mirror of
https://github.com/dogkeeper886/ollama37.git
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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
140 lines
3.9 KiB
Go
140 lines
3.9 KiB
Go
package openai
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import (
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"encoding/base64"
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"math"
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"testing"
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"github.com/ollama/ollama/api"
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)
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func TestToEmbeddingList(t *testing.T) {
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testCases := []struct {
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name string
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embeddings [][]float32
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format string
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expectType string // "float" or "base64"
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expectBase64 []string
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expectCount int
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promptEval int
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}{
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{"float format", [][]float32{{0.1, -0.2, 0.3}}, "float", "float", nil, 1, 10},
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{"base64 format", [][]float32{{0.1, -0.2, 0.3}}, "base64", "base64", []string{"zczMPc3MTL6amZk+"}, 1, 5},
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{"default to float", [][]float32{{0.1, -0.2, 0.3}}, "", "float", nil, 1, 0},
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{"invalid defaults to float", [][]float32{{0.1, -0.2, 0.3}}, "invalid", "float", nil, 1, 0},
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{"multiple embeddings", [][]float32{{0.1, 0.2}, {0.3, 0.4}, {0.5, 0.6}}, "base64", "base64", []string{"zczMPc3MTD4=", "mpmZPs3MzD4=", "AAAAP5qZGT8="}, 3, 0},
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{"empty embeddings", nil, "float", "", nil, 0, 0},
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}
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for _, tc := range testCases {
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t.Run(tc.name, func(t *testing.T) {
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resp := api.EmbedResponse{
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Embeddings: tc.embeddings,
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PromptEvalCount: tc.promptEval,
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}
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result := ToEmbeddingList("test-model", resp, tc.format)
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if tc.expectCount == 0 {
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if len(result.Data) != 0 {
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t.Errorf("expected 0 embeddings, got %d", len(result.Data))
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}
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return
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}
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if len(result.Data) != tc.expectCount {
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t.Fatalf("expected %d embeddings, got %d", tc.expectCount, len(result.Data))
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}
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if result.Model != "test-model" {
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t.Errorf("expected model 'test-model', got %q", result.Model)
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}
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// Check type of first embedding
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switch tc.expectType {
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case "float":
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if _, ok := result.Data[0].Embedding.([]float32); !ok {
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t.Errorf("expected []float32, got %T", result.Data[0].Embedding)
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}
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case "base64":
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for i, data := range result.Data {
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embStr, ok := data.Embedding.(string)
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if !ok {
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t.Errorf("embedding %d: expected string, got %T", i, data.Embedding)
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continue
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}
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// Verify it's valid base64
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if _, err := base64.StdEncoding.DecodeString(embStr); err != nil {
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t.Errorf("embedding %d: invalid base64: %v", i, err)
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}
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// Compare against expected base64 string if provided
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if tc.expectBase64 != nil && i < len(tc.expectBase64) {
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if embStr != tc.expectBase64[i] {
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t.Errorf("embedding %d: expected base64 %q, got %q", i, tc.expectBase64[i], embStr)
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}
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}
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}
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}
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// Check indices
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for i := range result.Data {
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if result.Data[i].Index != i {
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t.Errorf("embedding %d: expected index %d, got %d", i, i, result.Data[i].Index)
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}
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}
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if tc.promptEval > 0 && result.Usage.PromptTokens != tc.promptEval {
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t.Errorf("expected %d prompt tokens, got %d", tc.promptEval, result.Usage.PromptTokens)
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}
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})
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}
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}
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func TestFloatsToBase64(t *testing.T) {
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floats := []float32{0.1, -0.2, 0.3, -0.4, 0.5}
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result := floatsToBase64(floats)
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// Verify it's valid base64
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decoded, err := base64.StdEncoding.DecodeString(result)
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if err != nil {
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t.Fatalf("failed to decode base64: %v", err)
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}
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// Check length
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expectedBytes := len(floats) * 4
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if len(decoded) != expectedBytes {
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t.Errorf("expected %d bytes, got %d", expectedBytes, len(decoded))
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}
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// Decode and verify values
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for i, expected := range floats {
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offset := i * 4
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bits := uint32(decoded[offset]) |
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uint32(decoded[offset+1])<<8 |
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uint32(decoded[offset+2])<<16 |
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uint32(decoded[offset+3])<<24
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decodedFloat := math.Float32frombits(bits)
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if math.Abs(float64(decodedFloat-expected)) > 1e-6 {
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t.Errorf("float[%d]: expected %f, got %f", i, expected, decodedFloat)
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}
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}
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}
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func TestFloatsToBase64_EmptySlice(t *testing.T) {
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result := floatsToBase64([]float32{})
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// Should return valid base64 for empty slice
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decoded, err := base64.StdEncoding.DecodeString(result)
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if err != nil {
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t.Fatalf("failed to decode base64: %v", err)
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}
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if len(decoded) != 0 {
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t.Errorf("expected 0 bytes, got %d", len(decoded))
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}
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}
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