Enabled Dynamo exporter (#21713)

### Description
This PR modifies the run_dynamo_export function to ensure it mirrors the
behavior of run_torchscript_merged_export rather than
run_torchscript_separate_export. Additionally, I made adjustments to the
main function to ensure that run_dynamo is correctly invoked.



### Motivation and Context
The main motivation for this change is to enable successful export of
LLaMA-2 and LLaMA-3 models using the Dynamo exporter to ONNX.
Previously, the exporter was saving two copies of the weights, which is
inefficient. The modified approach ensures that only one copy of the
weights is saved, and the model can support both scenarios. These
changes enhance the compatibility of the exporter with LLaMA models and
subsequently other models and optimize the export process

onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py

@@ -11,6 +11,7 @@ import os
 import shutil
 import subprocess
 import sys
+import tempfile
 from itertools import chain
 
 import onnx
@@ -113,34 +114,6 @@ def save_onnx_model(onnx_model: onnx.ModelProto, output_path: str, data_path: st
     )
 
 
-# Notes:
-# 1) Dynamo export will not work automatically until this issue is resolved: https://github.com/microsoft/onnxscript/issues/493
-#
-# 2) Dynamo export will run manually if you set the ONNX file path to the same path that you use to save the model after export.
-# In other words, the value of `temp_path` should be set as the ONNX file path. You can open the issue in your browser to find
-# the location in ONNX Script where you have to make this change.
-#
-# Once the issue is resolved, we hope to modify the code below as follows for each export.
-#
-# Before:
-# temp_dir = args.output
-# temp_path = os.path.join(temp_dir, "temp.onnx")
-# ...
-# ...
-# ...
-# del onnx_model
-# os.system(f"rm {os.path.join(temp_dir, 'model.*')} && rm {os.path.join(temp_dir, '*.weight')} && rm {temp_path}")
-#
-#
-# After:
-# temp_dir = tempfile.TemporaryDirectory()
-# temp_path = os.path.join(temp_dir.name, "temp.onnx")
-# ...
-# ...
-# ...
-# del onnx_model
-# temp_dir.cleanup()
-#
 def run_dynamo_export(
     args: argparse.Namespace, l_config: AutoConfig, llama: AutoModelForCausalLM, rank: int = 0, world_size: int = 1
 ):
@@ -149,35 +122,25 @@ def run_dynamo_export(
     config.capture_scalar_outputs = True
 
     # Dummy values for export
-    batch_size, sequence_length = 2, 8
-    device = torch.device("cpu")
+    batch_size, sequence_length, past_sequence_length = 2, 8, 0
+    device = llama.device if args.model_name == "Llama-2-70b-hf" else torch.device("cpu")
 
-    # Export decoder_model.onnx
-    input_ids, attn_mask, pos_ids = get_sample_inputs(l_config, device, batch_size, sequence_length)
-    temp_dir = args.output  # tempfile.TemporaryDirectory()
-    temp_path = os.path.join(temp_dir, "temp.onnx")  # os.path.join(temp_dir.name, "temp.onnx")
-    torch.onnx.dynamo_export(
-        llama, input_ids, attn_mask, pos_ids, export_options=torch.onnx.ExportOptions(dynamic_shapes=True)
-    ).save(temp_path)
-
-    # Check decoder_model.onnx and save all external data to one file
-    onnx.checker.check_model(temp_path)
-    onnx.shape_inference.infer_shapes_path(temp_path)
-
-    output_path = os.path.join(args.output, f"rank_{rank}_{args.model_name}_decoder_model_fp32.onnx")
-    onnx_model = onnx.load_model(temp_path, load_external_data=True)
-    save_onnx_model(onnx_model, output_path, f"rank_{rank}_{args.model_name}_decoder_model_fp32.onnx.data")
-    del onnx_model
-    os.system(
-        f"rm {os.path.join(temp_dir, 'model.*')} && rm {os.path.join(temp_dir, '*.weight')} && rm {temp_path}"
-    )  # temp_dir.cleanup()
+    temp_name = args.model_name.lower().replace("-", "").replace("_", "")
+    max_sequence_length = 16384 if "codellama" in temp_name else 4096 if "llama2" in temp_name else 2048
 
     # Export decoder_with_past_model.onnx
-    input_ids, attn_mask, pos_ids, past_kv = get_sample_with_past_kv_inputs(
-        l_config, device, batch_size, sequence_length, world_size=world_size
+    input_ids, attn_mask, pos_ids, past_kv = get_merged_sample_with_past_kv_inputs(
+        l_config,
+        device,
+        batch_size,
+        sequence_length,
+        past_sequence_length,
+        max_seq_len=max_sequence_length,
+        use_fp16=False,
+        world_size=world_size,
     )
-    temp_dir = args.output  # tempfile.TemporaryDirectory()
-    temp_path = os.path.join(temp_dir, "temp.onnx")  # os.path.join(temp_dir.name, "temp.onnx")
+    temp_dir = tempfile.TemporaryDirectory()
+    temp_path = os.path.join(temp_dir.name, "temp.onnx")
     torch.onnx.dynamo_export(
         llama, input_ids, attn_mask, pos_ids, past_kv, export_options=torch.onnx.ExportOptions(dynamic_shapes=True)
     ).save(temp_path)
@@ -190,9 +153,7 @@ def run_dynamo_export(
     onnx_model = onnx.load_model(temp_path, load_external_data=True)
     save_onnx_model(onnx_model, output_path, f"rank_{rank}_{args.model_name}_decoder_with_past_model_fp32.onnx.data")
     del onnx_model
-    os.system(
-        f"rm {os.path.join(temp_dir, 'model.*')} && rm {os.path.join(temp_dir, '*.weight')} && rm {temp_path}"
-    )  # temp_dir.cleanup()
+    temp_dir.cleanup()
 
     logger.info(f"The {args.model_name} ONNX model has been successfully created with the Dynamo exporter!")
 
@@ -869,7 +830,7 @@ def main():
 
             # Export to ONNX
             if missing_separate_exports or missing_merged_export:
-                if args.use_dynamo_export and missing_separate_exports:
+                if args.use_dynamo_export:
                     logger.warning("Please ensure you have installed PyTorch, ONNX, and ONNX Script as follows.")
                     logger.warning("Step 1 - PyTorch nightly: https://pytorch.org/get-started/locally/")
                     logger.warning("Step 2 - ONNX weekly: https://pypi.org/project/onnx-weekly/")
@@ -902,7 +863,10 @@ def main():
                 decoder_merged_model_fp32_opt_path,
             ]
 
-            # Run the optimizer script
+            if args.use_dynamo_export:
+                continue
+
+            # Run the optimizer script.
             logger.info("Optimizing models...")
             for orig_path, opt_path in zip(old_paths, new_paths):
                 if os.path.exists(orig_path):
@@ -1007,6 +971,9 @@ def main():
                         remove_existing_model(fp_path)
         barrier()
 
+    if args.use_dynamo_export:
+        return
+
     logger.info("Verifying parity on all ONNX models created")
 
     # Use FP32 precision for FP32, INT8, INT4 CPU models, use FP16 precision for FP16 and INT4 GPU models