Add OnnxDiscrepancyCheck speedup metric with default timing updates

olive/passes/onnx/discrepancy_check.py

@@ -3,10 +3,12 @@
 # Licensed under the MIT License.
 # --------------------------------------------------------------------------
 import logging
+import time
 from typing import Optional
 
 from olive.data.config import DataConfig
 from olive.hardware import AcceleratorSpec
+from olive.hardware.accelerator import Device
 from olive.model import ONNXModelHandler
 from olive.passes import Pass
 from olive.passes.pass_config import BasePassConfig, PassConfigParam
@@ -61,6 +63,7 @@ class OnnxDiscrepancyCheck(Pass):
     - Maximum absolute error (MaxAE)
     - Number of elements where the absolute difference exceeds 0.1
     - Number of elements where the absolute difference exceeds 0.01
+    - Inference speedup of ONNX over PyTorch on the target device (or CPU fallback)
     - Longest common token sequence from the beginning between transformers
       generate and ONNX Runtime GenAI generate (when enabled)
 
@@ -107,6 +110,16 @@ def _default_config(cls, accelerator_spec: AcceleratorSpec) -> dict[str, PassCon
                     "computes the longest common token sequence from the beginning of their outputs."
                 ),
             ),
+            "warmup_iterations": PassConfigParam(
+                type_=int,
+                default_value=3,
+                description="Number of warmup iterations before timing inference for speedup measurement.",
+            ),
+            "timing_iterations": PassConfigParam(
+                type_=int,
+                default_value=10,
+                description="Number of timed iterations to measure inference speedup (ONNX vs PyTorch).",
+            ),
             "generate_prompt": PassConfigParam(
                 type_=str,
                 default_value="The capital of France is",
@@ -177,8 +190,22 @@ def _run_for_config(
         ref_model = AutoModelForCausalLM.from_pretrained(config.reference_model_path)
         ref_model.eval()
 
-        # Prepare ONNX session
-        session = model.prepare_session()
+        # Prepare ONNX session on the target device (fallback to CPU)
+        device = self.accelerator_spec.accelerator_type if self.accelerator_spec else None
+        execution_provider = self.accelerator_spec.execution_provider if self.accelerator_spec else None
+        if device is None:
+            device = Device.CPU
+
+        # Determine the torch device matching the accelerator spec
+        torch_device = torch.device("cpu")
+        if device == Device.GPU and torch.cuda.is_available():
+            torch_device = torch.device("cuda")
+        ref_model = ref_model.to(torch_device)
+
+        session = model.prepare_session(
+            device=device,
+            execution_providers=[execution_provider] if execution_provider else None,
+        )
         io_config = model.io_config
 
         # Run inference on both and compare
@@ -194,9 +221,9 @@ def _run_for_config(
 
                 # Run PyTorch inference
                 if isinstance(input_data, dict):
-                    torch_inputs = {k: v.clone() for k, v in input_data.items()}
+                    torch_inputs = {k: v.clone().to(torch_device) for k, v in input_data.items()}
                 else:
-                    torch_inputs = input_data
+                    torch_inputs = input_data.to(torch_device)
 
                 torch_output = ref_model(**torch_inputs)
                 torch_logits = torch_output.logits.detach().cpu().numpy()
@@ -225,6 +252,11 @@ def _run_for_config(
             total_elements,
         )
 
+        # Measure inference speedup (ONNX vs PyTorch) on the target device
+        self._measure_speedup(
+            ref_model, session, dataloader, io_config, torch_device, config.warmup_iterations, config.timing_iterations
+        )
+
         # Check thresholds
         failures = []
         if config.max_mae is not None and max_abs_error > config.max_mae:
@@ -254,6 +286,66 @@ def _run_for_config(
         # Return the model unchanged
         return model
 
+    def _measure_speedup(
+        self, ref_model, session, dataloader, io_config, torch_device, warmup_iterations, timing_iterations
+    ):
+        """Measure inference speedup of ONNX over PyTorch on the target device."""
+        import torch
+
+        from olive.common.utils import format_data
+
+        # Use the first batch for timing
+        first_batch = next(iter(dataloader))
+        input_data = first_batch[0] if isinstance(first_batch, (tuple, list)) else first_batch
+
+        if isinstance(input_data, dict):
+            torch_inputs = {k: v.clone().to(torch_device) for k, v in input_data.items()}
+        else:
+            torch_inputs = input_data.to(torch_device)
+
+        onnx_input_feed = format_data(input_data, io_config)
+        use_cuda_sync = torch_device.type == "cuda"
+
+        # Warmup PyTorch
+        with torch.no_grad():
+            for _ in range(warmup_iterations):
+                ref_model(**torch_inputs)
+            if use_cuda_sync:
+                torch.cuda.synchronize()
+
+        # Time PyTorch
+        with torch.no_grad():
+            if use_cuda_sync:
+                torch.cuda.synchronize()
+            start = time.perf_counter()
+            for _ in range(timing_iterations):
+                ref_model(**torch_inputs)
+            if use_cuda_sync:
+                torch.cuda.synchronize()
+            pytorch_time = (time.perf_counter() - start) / timing_iterations
+
+        # Warmup ONNX
+        for _ in range(warmup_iterations):
+            session.run(None, onnx_input_feed)
+
+        # Time ONNX
+        start = time.perf_counter()
+        for _ in range(timing_iterations):
+            session.run(None, onnx_input_feed)
+        onnx_time = (time.perf_counter() - start) / timing_iterations
+
+        speedup = pytorch_time / onnx_time if onnx_time > 0 else float("inf")
+
+        logger.info(
+            "OnnxDiscrepancyCheck speedup: pytorch_avg=%.4fs, onnx_avg=%.4fs, speedup=%.2fx (device=%s)",
+            pytorch_time,
+            onnx_time,
+            speedup,
+            torch_device,
+        )
+
+        return speedup
+
     def compare_generation(self, config: type[BasePassConfig], ref_model) -> int:
         """Run generation on both transformers and GenAI, return longest common token sequence length."""
         try: