Add qps_test

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

benchmark/qps_test.py

@@ -0,0 +1,111 @@
+import towhee
+from towhee.dc2 import pipe, ops
+from towhee import triton_client
+
+import onnxruntime
+import numpy
+import torch
+from statistics import mean
+
+import time
+import argparse
+
+import os
+import re
+import warnings
+import logging
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--pipe', action='store_true')
+parser.add_argument('--triton', action='store_true')
+parser.add_argument('--onnx', action='store_true')
+parser.add_argument('--atol', type=float, default=1e-3)
+parser.add_argument('--num', type=int, default=100)
+parser.add_argument('--device', type=str, default='cpu')
+args = parser.parse_args()
+
+p = (
+    pipe.input('path')
+        .map('path', 'frame', ops.audio_decode.ffmpeg())
+        .map('frame', 'vecs', ops.audio_embedding.nnfp(device=args.device))
+        .output('vecs')
+)
+
+data = '1sec.wav'
+out1 = p(data).get()[0]
+print('Pipe: OK', out1.shape)
+
+if args.num and args.pipe:
+    qps = []
+    for _ in range(10):
+        start = time.time()
+        # for _ in range(args.num):
+        #     p(data)
+        p.batch([data] * args.num)
+        end = time.time()
+        q = args.num / (end - start)
+        qps.append(q)
+    print('Pipe qps:', mean(qps))
+
+if args.triton:
+    client = triton_client.Client(url='localhost:8000')
+    out2 = client(data)[0][0]
+    print('Triton: OK')
+
+    if numpy.allclose(out1, out2, atol=args.atol):
+        print('Check accuracy: OK')
+    else:
+        max_diff = numpy.abs(out1 - out2).max()
+        min_diff = numpy.abs(out1 - out2).min()
+        mean_diff =  numpy.abs(out1 - out2).mean()
+        print(f'Check accuracy: atol is larger than {args.atol}.')
+        print(f'Maximum absolute difference is {max_diff}.')
+        print(f'Minimum absolute difference is {min_diff}.')
+        print(f'Mean difference is {mean_diff}.')
+    
+    if args.num:
+        qps = []
+        for _ in range(10):
+            start = time.time()
+            client.batch([data] * args.num)
+            end = time.time()
+            q = args.num / (end - start)
+            qps.append(q)
+        print('Triton qps:', mean(qps))
+
+if args.onnx:
+    op = ops.audio_embedding.nnfp(device=args.device).get_op()
+    decoder = ops.audio_decode.ffmpeg()
+    # if not os.path.exists('test.onnx'):
+    op.save_model('onnx', 'test.onnx')
+    sess = onnxruntime.InferenceSession('test.onnx',
+                                        providers=['CUDAExecutionProvider'])
+    inputs = [x for x in decoder(data)]
+    inputs = op.preprocess(inputs)
+    out3 = sess.run(None, input_feed={'input': inputs.cpu().detach().numpy()})
+    print('Onnx: OK', out3[0].shape)
+    if numpy.allclose(out1, out3, atol=args.atol):
+        print('Check accuracy: OK')
+    else:
+        max_diff = numpy.abs(out1 - out3).max()
+        min_diff = numpy.abs(out1 - out3).min()
+        mean_diff =  numpy.abs(out1 - out3).mean()
+        print(f'Check accuracy: atol is larger than {args.atol}.')
+        print(f'Maximum absolute difference is {max_diff}.')
+        print(f'Minimum absolute difference is {min_diff}.')
+        print(f'Mean difference is {mean_diff}.')
+
+    if args.num:
+        qps = []
+        for _ in range(10):
+            start = time.time()
+            for _ in range(args.num):
+                inputs = [x for x in decoder(data)]
+                inputs = op.preprocess(inputs)
+                sess.run(None, input_feed={'input': inputs.cpu().detach().numpy()})
+            end = time.time()
+            q = args.num / (end - start)
+            qps.append(q)
+        print('Onnx qps:', mean(qps))

nn_fingerprint.py

@@ -34,6 +34,7 @@ from .configs import default_params, hop25_params, distill_params
 
 warnings.filterwarnings('ignore')
 log = logging.getLogger('nnfp_op')
+log.setLevel(logging.ERROR)
 
 
 # @accelerate
@@ -199,7 +200,7 @@ class NNFingerprint(NNOperator):
                 raise ValueError(f'Invalid format {format}.')
         dummy_input = torch.rand(
             (1,) + (self.params['n_mels'], self.params['u'])
-        ).to(self.device)
+        )
         if format == 'pytorch':
             torch.save(self._model, path)
         elif format == 'torchscript':
@@ -210,14 +211,14 @@ class NNFingerprint(NNOperator):
                     log.warning(
                         'Failed to directly export as torchscript.'
                         'Using dummy input in shape of %s now.', dummy_input.shape)
-                    jit_model = torch.jit.trace(self._model, dummy_input, strict=False)
+                    jit_model = torch.jit.trace(self._model.to('cpu'), dummy_input, strict=False)
                 torch.jit.save(jit_model, path)
             except Exception as e:
                 log.error('Fail to save as torchscript: %s.', e)
                 raise RuntimeError(f'Fail to save as torchscript: {e}.')
         elif format == 'onnx':
             try:
-                torch.onnx.export(self._model,
+                torch.onnx.export(self._model.to('cpu'),
                                   dummy_input,
                                   path,
                                   input_names=['input'],