feat: Add FlashMLA submodule (#4449)

Co-authored-by: sleepcoo <sleepcoo@gmail.com>

.gitmodules

@@ -10,3 +10,6 @@
 [submodule "sgl-kernel/3rdparty/deepgemm"]
 	path = sgl-kernel/3rdparty/deepgemm
 	url = https://github.com/deepseek-ai/DeepGEMM
+[submodule "sgl-kernel/3rdparty/flashmla"]
+	path = sgl-kernel/3rdparty/flashmla
+	url = https://github.com/deepseek-ai/FlashMLA

sgl-kernel/setup.py

@@ -18,6 +18,21 @@ import shutil
 import sys
 from pathlib import Path
 
+# Setup flash_mla at the top level for tests to find
+# This makes the module importable without installation
+root_dir = Path(__file__).parent.resolve()
+module_src = root_dir / "3rdparty" / "flashmla" / "flash_mla"
+module_dest = root_dir / "flash_mla"
+
+if module_src.exists() and not module_dest.exists():
+    try:
+        os.symlink(module_src, module_dest, target_is_directory=True)
+        print(f"Created symbolic link from {module_src} to {module_dest}")
+    except (OSError, NotImplementedError):
+        if module_src.exists():
+            shutil.copytree(module_src, module_dest)
+            print(f"Copied directory from {module_src} to {module_dest}")
+
 import torch
 from setuptools import find_packages, setup
 from setuptools.command.build_py import build_py
@@ -55,6 +70,7 @@ cutlass_default = root / "3rdparty" / "cutlass"
 cutlass = Path(os.environ.get("CUSTOM_CUTLASS_SRC_DIR", default=cutlass_default))
 flashinfer = root / "3rdparty" / "flashinfer"
 deepgemm = root / "3rdparty" / "deepgemm"
+flashmla = root / "3rdparty" / "flashmla"
 include_dirs = [
     root / "include",
     root / "csrc",
@@ -63,6 +79,7 @@ include_dirs = [
     flashinfer.resolve() / "include",
     flashinfer.resolve() / "include" / "gemm",
     flashinfer.resolve() / "csrc",
+    flashmla.resolve() / "csrc",
     "cublas",
 ]
 
@@ -70,6 +87,7 @@ include_dirs = [
 class CustomBuildPy(build_py):
     def run(self):
         self.copy_deepgemm_to_build_lib()
+        self.copy_flashmla_to_build_lib()
         self.make_jit_include_symlinks()
         build_py.run(self)
 
@@ -93,6 +111,17 @@ class CustomBuildPy(build_py):
                 os.unlink(dst_dir)
             os.symlink(src_dir, dst_dir, target_is_directory=True)
 
+        # Create symbolic links for FlashMLA
+        flash_mla_include_dir = os.path.join(self.build_lib, "flash_mla/include")
+        os.makedirs(flash_mla_include_dir, exist_ok=True)
+
+        # Create empty directories for FlashMLA's include paths
+        # This is safer than creating symlinks as the targets might not exist in CI
+        for dirname in ["cute", "cutlass"]:
+            dst_dir = f"{flash_mla_include_dir}/{dirname}"
+            if not os.path.exists(dst_dir):
+                os.makedirs(dst_dir, exist_ok=True)
+
     def copy_deepgemm_to_build_lib(self):
         """
         This function copies DeepGemm to python's site-packages
@@ -110,6 +139,26 @@ class CustomBuildPy(build_py):
         # Copy the directory
         shutil.copytree(src_dir, dst_dir)
 
+    def copy_flashmla_to_build_lib(self):
+        """
+        This function copies FlashMLA to python's site-packages
+        """
+        dst_dir = os.path.join(self.build_lib, "flash_mla")
+        os.makedirs(dst_dir, exist_ok=True)
+
+        src_dir = os.path.join(str(flashmla.resolve()), "flash_mla")
+
+        if not os.path.exists(src_dir):
+            print(
+                f"Warning: Source directory {src_dir} does not exist, possibly the submodule is not properly initialized"
+            )
+            return
+
+        if os.path.exists(dst_dir):
+            shutil.rmtree(dst_dir)
+
+        shutil.copytree(src_dir, dst_dir)
+
 
 nvcc_flags = [
     "-DNDEBUG",

sgl-kernel/tests/test_flash_mla.py

+import argparse
+import math
+import random
+
+import torch
+import triton
+from flash_mla import flash_mla_with_kvcache, get_mla_metadata
+
+"""
+fork FlashMLA/tests/test_flash_mla.py
+"""
+
+
+def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False):
+    query = query.float()
+    key = key.float()
+    value = value.float()
+    key = key.repeat_interleave(h_q // h_kv, dim=0)
+    value = value.repeat_interleave(h_q // h_kv, dim=0)
+    attn_weight = query @ key.transpose(-2, -1) / math.sqrt(query.size(-1))
+    if is_causal:
+        s_q = query.shape[-2]
+        s_k = key.shape[-2]
+        attn_bias = torch.zeros(s_q, s_k, dtype=query.dtype)
+        temp_mask = torch.ones(s_q, s_k, dtype=torch.bool).tril(diagonal=s_k - s_q)
+        attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
+        attn_bias.to(query.dtype)
+        attn_weight += attn_bias
+    lse = attn_weight.logsumexp(dim=-1)
+    attn_weight = torch.softmax(attn_weight, dim=-1, dtype=torch.float32)
+    return attn_weight @ value, lse
+
+
+def cal_diff(x: torch.Tensor, y: torch.Tensor, name: str) -> None:
+    x, y = x.double(), y.double()
+    RMSE = ((x - y) * (x - y)).mean().sqrt().item()
+    cos_diff = 1 - 2 * (x * y).sum().item() / max((x * x + y * y).sum().item(), 1e-12)
+    amax_diff = (x - y).abs().max().item()
+    # print(f"{name}: {cos_diff=}, {RMSE=}, {amax_diff=}")
+    assert cos_diff < 1e-5
+
+
+@torch.inference_mode()
+def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen):
+    print(
+        f"{b=}, {s_q=}, {mean_sk=}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {varlen=}"
+    )
+
+    cache_seqlens = torch.full((b,), mean_sk, dtype=torch.int32)
+    if varlen:
+        for i in range(b):
+            cache_seqlens[i] = max(random.normalvariate(mean_sk, mean_sk / 2), s_q)
+    total_seqlens = cache_seqlens.sum().item()
+    mean_seqlens = cache_seqlens.float().mean().int().item()
+    max_seqlen = cache_seqlens.max().item()
+    max_seqlen_pad = triton.cdiv(max_seqlen, 256) * 256
+    # print(f"{total_seqlens=}, {mean_seqlens=}, {max_seqlen=}")
+
+    q = torch.randn(b, s_q, h_q, d)
+    block_size = 64
+    block_table = torch.arange(
+        b * max_seqlen_pad // block_size, dtype=torch.int32
+    ).view(b, max_seqlen_pad // block_size)
+    blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d)
+    for i in range(b):
+        blocked_k.view(b, max_seqlen_pad, h_kv, d)[i, cache_seqlens[i].item() :] = (
+            float("nan")
+        )
+    blocked_v = blocked_k[..., :dv]
+
+    tile_scheduler_metadata, num_splits = get_mla_metadata(
+        cache_seqlens, s_q * h_q // h_kv, h_kv
+    )
+
+    def flash_mla():
+        return flash_mla_with_kvcache(
+            q,
+            blocked_k,
+            block_table,
+            cache_seqlens,
+            dv,
+            tile_scheduler_metadata,
+            num_splits,
+            causal=causal,
+        )
+
+    def ref_mla():
+        out = torch.empty(b, s_q, h_q, dv, dtype=torch.float32)
+        lse = torch.empty(b, h_q, s_q, dtype=torch.float32)
+        for i in range(b):
+            begin = i * max_seqlen_pad
+            end = begin + cache_seqlens[i]
+            O, LSE = scaled_dot_product_attention(
+                q[i].transpose(0, 1),
+                blocked_k.view(-1, h_kv, d)[begin:end].transpose(0, 1),
+                blocked_v.view(-1, h_kv, dv)[begin:end].transpose(0, 1),
+                h_q=h_q,
+                h_kv=h_kv,
+                is_causal=causal,
+            )
+            out[i] = O.transpose(0, 1)
+            lse[i] = LSE
+        return out, lse
+
+    out_flash, lse_flash = flash_mla()
+    out_torch, lse_torch = ref_mla()
+    cal_diff(out_flash, out_torch, "out")
+    cal_diff(lse_flash, lse_torch, "lse")
+
+    t = triton.testing.do_bench(flash_mla)
+    FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2
+    bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (
+        torch.finfo(q.dtype).bits // 8
+    )
+    print(
+        f"{t:.3f} ms, {FLOPS / 10 ** 9 / t:.0f} TFLOPS, {bytes / 10 ** 6 / t:.0f} GB/s"
+    )
+
+
+def main(torch_dtype):
+    device = torch.device("cuda:0")
+    torch.set_default_dtype(torch_dtype)
+    torch.set_default_device(device)
+    torch.cuda.set_device(device)
+    torch.manual_seed(0)
+    random.seed(0)
+
+    h_kv = 1
+    d, dv = 576, 512
+    causal = True
+
+    for b in [128]:
+        for s in [4096, 8192]:
+            for h_q in [16, 32, 64, 128]:  # TP = 8, 4, 2, 1
+                for s_q in [1, 2]:  # MTP = 1, 2
+                    for varlen in [False, True]:
+                        test_flash_mla(b, s_q, s, h_q, h_kv, d, dv, causal, varlen)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        choices=["bf16", "fp16"],
+        default="bf16",
+        help="Data type to use for testing (bf16 or fp16)",
+    )
+    args = parser.parse_args()
+    torch_dtype = torch.bfloat16
+    if args.dtype == "fp16":
+        torch_dtype = torch.float16
+    main(torch_dtype)