vllm-project--vllm/tests/kernels/attention/test_flashinfer_mla_decode.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
import torch.nn.functional as F
from torch import Tensor

from vllm.platforms import current_platform

FLASHINFER_WORKSPACE_BUFFER_SIZE = 128 * 1024 * 1024

if not current_platform.has_device_capability(100):
    pytest.skip(
        reason="FlashInfer MLA Requires compute capability of 10 or above.",
        allow_module_level=True,
    )
else:
    from flashinfer.decode import trtllm_batch_decode_with_kv_cache_mla

# Deepseek R1 MLA config.
NUM_HEADS = 128
KV_LORA_RANK = 512
QK_NOPE_HEAD_DIM = 128
QK_ROPE_HEAD_DIM = 64
QK_HEAD_DIM = KV_LORA_RANK + QK_ROPE_HEAD_DIM
SCALE = (QK_NOPE_HEAD_DIM + QK_ROPE_HEAD_DIM) ** -0.5


def _make_decode_inputs(bs: int, block_size: int, dtype: torch.dtype):
    """Build valid trtllm MLA decode inputs on the current CUDA device."""
    max_seq_len_cap = 1024
    seq_lens = [torch.randint(2, max_seq_len_cap, (1,)).item() for _ in range(bs)]
    seq_lens[-1] = max_seq_len_cap
    max_seq_len = max(seq_lens)
    seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int32)

    # Generate block tables with random but unique block IDs
    # From https://github.com/flashinfer-ai/flashinfer/pull/1222
    blocks_per_seq = (seq_lens_tensor + block_size - 1) // block_size
    max_num_blocks_per_seq = max(blocks_per_seq.max().item(), 4)
    total_blocks_needed = int(sum(blocks_per_seq))
    all_block_ids = torch.randperm(total_blocks_needed)

    block_tables = torch.zeros((bs, max_num_blocks_per_seq), dtype=torch.int32)
    block_id = 0
    for i in range(bs):
        num_blocks_needed = blocks_per_seq[i]
        block_tables[i, :num_blocks_needed] = all_block_ids[
            block_id : block_id + num_blocks_needed
        ]
        block_id += num_blocks_needed

    kv_cache = torch.randn(block_tables.numel(), block_size, QK_HEAD_DIM).to(dtype)
    q = torch.randn(bs, NUM_HEADS, QK_HEAD_DIM).to(dtype)
    return q, kv_cache, block_tables, seq_lens_tensor, max_seq_len


def ref_mla(
    out: Tensor,  # (bs, num_heads, v_head_dim)
    query: Tensor,  # (bs, num_heads, head_dim)
    kv_cache: Tensor,  # (num_blocks, block_size, head_dim)
    scale: float,
    block_tables: Tensor,  # (bs, max_num_blocks)
    seq_lens: Tensor,  # (bs,)
):
    bs, num_heads, v_head_dim = out.shape
    head_dim = query.shape[2]

    for i in range(bs):
        # gather and flatten KV-cache
        kv = kv_cache[block_tables[i]]  # (max_num_blocks, block_size, head_dim)
        kv = kv.view(1, -1, head_dim)[:, : seq_lens[i]]  # (1, seq_len, head_dim)
        v = kv[:, :, :v_head_dim]

        q = query[i].view(num_heads, 1, head_dim)
        o = F.scaled_dot_product_attention(q, kv, v, scale=scale, enable_gqa=True)
        out[i] = o.view(num_heads, v_head_dim)

    return out


@pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("bs", [1, 2, 4, 16])
@pytest.mark.parametrize("block_size", [32, 64])
def test_flashinfer_mla_decode(dtype: torch.dtype, bs: int, block_size: int):
    torch.set_default_device("cuda")
    torch.manual_seed(42)

    q, kv_cache, block_tables, seq_lens_tensor, max_seq_len = _make_decode_inputs(
        bs, block_size, dtype
    )

    out_ref = q.new_zeros(bs, NUM_HEADS, KV_LORA_RANK)
    ref_mla(out_ref, q, kv_cache, SCALE, block_tables, seq_lens_tensor)

    workspace_buffer = torch.zeros(
        FLASHINFER_WORKSPACE_BUFFER_SIZE,
        dtype=torch.uint8,
        device=q.device,
    )
    # Flashinfer MLA expects the query to be of shape
    # (bs, q_len_per_request, num_heads, qk_head_dim),
    # where q_len_per_request is the MTP query length (=1 without MTP)
    q = q.unsqueeze(1)

    out_ans = trtllm_batch_decode_with_kv_cache_mla(
        query=q,
        kv_cache=kv_cache.unsqueeze(1),
        workspace_buffer=workspace_buffer,
        qk_nope_head_dim=QK_NOPE_HEAD_DIM,
        kv_lora_rank=KV_LORA_RANK,
        qk_rope_head_dim=QK_ROPE_HEAD_DIM,
        block_tables=block_tables,
        seq_lens=seq_lens_tensor,
        max_seq_len=max_seq_len,
        bmm1_scale=SCALE,
    )
    out_ans = out_ans.squeeze(1)
    torch.testing.assert_close(out_ans, out_ref, atol=1e-2, rtol=1e-2)


def test_flashinfer_mla_decode_workspace_supports_autotune():
    """vLLM's FlashInfer MLA decode workspace must be int8 for autotuning.

    Model Runner V2's warmup autotunes ``trtllm_batch_decode_mla``, which makes
    the FlashInfer autotuner enumerate the CuteDSL tactic. That tactic asserts
    ``workspace_buffer.dtype == torch.int8``; the trtllm-gen path (used for
    normal, non-autotuned inference) instead views the buffer as uint8, so a
    uint8 workspace only fails once the autotuner tries CuteDSL. That regressed
    every DeepSeek MLA test on Blackwell under V2 with
    ``workspace_buffer must be torch.int8`` (vllm-project/vllm#46646).
    """
    from flashinfer.autotuner import autotune

    from vllm.v1.attention.backends.mla.flashinfer_mla import _get_workspace_buffer

    torch.set_default_device("cuda")
    torch.manual_seed(0)

    workspace_buffer = _get_workspace_buffer(return_lse=False)
    assert workspace_buffer.dtype == torch.int8

    q, kv_cache, block_tables, seq_lens_tensor, max_seq_len = _make_decode_inputs(
        bs=1, block_size=64, dtype=torch.bfloat16
    )

    # Under the autotuner the CuteDSL tactic is instantiated with our workspace;
    # a uint8 buffer raises AssertionError here, an int8 buffer succeeds.
    with torch.inference_mode(), autotune(True):
        trtllm_batch_decode_with_kv_cache_mla(
            query=q.unsqueeze(1),
            kv_cache=kv_cache.unsqueeze(1),
            workspace_buffer=workspace_buffer,
            qk_nope_head_dim=QK_NOPE_HEAD_DIM,
            kv_lora_rank=KV_LORA_RANK,
            qk_rope_head_dim=QK_ROPE_HEAD_DIM,
            block_tables=block_tables,
            seq_lens=seq_lens_tensor,
            max_seq_len=max_seq_len,
            bmm1_scale=SCALE,
        )