Reference Implementation Of ReduceLogSumExp Crashes With Integer Input An In Depth Analysis

Jul 21, 2025 by gitftunila 92 views

Introduction

This article delves into a bug report concerning the reference implementation of the ReduceLogSumExp operator within the ONNX (Open Neural Network Exchange) framework. The bug manifests when the operator is used with integer inputs, leading to a crash due to data type incompatibility during the computation. This article will explore the details of the bug, the expected behavior of the operator, the underlying causes of the issue, and potential resolutions, emphasizing the importance of precise specifications and robust implementations in deep learning frameworks.

Bug Report Overview

Problem Description

The core issue arises when the ReduceLogSumExp operator, which is designed to compute the logarithm of the sum of exponentials of input elements, encounters integer data. The reference implementation, which serves as a standard for the operator's behavior, fails to handle integer inputs correctly, resulting in an OverflowError. This error occurs because the implementation attempts to assign a floating-point infinity value to an integer array, which is an invalid operation. The specific error message observed is OverflowError: cannot convert float infinity to integer.

Test Case

The bug can be reproduced using a simple test case written in Python. This test case involves creating an ONNX node for the ReduceLogSumExp operator, initializing a reference evaluator, and feeding it an integer array as input. The code snippet below illustrates the test case:

def test_reduce_log_sum_exp_int():
 node = oh.make_node("ReduceLogSumExp", inputs=["data"], outputs=["res"])

 sess = ReferenceEvaluator(node, optimized=False)
 data = np.asarray([1], np.int64)
 sess.run(None, {"data": data})

Error Traceback

When the above test case is executed, it produces a traceback that pinpoints the exact location of the error. The traceback shows that the error originates within the compute_log_sum_exp function, specifically when trying to set an element of the data_max array to negative infinity (-np.inf). Since data_max is an integer array, this operation is not permissible, leading to the OverflowError.

Traceback (most recent call last):
 File "<string>", line 17, in __PYTHON_EL_eval
 File "/Users/c.bourjau/repos/quantco/onnx-tests/t.py", line 41, in <module>
 test_reduce_log_sum_exp_int()
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
 File "/Users/c.bourjau/repos/quantco/onnx-tests/t.py", line 34, in test_reduce_log_sum_exp_int
 result, = sess.run(None, {"data": data})
 ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
 File "/Users/c.bourjau/repos/quantco/onnx-tests/.pixi/envs/default/lib/python3.13/site-packages/onnx/reference/reference_evaluator.py", line 593, in run
 outputs = node.run(*inputs, **linked_attributes)
 File "/Users/c.bourjau/repos/quantco/onnx-tests/.pixi/envs/default/lib/python3.13/site-packages/onnx/reference/op_run.py", line 472, in run
 res = self._run(*args, **kwargs)
 File "/Users/c.bourjau/repos/quantco/onnx-tests/.pixi/envs/default/lib/python3.13/site-packages/onnx/reference/ops/op_reduce_log_sum_exp.py", line 45, in _run
 return compute_log_sum_exp(data, axes, keepdims)
 File "/Users/c.bourjau/repos/quantco/onnx-tests/.pixi/envs/default/lib/python3.13/site-packages/onnx/reference/ops/op_reduce_log_sum_exp.py", line 14, in compute_log_sum_exp
 data_max[ind] = -np.inf
 ~~~~~~~~~^^^^^
OverflowError: cannot convert float infinity to integer

Expected Behavior and Specification Ambiguity

Specification Interpretation

The ONNX specification for the ReduceLogSumExp operator lists int64 as a supported data type. However, the underlying mathematical operations involved, namely the logarithm (Log) and exponential (Exp) functions, are not defined for integer inputs. This creates an ambiguity in the specification: while int64 is listed as a supported type, the operator's inherent functionality is geared towards floating-point numbers.

Potential Resolutions

There are two primary ways to resolve this issue:

Clarify the Specification: The specification could be updated to explicitly exclude integer types for the ReduceLogSumExp operator. This would align the specification with the operator's mathematical nature and prevent future confusion.
Implement Integer Handling: The reference implementation could be modified to handle integer inputs. This could involve casting the integer inputs to floating-point numbers before performing the logarithmic and exponential operations, and then potentially casting the result back to an integer type if necessary. This approach would require a careful consideration of numerical stability and potential precision loss.

Desired Outcome

The ideal outcome is to have a clear and unambiguous specification for the ReduceLogSumExp operator, along with a robust implementation that either correctly handles integer inputs or explicitly disallows them. This will ensure that users of the ONNX framework can rely on the operator's behavior and avoid unexpected errors.

Deep Dive into the Implementation and Error Context

Code Analysis

To understand the error in detail, let's examine the relevant parts of the reference implementation. The error occurs within the compute_log_sum_exp function, which is responsible for performing the core computation of the ReduceLogSumExp operator. The relevant code snippet is as follows:

def compute_log_sum_exp(data, axes, keepdims):
 # ...
 data_max = np.amax(data, axis=axes, keepdims=True)
 # ...
 data_max[ind] = -np.inf
 # ...

Here, data_max is a NumPy array that stores the maximum values along the specified axes. The error occurs when the code attempts to assign -np.inf (negative infinity) to an element of data_max. If data_max is an integer array (as is the case when the input data is an integer array), this assignment is invalid and raises an OverflowError.

Root Cause

The root cause of the error is the data type mismatch. The ReduceLogSumExp operator, by its nature, involves operations that produce floating-point results (logarithms and exponentials). When the input is an integer, the intermediate computations still require floating-point representation. The reference implementation, however, does not consistently handle this type conversion, leading to the error when a floating-point value is assigned to an integer array.

Contextual Understanding

It's crucial to understand the context in which this operator is used. The ReduceLogSumExp operator is commonly employed in neural networks, particularly in softmax-like operations where numerical stability is critical. The log-sum-exp trick helps prevent overflow issues when dealing with exponentials of large numbers. However, this numerical stability is typically relevant in the context of floating-point computations, not integer computations.

Proposed Solutions and Their Implications

Solution 1: Explicit Type Casting

One potential solution is to explicitly cast the integer input to a floating-point type (e.g., np.float64) at the beginning of the compute_log_sum_exp function. This would ensure that all subsequent computations are performed using floating-point arithmetic, avoiding the OverflowError. The code modification would look something like this:

def compute_log_sum_exp(data, axes, keepdims):
 data = data.astype(np.float64) # Explicit type casting
 # ...
 data_max = np.amax(data, axis=axes, keepdims=True)
 # ...
 data_max[ind] = -np.inf
 # ...

Implications

Pros: This approach is relatively straightforward to implement and addresses the immediate error. It allows the ReduceLogSumExp operator to function correctly with integer inputs by leveraging floating-point arithmetic.
Cons: Introducing type casting might have performance implications, as floating-point operations are generally slower than integer operations. Additionally, this approach might introduce subtle numerical differences compared to a purely integer-based implementation (if one were possible).

Solution 2: Specification Restriction

Another solution is to restrict the input data types for the ReduceLogSumExp operator to floating-point types only. This would eliminate the ambiguity in the specification and prevent users from inadvertently using the operator with integer inputs. This approach would involve updating the ONNX specification and potentially adding checks in the reference implementation to enforce the type restriction.

Implications

Pros: This approach provides a clear and unambiguous definition of the operator's behavior. It avoids the complexities of handling integer inputs and ensures that the operator is used in a manner consistent with its mathematical foundation.
Cons: This approach might limit the flexibility of the operator, as it would not be usable in scenarios where integer inputs are desired. However, given the operator's nature, such scenarios are likely rare.

Recommendation

Given the nature of the ReduceLogSumExp operator and its reliance on floating-point operations, the recommended solution is to restrict the input data types to floating-point types in the ONNX specification. This approach provides the clearest and most consistent behavior for the operator. If integer inputs are truly necessary, a separate operator or a combination of other ONNX operators could be used to achieve the desired functionality.

Updating the ONNX Specification and Ensuring Clarity

Importance of Clear Specifications

The ONNX specification serves as the definitive guide for operator behavior within the ONNX ecosystem. A clear and unambiguous specification is crucial for ensuring interoperability and predictability across different implementations and platforms. When specifications are vague or contradictory, it can lead to confusion among users and inconsistencies in implementations.

Proposed Specification Changes

To address the issue with the ReduceLogSumExp operator, the ONNX specification should be updated to explicitly state that the operator only supports floating-point input types (e.g., float32, float64). This can be achieved by modifying the operator's documentation to remove int64 (and other integer types) from the list of supported data types.

Example Specification Update

The current specification might list the supported data types as:

T: tensor(float16), tensor(float), tensor(double), tensor(int64), ...

The updated specification should be:

T: tensor(float16), tensor(float), tensor(double)

This simple change clearly communicates that the ReduceLogSumExp operator is intended for use with floating-point data only.

Additional Considerations

In addition to updating the data type specifications, it's also beneficial to include a note in the documentation explaining the rationale behind this restriction. This note could highlight the operator's reliance on logarithmic and exponential functions, which are inherently floating-point operations.

Ensuring Implementation Compliance

Once the specification is updated, it's essential to ensure that all ONNX implementations (including the reference implementation) comply with the new specification. This can be achieved by adding checks in the implementations to verify the input data types and raise an error if an unsupported type is encountered.

Conclusion

The bug report regarding the ReduceLogSumExp operator highlights the importance of clear specifications and robust implementations in deep learning frameworks. The ambiguity in the ONNX specification regarding integer inputs led to a crash in the reference implementation, underscoring the need for precise definitions of operator behavior. By updating the specification to explicitly restrict input data types to floating-point types, the ONNX framework can provide a more consistent and predictable experience for its users. This article has provided a comprehensive analysis of the issue, proposed solutions, and emphasized the crucial role of specifications in ensuring the reliability of deep learning tools and frameworks.