feat: add mtp ut and fix some bugs (#2453)

### What this PR does / why we need it?
Fix mtp mode ut

### Does this PR introduce _any_ user-facing change?
Nothing

### How was this patch tested?
This can be tested in the same way as a unit test.


- vLLM version: v0.10.0
- vLLM main:
53415653ff

Signed-off-by: 赵江江 <zhaojiangjiang1@h-partners.com>
Co-authored-by: 赵江江 <zhaojiangjiang1@h-partners.com>

tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py

@@ -1,43 +1,13 @@
 from __future__ import annotations
 
-import random
-from typing import Any
+import os
 
 import pytest
-from vllm import LLM, SamplingParams
+from vllm import SamplingParams
 
+from tests.e2e.conftest import VllmRunner
 
-@pytest.fixture
-def test_prompts():
-    prompt_types = ["repeat", "sentence"]
-    num_prompts = 10
-    prompts = []
-
-    random.seed(0)
-    random_prompt_type_choices = random.choices(prompt_types, k=num_prompts)
-
-    # Generate a mixed batch of prompts, some of which can be easily
-    # predicted by n-gram matching and some which likely cannot.
-    for kind in random_prompt_type_choices:
-        word_choices = ["test", "temp", "hello", "where"]
-        word = random.choice(word_choices)
-        if kind == "repeat":
-            prompt = f"""
-            please repeat the word '{word}' 10 times.
-            give no other output than the word at least ten times in a row,
-            in lowercase with spaces between each word and without quotes.
-            """
-        elif kind == "sentence":
-            prompt = f"""
-            please give a ten-word sentence that
-            uses the word {word} at least once.
-            give no other output than that simple sentence without quotes.
-            """
-        else:
-            raise ValueError(f"Unknown prompt type: {kind}")
-        prompts.append([{"role": "user", "content": prompt}])
-
-    return prompts
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 
 @pytest.fixture
@@ -50,39 +20,56 @@ def model_name():
     return "wemaster/deepseek_mtp_main_random_bf16"
 
 
-@pytest.mark.skipif(
-    True, reason="TODO: Enable me after test_mtp_correctness is fixed")
 def test_mtp_correctness(
-    test_prompts: list[list[dict[str, Any]]],
     sampling_config: SamplingParams,
     model_name: str,
 ):
+    example_prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
     '''
     Compare the outputs of a original LLM and a speculative LLM
     should be the same when using mtp speculative decoding.
     '''
-    ref_llm = LLM(model=model_name, max_model_len=256, enforce_eager=True)
-    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
-    del ref_llm
+    with VllmRunner(model_name,
+                    tensor_parallel_size=1,
+                    gpu_memory_utilization=0.7,
+                    max_model_len=256,
+                    enforce_eager=True) as ref_llm:
+        ref_outputs = ref_llm.generate(example_prompts, sampling_config)
+
+    with VllmRunner(
+            model_name,
+            tensor_parallel_size=1,
+            max_num_seqs=256,
+            gpu_memory_utilization=0.7,
+            distributed_executor_backend="mp",
+            enable_expert_parallel=True,
+            speculative_config={
+                "method": "deepseek_mtp",
+                "num_speculative_tokens": 1,
+            },
+            enforce_eager=True,
+            max_model_len=2000,
+            additional_config={"ascend_scheduler_config": {
+                "enabled": False
+            }}) as spec_llm:
+        spec_outputs = spec_llm.generate(example_prompts, sampling_config)
 
-    spec_llm = LLM(model=model_name,
-                   trust_remote_code=True,
-                   speculative_config={
-                       "method": "deepseek_mtp",
-                       "num_speculative_tokens": 1,
-                   },
-                   max_model_len=256,
-                   enforce_eager=True)
-    spec_outputs = spec_llm.chat(test_prompts, sampling_config)
     matches = 0
     misses = 0
     for ref_output, spec_output in zip(ref_outputs, spec_outputs):
-        if ref_output.outputs[0].text == spec_output.outputs[0].text:
+        ref_token_ids = ref_output[0][0]
+        spec_token_ids = spec_output[0][0]
+        if ref_token_ids == spec_token_ids[:len(ref_token_ids)]:
             matches += 1
         else:
             misses += 1
-            print(f"ref_output: {ref_output.outputs[0].text}")
-            print(f"spec_output: {spec_output.outputs[0].text}")
+            print(f"ref_output: {ref_output[1][0]}")
+            print(f"spec_output: {spec_output[1][0]}")
 
     # Heuristic: expect at least 66% of the prompts to match exactly
     # Upon failure, inspect the outputs to check for inaccuracy.

tests/ut/quantization/test_quant_config.py

@@ -113,6 +113,7 @@ class TestAscendQuantConfig(TestBase):
     def test_get_quant_method_for_fused_moe(self):
         fused_moe_layer = MagicMock(spec=FusedMoE)
         fused_moe_layer.moe = MagicMock(spec=FusedMoEConfig)
+        fused_moe_layer.moe_config = MagicMock(spec=FusedMoEConfig)
 
         # Test skipped layer
         with patch.object(self.ascend_config, 'is_layer_skipped_ascend', return_value=True), \

tests/ut/torchair/test_torchair_mla.py

@@ -1,11 +1,13 @@
 from unittest.mock import MagicMock, patch
 
 import torch
+from torch import nn
 from vllm.distributed.parallel_state import GroupCoordinator
 from vllm.model_executor.layers.linear import LinearBase
 
 from tests.ut.base import TestBase
 from vllm_ascend.attention.attention_v1 import AscendAttentionState
+from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
 from vllm_ascend.torchair.torchair_mla import (
     AscendMLATorchairBackend, AscendMLATorchairDecodeMetadata,
     AscendMLATorchairImpl, AscendMLATorchairMetadata,
@@ -398,6 +400,68 @@ class TestAscendMLATorchairMetadataBuilder(TestBase):
         assert torch.equal(sin_golden, metadata.decode.sin)
         assert torch.equal(cos_golden, metadata.decode.cos)
 
+    @patch("vllm_ascend.torchair.torchair_mla.get_ascend_config")
+    def test_build_decode(self, mock_ascend_config):
+        ascend_config = MagicMock()
+        mock_ascend_config.return_value = ascend_config
+        ascend_config.torchair_graph_config.enabled = False
+
+        mock_vllm_config = MagicMock()
+        mock_vllm_config.model_config.max_model_len = 1024
+        mock_vllm_config.cache_config.block_size = 16
+        mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
+        mock_vllm_config.get_head_size.return_value = 64
+        mock_vllm_config.model_config.dtype = torch.float16
+        mock_device = 'cpu'
+        model = MagicMock(spec=nn.Module)
+        model.model = MagicMock(spec=nn.Module)
+
+        builder = AscendMLATorchairMetadataBuilder(
+            mock_vllm_config,
+            mock_device,
+            metadata_cls=AscendMLATorchairMetadata)
+        builder.rope_dim = 64
+
+        builder.sin_cache = torch.tensor([10, 10])
+        builder.cos_cache = torch.tensor([10, 10])
+
+        with patch.object(builder,
+                          "_get_graph_runner_block_tables",
+                          side_effect=lambda x, y: y):
+            common_attn_metadata = AscendCommonAttentionMetadata(
+                query_start_loc=torch.tensor([0, 1, 2, 3]),
+                query_start_loc_cpu=torch.tensor([0, 1, 2, 3]),
+                seq_lens_cpu=torch.tensor([1, 1, 1]),
+                num_reqs=3,
+                num_actual_tokens=3,
+                max_query_len=1,
+                decode_token_per_req=torch.tensor([1, 1, 1]),
+                block_table_tensor=torch.zeros((10, 10)),
+                slot_mapping_cpu=torch.tensor(range(20)),
+                actual_seq_lengths_q=torch.tensor([0, 1, 2]),
+                positions=torch.tensor([1, 1]),
+                attn_mask=torch.ones((15, 15)),
+                spec_attn_mask=None,
+                attn_state=AscendAttentionState.ChunkedPrefill)
+
+            metadata = builder.build(common_attn_metadata, model)
+
+        self.assertIsInstance(metadata, AscendMLATorchairMetadata)
+        self.assertEqual(metadata.num_input_tokens, 0)
+        self.assertEqual(metadata.num_actual_tokens, 3)
+        self.assertEqual(metadata.num_decodes, 3)
+        self.assertEqual(metadata.num_decode_tokens, 3)
+        self.assertEqual(metadata.num_prefills, 0)
+        self.assertEqual(metadata.attn_state,
+                         AscendAttentionState.ChunkedPrefill)
+        self.assertIsNone(metadata.prefill)
+        self.assertIsInstance(metadata.decode, AscendMLATorchairDecodeMetadata)
+        self.assertEqual(metadata.block_tables.shape[0], 3)
+        self.assertEqual(metadata.block_tables.shape[1], 10)
+        self.assertEqual(metadata.seq_lens.shape[0], 3)
+        self.assertEqual(metadata.slot_mapping.shape[0], 3)
+        self.assertEqual(metadata.query_start_loc.shape[0], 4)
+
 
 class TestAscendMLATorchairImpl(TestBase):