Improvements to logging/metrics reporting

shortfin/python/shortfin_apps/sd/components/config_struct.py

@@ -46,6 +46,7 @@ class ModelParams:
     # Height and Width, respectively, for which Unet and VAE are compiled. e.g. [[512, 512], [1024, 1024]]
     dims: list[list[int]]
 
+    base_model_name: str = "SDXL"
     # Name of the IREE module for each submodel.
     clip_module_name: str = "compiled_clip"
     unet_module_name: str = "compiled_unet"
@@ -78,10 +79,13 @@ def max_unet_batch_size(self) -> int:
     def max_vae_batch_size(self) -> int:
         return self.vae_batch_sizes[-1]
 
+    @property
+    def all_batch_sizes(self) -> list:
+        return [self.clip_batch_sizes, self.unet_batch_sizes, self.vae_batch_sizes]
+
     @property
     def max_batch_size(self):
-        # TODO: a little work on the batcher should loosen this up.
-        return max(self.clip_batch_sizes, self.unet_batch_sizes, self.vae_batch_sizes)
+        return max(self.all_batch_sizes)
 
     @staticmethod
     def load_json(path: Path | str):
@@ -91,3 +95,11 @@ def load_json(path: Path | str):
         if isinstance(raw_params.unet_dtype, str):
             raw_params.unet_dtype = str_to_dtype[raw_params.unet_dtype]
         return raw_params
+
+    def __repr__(self):
+        return (
+            f"base model: {self.base_model_name} \n"
+            f"     output size (H,W): {self.dims} \n"
+            f"     max token sequence length : {self.max_seq_len} \n"
+            f"     classifier free guidance : {self.cfg_mode} \n"
+        )

shortfin/python/shortfin_apps/sd/components/service.py

@@ -155,11 +155,25 @@ def shutdown(self):
         self.batcher.shutdown()
 
     def __repr__(self):
+        modules = [
+            f"     {key} : {value}" for key, value in self.inference_modules.items()
+        ]
+        params = [
+            f"     {key} : {value}" for key, value in self.inference_parameters.items()
+        ]
         return (
             f"ServiceManager(\n"
-            f"  model_params={self.model_params}\n"
-            f"  inference_modules={self.inference_modules}\n"
-            f"  inference_parameters={self.inference_parameters}\n"
+            f"\n  INFERENCE DEVICES : \n"
+            f"     {self.sysman.ls.devices}\n"
+            f"\n  MODEL PARAMS: \n"
+            f"     {self.model_params}"
+            f"\n  SERVICE PARAMS: \n"
+            f"     fibers per device: {self.fibers_per_device}\n"
+            f"     program isolation mode: {self.prog_isolation}\n"
+            f"\n  INFERENCE MODULES : \n"
+            f"{'\n'.join(modules)}\n"
+            f"\n  INFERENCE PARAMETERS : \n"
+            f"{'\n'.join(params)}\n"
             f")"
         )
 

shortfin/python/shortfin_apps/sd/components/utils.py

@@ -31,6 +31,9 @@ async def wrapped_fn_async(*args: Any, **kwargs: Any) -> Any:
         ret = await fn(*args, **kwargs)
         duration_str = get_duration_str(start)
         logger.info(f"Completed {fn.__qualname__} in {duration_str}")
+        if fn.__qualname__ == "ClientGenerateBatchProcess.run":
+            sps_str = get_samples_per_second(start, *args)
+            logger.info(f"SAMPLES PER SECOND = {sps_str}")
         return ret
 
     if asyncio.iscoroutinefunction(fn):
@@ -39,6 +42,13 @@ async def wrapped_fn_async(*args: Any, **kwargs: Any) -> Any:
         return wrapped_fn
 
 
+def get_samples_per_second(start, *args: Any) -> str:
+    duration = time.time() - start
+    bs = args[0].gen_req.num_output_images
+    sps = str(float(bs) / duration)
+    return sps
+
+
 def get_duration_str(start: float) -> str:
     """Get human readable duration string from start time"""
     duration = time.time() - start