Add to ONNX reproduction logs (#2565)

docs/onnx-conversion.md

@@ -1,6 +1,8 @@
 # End to End ONNX Conversion for SPLADE++ Ensemble Distil
 This MD file will describe steps to convert particular PyTorch models (i.e., [SPLADE++](https://doi.org/10.1145/3477495.3531857)) to ONNX models and options to further optimize compute graph for Transformer-based models. For more details on how does ONNX Conversion work and how to optimize the compute graph, please refer to [ONNX Tutorials](https://github.com/onnx/tutorials#services).
 
+The SPLADE model takes a text input and generates sparse token-level representations as output, where each token is assigned a weight, enabling efficient information retrieval. A more in depth explantation can be found [here](https://www.pinecone.io/learn/splade/).
+
 All scripts are available for reference under in the following directory:
 ```
 src/main/python/onnx
@@ -331,4 +333,8 @@ cd src/main/python/onnx/models
 cp splade-cocondenser-ensembledistil-optimized.onnx splade-cocondenser-ensembledistil-vocab.txt ~/.cache/anserini/encoders/
 ```
 
-Second, now run the end to end regression as seen in the previously mentioned documentation with the generated ONNX model.
+Second, now run the end to end regression as seen in the previously mentioned documentation with the generated ONNX model.
+
+
+### Reproduction Log
++ Results reproduced by [@valamuri2020](https://github.com/valamuri2020) on 2024-08-06 (commit [`6178b40`](https://github.com/castorini/anserini/commit/6178b407fc791d62f81e751313771165c6e2c743))

src/main/python/onnx/convert_hf_model_to_onnx.py

@@ -1,10 +1,11 @@
-import torch
-from transformers import AutoTokenizer, AutoModel
-import onnx
-import onnxruntime
 import argparse
 import os
 
+import onnx
+import onnxruntime
+import torch
+from transformers import AutoModel, AutoTokenizer
+
 # device
 device = "cuda" if torch.cuda.is_available() else "cpu" # make sure torch is compiled with cuda if you have a cuda device
 
@@ -21,14 +22,6 @@ def get_model_output_names(model, test_input):
     else:
         return [f'output_{i}' for i in range(len(outputs))]
 
-def get_dynamic_axes(input_names, output_names):
-    dynamic_axes = {}
-    for name in input_names:
-        dynamic_axes[name] = {0: 'batch_size', 1: 'sequence'}
-    for name in output_names:
-        dynamic_axes[name] = {0: 'batch_size', 1: 'sequence'}
-    return dynamic_axes
-
 def convert_model_to_onnx(text, model, tokenizer, onnx_path, vocab_path, device):
     print(model) # this prints the model structure for better understanding (optional)
     model.eval()
@@ -38,7 +31,6 @@ def convert_model_to_onnx(text, model, tokenizer, onnx_path, vocab_path, device)
     test_input = {k: v.to(device) for k, v in test_input.items()}
 
     output_names = get_model_output_names(model, test_input)
-    dynamic_axes = get_dynamic_axes(input_names, output_names)
 
     model_type = model.config.model_type
     num_heads = model.config.num_attention_heads
@@ -50,7 +42,6 @@ def convert_model_to_onnx(text, model, tokenizer, onnx_path, vocab_path, device)
         onnx_path,
         input_names=input_names,
         output_names=output_names,
-        dynamic_axes=dynamic_axes,
         do_constant_folding=True,
         opset_version=14
     )