feat: Perf benchmark initial draft

Signed-off-by: Anurag Dixit <anuragd@nvidia.com>

examples/benchmark/py/config/vgg16.yml

@@ -0,0 +1,19 @@
+--- 
+backend: 
+  - torch
+  - torch_tensorrt
+input: 
+  input0: 
+    - 1
+    - 3
+    - 224
+    - 224
+  num_of_input: 1
+model: 
+  filename: vgg16_traced.jit.pt
+  name: vgg16
+runtime: 
+  device: 0
+  precision: 
+    - fp32
+    - fp16

examples/benchmark/py/perf_run.py

@@ -0,0 +1,290 @@
+from __future__ import print_function
+from __future__ import absolute_import
+from __future__ import division
+
+import argparse
+import timeit
+import numpy as np
+import torch.backends.cudnn as cudnn
+import yaml
+import os
+import pandas as pd
+
+# Backend
+import torch
+import torch_tensorrt as torchtrt
+import tensorrt as trt
+import pycuda.autoinit
+import pycuda.driver as cuda
+
+
+TRT_LOGGER = trt.Logger()
+EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+
+results = []
+
+def run_torch(model, input_tensors, params, precision):
+    print("Running Torch for precision: ", precision)
+
+    iters = 20 if not "iterations" in params else params['iterations']
+
+    # Warm up
+    with torch.no_grad():
+        for _ in range(20):
+            features = model(*input_tensors)
+
+    torch.cuda.synchronize()
+
+    timings = []
+    with torch.no_grad():
+        for i in range(iters):
+            start_time = timeit.default_timer()
+            features = model(*input_tensors)
+            torch.cuda.synchronize()
+            end_time = timeit.default_timer()
+            meas_time = end_time - start_time
+            timings.append(meas_time)
+            print("Iteration {}: {:.6f} s".format(i, end_time - start_time))
+
+    printStats("Torch", timings, precision)
+
+def onnx_to_trt_engine(onnx_model, precision):
+
+    with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, builder.create_builder_config() as config, trt.OnnxParser(network, TRT_LOGGER) as parser, trt.Runtime(TRT_LOGGER) as runtime:
+        config.max_workspace_size = 1 << 28 # 256MiB
+        builder.max_batch_size = 1
+
+        if precision == 'int8':
+            config.set_flag(trt.BuilderFlag.INT8)
+        elif precision == 'fp16' or precision == 'half':
+            config.set_flag(trt.BuilderFlag.HALF)
+
+        plan = builder.build_serialized_network(network, config)
+        model = runtime.deserialize_cuda_engine(plan)
+    return model
+
+def run_torch_tensorrt(model, input_tensors, params, precision):
+    print("Running Torch-TensorRT")
+
+    # Compiling Torch-TensorRT model
+    compile_settings = {
+       "inputs": input_tensors,
+       "enabled_precisions": {precision_to_dtype(precision)} 
+    }
+
+    model = torchtrt.compile(model, **compile_settings)
+
+    iters = 20 if not "iterations" in params else params['iterations']
+    # Warm up
+    with torch.no_grad():
+        for _ in range(20):
+            features = model(*input_tensors)
+
+    torch.cuda.synchronize()
+
+    timings = []
+    with torch.no_grad():
+        for i in range(iters):
+            start_time = timeit.default_timer()
+            features = model(*input_tensors)
+            torch.cuda.synchronize()
+            end_time = timeit.default_timer()
+            meas_time = end_time - start_time
+            timings.append(meas_time)
+            print("Iteration {}: {:.6f} s".format(i, end_time - start_time))
+
+    printStats("Torch-TensorRT", timings, precision)
+
+def run_tensorrt(model, input_tensors, params, precision):
+    print("Running TensorRT")
+    inputs = []
+    outputs = []
+    bindings = []
+    stream = cuda.Stream()
+    iters = 20 if not "iterations" in params else params['iterations']
+
+    if not "batch" in params:
+        batch_size = 1
+    else:
+        batch_size = params['batch_size']
+
+    with onnx_to_trt_engine(model, precision) as engine, engine.create_execution_context() as context:
+
+        for binding in engine:
+            size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
+            dtype = trt.nptype(engine.get_binding_dtype(binding))
+
+            # Input already allocated in input_tensors
+            mem = cuda.mem_alloc() 
+            # Allocate host and device buffers
+            host_mem = cuda.pagelocked_empty(size, dtype)
+            device_mem = cuda.mem_alloc(host_mem.nbytes)
+            # Append the device buffer to device bindings.
+            bindings.append(int(device_mem))
+            # Append to the appropriate list.
+            """
+            if engine.binding_is_input(binding):
+                inputs.append(HostDeviceMem(host_mem, device_mem))
+            else:
+                outputs.append(HostDeviceMem(host_mem, device_mem))
+            if not engine.binding_is_input(binding):
+                outputs.append(cuda.mem_alloc(cuda.pagelocked_empty(size, dtype).nbytes))
+            else:
+                bindings.append(input_tensors)
+            """
+        # Warm up
+        for _ in range(20):
+            context.execute_async(batch_size, bindings, stream.handle)
+
+        stream.synchronize()
+
+        for i in range(iters):
+            start_time = timeit.default_timer()
+            context.execute_async(batch_size, bindings, stream.handle)
+            stream.synchronize()
+            end_time = timeit.default_timer()
+            meas_time = end_time - start_time
+
+
+
+    iters = 20 if not "iterations" in params else params['iterations']
+    # Warm up
+    with torch.no_grad():
+        for _ in range(20):
+            features = model(input_tensors)
+
+    torch.cuda.synchronize()
+
+    timings = []
+    with torch.no_grad():
+        for i in range(iters):
+            start_time = timeit.default_timer()
+            features = model(input_tensors)
+            torch.cuda.synchronize()
+            end_time = timeit.default_timer()
+            meas_time = end_time - start_time
+            timings.append(meas_time)
+            print("Iteration {}: {:.6f} s".format(i, end_time - start_time))
+
+    printStats("TensorRT", timings, precision)
+
+def run(model, input_tensors, params, precision):
+    for backend in params['backend']:
+        if backend == 'all':
+            run_torch(model, input_tensors, params, precision)
+            run_torch_tensorrt(model, input_tensors, params, precision)
+            run_tensorrt(model, input_tensors, params, precision)
+
+        elif backend == "torch":
+            run_torch(model, input_tensors, params, precision)
+
+        elif backend == "torch_tensorrt":
+            run_torch_tensorrt(model, input_tensors, params, precision)
+
+        elif backend == "tensorrt":
+            run_tensorrt(model, input_tensors, params, precision)
+
+
+def printStats(backend, timings, precision, batch_size = 1):
+    times = np.array(timings)
+    steps = len(times)
+    speeds = batch_size / times
+    time_mean = np.mean(times)
+    time_med = np.median(times)
+    time_99th = np.percentile(times, 99)
+    time_std = np.std(times, ddof=0)
+    speed_mean = np.mean(speeds)
+    speed_med = np.median(speeds)
+
+    msg = ("\n%s =================================\n"
+            "batch size=%d, num iterations=%d\n"
+            "  Median FPS: %.1f, mean: %.1f\n"
+            "  Median latency: %.6f, mean: %.6f, 99th_p: %.6f, std_dev: %.6f\n"
+            ) % (backend,
+                batch_size, steps,
+                speed_med, speed_mean,
+                time_med, time_mean, time_99th, time_std)
+    print(msg)
+    meas = {
+        'Backend' : backend,
+        'precision' : precision,
+        'Median(FPS)' : speed_med,
+        'Mean(FPS)' : speed_mean,
+        'Median-Latency(ms)' : time_med,
+        'Mean-Latency(ms)' : time_mean,
+        '99th_p' : time_99th,
+        'std_dev': time_std
+    }
+    results.append(meas)
+
+def read_config(config_file):
+    with open(config_file, "r") as stream:
+        try:
+            params = yaml.safe_load(stream)
+        except yaml.YAMLError as exc:
+            print(exc)
+    return params
+
+def precision_to_dtype(pr):
+    if pr == 'fp32':
+        return torch.float
+    elif pr == 'fp16' or pr == 'half':
+        return torch.half
+    else:
+        return torch.int8
+
+def load_model(params):
+    model = None
+    # Load traced model
+    if "torch" in params['backend'] or "torch_tensorrt" in params['backend']:
+        model_path = os.path.join("models", params['model']['filename'])
+        model = torch.jit.load(model_path).cuda()
+
+    elif "tensorrt" in params['backend']:
+        onnx_model_file = os.path.join("models", params['model']['onnx_file'])
+        with open(onnx_model_file, 'rb') as onnx_model:
+            print('Beginning ONNX file parsing')
+            model = onnx_model.read()
+
+    return model
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser(description="Run inference on a model with random input values")
+    parser.add_argument("--config", help="Load YAML based configuration file to run the inference. If this is used other params will be ignored")
+    args = parser.parse_args()
+
+    # Load YAML params
+    params = read_config(args.config)
+
+    print("Loading model: ", params['model']['filename'])
+
+    model = None
+
+    if "device" in params['runtime']:
+        torch.cuda.set_device(params['runtime']['device'])
+
+    model = load_model(params)
+
+    cudnn.benchmark = True
+
+    # Create random input tensor of certain size
+    torch.manual_seed(12345)
+
+    num_input = params['input']['num_of_input']
+    for precision in params['runtime']['precision']:
+        input_tensors = []
+        num_input = params['input']['num_of_input']
+        for i in range(num_input):
+            inp_tensor = params['input']['input' + str(i)]
+            input_tensors.append(torch.randint(0, 2, tuple(d for d in inp_tensor), dtype=precision_to_dtype(precision)).cuda())
+
+        if precision == "fp16" or precision == "half":
+            #input_tensors = [x.half() for x in input_tensors]
+            model = model.half()
+
+        run(model, input_tensors, params, precision)
+
+    print('Model Summary:')
+    summary = pd.DataFrame(results)
+    print(summary)