Update benchmarking for diffusers (#487)

## What does this PR do?

**Type of change:** 
Example update

**Overview:** 
- Optimize the benchmarking function in the diffusers example

```python
python diffusion_trt.py --model flux-dev --benchmark --model-dtype BFloat16 --skip-image --torch
```

## Testing
```
Backbone-only inference latency (BFloat16):
  Average: 139.48 ms
  P50: 139.36 ms
  P95: 141.13 ms
  P99: 141.35 ms
```

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: No
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No

---------

Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>

Author: ajrasane

examples/diffusers/quantization/diffusion_trt.py

@@ -15,6 +15,7 @@
 
 import argparse
 
+import numpy as np
 import torch
 from onnx_utils.export import (
     generate_dummy_inputs_and_dynamic_axes_and_shapes,
@@ -49,6 +50,7 @@
 }
 
 
+@torch.inference_mode()
 def generate_image(pipe, prompt, image_name):
     seed = 42
     image = pipe(
@@ -61,56 +63,56 @@ def generate_image(pipe, prompt, image_name):
     print(f"Image generated saved as {image_name}")
 
 
-def benchmark_model(
-    pipe, prompt, num_warmup=10, num_runs=50, num_inference_steps=20, model_dtype=torch.float16
+@torch.inference_mode()
+def benchmark_backbone_standalone(
+    pipe,
+    num_warmup=10,
+    num_benchmark=100,
+    model_name="flux-dev",
 ):
-    """Benchmark the backbone model inference time."""
+    """Benchmark the backbone model directly without running the full pipeline."""
     backbone = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet
 
-    backbone_times = []
+    # Generate dummy inputs for the backbone
+    dummy_inputs, _, _ = generate_dummy_inputs_and_dynamic_axes_and_shapes(model_name, backbone)
+
+    # Extract the dict from the tuple and move to cuda
+    dummy_inputs_dict = {
+        k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in dummy_inputs[0].items()
+    }
+
+    # Warmup
+    print(f"Warming up: {num_warmup} iterations")
+    for _ in tqdm(range(num_warmup), desc="Warmup"):
+        _ = backbone(**dummy_inputs_dict)
+
+    # Benchmark
+    torch.cuda.synchronize()
     start_event = torch.cuda.Event(enable_timing=True)
     end_event = torch.cuda.Event(enable_timing=True)
 
-    def forward_pre_hook(_module, _input):
+    print(f"Benchmarking: {num_benchmark} iterations")
+    times = []
+    for _ in tqdm(range(num_benchmark), desc="Benchmark"):
+        torch.cuda.profiler.cudart().cudaProfilerStart()
         start_event.record()
-
-    def forward_hook(_module, _input, _output):
+        _ = backbone(**dummy_inputs_dict)
         end_event.record()
         torch.cuda.synchronize()
-        backbone_times.append(start_event.elapsed_time(end_event))
-
-    pre_handle = backbone.register_forward_pre_hook(forward_pre_hook)
-    post_handle = backbone.register_forward_hook(forward_hook)
-
-    try:
-        print(f"Starting warmup: {num_warmup} runs")
-        for _ in tqdm(range(num_warmup), desc="Warmup"):
-            with torch.amp.autocast("cuda", dtype=model_dtype):
-                _ = pipe(
-                    prompt,
-                    output_type="pil",
-                    num_inference_steps=num_inference_steps,
-                    generator=torch.Generator("cuda").manual_seed(42),
-                )
-
-        backbone_times.clear()
-
-        print(f"Starting benchmark: {num_runs} runs")
-        for _ in tqdm(range(num_runs), desc="Benchmark"):
-            with torch.amp.autocast("cuda", dtype=model_dtype):
-                _ = pipe(
-                    prompt,
-                    output_type="pil",
-                    num_inference_steps=num_inference_steps,
-                    generator=torch.Generator("cuda").manual_seed(42),
-                )
-    finally:
-        pre_handle.remove()
-        post_handle.remove()
-
-    total_backbone_time = sum(backbone_times)
-    avg_latency = total_backbone_time / (num_runs * num_inference_steps)
-    print(f"Inference latency of the torch backbone: {avg_latency:.2f} ms")
+        torch.cuda.profiler.cudart().cudaProfilerStop()
+        times.append(start_event.elapsed_time(end_event))
+
+    avg_latency = sum(times) / len(times)
+    p50 = np.percentile(times, 50)
+    p95 = np.percentile(times, 95)
+    p99 = np.percentile(times, 99)
+
+    print("\nBackbone-only inference latency:")
+    print(f"  Average: {avg_latency:.2f} ms")
+    print(f"  P50: {p50:.2f} ms")
+    print(f"  P95: {p95:.2f} ms")
+    print(f"  P99: {p99:.2f} ms")
+
     return avg_latency
 
 
@@ -196,7 +198,12 @@ def main():
         pipe.to("cuda")
 
         if args.benchmark:
-            benchmark_model(pipe, args.prompt, model_dtype=model_dtype)
+            benchmark_backbone_standalone(
+                pipe,
+                num_warmup=10,
+                num_benchmark=100,
+                model_name=args.model,
+            )
 
         if not args.skip_image:
             generate_image(pipe, args.prompt, image_name)

modelopt/torch/_deploy/utils/torch_onnx.py

@@ -29,7 +29,6 @@
 import torch.nn as nn
 from onnx import ModelProto
 from onnxconverter_common import convert_float_to_float16
-from packaging.version import Version
 from torch.nn.parallel import DataParallel, DistributedDataParallel
 
 from modelopt.onnx.autocast.convert import convert_to_f16
@@ -443,7 +442,7 @@ def get_onnx_bytes_and_metadata(
     )
     with torch.inference_mode(), autocast, quantizer_context:
         additional_kwargs = {}
-        if not dynamo_export and Version(torch.__version__) >= Version("2.8"):
+        if not dynamo_export:
             additional_kwargs["dynamic_axes"] = dynamic_axes
         torch.onnx.export(
             model,