[feature] support pcp + mtp in full graph (#4572)

1. support pcp + mtp in full graph
2. pcp/dcp related mtp bugfix
3. support pcp + mtpx

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: zhangsicheng5 <zhangsicheng5@huawei.com>

tests/e2e/multicard/long_sequence/test_mtp.py

@@ -0,0 +1,165 @@
+#
+# 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 pytest
+
+from tests.e2e.conftest import VllmRunner
+from vllm_ascend.utils import vllm_version_is
+
+os.environ["HCCL_BUFFSIZE"] = "512"
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_dcp_mtp1_eager():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "wemaster/deepseek_mtp_main_random_bf16"
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=2,
+            max_num_batched_tokens=1024,
+            enable_expert_parallel=True,
+            block_size=128,
+            speculative_config={
+                "num_speculative_tokens": 1,
+                "method": "deepseek_mtp",
+            },
+            enforce_eager=True,
+    ) as runner:
+        runner.generate_greedy(prompts, 32)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_dcp_mtp3_eager():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "wemaster/deepseek_mtp_main_random_bf16"
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=2,
+            max_num_batched_tokens=1024,
+            enable_expert_parallel=True,
+            block_size=128,
+            speculative_config={
+                "num_speculative_tokens": 3,
+                "method": "deepseek_mtp",
+            },
+            enforce_eager=True,
+    ) as runner:
+        runner.generate_greedy(prompts, 32)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_dcp_mtp3_piecewise_graph():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "wemaster/deepseek_mtp_main_random_bf16"
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=2,
+            max_num_batched_tokens=1024,
+            enable_expert_parallel=True,
+            block_size=128,
+            speculative_config={
+                "num_speculative_tokens": 3,
+                "method": "deepseek_mtp",
+            },
+            compilation_config={
+                "cudagraph_mode": "PIECEWISE",
+                "cudagraph_capture_sizes": [4, 8, 16],
+            },
+    ) as runner:
+        runner.generate_greedy(prompts, 32)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_dcp_mtp3_full_graph():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "wemaster/deepseek_mtp_main_random_bf16"
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=2,
+            max_num_batched_tokens=1024,
+            enable_expert_parallel=True,
+            block_size=128,
+            speculative_config={
+                "num_speculative_tokens": 3,
+                "method": "deepseek_mtp",
+            },
+            compilation_config={
+                "cudagraph_mode": "FULL_DECODE_ONLY",
+                "cudagraph_capture_sizes": [4, 8, 16],
+            },
+    ) as runner:
+        runner.generate_greedy(prompts, 32)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_dcp_mtp3_full_graph():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "wemaster/deepseek_mtp_main_random_bf16"
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            tensor_parallel_size=2,
+            decode_context_parallel_size=2,
+            max_num_batched_tokens=1024,
+            enable_expert_parallel=True,
+            block_size=128,
+            speculative_config={
+                "num_speculative_tokens": 3,
+                "method": "deepseek_mtp",
+            },
+            compilation_config={
+                "cudagraph_mode": "FULL_DECODE_ONLY",
+                "cudagraph_capture_sizes": [4, 8, 16],
+            },
+    ) as runner:
+        runner.generate_greedy(prompts, 32)