Delete all-mpnet-base-v2.py

all-mpnet-base-v2.py

@@ -1,47 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModel
-
-from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
-from executorch.exir import to_edge_transform_and_lower
-from torch.export import export, Dim
-from executorch.runtime import Runtime
-
-model_name = f"all-mpnet-base-v2.pte"
-tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2")
-model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2")
-model.eval()
-
-sentences = ["This is an example sentence"]
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
-executorch_input = (encoded_input["input_ids"], encoded_input["attention_mask"])
-
-# Export the model
-executorch_program = to_edge_transform_and_lower(
-    export(
-        model,
-        executorch_input,
-        dynamic_shapes={
-            "input_ids": {
-                1: Dim("tokens", min=1, max=384),
-            },
-            "attention_mask": {
-                1: Dim("tokens", min=1, max=384),
-            },
-        },
-    ),
-    partitioner=[XnnpackPartitioner()],
-).to_executorch()
-
-# Save the model
-with open(model_name, "wb") as file:
-    file.write(executorch_program.buffer)
-
-# Check the model output
-eager_reference_output = model(**encoded_input)
-
-runtime = Runtime.get()
-program = runtime.load_program(model_name)
-method = program.load_method("forward")
-output = method.execute(executorch_input)
-
-print(torch.allclose(output[0], eager_reference_output[0], rtol=1e-3, atol=1e-5))