adapt to sglang v0.5.2rc1 on dcu

sgl-kernel/tests/test_cutlass_mla.py

@@ -0,0 +1,103 @@
+import pytest
+import torch
+import torch.nn.functional as F
+from sgl_kernel import cutlass_mla_decode, cutlass_mla_get_workspace_size
+from torch import Tensor
+
+if torch.cuda.get_device_capability() < (10, 0):
+    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", [1, 16, 64, 128])
+@pytest.mark.parametrize("num_heads", [16, 32, 64, 128])
+@pytest.mark.parametrize("num_kv_splits", [-1, 1])
+def test_cutlass_mla_decode(
+    dtype: torch.dtype,
+    mean_seq_len: int,
+    bs: int,
+    varlen: bool,
+    block_size: int,
+    num_heads: int,
+    num_kv_splits: int,
+):
+    torch.set_default_dtype(dtype)
+    torch.set_default_device("cuda")
+    torch.manual_seed(42)
+
+    d = 576
+    h_q = num_heads
+    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
+
+    # Lager q values to detect split kv error
+    q = torch.randn(bs, h_q, d) * 100.0
+    block_table = torch.randint(0, bs * block_num, (bs, block_num), dtype=torch.int32)
+
+    kv_cache = torch.randn(block_table.numel(), block_size, d)
+
+    workspace_size = cutlass_mla_get_workspace_size(
+        block_num * block_size, bs, num_kv_splits=num_kv_splits
+    )
+    workspace = torch.empty(workspace_size, device="cuda", dtype=torch.uint8)
+
+    q_nope = torch.empty((h_q, bs, dv)).transpose(0, 1)
+    q_nope.copy_(q[:, :, :dv])
+    q_pe = q[:, :, dv:].clone()
+
+    out_ref = q.new_zeros(bs, h_q, dv)
+    ref_mla(out_ref, q, kv_cache, scale, block_table, seq_lens)
+    out = cutlass_mla_decode(
+        q_nope, q_pe, kv_cache, seq_lens, block_table, workspace, scale, num_kv_splits
+    )
+
+    torch.testing.assert_close(out, out_ref, atol=1e-2, rtol=1e-2)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])