Preserve model precision

[joein]

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[coderabbitai]

📝 Walkthrough

Walkthrough

The pull request introduces several changes across the codebase with a focus on type handling and output processing. A new type alias is added to include NDArray[np.float64] in the NumpyArray type definition. Multiple methods across the modules (such as in the Colbert, Colpali, OnnxTextEmbedding, PooledEmbedding, and PooledNormalizedEmbedding classes) have been modified to remove explicit conversion of numerical values to np.float32. This adjustment alters the data types of outputs by returning them in their original form. In addition, the project version has been updated from "0.5.1" to "0.6.0" in the pyproject.toml file, and test cases have been updated to reflect the removal of type conversion for dummy embeddings.

Suggested Reviewers

• I8dNLo
• generall

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📥 Commits

Reviewing files that changed from the base of the PR and between 6cda2ce and 32112dd.

📒 Files selected for processing (8)

• fastembed/common/types.py (1 hunks)
• fastembed/late_interaction/colbert.py (2 hunks)
• fastembed/late_interaction_multimodal/colpali.py (2 hunks)
• fastembed/text/onnx_embedding.py (1 hunks)
• fastembed/text/pooled_embedding.py (1 hunks)
• fastembed/text/pooled_normalized_embedding.py (1 hunks)
• pyproject.toml (1 hunks)
• tests/test_custom_models.py (2 hunks)

✅ Files skipped from review due to trivial changes (1)

• pyproject.toml

⏰ Context from checks skipped due to timeout of 90000ms (16)

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🔇 Additional comments (12)

fastembed/common/types.py (1)

19-19: Excellent addition of float64 support to NumpyArray type.

Adding NDArray[np.float64] to the NumpyArray type definition aligns perfectly with the PR objective of preserving model precision. This change enables the codebase to properly type-hint methods that return higher precision float arrays without forcing conversion to lower precision types.

fastembed/late_interaction_multimodal/colpali.py (2)

142-145: Preserving original precision by removing float32 conversion.

This change maintains the original precision of the model output by removing the previous explicit conversion to np.float32, which is consistent with the PR's objective.

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160-160: Preserving original precision by removing float32 conversion.

Removing the explicit conversion to np.float32 for the text model output ensures that the original precision from the model is preserved.

fastembed/late_interaction/colbert.py (3)

49-49: Preserving original precision by removing float32 conversion for query outputs.

This change maintains the original precision of query embeddings by removing the previous explicit conversion to np.float32, which is consistent with the PR's objective.

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61-61: Preserving original precision in attention mask application.

The removal of explicit type conversion in the attention mask application step ensures that precision is maintained throughout the computation.

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65-65: Preserving original precision in the final document output.

This change ensures the normalized document embeddings maintain their original precision by removing the explicit float32 conversion.

fastembed/text/pooled_embedding.py (1)

118-118:

Details

❓ Verification inconclusive

Preserving original precision in pooled embeddings.

Removing the float32 conversion from the mean pooling output ensures that the embeddings maintain their original precision, which aligns with the PR objective.

Note that this change might increase memory usage for applications using these embeddings. Have you verified that this precision increase doesn't significantly impact performance or memory usage in typical use cases?

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🌐 Web query:

What are the performance and memory trade-offs between np.float32 and np.float64 in numpy?

💡 Result:

Using np.float32 and np.float64 in NumPy involves significant trade-offs between memory usage, computational speed, and numerical precision:

Memory Usage

• Halved memory footprint:
  np.float32 uses 4 bytes per value vs np.float64's 8 bytes, reducing array memory usage by 50%[1][2][7]. For example:
  • A 500x2000x2000 array consumes 16 GB as float64 vs 8 GB as float32[2].
  • This enables larger datasets to fit into RAM or GPU memory[8].

Computational Performance

• Potential speed gains:
  Operations on float32 arrays can be ~2x faster due to reduced data transfer (e.g., summing arrays: 23 ms vs 50 ms)[7]. Benefits are more pronounced when:
  1. Algorithms are memory-bandwidth limited[8]
  2. Hardware (GPUs) natively optimizes for 32-bit math[4]
• Mixed-type penalties:
  Operations combining float32/float64 may incur conversion overheads (e.g., Julia saw 100x slowdowns, while NumPy showed minimal impact)[3][6].

Numerical Considerations

• Precision limitations:
  float32 only represents ~16 million distinct values at any given scale vs float64's ~9 quintillion[1]. Critical implications include:
  1. Loss of sub-integer precision above 16 million (e.g., 16777217 → 16777216)[1]
  2. Limited temporal resolution (~4 hours at millisecond precision)[1]
• Error propagation:
  32-bit floats accumulate rounding errors faster in iterative calculations[10].

When to Use Each

Scenario	Recommended Type
Memory-constrained systems	float32
High-precision science	float64
GPU-accelerated ML models	float32 (or lower)
Time-series with wide range	int32 + scaling[1]

For many machine learning applications where exact precision isn't critical, float32 provides a good balance of performance and memory efficiency[7][8]. Scientific computing often requires float64 to maintain numerical stability through long computation chains[10].

Citations:

• 1: https://pythonspeed.com/articles/float64-float32-precision/
• 2: https://gavinw.me/notes/numpy/large-array-memory.html
• 3: https://discourse.julialang.org/t/drastic-performance-hit-matrix-multiply-different-types-internal-cast-julia-vs-numpy/17120
• 4: https://discourse.julialang.org/t/moving-from-float64-to-float32-not-improving-performance/88516
• 5: https://numpy.org/devdocs/user/basics.types.html
• 6: float64 is faster than float32?? Theano/Theano#1913
• 7: https://vinson2233.wordpress.com/2020/11/30/numpy-and-pandas-numerical-data-types-stability-speed-and-memory-usage/
• 8: float32 vs float64 erikbern/ann-benchmarks#20
• 9: numpy strings use too much memory numpy/numpy#15347
• 10: https://neurostars.org/t/consequence-of-using-single-float32-or-double-float64-precision-for-saving-interpolated-data/224

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Maintaining Full Numerical Precision in Mean Pooling

The updated code now directly returns the result from self.mean_pooling(embeddings, attn_mask), thereby preserving the original precision of the embeddings. This change ensures fidelity to the PR objective; however, it comes with a trade-off: higher precision types (e.g., np.float64) consume roughly twice the memory compared to np.float32 and can be slower in memory-bound operations. Please verify that the increased memory usage doesn’t adversely affect performance in your typical use cases.

• File: fastembed/text/pooled_embedding.py
• Line: 118

tests/test_custom_models.py (3)

74-75: Improved precision by removing float32 conversion.

The changes remove the explicit conversion to np.float32, allowing the arrays to maintain their default np.float64 precision from np.random.random().

This aligns with the PR objective to preserve model precision.

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95-96: Consistent precision handling in expected output.

Removing the float32 type conversion ensures the expected output matches the new behavior of maintaining higher precision.

This change correctly updates test expectations to match the implementation changes.

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97-98: Normalized embeddings now preserve precision.

The expected output for normalized embeddings now correctly maintains the original precision without downconverting to float32.

This is consistent with changes in the implementation code.

fastembed/text/onnx_embedding.py (1)

311-311: Return embeddings with original precision.

This change removes the explicit conversion to np.float32, allowing the embeddings to retain their original precision after normalization.

This change directly implements the PR objective of preserving model precision. Note that downstream code must now handle potentially higher precision values (likely float64) correctly.

fastembed/text/pooled_normalized_embedding.py (1)

146-146: Maintain precision in normalized pooled embeddings.

Removed the explicit conversion to np.float32, allowing the normalized embeddings to retain their original data type.

This change aligns with the PR goal of preserving model precision. The higher precision output (likely float64) may improve numerical stability in downstream calculations.

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