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jasperdewintherjasperdewinther
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simplify file includes
1 parent 1b0124d commit 1f93e68

4 files changed

Lines changed: 90 additions & 25 deletions

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‎scripts/deep_analysis_plotter.ipynb‎

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@@ -34,10 +34,8 @@
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"outputs": [],
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"source": [
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"files = [\n",
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" 'a_inline_deep.json',\n",
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" 'b_inline_deep.json',\n",
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" 'a_pipeline_deep.json',\n",
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" 'b_pipeline_deep.json',\n",
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" 'a_deep_analysis.json',\n",
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" 'b_deep_analysis.json',\n",
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"]"
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]
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},

‎scripts/frame_breakdown_stackplot.ipynb‎

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"outputs": [],
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"source": [
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"files = [\n",
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" 'a_inline_deep.json',\n",
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" 'b_inline_deep.json',\n",
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" 'a_pipeline_deep.json',\n",
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" 'b_pipeline_deep.json',\n",
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" 'a_deep_analysis.json',\n",
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" 'b_deep_analysis.json',\n",
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"]"
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]
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},

‎scripts/per_frame_plotter.ipynb‎

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@@ -6,7 +6,10 @@
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"id": "1d9d9451-25fe-40d7-a67e-2fcaf6a9f54a",
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"metadata": {},
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"outputs": [],
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"source": "# Install dependencies into the active kernel\n%pip install pandas matplotlib"
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"source": [
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"# Install dependencies into the active kernel\n",
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"%pip install pandas matplotlib"
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]
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},
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{
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"cell_type": "code",
@@ -29,10 +32,8 @@
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"outputs": [],
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"source": [
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"files = [\n",
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" 'a_inline_perframe.csv',\n",
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" 'a_pipeline_perframe.csv',\n",
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" 'b_inline_perframe.csv',\n",
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" 'b_pipeline_perframe.csv',\n",
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" 'a_deep_analysis.json',\n",
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" 'b_deep_analysis.json',\n",
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"]"
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]
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},
@@ -42,23 +43,74 @@
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"id": "a5f9ea81-7bd2-4f70-a0b8-45164afe8f76",
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"metadata": {},
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"outputs": [],
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"source": "scores = {}\nfor file in files:\n file_scores = pd.read_csv(file)\n # Drop all-zero (empty) columns\n file_scores = file_scores.loc[:, (file_scores != 0).any(axis=0)]\n scores[file] = file_scores\nscores = pd.concat(scores)\n\n# Frame time = delta between consecutive sequence timestamps\nscores['Frame time (ms)'] = (scores['Sequence Time (ns)'].shift(-1) - scores['Sequence Time (ns)']) / 1_000_000\n# The final frame's delta is negative (the benchmark loops) and the first 3 frames\n# are huge outliers, so drop both.\nscores['Frame time (ms)'] = scores['Frame time (ms)'].mask(\n (scores['Frame time (ms)'] < 0) | (scores['Frame'] < 3)\n)\n\n# Reshape into row per input file + sequence time, column per metric\nscores = scores.reset_index().set_index(['level_0', 'Sequence Time (ns)']).drop(['level_1', 'Loop Index', 'Frame'], axis=1)\n\n# Convert the nanosecond timing columns to milliseconds\nfor col in ['Ray Tracing', 'Acceleration Structure Build', 'Rasterization', 'Compute', 'Driver']:\n scores[f'{col} (ms)'] = scores.pop(f'{col} (ns)') / 1_000_000\nscores"
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"source": [
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"scores = {}\n",
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"for file in files:\n",
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" file_scores = pd.read_csv(file)\n",
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" # Drop all-zero (empty) columns\n",
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" file_scores = file_scores.loc[:, (file_scores != 0).any(axis=0)]\n",
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" scores[file] = file_scores\n",
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"scores = pd.concat(scores)\n",
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"\n",
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"# Frame time = delta between consecutive sequence timestamps\n",
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"scores['Frame time (ms)'] = (scores['Sequence Time (ns)'].shift(-1) - scores['Sequence Time (ns)']) / 1_000_000\n",
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"# The final frame's delta is negative (the benchmark loops) and the first 3 frames\n",
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"# are huge outliers, so drop both.\n",
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"scores['Frame time (ms)'] = scores['Frame time (ms)'].mask(\n",
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" (scores['Frame time (ms)'] < 0) | (scores['Frame'] < 3)\n",
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")\n",
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"\n",
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"# Reshape into row per input file + sequence time, column per metric\n",
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"scores = scores.reset_index().set_index(['level_0', 'Sequence Time (ns)']).drop(['level_1', 'Loop Index', 'Frame'], axis=1)\n",
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"\n",
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"# Convert the nanosecond timing columns to milliseconds\n",
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"for col in ['Ray Tracing', 'Acceleration Structure Build', 'Rasterization', 'Compute', 'Driver']:\n",
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" scores[f'{col} (ms)'] = scores.pop(f'{col} (ns)') / 1_000_000\n",
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"scores"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b4bbc8ad-fbf4-4c96-8aa4-afb02ff305a0",
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"metadata": {},
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"outputs": [],
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"source": "# Reshape into row per sequence time + metric, column per input file\nnew_scores = scores.stack().unstack(0).reset_index()\nnew_scores.columns.name = None # drop the leftover index name so it isn't a \"level_0\" legend title\n\n# ns to s for the sequence time\nnew_scores['Sequence Time (s)'] = new_scores['Sequence Time (ns)'] / 1_000_000_000\nnew_scores = new_scores.drop('Sequence Time (ns)', axis=1)\nnew_scores"
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"source": [
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"# Reshape into row per sequence time + metric, column per input file\n",
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"new_scores = scores.stack().unstack(0).reset_index()\n",
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"new_scores.columns.name = None # drop the leftover index name so it isn't a \"level_0\" legend title\n",
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"\n",
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"# ns to s for the sequence time\n",
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"new_scores['Sequence Time (s)'] = new_scores['Sequence Time (ns)'] / 1_000_000_000\n",
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"new_scores = new_scores.drop('Sequence Time (ns)', axis=1)\n",
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"new_scores"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2d6b6f75-251b-4bb2-aa44-e6393cf8140a",
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"metadata": {},
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"outputs": [],
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"source": "pathlib.Path('output_per_frame').mkdir(parents=True, exist_ok=True)\nfor graph_name in new_scores['level_1'].unique():\n graph_data = new_scores[new_scores['level_1'] == graph_name].drop('level_1', axis=1)\n graph_data = graph_data.apply(pd.to_numeric, errors='coerce').interpolate(method='linear')\n\n ax = graph_data.plot(\n x='Sequence Time (s)',\n xlabel='benchmark timeline in seconds',\n ylabel='value',\n title=graph_name,\n figsize=(20, 5),\n colormap='Dark2',\n grid=True,\n rot=0,\n )\n ax.figure.savefig(f'output_per_frame/{graph_name}.png')\n plt.close(ax.figure) # free the figure so we don't keep one open per metric\n"
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"source": [
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"pathlib.Path('output_per_frame').mkdir(parents=True, exist_ok=True)\n",
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"for graph_name in new_scores['level_1'].unique():\n",
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" graph_data = new_scores[new_scores['level_1'] == graph_name].drop('level_1', axis=1)\n",
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" graph_data = graph_data.apply(pd.to_numeric, errors='coerce').interpolate(method='linear')\n",
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"\n",
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" ax = graph_data.plot(\n",
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" x='Sequence Time (s)',\n",
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" xlabel='benchmark timeline in seconds',\n",
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" ylabel='value',\n",
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" title=graph_name,\n",
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" figsize=(20, 5),\n",
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" colormap='Dark2',\n",
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" grid=True,\n",
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" rot=0,\n",
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" )\n",
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" ax.figure.savefig(f'output_per_frame/{graph_name}.png')\n",
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" plt.close(ax.figure) # free the figure so we don't keep one open per metric\n"
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]
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}
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],
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"metadata": {
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

‎scripts/scores_plotter.ipynb‎

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Original file line numberDiff line numberDiff line change
@@ -6,15 +6,22 @@
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"id": "c7a3c3bd-54d5-4979-8502-ac69a32a13fb",
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"metadata": {},
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"outputs": [],
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"source": "# Install dependencies into the active kernel\n%pip install pandas matplotlib"
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"source": [
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"# Install dependencies into the active kernel\n",
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"%pip install pandas matplotlib"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "21658370-184d-4ab0-b210-d8ab93f0818b",
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"metadata": {},
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"outputs": [],
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"source": "import pandas as pd\nimport os\nimport matplotlib.pyplot as plt"
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"source": [
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"import pandas as pd\n",
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"import os\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"files = [\n",
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" 'scores/5070ti_inline.csv',\n",
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" 'scores/5070ti_pipeline.csv',\n",
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" 'scores/9070xt_inline.csv',\n",
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" 'scores/9070xt_pipeline.csv',\n",
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" 'a_deep_analysis.json',\n",
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" 'b_deep_analysis.json',\n",
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"]"
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]
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},
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"id": "d89bab16-7553-4fd2-953b-05a054362999",
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"metadata": {},
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"outputs": [],
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"source": "os.makedirs('output_scores', exist_ok=True)\nax = scores.plot(\n kind=\"bar\",\n figsize=(20, 5),\n colormap='Dark2',\n grid=True,\n rot=0,\n title=\"Evolve scores (higher is better)\",\n ylabel=\"score\",\n)\nax.figure.savefig('output_scores/evolve_scores.png', bbox_inches='tight')"
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"source": [
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"os.makedirs('output_scores', exist_ok=True)\n",
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"ax = scores.plot(\n",
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" kind=\"bar\",\n",
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" figsize=(20, 5),\n",
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" colormap='Dark2',\n",
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" grid=True,\n",
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" rot=0,\n",
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" title=\"Evolve scores (higher is better)\",\n",
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" ylabel=\"score\",\n",
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")\n",
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"ax.figure.savefig('output_scores/evolve_scores.png', bbox_inches='tight')"
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]
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}
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],
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"metadata": {
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

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