[Enhancement] Introduce High-Performance MoT (Mixture-of-Tokens) Kernels: Triton Implementation & A100 Tuning

benchmarks/diffusion/diffusion_benchmark_serving.py

@@ -64,6 +64,7 @@
 import argparse
 import ast
 import asyncio
+import base64
 import glob
 import json
 import logging
@@ -818,6 +819,73 @@ def calculate_metrics(
     return metrics
 
 
+def _save_generated_outputs(
+    outputs: list[RequestFuncOutput],
+    requests_list: list[RequestFuncInput],
+    save_dir: str,
+) -> None:
+    """Decode and save base64 images/videos from successful responses."""
+    os.makedirs(save_dir, exist_ok=True)
+    saved = 0
+    failed = 0
+
+    for idx, (req, out) in enumerate(zip(requests_list, outputs)):
+        if not out.success or not out.response_body:
+            continue
+
+        media_urls: list[str] = []
+
+        # Chat-completions style: choices[*].message.content[*].image_url.url
+        choices = out.response_body.get("choices", [])
+        if isinstance(choices, list):
+            for choice in choices:
+                content = (choice or {}).get("message", {}).get("content")
+                if not isinstance(content, list):
+                    continue
+                for item in content:
+                    if not isinstance(item, dict) or item.get("type") != "image_url":
+                        continue
+                    url = (item.get("image_url") or {}).get("url", "")
+                    if isinstance(url, str) and url.startswith("data:"):
+                        media_urls.append(url)
+
+        # Images endpoint style: data[*].b64_json
+        data_items = out.response_body.get("data", [])
+        if isinstance(data_items, list):
+            for data_item in data_items:
+                if not isinstance(data_item, dict):
+                    continue
+                b64_json = data_item.get("b64_json", "")
+                if isinstance(b64_json, str) and b64_json:
+                    media_urls.append(f"data:image/png;base64,{b64_json}")
+
+        for img_idx, url in enumerate(media_urls):
+            if "," not in url:
+                continue
+
+            try:
+                header, b64_data = url.split(",", 1)
+                ext = "png"
+                if "image/jpeg" in header:
+                    ext = "jpg"
+                elif "image/webp" in header:
+                    ext = "webp"
+                elif "video/mp4" in header:
+                    ext = "mp4"
+
+                img_bytes = base64.b64decode(b64_data)
+                fname = f"req_{idx:04d}_{img_idx}.{ext}"
+                fpath = os.path.join(save_dir, fname)
+                with open(fpath, "wb") as f:
+                    f.write(img_bytes)
+                saved += 1
+            except Exception as e:
+                failed += 1
+                logger.warning(f"Failed to save image for request {idx}: {e}", exc_info=True)
+
+    logger.info(f"Saved {saved} generated image(s) to {save_dir}. Failed to save {failed} image(s).")
+
+
 def wait_for_service(base_url: str, timeout: int = 120) -> None:
     print(f"Waiting for service at {base_url}...")
     start_time = time.time()
@@ -995,6 +1063,9 @@ async def limited_request_func(req, session, pbar):
 
     print("\n" + "=" * 60)
 
+    if args.save_dir:
+        _save_generated_outputs(outputs, requests_list, args.save_dir)
+
     if args.output_file:
         with open(args.output_file, "w") as f:
             json.dump(metrics, f, indent=2)
@@ -1066,11 +1137,18 @@ async def limited_request_func(req, session, pbar):
         default=1,
         help="Number of warmup requests to run before measurement.",
     )
+    # NOTE Changed default from 1 to 2 because some models (e.g., Bagel) run
+    # `num_timesteps - 1` denoising iterations. A default of 1 results in 0 steps,
+    # which causes errors.
+    # TODO If this slightly longer warmup causes regression issues for other
+    # diffusion pipelines in the future, consider implementing model-specific
+    # overrides instead of a global default.
     parser.add_argument(
         "--warmup-num-inference-steps",
         type=int,
-        default=1,
-        help="num_inference_steps used for warmup requests.",
+        default=2,
+        help="Number of inference steps used for warmup requests. "
+        "Default is 2 to ensure at least one denoising step is executed.",
     )
     parser.add_argument("--width", type=int, default=None, help="Image/Video width.")
     parser.add_argument("--height", type=int, default=None, help="Image/Video height.")
@@ -1104,6 +1182,13 @@ async def limited_request_func(req, session, pbar):
         default=3.0,
         help="SLO target multiplier: slo_ms = estimated_exec_time_ms * slo_scale (default: 3).",
     )
+    parser.add_argument(
+        "--save-dir",
+        type=str,
+        default=None,
+        help="Directory to save generated images/outputs for visual inspection. "
+        "If not set, generated outputs are discarded after metric collection.",
+    )
     parser.add_argument("--disable-tqdm", action="store_true", help="Disable progress bar.")
     parser.add_argument(
         "--enable-negative-prompt",