test: test batch queue flush all

jina/serve/executors/decorators.py

@@ -416,6 +416,7 @@ def dynamic_batching(
     *,
     preferred_batch_size: Optional[int] = None,
     timeout: Optional[float] = 10_000,
+    flush_all: bool = False
 ):
     """
     `@dynamic_batching` defines the dynamic batching behavior of an Executor.
@@ -426,11 +427,13 @@ def dynamic_batching(
 
     :param func: the method to decorate
     :param preferred_batch_size: target number of Documents in a batch. The batcher will collect requests until `preferred_batch_size` is reached,
-        or until `timeout` is reached. Therefore, the actual batch size can be smaller or larger than `preferred_batch_size`.
+        or until `timeout` is reached. Therefore, the actual batch size can be smaller or equal to `preferred_batch_size`, except if `flush_all` is set to True
     :param timeout: maximum time in milliseconds to wait for a request to be assigned to a batch.
         If the oldest request in the queue reaches a waiting time of `timeout`, the batch will be passed to the Executor,
         even if it contains fewer than `preferred_batch_size` Documents.
         Default is 10_000ms (10 seconds).
+    :param flush_all: Determines if once the batches is triggered by timeout or preferred_batch_size, the function will receive everything that the batcher has accumulated or not.
+        If this is true, `preferred_batch_size` is used as a trigger mechanism.
     :return: decorated function
     """
 
@@ -476,6 +479,7 @@ def _inject_owner_attrs(self, owner, name):
                 'preferred_batch_size'
             ] = preferred_batch_size
             owner.dynamic_batching[fn_name]['timeout'] = timeout
+            owner.dynamic_batching[fn_name]['flush_all'] = flush_all
             setattr(owner, name, self.fn)
 
         def __set_name__(self, owner, name):

tests/integration/dynamic_batching/test_dynamic_batching.py

@@ -629,11 +629,17 @@ def test_failure_propagation():
             )
 
 
-@pytest.mark.repeat(10)
-def test_exception_handling_in_dynamic_batch():
+@pytest.mark.parametrize(
+    'flush_all',
+    [
+        False,
+        True
+    ],
+)
+def test_exception_handling_in_dynamic_batch(flush_all):
     class SlowExecutorWithException(Executor):
 
-        @dynamic_batching(preferred_batch_size=3, timeout=1000)
+        @dynamic_batching(preferred_batch_size=3, timeout=5000, flush_all=flush_all)
         @requests(on='/foo')
         def foo(self, docs, **kwargs):
             for doc in docs:
@@ -659,4 +665,50 @@ def foo(self, docs, **kwargs):
             if r.header.status.code == jina_pb2.StatusProto.StatusCode.ERROR:
                 num_failed_requests += 1
 
-        assert 1 <= num_failed_requests <= 3  # 3 requests in the dynamic batch failing
+        if not flush_all:
+            assert 1 <= num_failed_requests <= 3  # 3 requests in the dynamic batch failing
+        else:
+            assert 1 <= num_failed_requests <= len(da)  # 3 requests in the dynamic batch failing
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    'flush_all',
+    [
+        False,
+        True
+    ],
+)
+async def test_num_docs_processed_in_exec(flush_all):
+    class DynBatchProcessor(Executor):
+
+        @dynamic_batching(preferred_batch_size=5, timeout=5000, flush_all=flush_all)
+        @requests(on='/foo')
+        def foo(self, docs, **kwargs):
+            for doc in docs:
+                doc.text = f"{len(docs)}"
+
+    depl = Deployment(uses=DynBatchProcessor, protocol='http')
+
+    with depl:
+        da = DocumentArray([Document(text='good') for _ in range(50)])
+        cl = Client(protocol=depl.protocol, port=depl.port, asyncio=True)
+        res = []
+        async for r in cl.post(
+            on='/foo',
+            inputs=da,
+            request_size=7,
+            continue_on_error=True,
+            results_in_order=True,
+        ):
+            res.extend(r)
+        assert len(res) == 50  # 1 request per input
+        if not flush_all:
+            for d in res:
+                assert int(d.text) <= 5
+        else:
+            larger_than_5 = 0
+            for d in res:
+                if int(d.text) > 5:
+                    larger_than_5 += 1
+                assert int(d.text) >= 5
+            assert larger_than_5 > 0

tests/unit/serve/dynamic_batching/test_batch_queue.py

@@ -9,7 +9,8 @@
 
 
 @pytest.mark.asyncio
-async def test_batch_queue_timeout():
+@pytest.mark.parametrize('flush_all', [False, True])
+async def test_batch_queue_timeout(flush_all):
     async def foo(docs, **kwargs):
         await asyncio.sleep(0.1)
         return DocumentArray([Document(text='Done') for _ in docs])
@@ -20,6 +21,7 @@ async def foo(docs, **kwargs):
         response_docarray_cls=DocumentArray,
         preferred_batch_size=4,
         timeout=2000,
+        flush_all=flush_all,
     )
 
     three_data_requests = [DataRequest() for _ in range(3)]
@@ -59,7 +61,8 @@ async def process_request(req):
 
 
 @pytest.mark.asyncio
-async def test_batch_queue_timeout_does_not_wait_previous_batch():
+@pytest.mark.parametrize('flush_all', [False, True])
+async def test_batch_queue_timeout_does_not_wait_previous_batch(flush_all):
     batches_lengths_computed = []
 
     async def foo(docs, **kwargs):
@@ -73,6 +76,7 @@ async def foo(docs, **kwargs):
         response_docarray_cls=DocumentArray,
         preferred_batch_size=5,
         timeout=3000,
+        flush_all=flush_all
     )
 
     data_requests = [DataRequest() for _ in range(3)]
@@ -93,19 +97,28 @@ async def process_request(req, sleep=0):
     init_time = time.time()
     tasks = [asyncio.create_task(process_request(req)) for req in data_requests]
     tasks.append(asyncio.create_task(process_request(extra_data_request, sleep=2)))
-    responses = await asyncio.gather(*tasks)
+    _ = await asyncio.gather(*tasks)
     time_spent = (time.time() - init_time) * 1000
-    # TIME TAKEN: 8000 for first batch of requests, plus 4000 for second batch that is fired inmediately
-    # BEFORE FIX in https://github.com/jina-ai/jina/pull/6071, this would take: 8000 + 3000 + 4000 (Timeout would start counting too late)
-    assert time_spent >= 12000
-    assert time_spent <= 12500
-    assert batches_lengths_computed == [5, 1, 2]
+
+    if flush_all is False:
+        # TIME TAKEN: 8000 for first batch of requests, plus 4000 for second batch that is fired inmediately
+        # BEFORE FIX in https://github.com/jina-ai/jina/pull/6071, this would take: 8000 + 3000 + 4000 (Timeout would start counting too late)
+        assert time_spent >= 12000
+        assert time_spent <= 12500
+    else:
+        assert time_spent >= 8000
+        assert time_spent <= 8500
+    if flush_all is False:
+        assert batches_lengths_computed == [5, 1, 2]
+    else:
+        assert batches_lengths_computed == [6, 2]
 
     await bq.close()
 
 
 @pytest.mark.asyncio
-async def test_batch_queue_req_length_larger_than_preferred():
+@pytest.mark.parametrize('flush_all', [False, True])
+async def test_batch_queue_req_length_larger_than_preferred(flush_all):
     async def foo(docs, **kwargs):
         await asyncio.sleep(0.1)
         return DocumentArray([Document(text='Done') for _ in docs])
@@ -116,6 +129,7 @@ async def foo(docs, **kwargs):
         response_docarray_cls=DocumentArray,
         preferred_batch_size=4,
         timeout=2000,
+        flush_all=flush_all,
     )
 
     data_requests = [DataRequest() for _ in range(3)]
@@ -240,7 +254,8 @@ async def process_request(req):
 
 
 @pytest.mark.asyncio
-async def test_exception_all():
+@pytest.mark.parametrize('flush_all', [False, True])
+async def test_exception_all(flush_all):
     async def foo(docs, **kwargs):
         raise AssertionError
 
@@ -249,6 +264,7 @@ async def foo(docs, **kwargs):
         request_docarray_cls=DocumentArray,
         response_docarray_cls=DocumentArray,
         preferred_batch_size=2,
+        flush_all=flush_all,
         timeout=500,
     )
 
@@ -287,8 +303,9 @@ async def foo(docs, **kwargs):
 @pytest.mark.parametrize('num_requests', [61, 127, 100])
 @pytest.mark.parametrize('preferred_batch_size', [7, 27, 61, 73, 100])
 @pytest.mark.parametrize('timeout', [0.3, 500])
+@pytest.mark.parametrize('flush_all', [False, True])
 @pytest.mark.asyncio
-async def test_return_proper_assignment(num_requests, preferred_batch_size, timeout):
+async def test_return_proper_assignment(num_requests, preferred_batch_size, timeout, flush_all):
     import random
 
     async def foo(docs, **kwargs):
@@ -301,6 +318,7 @@ async def foo(docs, **kwargs):
         request_docarray_cls=DocumentArray,
         response_docarray_cls=DocumentArray,
         preferred_batch_size=preferred_batch_size,
+        flush_all=flush_all,
         timeout=timeout,
     )
 
@@ -331,3 +349,58 @@ async def process_request(req):
         assert len(resp.docs) == length
         for j, d in enumerate(resp.docs):
             assert d.text == f'Text {j} from request {i} with len {length} Processed'
+
+
+@pytest.mark.parametrize('num_requests', [61, 127, 100])
+@pytest.mark.parametrize('preferred_batch_size', [7, 27, 61, 73, 100])
+@pytest.mark.parametrize('timeout', [0.3, 500])
+@pytest.mark.parametrize('flush_all', [False, True])
+@pytest.mark.asyncio
+async def test_length_processed_in_func(num_requests, preferred_batch_size, timeout, flush_all):
+    import random
+
+    async def foo(docs, **kwargs):
+        if not flush_all:
+            assert len(docs) <= preferred_batch_size
+        else:
+            assert len(docs) >= preferred_batch_size
+        await asyncio.sleep(0.1)
+        for doc in docs:
+            doc.text += ' Processed'
+
+    bq: BatchQueue = BatchQueue(
+        foo,
+        request_docarray_cls=DocumentArray,
+        response_docarray_cls=DocumentArray,
+        preferred_batch_size=preferred_batch_size,
+        flush_all=flush_all,
+        timeout=timeout,
+    )
+
+    data_requests = [DataRequest() for _ in range(num_requests)]
+    len_requests = []
+    for i, req in enumerate(data_requests):
+        len_request = random.randint(preferred_batch_size, preferred_batch_size * 10)
+        len_requests.append(len_request)
+        req.data.docs = DocumentArray(
+            [
+                Document(text=f'Text {j} from request {i} with len {len_request}')
+                for j in range(len_request)
+            ]
+        )
+
+    async def process_request(req):
+        q = await bq.push(req)
+        item = await q.get()
+        q.task_done()
+        return item
+
+    tasks = [asyncio.create_task(process_request(req)) for req in data_requests]
+    items = await asyncio.gather(*tasks)
+    for i, item in enumerate(items):
+        assert item is None
+
+    for i, (resp, length) in enumerate(zip(data_requests, len_requests)):
+        assert len(resp.docs) == length
+        for j, d in enumerate(resp.docs):
+            assert d.text == f'Text {j} from request {i} with len {length} Processed'

tests/unit/serve/executors/test_executor.py

@@ -614,15 +614,15 @@ class C(B):
     [
         (
             dict(preferred_batch_size=4, timeout=5_000),
-            dict(preferred_batch_size=4, timeout=5_000),
+            dict(preferred_batch_size=4, timeout=5_000, flush_all=False),
         ),
         (
-            dict(preferred_batch_size=4, timeout=5_000),
-            dict(preferred_batch_size=4, timeout=5_000),
+            dict(preferred_batch_size=4, timeout=5_000, flush_all=True),
+            dict(preferred_batch_size=4, timeout=5_000, flush_all=True),
         ),
         (
             dict(preferred_batch_size=4),
-            dict(preferred_batch_size=4, timeout=10_000),
+            dict(preferred_batch_size=4, timeout=10_000, flush_all=False),
         ),
     ],
 )
@@ -641,15 +641,15 @@ def foo(self, docs, **kwargs):
     [
         (
             dict(preferred_batch_size=4, timeout=5_000),
-            dict(preferred_batch_size=4, timeout=5_000),
+            dict(preferred_batch_size=4, timeout=5_000, flush_all=False),
         ),
         (
-            dict(preferred_batch_size=4, timeout=5_000),
-            dict(preferred_batch_size=4, timeout=5_000),
+            dict(preferred_batch_size=4, timeout=5_000, flush_all=True),
+            dict(preferred_batch_size=4, timeout=5_000, flush_all=True),
         ),
         (
             dict(preferred_batch_size=4),
-            dict(preferred_batch_size=4, timeout=10_000),
+            dict(preferred_batch_size=4, timeout=10_000, flush_all=False),
         ),
     ],
 )