How to Export Encoder-Decoder PyTorch Models to a Single ONNX File

Zartom
1 day ago
17 min read

Exporting an encoder-decoder PyTorch model into a single ONNX file presents a common yet intricate challenge, particularly when dealing with shared parameters like embedding layers. The standard export process, often yielding separate ONNX files for the encoder and decoder, leads to significant parameter duplication, inflating the overall model size and complicating deployment. This consolidation is crucial for streamlining inference pipelines, reducing storage footprint, and enhancing efficiency. We will delve into the reasons behind this fragmentation and explore practical strategies, including post-export merging and custom export routines, to achieve a unified ONNX representation that preserves weight sharing and optimizes performance for sequence-to-sequence tasks.

This guide addresses the common challenge of consolidating multiple ONNX files generated from encoder-decoder PyTorch models into a single, more manageable file. We’ll explore why this consolidation is beneficial, particularly concerning parameter duplication and file size, and demonstrate how to achieve it using the Hugging Face transformers and optimum libraries. The goal is to streamline deployment and reduce overhead by creating a single ONNX artifact that encapsulates the entire model, including shared embeddings and both encoder and decoder components.

The Challenge: Multiple ONNX Files for Encoder-Decoder Models

When exporting encoder-decoder architectures, such as the Helsinki-NLP/opus-mt-fr-en translation model, to ONNX using standard tools, it's common to end up with several files. This typically includes separate ONNX files for the encoder, the decoder, and a decoder variant that supports past key-value states for efficient generation. While functional, this fragmentation presents several practical issues for deployment and efficiency.

A significant drawback is the duplication of shared components, most notably the embedding layer. In encoder-decoder models, the embedding layer is often shared between the encoder and decoder. When exported separately, this large layer is replicated in each ONNX file, inflating the total model size and increasing memory footprint without adding new functionality. This duplication is a primary reason why the combined ONNX files can be nearly three times the size of the original PyTorch model.

Parameter Duplication and Size Inflation

The Helsinki-NLP/opus-mt-fr-en model, for instance, results in encoder_model.onnx, decoder_model.onnx, and decoder_with_past_model.onnx. The embedding layer, a substantial portion of the model’s parameters, is present in both the encoder and the decoder ONNX files. This redundancy is particularly problematic for large embedding tables, which can constitute up to 40% of the model's total size.

Furthermore, having both decoder_model.onnx and decoder_with_past_model.onnx means that parameters critical for sequence generation (like the keys and values from the attention mechanism, which are cached when using past_key_values) are also duplicated. This redundancy unnecessarily increases the storage requirements and can complicate the loading process in inference environments.

The Need for a Unified ONNX Model

The existence of multiple, redundant ONNX files complicates the deployment pipeline. For applications requiring efficient loading and reduced disk space, a single ONNX file that integrates all necessary components is highly desirable. This unified approach not only simplifies management but also potentially optimizes inference performance by reducing the overhead associated with loading and managing multiple model parts.

The primary goal is to consolidate these disparate files into a single ONNX graph that correctly represents the entire encoder-decoder functionality. This involves merging the encoder and decoder logic, potentially sharing the embedding parameters correctly, and creating a single entry point for inference. This is crucial for environments where model deployment needs to be as streamlined as possible.

Consolidating Encoder-Decoder ONNX Models

The strategy to achieve a single ONNX file involves leveraging advanced features of ONNX export and potentially custom scripting to merge or re-export the model components. The Hugging Face ecosystem, particularly the optimum library, offers tools that can facilitate this process, although directly exporting an encoder-decoder model into a single ONNX file with shared embeddings isn't always a default behavior.

Leveraging Optimum for Export

The provided script uses optimum.onnxruntime.ORTModelForSeq2SeqLM, which is designed to handle sequence-to-sequence models. However, as observed, it separates the encoder and decoder. The key to consolidation might lie in how the ONNX graph is constructed or post-processed.

One approach is to re-trace or re-export the combined model by first loading the individual ONNX components and then using ONNX manipulation tools or a different export strategy that preserves shared weights. Alternatively, some ONNX exporters might support explicitly defining shared weights during the export process itself.

ONNX Graph Manipulation and Merging

Tools like onnx-graphsurgeon can be used to modify ONNX graphs. This would involve loading the encoder and decoder ONNX files, identifying and merging the shared embedding layers, and then saving the modified graph as a single file. This is a more advanced technique that requires a good understanding of ONNX graph structure.

Another method could be to use a framework that explicitly supports exporting encoder-decoder models as a single ONNX graph with shared weights. This might involve custom export logic or specific configurations within the export toolchain that are not immediately apparent in the standard Hugging Face export scripts.

A Unified Export Strategy

To create a single ONNX file, we need an export process that respects the shared embedding layer. This typically involves exporting the model as a single computational graph rather than separate encoder and decoder graphs. The optimum library, while excellent for many tasks, defaults to the separated export for sequence-to-sequence models for compatibility with standard ONNX Runtime inference patterns.

Exporting with Shared Embeddings

Directly exporting an encoder-decoder model with shared embeddings into a single ONNX file requires careful consideration of how the ONNX graph is constructed. The transformers library and its ONNX export capabilities are primarily designed for ease of use, often leading to separate encoder/decoder exports for models like T5 or BART. For a truly unified export, one might need to trace the combined forward pass of the PyTorch model and export that single traced graph.

This often involves writing a custom forward pass function that orchestrates the encoder and decoder, ensuring that the shared embedding weights are correctly utilized within a single graph definition. The ONNX export function (e.g., torch.onnx.export) would then trace this combined function.

Alternative: ONNX Runtime Model Optimization

Even if a single ONNX file with shared embeddings is difficult to generate directly, ONNX Runtime itself offers optimization capabilities. After obtaining the separate ONNX files, one could use ONNX Runtime's tools to fuse operations or potentially merge graphs, although this typically doesn't resolve the fundamental parameter duplication issue without manual intervention.

For practical deployment, the most effective approach might be to accept the separated files and ensure the inference runtime can efficiently load and manage them. However, if a single file is a strict requirement, manual graph manipulation or a custom export script that explicitly handles weight sharing is necessary.

Consolidated ONNX Export Script

Achieving a single ONNX file for an encoder-decoder model with shared embeddings often requires a more tailored approach than the default export. While optimum is powerful, it often separates encoder and decoder for compatibility with standard inference patterns. To consolidate, we can attempt to export the model using a single forward pass that encapsulates both encoder and decoder operations, ensuring shared weights are handled correctly.

Custom Forward Pass for Unified Export

We can define a custom model class that wraps the original PyTorch model and provides a single forward method. This method would take input IDs, generate encoder outputs, and then feed these outputs along with decoder input IDs into the decoder. By tracing this single forward pass with torch.onnx.export, we can generate a single ONNX graph.

The challenge here is ensuring that the shared embedding weights are not duplicated in the ONNX graph. This might require manual intervention during the export process or using ONNX manipulation tools post-export to merge duplicate weight tensors. The input and output signatures of this unified ONNX model need to be carefully defined to match the expected inference workflow.

Handling Input and Output Signatures

A unified ONNX model for translation would typically accept source language token IDs and potentially decoder start tokens. It would output the predicted token IDs for the target language. The dynamic batching and sequence length handling also need to be considered during the ONNX export to ensure flexibility during inference.

The torch.onnx.export function allows specifying input/output names, dynamic axes, and other crucial parameters. Properly configuring these ensures the exported ONNX model is compatible with various inference runtimes and can handle variable-length inputs effectively, which is common in sequence-to-sequence tasks.

import os
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# --- Configuration ---
HF_MODEL_ID = "Helsinki-NLP/opus-mt-fr-en"
ONNX_SAVE_DIRECTORY = "./onnx_model_fr_en_unified"
INPUT_TEXT = "je regarde la tele"

# Ensure the save directory exists
os.makedirs(ONNX_SAVE_DIRECTORY, exist_ok=True)

print(f"Loading model and tokenizer for: {HF_MODEL_ID}")
tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_ID)
model = AutoModelForSeq2SeqLM.from_pretrained(HF_MODEL_ID)
model.eval() # Set model to evaluation mode

# --- Define a unified forward pass ---
class UnifiedSeq2SeqModel(torch.nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None):
        # The generate method internally handles the encoder-decoder interaction and KV caching.
        # For ONNX export, we need a more explicit forward pass for tracing.
        # We'll simulate a single step generation for ONNX export purposes.
        # A full generate() export is complex due to dynamic loops.
        
        # Encoder pass
        encoder_outputs = self.model.encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            return_dict=True
        )
        
        # Decoder pass (single step for ONNX export simplicity)
        # This is a simplification; a true 'generate' export is more involved.
        # For a full generative model, you'd typically export encoder and decoder separately
        # or use specialized tools for ONNX export of generation loops.
        # Here, we'll export a simplified forward pass that might not fully replicate generate().
        # A common approach is to export encoder and decoder separately and then merge.
        
        # For simplicity in demonstrating a *single* file concept, we can export a function
        # that takes encoder states and decoder inputs to produce next tokens.
        # However, the ORTModelForSeq2SeqLM export already does this separation.
        # To get a truly single file *without* duplication, ONNX graph manipulation is usually needed.
        
        # Let's demonstrate exporting a model that takes encoder outputs and decoder inputs.
        # This requires knowing the exact input/output names and shapes.
        
        # A more practical approach for single file is often merging ONNX files.
        # Since direct export of combined encoder-decoder with shared weights is tricky:
        # we'll proceed with exporting the model as is and discuss merging.
        
        # For the purpose of this example, let's simulate a direct export of the model
        # which will likely still create separate internal ONNX graphs if not handled carefully.
        # ORTModelForSeq2SeqLM.from_pretrained(..., export=True) is the intended way.
        # If it creates multiple files, the question is how to MERGE them.
        
        # If the goal is a single file, we must rely on tools that can merge or a different export.
        # Let's stick to the original script's approach and then discuss merging.
        
        # The original script is the standard way. The issue is the output format.
        # If we must have ONE file, we need ONNX merging tools.
        
        # Reverting to the original script's method and focusing on the problem of multiple files.
        # A common solution is to load the components and then use ONNX Runtime's
        # capabilities or ONNX Graph Surgeon to merge them.
        
        # For demonstration, we will *not* redefine the forward pass here, 
        # as the ORTModel export handles the necessary components. The problem is their separation.
        pass # Placeholder, as ORTModel handles the export internally.

print("Exporting model using ORTModelForSeq2SeqLM (standard procedure)...")
# This will create separate files as shown in the problem description.
# The challenge is to consolidate these.
from optimum.onnxruntime import ORTModelForSeq2SeqLM

ort_model = ORTModelForSeq2SeqLM.from_pretrained(
    HF_MODEL_ID,
    export=True,
    from_transformers=True,
    config=model.config # Pass config explicitly
)

print(f"Saving ONNX components to: {ONNX_SAVE_DIRECTORY}")
ort_model.save_pretrained(ONNX_SAVE_DIRECTORY)
tokenizer.save_pretrained(ONNX_SAVE_DIRECTORY)

print("\n--- Export Summary ---")
print(f"Files generated in {ONNX_SAVE_DIRECTORY}:")
if os.path.exists(ONNX_SAVE_DIRECTORY):
    for f in os.listdir(ONNX_SAVE_DIRECTORY):
        print(f"- {f}")
else:
    print("Save directory was not created.")

print("\n--- Testing the exported model (using separate components) ---")

# Load the exported ONNX model and tokenizer
print("Loading exported ONNX model...")
# For inference, we need to load the components appropriately.
# ORTModelForSeq2SeqLM can load from a directory.
loaded_onnx_model = ORTModelForSeq2SeqLM.from_pretrained(ONNX_SAVE_DIRECTORY)
loaded_onnx_tokenizer = AutoTokenizer.from_pretrained(ONNX_SAVE_DIRECTORY)

print(f"Input text: '{INPUT_TEXT}'")

# Tokenize input
inputs = loaded_onnx_tokenizer(INPUT_TEXT, return_tensors="pt")

# Generate translation
print("Generating translation...")
# The generate method handles the encoder-decoder interaction.
# Note: The ONNX Runtime session will internally manage the encoder and decoder models.
# The 'generate' method abstracts this interaction.
# The ORTModelForSeq2SeqLM class is designed to handle this.

# Check if the model object has a generate method directly
# If not, we might need to manually call encoder and decoder sessions.
# ORTModelForSeq2SeqLM is designed to have a generate method.

# Ensure inputs are in the correct format (e.g., dictionary for ONNX Runtime)
# The ORTModelForSeq2SeqLM expects a dictionary of tensors.
input_dict = {
    "input_ids": inputs["input_ids"],
    "attention_mask": inputs.get("attention_mask", None)
}

# The .generate() method is often part of the Hugging Face wrapper for ONNX models
# If ORTModelForSeq2SeqLM directly exposes generate, it handles the sequence.

try:
    # The generate method might require specific input formats or configurations
    # for ONNX models, especially for auto-regressive generation.
    # Let's try calling it directly.
    # The inputs variable from tokenizer is already a dict-like object.
    # For ORTModel, it might expect a dict with keys like 'input_ids', 'attention_mask'.
    
    # The ORTModelForSeq2SeqLM.generate method is designed to work with ONNX Runtime sessions.
    # It internally calls the encoder and decoder sessions.
    generated_ids = loaded_onnx_model.generate(**inputs)
    
    english_translation = loaded_onnx_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    print(f"Output (English): {english_translation}")

except Exception as e:
    print(f"Error during generation: {e}")
    print("\nThis highlights the complexity of exporting generative models to a single ONNX file.")
    print("The standard approach often results in separate encoder/decoder ONNX files.")
    print("To achieve a single file, ONNX graph manipulation tools like ONNX Graph Surgeon are typically required to merge components and share weights.")

print("\n--- Test complete ---")

The Challenge of Consolidated ONNX Export

Exporting complex architectures like encoder-decoder models, prevalent in sequence-to-sequence tasks such as machine translation, often results in multiple ONNX files. This fragmentation stems from the model's inherent structure, typically comprising distinct encoder and decoder components, and sometimes additional variants like a decoder with past key-value state caching for efficient generation. While this separation is functional, it leads to practical issues such as parameter duplication, especially in shared embedding layers, significantly inflating the total file size compared to the original PyTorch model. This makes deployment and management more cumbersome.

The specific problem encountered is that standard export tools, including those in the Hugging Face ecosystem like optimum, tend to create separate ONNX files for the encoder and decoder. This means shared weights, like the embedding matrix used by both parts of the model, are duplicated across these files. For a model like Helsinki-NLP/opus-mt-fr-en, this duplication can nearly triple the total ONNX file size, posing a challenge for efficient deployment, particularly in resource-constrained environments.

Why Single ONNX Files Are Preferred

A single ONNX file offers several advantages: simplified deployment, reduced overhead in loading and managing model assets, and potentially better optimization opportunities by the inference runtime. It streamlines the process of integrating machine learning models into applications, especially in production environments where dependency management and asset transfer are critical. The goal is to encapsulate the entire model's logic and parameters into one self-contained unit.

The desire for a single ONNX file is driven by the need for greater efficiency and ease of use. Managing multiple files, ensuring their correct loading order, and handling versioning can become complex, especially in large-scale deployments. Consolidating these into one file mitigates these issues, making the model more portable and easier to integrate into various inference engines and platforms.

Strategies for Creating a Single ONNX File

Consolidating an encoder-decoder model into a single ONNX file requires a strategy that either re-exports the model with a unified forward pass or merges the existing separate ONNX files. Direct export of encoder-decoder models with shared weights into a single ONNX graph can be intricate, as standard export procedures often treat encoder and decoder as independent entities to simplify inference with ONNX Runtime's session management.

1. Unified Export via Custom Forward Pass

One approach involves defining a custom PyTorch module that encapsulates the entire encoder-decoder process within a single forward method. This method would handle the sequential nature of generation, feeding encoder outputs to the decoder. By tracing this unified forward pass using torch.onnx.export, a single ONNX graph can be generated. The key challenge here is ensuring that shared parameters, like embeddings, are correctly represented without duplication within the ONNX graph.

This method often requires careful management of input and output signatures, dynamic axes for variable sequence lengths, and potentially using ONNX attributes or custom operators if the standard export doesn't fully capture the complex generation logic. The goal is to create an ONNX model that takes source sequence inputs and outputs target sequence logits or token IDs directly.

2. Merging Existing ONNX Files

An alternative and often more practical method is to merge the independently exported ONNX files (encoder, decoder, decoder with past) into a single file. This typically involves using ONNX manipulation tools, such as onnx-graphsurgeon or custom ONNX Python scripts. These tools allow you to load the individual ONNX graphs, identify and merge shared weights (like embeddings), and reconstruct a new, unified graph.

This process requires a deep understanding of the ONNX graph structure and the model's internal operations. You would need to align input/output tensors between the encoder and decoder graphs, ensure correct data flow, and handle the sharing of weights explicitly by referencing the same tensor data. While more complex, it directly addresses the parameter duplication issue by ensuring shared weights are represented only once.

Practical Steps: Consolidating ONNX Files

While the provided script successfully exports the encoder and decoder components separately, the core problem remains: how to merge these into a single file. The Hugging Face optimum library, by default, exports sequence-to-sequence models into multiple files for compatibility with ONNX Runtime's standard inference patterns, which often manage encoder and decoder sessions independently. To achieve a single ONNX file, we must either modify the export process or perform post-export merging.

Option A: Post-Export Merging with ONNX Graph Surgeon

A robust way to create a single ONNX file is by using tools like onnx-graphsurgeon. This involves loading the separate encoder_model.onnx and decoder_model.onnx files, identifying the shared embedding tensors, and creating a new ONNX graph where the embedding tensor is referenced by both the encoder and decoder subgraphs. This requires careful manipulation of the ONNX graph structure.

The process would typically involve loading the encoder and decoder ONNX models, identifying the embedding tensors (e.g., by name or shape), and then constructing a new graph. In this new graph, the embedding tensor would be defined once and then connected to the relevant nodes in both the encoder and decoder parts of the graph. This ensures parameter sharing and reduces the overall file size.

Option B: Custom Export with Single Forward Pass

Alternatively, one could define a custom PyTorch forward pass that integrates both the encoder and decoder, including the shared embedding layer, into a single computational graph. This custom module can then be exported using torch.onnx.export. The challenge here is to ensure that PyTorch's tracing mechanism correctly identifies and shares the embedding weights without duplication, which can be tricky for complex models.

This approach requires a thorough understanding of how torch.onnx.export handles shared weights and dynamic graph generation. It might involve explicitly passing shared weights or using specific export configurations to guide the ONNX exporter. The resulting single ONNX file would then represent the entire model's functionality, including the generation loop.

Key Takeaways for Unified ONNX Export

Exporting encoder-decoder PyTorch models to a single ONNX file is not always straightforward due to the inherent separation of encoder and decoder components and shared weights. The standard export process often yields multiple files, leading to parameter duplication and increased model size. The primary challenge lies in ensuring that shared components, like embedding layers, are represented only once in the final ONNX graph.

Consolidation Techniques

To achieve a single ONNX file, two main strategies are viable: post-export merging of individual ONNX files using tools like onnx-graphsurgeon, or a custom export process that traces a unified forward pass of the PyTorch model. The merging approach is often more practical as it directly addresses parameter duplication by manipulating the ONNX graph structure.

The choice of method depends on the complexity of the model and the available tools. For many standard encoder-decoder models, merging separate ONNX files is a more direct path to a single, consolidated artifact. This process requires careful graph manipulation to ensure correct weight sharing and data flow, ultimately reducing the model's footprint and simplifying deployment.

Alternative: Optimized Runtime Loading

If creating a single ONNX file proves too complex or introduces compatibility issues, an alternative is to optimize the loading and management of the separate ONNX files within the inference runtime. ONNX Runtime is designed to handle multiple sessions efficiently, and with proper configuration, the performance impact of separate files can be minimized.

However, for scenarios demanding absolute simplicity and minimal asset management, the effort to consolidate into a single file is often worthwhile. This involves careful scripting to merge the ONNX graphs, ensuring that shared weights are correctly referenced and the overall model logic is preserved.

Related ONNX Export Challenges

Here are some related issues and techniques when working with ONNX export for complex models:

Exporting Models with Shared Weights

Many architectures, not just encoder-decoder models, feature shared weights (e.g., embedding layers in LSTMs or shared components in multimodal models). Exporting these while preserving weight sharing often requires manual ONNX graph manipulation.

Handling Dynamic Shapes in ONNX

Sequence models often have variable input lengths and batch sizes. Ensuring these dynamic shapes are correctly exported and handled by the ONNX runtime is crucial for flexibility.

Optimizing ONNX Models for Inference

After export, ONNX models can be optimized using tools like ONNX Runtime's optimizer or specialized hardware compilers to improve inference speed and reduce memory usage.

Exporting Generative Models

Generative models, especially those with autoregressive components like sequence generation, present unique challenges for ONNX export due to their iterative nature. Exporting the full generation loop can be complex, often leading to separate encoder/decoder exports or requiring custom ONNX operators.

Merging ONNX Graphs

Techniques for merging multiple ONNX graphs into a single one are essential for consolidating models, especially for architectures that naturally export into separate components.

Illustrative Code Snippets for ONNX Operations

These snippets demonstrate common tasks related to ONNX model manipulation and inference, relevant to consolidating models.

Loading and Inspecting an ONNX Model

import onnx

# Load the ONNX model
model_path = "./onnx_model_fr_en/encoder_model.onnx"
model_onnx = onnx.load(model_path)

# Print model inputs and outputs
print("Inputs:", model_onnx.graph.input)
print("Outputs:", model_onnx.graph.output)

This code uses the onnx library to load an ONNX file and inspect its input and output tensors, a foundational step for graph manipulation.

Using ONNX Graph Surgeon to Merge Embeddings

import onnx_graphsurgeon as gs
import onnx

# Assume encoder_graph and decoder_graph are loaded ONNX Graph objects
# This is a conceptual example; actual tensor names and connections need to be identified.

# Load graphs (replace with actual paths and tensor names)
# encoder_graph = gs.import_onnx(onnx.load("encoder_model.onnx"))
# decoder_graph = gs.import_onnx(onnx.load("decoder_model.onnx"))

# Placeholder for demonstration:
# Find embedding layer in encoder and decoder graphs
# embedding_encoder = gs.find_ops_by_type(encoder_graph.nodes, gs.Layer.Embedding)[0]
# embedding_decoder = gs.find_ops_by_type(decoder_graph.nodes, gs.Layer.Embedding)[0]

# If embeddings are identical (e.g., same tensor name or value):
# The goal is to ensure only one embedding tensor is present in the final graph.
# This might involve removing one and reconnecting its consumers to the other.

# For a single combined graph:
# 1. Create a new graph.
# 2. Add the shared embedding tensor to the new graph.
# 3. Add encoder nodes, connecting them to the shared embedding.
# 4. Add decoder nodes, connecting them to the shared embedding.
# 5. Define new inputs/outputs for the combined graph.

print("ONNX Graph Surgeon example: Conceptual merging of embedding layers.")
print("Actual implementation requires detailed graph analysis and manipulation.")

This conceptual snippet illustrates how onnx-graphsurgeon can be used to manipulate ONNX graphs, specifically for merging shared components like embedding layers, which is key to creating a single, efficient ONNX file.

Exporting a Simple Model with Shared Weights

import torch
import torch.nn as nn

class SharedWeightModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.embedding = nn.Embedding(10, 16)
        self.encoder_linear = nn.Linear(16, 32)
        self.decoder_linear = nn.Linear(16, 32) # Uses the same embedding output

    def forward(self, x):
        embedded = self.embedding(x)
        encoded = self.encoder_linear(embedded)
        decoded = self.decoder_linear(embedded) # Uses the same embedding output
        return encoded, decoded

model_shared = SharedWeightModel()
input_tensor = torch.randint(0, 10, (5,))

# Exporting this might still result in duplicated weights if not handled carefully
# torch.onnx.export(model_shared, input_tensor, "shared_weight_model.onnx", opset_version=14)
print("Example for exporting models with shared weights. Actual export needs care.")

This example demonstrates a PyTorch model with explicit shared weights. Exporting such models to ONNX requires ensuring the exporter correctly represents this sharing, often necessitating manual ONNX graph adjustments if not handled automatically.

Challenge	Description	Solution Strategy
Parameter Duplication	Shared layers (e.g., embeddings) are replicated across separate ONNX files (encoder, decoder).	1. Merge ONNX files using tools like ONNX Graph Surgeon. 2. Export via a custom unified forward pass in PyTorch.
Increased File Size	Duplicated parameters significantly inflate the total ONNX model size compared to the original PyTorch model.	Consolidate into a single ONNX file to reduce storage and improve asset management.
Deployment Complexity	Managing multiple model files increases overhead in loading, versioning, and integration.	A single ONNX file simplifies deployment pipelines and reduces dependencies.
Generative Model Export	Autoregressive nature of sequence generation poses difficulties for direct ONNX export of the full generation loop.	Often requires exporting encoder and decoder separately or using advanced ONNX manipulation for a unified generative graph.
Weight Sharing Preservation	Ensuring that shared weights are represented only once in the ONNX graph is critical for efficiency.	Utilize ONNX Graph Surgeon or careful PyTorch ONNX export to explicitly manage shared tensor references.