update phi-3-mini readme doc (#4377)

Summary:
Update phi-3-mini readme to reflect latest changes.

It also fallbacks to use `torch.export._trace._export` due to issue pytorch/pytorch#128394

Pull Request resolved: #4377

Reviewed By: kirklandsign

Differential Revision: D60130144

Pulled By: helunwencser

fbshipit-source-id: 1d8d9a3791b877f43ad9312b4e704d5ca6d7f69e

examples/models/phi-3-mini/README.md

@@ -4,8 +4,6 @@ This example demonstrates how to run a [Phi-3-mini](https://huggingface.co/micro
 # Instructions
 ## Step 1: Setup
 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. For installation run `./install_requirements.sh --pybind xnnpack`
-2. Phi-3 Mini-128K-Instruct has been integrated in the development version (4.41.0.dev0) of transformers. Make sure that you install transformers with version at least 4.41.0: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`
-
 
 ## Step 2: Prepare and run the model
 1. Download the `tokenizer.model` from HuggingFace.
@@ -15,7 +13,7 @@ wget -O tokenizer.model https://huggingface.co/microsoft/Phi-3-mini-128k-instruc
 ```
 2. Export the model. This step will take a few minutes to finish.
 ```
-python export_model.py
+python3 export_phi-3-mini.py
 ```
 3. Build and run the runner.
 ```

examples/models/phi-3-mini/export_phi-3-mini.py

@@ -5,47 +5,66 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-from executorch.extension.llm.export.builder import DType, LLMEdgeManager
 
-from executorch.extension.llm.export.partitioner_lib import get_xnnpack_partitioner
-from executorch.extension.llm.export.quantizer_lib import (
-    DynamicQuantLinearOptions,
-    get_pt2e_quantizers,
-    PT2EQuantOptions,
+from executorch.backends.transforms.duplicate_dynamic_quant_chain import (
+    DuplicateDynamicQuantChainPass,
+)
+from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
+from executorch.backends.xnnpack.utils.configs import get_xnnpack_edge_compile_config
+from executorch.exir import to_edge
+from torch._export import capture_pre_autograd_graph
+from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
+
+from torch.ao.quantization.quantizer.xnnpack_quantizer import (
+    get_symmetric_quantization_config,
+    XNNPACKQuantizer,
 )
 
 from transformers import Phi3ForCausalLM
 
 
 def main() -> None:
-    torch.manual_seed(42)
+    torch.manual_seed(0)
 
     # pyre-ignore: Undefined attribute [16]: Module `transformers` has no attribute `Phi3ForCausalLM`
     model = Phi3ForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
 
-    modelname = "phi-3-mini"
-
-    (
-        LLMEdgeManager(
-            model=model,
-            modelname=modelname,
-            max_seq_len=128,
-            dtype=DType.fp32,
-            use_kv_cache=False,
-            example_inputs=(torch.randint(0, 100, (1, 100), dtype=torch.long),),
-            enable_dynamic_shape=True,
-            verbose=True,
+    example_inputs = (torch.randint(0, 100, (1, 100), dtype=torch.long),)
+    dynamic_shape = {"input_ids": {1: torch.export.Dim("sequence_length", max=128)}}
+
+    xnnpack_quant_config = get_symmetric_quantization_config(
+        is_per_channel=True, is_dynamic=True
+    )
+    xnnpack_quantizer = XNNPACKQuantizer()
+    xnnpack_quantizer.set_global(xnnpack_quant_config)
+
+    with torch.nn.attention.sdpa_kernel(
+        [torch.nn.attention.SDPBackend.MATH]
+    ), torch.no_grad():
+        model = capture_pre_autograd_graph(
+            model, example_inputs, dynamic_shapes=dynamic_shape
         )
-        .set_output_dir(".")
-        .capture_pre_autograd_graph()
-        .pt2e_quantize(
-            get_pt2e_quantizers(PT2EQuantOptions(None, DynamicQuantLinearOptions()))
+        model = prepare_pt2e(model, xnnpack_quantizer)
+        model(*example_inputs)
+        model = convert_pt2e(model, fold_quantize=False)
+        DuplicateDynamicQuantChainPass()(model)
+        # TODO(lunwenh): update it to use export once
+        # https://github.com/pytorch/pytorch/issues/128394 is resolved.
+        model = torch.export._trace._export(
+            model,
+            example_inputs,
+            dynamic_shapes=dynamic_shape,
+            strict=False,
+            pre_dispatch=False,
         )
-        .export_to_edge()
-        .to_backend([get_xnnpack_partitioner()])
-        .to_executorch()
-        .save_to_pte(f"{modelname}.pte")
-    )
+
+    edge_config = get_xnnpack_edge_compile_config()
+    edge_manager = to_edge(model, compile_config=edge_config)
+    edge_manager = edge_manager.to_backend(XnnpackPartitioner(has_dynamic_shapes=True))
+    et_program = edge_manager.to_executorch()
+
+    with open("phi-3-mini.pte", "wb") as file:
+        file.write(et_program.buffer)
 
 
 if __name__ == "__main__":