Port cpu moe options from mainline #672
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Co-Authored-By: Parsa <[email protected]>
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Do people think that this really useful?
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I did before I looked at mainline and realized it's just what I'm doing in bash, while wondering if it's even any faster than some alternatives.
Yeah, speaking as a noob here but with some user-facing experience as a dev, this feels like solving a papercut by potentially misleading people. Speaking as someone who has been on the internet, I get the feeling we're looking at something like blogspam making one piece of isolated advice into a pseudo best practice. The most credible source I can find for this exact advice are Unsloth guides for big MoE models and this Reddit post. As I understand it with my beginner mind, it's a combo of I do think we're going to see a lot of users like me trying to cram fairly sizeable MoEs into modest-to-meager hardware. I've got a 3090 on PCIe gen3, ~192GB DDR4 2666MHz and 16 threads dedicated to inference. Until I can afford to upgrade the host and leapfrog the GPU this a topic that really matters to me. If someone with deeper technical knowledge can help fill in some of the details about the tradeoffs of whole-layer vs. expert-tensor offload, I can happily test on my setup and share results on Reddit to get some evidence-based thinking going out there. I also am fuzzy on exactly why I'm told to offload from GPU to CPU starting with the last layers and working forward -- prompt processing? I think there is a need for "hybrid MoE strategies" and it would be a good backdoor for getting people to think beyond VRAM+RAM+TC. Also, I know there are people who have done statistical analysis of activations to guide offloading choices but that seems heavily workload dependent. I think any efforts on this front should be leading towards teaching someone how to read the architecture diagram and deduce which layers/tensors belong where on their hardware. |
* Port cpu moe options from mainline * Use strdup and int32_t to follow coding guidelines
* mxfp4: basics * mxfp4: Zen4 GEMM * mxfp4: repacked GEMM (AVX2/Zen4) * mxfp4: AVX2 GEMM * mxfp4: NEON GEMM * mxfp4: repacked GEMM (NEON) * mxfp4: Metal * Fix quantized K cache without FA (#680) * Prevent assert with quantized K cache and no FA * Fix MMQ when running with quantized K cache without FA --------- Co-authored-by: Iwan Kawrakow <[email protected]> * Fix for Deepseek r1 parsing (#676) * Implement function calling / tools for ik_llama.cpp for Kimi K2 * Implement basic tool choice * Backport llama.cpp tool calls support * Enhance function calls with improved chat parser and string utilities - Add new chat.h/chat.cpp and chat-parser.h/chat-parser.cpp for better chat handling - Improve function calls parsing with fallback to llama.cpp builder pattern - Add string utility functions (starts_with, ends_with, find_partial_stop) - Update README with function calls testing instructions - Enhance Kimi K2 parser and function calls documentation - Add comprehensive test suite for function calls - Update CMakeLists.txt and Makefile for new components * Enhance function calling with unified streaming and parser improvements - Fix streaming content cleanup to prevent function syntax in output - Unify content extraction patterns with llama.cpp approach - Improve Kimi K2 parser robustness and partial content handling - Add comprehensive test coverage for function call scenarios - Optimize chat message parsing and diff computation * Replace hardcoded values in kimi_k2_parser.hpp with named constants - Add compile-time constants for all token format markers - Add compile-time constants for XML format markers - Add compile-time constants for simple format patterns - Replace all hardcoded string literals with named constants - Use compile-time length calculation to avoid manual counting - Improve maintainability and reduce magic numbers throughout parser * Fix duplicate common_chat_parse definition - Remove duplicate implementation from chat-parser.cpp - Keep single implementation in chat.cpp following llama.cpp patterns - Resolves linker error: multiple definition of common_chat_parse * Fix JSON assertion failure in function call parsing - Add proper validation that 'function' field is an object before accessing nested keys - Handle missing 'arguments' field gracefully with default "{}" - Prevents crash when parsing malformed tool call JSON structures * Add comprehensive Qwen3 XML tool calling support with unit tests - Implement Qwen3 XML parser with <tool_call>{"name": "func", "arguments": {...}}</tool_call> format - Add model detection and routing for Qwen3 vs Kimi-K2 formats - Create 8 comprehensive unit tests covering parsing, streaming, error handling - Fix token format cleaning bug in kimi_k2_parser.hpp processing order - Remove progressive parsing code and related utilities - Add tool injection support for Qwen3 format in server utils * Add DeepSeek R1 function calling support with comprehensive unit tests - Implement complete DeepSeek R1 tool call parsing in common_chat_parser.cpp - Add DeepSeek R1 model detection and tool injection in deepseek_r1_tools.hpp - Update function_calls.hpp with DeepSeek R1 integration and content extraction - Update documentation to reflect support for Kimi-K2, Qwen3, and DeepSeek R1 models - Add comprehensive unit tests for DeepSeek R1 reasoning, tool calls, and integration - Port exact implementation patterns from original llama.cpp for compatibility Key features: - Native DeepSeek R1 format: <｜tool▁calls▁begin｜>function<｜tool▁sep｜>name```json{}```<｜tool▁call▁end｜><｜tool▁calls▁end｜> - Reasoning content extraction from <think>...</think> tags - Multiple tool calls support with separate call blocks - Model detection for deepseek-r1, deepseek_r1 naming patterns - Integration with incremental parsing and streaming support * Add partial parsing support for JSON and regex - json-partial.h/cpp: JSON partial parsing functionality - regex-partial.h/cpp: Regex partial parsing functionality * Add format_chat integration tests for Qwen3 tool injection - Add test_qwen3_format_chat_integration() to validate tool injection pipeline - Test tool injection conditions and system message enhancement - Verify JSON formatting and anti-preamble instructions - Add comprehensive test documentation Tests confirm tool injection works correctly - conversational preamble issue is not in ik_llama.cpp but likely in UI configuration. * Fix Qwen3 tool call parsing - pass model name to parser Server was not passing model name to parse_chat_message_incremental(), causing Qwen3 to fall back to Kimi-K2 parser and return tool calls as content instead of proper tool_calls array. * Fix non-streaming path to use model-specific parsing Non-streaming responses were hardcoded to use Kimi-K2 format, causing Qwen3 XML tool calls to be returned as content instead of proper tool_calls array. Now uses same model detection as streaming path for consistency. * Update Qwen3 function call handling in server and tests - Enhanced server function call detection and response formatting - Improved test coverage for Qwen3 tool call scenarios - Refined XML parsing for better tool execution support * Add DeepSeek-R1 function call parsing support Implements comprehensive parsing for all 4 DeepSeek-R1 function call formats: - Format 1: Standard function call syntax (already supported) - Format 2: Alternative function call patterns (already supported) - Format 3: Tools array format - function\n```json\n{"tools": [...]} - Format 4: XML wrapped format - <tool_call>function</think>Name\n```json\n{...}```</tool_call> Key changes: - Added parse_deepseek_r1_tools_array() following original parse_prefixed_json_tool_call_array pattern - Added parse_deepseek_r1_xml_wrapped() following Hermes-2-Pro XML wrapper patterns - Integrated both parsers into exception handling chain for robust fallback - Added comprehensive TDD test coverage for all formats - Anonymized all confidential information while preserving functionality Resolves tool_calls_count=0 issue where DeepSeek-R1 models generated valid tool calls but server failed to parse them correctly. * Update function_calls.md documentation for DeepSeek-R1 Format 4 - Added Format 4 (XML wrapped) documentation with examples - Updated implementation notes with correct parser order (3→4→1→2) - Marked all DeepSeek-R1 formats as working (July 2025 update) - Updated test status for Format 3 and 4 as passing - Added parse_deepseek_r1_xml_wrapped() function reference - Corrected implementation file line numbers * Fix merge conflict in test-function-calls.cpp - Removed incomplete merge conflict marker from line 3027 - Ensured all tests compile and pass successfully - All DeepSeek-R1 formats (1-4) working correctly - All streaming and content cleaning tests passing * Fix DeepSeek R1 parsing issue with responses wrapped in think tags Restore missing consume_rest() call from working PR #648 implementation. When responses don't contain tool calls, remaining content after reasoning parsing must be preserved as displayable content. Fixes issue where entire responses wrapped in <think> tags resulted in empty content output. * Implement proper reasoning handling following original llama.cpp patterns - Add missing reasoning_format and reasoning_in_content fields to common_chat_syntax - Update try_parse_reasoning to match original llama.cpp logic exactly - Add TDD test case with reasoning_in_content=true for DeepSeek R1 - Following TDD: test should now pass with proper syntax configuration Based on original llama.cpp implementation patterns. * TDD SUCCESS: Fix DeepSeek R1 thinking tag termination issue ✅ Test passes with reasoning_in_content=true configuration - Content properly preserved: '<think>content</think>' displays fully - Reasoning field empty as expected - Following TDD: test-first approach validates the fix Next: Update server to automatically apply this configuration. * Complete server integration fix for DeepSeek R1 thinking tag termination - Server now automatically sets reasoning_in_content=true for DeepSeek R1 models - Fixes issue where responses wrapped in <think> tags appear empty to users * Add TDD test case for DeepSeek R1 thinking tag termination issue - Test reproduces the exact failure scenario reported by user - Validates that reasoning_in_content=true fixes the issue - Demonstrates empty content problem and working solution * Add remaining TDD test changes for DeepSeek R1 thinking tag fix * Add debug output after upstream merge * Remove temporary benchmark and debug files - Remove tests/benchmark-progressive-parsing.cpp (development tool, not part of core functionality) - Remove tests/reproduce_bug.sh (debugging script, not needed for PR) * Port cpu moe options from mainline (#672) * Port cpu moe options from mainline * Use strdup and int32_t to follow coding guidelines * maxfp4: CUDA dequantize * mxfp4: CUDA GEMV * mxfp4: CUDA MMQ * mxfp4: minor CUDA tweaks --------- Co-authored-by: Iwan Kawrakow <[email protected]> Co-authored-by: Anton Sokolchenko <[email protected]> Co-authored-by: Parsa <[email protected]>
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Simple port of ggml-org/llama.cpp#15077.