support fp16

README.md

@@ -3,7 +3,7 @@
 FlashMLA is an efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences serving.
 
 Currently released:
-- BF16
+- BF16, FP16
 - Paged kvcache with block size of 64
 
 ## Quick start

csrc/flash_api.cpp

@@ -77,7 +77,7 @@ mha_fwd_kvcache_mla(
     at::Tensor vcache = vcache_.has_value() ? vcache_.value() : kcache;
 
     auto q_dtype = q.dtype();
-    TORCH_CHECK(q_dtype == torch::kBFloat16);
+    TORCH_CHECK(q_dtype == torch::kBFloat16 || q_dtype == torch::kFloat16);
     TORCH_CHECK(kcache.dtype() == q_dtype, "query and key must have the same dtype");
 
     CHECK_DEVICE(q); CHECK_DEVICE(kcache); CHECK_DEVICE(vcache);
@@ -186,7 +186,12 @@ mha_fwd_kvcache_mla(
 
     auto stream = at::cuda::getCurrentCUDAStream().stream();
     TORCH_CHECK(head_size == 576);
-    run_mha_fwd_splitkv_mla<cutlass::bfloat16_t, 576>(params, stream);
+
+    if (q_dtype == torch::kBFloat16) {
+        run_mha_fwd_splitkv_mla<cutlass::bfloat16_t, 576>(params, stream);
+    } else {
+        run_mha_fwd_splitkv_mla<cutlass::half_t, 576>(params, stream);
+    }
 
     out = out.view({batch_size, seqlen_q_ori, ngroups, num_heads_k, head_size_v}).transpose(2, 3)
             .reshape({batch_size, seqlen_q_ori, num_heads_ori, head_size_v});

csrc/flash_fwd_mla_fp16_sm90.cu

+#include "flash_fwd_mla_kernel.h"
+
+template void run_mha_fwd_splitkv_mla<cutlass::half_t, 576>(Flash_fwd_mla_params &params, cudaStream_t stream);

csrc/flash_fwd_mla_kernel.h

@@ -601,79 +601,3 @@ void run_mha_fwd_splitkv_mla(Flash_fwd_mla_params &params, cudaStream_t stream)
     using Kernel_traits = Flash_fwd_kernel_traits_mla<576, 64, 64, 8, T, 512>;
     run_flash_splitkv_fwd_mla<Kernel_traits, flash::SharedStorageMLA<Kernel_traits>>(params, stream);
 }
-
-static constexpr int MaxBatchSize = 4096;
-
-__global__ void __launch_bounds__(256, 1, 1)
-get_mla_metadata_kernel(__grid_constant__ const Mla_metadata_params params) {
-    int *seqlens_k_ptr = params.seqlens_k_ptr;
-    int *tile_scheduler_metadata_ptr = params.tile_scheduler_metadata_ptr;
-    int *num_splits_ptr = params.num_splits_ptr;
-    int batch_size = params.batch_size;
-    int block_size_n = params.block_size_n;
-    int fixed_overhead_num_blocks = params.fixed_overhead_num_blocks;
-    int num_sm_parts = params.num_sm_parts;
-
-    __shared__ int num_blocks_shared[MaxBatchSize];
-    __shared__ int num_splits_shared[MaxBatchSize];
-
-    int total_num_blocks = 0;
-    for (int i = threadIdx.x; i < batch_size; i += 32) {
-        int num_blocks = cutlass::ceil_div(seqlens_k_ptr[i], block_size_n);
-        total_num_blocks += num_blocks + fixed_overhead_num_blocks;
-        num_blocks_shared[i] = num_blocks;
-    }
-    for (int offset = 16; offset >= 1; offset /= 2) {
-        total_num_blocks += __shfl_xor_sync(uint32_t(-1), total_num_blocks, offset);
-    }
-    __syncwarp();
-
-    if (threadIdx.x == 0) {
-        int payload = cutlass::ceil_div(total_num_blocks, num_sm_parts) + fixed_overhead_num_blocks;
-
-        int now_idx = 0, now_block = 0, now_n_split_idx = 0, cum_num_splits = 0;
-        num_splits_shared[0] = 0;
-        for (int i = 0; i < num_sm_parts; ++i) {
-            int tile_scheduler_metadata0[4], tile_scheduler_metadata1;
-            tile_scheduler_metadata0[0] = now_idx;
-            tile_scheduler_metadata0[1] = now_block * block_size_n;
-            tile_scheduler_metadata1 = now_n_split_idx;
-            int remain_payload = payload;
-            while (now_idx < batch_size) {
-                int num_blocks = num_blocks_shared[now_idx];
-                int now_remain_blocks = num_blocks - now_block;
-                if (remain_payload >= now_remain_blocks + fixed_overhead_num_blocks) {
-                    cum_num_splits += now_n_split_idx + 1;
-                    num_splits_shared[now_idx + 1] = cum_num_splits;
-                    remain_payload -= now_remain_blocks + fixed_overhead_num_blocks;
-                    ++now_idx;
-                    now_block = 0;
-                    now_n_split_idx = 0;
-                } else {
-                    if (remain_payload - fixed_overhead_num_blocks > 0) {
-                        now_block += remain_payload - fixed_overhead_num_blocks;
-                        ++now_n_split_idx;
-                        remain_payload = 0;
-                    }
-                    break;
-                }
-            }
-            tile_scheduler_metadata0[2] = now_block > 0 ? now_idx : now_idx - 1;
-            tile_scheduler_metadata0[3] = now_block > 0 ? now_block * block_size_n : seqlens_k_ptr[now_idx - 1];
-            *reinterpret_cast<int4 *>(tile_scheduler_metadata_ptr + i * TileSchedulerMetaDataSize) = *reinterpret_cast<int4 *>(tile_scheduler_metadata0);
-            tile_scheduler_metadata_ptr[i * TileSchedulerMetaDataSize + 4] = tile_scheduler_metadata1;
-        }
-        FLASH_DEVICE_ASSERT(now_idx == batch_size && now_block == 0 && now_n_split_idx == 0);
-    }
-    __syncwarp();
-
-    for (int i = threadIdx.x; i <= batch_size; i += 32) {
-        num_splits_ptr[i] = num_splits_shared[i];
-    }
-}
-
-void get_mla_metadata_func(Mla_metadata_params &params, cudaStream_t stream) {
-    FLASH_ASSERT(params.batch_size < MaxBatchSize);
-    get_mla_metadata_kernel<<<1, 32, 0, stream>>>(params);
-    CHECK_CUDA_KERNEL_LAUNCH();
-}

csrc/flash_fwd_mla_metadata.cu

+#include "flash_fwd_mla_kernel.h"
+
+static constexpr int MaxBatchSize = 4096;
+
+__global__ void __launch_bounds__(256, 1, 1)
+get_mla_metadata_kernel(__grid_constant__ const Mla_metadata_params params) {
+    int *seqlens_k_ptr = params.seqlens_k_ptr;
+    int *tile_scheduler_metadata_ptr = params.tile_scheduler_metadata_ptr;
+    int *num_splits_ptr = params.num_splits_ptr;
+    int batch_size = params.batch_size;
+    int block_size_n = params.block_size_n;
+    int fixed_overhead_num_blocks = params.fixed_overhead_num_blocks;
+    int num_sm_parts = params.num_sm_parts;
+
+    __shared__ int num_blocks_shared[MaxBatchSize];
+    __shared__ int num_splits_shared[MaxBatchSize];
+
+    int total_num_blocks = 0;
+    for (int i = threadIdx.x; i < batch_size; i += 32) {
+        int num_blocks = cutlass::ceil_div(seqlens_k_ptr[i], block_size_n);
+        total_num_blocks += num_blocks + fixed_overhead_num_blocks;
+        num_blocks_shared[i] = num_blocks;
+    }
+    for (int offset = 16; offset >= 1; offset /= 2) {
+        total_num_blocks += __shfl_xor_sync(uint32_t(-1), total_num_blocks, offset);
+    }
+    __syncwarp();
+
+    if (threadIdx.x == 0) {
+        int payload = cutlass::ceil_div(total_num_blocks, num_sm_parts) + fixed_overhead_num_blocks;
+
+        int now_idx = 0, now_block = 0, now_n_split_idx = 0, cum_num_splits = 0;
+        num_splits_shared[0] = 0;
+        for (int i = 0; i < num_sm_parts; ++i) {
+            int tile_scheduler_metadata0[4], tile_scheduler_metadata1;
+            tile_scheduler_metadata0[0] = now_idx;
+            tile_scheduler_metadata0[1] = now_block * block_size_n;
+            tile_scheduler_metadata1 = now_n_split_idx;
+            int remain_payload = payload;
+            while (now_idx < batch_size) {
+                int num_blocks = num_blocks_shared[now_idx];
+                int now_remain_blocks = num_blocks - now_block;
+                if (remain_payload >= now_remain_blocks + fixed_overhead_num_blocks) {
+                    cum_num_splits += now_n_split_idx + 1;
+                    num_splits_shared[now_idx + 1] = cum_num_splits;
+                    remain_payload -= now_remain_blocks + fixed_overhead_num_blocks;
+                    ++now_idx;
+                    now_block = 0;
+                    now_n_split_idx = 0;
+                } else {
+                    if (remain_payload - fixed_overhead_num_blocks > 0) {
+                        now_block += remain_payload - fixed_overhead_num_blocks;
+                        ++now_n_split_idx;
+                        remain_payload = 0;
+                    }
+                    break;
+                }
+            }
+            tile_scheduler_metadata0[2] = now_block > 0 ? now_idx : now_idx - 1;
+            tile_scheduler_metadata0[3] = now_block > 0 ? now_block * block_size_n : seqlens_k_ptr[now_idx - 1];
+            *reinterpret_cast<int4 *>(tile_scheduler_metadata_ptr + i * TileSchedulerMetaDataSize) = *reinterpret_cast<int4 *>(tile_scheduler_metadata0);
+            tile_scheduler_metadata_ptr[i * TileSchedulerMetaDataSize + 4] = tile_scheduler_metadata1;
+        }
+        FLASH_DEVICE_ASSERT(now_idx == batch_size && now_block == 0 && now_n_split_idx == 0);
+    }
+    __syncwarp();
+
+    for (int i = threadIdx.x; i <= batch_size; i += 32) {
+        num_splits_ptr[i] = num_splits_shared[i];
+    }
+}
+
+void get_mla_metadata_func(Mla_metadata_params &params, cudaStream_t stream) {
+    FLASH_ASSERT(params.batch_size < MaxBatchSize);
+    get_mla_metadata_kernel<<<1, 32, 0, stream>>>(params);
+    CHECK_CUDA_KERNEL_LAUNCH();
+}
\ No newline at end of file

setup.py

@@ -37,6 +37,8 @@ ext_modules.append(
         sources=[
             "csrc/flash_api.cpp",
             "csrc/flash_fwd_mla_bf16_sm90.cu",
+            "csrc/flash_fwd_mla_fp16_sm90.cu",
+            "csrc/flash_fwd_mla_metadata.cu",
         ],
         extra_compile_args={
             "cxx": cxx_args,

tests/test_flash_mla.py

+import argparse
 import math
 import random
 
 import torch
 import triton
 
-from flash_mla import get_mla_metadata, flash_mla_with_kvcache
+from flash_mla import flash_mla_with_kvcache, get_mla_metadata
 
 
 def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False):
@@ -38,7 +39,9 @@ def cal_diff(x: torch.Tensor, y: torch.Tensor, name: str) -> None:
 
 @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=}")
+    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:
@@ -52,18 +55,30 @@ def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen):
 
     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)
+    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_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)
+    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,
+            q,
+            blocked_k,
+            block_table,
+            cache_seqlens,
+            dv,
+            tile_scheduler_metadata,
+            num_splits,
+            causal=causal,
         )
 
     def ref_mla():
@@ -91,14 +106,17 @@ def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen):
 
     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(dtype).bits // 8)
-    print(f"{t:.3f} ms, {FLOPS / 10 ** 9 / t:.0f} TFLOPS, {bytes / 10 ** 6 / t:.0f} GB/s")
+    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"
+    )
 
 
-if __name__ == "__main__":
-    dtype = torch.bfloat16
+def main(torch_dtype):
     device = torch.device("cuda:0")
-    torch.set_default_dtype(dtype)
+    torch.set_default_dtype(torch_dtype)
     torch.set_default_device(device)
     torch.cuda.set_device(device)
     torch.manual_seed(0)
@@ -114,3 +132,22 @@ if __name__ == "__main__":
                 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)