add db population script.

Author: sayakpaul

.github/workflows/benchmark.yml

@@ -11,6 +11,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
+  BASE_PATH: benchmark_outputs
 
 jobs:
   torch_models_cuda_benchmark_tests:
@@ -43,8 +44,7 @@ jobs:
           python utils/print_env.py
       - name: Diffusers Benchmarking
         env:
-            HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
-            BASE_PATH: benchmark_outputs
+          HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
         run: |
           cd benchmarks && python run_all.py && python push_results.py
           mkdir ${BASE_PATH} && mv *.csv ${BASE_PATH}
@@ -56,6 +56,14 @@ jobs:
           name: benchmark_test_reports
           path: benchmarks/benchmark_outputs
 
+      - name: Update benchmarking results to DB
+        env:
+          PGDATABASE: metrics
+          PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }}
+          PGUSER: transformers_benchmarks
+          PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }}
+        run: cd benchmarks && python populate_into_db.py
+
       - name: Report success status
         if: ${{ success() }}
         run: |

benchmarks/populate_into_db.py

@@ -0,0 +1,124 @@
+import os
+
+import pandas as pd
+import psycopg2
+import psycopg2.extras
+
+
+FINAL_CSV_FILENAME = "benchmark_outputs/collated_results.csv"
+TABLE_NAME = "diffusers_benchmarks"
+
+if __name__ == "__main__":
+    conn = psycopg2.connect(
+        host=os.getenv("PGHOST"),
+        database=os.getenv("PGDATABASE"),
+        user=os.getenv("PGUSER"),
+        password=os.getenv("PGPASSWORD"),
+    )
+    cur = conn.cursor()
+
+    cur.execute(f"""
+    CREATE TABLE IF NOT EXISTS {TABLE_NAME} (
+        scenario       TEXT,
+        model_cls      TEXT,
+        num_params_M   REAL,
+        flops_M        REAL,
+        time_plain_s   REAL,
+        mem_plain_GB   REAL,
+        time_compile_s REAL,
+        mem_compile_GB REAL,
+        fullgraph      BOOLEAN,
+        mode           TEXT,
+        github_sha     TEXT
+    );
+    """)
+    conn.commit()
+
+    df = pd.read_csv(FINAL_CSV_FILENAME)
+
+    # Helper to cast values (or None) given a dtype
+    def _cast_value(val, dtype: str):
+        if pd.isna(val):
+            return None
+
+        if dtype == "text":
+            return str(val).strip()
+
+        if dtype == "float":
+            try:
+                return float(val)
+            except ValueError:
+                return None
+
+        if dtype == "bool":
+            s = str(val).strip().lower()
+            if s in ("true", "t", "yes", "1"):
+                return True
+            if s in ("false", "f", "no", "0"):
+                return False
+            if val in (1, 1.0):
+                return True
+            if val in (0, 0.0):
+                return False
+            return None
+
+        return val
+
+    rows_to_insert = []
+    for _, row in df.iterrows():
+        scenario = _cast_value(row.get("scenario"), "text")
+        model_cls = _cast_value(row.get("model_cls"), "text")
+        num_params_M = _cast_value(row.get("num_params_M"), "float")
+        flops_M = _cast_value(row.get("flops_M"), "float")
+        time_plain_s = _cast_value(row.get("time_plain_s"), "float")
+        mem_plain_GB = _cast_value(row.get("mem_plain_GB"), "float")
+        time_compile_s = _cast_value(row.get("time_compile_s"), "float")
+        mem_compile_GB = _cast_value(row.get("mem_compile_GB"), "float")
+        fullgraph = _cast_value(row.get("fullgraph"), "bool")
+        mode = _cast_value(row.get("mode"), "text")
+
+        # If "github_sha" column exists in the CSV, cast it; else default to None
+        if "github_sha" in df.columns:
+            github_sha = _cast_value(row.get("github_sha"), "text")
+        else:
+            github_sha = None
+
+        rows_to_insert.append(
+            (
+                scenario,
+                model_cls,
+                num_params_M,
+                flops_M,
+                time_plain_s,
+                mem_plain_GB,
+                time_compile_s,
+                mem_compile_GB,
+                fullgraph,
+                mode,
+                github_sha,
+            )
+        )
+
+    # Batch-insert all rows (with NULL for any None)
+    insert_sql = """
+    INSERT INTO benchmarks (
+        scenario,
+        model_cls,
+        num_params_M,
+        flops_M,
+        time_plain_s,
+        mem_plain_GB,
+        time_compile_s,
+        mem_compile_GB,
+        fullgraph,
+        mode,
+        github_sha
+    )
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);
+    """
+
+    psycopg2.extras.execute_batch(cur, insert_sql, rows_to_insert)
+    conn.commit()
+
+    cur.close()
+    conn.close()

benchmarks/requirements.txt

@@ -1,4 +1,5 @@
 pandas 
 peft 
 torchprofile
-bitsandbytes
+bitsandbytes
+psycopg2==2.9.9