[E2E] add E2E for Prefix Caching cp & Chunked Prefill cp (#5149)

### What this PR does / why we need it?
Add E2E for Prefix Caching cp & Chunked Prefill cp 
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: F.Liu <liufeng248@huawei.com>
Signed-off-by: Feng Liu <46866849+ader47@users.noreply.github.com>
Co-authored-by: F.Liu <liufeng248@huawei.com>

.github/workflows/scripts/config.yaml

@@ -139,7 +139,9 @@ e2e-multicard-4-cards:
     estimated_time: 60
   - name: tests/e2e/multicard/4-cards/long_sequence/test_basic.py
     estimated_time: 60
-  - name: tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill.py
+  - name: tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill_cp.py
+    estimated_time: 60
+  - name: tests/e2e/multicard/4-cards/long_sequence/test_prefix_caching_cp.py
     estimated_time: 60
   - name: tests/e2e/multicard/4-cards/long_sequence/test_mtp.py
     estimated_time: 60

tests/e2e/conftest.py

@@ -78,6 +78,7 @@ PromptVideoInput = _PromptMultiModalInput[np.ndarray]
 logger = logging.getLogger(__name__)
 
 _TEST_DIR = os.path.dirname(__file__)
+_LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "long_prompt.txt")]
 
 
 def _check_npu_memory_worker(target_free_percentage: float, max_wait_seconds: float):

tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill.py

@@ -1,122 +0,0 @@
-#
-# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
-# Copyright 2023 The vLLM team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# This file is a part of the vllm-ascend project.
-# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
-#
-import os
-import random
-import string
-from unittest.mock import patch
-
-import pytest
-from vllm import SamplingParams
-
-from tests.e2e.conftest import VllmRunner
-
-MODELS = [
-    "vllm-ascend/Qwen3-30B-A3B-W8A8",
-    "vllm-ascend/DeepSeek-V2-Lite-W8A8",
-]
-
-
-def generate_prompts(input_len, batchsize):
-    prompts = [
-        " ".join([
-            f"{random.choice(string.ascii_letters)}" for _ in range(input_len)
-        ]) for _ in range(batchsize)
-    ]
-    return prompts
-
-
-@patch.dict(
-    os.environ, {
-        "HCCL_BUFFSIZE": "768",
-        "VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
-        "VLLM_ALLOW_LONG_MAX_MODEL_LEN": "1"
-    })
-@pytest.mark.parametrize("model", MODELS)
-def test_models_chunked_prefill_mixed_length_prompts_including_1_token(
-        model: str):
-    TEST_ROPE_PARAMETERS = {
-        "rope_theta": 1000000,
-        "rope_type": "yarn",
-        "factor": 4,
-        "original_max_position_embeddings": 32768
-    }
-    prompts = [
-        generate_prompts(128 * 1024, 1)[0],
-        generate_prompts(1, 1)[0],
-        generate_prompts(9104, 1)[0],
-    ]
-    sampling_params = SamplingParams(max_tokens=1, temperature=0.0)
-
-    with VllmRunner(
-            model,
-            enforce_eager=True,
-            max_num_seqs=2,
-            max_num_batched_tokens=131000,
-            max_model_len=132000,
-            tensor_parallel_size=2,
-            prefill_context_parallel_size=2,
-            decode_context_parallel_size=1,
-            enable_expert_parallel=True,
-            block_size=128,
-            quantization="ascend",
-            hf_overrides={"rope_parameters": TEST_ROPE_PARAMETERS},
-    ) as runner:
-        runner.model.generate(prompts, sampling_params)
-
-
-@patch.dict(
-    os.environ, {
-        "HCCL_BUFFSIZE": "768",
-        "VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
-        "VLLM_ALLOW_LONG_MAX_MODEL_LEN": "1"
-    })
-@pytest.mark.parametrize("model", MODELS)
-def test_models_chunked_prefill_with_empty_kvcache(model: str):
-    TEST_ROPE_PARAMETERS = {
-        "rope_theta": 1000000,
-        "rope_type": "yarn",
-        "factor": 4,
-        "original_max_position_embeddings": 32768
-    }
-    # Note(qcs): we use chunk_size=50, kv_cache_interleave_size=128
-    # to simulate certain edge cases.
-    prompts = [
-        generate_prompts(128, 1)[0],
-        generate_prompts(1, 1)[0],
-        generate_prompts(130, 1)[0],
-        generate_prompts(51, 1)[0],
-        generate_prompts(129, 1)[0],
-    ]
-    sampling_params = SamplingParams(max_tokens=1, temperature=0.0)
-
-    with VllmRunner(
-            model,
-            enforce_eager=True,
-            max_num_seqs=2,
-            tensor_parallel_size=2,
-            prefill_context_parallel_size=2,
-            decode_context_parallel_size=1,
-            enable_expert_parallel=True,
-            long_prefill_token_threshold=50,
-            block_size=128,
-            cp_kv_cache_interleave_size=128,
-            quantization="ascend",
-            hf_overrides={"rope_parameters": TEST_ROPE_PARAMETERS},
-    ) as runner:
-        runner.model.generate(prompts, sampling_params)

tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill_cp.py

@@ -0,0 +1,230 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
+#
+import os
+import random
+import string
+from typing import Any, Dict
+from unittest.mock import patch
+
+import pytest
+from vllm import SamplingParams
+
+from tests.e2e.conftest import _LONG_PROMPTS, VllmRunner
+
+MODELS = [
+    "vllm-ascend/Qwen3-30B-A3B-W8A8",
+    "vllm-ascend/DeepSeek-V2-Lite-W8A8",
+]
+
+SETTINGS: Dict[str, Dict[str, Any]] = {
+    "vllm-ascend/Qwen3-30B-A3B-W8A8": {
+        "TP": 2,
+        "PCP": 2,
+        "DCP": 1,
+        "quantization": "ascend",
+    },
+    "vllm-ascend/DeepSeek-V2-Lite-W8A8": {
+        "TP": 2,
+        "PCP": 2,
+        "DCP": 2,
+        "quantization": "ascend",
+    }
+}
+
+# A prompt containing a large markdown table. The table is randomly generated by GPT-4.
+with open(_LONG_PROMPTS[0], 'r', encoding='utf-8') as file:
+    LONG_PROMPT = file.read()
+
+INPUT_PROMPTS = [
+    LONG_PROMPT +
+    "Question: what is the age of John Doe? Your answer: The age of John Doe is ",
+    LONG_PROMPT +
+    "Question: what is the age of Alice Johnson? Your answer: The age of Alice Johnson is "
+]
+
+VLLM_OUTPUT = [INPUT_PROMPTS[0] + "29", INPUT_PROMPTS[1] + "27"]
+
+
+def generate_prompts(input_len, batchsize):
+    prompts = [
+        " ".join([
+            f"{random.choice(string.ascii_letters)}" for _ in range(input_len)
+        ]) for _ in range(batchsize)
+    ]
+    return prompts
+
+
+@patch.dict(
+    os.environ, {
+        "HCCL_BUFFSIZE": "768",
+        "VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
+        "VLLM_ALLOW_LONG_MAX_MODEL_LEN": "1"
+    })
+@pytest.mark.parametrize("model", MODELS)
+def test_models_chunked_prefill_mixed_length_prompts_including_1_token(
+        model: str):
+    TEST_ROPE_PARAMETERS = {
+        "rope_theta": 1000000,
+        "rope_type": "yarn",
+        "factor": 4,
+        "original_max_position_embeddings": 32768
+    }
+    prompts = [
+        generate_prompts(128 * 1024, 1)[0],
+        generate_prompts(1, 1)[0],
+        generate_prompts(9104, 1)[0],
+    ]
+    sampling_params = SamplingParams(max_tokens=1, temperature=0.0)
+
+    with VllmRunner(
+            model,
+            enforce_eager=True,
+            max_num_seqs=2,
+            max_num_batched_tokens=131000,
+            max_model_len=132000,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=1,
+            enable_expert_parallel=True,
+            block_size=128,
+            quantization="ascend",
+            hf_overrides={"rope_parameters": TEST_ROPE_PARAMETERS},
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@patch.dict(
+    os.environ, {
+        "HCCL_BUFFSIZE": "768",
+        "VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
+        "VLLM_ALLOW_LONG_MAX_MODEL_LEN": "1"
+    })
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.skip(reason="skip for bad adaptability with main2main")
+def test_models_chunked_prefill_with_empty_kvcache(model: str):
+    TEST_ROPE_PARAMETERS = {
+        "rope_theta": 1000000,
+        "rope_type": "yarn",
+        "factor": 4,
+        "original_max_position_embeddings": 32768
+    }
+    # Note(qcs): we use chunk_size=50, kv_cache_interleave_size=128
+    # to simulate certain edge cases.
+    prompts = [
+        generate_prompts(128, 1)[0],
+        generate_prompts(1, 1)[0],
+        generate_prompts(130, 1)[0],
+        generate_prompts(51, 1)[0],
+        generate_prompts(129, 1)[0],
+    ]
+    sampling_params = SamplingParams(max_tokens=1, temperature=0.0)
+
+    with VllmRunner(
+            model,
+            enforce_eager=True,
+            max_num_seqs=2,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=1,
+            enable_expert_parallel=True,
+            long_prefill_token_threshold=50,
+            block_size=128,
+            cp_kv_cache_interleave_size=128,
+            quantization="ascend",
+            hf_overrides={"rope_parameters": TEST_ROPE_PARAMETERS},
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [2])
+@patch.dict(os.environ, {"HCCL_BUFFSIZE": "768"})
+def test_models_chunked_prefill_with_cp_basic(model: str,
+                                              max_tokens: int) -> None:
+    with VllmRunner(
+            model,
+            block_size=128,
+            max_model_len=4096,
+            enforce_eager=True,
+            max_num_batched_tokens=128,
+            enable_expert_parallel=True,
+            enable_prefix_caching=False,
+            enable_chunked_prefill=True,
+            tensor_parallel_size=SETTINGS[model]['TP'],
+            quantization=SETTINGS[model]["quantization"],
+            prefill_context_parallel_size=SETTINGS[model]['PCP'],
+            decode_context_parallel_size=SETTINGS[model]['DCP']) as vllm_model:
+        chunked_prefill_outputs = vllm_model.generate_greedy(
+            INPUT_PROMPTS, max_tokens)
+
+    for i in range(len(chunked_prefill_outputs)):
+        assert chunked_prefill_outputs[i][1] == VLLM_OUTPUT[i]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [2])
+@patch.dict(os.environ, {"HCCL_BUFFSIZE": "768"})
+def test_models_chunked_prefill_with_cp_piecewise(model: str,
+                                                  max_tokens: int) -> None:
+    with VllmRunner(
+            model,
+            block_size=128,
+            max_model_len=4096,
+            enforce_eager=False,
+            max_num_batched_tokens=128,
+            enable_expert_parallel=True,
+            enable_prefix_caching=False,
+            enable_chunked_prefill=True,
+            tensor_parallel_size=SETTINGS[model]['TP'],
+            quantization=SETTINGS[model]["quantization"],
+            prefill_context_parallel_size=SETTINGS[model]['PCP'],
+            decode_context_parallel_size=SETTINGS[model]['DCP']) as vllm_model:
+        chunked_prefill_outputs = vllm_model.generate_greedy(
+            INPUT_PROMPTS, max_tokens)
+
+    for i in range(len(chunked_prefill_outputs)):
+        assert chunked_prefill_outputs[i][1] == VLLM_OUTPUT[i]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [2])
+@patch.dict(os.environ, {"HCCL_BUFFSIZE": "768"})
+def test_models_chunked_prefill_with_cp_full_graph(model: str,
+                                                   max_tokens: int) -> None:
+    with VllmRunner(model,
+                    block_size=128,
+                    max_model_len=4096,
+                    enforce_eager=False,
+                    max_num_batched_tokens=128,
+                    enable_expert_parallel=True,
+                    enable_prefix_caching=False,
+                    enable_chunked_prefill=True,
+                    tensor_parallel_size=SETTINGS[model]['TP'],
+                    quantization=SETTINGS[model]["quantization"],
+                    prefill_context_parallel_size=SETTINGS[model]['PCP'],
+                    decode_context_parallel_size=SETTINGS[model]['DCP'],
+                    compilation_config={
+                        "cudagraph_capture_sizes": [4, 8, 24, 48, 60],
+                        "cudagraph_mode": "FULL_DECODE_ONLY"
+                    }) as vllm_model:
+        chunked_prefill_outputs = vllm_model.generate_greedy(
+            INPUT_PROMPTS, max_tokens)
+
+    for i in range(len(chunked_prefill_outputs)):
+        assert chunked_prefill_outputs[i][1] == VLLM_OUTPUT[i]

tests/e2e/multicard/4-cards/long_sequence/test_prefix_caching_cp.py

@@ -0,0 +1,135 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
+#
+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/e2e/multicard/long_sequence/test_prefix_caching_cp.py`
+"""
+
+from typing import Any, Dict
+
+import pytest
+
+from tests.e2e.conftest import _LONG_PROMPTS, VllmRunner
+
+MODELS = [
+    "vllm-ascend/Qwen3-30B-A3B-W8A8", "vllm-ascend/DeepSeek-V2-Lite-W8A8"
+]
+
+SETTINGS: Dict[str, Dict[str, Any]] = {
+    "vllm-ascend/Qwen3-30B-A3B-W8A8": {
+        "TP": 2,
+        "PCP": 2,
+        "DCP": 1,
+        "quantization": "ascend",
+    },
+    "vllm-ascend/DeepSeek-V2-Lite-W8A8": {
+        "TP": 2,
+        "PCP": 2,
+        "DCP": 2,
+        "quantization": "ascend",
+    }
+}
+
+# A prompt containing a large markdown table. The table is randomly generated by GPT-4.
+with open(_LONG_PROMPTS[0], 'r', encoding='utf-8') as file:
+    LONG_PROMPT = file.read()
+
+INPUT_PROMPTS = [
+    LONG_PROMPT +
+    "Question: what is the age of John Doe? Your answer: The age of John Doe is ",
+    LONG_PROMPT +
+    "Question: what is the age of Umar Black? Your answer: The age of Umar Black is "
+]
+
+VLLM_OUTPUT = [INPUT_PROMPTS[0] + "29", INPUT_PROMPTS[1] + "39"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [2])
+def test_models_prefix_cache_with_cp_basic(
+        model: str, max_tokens: int, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("HCCL_BUFFSIZE", "768")
+    with VllmRunner(
+            model,
+            block_size=128,
+            enforce_eager=True,
+            max_model_len=4096,
+            enable_prefix_caching=True,
+            enable_expert_parallel=True,
+            max_num_batched_tokens=4096,
+            tensor_parallel_size=SETTINGS[model]['TP'],
+            quantization=SETTINGS[model]["quantization"],
+            prefill_context_parallel_size=SETTINGS[model]['PCP'],
+            decode_context_parallel_size=SETTINGS[model]['DCP']) as vllm_model:
+        prefix_cache_outputs = vllm_model.generate_greedy(
+            INPUT_PROMPTS, max_tokens)
+
+    for i in range(len(prefix_cache_outputs)):
+        assert prefix_cache_outputs[i][1] == VLLM_OUTPUT[i]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [2])
+def test_models_prefix_cache_with_cp_piecewise(
+        model: str, max_tokens: int, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("HCCL_BUFFSIZE", "768")
+    with VllmRunner(
+            model,
+            block_size=128,
+            max_model_len=4096,
+            enforce_eager=False,
+            enable_prefix_caching=True,
+            enable_expert_parallel=True,
+            max_num_batched_tokens=4096,
+            tensor_parallel_size=SETTINGS[model]['TP'],
+            quantization=SETTINGS[model]["quantization"],
+            prefill_context_parallel_size=SETTINGS[model]['PCP'],
+            decode_context_parallel_size=SETTINGS[model]['DCP']) as vllm_model:
+        prefix_cache_outputs = vllm_model.generate_greedy(
+            INPUT_PROMPTS, max_tokens)
+
+    for i in range(len(prefix_cache_outputs)):
+        assert prefix_cache_outputs[i][1] == VLLM_OUTPUT[i]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [2])
+def test_models_prefix_cache_with_cp_full_graph(
+        model: str, max_tokens: int, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("HCCL_BUFFSIZE", "768")
+    with VllmRunner(model,
+                    block_size=128,
+                    max_model_len=4096,
+                    enforce_eager=False,
+                    enable_prefix_caching=True,
+                    enable_expert_parallel=True,
+                    max_num_batched_tokens=4096,
+                    tensor_parallel_size=SETTINGS[model]['TP'],
+                    quantization=SETTINGS[model]["quantization"],
+                    prefill_context_parallel_size=SETTINGS[model]['PCP'],
+                    decode_context_parallel_size=SETTINGS[model]['DCP'],
+                    compilation_config={
+                        "cudagraph_capture_sizes": [4, 8, 24, 48, 60],
+                        "cudagraph_mode": "FULL_DECODE_ONLY"
+                    }) as vllm_model:
+        prefix_cache_outputs = vllm_model.generate_greedy(
+            INPUT_PROMPTS, max_tokens)
+
+    for i in range(len(prefix_cache_outputs)):
+        assert prefix_cache_outputs[i][1] == VLLM_OUTPUT[i]

tests/e2e/prompts/long_prompt.txt

@@ -0,0 +1,35 @@
+You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as follows.
+# Table
+
+| ID  | Name          | Age | Occupation    | Country       | Email                  | Phone Number   | Address                       |
+|-----|---------------|-----|---------------|---------------|------------------------|----------------|------------------------------|
+| 1   | John Doe      | 29  | Engineer      | USA           | john.doe@example.com   | 555-1234       | 123 Elm St, Springfield, IL  |
+| 2   | Jane Smith    | 34  | Doctor        | Canada        | jane.smith@example.com | 555-5678       | 456 Oak St, Toronto, ON      |
+| 3   | Alice Johnson | 27  | Teacher       | UK            | alice.j@example.com    | 555-8765       | 789 Pine St, London, UK      |
+| 4   | Bob Brown     | 45  | Artist        | Australia     | bob.b@example.com      | 555-4321       | 321 Maple St, Sydney, NSW    |
+| 5   | Carol White   | 31  | Scientist     | New Zealand   | carol.w@example.com    | 555-6789       | 654 Birch St, Wellington, NZ |
+| 6   | Dave Green    | 28  | Lawyer        | Ireland       | dave.g@example.com     | 555-3456       | 987 Cedar St, Dublin, IE     |
+| 7   | Emma Black    | 40  | Musician      | USA           | emma.b@example.com     | 555-1111       | 246 Ash St, New York, NY     |
+| 8   | Frank Blue    | 37  | Chef          | Canada        | frank.b@example.com    | 555-2222       | 135 Spruce St, Vancouver, BC |
+| 9   | Grace Yellow  | 50  | Engineer      | UK            | grace.y@example.com    | 555-3333       | 864 Fir St, Manchester, UK   |
+| 10  | Henry Violet  | 32  | Artist        | Australia     | henry.v@example.com    | 555-4444       | 753 Willow St, Melbourne, VIC|
+| 11  | Irene Orange  | 26  | Scientist     | New Zealand   | irene.o@example.com    | 555-5555       | 912 Poplar St, Auckland, NZ  |
+| 12  | Jack Indigo   | 38  | Teacher       | Ireland       | jack.i@example.com     | 555-6666       | 159 Elm St, Cork, IE         |
+| 13  | Karen Red     | 41  | Lawyer        | USA           | karen.r@example.com    | 555-7777       | 357 Cedar St, Boston, MA     |
+| 14  | Leo Brown     | 30  | Chef          | Canada        | leo.b@example.com      | 555-8888       | 246 Oak St, Calgary, AB      |
+| 15  | Mia Green     | 33  | Musician      | UK            | mia.g@example.com      | 555-9999       | 975 Pine St, Edinburgh, UK   |
+| 16  | Noah Yellow   | 29  | Doctor        | Australia     | noah.y@example.com     | 555-0000       | 864 Birch St, Brisbane, QLD  |
+| 17  | Olivia Blue   | 35  | Engineer      | New Zealand   | olivia.b@example.com   | 555-1212       | 753 Maple St, Hamilton, NZ   |
+| 18  | Peter Black   | 42  | Artist        | Ireland       | peter.b@example.com    | 555-3434       | 912 Fir St, Limerick, IE     |
+| 19  | Quinn White   | 28  | Scientist     | USA           | quinn.w@example.com    | 555-5656       | 159 Willow St, Seattle, WA   |
+| 20  | Rachel Red    | 31  | Teacher       | Canada        | rachel.r@example.com   | 555-7878       | 357 Poplar St, Ottawa, ON    |
+| 21  | Steve Green   | 44  | Lawyer        | UK            | steve.g@example.com    | 555-9090       | 753 Elm St, Birmingham, UK   |
+| 22  | Tina Blue     | 36  | Musician      | Australia     | tina.b@example.com     | 555-1213       | 864 Cedar St, Perth, WA      |
+| 23  | Umar Black    | 39  | Chef          | New Zealand   | umar.b@example.com     | 555-3435       | 975 Spruce St, Christchurch, NZ|
+| 24  | Victor Yellow | 43  | Engineer      | Ireland       | victor.y@example.com   | 555-5657       | 246 Willow St, Galway, IE    |
+| 25  | Wendy Orange  | 27  | Artist        | USA           | wendy.o@example.com    | 555-7879       | 135 Elm St, Denver, CO       |
+| 26  | Xavier Green  | 34  | Scientist     | Canada        | xavier.g@example.com   | 555-9091       | 357 Oak St, Montreal, QC     |
+| 27  | Yara Red      | 41  | Teacher       | UK            | yara.r@example.com     | 555-1214       | 975 Pine St, Leeds, UK       |
+| 28  | Zack Blue     | 30  | Lawyer        | Australia     | zack.b@example.com     | 555-3436       | 135 Birch St, Adelaide, SA   |
+| 29  | Amy White     | 33  | Musician      | New Zealand   | amy.w@example.com      | 555-5658       | 159 Maple St, Wellington, NZ |
+| 30  | Ben Black     | 38  | Chef          | Ireland       | ben.b@example.com      | 555-7870       | 246 Fir St, Waterford, IE    |