perf: Conditional gallocr caching for Fast-AR decode on pure CPU runs (%4~ faster)#43
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Skyrion9 wants to merge 1 commit into
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perf: Conditional gallocr caching for Fast-AR decode on pure CPU runs (%4~ faster)#43Skyrion9 wants to merge 1 commit into
Skyrion9 wants to merge 1 commit into
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… %4~ faster CPU inference. - Implemented ggml_gallocr_t with proper lifecycle management. - We check if we're running purely on CPU and use the cached codepath, or fallback to old behavior when any GPU backend is involved. - We effectively eliminate redundant allocation cycles in Fast-AR decoding loop for pure CPU configs.
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Estimated code review effort: 3 (Moderate) | ~15–30 minutes Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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🧹 Nitpick comments (1)
src/s2_model.cpp (1)
1238-1289: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winDeduplicate the tensor-set calls shared by both branches.
The
ggml_backend_tensor_setcalls forhidden0,positions, andprefix_ids(Lines 1254-1258) are byte-for-byte identical to the ones in the GPU-offload branch (Lines 1275-1279). Only the allocation and compute calls actually differ between branches; the input-setting step can be pulled out into a single shared block after allocation succeeds in either branch, avoiding the two copies drifting apart later.♻️ Proposed refactor
if (is_cpu_only) { if (!fast_gallocr_) { fast_gallocr_ = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend_cpu_)); if (!fast_gallocr_) { std::fprintf(stderr, "[fast_decode] gallocr creation failed\n"); ggml_free(ctx0); return false; } } if (!ggml_gallocr_alloc_graph(fast_gallocr_, gf)) { std::fprintf(stderr, "[fast_decode] gallocr alloc failed\n"); ggml_free(ctx0); return false; } - - ggml_backend_tensor_set(hidden0, hidden_in.data(), 0, hidden_in.size() * sizeof(float)); - ggml_backend_tensor_set(positions, pos_vals.data(), 0, pos_vals.size() * sizeof(int32_t)); - if (prefix_ids) { - ggml_backend_tensor_set(prefix_ids, prefix_tokens.data(), 0, prefix_tokens.size() * sizeof(int32_t)); - } - if (ggml_backend_graph_compute(backend_cpu_, gf) != GGML_STATUS_SUCCESS) { std::fprintf(stderr, "[fast_decode] cpu compute failed\n"); ggml_free(ctx0); return false; } } else { ggml_backend_sched_reset(fast_sched_); if (!ggml_backend_sched_alloc_graph(fast_sched_, gf)) { std::fprintf(stderr, "[fast_decode] sched alloc failed\n"); ggml_backend_sched_reset(fast_sched_); ggml_free(ctx0); return false; } - - ggml_backend_tensor_set(hidden0, hidden_in.data(), 0, hidden_in.size() * sizeof(float)); - ggml_backend_tensor_set(positions, pos_vals.data(), 0, pos_vals.size() * sizeof(int32_t)); - if (prefix_ids) { - ggml_backend_tensor_set(prefix_ids, prefix_tokens.data(), 0, prefix_tokens.size() * sizeof(int32_t)); - } - + + } + + ggml_backend_tensor_set(hidden0, hidden_in.data(), 0, hidden_in.size() * sizeof(float)); + ggml_backend_tensor_set(positions, pos_vals.data(), 0, pos_vals.size() * sizeof(int32_t)); + if (prefix_ids) { + ggml_backend_tensor_set(prefix_ids, prefix_tokens.data(), 0, prefix_tokens.size() * sizeof(int32_t)); + } + + if (is_cpu_only) { + if (ggml_backend_graph_compute(backend_cpu_, gf) != GGML_STATUS_SUCCESS) { + std::fprintf(stderr, "[fast_decode] cpu compute failed\n"); + ggml_free(ctx0); + return false; + } + } else { if (ggml_backend_sched_graph_compute(fast_sched_, gf) != GGML_STATUS_SUCCESS) { std::fprintf(stderr, "[fast_decode] sched compute failed\n"); ggml_backend_sched_reset(fast_sched_); ggml_free(ctx0); return false; } ggml_backend_sched_reset(fast_sched_); }🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/s2_model.cpp` around lines 1238 - 1289, Deduplicate the input tensor setup in the fast decode flow: remove the identical ggml_backend_tensor_set calls for hidden0, positions, and optional prefix_ids from both is_cpu_only branches, and place one shared block after the branch-specific graph allocation succeeds and before computation. Preserve the existing allocation, compute, cleanup, and failure behavior for both CPU and scheduled backends.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Nitpick comments:
In `@src/s2_model.cpp`:
- Around line 1238-1289: Deduplicate the input tensor setup in the fast decode
flow: remove the identical ggml_backend_tensor_set calls for hidden0, positions,
and optional prefix_ids from both is_cpu_only branches, and place one shared
block after the branch-specific graph allocation succeeds and before
computation. Preserve the existing allocation, compute, cleanup, and failure
behavior for both CPU and scheduled backends.
ℹ️ Review info
⚙️ Run configuration
Configuration used: defaults
Review profile: CHILL
Plan: Pro Plus
Run ID: 95e6695c-8e20-49e1-a5ed-373709072048
📒 Files selected for processing (2)
include/s2_model.hsrc/s2_model.cpp
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Implemented ggml_gallocr_t with proper lifecycle management.
We check if we're running purely on CPU and use the cached codepath, or fallback to old behavior when any GPU backend is involved.
This effectively eliminates redundant allocation cycles in Fast-AR decoding loop for pure CPU configs.
Before/After PR test
CPU: AMD Ryzen 7 5700X3D (8C/16T, pinned to physical cores)
Model:
s2-pro-q8_0.gguf| Backend: CPU (-ngl 0) | Allocator: mimallocPrompt: "hello world, how are you today?" (~204 tokens prefill)
My build script:
My runtime setup:
Summary by CodeRabbit
Performance
Bug Fixes