UPSTREAM PR #16937: common : move gpt-oss reasoning processing to init params

[DajanaV]

Mirrored from ggml-org/llama.cpp#16937

A bit of clean up.

Move assignment of the thinking field to common_chat_params_init_gpt_oss(), since it is gpt-oss specific.

ref: ggml-org/llama.cpp#16934 (comment)

[loci-agentic-ai]

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Performance Analysis Report

Summary of Performance Changes

The analysis reveals minimal performance changes between versions, with degradations measured in sub-nanosecond precision that round to zero when expressed as integers. The changes are concentrated in utility functions within the vocabulary processing module rather than core inference functions.

Critical Function Analysis

Core Inference Functions - No Impact

• llama_decode(): No measurable change (Response Time: 49,003,908 ns, unchanged)
• llama_encode(): No measurable change (Response Time: 12,329,225 ns, unchanged)
• llama_tokenize(): No measurable change (Response Time: 834,830 ns, unchanged)

Affected Utility Functions

• std::make_pair (vocabulary processing): +0.11% throughput degradation (+0.13 ns)
• Unicode operator: +0.08% response time degradation (+0.015 ns)

KPI Impact Assessment

1. Tokens Per Second - No Impact

Status: No measurable impact on inference throughput

Analysis: The core tokenization and inference functions (llama_decode, llama_encode, llama_tokenize) show no performance changes. Given the reference that a 2ms slower llama_decode function results in 7% fewer tokens per second, the observed sub-nanosecond changes in utility functions will not affect tokens per second performance.

Impacted Functions: None of the critical inference functions show measurable changes.

2. Power Consumption - Negligible Impact

Status: Effectively no change across all binaries

Impacted Binaries:

• build.bin.libllama.so: +1.27 nJ (+0.0004% change)
• build.bin.libggml-base.so: No change
• build.bin.libggml-cpu.so: No change
• build.bin.libggml.so: No change

Analysis: The minimal power consumption increase in libllama.so is within measurement noise tolerance and represents no practical impact on energy efficiency.

3. Quantization Efficiency - No Impact

Status: No changes detected in quantization-related functions

Analysis: Functions like llama_model_quantize() and quantization format handling show no performance changes. The observed degradations are in vocabulary processing utilities, not quantization pathways.

4. Memory Usage - No Impact

Status: No changes in memory management functions

Analysis: Memory management functions (llama_memory_clear, llama_memory_seq_rm, KV cache operations) show no performance changes. The utility function changes do not affect memory allocation patterns or cache efficiency.

5. Batch Processing - No Impact

Status: No changes in batch processing functions

Analysis: Batch processing functions (llama_batch_init, llama_batch_get_one, llama_decode with batches) show no performance changes. The observed degradations are isolated to vocabulary processing utilities.

Root Cause Analysis

GPT-OSS Template Processing Changes

The performance changes correlate with the GPT-OSS reasoning processing modifications in PR #44:

• std::make_pair degradation: Located in llama-vocab.cpp:922:928, likely related to enhanced message preprocessing logic that creates adjusted message copies
• Unicode operator degradation: Part of text processing pipeline affected by template-specific message handling

Technical Factors

• Template Specialization Overhead: New conditional logic for GPT-OSS templates introduces minimal processing overhead
• Message Object Copying: Creation of adjusted message arrays adds negligible memory allocation overhead
• Compiler Optimization Variations: Sub-nanosecond changes may reflect different optimization decisions in template instantiation

Action Items

Code Optimization

• Template Processing: Consider lazy evaluation for message adjustments in GPT-OSS template initialization
• Memory Allocation: Optimize object copying patterns in message preprocessing logic
• Compiler Optimization: Verify consistent optimization flags across builds to minimize template instantiation variations

Build System

• Optimization Flags: Ensure aggressive optimization (-O3, -march=native) is consistently applied to vocabulary processing modules
• Link-Time Optimization: Enable LTO to optimize cross-module template instantiations
• Profile-Guided Optimization: Consider PGO for template-heavy code paths

Conclusion

The version comparison shows no meaningful performance impact on inference capabilities. The sub-nanosecond degradations in utility functions are within measurement precision limits and do not affect any critical performance metrics. The changes represent successful architectural improvements that enhance template system flexibility without compromising inference performance.