test onnx and qps

Signed-off-by: ChengZi <chen.zhang@zilliz.com>

test_onnx_and_qps.py

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+import time
+
+import numpy
+import torch
+from towhee import ops
+import onnx
+import onnxruntime
+from tqdm import tqdm
+
+from rerank import ReRank
+
+test_query = 'abc'
+test_docs = ['123', 'ABC', 'ABCabc']
+atol = 1e-3
+
+try_times = 500
+batch_size_list = [2, 4, 8, 16, 32, 64]
+
+model_name_list = [
+    # q2p models:
+    'cross-encoder/ms-marco-TinyBERT-L-2-v2',
+    'cross-encoder/ms-marco-MiniLM-L-2-v2',
+    'cross-encoder/ms-marco-MiniLM-L-4-v2',
+    'cross-encoder/ms-marco-MiniLM-L-6-v2',
+    'cross-encoder/ms-marco-MiniLM-L-12-v2',
+    'cross-encoder/ms-marco-TinyBERT-L-2',
+    'cross-encoder/ms-marco-TinyBERT-L-4',
+    'cross-encoder/ms-marco-TinyBERT-L-6',
+    'cross-encoder/ms-marco-electra-base',
+    'nboost/pt-tinybert-msmarco',
+    'nboost/pt-bert-base-uncased-msmarco',
+    'nboost/pt-bert-large-msmarco',
+    'Capreolus/electra-base-msmarco',
+    'amberoad/bert-multilingual-passage-reranking-msmarco',
+
+    # q2q models:
+    'cross-encoder/quora-distilroberta-base',
+    'cross-encoder/quora-roberta-base',
+    'cross-encoder/quora-roberta-large',
+]
+
+
+for name in tqdm(model_name_list):
+    print('######################\nname=', name)
+
+    ### Test python qps
+    for device in ['cpu', 'cuda:3']:
+    # op = ReRank(model_name=name, threshold=0, device='cpu')
+        op = ops.rerank(model_name=name, threshold=0, device=device)
+
+        for batch_size in batch_size_list:
+            qps = []
+            for ind, _ in enumerate(range(try_times)):
+                start = time.time()
+                out = op(test_query, ['dump input'] * batch_size)
+                end = time.time()
+                if ind == 0:
+                    continue
+                q = 1 / (end - start)
+                qps.append(q)
+            print(f'device = {device}, batch_size = {batch_size}, mean qps = {sum(qps) / len(qps)}')
+
+
+
+    ### Test onnx checking
+    op = ops.rerank(model_name=name, threshold=0, device='cpu').get_op()
+    out1 = op(test_query, test_docs)
+    scores1 = out1[1]
+    onnx_path = str(op.save_model(format='onnx'))
+    onnx_model = onnx.load(onnx_path)
+    onnx.checker.check_model(onnx_model)
+
+    batch = [(test_query, doc) for doc in test_docs]
+    texts = [[] for _ in range(len(batch[0]))]
+
+    for example in batch:
+        for idx, text in enumerate(example):
+            texts[idx].append(text.strip())
+
+
+    sess = onnxruntime.InferenceSession(onnx_path, providers=onnxruntime.get_available_providers())
+
+    inputs = op.tokenizer(*texts, padding=True, truncation='longest_first', return_tensors="np",
+                               max_length=op.max_length)
+    out2 = sess.run(output_names=['last_hidden_state'], input_feed=dict(inputs))[0]
+    scores2 = op.post_proc(torch.from_numpy(out2))
+    scores2 = sorted(scores2, reverse=True)
+    assert numpy.allclose(scores1, scores2, atol=atol) is True
+
+
+    ### Test onnx qps
+    for batch_size in batch_size_list:
+        qps = []
+        model_qps = []
+        for ind, _ in enumerate(range(try_times)):
+            start = time.time()
+            batch = [(test_query, doc) for doc in ['dump input'] * batch_size]
+            texts = [[] for _ in range(len(batch[0]))]
+
+            for example in batch:
+                for idx, text in enumerate(example):
+                    texts[idx].append(text.strip())
+
+            inputs = op.tokenizer(*texts, padding=True, truncation='longest_first', return_tensors="np",
+                                  max_length=op.max_length)
+            model_start = time.time()
+            out2 = sess.run(output_names=['last_hidden_state'], input_feed=dict(inputs))[0]
+            model_end = time.time()
+            scores2 = op.post_proc(torch.from_numpy(out2))
+            scores2 = sorted(scores2, reverse=True)
+            end = time.time()
+            if ind == 0:
+                continue
+            q = 1 / (end - start)
+            model_q = 1 / (model_end - model_start)
+            qps.append(q)
+            model_qps.append(model_q)
+        print(f'onnx, batch_size = {batch_size}, mean qps = {sum(qps) / len(qps)}')
+        print(f'model onnx, batch_size = {batch_size}, mean qps = {sum(model_qps) / len(model_qps)}')
+
+