_layers=30, num_attention_heads=16)
+        else:
+            raise TypeError(f"model_builder {model_type}")
+
+    def gpt2_data_gen(device="cuda"):
+        input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=device)
+        attention_mask = torch.ones_like(input_ids, device=device)
+        kwargs = dict(input_ids=input_ids, attention_mask=attention_mask)
+        return kwargs
+
+    return gpt2_model_builder, gpt2_data_gen