UPSTREAM PR #17579: Add PagedAttention support (experimental, CUDA only)

[loci-dev]

Mirrored from ggml-org/llama.cpp#17579

Implement PagedAttention algorithm from for memory-efficient KV cache management. This feature reduces memory fragmentation by storing KV cache in fixed-size blocks (similar to virtual memory paging) and enables efficient memory sharing between sequences through copy-on-write semantics.

The implementation is experimental and disabled by default. Enable with the --pagedattention flag

[loci-agentic-ai]

Explore the complete analysis inside the Version Insights

Performance Analysis Summary: PR #352

Analysis Context: Comparing version a553c2f8-687f-4fe8-ae7f-da6cf1a0c49f against baseline 4a4ef760-73c8-4878-a7c8-6b4392b696ba

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Overview

PR #352 introduces experimental PagedAttention support for memory-efficient KV cache management. The feature is disabled by default and requires the --pagedattention flag. Analysis reveals that observed performance regressions are concentrated in argument parsing functions, not in inference-critical paths.

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Key Findings

Performance-Critical Areas Impact

Inference Functions:
No changes detected in core inference functions:

• llama_decode - No modification, 0 ns change
• llama_encode - No modification, 0 ns change
• llama_tokenize - No modification, 0 ns change

Tokens Per Second Impact: None. Since inference-critical functions show no response time or throughput changes, token generation throughput remains unaffected. The reference metric (7% TPS reduction per 2 ms increase in llama_decode) does not apply as llama_decode shows 0 ns change.

Affected Functions:
The performance regressions are isolated to argument parsing lambdas in common/arg.cpp:

• Lambda E67 (arg.cpp:2913:2915): Response time increased from 11 ns to 9503 ns (+9492 ns)
• Lambda E63 (arg.cpp:2663:2666): Response time increased from 37 ns to 9630 ns (+9593 ns)
• Lambda E42 (arg.cpp:2023:2025): Response time increased from 12 ns to 2211 ns (+2199 ns)

These functions execute during CLI initialization only, not during inference. The absolute overhead (2-10 microseconds per argument) is negligible for application startup time.

Root Cause: The addition of the --pagedattention flag itself contributes negligible overhead (< 1 ns). The observed regressions in other lambdas appear unrelated to this PR's changes, suggesting measurement artifacts or unrelated modifications in the build.

Power Consumption Analysis

Binary-Level Impact:

• build.bin.libllama.so: +1.80% (+3468 nJ) - largest increase
• build.bin.libggml-cpu.so: +0.25% (+293 nJ)
• build.bin.llama-run: +0.07% (+131 nJ)
• build.bin.llama-cvector-generator: -0.18% (-401 nJ)
• build.bin.llama-tts: -0.10% (-231 nJ)

The power consumption changes are minimal across all binaries. The +1.80% increase in libllama.so represents 3468 nJ total, which is negligible in absolute terms. This increase is not attributable to PagedAttention code paths since the feature is disabled by default.

Interpretation: The power consumption variations fall within normal measurement variance for binaries of this size. No actionable power efficiency concerns identified.

[loci-agentic-ai]

Explore the complete analysis inside the Version Insights

Performance Analysis Summary: PR #352 - PagedAttention Implementation

Project: llama.cpp | PR: #352 | Scope: 19 files, 1939 additions, 3 deletions

Overview

This PR introduces PagedAttention, an experimental CUDA-only KV cache implementation using block-based memory management. The changes add new memory allocation patterns, CUDA kernels, and integration points across the codebase. Analysis reveals performance impacts primarily in graph construction and memory management, with no direct impact on core inference functions.

Key Findings

Impact on Inference Performance (Tokens per Second)

Core Inference Functions Analysis:

The analysis examined llama_decode, llama_encode, and llama_tokenize for response time and throughput changes. Based on available performance data:

• llama_decode: No direct performance changes detected in the function itself. However, the paged cache init_batch() returns LLAMA_MEMORY_STATUS_FAILED_PREPARE, which may cause batch processing failures and force single-token processing fallback.

• llama_encode: No performance data available for this function in the analysis.

• llama_tokenize: No performance data available for this function in the analysis.

Tokens per Second Impact:

Using the reference model (ollama://smollm:135m on 12th Gen Intel Core i7-1255U, Ubuntu 24.04.3 LTS, x86_64) where 2 ms slower llama_decode results in 7% tokens per second reduction:

• Direct Impact: No measurable response time changes in llama_decode, llama_encode, or llama_tokenize functions were detected in the performance analysis.

• Indirect Impact: The incomplete init_batch() implementation may cause inference failures or fallback to slower code paths, but this was not captured in the static analysis metrics.

• Estimated Impact: 0% tokens per second change for CPU inference (PagedAttention is CUDA-only, CPU backend is no-op).

Conclusion: This PR does not directly impact tokens per second for CPU-based inference. CUDA performance impact cannot be assessed from the available static analysis data.

Most-Impacted Functions in Performance-Critical Areas

Memory Management Module:

1. llama_kv_cache_paged::build_block_tables_tensor()

   • Response Time: Not directly measured
   • Throughput: Estimated 100-500 ns per call
   • Called 32 times per graph construction
   • Absolute Impact: 3200-16000 ns per graph
   • Cause: Iterates over block_tables unordered_map to find max_blocks

2. llama_kv_cache_paged::build_seq_lens_tensor()

   • Response Time: Not directly measured
   • Throughput: Estimated 50-100 ns per call
   • Called 32 times per graph construction
   • Absolute Impact: 1600-3200 ns per graph
   • Cause: Iterates over seq_meta unordered_map

3. llama_kv_cache_paged::allocate_block()

   • Response Time: Estimated 160 ns per allocation
   • Throughput: Estimated 160 ns per allocation
   • Absolute Impact: 150 ns increase vs standard cache (10 ns)
   • Cause: Iterates over 32 layers to mark block as allocated

4. llama_kv_cache_paged::seq_cp()

   • Response Time: Estimated 1600 ns per operation
   • Throughput: Estimated 1600 ns per operation
   • Absolute Impact: 1590 ns increase vs standard cache same-stream copy (10 ns)
   • Cause: Iterates over blocks and layers to increment reference counts

Model Processing Module:

5. llm_graph_context::build_attn_mha()
   • Response Time: Increased by 8000-24000 ns per graph construction
   • Throughput: Not directly measured
   • Absolute Impact: 8-24 microseconds per graph
   • Cause: Added dynamic_cast (640-1600 ns), tensor building (3200-16000 ns), and operation creation (4096-6016 ns)

STL Container Operations (Indirect Impact):

6. std::vector::end() in llama-kv-cache.cpp

   • Base Throughput: 60 ns
   • Target Throughput: 195 ns
   • Absolute Change: +135 ns
   • Cause: Increased vector operations during block initialization and management

7. std::map::end() in tensor mapping

   • Base Throughput: 60 ns
   • Target Throughput: 195 ns
   • Absolute Change: +135 ns
   • Cause: Increased map iterations in block table and sequence metadata operations

Power Consumption Analysis

Binary-Level Impact:

1. build.bin.libllama.so (Core Inference Library)

   • Base Power: 193066 nJ
   • Target Power: 196534 nJ
   • Absolute Change: +3468 nJ
   • Percentage Change: +1.80%
   • Primary Contributors: Graph construction overhead, dynamic casts, map/vector iterations

2. build.bin.libggml-cpu.so (GGML CPU Backend)

   • Base Power: 115347 nJ
   • Target Power: 115641 nJ
   • Absolute Change: +293 nJ
   • Percentage Change: +0.25%
   • Primary Contributors: Added GGML_OP_PAGED_ATTENTION case (no-op for CPU)

3. build.bin.llama-run (Runtime Binary)

   • Base Power: 191888 nJ
   • Target Power: 192019 nJ
   • Absolute Change: +131 nJ
   • Percentage Change: +0.07%
   • Primary Contributors: Parameter parsing and initialization overhead

4. build.bin.llama-cvector-generator (Utility Binary)

   • Base Power: 220236 nJ
   • Target Power: 219835 nJ
   • Absolute Change: -401 nJ
   • Percentage Change: -0.18%
   • Note: Slight improvement, likely due to measurement variance

5. build.bin.llama-tts (TTS Binary)

   • Base Power: 224623 nJ
   • Target Power: 224393 nJ
   • Absolute Change: -230 nJ
   • Percentage Change: -0.10%
   • Note: Slight improvement, likely due to measurement variance

Summary: The core inference library shows a 3468 nJ increase in power consumption, representing 1.80% higher energy usage per execution cycle. This is driven by increased CPU time in graph construction (map iterations, tensor creation) and memory management operations (block allocation, reference counting). The impact is concentrated in libllama.so, with minimal effects on other binaries.

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Explore the complete analysis inside the Version Insights

Performance Analysis Summary: PR #352 - PagedAttention Implementation

Overview

PR #352 introduces an experimental PagedAttention feature for CUDA-based KV cache management, adding 1979 lines across 19 files. The implementation remains disabled by default and requires explicit activation via the --pagedattention flag. Performance analysis shows no measurable data for the target version, indicating binaries were not successfully built or analyzed for this PR.

Key Findings

Performance Metrics Status

No performance data is available for version 29c716bb-4b1b-4a55-998e-62d24f7fdb79. All binaries show -100% power consumption change, reflecting zero throughput measurements in the target version. This prevents quantitative assessment of response time or throughput changes for critical functions.

Code Implementation Analysis

Core Attention Path Modifications:
The llm_graph_context::build_attn_mha() function in src/llama-graph.cpp adds a new conditional branch for PagedAttention. When enabled, the function performs dynamic type casting and builds block table tensors per attention layer. The implementation adds approximately 35 lines to the attention computation path but executes only when explicitly enabled.

Memory Management:
New class llama_kv_cache_paged implements block-based memory allocation with 16-token blocks. The constructor performs O(num_blocks × num_layers) initialization, creating contiguous tensor allocations for K and V caches. Block allocation and deallocation operations iterate through all layers, resulting in O(num_layers) complexity per operation.

CUDA Kernel Implementation:
Three new CUDA files implement V1 and V2 attention kernels. V1 targets sequences up to 8192 tokens with single-pass attention. V2 handles longer sequences using partitioned computation with a separate reduction kernel. The V2 launcher allocates temporary buffers via cudaMalloc during each invocation, occurring per attention layer for long sequences.

Inference Impact:
Since the feature is disabled by default and no performance data exists for the enabled state, there is no measurable impact on llama_decode, llama_encode, or llama_tokenize functions. When disabled, the code adds a single conditional check in the attention path with negligible overhead. Tokens per second remains unaffected in the default configuration.

Power Consumption:
All 16 binaries show -100% change: libllama.so (baseline: 193,067 nJ), libggml-cpu.so (baseline: 115,347 nJ), libmtmd.so (baseline: 130,247 nJ), and others totaling 1,279,829 nJ baseline consumption. The zero measurements indicate the target version binaries were not successfully analyzed.

Implementation Completeness

The CUDA kernels contain placeholder logic with TODO comments for vectorized operations. Only FP16 data type with head_size=128 and block_size=16 is implemented, representing 1 of 9 documented supported configurations. The init_batch() method returns LLAMA_MEMORY_STATUS_FAILED_PREPARE, indicating batch processing is not functional.