[Autotuner] Plotting utility + test

docs/user/InstructionsForAutoTuner.md

@@ -145,6 +145,19 @@ python3 -m autotuner.distributed --design gcd --platform sky130hd \
                        sweep
 ```
 
+#### Plot images
+
+After running an AutoTuner experiment, you can generate a graph to understand the results better.
+The graph will show the progression of one metric (see list below) over the execution of the experiment.
+
+- QoR
+- Runtime per trial
+- Clock Period
+- Worst slack
+
+```shell
+python3 utils/plot.py --results_dir <your-autotuner-result-path>
+```
 
 ### Google Cloud Platform (GCP) distribution with Ray
 

flow/test/test_autotuner.sh

@@ -8,11 +8,6 @@ cd ../
 ./tools/AutoTuner/installer.sh
 . ./tools/AutoTuner/setup.sh
 
-# remove dashes and capitalize platform name
-PLATFORM=${PLATFORM//-/}
-# convert to uppercase
-PLATFORM=${PLATFORM^^}
-
 echo "Running Autotuner smoke tune test"
 python3 -m unittest tools.AutoTuner.test.smoke_test_tune.${PLATFORM}TuneSmokeTest.test_tune
 
@@ -30,4 +25,14 @@ if [ "$PLATFORM" == "asap7" ] && [ "$DESIGN_NAME" == "gcd" ]; then
   python3 -m unittest tools.AutoTuner.test.resume_check.ResumeCheck.test_tune_resume
 fi
 
+echo "Running Autotuner plotting smoke test"
+all_experiments=$(ls -d ./flow/logs/${PLATFORM}/${DESIGN_NAME}/smoke-test-tune*)
+all_experiments=$(basename -a $all_experiments)
+for expt in $all_experiments; do
+  python3 tools/AutoTuner/src/autotuner/utils/plot.py \
+    --platform ${PLATFORM} \
+    --design ${DESIGN_NAME} \
+    --experiment $expt
+done
+
 exit $ret

flow/util/genReport.py

@@ -19,6 +19,7 @@
 METRICS_CHECK_FMT = "{}/metadata-{}-check.log"
 REGEX_ERROR = re.compile(r"^\[error ?(\w+-\d+)?\]", re.IGNORECASE)
 REGEX_WARNING = re.compile(r"^\[warning ?(\w+-\d+)?\]", re.IGNORECASE)
+SKIPPED_FLOW_VARIANT_KEYWORDS = ["test", "tune"]
 STATUS_GREEN = "Passing"
 STATUS_RED = "Failing"
 
@@ -248,7 +249,9 @@ def write_summary():
     dir_list = log_dir.split(os.sep)
     # Handles autotuner folders, which do not have `report.log` natively.
     # TODO: Can we log something for autotuner?
-    if len(dir_list) != 4 or "test-" in dir_list[-1]:
+    if len(dir_list) != 4 or any(
+        word in dir_list[-1] for word in SKIPPED_FLOW_VARIANT_KEYWORDS
+    ):
         continue
     report_dir = log_dir.replace(LOGS_FOLDER, REPORTS_FOLDER)
 

tools/AutoTuner/requirements.txt

@@ -9,3 +9,4 @@ tensorboard>=2.14.0,<=2.16.2
 protobuf==3.20.3
 SQLAlchemy==1.4.17
 urllib3<=1.26.15
+matplotlib==3.10.0

tools/AutoTuner/src/autotuner/utils/plot.py

@@ -0,0 +1,195 @@
+import glob
+import json
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import re
+import os
+import argparse
+import sys
+
+# Only does plotting for AutoTunerBase variants
+AT_REGEX = r"variant-AutoTunerBase-([\w-]+)-\w+"
+
+# TODO: Make sure the distributed.py METRIC variable is consistent with this, single source of truth.
+METRIC = "metric"
+
+cur_dir = os.path.dirname(os.path.abspath(__file__))
+root_dir = os.path.join(cur_dir, "../../../../../")
+os.chdir(root_dir)
+
+
+def load_dir(dir: str) -> pd.DataFrame:
+    """
+    Load and merge progress, parameters, and metrics data from a specified directory.
+    This function searches for `progress.csv`, `params.json`, and `metrics.json` files within the given directory,
+    concatenates the data, and merges them into a single pandas DataFrame.
+    Args:
+        dir (str): The directory path containing the subdirectories with `progress.csv`, `params.json`, and `metrics.json` files.
+    Returns:
+        pd.DataFrame: A DataFrame containing the merged data from the progress, parameters, and metrics files.
+    """
+
+    # Concatenate progress DFs
+    progress_csvs = glob.glob(f"{dir}/*/progress.csv")
+    if len(progress_csvs) == 0:
+        print("No progress.csv files found.")
+        sys.exit(1)
+    progress_df = pd.concat([pd.read_csv(f) for f in progress_csvs])
+
+    # Concatenate params.json & metrics.json file
+    params = []
+    failed = []
+    for params_fname in glob.glob(f"{dir}/*/params.json"):
+        metrics_fname = params_fname.replace("params.json", "metrics.json")
+        try:
+            with open(params_fname, "r") as f:
+                _dict = json.load(f)
+                _dict["trial_id"] = re.search(AT_REGEX, params_fname).group(1)
+            with open(metrics_fname, "r") as f:
+                metrics = json.load(f)
+                ws = metrics["finish"]["timing__setup__ws"]
+                metrics["worst_slack"] = ws
+                _dict.update(metrics)
+            params.append(_dict)
+        except Exception as e:
+            failed.append(metrics_fname)
+            continue
+
+    # Merge all dataframe
+    params_df = pd.DataFrame(params)
+    try:
+        progress_df = progress_df.merge(params_df, on="trial_id")
+    except KeyError:
+        print(
+            "Unable to merge DFs due to missing trial_id in params.json (possibly due to failed trials.)"
+        )
+        sys.exit(1)
+
+    # Print failed, if any
+    if failed:
+        failed_files = "\n".join(failed)
+        print(f"Failed to load {len(failed)} files:\n{failed_files}")
+    return progress_df
+
+
+def preprocess(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Preprocess the input DataFrame by renaming columns, removing unnecessary columns,
+    filtering out invalid rows, and normalizing the timestamp.
+    Args:
+        df (pd.DataFrame): The input DataFrame to preprocess.
+    Returns:
+        pd.DataFrame: The preprocessed DataFrame with renamed columns, removed columns,
+                      filtered rows, and normalized timestamp.
+    """
+
+    cols_to_remove = [
+        "done",
+        "training_iteration",
+        "date",
+        "pid",
+        "hostname",
+        "node_ip",
+        "time_since_restore",
+        "time_total_s",
+        "iterations_since_restore",
+    ]
+    rename_dict = {
+        "time_this_iter_s": "runtime",
+        "_SDC_CLK_PERIOD": "clk_period",  # param
+    }
+    try:
+        df = df.rename(columns=rename_dict)
+        df = df.drop(columns=cols_to_remove)
+        df = df[df[METRIC] != 9e99]
+        df["timestamp"] -= df["timestamp"].min()
+        return df
+    except KeyError as e:
+        print(
+            f"KeyError: {e} in the DataFrame. Dataframe does not contain necessary columns."
+        )
+        sys.exit(1)
+
+
+def plot(df: pd.DataFrame, key: str, dir: str):
+    """
+    Plots a scatter plot with a linear fit and a box plot for a specified key from a DataFrame.
+    Args:
+        df (pd.DataFrame): The DataFrame containing the data to plot.
+        key (str): The column name in the DataFrame to plot.
+        dir (str): The directory where the plots will be saved. The directory must exist.
+    Returns:
+        None
+    """
+
+    assert os.path.exists(dir), f"Directory {dir} does not exist."
+    # Plot box plot and time series plot for key
+    fig, ax = plt.subplots(1, figsize=(15, 10))
+    ax.scatter(df["timestamp"], df[key])
+    ax.set_xlabel("Time (s)")
+    ax.set_ylabel(key)
+    ax.set_title(f"{key} vs Time")
+
+    try:
+        coeff = np.polyfit(df["timestamp"], df[key], 1)
+        poly_func = np.poly1d(coeff)
+        ax.plot(
+            df["timestamp"],
+            poly_func(df["timestamp"]),
+            "r--",
+            label=f"y={coeff[0]:.2f}x+{coeff[1]:.2f}",
+        )
+        ax.legend()
+    except np.linalg.LinAlgError:
+        print("Cannot fit a line to the data, plotting only scatter plot.")
+
+    fig.savefig(f"{dir}/{key}.png")
+
+    plt.figure(figsize=(15, 10))
+    plt.boxplot(df[key])
+    plt.ylabel(key)
+    plt.title(f"{key} Boxplot")
+    plt.savefig(f"{dir}/{key}-boxplot.png")
+
+
+def main(platform: str, design: str, experiment: str):
+    """
+    Main function to process results from a specified directory and plot the results.
+    Args:
+        platform (str): The platform name.
+        design (str): The design name.
+        experiment (str): The experiment name.
+    Returns:
+        None
+    """
+
+    results_dir = os.path.join(
+        root_dir, f"./flow/logs/{platform}/{design}/{experiment}"
+    )
+    img_dir = os.path.join(
+        root_dir, f"./flow/reports/images/{platform}/{design}/{experiment}"
+    )
+    print("Processing results from", results_dir)
+    os.makedirs(img_dir, exist_ok=True)
+    df = load_dir(results_dir)
+    df = preprocess(df)
+    keys = [METRIC] + ["runtime", "clk_period", "worst_slack"]
+
+    # Plot only if more than one entry
+    if len(df) < 2:
+        print("Less than 2 entries, skipping plotting.")
+        sys.exit(0)
+    for key in keys:
+        plot(df, key, img_dir)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Plot AutoTuner results.")
+    parser.add_argument("--platform", type=str, help="Platform name.", required=True)
+    parser.add_argument("--design", type=str, help="Design name.", required=True)
+    parser.add_argument(
+        "--experiment", type=str, help="Experiment name.", required=True
+    )
+    args = parser.parse_args()
+    main(platform=args.platform, design=args.design, experiment=args.experiment)

tools/AutoTuner/test/resume_check.py

@@ -51,7 +51,7 @@ def setUp(self):
             f" --platform {self.platform}"
             f" --config {self.config}"
             f" --jobs {self.jobs}"
-            f" --experiment test_resume"
+            f" --experiment test-resume"
             f" tune --iterations {self.iterations} --samples {self.samples}"
             f" --resources_per_trial {res_per_trial}"
             f" {c}"

tools/AutoTuner/test/smoke_test_algo_eval.py

@@ -53,17 +53,17 @@ def test_algo_eval(self):
             self.assertTrue(successful)
 
 
-class ASAP7AlgoEvalSmokeTest(BaseAlgoEvalSmokeTest):
+class asap7AlgoEvalSmokeTest(BaseAlgoEvalSmokeTest):
     platform = "asap7"
     design = "gcd"
 
 
-class IHPSG13G2AlgoEvalSmokeTest(BaseAlgoEvalSmokeTest):
+class ihpsg13g2AlgoEvalSmokeTest(BaseAlgoEvalSmokeTest):
     platform = "ihp-sg13g2"
     design = "gcd"
 
 
-class SKY130HDAlgoEvalSmokeTest(BaseAlgoEvalSmokeTest):
+class sky130hdAlgoEvalSmokeTest(BaseAlgoEvalSmokeTest):
     platform = "sky130hd"
     design = "gcd"
 

tools/AutoTuner/test/smoke_test_sample_iteration.py

@@ -36,17 +36,17 @@ def test_sample_iteration(self):
             self.assertTrue(successful)
 
 
-class ASAP7SampleIterationSmokeTest(BaseSampleIterationSmokeTest):
+class asap7SampleIterationSmokeTest(BaseSampleIterationSmokeTest):
     platform = "asap7"
     design = "gcd"
 
 
-class SKY130HDSampleIterationSmokeTest(BaseSampleIterationSmokeTest):
+class sky130hdSampleIterationSmokeTest(BaseSampleIterationSmokeTest):
     platform = "sky130hd"
     design = "gcd"
 
 
-class IHPSG13G2SampleIterationSmokeTest(BaseSampleIterationSmokeTest):
+class ihpsg13g2SampleIterationSmokeTest(BaseSampleIterationSmokeTest):
     platform = "ihp-sg13g2"
     design = "gcd"
 

tools/AutoTuner/test/smoke_test_sweep.py

@@ -48,17 +48,17 @@ def test_sweep(self):
         self.assertTrue(successful)
 
 
-class ASAP7SweepSmokeTest(BaseSweepSmokeTest):
+class asap7SweepSmokeTest(BaseSweepSmokeTest):
     platform = "asap7"
     design = "gcd"
 
 
-class SKY130HDSweepSmokeTest(BaseSweepSmokeTest):
+class sky130hdSweepSmokeTest(BaseSweepSmokeTest):
     platform = "sky130hd"
     design = "gcd"
 
 
-class IHPSG13G2SweepSmokeTest(BaseSweepSmokeTest):
+class ihpsg13g2SweepSmokeTest(BaseSweepSmokeTest):
     platform = "ihp-sg13g2"
     design = "gcd"
 

tools/AutoTuner/test/smoke_test_tune.py

@@ -32,17 +32,17 @@ def test_tune(self):
         self.assertTrue(successful)
 
 
-class ASAP7TuneSmokeTest(BaseTuneSmokeTest):
+class asap7TuneSmokeTest(BaseTuneSmokeTest):
     platform = "asap7"
     design = "gcd"
 
 
-class SKY130HDTuneSmokeTest(BaseTuneSmokeTest):
+class sky130hdTuneSmokeTest(BaseTuneSmokeTest):
     platform = "sky130hd"
     design = "gcd"
 
 
-class IHPSG13G2TuneSmokeTest(BaseTuneSmokeTest):
+class ihpsg13g2TuneSmokeTest(BaseTuneSmokeTest):
     platform = "ihp-sg13g2"
     design = "gcd"