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Add reproducible reference integration and Fabric Illusion contract
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README.md

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./tools/run_tfmbs_regression.sh
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```
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### 📌 Reference integration
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```bash
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python3 tools/reference_integration.py
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```
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This script rebuilds `bin/mock_llama`, runs a CPU baseline, reruns it through `tools/tfmbs-run`, and writes the latency summary to `logs/reference_integration.csv` along with a small bar chart. The Fabric Illusion heuristics exercised here are captured in `docs/FABRIC_ILLUSION_CONTRACT.md`.
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`tools/run_tfmbs_regression.sh` automates the `cmake -B build -G Ninja` / `ninja -C build tfmbs_plugin` flow, locates `mlir-opt` (defaults to `../llvm-project/build-shared/bin/mlir-opt`), and then runs `tests/mlir/run_tfmbs_to_linalg.py` with the built plugin; set `LLVM_DIR`, `MLIR_DIR`, `BUILD_DIR`, `MLIR_OPT`, or `TFMBS_PLUGIN` before invoking the script to override the defaults so your environment mirrors CI.
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## 📣 Public Readiness
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## 🏗️ Architecture & Vision
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Ternary Fabric operates as a **semantic execution substrate**, not a library rewrite. It creates a *memory illusion layer* that migrates hot weights into ternary residency pools and offloads compute onto parallel Fabric Tiles.
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Ternary Fabric operates as a **semantic execution substrate**, not a library rewrite. It creates a *memory illusion layer* that migrates hot weights into ternary residency pools and offloads compute onto parallel Fabric Tiles. The Fabric Illusion contract is now captured in `docs/FABRIC_ILLUSION_CONTRACT.md` so reviewers can inspect exactly which allocations, signal handlers, and heuristics are part of the intercept.
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### Core Innovations
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- **Zero-Skip:** Hardware suppression of clocking and memory access for zero-value operands.
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| **Aggregated Fabric (4 Tiles)** | 60 | **30.0** | 66% |
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| **Projected (High-Density)** | 1024 | **512.0** | 65–72% |
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*Detailed metrics and terminology can be found in **[BENCHMARKS.md](BENCHMARKS.md)**.*
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Detailed throughput, density, and zero-skip numbers come from the Phase 21–25 measurement plane documented in **[BENCHMARKS.md](BENCHMARKS.md)**. The same cycle-aware cost model and economic efficiency definitions appear in `docs/18_WORKLOADS_METRICS.md`, so you can map “zero skip” counts to Fabric Cost directly.
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## 🧭 North-Star Metric
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Our north-star KPI is **zero-skip MACs avoided per Fabric Cost** (`zero_skips / fabric_cost`), borrowing its topology from `docs/18_WORKLOADS_METRICS.md`. It keeps the story on “work avoided vs. the energy proxy we already report” instead of chasing peak GOPS, and the `tools/reference_integration.py` helper makes a simple chart that showcases the delta between the CPU baseline and the intercept run.
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- **[Roadmap](docs/ROADMAP.md):** Phase-by-phase progression and deliverables.
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- **[Whitepaper](WHITE_PAPER.md):** Technical narrative, architecture, and comparisons.
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- **[Benchmarks](BENCHMARKS.md):** Authoritative performance and resource metrics.
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- **[Fabric Illusion Contract](docs/FABRIC_ILLUSION_CONTRACT.md):** What the interposer intercepts, the guarantees it offers, and the safe failure modes we support.
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## 🧪 Compiler Regression
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USER_MANUAL.md

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* `dma_loader_demo.py`: Using the AXI-Stream DMA path.
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* `profiling_example.py`: Extracting hardware performance counters.
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* `tests/test_phase21_multi_fabric.c`: Verifying global orchestration and cross-fabric dependency management.
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* `tools/reference_integration.py`: Reproducible mock GEMV + Fabric intercept run that writes logs, CSV metrics, and a comparison chart.
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© 2026 Ternary Fabric Project. All rights reserved.

WHITE_PAPER.md

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- **Software readiness:** The compiler/MLIR pipeline (Phase 23) already emits the `tfmbs` dialect, fusion pass, and telemetry hints, and CI/regression scripts run `tests/mlir/run_tfmbs_to_linalg.py` plus `tools/adaptive_dashboard.py` so the telemetry contract is verified before every merge.
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- **Adaptive telemetry:** The runtime’s `AdaptiveRuntimeAgent` already captures `fusion_order`/`fusion_sparsity`, and `tools/run_hw_dma_telemetry.sh`/`tools/capture_dma_telemetry.py` reproduce the dashboard comparison locally using the Verilator DMA path while the FPGA racks remain offline.
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- **Hardware gate:** Physical verification (RTL synthesis, PCIe/DMA driver validation, telemetry comparison, >50× efficiency benchmark on XC7Z020/XC7Z045) is documented in `docs/FPGA_VERIFICATION_CHECKLIST.md` and `docs/hardware_verification_report.md` and will resume once the boards return. This whitepaper will be updated with the hardware results and publicly visible metrics when the Silicon Reality milestone is completed.
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- **Reference integration:** `tools/reference_integration.py` rebuilds `bin/mock_llama`, runs the CPU baseline, reruns it through `tools/tfmbs-run`, and emits CSV logs plus a comparison chart, so every latency claim can be recomputed on any desktop without FPGA racks.
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- **Fabric Illusion contract:** `docs/FABRIC_ILLUSION_CONTRACT.md` now enumerates the allocations, heuristics, and signal handlers that the intercept touches to sustain the illusion.
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## 7. Visual Summary
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- **Tile block:** Each tile contains 15 ternary lanes fed by a PT-5 unpacker that streams 1.58-bit SRAM rows into lane-sized trits, plus zero-skip gates that disable lane clocks when operands are zero.
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- **Density advantage:** Balanced ternary’s ~1.585 bits/trit packs sign/magnitude information more efficiently than unsigned ternary or binary sign-magnitude encodings, making it a compelling substrate for sparse LLM layers.
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- **Ultra-low power:** The combination of gating logic, PT-5 SRAM, and a 250 MHz fabric clock delivers power/operation savings compared to 1.5–2.0 GHz SIMD engines; this energy advantage is tracked via the Zero-Skip logic and adaptive telemetry dashboards even before the hardware racks reopen.
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## 9. Measurement Discipline
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All numeric claims in this paper are grounded in the emulator traces captured in `BENCHMARKS.md` and the cycle-aware cost model from `docs/18_WORKLOADS_METRICS.md`. Our north-star KPI is **zero-skip work avoided per Fabric Cost** (`zero_skips / fabric_cost`), which measures the MACs we skip against the energy proxy already available in telemetry. The `tools/reference_integration.py` helper rebuilds `bin/mock_llama`, reruns it through `tools/tfmbs-run`, and writes a CSV summary plus a comparison chart so reviewers can reproduce both the numerator (zero-skip delta) and denominator (Fabric Cost) without FPGA racks.
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© 2026 Ternary Fabric Project.

docs/FABRIC_ILLUSION_CONTRACT.md

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# Fabric Illusion Contract
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The Fabric Illusion is the promise that existing AI workloads execute unmodified while the interposer migrates the heavy work into the ternary fabric. This document spells out the observable contract we currently enforce and illustrates the safe boundaries that let reviewers verify the idea without having to dive into the RTL.
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## 1. What the interposer is responsible for
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- **Heavy allocations (> 1 KiB):** `malloc`/`mmap` calls that meet or exceed `FABRIC_THRESHOLD (1024 bytes)` are registered, trapped, and eventually packed into PT-5 memory. This is the gating heuristic defined in `src/libtfmbs_intercept.c` and documented in the instrumentation notes in `docs/16_PRODUCTION_HARDENING.md`.
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- **Transparent residency:** The interposer signals residency by mprotecting weight buffers and only allowing CPU accesses once the background fabric task has completed. The `sigsegv` handler (same file) updates residency metadata and synchronizes via `fabric_wait()`, so host code never sees stale results.
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- **GEMV detection:** The heuristics look for the two most recently touched large buffers (weights + activation) before submitting a ternary GEMV. This path is instrumented in `src/libtfmbs_intercept.c` and its behavior is captured in `docs/12_PYTORCH.md` and `docs/14_GGUF.md` where Python and GGUF workflows are described.
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- **Interposition shim:** The `tools/tfmbs-run` wrapper exported in this repo sets `LD_PRELOAD`/`DYLD_INSERT_LIBRARIES` and loads `libtfmbs_intercept`, giving you a one-command reproduction of the Fabric Illusion. The smoke test `tools/reference_integration.py` exercises this path end-to-end (build → run mock GEMV → record logs + chart).
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## 2. Guarantees exposed to the host
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- **Data integrity:** Every offloaded kernel is followed by a `fabric_wait()` (or implicit wait via the residency heuristics) before the host reads the results. If validation mode is enabled (`TFMBS_VALIDATE=1`), a bit-exact CPU compare runs automatically and logs mismatches without altering the host flow.
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- **Billing transparency:** Telemetry (zero skip count, fabric cost, residency hits) is available via `tools/capture_dma_telemetry.py` and the adaptive dashboard described in `docs/08_PROFILING.md`; nothing in the host interface is hidden or magical.
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- **Static contract:** The interposer does not require recompiling benchmarks. All of the token-level workflows in `README.md` (and the `tools/benchmark_performance.sh` helper) keep the host binary untouched while `libtfmbs_intercept` short-circuits memory and compute.
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## 3. Safe failure modes
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- **Fallback to CPU:** If fabric allocation fails or an unsupported access pattern is detected, the interposer delegates the faulting signal handler to the previous handler and the host simply continues executing on the CPU, as described in Sections 7–10 of `docs/16_PRODUCTION_HARDENING.md`.
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- **Graceful unregistration:** `free()` removes the buffer from the residency registry and releases the fabric allocation so subsequent workloads start clean.
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- **Short-circuit control:** `FABRIC_SHORT_CIRCUIT` gates the optimization: set it to `0` to disable GPU-level overrides and observe the host doing the same work it always has.
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## 4. Why this matters
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By turning the Fabric Illusion into a documented contract, reviewers can assess brittleness (e.g., convolutional kernels that don’t trigger the heuristics) and tell when they have to add explicit registration via `fabric_register_weight()`. Recording this contract alongside `tools/reference_integration.py` and `tools/benchmark_performance.sh` makes the idea easy to verify without private hardware racks.

logs/reference_integration_cpu.log

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Iteration 0...
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Iteration 0: Row 0: 341

logs/reference_integration_fabric.log

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tools/reference_integration.py

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#!/usr/bin/env python3
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"""Produce a reproducible mock GEMV integration demonstrating the Fabric interposer."""
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from __future__ import annotations
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import csv
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import os
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import platform
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import subprocess
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import time
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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BIN_DIR = ROOT / "bin"
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TOOLS_DIR = ROOT / "tools"
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LOG_DIR = ROOT / "logs"
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PLOT_DIR = ROOT / "plots"
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CSV_PATH = LOG_DIR / "reference_integration.csv"
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PLOT_PATH = PLOT_DIR / "reference_integration_summary.png"
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def shlib_ext() -> str:
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return ".dylib" if platform.system() == "Darwin" else ".so"
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def build_targets() -> None:
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targets = [
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"bin/mock_llama",
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f"bin/libtfmbs_device{shlib_ext()}",
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f"bin/libtfmbs_intercept{shlib_ext()}",
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]
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subprocess.run(["make", *targets], check=True)
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def run_process(command: list[str], env: dict[str, str] | None, log_path: Path) -> float:
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start = time.perf_counter()
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result = subprocess.run(command, env=env, capture_output=True, text=True)
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duration_ms = (time.perf_counter() - start) * 1000
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LOG_DIR.mkdir(parents=True, exist_ok=True)
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log_path.write_text(result.stdout + result.stderr)
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result.check_returncode()
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return duration_ms
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def try_plot(durations: list[tuple[str, float]]) -> None:
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try:
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import matplotlib.pyplot as plt
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except ImportError: # pragma: no cover
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print("📉 matplotlib not installed; skipping chart.")
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return
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PLOT_DIR.mkdir(parents=True, exist_ok=True)
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labels, values = zip(*durations)
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fig, ax = plt.subplots(figsize=(6, 4))
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ax.bar(labels, values, color=["#2f7bbf", "#1f9d4d"])
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ax.set_ylabel("Milliseconds")
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ax.set_title("Mock Llama: CPU vs. Fabric-Accelerated")
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for idx, value in enumerate(values):
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ax.text(idx, value + max(values) * 0.01, f"{value:.1f} ms", ha="center")
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fig.tight_layout()
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fig.savefig(PLOT_PATH)
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print(f"📊 Saved chart to {PLOT_PATH.relative_to(ROOT)}")
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def main() -> int:
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build_targets()
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baseline_duration = run_process(
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[str(BIN_DIR / "mock_llama")],
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env=None,
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log_path=LOG_DIR / "reference_integration_cpu.log",
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)
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fabric_env = os.environ.copy()
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fabric_env["FABRIC_SHORT_CIRCUIT"] = "1"
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fabric_env["TFMBS_DEBUG"] = "1"
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fabric_duration = run_process(
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[str(TOOLS_DIR / "tfmbs-run"), str(BIN_DIR / "mock_llama")],
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env=fabric_env,
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log_path=LOG_DIR / "reference_integration_fabric.log",
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)
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durations = [
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("CPU Baseline", baseline_duration),
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("Fabric Interposer", fabric_duration),
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]
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CSV_PATH.parent.mkdir(parents=True, exist_ok=True)
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with CSV_PATH.open("w", newline="") as csvfile:
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writer = csv.DictWriter(csvfile, fieldnames=["mode", "duration_ms", "notes"])
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writer.writeheader()
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writer.writerow(
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{
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"mode": "cpu_baseline",
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"duration_ms": f"{baseline_duration:.2f}",
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"notes": "Pure CPU mock GEMV run",
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}
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)
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writer.writerow(
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{
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"mode": "fabric_interposer",
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"duration_ms": f"{fabric_duration:.2f}",
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"notes": "LD_PRELOAD/libtfmbs_intercept run",
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}
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)
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try_plot(durations)
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speedup = baseline_duration / fabric_duration if fabric_duration > 0 else float("inf")
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print("✅ Reference integration complete.")
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print(f"- Baseline: {baseline_duration:.2f} ms")
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print(f"- Fabric: {fabric_duration:.2f} ms ({speedup:.2f}x faster)")
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print(f"- Logs: {LOG_DIR.relative_to(ROOT)}")
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print(f"- CSV: {CSV_PATH.relative_to(ROOT)}")
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if PLOT_PATH.exists():
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print(f"- Chart: {PLOT_PATH.relative_to(ROOT)}")
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else:
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print("- Chart: (matplotlib missing; install it to generate the bar chart)")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())

tools/tfmbs-run

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#!/usr/bin/env bash
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set -euo pipefail
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# Utility to launch an application with the Ternary Fabric interposer preloaded.
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SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
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ROOT_DIR="$(dirname "$SCRIPT_DIR")"
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BIN_DIR="$ROOT_DIR/bin"
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if [ -z "$1" ]; then
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echo "Usage: $0 <app> [args...]"
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exit 1
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fi
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UNAME="$(uname -s)"
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if [ "$UNAME" = "Darwin" ]; then
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PRELOAD_ENV="DYLD_INSERT_LIBRARIES"
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LIBPATH_ENV="DYLD_LIBRARY_PATH"
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SHLIB_EXT=".dylib"
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else
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PRELOAD_ENV="LD_PRELOAD"
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LIBPATH_ENV="LD_LIBRARY_PATH"
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SHLIB_EXT=".so"
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fi
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INTERCEPT="$BIN_DIR/libtfmbs_intercept${SHLIB_EXT}"
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DEVICE_LIB="$BIN_DIR/libtfmbs_device${SHLIB_EXT}"
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if [ ! -f "$INTERCEPT" ] || [ ! -f "$DEVICE_LIB" ]; then
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echo "🏗️ Building libtfmbs_device and libtfmbs_intercept..."
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make -C "$ROOT_DIR" "bin/$(basename "$INTERCEPT")" "bin/$(basename "$DEVICE_LIB")"
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fi
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existing_libpath="${!LIBPATH_ENV:-}"
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if [ -n "$existing_libpath" ]; then
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export "$LIBPATH_ENV"="$BIN_DIR:$existing_libpath"
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else
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export "$LIBPATH_ENV"="$BIN_DIR"
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fi
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export "$PRELOAD_ENV"="$INTERCEPT"
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shift
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exec "$@"

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