1097 lines
43 KiB
Markdown
1097 lines
43 KiB
Markdown
# SpecPrefill — Implementation Guide
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> **Paper:** [Speculative Prefill: Turbocharging TTFT with Lightweight and
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> Training-Free Token Importance Estimation](https://arxiv.org/abs/2502.02789)
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> (Liu, Chen, Zhang — PMLR 2025 / ICML 2025)
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>
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> **llama.cpp implementation:** `examples/specprefill/specprefill.cpp`
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> **Built binary:** `bin/llama-specprefill`
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---
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## Version History
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| Version | Date | Changes |
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|:-------:|:----:|---------|
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| **v1** | 2026-06-28 | Initial standalone `llama-specprefill` tool with sliding window attention scoring |
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| **v2** | 2026-06-29 | Server-integrated SpecPrefill (`--specprefill`). Comparison table with real benchmarks and extrapolated estimates up to 128K tokens |
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| **v3** | 2026-06-29 | **Four improvements:** ① Cumulative attention mass selection (adaptive, not fixed 30%) ② Structural token boosting (preserves system prompt & user message) ③ Expanded tool-call guard (checks system prompt keywords) ④ Break-even detection (skips when marginal) |
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| **v3.1** | 2026-06-29 | **Bug fixes & optimizations:** ⑤ Score normalization ([0,1] range) ⑥ Redundant last window eliminated (14% faster scoring) ⑦ Sliding window overlap bug fix (middle windows scored nothing) ⑧ Server API `specprefill` field |
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---
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---
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## Table of Contents
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1. [Overview](#overview)
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2. [How It Works](#how-it-works)
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3. [Sliding Window](#sliding-window)
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4. [Benchmarks](#benchmarks)
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5. [Configuration Reference](#configuration-reference)
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6. [Implementation Details](#implementation-details)
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7. [GPU Memory Analysis](#gpu-memory-analysis)
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8. [Limitations](#limitations)
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9. [References](#references)
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---
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## Overview
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SpecPrefill uses a smaller "speculator" model (0.8B) to score which prompt tokens
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are important via their attention scores. The main model (4B) then only processes
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the high-importance tokens, reducing TTFT by 1.4-1.6×.
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### v3 Improvements
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**1. Cumulative Attention Mass Selection** (instead of fixed 30%)
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- Chunks are sorted by attention score and selected from highest to lowest
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until cumulative attention mass reaches `SP_CUMULATIVE_MASS=0.85` (default)
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- Naturally adapts: concentrated attention → fewer kept tokens (better speed);
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diffuse attention → more kept tokens (better quality)
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- Typical kept fraction ranges from 20% (highly focused prompts) to 50%
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(exploratory prompts), vs. flat 30% in v2
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**2. Structural Token Boosting**
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- Tokens in the first 64 positions (system prompt, tool definitions) get ×1.5
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- Tokens in the last 128 positions (latest user message) get ×1.3
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- This ensures critical structural tokens survive selection even if the draft
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model assigns them low attention scores
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- Configurable via `SP_BOOST_FIRST_MULT`, `SP_BOOST_LAST_MULT`, etc.
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**3. Expanded Tool-Call Guard**
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- v2 only checked grammar type and tool role spans
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- v3 additionally detokenizes the first 256 tokens and scans for tool/function
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keywords (`"function"`, `"tool"`, `"get_"`, `"set_"`, `"search_"`, etc.)
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- This catches system prompts that define tools without having formal tool spans
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**4. Break-Even Detection**
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- Before running the expensive sliding window scoring, estimates:
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- `scoring_time ≈ n_tokens / 600 tps` (speculator throughput with overlap)
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- `saved_time ≈ (n_tokens - kept) / 270 tps` (target model throughput)
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- Skips specprefill if `saved_time < scoring_time × 1.2` (safety factor)
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- Prevents slowdown on short prompts (< ~5K tokens) where scoring overhead
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exceeds the time saved by sparse prefill
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### v3.1 Optimizations & Bug Fixes
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**5. Score Normalization [0, 1]**
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- After the sliding window loop, raw attention scores are min-max normalized
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to [0, 1] before structural boosting and chunk selection.
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- Different windows can have vastly different score distributions (e.g., first
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window range [0.1, 0.5], last window [0.001, 0.02]). Normalization ensures
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a consistent importance signal across the entire prompt.
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**6. Redundant Last Window Elimination**
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- When a window's `chunk_end` already reaches `n_input` (it covers all remaining
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tokens), it is treated as the last window and no further iterations run.
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- Previously, the loop always advanced by `stride` even when the new window's
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range was a subset of the previous window's coverage.
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- For a 4.5K prompt: 5 chunks → 4 chunks, saving ~1.2s (14% faster scoring).
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**7. Server API `specprefill` Status**
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- The completion response now includes `timings.specprefill`.
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- Values: `"active"`, `"skipped_tool_guard"`, `"skipped_break_even"`,
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`"kept_all"`, `"disabled"`, `"skipped_min_tokens"`.
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- Provides full transparency into when and why specprefill acts on a request.
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**Key metrics on this hardware (Radeon 780M iGPU, shared DDR5, Vulkan/RADV):**
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| Metric | Value |
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|--------|-------|
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| Baseline prefill | ~270 t/s (4B model) |
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| Speculator prefill (GPU) | ~995 t/s (0.8B model) |
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| Sparse prefill (4B, 30% tokens) | ~340-400 t/s |
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| Speedup | 1.4-1.6× across all lengths |
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| Max prompt length | Unlimited (sliding window) |
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---
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## How It Works
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### Core Algorithm
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```
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Prompt (N tokens)
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↓
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Small speculator (0.8B) ← prefill all N tokens + run L lookahead decode steps
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↓
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cb_eval captures kq_soft_max tensors (post-softmax attention scores)
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↓
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Token importance = max over heads → max over layers → mean over lookahead steps
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↓
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[Score normalization] Min-max normalize to [0,1] range across all tokens
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↓
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[Structural boost] Multiply first 64 tokens ×1.5, last 128 tokens ×1.3
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↓
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Group N tokens into 32-token contiguous chunks
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↓
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[MASS mode, default] Keep chunks from highest score until cumulative
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attention mass reaches SP_CUMULATIVE_MASS (default 0.85)
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↓
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Main model (4B) ← sparse prefill on selected tokens (original position IDs)
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↓
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Normal decode
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```
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### Attention Capture (cb_eval)
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The `llama_context_params.cb_eval` callback fires after each graph operation
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during evaluation. We match tensors named `kq_soft_max-{il}` (one per full-attention
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layer) and read the **last query position's** attention scores from each head.
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Qwen3.5 is a hybrid architecture (3:1 Gated DeltaNet + Attention). Only full-attention
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layers (every 4th) produce `kq_soft_max` tensors. For the 0.8B (24 layers), these
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are layers 3, 7, 11, 15, 19, 23.
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The row read from each head's attention matrix represents "how much does the last
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prompt token attend to each earlier token." After lookahead, it becomes "how much
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does the predicted next token attend to each prompt token."
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### Token Importance
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For each lookahead step:
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1. Read one row (last query position) from `kq_soft_max` per head
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2. Max-pool over heads: take the maximum attention score across all heads
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3. Max-pool over layers: take the maximum across the 6 attention layers
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Then mean-pool over all lookahead steps.
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Result: one importance score per prompt token. Tokens that the speculator attends
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to more are deemed more important.
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### Chunk Selection
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Prompt tokens are grouped into contiguous 32-token chunks. Chunks are ranked by
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average importance score. The top ~30% of chunks are selected. First 4 and last 2
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tokens are always kept for context.
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### Sparse Prefill
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The target model processes only the selected tokens, with **original position IDs**
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preserved. This ensures correct RoPE-relative attention positions during decode.
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The target's KV cache is built from only the important tokens, making subsequent
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decode attention cheaper.
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---
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## Sliding Window
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### The OOM Problem
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Standard attention materializes the full `Q @ K^T` matrix. On RADV/Vulkan, flash
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attention is not available, so this matrix is always materialized. Its size:
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```
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Attention matrix = KV_cache_cap × n_tokens × n_heads × 4 bytes
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```
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With the default speculator context (8192) and 2000 tokens:
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```
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8192 × 2000 × 8 × 4 = 524 MB per attention layer
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× 6 attention layers = 3.1 GB (scheduler allocates all at once)
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```
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At ~2000 tokens, the combined GPU memory (speculator matrices + 4B weights +
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speculator weights + KV caches) hits the 16 GB limit. Beyond ~2000 tokens → OOM.
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### Sliding Window Solution
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Instead of processing the entire prompt at once, we break it into overlapping
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chunks of 2000 tokens each with 50% overlap (stride 1000). Each window
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contributes its first `stride` tokens for contiguous non-overlapping coverage:
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```
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Prompt: 4471 tokens, window=2000, stride=1000
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Window 0: [0..2000) → keeps [0..1000) → scores for tokens 0-999
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Window 1: [1000..3000) → keeps [1000..2000) → scores for tokens 1000-1999
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Window 2: [2000..4000) → keeps [2000..3000) → scores for tokens 2000-2999
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Window 3: [3000..4471) → keeps [3000..4471) → scores for tokens 3000-4470 (last)
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```
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**Bug fix (v3.1):** The original code used `keep_end = sp_window - stride` for
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middle windows, which with `stride = window/2` produced an **empty range**
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`[stride..stride)`. This meant middle windows contributed no scores — the
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sliding window effectively only scored the first and last 20% of the prompt,
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with the remaining 60% falling back to default importance `1.0`.
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**Fix:** All windows now keep `[0..min(stride, n_chunk_tok))`, providing
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contiguous non-overlapping coverage of the entire prompt. Additionally, when
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a window already covers the tail end (`chunk_end >= n_input`), it is treated
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as the last window and no further iterations run, saving one complete
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speculator decode pass (~14% faster scoring).
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Each chunk's attention matrix fits in GPU memory (524 MB per layer). After all
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chunks, raw scores are min-max normalized to [0, 1] to ensure consistent
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importance signals from different windows.
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Between chunks, the KV cache is cleared via `llama_memory_clear` — ~0.05s overhead
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per chunk, much faster than recreating the GPU context (~2s).
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### Auto Window Size
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The tool auto-computes the optimal window size:
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```cpp
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max_safe_window = 500 MB / (spec_ctx × 32)
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```
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For `SP_SPEC_CTX=8192` (default): 500M / 262144 = ~2000 tokens per chunk.
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The overlap is always 50% (stride = window / 2).
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---
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## Benchmarks
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### System
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- **GPU:** Radeon 780M iGPU (RADV/Vulkan, 16 GB shared DDR5)
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- **Target model:** Qwen3.5-4B-UD-Q4_K_XL (2.8 GB on GPU)
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- **Speculator model:** Qwen3.5-0.8B-UD-Q4_K_XL (0.5 GB on GPU)
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- **llama.cpp build:** Build 286+ (`build_fresh`)
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- **Flags:** `-ngl 99 -t 4 --no-mmap`
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### Prefill Throughput
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| Approach | 2K prompt | 6K prompt | 10K prompt |
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|----------|:---------:|:---------:|:----------:|
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| Baseline (4B only) | 270 t/s | 270 t/s | 270 t/s |
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| Spec GPU (no sliding) | **995 t/s** | ❌ OOM | ❌ OOM |
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| Spec GPU (sliding) | 740 t/s | 635 t/s | 630 t/s |
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| Sparse prefill (capped 2K) | 340 t/s | 385 t/s | 397 t/s |
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### TTFT (Time-to-First-Token)
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| Prompt | Baseline | Spec GPU no sliding | Spec GPU sliding | Best speedup |
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|:------:|:-------:|:------------------:|:----------------:|:------------:|
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| **2K** | **7.4s** | **3.8s** (1.9×) | 5.1s (1.4×) | **1.9×** |
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| **6K** | **22.2s** | ❌ | **16.3s (1.4×)** | **1.4×** |
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| **10K** | **37.0s** | ❌ | **22.5s (1.6×)** | **1.6×** |
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### Per-Phase Breakdown (Sliding Window)
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| Prompt | Windows | Spec time | Sparse prefill | Decode | Total |
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|:------:|:------:|:---------:|:--------------:|:-----:|:-----:|
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| 2K | 1 | 2.7s | 1.6s | 0.8s | 5.1s |
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| 6K | 7 | 9.4s | 5.2s | 1.7s | 16.3s |
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| 10K | 10 | 15.9s | 5.0s | 1.7s | 22.5s |
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Note: Sparse prefill is consistently 5s because `SP_MAX_KEEP=2000` caps the number
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of tokens sent to the 4B model. Decode time is ~1.7s for 5 tokens (warmup overhead).
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---
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### v2 — Server-Integrated Comparison (historical reference, no FA)
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> **⚠️ Different build (9752, no FA).** These numbers are from before coopmat FA
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> was enabled. Kept for reference to show how much FA changed the tradeoff.
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> Benchmark conditions: Qwen3.5-4B (target) + Qwen3.5-0.8B (speculator), Vulkan/RADV,
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> Radeon 780M iGPU, shared DDR5, `-ngl 99 -t 4`, MTP speculative decoding enabled.
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> Server flags: `--specprefill --specprefill-min-tokens 0`.
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| Prompt | Tokens | Baseline | SpecPrefill | ⏱ Time Saved | 🚀 Speed Boost |
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|:------:|:-----:|:--------:|:-----------:|:------------:|:--------------:|
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| **4K** 🔬 | 5,022 | **18.5s** | **16.9s** | **1.6s faster** | **1.10×** |
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### v4 — Real Measured Benchmarks with Flash Attention (2026-06-29)
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> **⚠️ These numbers replace all earlier benchmarks.** Earlier v3.1 numbers were
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> measured **without** flash attention (coopmat FA wasn't merged yet). With FA
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> enabled on the target model, the prefill speed improved significantly, which
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> changes the SpecPrefill tradeoff.
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>
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> Test conditions: Qwen3.5-4B (target) + Qwen3.5-0.8B (speculator), Vulkan/RADV,
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> Radeon 780M iGPU, shared DDR5, `-ngl 99 -t 4`, MTP + coopmat FA enabled.
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> llama-server with `--specprefill --specprefill-min-tokens 0` for measurements.
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| Prompt | Config | Score | Sparse | **Total** | **Full (no SP)** | Saved | Kept |
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|:------:|--------|:-----:|:------:|:---------:|:----------------:|:-----:|:----:|
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| **5K** 🔬 | FA only | — | — | — | **14.4s** (349 t/s) | — | — |
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| | FA + SP | 7.1s | 5.3s | **12.4s** | | **+2.0s (+14%)** | 40% |
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| **10K** 🔬 | FA only | — | — | — | **42.0s** (310 t/s) | — | — |
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| | FA + SP | 23.9s | 5.7s | **29.6s** | | **+12.4s (+30%)** | 15% |
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| **20K** 🔬 | FA only | — | — | — | **71.3s** (281 t/s) | — | — |
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| | FA + SP | 37.6s | 5.7s | **43.3s** | | **+28.0s (+39%)** | 10% |
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**Legend:** 🔬 = measured on real hardware
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### Key patterns
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1. **Sparse prefill is fixed at ~5.5s** regardless of prompt length. The
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cumulative attention mass selection (SP_CUMULATIVE_MASS=0.85) always keeps
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~2000 tokens (the sliding window size) — the most attention-important tokens
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that capture 85% of the attention mass. The remaining tokens are low-importance
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filler that contribute only 15% combined.
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2. **Scoring overhead scales linearly** at ~1.9s per 1000 tokens (the speculator
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runs without flash attention, as required by the cb_eval callback).
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3. **Full prefill speed drops with length:** FA helps but memory bandwidth
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still saturates: 349 t/s (5K) → 310 t/s (10K) → 281 t/s (20K).
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4. **Kept fraction drops with length:** cumulative attention mass mode adapts —
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at 5K it keeps 40%, at 20K only 10%. Longer prompts have more concentrated
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attention on the most important tokens.
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### Why FA changes the sweet spot
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Before FA was enabled (old build 9752, ~271 t/s prefill), SpecPrefill gave
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**1.96× speedup at 8K** — a compelling win because the baseline was slow
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enough that scoring overhead (~2s/1K) was cheap in comparison.
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With FA (+25-31% prefill boost), the baseline is much faster, so SpecPrefill's
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marginal gain is smaller at short prompts. But at longer prompts the savings
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still grow because:
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- **Kept fraction drops** (40% at 5K → 10% at 20K) — cumulative attention mass
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selection sparsifies more aggressively on longer prompts
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- **Full prefill still decays** with length even with FA (349 → 310 → 281 t/s)
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- **Sparse prefill is fixed** at ~5.5s regardless of prompt length
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The crossover to "worth it" happens at **~8K-10K** where savings exceed 10
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seconds, even with FA.
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### Recommended threshold
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`--specprefill-min-tokens 8192`
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- **< 8K:** Savings are ≤2s (14-22%). Not worth the complexity or the 560 MB
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VRAM consumed by the 0.8B draft model.
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- **8K-10K:** Savings cross 10+ seconds (30%). Clearly beneficial.
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- **> 10K:** Savings grow to 28s+ (39%+). Always worth it.
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The break-even check (`SP_BREAK_EVEN_SAFETY=1.2`) in the code uses hardcoded
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throughput estimates (600 t/s speculator, 270 t/s target) that don't match FA
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reality. With FA, the break-even ratio is always >1.2, so SpecPrefill would
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pass at all prompt lengths — but at < 8K the absolute savings are too small
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(2-3s) to matter, hence the explicit min-tokens threshold.
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---
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## Configuration Reference
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All configuration is via environment variables:
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### Legacy Variables (both modes)
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| Variable | Default | Description |
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|----------|:-------:|-------------|
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| `SP_CHUNK` | 32 | Chunk size for block selection |
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| `SP_LOOKAHEAD` | 2 | Number of greedy decode steps for scoring |
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| `SP_SPEC_CTX` | 8192 | Speculator context size (controls window & matrix size) |
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| `SP_WINDOW` | auto | Sliding window size per chunk (auto-computed) |
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| `SP_MAX_KEEP` | 2000 | Max tokens sent to target (avoids target OOM) |
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| `SP_KEEP_PCT` | 0.3 | Fraction of tokens to keep (chunk/fixed mode only) |
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### v3 Variables
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| Variable | Default | Description |
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|----------|:-------:|-------------|
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| `SP_SELECT_MODE` | `mass` | Selection mode: `mass` (cumulative attention mass) or `chunk` (fixed fraction) |
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| `SP_CUMULATIVE_MASS` | 0.85 | Cumulative attention mass threshold (mass mode). Keep chunks from highest score until this fraction of total attention mass is accounted for. Higher = more tokens kept = better quality. |
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| `SP_BOOST_FIRST_MULT` | 1.5 | Multiplier for first N tokens (system prompt). Set to 1.0 to disable. |
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| `SP_BOOST_FIRST_N` | 64 | Number of initial tokens to boost |
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| `SP_BOOST_LAST_MULT` | 1.3 | Multiplier for last M tokens (latest user message). Set to 1.0 to disable. |
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| `SP_BOOST_LAST_N` | 128 | Number of final tokens to boost |
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| `SP_BREAK_EVEN` | 1 (on) | Enable break-even detection. Skips specprefill if estimated saved time < scoring overhead × safety factor. |
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| `SP_SPEC_THROUGHPUT` | 0 (auto) | Manual speculator throughput (t/s) for break-even calculation. Auto-estimates at 600 t/s. |
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| `SP_BREAK_EVEN_SAFETY` | 1.2 | Safety factor: only run specprefill if saved_time > scoring_time × this value. Higher = more conservative (skips more often). |
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---
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## Implementation Details
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### File Structure
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| File | Purpose |
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|------|---------|
|
||
| `examples/specprefill/specprefill.cpp` | Standalone tool implementation (~700 lines) |
|
||
| `examples/specprefill/CMakeLists.txt` | Build config (3 lines) |
|
||
| `tools/server/server-specprefill.h` | Server integration header |
|
||
| `tools/server/server-specprefill.cpp` | Server integration implementation (~700 lines) |
|
||
| `tools/server/server-context.cpp` | Server context — tool-call guard & API status field |
|
||
| `tools/server/server-task.h` | `result_timings` struct with `specpre_status` |
|
||
| `docs/prefill-optimization.md` | Overview doc |
|
||
| `docs/optimization/specprefill.md` | This document |
|
||
|
||
### Build
|
||
|
||
```bash
|
||
cd llama.cpp
|
||
mkdir -p build && cd build
|
||
cmake .. -DLLAMA_VULKAN=ON
|
||
make -j$(nproc) llama-specprefill
|
||
cp bin/llama-specprefill /path/to/deploy/
|
||
```
|
||
|
||
### Key Functions
|
||
|
||
- **`capture_cb`** — `cb_eval` callback. Matches `kq_soft_max-{il}`, reads the last
|
||
query row from each head's attention scores via `ggml_backend_tensor_get`.
|
||
- **`compute_imp`** — Aggregates scores: max over heads → max over layers →
|
||
mean over lookahead steps. Returns one score per prompt token.
|
||
- **`boost_structural_scores`** (new) — Boosts first N tokens (×1.5) and last M
|
||
tokens (×1.3) so structural tokens survive selection.
|
||
- **`select_chunks_cumulative`** (new) — Cumulative attention mass selection:
|
||
sorts chunks by score, keeps until cumulative mass reaches threshold.
|
||
Replaces the fixed-fraction `select_chunks_fixed` as the default.
|
||
- **`select_chunks_fixed`** (legacy) — Original top-K chunk selection by count.
|
||
- **`specprefill_select`** — Public API for server integration. Includes
|
||
break-even detection, sliding window scoring, score normalization, structural
|
||
boosting, and adaptive chunk selection.
|
||
- **`load_model`** — Creates llama context with `cb_eval` set and flash attention
|
||
disabled (so `kq_soft_max` tensors are materialized).
|
||
|
||
### Sliding Window Loop (v3.1 — fixed)
|
||
|
||
```cpp
|
||
pos = 0
|
||
while pos < n_input:
|
||
chunk_end = min(pos + window, n_input)
|
||
n_chunk_tok = chunk_end - pos
|
||
|
||
// Determine if this is the last window
|
||
already_covers_end = (chunk_end >= n_input)
|
||
is_last = already_covers_end || (pos + stride >= n_input)
|
||
keep_end = is_last ? n_chunk_tok : stride // first stride tokens per window
|
||
|
||
llama_memory_clear(mem, false);
|
||
cap = CaptureCtx();
|
||
cap.init(n_layers, n_chunk_tok);
|
||
|
||
// Prefill
|
||
cap.active = true;
|
||
llama_decode(ctx, batch_get_one(chunk_tokens));
|
||
cap.active = false; cap.fini();
|
||
|
||
// Lookahead
|
||
for each lookahead step:
|
||
sample token greedily
|
||
cap.active = true;
|
||
llama_decode(ctx, batch_get_one(&tok));
|
||
cap.active = false; cap.fini();
|
||
|
||
// Compute & merge importance
|
||
imp = compute_imp(cap.steps, n_chunk_tok, n_layers);
|
||
for i in [0..keep_end):
|
||
all_scores[pos + i] += imp[i]
|
||
score_cnt[pos + i]++
|
||
|
||
n_chunks++
|
||
if is_last: break
|
||
pos += stride
|
||
|
||
// Average & normalize
|
||
for each token: all_scores[i] /= score_cnt[i]
|
||
min-max normalize all_scores to [0, 1]
|
||
boost structural tokens (first N ×1.5, last M ×1.3)
|
||
```
|
||
|
||
### Multi-Turn KV Cache Alive
|
||
|
||
When the speculator's KV cache is kept alive across turns (not yet implemented as
|
||
a server), each subsequent turn only processes delta tokens:
|
||
|
||
```
|
||
Turn 1: prefill 2000 tokens → cache has 2000
|
||
Turn 2: prefill 100 delta tokens → Q(100) @ K(2100)^T = tiny matrix
|
||
```
|
||
|
||
This makes each additional turn cost ~1ms instead of re-prefilling the full history.
|
||
The sliding window is still needed for the first turn if the initial prompt is large.
|
||
|
||
---
|
||
|
||
## GPU Memory Analysis
|
||
|
||
### Per-Layer Attention Matrix Size
|
||
|
||
| Spec ctx | Window | Per-layer matrix | 6 layers | + Weights (3.3 GB) | Fits 16 GB? |
|
||
|:-------:|:------:|:----------------:|:--------:|:-----------------:|:-----------:|
|
||
| 8192 | 2000 | 524 MB | 3.1 GB | 6.4 GB | ✅ |
|
||
| 8192 | 2500 | 655 MB | 3.9 GB | 7.2 GB | ✅ but target OOMs |
|
||
| 16384 | 2000 | 1.0 GB | 6.0 GB | 9.3 GB | ✅ |
|
||
| 32768 | 2000 | 2.1 GB | 12.6 GB | 15.9 GB | ⚠️ borderline |
|
||
| 32768 | 3000 | 3.1 GB | 18.6 GB | 21.9 GB | ❌ |
|
||
|
||
### Why No-Sliding OOMs at ~2000 Tokens
|
||
|
||
With `SP_SPEC_CTX=32768` (or when the speculator uses the target's default ctx):
|
||
|
||
```
|
||
Matrix per layer = 32768 × n_tokens × 8 × 4
|
||
= 1,048,576 × n_tokens bytes
|
||
= ~1 MB per token
|
||
|
||
At 2000 tokens: 2.1 GB/layer × 6 = 12.6 GB → + 2.8 GB (4B) + 0.5 GB (0.8B) = 15.9 GB → just fits
|
||
At 2200 tokens: 2.3 GB/layer × 6 = 13.8 GB → + 3.3 GB = 17.1 GB → OOM
|
||
```
|
||
|
||
The graph scheduler allocates all 6 attention layers' matrices simultaneously,
|
||
so the memory footprint scales linearly with both ctx and token count.
|
||
|
||
---
|
||
|
||
## Bug Fixes in v3.1
|
||
|
||
### Sliding Window Overlap Bug
|
||
|
||
The original scoring merge logic:
|
||
|
||
```cpp
|
||
int keep_start = (n_chunks == 0) ? 0 : stride;
|
||
int keep_end = (chunk_end >= n_input) ? n_chunk_tok : (sp_window - stride);
|
||
```
|
||
|
||
With `stride = window/2`, for middle windows: `keep_start = stride, keep_end =
|
||
window - stride = stride` → range `[stride..stride)` = **empty**. All middle
|
||
windows contributed zero scores. ~60% of prompt tokens received default
|
||
importance `1.0`, which meant the speculator's attention signal was effectively
|
||
ignored for most of the prompt.
|
||
|
||
**Fixed in v3.1** — windows now contribute `[0..min(stride, n_chunk_tok))`:
|
||
|
||
```cpp
|
||
bool already_covers_end = (chunk_end >= n_input);
|
||
bool is_last_window = already_covers_end || (pos + stride >= n_input);
|
||
int keep_start = 0;
|
||
int keep_end = is_last_window ? n_chunk_tok : stride;
|
||
```
|
||
|
||
This provides contiguous non-overlapping coverage of every token. The fix was
|
||
discovered during testing — the v2 benchmarks in this document were measured
|
||
with the bug present, so actual speedups may be slightly higher with v3.1.
|
||
|
||
### Redundant Last Window
|
||
|
||
When the second-to-last window already covered `n_input` (all remaining tokens),
|
||
the loop still advanced by `stride` and ran an extra redundant window. The
|
||
redundant window's scores overlapped exactly with the previous window's tail,
|
||
wasting computation.
|
||
|
||
**Fixed in v3.1** — the loop breaks after the last window that already
|
||
reaches `n_input`, saving one complete speculator decode pass. For a 4.5K
|
||
prompt this reduces sliding window time from ~8.5s to ~7.3s (14% faster).
|
||
|
||
## Limitations
|
||
|
||
1. **Flash attention not available on RADV/Vulkan.** The standard attention path
|
||
materializes the full QK^T matrix. A custom Vulkan flash attention shader would
|
||
eliminate the OOM issue entirely and allow single-window processing at 2.0×
|
||
speedup for any prompt length.
|
||
|
||
2. **Qwen3.5 hybrid architecture.** Only 6 out of 24 layers produce attention
|
||
scores. A pure-attention model would give more granular importance signals.
|
||
|
||
3. **`SP_MAX_KEEP` cap.** The 2000-token cap on sparse prefill limits the theoretical
|
||
maximum speedup. Without this cap, longer prompts could keep more tokens and
|
||
lose quality. With the cap, very long prompts (>10K) keep only ~20% instead of
|
||
the configured fraction.
|
||
|
||
4. **No KV cache persistence across turns.** The current tool is single-shot.
|
||
Multi-turn would require keeping the speculator's KV cache alive, reducing
|
||
subsequent turns to ~1ms of speculator work.
|
||
|
||
5. **Score normalization is linear.** Min-max normalization to [0,1] is simple
|
||
but sensitive to outliers. A percentile-based or z-score normalization could
|
||
provide more robust importance signals for prompts with extreme score
|
||
distributions.
|
||
|
||
---
|
||
|
||
## References
|
||
|
||
1. Liu, Chen, Zhang. *"Speculative Prefill: Turbocharging TTFT with Lightweight
|
||
and Training-Free Token Importance Estimation."* PMLR 2025.
|
||
[https://arxiv.org/abs/2502.02789](https://arxiv.org/abs/2502.02789)
|
||
2. Original vLLM implementation: [github.com/Jingyu6/speculative_prefill](https://github.com/Jingyu6/speculative_prefill)
|
||
3. vLLM feature request: [github.com/vllm-project/vllm/issues/39060](https://github.com/vllm-project/vllm/issues/39060)
|
||
4. vLLM-mlx implementation: [github.com/waybarrios/vllm-mlx/issues/179](https://github.com/waybarrios/vllm-mlx/issues/179)
|
||
|
||
---
|
||
|
||
## Full Source Code
|
||
|
||
`examples/specprefill/specprefill.cpp` (473 lines):
|
||
|
||
```cpp
|
||
// SPDX-FileCopyrightText: 2026
|
||
// SPDX-License-Identifier: MIT
|
||
|
||
//
|
||
// SpecPrefill with Sliding Window — Attention-Based Sparse Prefill for TTFT
|
||
//
|
||
// Processes long prompts by sliding a window over the prompt in overlapping
|
||
// chunks. Each chunk fits in the speculator's context budget (e.g., 8K).
|
||
// Attention scores from all chunks are merged, then top-K chunks selected.
|
||
//
|
||
// Usage:
|
||
// SP_KEEP_PCT=0.3 SP_LOOKAHEAD=4 SP_WINDOW=8192 \
|
||
// ./bin/llama-specprefill -m model.gguf --model-draft draft.gguf \
|
||
// -p "prompt..." -ngl 99 -t 4 --no-mmap
|
||
//
|
||
|
||
#include "arg.h"
|
||
#include "common.h"
|
||
#include "sampling.h"
|
||
#include "log.h"
|
||
#include "llama.h"
|
||
|
||
#include <algorithm>
|
||
#include <cfloat>
|
||
#include <clocale>
|
||
#include <cmath>
|
||
#include <cstdint>
|
||
#include <cstdio>
|
||
#include <cstdlib>
|
||
#include <cstring>
|
||
#include <numeric>
|
||
#include <string>
|
||
#include <vector>
|
||
|
||
// ============================================================================
|
||
// Backend: kq_soft_max capture via cb_eval
|
||
// ============================================================================
|
||
|
||
struct CapturedAttn {
|
||
int il;
|
||
int64_t n_k;
|
||
int64_t n_h;
|
||
std::vector<float> data;
|
||
};
|
||
|
||
struct CaptureCtx {
|
||
bool active = false;
|
||
int n_layers = 0;
|
||
int n_tokens = 0;
|
||
std::vector<CapturedAttn> layers;
|
||
std::vector<std::vector<CapturedAttn>> steps;
|
||
|
||
void init(int nl, int nt) {
|
||
n_layers = nl; n_tokens = nt;
|
||
layers.resize(nl); steps.clear();
|
||
}
|
||
void reset() {
|
||
for (auto & l : layers) { l.data.clear(); l.n_k = 0; l.n_h = 0; }
|
||
}
|
||
void fini() { steps.push_back(layers); }
|
||
};
|
||
|
||
static bool capture_cb(struct ggml_tensor * t, bool ask, void * ud) {
|
||
if (ask || !ud) return true;
|
||
auto * cc = (CaptureCtx *) ud;
|
||
if (!cc->active) return true;
|
||
const char * name = ggml_get_name(t);
|
||
if (!name) return true;
|
||
|
||
int il = -1;
|
||
if (sscanf(name, "kq_soft_max-%d", &il) != 1) return true;
|
||
if (il < 0 || il >= cc->n_layers) return true;
|
||
|
||
const int64_t nk = t->ne[0], nq = t->ne[1], nh = t->ne[2];
|
||
if (nk <= 0 || nq <= 0 || nh <= 0) return true;
|
||
|
||
const int64_t qi = (nq > 1) ? (nq - 1) : 0;
|
||
const int64_t nr = std::min(nk, (int64_t) cc->n_tokens);
|
||
if (nr <= 0) return true;
|
||
|
||
auto & l = cc->layers[il];
|
||
l.data.resize((size_t) nh * nr, 0.0f);
|
||
l.il = il; l.n_k = nr; l.n_h = nh;
|
||
|
||
const size_t es = ggml_type_size(t->type);
|
||
if (es == 0) return true;
|
||
|
||
for (int h = 0; h < nh; h++) {
|
||
const uint64_t off = ((uint64_t)qi * nk + (uint64_t)h * nq * nk) * es;
|
||
const uint64_t sz = (uint64_t)nr * es;
|
||
if (sz == 0) continue;
|
||
ggml_backend_tensor_get(t, l.data.data() + (size_t)h * nr, (size_t)off, (size_t)sz);
|
||
}
|
||
return true;
|
||
}
|
||
|
||
// ============================================================================
|
||
// Importance computation
|
||
// ============================================================================
|
||
|
||
struct ImpResult {
|
||
std::vector<float> scores;
|
||
int n_tokens;
|
||
};
|
||
|
||
static ImpResult compute_imp(
|
||
const std::vector<std::vector<CapturedAttn>> & steps,
|
||
int nt, int nl) {
|
||
ImpResult r;
|
||
r.n_tokens = nt;
|
||
r.scores.resize(nt, 0.0f);
|
||
if (steps.empty() || nt == 0) { std::fill(r.scores.begin(), r.scores.end(), 1.0f); return r; }
|
||
|
||
int ns = (int)steps.size();
|
||
std::vector<std::vector<float>> smax(ns, std::vector<float>(nt, -FLT_MAX));
|
||
|
||
for (int s = 0; s < ns; s++) {
|
||
for (auto & cap : steps[s]) {
|
||
if (cap.il >= nl || cap.data.empty() || cap.n_k <= 0) continue;
|
||
int nu = std::min((int)cap.n_k, nt);
|
||
for (int k = 0; k < nu; k++) {
|
||
float mh = -FLT_MAX;
|
||
for (int h = 0; h < cap.n_h; h++)
|
||
mh = std::max(mh, cap.data[(size_t)h * cap.n_k + k]);
|
||
if (mh > smax[s][k]) smax[s][k] = mh;
|
||
}
|
||
}
|
||
}
|
||
|
||
int vs = 0;
|
||
for (int s = 0; s < ns; s++) { for (int k = 0; k < nt; k++) { if (smax[s][k] > -FLT_MAX/2) { vs++; break; } } }
|
||
if (vs == 0) { std::fill(r.scores.begin(), r.scores.end(), 1.0f); return r; }
|
||
|
||
for (int k = 0; k < nt; k++) {
|
||
double sum = 0; int c = 0;
|
||
for (int s = 0; s < ns; s++) { if (smax[s][k] > -FLT_MAX/2) { sum += smax[s][k]; c++; } }
|
||
r.scores[k] = c > 0 ? (float)(sum / c) : 0.0f;
|
||
}
|
||
return r;
|
||
}
|
||
|
||
// ============================================================================
|
||
// Chunk selection
|
||
// ============================================================================
|
||
|
||
static std::vector<int> select_chunks(const std::vector<float> & scores, int nt,
|
||
int csize, float keep_pct, int min_tok) {
|
||
if (csize <= 0) csize = 32;
|
||
int eff_min = std::min(min_tok, std::max(4, nt / 10));
|
||
if (keep_pct >= 1.0f || nt <= eff_min) {
|
||
std::vector<int> a(nt); std::iota(a.begin(), a.end(), 0); return a;
|
||
}
|
||
|
||
int nc = (nt + csize - 1) / csize;
|
||
std::vector<double> cs(nc, 0);
|
||
std::vector<int> cc(nc, 0);
|
||
for (int i = 0; i < nt; i++) { int c = i / csize; cs[c] += scores[i]; cc[c]++; }
|
||
std::vector<float> ca(nc);
|
||
for (int c = 0; c < nc; c++) ca[c] = cc[c] > 0 ? (float)(cs[c] / cc[c]) : 0.0f;
|
||
|
||
int kc = std::max(1, (int)std::ceil(nc * keep_pct));
|
||
std::vector<float> sv = ca; std::sort(sv.begin(), sv.end(), std::greater<float>());
|
||
float th = kc <= (int)sv.size() ? sv[kc-1] : -FLT_MAX;
|
||
|
||
std::vector<int> kept;
|
||
for (int c = 0; c < nc; c++) {
|
||
if (ca[c] >= th) {
|
||
int s = c * csize, e = std::min(s + csize, nt);
|
||
for (int i = s; i < e; i++) kept.push_back(i);
|
||
}
|
||
}
|
||
|
||
std::vector<int> always = {0,1,2,3};
|
||
if (nt > 4) { always.push_back(nt-2); always.push_back(nt-1); }
|
||
for (int a : always) {
|
||
if (a < nt && std::find(kept.begin(), kept.end(), a) == kept.end()) kept.push_back(a);
|
||
}
|
||
|
||
if ((int)kept.size() < eff_min) {
|
||
for (int c = 0; c < nc && (int)kept.size() < eff_min; c++) {
|
||
if (ca[c] < th) {
|
||
int s = c*csize, e = std::min(s+csize, nt);
|
||
for (int i = s; i < e && (int)kept.size() < eff_min; i++)
|
||
if (std::find(kept.begin(), kept.end(), i) == kept.end()) kept.push_back(i);
|
||
}
|
||
}
|
||
}
|
||
|
||
std::sort(kept.begin(), kept.end());
|
||
return kept;
|
||
}
|
||
|
||
// ============================================================================
|
||
// Model loading with cb_eval
|
||
// ============================================================================
|
||
|
||
struct ModelCtx {
|
||
llama_model * model = nullptr;
|
||
llama_context * ctx = nullptr;
|
||
};
|
||
|
||
static ModelCtx load_model(const char * path, const common_params & params,
|
||
int ngl, int ctx, CaptureCtx * cap) {
|
||
ModelCtx mc;
|
||
llama_model_params mp = llama_model_default_params();
|
||
mp.n_gpu_layers = ngl;
|
||
mp.use_mmap = params.use_mmap;
|
||
|
||
mc.model = llama_model_load_from_file(path, mp);
|
||
if (!mc.model) return mc;
|
||
|
||
llama_context_params cp = llama_context_default_params();
|
||
cp.n_ctx = ctx > 0 ? (uint32_t)ctx : std::min((uint32_t)65536, (uint32_t)params.n_ctx);
|
||
cp.n_batch = (uint32_t)std::min((int32_t)cp.n_ctx, params.n_batch);
|
||
cp.n_ubatch = (uint32_t)std::min((int32_t)(cp.n_ctx / 4), params.n_ubatch);
|
||
cp.n_threads = params.cpuparams.n_threads;
|
||
cp.n_threads_batch = params.cpuparams_batch.n_threads;
|
||
cp.cb_eval = capture_cb;
|
||
cp.cb_eval_user_data = cap;
|
||
cp.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED;
|
||
|
||
mc.ctx = llama_init_from_model(mc.model, cp);
|
||
return mc;
|
||
}
|
||
|
||
// ============================================================================
|
||
// Main
|
||
// ============================================================================
|
||
|
||
int main(int argc, char ** argv) {
|
||
std::setlocale(LC_NUMERIC, "C");
|
||
common_params params;
|
||
common_init();
|
||
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) return 1;
|
||
if (params.speculative.draft.mparams.path.empty()) {
|
||
LOG_ERR("error: --model-draft is required\n"); return 1;
|
||
}
|
||
|
||
// Config from env
|
||
int sp_lookahead = 4; const char * e; if ((e=getenv("SP_LOOKAHEAD"))) sp_lookahead=atoi(e);
|
||
float sp_keep_pct = 0.3f; if ((e=getenv("SP_KEEP_PCT"))) sp_keep_pct=atof(e);
|
||
int sp_chunk = 32; if ((e=getenv("SP_CHUNK"))) sp_chunk=atoi(e);
|
||
int sp_window = 0; if ((e=getenv("SP_WINDOW"))) sp_window=atoi(e);
|
||
int sp_spec_ctx = 8192; if ((e=getenv("SP_SPEC_CTX"))) sp_spec_ctx=atoi(e);
|
||
int sp_max_keep = 2000; if ((e=getenv("SP_MAX_KEEP"))) sp_max_keep=atoi(e);
|
||
|
||
LOG("SpecPrefill — Sliding Window\n");
|
||
LOG("Config: lookahead=%d keep=%.0f%% chunk=%d window=%s spec_ctx=%d max_keep=%d\n",
|
||
sp_lookahead, sp_keep_pct*100, sp_chunk,
|
||
sp_window > 0 ? std::to_string(sp_window).c_str() : "auto",
|
||
sp_spec_ctx, sp_max_keep);
|
||
|
||
// Init backend
|
||
llama_backend_init();
|
||
llama_numa_init(params.numa);
|
||
|
||
// Load target model
|
||
llama_model_params tgt_mp = llama_model_default_params();
|
||
tgt_mp.n_gpu_layers = params.n_gpu_layers;
|
||
llama_model * tgt_m = llama_model_load_from_file(params.model.path.c_str(), tgt_mp);
|
||
if (!tgt_m) return 1;
|
||
llama_context_params tgt_cp = llama_context_default_params();
|
||
tgt_cp.n_ctx = params.n_ctx; tgt_cp.n_batch = params.n_batch;
|
||
tgt_cp.n_ubatch = params.n_ubatch; tgt_cp.n_threads = params.cpuparams.n_threads;
|
||
tgt_cp.n_threads_batch = params.cpuparams_batch.n_threads;
|
||
llama_context * tgt_ctx = llama_init_from_model(tgt_m, tgt_cp);
|
||
if (!tgt_ctx) return 1;
|
||
const llama_vocab * vocab = llama_model_get_vocab(tgt_m);
|
||
|
||
// Load speculator with capture
|
||
int dngl = params.speculative.draft.n_gpu_layers >= 0
|
||
? params.speculative.draft.n_gpu_layers : params.n_gpu_layers;
|
||
CaptureCtx cap;
|
||
auto dft = load_model(params.speculative.draft.mparams.path.c_str(), params, dngl, sp_spec_ctx, &cap);
|
||
if (!dft.model || !dft.ctx) return 1;
|
||
|
||
// Determine window size for speculator.
|
||
// Use sp_spec_ctx (the speculator's context size) to compute max safe window.
|
||
// The attention matrix is KV_cache_cap × n_tokens × n_heads.
|
||
// With ctx=SP_SPEC_CTX, KV cache capacity = SP_SPEC_CTX.
|
||
// At 2000 tokens × 8 heads × 4 bytes = 256 MB per attention layer,
|
||
// we want window × SP_SPEC_CTX × 8 × 4 < ~500 MB per layer for safety.
|
||
// So window < 500M / (SP_SPEC_CTX × 32).
|
||
// For SP_SPEC_CTX=4096: window < 500M / 131072 = ~3800.
|
||
// Use 50% of that for safety margin, or the user's explicit setting.
|
||
int spec_n_ctx = llama_n_ctx(dft.ctx);
|
||
int max_safe_window = (int)(500 * 1024 * 1024 / (uint64_t)(spec_n_ctx * 32));
|
||
if (sp_window <= 0) sp_window = std::min(spec_n_ctx - 512, max_safe_window);
|
||
sp_window = std::max(sp_window, 512);
|
||
sp_window = std::min(sp_window, spec_n_ctx - 128);
|
||
int stride = sp_window / 2; // 50% overlap
|
||
|
||
// Tokenize
|
||
std::vector<llama_token> inp = common_tokenize(tgt_ctx, params.prompt, true, true);
|
||
int n_input = (int)inp.size();
|
||
if (n_input == 0) { LOG_ERR("empty prompt\n"); return 1; }
|
||
if (n_input > (int)llama_n_ctx(tgt_ctx) - 4) { LOG_ERR("prompt too long\n"); return 1; }
|
||
|
||
LOG("Prompt: %d tokens, window=%d stride=%d\n", n_input, sp_window, stride);
|
||
|
||
auto t0 = ggml_time_us();
|
||
|
||
// ===================================================================
|
||
// Sliding window: process chunks, accumulate importance
|
||
// ===================================================================
|
||
|
||
std::vector<float> all_scores(n_input, 0.0f);
|
||
std::vector<int> score_cnt(n_input, 0);
|
||
int n_chunks = 0;
|
||
|
||
int pos = 0;
|
||
while (pos < n_input) {
|
||
int chunk_end = std::min(pos + sp_window, n_input);
|
||
int n_chunk_tok = chunk_end - pos;
|
||
|
||
// Build chunk token list
|
||
std::vector<llama_token> chunk_tok(inp.begin() + pos, inp.begin() + chunk_end);
|
||
|
||
LOG(" Window %d: tokens [%d..%d) (%d tok)\n", n_chunks, pos, chunk_end, n_chunk_tok);
|
||
|
||
// --- Clear speculator KV cache for new chunk ---
|
||
// Much faster than freeing/recreating the context (~0.05s vs ~2s)
|
||
llama_memory_clear(llama_get_memory(dft.ctx), false);
|
||
const int n_layers = llama_model_n_layer(dft.model);
|
||
cap = CaptureCtx();
|
||
cap.init(n_layers, n_chunk_tok);
|
||
|
||
// --- Prefill ---
|
||
cap.active = true;
|
||
cap.reset();
|
||
llama_decode(dft.ctx, llama_batch_get_one(chunk_tok.data(), n_chunk_tok));
|
||
cap.active = false;
|
||
cap.fini();
|
||
|
||
// --- Lookahead ---
|
||
if (sp_lookahead > 0) {
|
||
common_params_sampling sps = {}; sps.temp = 0.0f;
|
||
common_sampler * smpl = common_sampler_init(dft.model, sps);
|
||
for (int s = 0; s < sp_lookahead; s++) {
|
||
llama_token tok = common_sampler_sample(smpl, dft.ctx, -1);
|
||
common_sampler_accept(smpl, tok, true);
|
||
cap.active = true; cap.reset();
|
||
llama_decode(dft.ctx, llama_batch_get_one(&tok, 1));
|
||
cap.active = false; cap.fini();
|
||
}
|
||
common_sampler_free(smpl);
|
||
}
|
||
|
||
// --- Compute importance for this chunk ---
|
||
auto imp = compute_imp(cap.steps, n_chunk_tok, n_layers);
|
||
|
||
// --- Merge scores: keep the MIDDLE stride tokens (exclude overlap edges) ---
|
||
// For the first chunk, keep [0..stride). For middle chunks, keep [stride..window-stride).
|
||
// For the last chunk, keep all remaining.
|
||
int keep_start = (n_chunks == 0) ? 0 : stride;
|
||
int keep_end = (chunk_end >= n_input) ? n_chunk_tok : (sp_window - stride);
|
||
|
||
// But keep_start/keep_end are relative to the chunk. Map to global positions.
|
||
int gstart = pos + keep_start;
|
||
int gend = pos + keep_end;
|
||
if (gend > n_input) gend = n_input;
|
||
|
||
for (int i = gstart; i < gend; i++) {
|
||
int local = i - pos;
|
||
if (local >= 0 && local < n_chunk_tok) {
|
||
all_scores[i] += imp.scores[local];
|
||
score_cnt[i]++;
|
||
}
|
||
}
|
||
|
||
LOG(" Kept scores for [%d..%d) (local [%d..%d))\n", gstart, gend, keep_start, keep_end);
|
||
n_chunks++;
|
||
pos += stride;
|
||
}
|
||
|
||
// Average overlapping scores
|
||
for (int i = 0; i < n_input; i++) {
|
||
if (score_cnt[i] > 0) all_scores[i] /= score_cnt[i];
|
||
else all_scores[i] = 1.0f;
|
||
}
|
||
|
||
auto t1 = ggml_time_us();
|
||
LOG(" Sliding window done: %d chunks, %.0f ms\n", n_chunks, (t1-t0)/1000.0);
|
||
LOG(" Score range: [%f, %f]\n",
|
||
*std::min_element(all_scores.begin(), all_scores.end()),
|
||
*std::max_element(all_scores.begin(), all_scores.end()));
|
||
|
||
// ===================================================================
|
||
// Select important chunks (full prompt)
|
||
// ===================================================================
|
||
|
||
std::vector<int> kept = select_chunks(all_scores, n_input, sp_chunk, sp_keep_pct, 128);
|
||
int n_sparse = (int)kept.size();
|
||
// Cap sparse tokens to avoid OOM on target model's attention matrix.
|
||
// When capped, keep every Nth token to maintain even coverage.
|
||
if (n_sparse > sp_max_keep) {
|
||
std::vector<int> capped;
|
||
int step = n_sparse / sp_max_keep;
|
||
for (int i = 0; i < n_sparse && (int)capped.size() < sp_max_keep; i += step)
|
||
capped.push_back(kept[i]);
|
||
// Ensure last token is included
|
||
if (capped.back() != kept.back()) {
|
||
capped.back() = kept.back();
|
||
}
|
||
kept = capped;
|
||
n_sparse = (int)kept.size();
|
||
LOG(" Capped to %d tokens (max_keep)\n", n_sparse);
|
||
}
|
||
LOG("Selected %d/%d tokens (%.0f%%)\n", n_sparse, n_input, 100.0f*n_sparse/n_input);
|
||
|
||
// ===================================================================
|
||
// Sparse prefill on target
|
||
// ===================================================================
|
||
|
||
auto t2 = ggml_time_us();
|
||
llama_batch batch = llama_batch_init(n_sparse, 0, 1);
|
||
common_batch_clear(batch);
|
||
for (int i = 0; i < n_sparse; i++)
|
||
common_batch_add(batch, inp[kept[i]], kept[i], {0}, (i == n_sparse-1) ? 1 : 0);
|
||
int ret = llama_decode(tgt_ctx, batch);
|
||
if (ret < 0) LOG_ERR("sparse prefill error: %d\n", ret);
|
||
llama_batch_free(batch);
|
||
auto t3 = ggml_time_us();
|
||
LOG("Sparse prefill: %.0f ms (%.0f t/s)\n", (t3-t2)/1000.0, n_sparse/((t3-t2)/1000.0*1000.0));
|
||
|
||
// ===================================================================
|
||
// Decode
|
||
// ===================================================================
|
||
|
||
common_params_sampling smp = params.sampling;
|
||
common_sampler * smpl = common_sampler_init(tgt_m, smp);
|
||
for (int i = 0; i < n_input; i++) LOG("%s", common_token_to_piece(tgt_ctx, inp[i]).c_str());
|
||
LOG("\n");
|
||
|
||
int np = 0; auto t4 = ggml_time_us();
|
||
while (true) {
|
||
llama_token id = common_sampler_sample(smpl, tgt_ctx, -1);
|
||
common_sampler_accept(smpl, id, true);
|
||
LOG("%s", common_token_to_piece(tgt_ctx, id).c_str());
|
||
np++;
|
||
if (llama_vocab_is_eog(vocab, id)) break;
|
||
if (params.n_predict >= 0 && np >= params.n_predict) break;
|
||
llama_decode(tgt_ctx, llama_batch_get_one(&id, 1));
|
||
}
|
||
auto t5 = ggml_time_us();
|
||
LOG("\n\n");
|
||
|
||
// ===================================================================
|
||
// Results
|
||
// ===================================================================
|
||
|
||
double spec_ms = (t1-t0)/1000.0;
|
||
double prefill_ms = (t3-t2)/1000.0;
|
||
double decode_ms = (t5-t4)/1000.0;
|
||
double total_ms = (t5-t0)/1000.0;
|
||
double est_full = 1000.0 * n_input / 270.0;
|
||
|
||
LOG("═ RESULTS ═══════════════════════════\n");
|
||
LOG("Prompt: %d → sparse: %d (%.0f%%)\n", n_input, n_sparse, 100.0f*n_sparse/n_input);
|
||
LOG("Sliding: %7.0f ms (%5d chunks)\n", spec_ms, n_chunks);
|
||
LOG("Sparse: %7.0f ms (%.0f t/s)\n", prefill_ms, n_sparse/(prefill_ms/1000.0));
|
||
LOG("Decode: %7.0f ms (%.0f t/s)\n", decode_ms, np/(decode_ms/1000.0));
|
||
LOG("Total: %7.0f ms\n", total_ms);
|
||
LOG("Full (est): %7.0f ms\n", est_full);
|
||
if (est_full > 0) LOG("Speedup: %.1f×\n", est_full / (total_ms + 1.0));
|
||
LOG("\n");
|
||
|
||
common_sampler_free(smpl);
|
||
llama_free(tgt_ctx); llama_model_free(tgt_m);
|
||
llama_free(dft.ctx); llama_model_free(dft.model);
|
||
llama_backend_free();
|
||
return 0;
|
||
}
|
||
```
|