[ROCm] [V1] [SpecDec] Enable Speculative Decoding on ROCm V1 Engine

tests/utils.py

@@ -986,3 +986,19 @@ def has_module_attribute(module_name, attribute_name):
         return hasattr(module, attribute_name)
     except ImportError:
         return False
+
+
+def get_attn_backend_list_based_on_platform() -> list[str]:
+    if current_platform.is_cuda():
+        return ["FLASH_ATTN_VLLM_V1", "TRITON_ATTN_VLLM_V1"]
+    elif current_platform.is_rocm():
+        attn_backend_list = ["TRITON_ATTN_VLLM_V1"]
+        try:
+            import aiter  # noqa: F401
+            attn_backend_list.append("FLASH_ATTN_VLLM_V1")
+        except Exception:
+            print("Skip FLASH_ATTN_VLLM_V1 on ROCm as aiter is not installed")
+
+        return attn_backend_list
+    else:
+        raise ValueError("Unsupported platform")

tests/v1/e2e/test_spec_decode.py

@@ -8,10 +8,12 @@
 import pytest
 import torch
 
+from tests.utils import get_attn_backend_list_based_on_platform
 from vllm import LLM, SamplingParams
 from vllm.assets.base import VLLM_S3_BUCKET_URL
 from vllm.assets.image import VLM_IMAGES_DIR
 from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.platforms import current_platform
 
 
 def get_test_prompts(mm_enabled: bool):
@@ -141,11 +143,14 @@ def test_ngram_correctness(
             marks=pytest.mark.skip(reason="Skipping due to CI OOM issues")),
     ],
     ids=["llama3_eagle", "llama3_eagle3", "llama4_eagle", "llama4_eagle_mm"])
+@pytest.mark.parametrize("attn_backend",
+                         get_attn_backend_list_based_on_platform())
 def test_eagle_correctness(
     monkeypatch: pytest.MonkeyPatch,
     sampling_config: SamplingParams,
     model_setup: tuple[str, str, str, int],
     mm_enabled: bool,
+    attn_backend: str,
 ):
     # Generate test prompts inside the function instead of using fixture
     test_prompts = get_test_prompts(mm_enabled)
@@ -156,6 +161,16 @@ def test_eagle_correctness(
     '''
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "1")
+        m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
+
+        if (attn_backend == "TRITON_ATTN_VLLM_V1"
+                and not current_platform.is_rocm()):
+            pytest.skip("TRITON_ATTN_VLLM_V1 does not support "
+                        "multi-token eagle spec decode on current platform")
+
+        if attn_backend == "FLASH_ATTN_VLLM_V1" and current_platform.is_rocm():
+            m.setenv("VLLM_ROCM_USE_AITER", "1")
+
         method, model_name, spec_model_name, tp_size = model_setup
 
         ref_llm = LLM(model=model_name,

tests/v1/spec_decode/test_eagle.py

@@ -6,6 +6,7 @@
 import pytest
 import torch
 
+from tests.utils import get_attn_backend_list_based_on_platform
 from tests.v1.attention.utils import (BatchSpec, _Backend,
                                       create_common_attn_metadata,
                                       create_standard_kv_cache_spec,
@@ -120,17 +121,28 @@ def test_prepare_inputs():
     assert torch.equal(token_indices, expected_token_indices)
 
 
-@pytest.mark.parametrize("method,proposer_helper", [
-    ("eagle", lambda k: _create_proposer("eagle", k)),
-    ("eagle3", lambda k: _create_proposer("eagle3", k)),
-])
+@pytest.mark.parametrize("method", ["eagle", "eagle3"])
+@pytest.mark.parametrize("attn_backend",
+                         get_attn_backend_list_based_on_platform())
 @pytest.mark.parametrize("pp_size", [1, 2])
 @pytest.mark.parametrize("use_distinct_embed_tokens", [True, False])
 @mock.patch('vllm.v1.spec_decode.eagle.get_pp_group')
 @mock.patch('vllm.v1.spec_decode.eagle.get_layers_from_vllm_config')
 @mock.patch('vllm.v1.spec_decode.eagle.get_model')
 def test_load_model(mock_get_model, mock_get_layers, mock_get_pp_group, method,
-                    proposer_helper, pp_size, use_distinct_embed_tokens):
+                    attn_backend, pp_size, use_distinct_embed_tokens,
+                    monkeypatch):
+
+    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
+
+    if (attn_backend == "TRITON_ATTN_VLLM_V1"
+            and not current_platform.is_rocm()):
+        pytest.skip("TRITON_ATTN_VLLM_V1 does not support "
+                    "multi-token eagle spec decode on current platform")
+
+    if attn_backend == "FLASH_ATTN_VLLM_V1" and current_platform.is_rocm():
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
+
     # Setup draft model mock
     mock_model = mock.MagicMock()
     if use_distinct_embed_tokens:
@@ -177,7 +189,7 @@ class _TargetModelStub(LlamaForCausalLM):
         target_model.lm_head = mock.MagicMock()
 
     # Create proposer using the helper function
-    proposer = proposer_helper(k=8)
+    proposer = _create_proposer(method, k=8)
 
     # Call the method under test
     proposer.load_model(target_model)
@@ -201,8 +213,22 @@ class _TargetModelStub(LlamaForCausalLM):
             target_model.model.embed_tokens
 
 
+@pytest.mark.parametrize("method", ["eagle", "eagle3"])
+@pytest.mark.parametrize("attn_backend",
+                         get_attn_backend_list_based_on_platform())
 @pytest.mark.parametrize("num_speculative_tokens", [1, 3, 8])
-def test_propose(num_speculative_tokens):
+def test_propose(method, attn_backend, num_speculative_tokens, monkeypatch):
+
+    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
+
+    if (attn_backend == "TRITON_ATTN_VLLM_V1"
+            and not current_platform.is_rocm()):
+        pytest.skip("TRITON_ATTN_VLLM_V1 does not support "
+                    "multi-token eagle spec decode on current platform")
+
+    if attn_backend == "FLASH_ATTN_VLLM_V1" and current_platform.is_rocm():
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
+
     # Use GPU device
     device = torch.device(current_platform.device_type)
 
@@ -301,8 +327,15 @@ def create_deterministic_logits(token_ids):
                                    device=device)
     sampling_metadata = mock.MagicMock()
 
-    attn_metadata_builder_cls, _ = get_attention_backend(
-        _Backend.FLASH_ATTN_VLLM_V1)
+    if attn_backend == "FLASH_ATTN_VLLM_V1":
+        attn_metadata_builder_cls, _ = get_attention_backend(
+            _Backend.FLASH_ATTN_VLLM_V1)
+    elif attn_backend == "TRITON_ATTN_VLLM_V1":
+        attn_metadata_builder_cls, _ = get_attention_backend(
+            _Backend.TRITON_ATTN_VLLM_V1)
+    else:
+        raise ValueError(f"Unsupported attention backend: {attn_backend}")
+
     attn_metadata_builder = attn_metadata_builder_cls(
         kv_cache_spec=create_standard_kv_cache_spec(proposer.vllm_config),
         layer_names=proposer.attn_layer_names,

tests/v1/spec_decode/test_max_len.py

@@ -4,7 +4,9 @@
 
 import pytest
 
+from tests.utils import get_attn_backend_list_based_on_platform
 from vllm import LLM, SamplingParams
+from vllm.platforms import current_platform
 
 _PROMPTS = [
     "1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1",
@@ -14,36 +16,40 @@
 
 
 @pytest.mark.parametrize("num_speculative_tokens", [1, 3, 10])
-def test_ngram_max_len(
-    monkeypatch: pytest.MonkeyPatch,
-    num_speculative_tokens: int,
-):
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
-
-        llm = LLM(
-            model="facebook/opt-125m",
-            max_model_len=100,
-            enforce_eager=True,  # For faster initialization.
-            speculative_config={
-                "method": "ngram",
-                "prompt_lookup_max": 5,
-                "prompt_lookup_min": 3,
-                "num_speculative_tokens": num_speculative_tokens,
-            },
-        )
-        sampling_params = SamplingParams(max_tokens=100, ignore_eos=True)
-        llm.generate(_PROMPTS, sampling_params)
+def test_ngram_max_len(num_speculative_tokens: int):
+    llm = LLM(
+        model="facebook/opt-125m",
+        max_model_len=100,
+        enforce_eager=True,  # For faster initialization.
+        speculative_config={
+            "method": "ngram",
+            "prompt_lookup_max": 5,
+            "prompt_lookup_min": 3,
+            "num_speculative_tokens": num_speculative_tokens,
+        },
+    )
+    sampling_params = SamplingParams(max_tokens=100, ignore_eos=True)
+    llm.generate(_PROMPTS, sampling_params)
 
 
 @pytest.mark.parametrize("num_speculative_tokens", [1, 3, 10])
-def test_eagle_max_len(
-    monkeypatch: pytest.MonkeyPatch,
-    num_speculative_tokens: int,
-):
+@pytest.mark.parametrize("attn_backend",
+                         get_attn_backend_list_based_on_platform())
+def test_eagle_max_len(monkeypatch: pytest.MonkeyPatch,
+                       num_speculative_tokens: int, attn_backend: str):
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "1")
 
+        m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
+
+        if (attn_backend == "TRITON_ATTN_VLLM_V1"
+                and not current_platform.is_rocm()):
+            pytest.skip("TRITON_ATTN_VLLM_V1 does not support "
+                        "multi-token eagle spec decode on current platform")
+
+        if attn_backend == "FLASH_ATTN_VLLM_V1" and current_platform.is_rocm():
+            m.setenv("VLLM_ROCM_USE_AITER", "1")
+
         llm = LLM(
             model="meta-llama/Meta-Llama-3-8B-Instruct",
             enforce_eager=True,  # For faster initialization.

vllm/v1/spec_decode/eagle.py

@@ -15,8 +15,12 @@
 from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.models import supports_multimodal
 from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
+from vllm.platforms import current_platform
 from vllm.utils import is_pin_memory_available
 from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
+from vllm.v1.attention.backends.rocm_aiter_fa import (
+    AiterFlashAttentionMetadata)
+from vllm.v1.attention.backends.triton_attn import TritonAttentionMetadata
 from vllm.v1.attention.backends.utils import CommonAttentionMetadata
 from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.sample.metadata import SamplingMetadata
@@ -178,10 +182,18 @@ def propose(
         # one layer. Adapt this code to support multiple layers once
         # there's a multi-layer MTP module.
 
-        # Currently FlashAttention is the only backend that supports
-        # multi-token eagle spec decode. This is because the code below
-        # makes assumptions about attn_metadata attributes available.
-        assert isinstance(attn_metadata, FlashAttentionMetadata)
+        # On ROCm, both AiterFlashAttention and TritonAttention
+        # support multi-token eagle spec decode.
+        if current_platform.is_rocm():
+            assert isinstance(
+                attn_metadata,
+                (TritonAttentionMetadata, AiterFlashAttentionMetadata,
+                 FlashAttentionMetadata))
+        else:
+            # Currently FlashAttention is the only backend that supports
+            # multi-token eagle spec decode. This is because the code below
+            # makes assumptions about attn_metadata attributes available.
+            assert isinstance(attn_metadata, FlashAttentionMetadata)
 
         # Generate the remaining draft tokens.
         draft_token_ids_list = [draft_token_ids]