[MTP] follow custom deepseek modeling changes to support graph mode (#636)

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### What this PR does / why we need it?

As custom deepseek modeling do some changes to support graph mode in
https://github.com/vllm-project/vllm-ascend/pull/585, so i follow it to
change custom deepseek_mtp modeling.

And some modifications for k>1 were not carried over by the
https://github.com/vllm-project/vllm-ascend/pull/429, now i add it.

In order to better take care of the MTP feature in the vllm-ascend
repository, I added cases related to graph mode(torchair), but i skip it
since torchair can not correctly clean up memory in vllmrunner.

Also i add some case for MTP quantization weights, but test weight is
not ready, so i skip it and i will open it when test quant weights is
ready.

https://github.com/vllm-project/vllm-ascend/pull/648 did not completely
fix the sample
change(https://github.com/vllm-project/vllm-ascend/issues/660) issue, I
added the relevant changes.

### Does this PR introduce _any_ user-facing change?
now, u can use following method to use mtp in deepseek v3/r1 float or
quant weights with eager mode.
```python
llm = LLM(
    model="wemaster/deepseek_mtp_main_random_bf16",
    tensor_parallel_size=2,
    speculative_config={
        "num_speculative_tokens": 1,
    },
    enforce_eager=True,
    trust_remote_code=True,
    disable_log_stats=False,
    gpu_memory_utilization=0.8,
    max_model_len=64,
)
```

or use mtp in deepseek v3/r1 float or quant weights with graph
mode（torchair）
```python
llm = LLM(
    model="wemaster/deepseek_mtp_main_random_bf16",
    tensor_parallel_size=2,
    speculative_config={
        "num_speculative_tokens": 1,
    },
    trust_remote_code=True,
    additional_config={
        'enable_graph_mode': True,
    },
    disable_log_stats=False,
    gpu_memory_utilization=0.8,
    max_model_len=64,
)
```

add notes:
1. now, we support k>1, so u can set num_speculative_tokens > 1 if there
is sufficient redundant computing power;
2. MTP is not supported in V1, we will support it when vLLM does it in
https://github.com/vllm-project/vllm/issues/13500.
3. if u run MTP failed by `segmentation fault`, u can follow v0.7.3
patch https://github.com/vllm-project/vllm-ascend/pull/236 file
`vllm_ascend/patch/patch_metrics.py` method
`__npu_async_metrics_collector_init__`

### How was this patch tested?
local tested passed and test by CI

Signed-off-by: mengwei805 <mengwei25@huawei.com>

tests/singlecard/spec_decode/__init__.py

@@ -0,0 +1,18 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# 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.
+#
+
+from vllm_ascend.patch import worker  # noqa: F401

tests/singlecard/spec_decode/e2e/conftest.py

@@ -17,7 +17,9 @@
 # limitations under the License.
 #
 
+import shutil
 from itertools import cycle
+from pathlib import Path
 from typing import List, Optional, Sequence, Tuple, Union
 
 import pytest
@@ -177,6 +179,12 @@ def _check_logprobs_when_output_disabled(
         assert spec_pos_logprob_token_id in baseline_pos_logprobs
 
 
+def _clean_torchair_cache():
+    cache_path = Path.cwd() / '.torchair_cache'
+    if cache_path.exists() and cache_path.is_dir():
+        shutil.rmtree(cache_path)
+
+
 def run_equality_correctness_test(
         vllm_runner,
         common_llm_kwargs,
@@ -219,10 +227,20 @@ def run_equality_correctness_test(
                                      logprobs=logprobs,
                                      prompt_logprobs=prompt_logprobs)
 
+    # TODO current torchair graph mode needs clean torchair cache.
+    # if do not clean, it will raise error
+    additional_config = common_llm_kwargs.get("additional_config")
+    enable_graph_mode = additional_config.get(
+        "enable_graph_mode") if additional_config else False
+
     with vllm_runner(**org_args) as vllm_model:
+        if enable_graph_mode:
+            _clean_torchair_cache()
         org_outputs = vllm_model.generate_w_logprobs(prompts, sampling_params)
 
     with vllm_runner(**sd_args) as vllm_model:
+        if enable_graph_mode:
+            _clean_torchair_cache()
         if ensure_all_accepted or expected_acceptance_rate is not None:
             # Force log interval to be 0 to catch all metrics.
             stat_logger = vllm_model.model.llm_engine.stat_loggers[

tests/singlecard/spec_decode/e2e/test_mtp_correctness.py

@@ -36,14 +36,20 @@ However, we still need to verify below scenario could be passed:
 With those tests, we can say at least, mtp would not break the
 correctess for the target model outputs.
 """
+import os
 
 import pytest
 
 from .conftest import run_equality_correctness_test
 
-# main model
-# NOTE vLLM use fp8 model, vllm-ascend use bf16 model
-MAIN_MODEL = "wemaster/deepseek_mtp_main_random_bf16"
+# NOTE both main model and MTP are bfloat16
+FLOAT_MODEL = "wemaster/deepseek_mtp_main_random_bf16"
+
+# NOTE main model is w8a8, MTP is bfloat16
+QUANT_MODEL = "wemaster/deepseek_mtp_main_random_w8a8_part"
+
+# TODO when msmodelslim can quantify both main and MTP model
+# This UT should use w8a8 fully weights.
 
 # max. number of speculative tokens: this corresponds to
 # num_nextn_predict_layers in the config.json of the speculator model.
@@ -51,8 +57,11 @@ MAX_SPEC_TOKENS = 1
 
 # precision
 PRECISION = "bfloat16"
+os.environ["VLLM_USE_MODELSCOPE"] = "True"
 
 
+@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
+                    reason="mtp is not supported on v1")
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
@@ -66,7 +75,7 @@ PRECISION = "bfloat16"
         "dtype": PRECISION,
 
         # Main model
-        "model_name": MAIN_MODEL,
+        "model_name": FLOAT_MODEL,
 
         # GPU memory utilization
         "gpu_memory_utilization": 0.85
@@ -97,6 +106,7 @@ def test_mtp_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
                                   batch_size, output_len, seed)
 
 
+@pytest.mark.skipif(True, reason="quant model is not ready.")
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
@@ -110,7 +120,53 @@ def test_mtp_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
         "dtype": PRECISION,
 
         # Main model
-        "model_name": MAIN_MODEL,
+        "model_name": QUANT_MODEL,
+
+        # GPU memory utilization
+        "gpu_memory_utilization": 0.85
+    }])
+@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
+@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
+@pytest.mark.parametrize("test_llm_kwargs", [
+    {
+        "speculative_config": {
+            "num_speculative_tokens": MAX_SPEC_TOKENS,
+        },
+    },
+])
+@pytest.mark.parametrize("output_len", [
+    128,
+])
+@pytest.mark.parametrize("batch_size", [1, 32])
+@pytest.mark.parametrize("seed", [1])
+def test_mtp_e2e_quant_greedy_correctness(vllm_runner, common_llm_kwargs,
+                                          per_test_common_llm_kwargs,
+                                          baseline_llm_kwargs, test_llm_kwargs,
+                                          batch_size: int, output_len: int,
+                                          seed: int):
+
+    run_equality_correctness_test(vllm_runner, common_llm_kwargs,
+                                  per_test_common_llm_kwargs,
+                                  baseline_llm_kwargs, test_llm_kwargs,
+                                  batch_size, output_len, seed)
+
+
+@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
+                    reason="mtp is not supported on v1")
+@pytest.mark.parametrize(
+    "common_llm_kwargs",
+    [{
+        # Skip cuda graph recording for fast test.
+        "enforce_eager": True,
+
+        # Print spec metrics.
+        "disable_log_stats": False,
+
+        # Precision
+        "dtype": PRECISION,
+
+        # Main model
+        "model_name": FLOAT_MODEL,
 
         # GPU memory utilization
         "gpu_memory_utilization": 0.85
@@ -158,15 +214,13 @@ def test_mtp_e2e_greedy_logprobs(vllm_runner, common_llm_kwargs,
         ["disable_logprobs"])
 
 
-@pytest.mark.skipif(
-    True,
-    reason=
-    "Open it when vllm-ascend support graph mode and support enforce_eager status is False to run model in graph mode"
-)
+@pytest.mark.skipif(True, reason="torchair ut can not clean mem.")
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
-        "enforce_eager": False,
+        "additional_config": {
+            'enable_graph_mode': True,
+        },
 
         # Print spec metrics.
         "disable_log_stats": False,
@@ -175,7 +229,7 @@ def test_mtp_e2e_greedy_logprobs(vllm_runner, common_llm_kwargs,
         "dtype": PRECISION,
 
         # Main model
-        "model_name": MAIN_MODEL,
+        "model_name": FLOAT_MODEL,
         "gpu_memory_utilization": 0.85
     }])
 @pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@@ -192,20 +246,64 @@ def test_mtp_e2e_greedy_logprobs(vllm_runner, common_llm_kwargs,
 ])
 @pytest.mark.parametrize("batch_size", [1, 32])
 @pytest.mark.parametrize("seed", [1])
-def test_mtp_e2e_greedy_correctness_cuda_graph(vllm_runner, common_llm_kwargs,
-                                               per_test_common_llm_kwargs,
-                                               baseline_llm_kwargs,
-                                               test_llm_kwargs,
-                                               batch_size: int,
-                                               output_len: int, seed: int):
-    """Verify greedy equality with cuda graph enabled and different
-    batch sizes."""
+def test_mtp_e2e_greedy_correctness_torchair_graph(
+        vllm_runner, common_llm_kwargs, per_test_common_llm_kwargs,
+        baseline_llm_kwargs, test_llm_kwargs, batch_size: int, output_len: int,
+        seed: int):
+    """Verify greedy equality with torchair graph enabled and different
+    batch sizes using bfloat16 weights."""
     run_equality_correctness_test(vllm_runner, common_llm_kwargs,
                                   per_test_common_llm_kwargs,
                                   baseline_llm_kwargs, test_llm_kwargs,
                                   batch_size, output_len, seed)
 
 
+@pytest.mark.skipif(True, reason="quant model is not ready.")
+@pytest.mark.parametrize(
+    "common_llm_kwargs",
+    [{
+        "additional_config": {
+            'enable_graph_mode': True,
+        },
+
+        # Print spec metrics.
+        "disable_log_stats": False,
+
+        # Precision
+        "dtype": PRECISION,
+
+        # Main model
+        "model_name": QUANT_MODEL,
+        "gpu_memory_utilization": 0.85
+    }])
+@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
+@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
+@pytest.mark.parametrize("test_llm_kwargs", [
+    {
+        "speculative_config": {
+            "num_speculative_tokens": MAX_SPEC_TOKENS,
+        },
+    },
+])
+@pytest.mark.parametrize("output_len", [
+    128,
+])
+@pytest.mark.parametrize("batch_size", [1, 32])
+@pytest.mark.parametrize("seed", [1])
+def test_mtp_e2e_quant_greedy_correctness_torchair_graph(
+        vllm_runner, common_llm_kwargs, per_test_common_llm_kwargs,
+        baseline_llm_kwargs, test_llm_kwargs, batch_size: int, output_len: int,
+        seed: int):
+    """Verify greedy equality with torchair graph enabled and different
+    batch sizes using quant weights."""
+    run_equality_correctness_test(vllm_runner, common_llm_kwargs,
+                                  per_test_common_llm_kwargs,
+                                  baseline_llm_kwargs, test_llm_kwargs,
+                                  batch_size, output_len, seed)
+
+
+@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
+                    reason="mtp is not supported on v1")
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
@@ -221,7 +319,7 @@ def test_mtp_e2e_greedy_correctness_cuda_graph(vllm_runner, common_llm_kwargs,
         "dtype": PRECISION,
 
         # Main model
-        "model_name": MAIN_MODEL,
+        "model_name": FLOAT_MODEL,
 
         # GPU memory utilization
         "gpu_memory_utilization": 0.9
@@ -256,6 +354,8 @@ def test_mtp_e2e_greedy_correctness_with_preemption(
                                   batch_size, output_len, seed)
 
 
+@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
+                    reason="mtp is not supported on v1")
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
@@ -266,7 +366,7 @@ def test_mtp_e2e_greedy_correctness_with_preemption(
         "dtype": PRECISION,
 
         # Main model
-        "model_name": MAIN_MODEL,
+        "model_name": FLOAT_MODEL,
 
         # GPU memory utilization
         "gpu_memory_utilization": 0.9
@@ -305,6 +405,8 @@ def test_mtp_different_k(vllm_runner, common_llm_kwargs,
                                   batch_size, output_len, seed)
 
 
+@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
+                    reason="mtp is not supported on v1")
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
@@ -315,7 +417,7 @@ def test_mtp_different_k(vllm_runner, common_llm_kwargs,
         "dtype": PRECISION,
 
         # Main model
-        "model_name": MAIN_MODEL,
+        "model_name": FLOAT_MODEL,
 
         # GPU memory utilization
         "gpu_memory_utilization": 0.9

tests/singlecard/spec_decode/test_dynamic_spec_decode.py

@@ -29,7 +29,6 @@ from vllm.spec_decode.top1_proposer import Top1Proposer
 
 from tests.singlecard.spec_decode.test_utils import mock_spec_decode_sampler
 from tests.singlecard.spec_decode.utils import create_batch, mock_worker
-from vllm_ascend.patch.worker import patch_common  # noqa: F401
 
 
 @pytest.mark.parametrize('queue_size', [4])

tests/singlecard/spec_decode/test_multi_step_worker.py

@@ -33,7 +33,6 @@ from tests.singlecard.spec_decode.utils import (
     assert_logprobs_dict_allclose, create_batch,
     create_seq_group_metadata_from_prompts, create_worker,
     patch_execute_model_with_seeds, zero_kv_cache)
-from vllm_ascend.patch.worker import patch_common  # noqa: F401
 from vllm_ascend.worker.draft_model_runner import TP1DraftModelRunner
 from vllm_ascend.worker.worker import NPUWorker
 

tests/singlecard/spec_decode/test_ngram_worker.py

@@ -24,7 +24,6 @@ from vllm.spec_decode.top1_proposer import Top1Proposer
 
 from tests.singlecard.spec_decode.utils import (
     create_seq_group_metadata_from_prompts, create_worker)
-from vllm_ascend.patch.worker import patch_common  # noqa: F401
 
 
 def test_ngram_algo_correctness_for_single_no_match():

tests/singlecard/spec_decode/test_spec_decode_worker.py

@@ -39,8 +39,6 @@ from tests.singlecard.spec_decode.test_utils import mock_spec_decode_sampler
 from tests.singlecard.spec_decode.utils import (create_batch,
                                                 create_sampler_output_list,
                                                 create_worker, mock_worker)
-# patch SpecDecodeWorker, AsyncMetricsCollector
-from vllm_ascend.patch.worker import patch_common  # noqa: F401
 from vllm_ascend.worker.draft_model_runner import TP1DraftModelRunner
 from vllm_ascend.worker.worker import NPUWorker