[NVIDIA] Support Cutlass MLA for Blackwell GPUs (#16032)

Signed-off-by: kaixih <kaixih@nvidia.com>

tests/kernels/test_cutlass_mla_decode.py

@@ -0,0 +1,93 @@
+# SPDX-License-Identifier: Apache-2.0
+import pytest
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+import vllm._custom_ops as ops
+from vllm.platforms import current_platform
+
+if not current_platform.has_device_capability(100):
+    pytest.skip(
+        reason="Cutlass MLA Requires compute capability of 10 or above.",
+        allow_module_level=True)
+
+
+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, torch.float16])
+@pytest.mark.parametrize("mean_seq_len", [128, 1024, 4096])
+@pytest.mark.parametrize("bs", [1, 2, 4])
+@pytest.mark.parametrize("varlen", [False, True])
+@pytest.mark.parametrize("block_size", [16, 64, 128])
+def test_cutlass_mla_decode(dtype: torch.dtype, mean_seq_len: int, bs: int,
+                            varlen: bool, block_size: int):
+    torch.set_default_dtype(dtype)
+    torch.set_default_device('cuda')
+    torch.manual_seed(42)
+
+    d = 576
+    h_q = 128
+    dv = 512
+
+    q_nope_dim = 128
+    q_pe_dim = 64
+    scale = (q_nope_dim + q_pe_dim)**(-0.5)
+    if varlen:
+        seq_lens = torch.empty(bs).normal_(mean_seq_len, mean_seq_len / 2)
+        seq_lens = seq_lens.clip(2).to(torch.int32)
+    else:
+        seq_lens = torch.full((bs, ), mean_seq_len, dtype=torch.int32)
+    max_seq_len = seq_lens.max().item()
+    block_num = (max_seq_len + block_size - 1) // block_size
+
+    # Pad block_num so that small blocks can be packed into full 128-sized
+    # CUTLASS tiles. One 128-wide tile can hold (128 // block_size) small
+    # blocks.
+    pack_factor = 128 // block_size
+    block_num = ((block_num + pack_factor - 1) // pack_factor) * pack_factor
+
+    q = torch.randn(bs, h_q, d)
+    block_table = torch.randint(0,
+                                bs * block_num, (bs, block_num),
+                                dtype=torch.int32)
+
+    kv_cache = torch.randn(block_table.numel(), block_size, d)
+
+    out_ref = q.new_zeros(bs, h_q, dv)
+    ref_mla(out_ref, q, kv_cache, scale, block_table, seq_lens)
+    out_ans = torch.zeros_like(out_ref)
+    q_nope = q[:, :, :dv].clone()
+    q_pe = q[:, :, dv:].clone()
+    ops.cutlass_mla_decode(out_ans, q_nope, q_pe, kv_cache, seq_lens,
+                           block_table, scale)
+
+    torch.testing.assert_close(out_ans, out_ref, atol=1e-2, rtol=1e-2)