[Attention][Spec Decode] FlashMLA spec decode support

tests/v1/attention/test_mla_backends.py

@@ -1,6 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Tests for v1 MLA backends without GPUModelRunner dependency."""
+"""Tests for v1 MLA backends without GPUModelRunner dependency.
+
+Known Issues:
+- FLASH_ATTN_MLA backend occasionally produces NaN values in
+  test_backend_correctness[mixed_small] when run after
+  test_backend_correctness[small_prefill], but passes when run alone.
+"""
 
 import pytest
 import torch
@@ -14,6 +20,8 @@
 )
 from vllm import _custom_ops as ops
 from vllm.attention.backends.registry import _Backend
+from vllm.attention.ops.flashmla import is_flashmla_dense_supported
+from vllm.config.vllm import set_current_vllm_config
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv
 from vllm.v1.attention.backends.utils import CommonAttentionMetadata
 from vllm.v1.kv_cache_interface import FullAttentionSpec
@@ -29,6 +37,10 @@
 if not torch.cuda.is_available() or torch.cuda.get_device_properties(0).major < 10:
     BACKENDS_TO_TEST.remove(_Backend.CUTLASS_MLA)
 
+# Remove FLASHMLA from the list if not supported
+if not is_flashmla_dense_supported()[0]:
+    BACKENDS_TO_TEST.remove(_Backend.FLASHMLA)
+
 torch.manual_seed(42)
 
 
@@ -66,6 +78,12 @@ def _convert_dtype_to_torch(dtype):
     "large_prefill": BatchSpec(seq_lens=[4096] * 8, query_lens=[32] * 8),
     "single_decode": BatchSpec(seq_lens=[1024], query_lens=[1]),
     "single_prefill": BatchSpec(seq_lens=[1024], query_lens=[64]),
+    "spec_decode_small": BatchSpec(
+        seq_lens=[128, 256, 512, 1024], query_lens=[4, 4, 4, 4]
+    ),
+    "spec_decode_medium": BatchSpec(
+        seq_lens=[512, 1024, 2048, 512, 1024, 2048], query_lens=[8, 8, 8, 8, 8, 8]
+    ),
 }
 
 
@@ -239,61 +257,64 @@ def run_attention_backend(
 
     builder_cls, impl_cls = try_get_attention_backend(backend)
 
-    # Build metadata
-    builder = builder_cls(kv_cache_spec, layer_names, vllm_config, device)
-    attn_metadata = builder.build(
-        common_prefix_len=0,
-        common_attn_metadata=common_attn_metadata,
-    )
+    # Set the current vllm config so that get_current_vllm_config() works
+    # in the backend implementations
+    with set_current_vllm_config(vllm_config):
+        # Build metadata
+        builder = builder_cls(kv_cache_spec, layer_names, vllm_config, device)
+        attn_metadata = builder.build(
+            common_prefix_len=0,
+            common_attn_metadata=common_attn_metadata,
+        )
 
-    # Instantiate MLA implementation
-    num_heads = vllm_config.model_config.get_num_attention_heads(
-        vllm_config.parallel_config
-    )
-    num_kv_heads = vllm_config.model_config.get_num_kv_heads(
-        vllm_config.parallel_config
-    )
-    head_size = vllm_config.model_config.get_head_size()
-    scale = 1.0 / (head_size**0.5)
-    impl = impl_cls(
-        num_heads=num_heads,
-        head_size=head_size,
-        scale=scale,
-        num_kv_heads=num_kv_heads,
-        alibi_slopes=None,
-        sliding_window=None,
-        kv_cache_dtype="auto",
-        logits_soft_cap=None,
-        attn_type="decoder",
-        kv_sharing_target_layer_name=None,
-        q_lora_rank=None,
-        kv_lora_rank=kv_lora_rank,
-        qk_nope_head_dim=qk_nope_head_dim,
-        qk_rope_head_dim=qk_rope_head_dim,
-        qk_head_dim=qk_nope_head_dim + qk_rope_head_dim,
-        v_head_dim=v_head_dim,
-        kv_b_proj=mock_kv_b_proj,
-    )
+        # Instantiate MLA implementation
+        num_heads = vllm_config.model_config.get_num_attention_heads(
+            vllm_config.parallel_config
+        )
+        num_kv_heads = vllm_config.model_config.get_num_kv_heads(
+            vllm_config.parallel_config
+        )
+        head_size = vllm_config.model_config.get_head_size()
+        scale = 1.0 / (head_size**0.5)
+        impl = impl_cls(
+            num_heads=num_heads,
+            head_size=head_size,
+            scale=scale,
+            num_kv_heads=num_kv_heads,
+            alibi_slopes=None,
+            sliding_window=None,
+            kv_cache_dtype="auto",
+            logits_soft_cap=None,
+            attn_type="decoder",
+            kv_sharing_target_layer_name=None,
+            q_lora_rank=None,
+            kv_lora_rank=kv_lora_rank,
+            qk_nope_head_dim=qk_nope_head_dim,
+            qk_rope_head_dim=qk_rope_head_dim,
+            qk_head_dim=qk_nope_head_dim + qk_rope_head_dim,
+            v_head_dim=v_head_dim,
+            kv_b_proj=mock_kv_b_proj,
+        )
 
-    # Process weights to create W_UK_T and W_UV attributes needed by MLA
-    act_dtype = _convert_dtype_to_torch(vllm_config.model_config.dtype)
-    impl.process_weights_after_loading(act_dtype)
+        # Process weights to create W_UK_T and W_UV attributes needed by MLA
+        act_dtype = _convert_dtype_to_torch(vllm_config.model_config.dtype)
+        impl.process_weights_after_loading(act_dtype)
 
-    # Create mock layer and output buffer
-    mock_layer = MockAttentionLayer(device)
-    num_tokens = query.shape[0]
-    output = torch.empty(
-        num_tokens, num_heads * v_head_dim, dtype=query.dtype, device=query.device
-    )
+        # Create mock layer and output buffer
+        mock_layer = MockAttentionLayer(device)
+        num_tokens = query.shape[0]
+        output = torch.empty(
+            num_tokens, num_heads * v_head_dim, dtype=query.dtype, device=query.device
+        )
 
-    # Run forward pass
-    # NOTE: The query, key, and value are already shaped correctly
-    # in the calling test function.
-    output = impl.forward(
-        mock_layer, query, kv_c, k_pe, kv_cache, attn_metadata, output=output
-    )
+        # Run forward pass
+        # NOTE: The query, key, and value are already shaped correctly
+        # in the calling test function.
+        output = impl.forward(
+            mock_layer, query, kv_c, k_pe, kv_cache, attn_metadata, output=output
+        )
 
-    return output
+        return output
 
 
 @pytest.mark.parametrize(
@@ -309,6 +330,8 @@ def run_attention_backend(
         "large_prefill",
         "single_decode",
         "single_prefill",
+        "spec_decode_small",
+        "spec_decode_medium",
     ],
 )
 @pytest.mark.parametrize("model", ["deepseek-ai/DeepSeek-V2-Lite-Chat"])
@@ -328,10 +351,39 @@ def test_backend_correctness(dist_init, batch_spec_name: str, model: str):
        simulated paged KV cache.
     5. Comparing the vLLM backend's output to the ground-truth SDPA output.
     """
+    from vllm.v1.attention.backends.mla.common import QueryLenSupport
+
     batch_spec = BATCH_SPECS[batch_spec_name]
+    is_spec_decode_test = batch_spec_name.startswith("spec_decode")
+    spec_decode_backends = {_Backend.FLASH_ATTN_MLA, _Backend.FLASHMLA}
+
+    block_size = 16
+    required_blocks = sum(
+        (seq_len + block_size - 1) // block_size for seq_len in batch_spec.seq_lens
+    )
+    # Add 1 for null block at index 0, and some buffer
+    num_gpu_blocks = required_blocks + 1 + 100
+
     vllm_config = create_vllm_config(
-        model_name=model, max_model_len=max(batch_spec.seq_lens), num_gpu_blocks=2048
+        model_name=model,
+        max_model_len=max(batch_spec.seq_lens),
+        num_gpu_blocks=num_gpu_blocks,
+        block_size=block_size,
     )
+
+    # For spec decode tests, add a speculative_config to set the reorder_batch_threshold
+    if is_spec_decode_test:
+        from vllm.config import SpeculativeConfig
+
+        # Get the query length from the batch spec (they should all be uniform)
+        query_len = batch_spec.query_lens[0]
+        # Set num_speculative_tokens to query_len - 1
+        # (since threshold is 1 + num_spec_tokens)
+        # Use ngram method which doesn't require a draft model
+        vllm_config.speculative_config = SpeculativeConfig(
+            method="ngram", num_speculative_tokens=query_len - 1
+        )
+
     device = torch.device("cuda:0")
 
     kv_cache_spec = create_standard_kv_cache_spec(vllm_config)
@@ -395,11 +447,37 @@ def test_backend_correctness(dist_init, batch_spec_name: str, model: str):
         # K_PE (rope component): [s_len, 1, qk_rope_head_dim]
         k_pe_full = torch.randn(s_len, 1, qk_rope_head_dim, dtype=dtype, device=device)
 
-        # Determine if this is decode or prefill
+        # Determine if this sequence uses the decode pipeline or prefill
+        # pipeline for each backend
+        # NOTE: For spec decode tests with uniform query_len > 1, backends that
+        # support spec decode (FLASH_ATTN_MLA with varlen support, FLASHMLA with
+        # uniform support) will use the decode pipeline (MQA-style), while
+        # backends that only support single-token queries will use the prefill
+        # pipeline (MHA-style). This ensures the reference implementation
+        # matches each backend's actual decode/prefill pipeline path.
         is_decode = []
-        for i, backend in enumerate(BACKENDS_TO_TEST):
+        for backend_idx, backend in enumerate(BACKENDS_TO_TEST):
             builder_cls, _ = try_get_attention_backend(backend)
-            is_decode.append(q_len <= builder_cls.reorder_batch_threshold)
+            if is_spec_decode_test:
+                query_len_support = getattr(
+                    builder_cls, "query_len_support", QueryLenSupport.SINGLE_ONLY
+                )
+                supports_spec = query_len_support != QueryLenSupport.SINGLE_ONLY
+                is_decode.append(supports_spec)
+            else:
+                threshold = getattr(builder_cls, "reorder_batch_threshold", None)
+                query_len_support = getattr(
+                    builder_cls, "query_len_support", QueryLenSupport.SINGLE_ONLY
+                )
+                within_threshold = q_len <= threshold if threshold else False
+                if (
+                    within_threshold
+                    and query_len_support == QueryLenSupport.UNIFORM
+                    and i > 0
+                ):
+                    first_q_len = query_lens[0]
+                    within_threshold = q_len == first_q_len
+                is_decode.append(within_threshold)
 
         # Split q into nope and rope components
         q_nope, q_pe = q_c.split([qk_nope_head_dim, qk_rope_head_dim], dim=-1)
@@ -478,11 +556,11 @@ def test_backend_correctness(dist_init, batch_spec_name: str, model: str):
         sdpa_out_i_prefill = sdpa_out_i_prefill.transpose(1, 2).squeeze(0)
         sdpa_out_i_prefill = sdpa_out_i_prefill.flatten(start_dim=-2)
 
-        for i, backend in enumerate(BACKENDS_TO_TEST):
-            if is_decode[i]:
-                all_sdpa_outputs[i].append(sdpa_out_i_decode)
+        for backend_idx, backend in enumerate(BACKENDS_TO_TEST):
+            if is_decode[backend_idx]:
+                all_sdpa_outputs[backend_idx].append(sdpa_out_i_decode)
             else:
-                all_sdpa_outputs[i].append(sdpa_out_i_prefill)
+                all_sdpa_outputs[backend_idx].append(sdpa_out_i_prefill)
 
         # Inputs for vLLM MLA backends are just the new tokens
         all_q_vllm.append(q_c)
@@ -497,9 +575,9 @@ def test_backend_correctness(dist_init, batch_spec_name: str, model: str):
     query_vllm = torch.cat(all_q_vllm, dim=0)
     kv_c_vllm = torch.cat(all_kv_c_vllm, dim=0)
     k_pe_vllm = torch.cat(all_k_pe_vllm, dim=0)
-    sdpa_outputs = []
-    for i, backend in enumerate(BACKENDS_TO_TEST):
-        sdpa_outputs.append(torch.cat(all_sdpa_outputs[i], dim=0))
+    sdpa_outputs = {}
+    for backend_idx, backend in enumerate(BACKENDS_TO_TEST):
+        sdpa_outputs[backend] = torch.cat(all_sdpa_outputs[backend_idx], dim=0)
 
     # Create mock kv_b_proj using the same weights as reference implementation
     from vllm.model_executor.layers.linear import ColumnParallelLinear
@@ -516,7 +594,7 @@ def test_backend_correctness(dist_init, batch_spec_name: str, model: str):
     kv_b_proj_weight = kv_b_proj_weight.view(
         kv_lora_rank, num_q_heads * (qk_nope_head_dim + v_head_dim)
     )
-    mock_kv_b_proj.weight = torch.nn.Parameter(kv_b_proj_weight.T)
+    mock_kv_b_proj.weight = torch.nn.Parameter(kv_b_proj_weight.T, requires_grad=False)
 
     # Create metadata using original batch spec
     common_attn_metadata = create_common_attn_metadata(
@@ -537,7 +615,11 @@ def test_backend_correctness(dist_init, batch_spec_name: str, model: str):
     )
 
     # 4. Run vLLM backends and compare
-    for i, backend_name in enumerate(BACKENDS_TO_TEST):
+    for backend_idx, backend_name in enumerate(BACKENDS_TO_TEST):
+        # Skip backends that don't support spec decode for spec decode tests
+        if is_spec_decode_test and backend_name not in spec_decode_backends:
+            continue
+
         backend_output = run_attention_backend(
             backend_name,
             kv_cache_spec,
@@ -556,14 +638,17 @@ def test_backend_correctness(dist_init, batch_spec_name: str, model: str):
             mock_kv_b_proj,
         )
 
+        # Use backend_idx to get the correct SDPA output for this backend
+        expected_output = sdpa_outputs[backend_name]
+
         # Check shape and dtype consistency
-        assert backend_output.shape == sdpa_outputs[i].shape, (
+        assert backend_output.shape == expected_output.shape, (
             f"[{backend_name}] shape {backend_output.shape} != "
-            f"SDPA shape {sdpa_outputs[i].shape}"
+            f"SDPA shape {expected_output.shape}"
         )
-        assert backend_output.dtype == sdpa_outputs[i].dtype, (
+        assert backend_output.dtype == expected_output.dtype, (
             f"[{backend_name}] dtype {backend_output.dtype} != "
-            f"SDPA dtype {sdpa_outputs[i].dtype}"
+            f"SDPA dtype {expected_output.dtype}"
         )
 
         assert torch.isfinite(backend_output).all(), (
@@ -574,12 +659,12 @@ def test_backend_correctness(dist_init, batch_spec_name: str, model: str):
         rtol = 1e-2
         atol = 5e-1
 
-        max_diff = torch.max(torch.abs(backend_output - sdpa_outputs[i])).item()
+        max_diff = torch.max(torch.abs(backend_output - expected_output)).item()
         max_rel_diff = torch.max(
-            torch.abs(backend_output - sdpa_outputs[i]) / torch.abs(sdpa_outputs[i])
+            torch.abs(backend_output - expected_output) / torch.abs(expected_output)
         ).item()
         all_close = torch.allclose(
-            backend_output, sdpa_outputs[i], rtol=rtol, atol=atol
+            backend_output, expected_output, rtol=rtol, atol=atol
         )
 
         assert all_close, (