[Feature Enhancement] Add a hack extractor script to solve vmap and autocast

graph_net/test/hack_llm_extractor.py

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+import torch
+from transformers import AutoModel, AutoTokenizer, AutoConfig
+import graph_net.torch
+from transformers.masking_utils import ALL_MASK_ATTENTION_FUNCTIONS
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+from transformers.integrations.executorch import sdpa_mask_without_vmap
+
+
+def get_model_name():
+    return "Qwen/Qwen2.5-3B"
+
+
+def create_model():
+    config = AutoConfig.from_pretrained(get_model_name())
+    model = AutoModel.from_config(config)
+    # https://github.com/huggingface/transformers/blob/6b5bd117231f969713ed79fd4870903ab3c93edf/docs/source/en/attention_interface.md?plain=1#L194-L195
+    ALL_MASK_ATTENTION_FUNCTIONS.register("sdpa_without_vmap", sdpa_mask_without_vmap)
+    ALL_ATTENTION_FUNCTIONS.register(
+        "sdpa_without_vmap", ALL_ATTENTION_FUNCTIONS["sdpa"]
+    )
+    model.config._attn_implementation = "sdpa_without_vmap"
+    model.eval()
+    return model.to(device)
+
+
+if __name__ == "__main__":
+    tokenizer = AutoTokenizer.from_pretrained(get_model_name())
+
+    text = "Hello world"
+    inputs = tokenizer(text, return_tensors="pt")
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    class DummyAutocast:
+        def __init__(self, *args, **kwargs):
+            pass
+
+        def __enter__(self):
+            pass
+
+        def __exit__(self, *args):
+            pass
+
+    class PatchedModel(torch.nn.Module):
+        def __init__(self, model):
+            super().__init__()
+            self.model = model
+
+        def forward(self, *args, **kwargs):
+            # TODO
+            original_autocast1 = torch.cuda.amp.autocast
+            original_autocast2 = torch.amp.autocast
+            original_autocast3 = torch.autocast
+            try:
+                torch.cuda.amp.autocast = DummyAutocast
+                torch.amp.autocast = DummyAutocast
+                torch.autocast = DummyAutocast
+                return self.model.forward(*args, **kwargs)
+            finally:
+                torch.cuda.amp.autocast = original_autocast1
+                torch.amp.autocast = original_autocast2
+                torch.autocast = original_autocast3
+
+    model = create_model()
+    model = PatchedModel(model)
+    model = graph_net.torch.extract(name=get_model_name(), dynamic=False)(model)
+
+    print("Running inference...")
+    output = model(**inputs)
+    print("Inference finished. Output shape:", output.last_hidden_state.shape)

samples/transformers-auto-model/Qwen/Qwen2.5-0.5B/graph_hash.txt

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+70d4001ac71bdeb9de5fe851688032c3ba4cd2b9abf22950fc2061c98358408b

samples/transformers-auto-model/Qwen/Qwen2.5-0.5B/graph_net.json

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+{
+    "framework": "torch",
+    "num_devices_required": 1,
+    "num_nodes_required": 1,
+    "dynamic": false
+}