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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zYI71djJxM63"
+ },
+ "source": [
+ "# Evaluating Sustainability using EvaluationAgent"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {
+ "id": "FZ-4_M4fxM64",
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "\n",
+ " ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1odA2-O0aztf"
+ },
+ "source": [
+ "| Component | Details |\n",
+ "|-----------|---------|\n",
+ "| **Goal** | Show how to evaluate the sustainability of optimization algorithms by measuring inference time, energy consumption, CO₂ emissions, and quality trade-offs. |\n",
+ "| **Model** | [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) |\n",
+ "| **Dataset** | [LAION256](https://huggingface.co/datasets/nannullna/laion_subset) (10-sample subset) |\n",
+ "| **Device** | 1 x T4 (16GB) |\n",
+ "| **Optimization Algorithms** | cacher(deepcache), compiler(torch_compile) |\n",
+ "| **Evaluation Metrics** | `total_time`, `energy_consumed`, `co2_emissions`, `clip_score` |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hXDK5Mt1o6qc"
+ },
+ "source": [
+ "## Getting Started\n",
+ "\n",
+ "To install the required dependencies, you can run the following command:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "HfEXSjYWxM64"
+ },
+ "outputs": [],
+ "source": [
+ "%pip install pruna"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ixOOuXkao6qd"
+ },
+ "source": [
+ "For more information about how to install Pruna, please refer to the [Installation](https://docs.pruna.ai/en/stable/setup/install.html) page."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OCoHdvckxM64"
+ },
+ "source": [
+ "## 1. Load the Model\n",
+ "\n",
+ "First, we load the model. We use [stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5), one of the most widely used text-to-image diffusion models: it is small enough to run on a T4 GPU while still being representative of the compute and energy profile of real image-generation workloads. Feel free to swap in any other diffusion model from Hugging Face."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4PMNwfOOxM64"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from diffusers import AutoPipelineForText2Image\n",
+ "\n",
+ "from pruna.engine.pruna_model import PrunaModel\n",
+ "\n",
+ "pipe = AutoPipelineForText2Image.from_pretrained(\n",
+ " \"stable-diffusion-v1-5/stable-diffusion-v1-5\",\n",
+ " torch_dtype=torch.float16,\n",
+ " use_safetensors=True,\n",
+ ")\n",
+ "pipe = pipe.to(\"cuda\")\n",
+ "model = PrunaModel(pipe)\n",
+ "pipe.set_progress_bar_config(disable=True)\n",
+ "\n",
+ "# Shared generation parameters, applied to every configuration so results are comparable\n",
+ "GEN_ARGS = {\"num_inference_steps\": 25, \"guidance_scale\": 7.5}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Zgvuf3dZxM64"
+ },
+ "source": [
+ "## 2. Define the Evaluation Metrics\n",
+ "\n",
+ "`pruna` tracks three sustainability metrics:\n",
+ "\n",
+ "- **`total_time`**: wall-clock time to run the benchmark iterations (ms)\n",
+ "- **`energy_consumed`**: total energy drawn during inference (kWh)\n",
+ "- **`co2_emissions`**: estimated CO₂-equivalent emissions based on energy consumed and hardware location (kg)\n",
+ "\n",
+ "We also include **`clip_score`**, a quality metric measuring how well generated images match their prompts, so we can check whether efficiency gains come at a quality cost.\n",
+ "\n",
+ "We will pass these metrics to the evaluation `Task` as a list of metric instances. For other ways to specify metrics, see the [evaluation documentation](https://docs.pruna.ai/en/stable/docs_pruna/user_manual/evaluate.html)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yTBJuCTNxM65"
+ },
+ "outputs": [],
+ "source": [
+ "from pruna.evaluation.metrics import (\n",
+ " CO2EmissionsMetric,\n",
+ " EnergyConsumedMetric,\n",
+ " TorchMetricWrapper,\n",
+ " TotalTimeMetric,\n",
+ ")\n",
+ "\n",
+ "request = [\n",
+ " TotalTimeMetric(n_iterations=10, n_warmup_iterations=3),\n",
+ " EnergyConsumedMetric(n_iterations=10, n_warmup_iterations=3),\n",
+ " CO2EmissionsMetric(n_iterations=10, n_warmup_iterations=3),\n",
+ " TorchMetricWrapper(\"clip_score\"),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vFhRH1YCxM65"
+ },
+ "source": [
+ "## 3. Create an EvaluationAgent and a Task\n",
+ "\n",
+ "Pruna's evaluation process uses a Task to define which metrics to calculate and provide the evaluation data. The EvaluationAgent then takes this Task and handles running the model inference, passing the inputs, ground truth, and predictions to each metric, and collecting the results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wSIDHfEoxM65"
+ },
+ "outputs": [],
+ "source": [
+ "from pruna.data.pruna_datamodule import PrunaDataModule\n",
+ "from pruna.evaluation.evaluation_agent import EvaluationAgent\n",
+ "from pruna.evaluation.task import Task\n",
+ "\n",
+ "datamodule = PrunaDataModule.from_string(\"LAION256\")\n",
+ "datamodule.limit_datasets(10) # Quality metrics run over these 10 samples. Timing metrics benchmark a single batch\n",
+ "task = Task(request, datamodule)\n",
+ "eval_agent = EvaluationAgent(task)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7tKFVEQPxM65"
+ },
+ "source": [
+ "## 4. Evaluate the Baseline Model\n",
+ "\n",
+ "We can evaluate a model by calling the `evaluate` method of the EvaluationAgent."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wWvxYQvMxM65"
+ },
+ "outputs": [],
+ "source": [
+ "model.inference_handler.model_args.update(GEN_ARGS)\n",
+ "\n",
+ "base_results = eval_agent.evaluate(model)\n",
+ "for r in base_results:\n",
+ " print(f\"{r.name}: {r.result:.4g}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KT_eDmagxM65"
+ },
+ "source": [
+ "## 5. Smash the Model with DeepCache\n",
+ "\n",
+ "[DeepCache](https://docs.pruna.ai/en/stable/compression.html#deepcache) is a caching algorithm for diffusion models: it exploits the redundancy between consecutive denoising steps by caching intermediate UNet feature maps and reusing them in later steps, skipping part of the computation. Since the GPU does less work per image, we expect inference time (and with it energy consumption and CO₂ emissions) to drop, possibly at a small cost in image quality."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XGcu7t-8xM65"
+ },
+ "outputs": [],
+ "source": [
+ "import copy\n",
+ "\n",
+ "from pruna import smash\n",
+ "from pruna.config.smash_config import SmashConfig\n",
+ "from pruna.engine.utils import safe_memory_cleanup\n",
+ "\n",
+ "smash_config = SmashConfig()\n",
+ "smash_config.add(dict(deepcache=True))\n",
+ "\n",
+ "pipe = pipe.to(\"cpu\")\n",
+ "safe_memory_cleanup()\n",
+ "\n",
+ "copy_pipe = copy.deepcopy(pipe).to(\"cuda\")\n",
+ "smashed_pipe = smash(copy_pipe, smash_config)\n",
+ "smashed_pipe.set_progress_bar_config(disable=True)\n",
+ "smashed_pipe.inference_handler.model_args.update(GEN_ARGS)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_g5UNUW2xM65"
+ },
+ "source": [
+ "## 6. Evaluate the Smashed Model (DeepCache)\n",
+ "\n",
+ "We now evaluate the smashed model by calling evaluate again."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "DBPyC0O4xM65"
+ },
+ "outputs": [],
+ "source": [
+ "smashed_results = eval_agent.evaluate(smashed_pipe)\n",
+ "for r in smashed_results:\n",
+ " print(f\"{r.name}: {r.result:.4g}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fbtvUWW9xM65"
+ },
+ "source": [
+ "## 7. Analyze Baseline vs. DeepCache\n",
+ "\n",
+ "With both configurations evaluated on the same task, we can now compare them side by side. For time, energy, and CO₂ a negative change is an improvement. For CLIP score, higher is better."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "M8baXC6axM65"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "base = {r.name: r.result for r in base_results}\n",
+ "smashed = {r.name: r.result for r in smashed_results}\n",
+ "\n",
+ "metrics = {\n",
+ " \"Inference time (s)\": (\"total_time\", 1e-3), # ms -> s\n",
+ " \"Energy (Wh)\": (\"energy_consumed\", 1e3), # kWh -> Wh\n",
+ " \"CO2 emissions (g)\": (\"co2_emissions\", 1e3), # kg -> g\n",
+ " \"CLIP score\": (\"clip_score\", 1),\n",
+ "}\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 4, figsize=(15, 4), layout=\"constrained\")\n",
+ "\n",
+ "for ax, (label, (key, scale)) in zip(axes, metrics.items()):\n",
+ " values = [base[key] * scale, smashed[key] * scale]\n",
+ " bars = ax.bar([\"Baseline\", \"DeepCache\"], values, color=[\"#8a8a8a\", \"#7c3aed\"])\n",
+ " change = (values[1] / values[0] - 1) * 100\n",
+ " ax.bar_label(bars, labels=[f\"{values[0]:.3f}\", f\"{values[1]:.3f}\\n({change:+.1f}%)\"], fontsize=9)\n",
+ " ax.set_ylim(0, max(values) * 1.3)\n",
+ " ax.set_title(label)\n",
+ "\n",
+ "fig.suptitle(\"Benchmark metrics: baseline vs. DeepCache\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jRn8xPlotA19"
+ },
+ "source": [
+ "DeepCache roughly halves inference time (-46%) and cuts energy consumption and CO₂ emissions by about 38%, at the cost of a 2.4% drop in CLIP score."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lf5yG4hBsGr1"
+ },
+ "source": [
+ "## 8. Smash the Model with torch_compile\n",
+ "\n",
+ "In contrast to DeepCache, [torch_compile](https://docs.pruna.ai/en/stable/compression.html#torch-compile) does not skip any computation: it compiles the model into optimized GPU kernels, so the exact same work runs faster. The generated images stay essentially identical, so quality is unaffected, but because the savings come purely from better kernel execution, how much time, energy, and CO₂ you save depends on your hardware."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5KpMar6gsdH3"
+ },
+ "outputs": [],
+ "source": [
+ "# Free the DeepCache pipeline before building the next configuration\n",
+ "del smashed_pipe\n",
+ "safe_memory_cleanup()\n",
+ "\n",
+ "# Smash with torch.compile (start from a fresh copy of the original pipe)\n",
+ "compile_config = SmashConfig()\n",
+ "compile_config.add(dict(torch_compile=True))\n",
+ "\n",
+ "compile_pipe = smash(copy.deepcopy(pipe).to(\"cuda\"), compile_config)\n",
+ "compile_pipe.set_progress_bar_config(disable=True)\n",
+ "compile_pipe.inference_handler.model_args.update(GEN_ARGS)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T6Igrkx1tR2Y"
+ },
+ "source": [
+ "## 9. Evaluate the Smashed Model (torch_compile)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8v_mndBYtJPf"
+ },
+ "outputs": [],
+ "source": [
+ "compiled_results = eval_agent.evaluate(compile_pipe)\n",
+ "for r in compiled_results:\n",
+ " print(f\"{r.name}: {r.result:.4g}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BMlNwrSytRdX"
+ },
+ "source": [
+ "## 10. Analyze Baseline vs. DeepCache vs. torch_compile\n",
+ "\n",
+ "With all three configurations evaluated on the same task, we can compare them in a single visual."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zEAcXrrCtJ87"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "all_results = {\n",
+ " \"Baseline\": base_results,\n",
+ " \"DeepCache\": smashed_results,\n",
+ " \"torch.compile\": compiled_results,\n",
+ "}\n",
+ "data = {name: {r.name: r.result for r in res} for name, res in all_results.items()}\n",
+ "\n",
+ "# Convert to more readable units for small workloads\n",
+ "metrics = {\n",
+ " \"Inference time (s)\": (\"total_time\", 1e-3), # ms -> s\n",
+ " \"Energy (Wh)\": (\"energy_consumed\", 1e3), # kWh -> Wh\n",
+ " \"CO2 emissions (g)\": (\"co2_emissions\", 1e3), # kg -> g\n",
+ " \"CLIP score\": (\"clip_score\", 1),\n",
+ "}\n",
+ "\n",
+ "names = list(all_results)\n",
+ "colors = [\"#8a8a8a\", \"#7c3aed\", \"#0d9488\"]\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 4, figsize=(15, 4), layout=\"constrained\")\n",
+ "\n",
+ "for ax, (label, (key, scale)) in zip(axes, metrics.items()):\n",
+ " vals = [data[name][key] * scale for name in names]\n",
+ " bars = ax.bar(names, vals, color=colors)\n",
+ " labels = [f\"{v:.3f}\" if i == 0 else f\"{v:.3f}\\n({(v / vals[0] - 1) * 100:+.1f}%)\"\n",
+ " for i, v in enumerate(vals)]\n",
+ " ax.bar_label(bars, labels=labels, fontsize=9)\n",
+ " ax.set_ylim(0, max(vals) * 1.3)\n",
+ " ax.set_title(label)\n",
+ "\n",
+ "fig.suptitle(\"Benchmark metrics vs. baseline\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kQm2xPlotB25"
+ },
+ "source": [
+ "DeepCache delivers the largest savings but with a small quality dip, while torch.compile saves less (16% time, 10% energy/CO₂) yet leaves quality untouched (CLIP score +0.9%)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FhhTk9tK8Uhr"
+ },
+ "source": [
+ "## Conclusions\n",
+ "\n",
+ "All three sustainability metrics move in the same direction: less inference time means less energy consumption and CO₂ emissions. In this workflow, DeepCache cuts inference time by about 46%, and energy and CO₂ by about 38% because baseline power draw (such as RAM and idle GPU power) is consumed regardless of how fast the computation runs. For torch.compile, inference time is cut by 16% while energy and CO₂ are cut by 10%. DeepCache achieves this by skipping redundant UNet computation, while torch.compile runs the same computation with faster kernels, so its gains depend on the hardware.\n",
+ "\n",
+ "CLIP score shows a slight drop for DeepCache, hinting at a small quality tradeoff even in this limited run. For another comparison, see the [CMMD evaluation tutorial](./evaluation_agent_cmmd.ipynb)."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst
index 142b6fcb..12f28a5c 100644
--- a/docs/tutorials/index.rst
+++ b/docs/tutorials/index.rst
@@ -58,6 +58,12 @@ These tutorials will guide you through the process of using |pruna| to optimize
``Evaluate`` image generation quality with ``CMMD`` and ``EvaluationAgent``.
+ .. grid-item-card:: Evaluating Sustainability using EvaluationAgent
+ :text-align: center
+ :link: ./evaluation_agent_sustainability.ipynb
+
+ ``Evaluate`` energy, CO2, and time with ``EvaluationAgent``, comparing ``deepcache`` and ``torch_compile`` against a baseline.
+
.. grid-item-card:: x2 smaller Sana diffusers in action
:text-align: center
:link: ./sana_diffusers_int8.ipynb
@@ -125,4 +131,3 @@ These tutorials will guide you through the process of using |pruna| to optimize
:glob:
./*
-