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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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
6.1 KiB
Diff
140 lines
6.1 KiB
Diff
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
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From: Jesse Gross <jesse@ollama.com>
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Date: Fri, 18 Apr 2025 15:58:19 -0700
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Subject: [PATCH] graph memory reporting on failure
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---
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ggml/include/ggml-alloc.h | 1 +
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ggml/include/ggml-backend.h | 1 +
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ggml/src/ggml-alloc.c | 34 +++++++++++++++++++++++++++++++---
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ggml/src/ggml-backend.cpp | 7 +++++++
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4 files changed, 40 insertions(+), 3 deletions(-)
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diff --git a/ggml/include/ggml-alloc.h b/ggml/include/ggml-alloc.h
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index 2cb150fd..7ab3f019 100644
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--- a/ggml/include/ggml-alloc.h
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+++ b/ggml/include/ggml-alloc.h
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@@ -65,6 +65,7 @@ GGML_API bool ggml_gallocr_reserve_n(
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GGML_API bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
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GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id);
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+GGML_API size_t ggml_gallocr_get_attempted_buffer_size(ggml_gallocr_t galloc, int buffer_id);
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// Utils
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// Create a buffer and allocate all the tensors in a ggml_context
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diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
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index f1b74078..c54ff98b 100644
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--- a/ggml/include/ggml-backend.h
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+++ b/ggml/include/ggml-backend.h
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@@ -318,6 +318,7 @@ extern "C" {
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GGML_API ggml_backend_buffer_type_t ggml_backend_sched_get_buffer_type(ggml_backend_sched_t sched, ggml_backend_t backend);
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GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
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+ GGML_API size_t ggml_backend_sched_get_attempted_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
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GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
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GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
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diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
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index 929bc448..eee9d3b1 100644
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--- a/ggml/src/ggml-alloc.c
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+++ b/ggml/src/ggml-alloc.c
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@@ -486,6 +486,7 @@ struct node_alloc {
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struct ggml_gallocr {
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ggml_backend_buffer_type_t * bufts; // [n_buffers]
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struct vbuffer ** buffers; // [n_buffers]
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+ size_t *buffer_sizes; // [n_buffers]
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struct ggml_dyn_tallocr ** buf_tallocs; // [n_buffers]
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int n_buffers;
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@@ -509,6 +510,9 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
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galloc->buffers = calloc(n_bufs, sizeof(struct vbuffer *));
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GGML_ASSERT(galloc->buffers != NULL);
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+ galloc->buffer_sizes = calloc(n_bufs, sizeof(size_t));
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+ GGML_ASSERT(galloc->buffer_sizes != NULL);
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+
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galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
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GGML_ASSERT(galloc->buf_tallocs != NULL);
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@@ -576,6 +580,7 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) {
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ggml_hash_set_free(&galloc->hash_set);
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free(galloc->hash_values);
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free(galloc->bufts);
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+ free(galloc->buffer_sizes);
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free(galloc->buffers);
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free(galloc->buf_tallocs);
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free(galloc->node_allocs);
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@@ -869,6 +874,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
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}
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}
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+ bool success = true;
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+
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// reallocate buffers if needed
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for (int i = 0; i < galloc->n_buffers; i++) {
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// if the buffer type is used multiple times, we reuse the same buffer
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@@ -898,14 +905,19 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
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ggml_vbuffer_free(galloc->buffers[i]);
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galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
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- if (galloc->buffers[i] == NULL) {
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+ if (galloc->buffers[i]) {
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+ galloc->buffer_sizes[i] = ggml_vbuffer_size(galloc->buffers[i]);
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+ } else {
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GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
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- return false;
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+ galloc->buffer_sizes[i] = new_size;
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+ success = false;
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}
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+ } else {
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+ galloc->buffer_sizes[i] = ggml_vbuffer_size(galloc->buffers[i]);
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}
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}
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- return true;
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+ return success;
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}
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bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
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@@ -1060,6 +1072,22 @@ size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
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return ggml_vbuffer_size(galloc->buffers[buffer_id]);
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}
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+size_t ggml_gallocr_get_attempted_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
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+ GGML_ASSERT(buffer_id >= 0 && buffer_id < galloc->n_buffers);
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+
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+ for (int i = 0; i < buffer_id; i++) {
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+ if (galloc->buf_tallocs[i] == galloc->buf_tallocs[buffer_id]) {
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+ // This buffer is the same as a previous one due to the same buffer type being used multiple times
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+ // (See above.) However, we need a different check because multiple buffers might be NULL in our
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+ // case and we still want to know the attempted size.
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+
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+ return 0;
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+ }
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+ }
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+
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+ return galloc->buffer_sizes[buffer_id];
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+}
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+
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// utils
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static void free_buffers(ggml_backend_buffer_t ** buffers, const size_t * n_buffers) {
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diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
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index 8ba86f82..cb2b9956 100644
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--- a/ggml/src/ggml-backend.cpp
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+++ b/ggml/src/ggml-backend.cpp
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@@ -1809,6 +1809,13 @@ size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backe
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return ggml_gallocr_get_buffer_size(sched->galloc, backend_index);
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}
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+size_t ggml_backend_sched_get_attempted_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
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+ int backend_index = ggml_backend_sched_backend_id(sched, backend);
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+ GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
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+
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+ return ggml_gallocr_get_attempted_buffer_size(sched->galloc, backend_index);
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+}
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+
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void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
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GGML_ASSERT(sched);
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int backend_index = ggml_backend_sched_backend_id(sched, backend);
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