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This commit represents a complete rework after pulling the latest changes from official ollama/ollama repository and re-applying Tesla K80 compatibility patches. ## Key Changes ### CUDA Compute Capability 3.7 Support (Tesla K80) - Added sm_37 (compute 3.7) to CMAKE_CUDA_ARCHITECTURES in CMakeLists.txt - Updated CMakePresets.json to include compute 3.7 in "CUDA 11" preset - Using 37-virtual (PTX with JIT compilation) for maximum compatibility ### Legacy Toolchain Compatibility - **NVIDIA Driver**: 470.256.02 (last version supporting Kepler/K80) - **CUDA Version**: 11.4.4 (last CUDA 11.x supporting compute 3.7) - **GCC Version**: 10.5.0 (required by CUDA 11.4 host_config.h) ### CPU Architecture Trade-offs Due to GCC 10.5 limitation, sacrificed newer CPU optimizations: - Alderlake CPU variant enabled WITHOUT AVX_VNNI (requires GCC 11+) - Still supports: SSE4.2, AVX, F16C, AVX2, BMI2, FMA - Performance impact: ~3-7% on newer CPUs (acceptable for K80 compatibility) ### Build System Updates - Modified ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt for compute 3.7 - Added -Wno-deprecated-gpu-targets flag to suppress warnings - Updated ml/backend/ggml/ggml/src/CMakeLists.txt for Alderlake without AVX_VNNI ### Upstream Sync Merged latest llama.cpp changes including: - Enhanced KV cache management with ISWA and hybrid memory support - Improved multi-modal support (mtmd framework) - New model architectures (Gemma3, Llama4, Qwen3, etc.) - GPU backend improvements for CUDA, Metal, and ROCm - Updated quantization support and GGUF format handling ### Documentation - Updated CLAUDE.md with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was dropped in official Ollama due to legacy driver/CUDA requirements. The toolchain constraint creates a deadlock: - K80 → Driver 470 → CUDA 11.4 → GCC 10 → No AVX_VNNI We accept the loss of cutting-edge CPU optimizations to enable running modern LLMs on legacy but still capable Tesla K80 hardware (12GB VRAM per GPU). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
73 lines
2.4 KiB
Go
73 lines
2.4 KiB
Go
package input
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import "github.com/ollama/ollama/ml"
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// Multimodal is a multimodal embedding or a component of one.
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// For example, it could be a row of an image that can be processed
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// independently.
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type Multimodal struct {
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// Tensor is the embedding data. Implementations may chose what to
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// store here or it may be nil if not needed. However, any ml.Tensor
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// objects must be stored here and not in Data.
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Tensor ml.Tensor
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// Data is implementation-specific opaque data, such as metadata on how
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// to layout Tensor. It may be nil if not needed. It may also store larger
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// objects such as complete images if they are to be processed later.
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Data any
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}
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// Input represents one token in the input stream
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type Input struct {
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// Token is a single element of text.
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Token int32
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// Multimodal is represents a non-text element such as an
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// image (or part of one if the image can be processed in pieces).
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// It may be used either together with Token or on its own.
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Multimodal []Multimodal
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// MultimodalHash is a unique representation of the data
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// stored in Multimodal, used for caching and comparing
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// equality.
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MultimodalHash uint64
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// SameBatch forces the following number of tokens to be processed
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// in a single batch, breaking and extending batches as needed.
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// Useful for things like images that must be processed in one
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// shot.
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SameBatch int
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}
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// MultimodalIndex is a multimodal element (such as an image)
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// together with an index into the slice of Inputs with the
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// corresponding token. Note that the index is not the same
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// as the position - to find that use the index with the
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// Positions slice.
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type MultimodalIndex struct {
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Index int
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Multimodal []Multimodal
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}
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// Batch contains the inputs for a model forward pass
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type Batch struct {
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// Inputs is the input tokens, including placeholders for multimodal inputs.
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Inputs ml.Tensor
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// Outputs are the set of indicies into Inputs for which output data should
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// be returned.
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Outputs ml.Tensor
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// Positions is the position for each Input, relative to its sequence. Equal
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// in length to Inputs.
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Positions []int32
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// Sequences is the sequence for each Input. Equal in length to Inputs.
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Sequences []int
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// Multimodal is a set of multimodal embeddings previously created by
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// EncodeMultimodal, along with an index into Inputs. Unused for text-only
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// models or for batches without multimodal elements.
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Multimodal []MultimodalIndex
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}
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