[WIP] support more dtypes for tcgen05 (#1229)

support ld with pack for fp32 dtype

add dump

add tempalte expand

remove unused dtype and change to rebased apis

examples/gemm_fp8/example_tilelang_gemm_fp8_sm100.py

+import torch
+import tilelang
+import tilelang.language as T
+from tilelang.utils.tensor import map_torch_type
+
+
+def matmul(
+    M,
+    N,
+    K,
+    block_M,
+    block_N,
+    block_K,
+    trans_A,
+    trans_B,
+    in_dtype,
+    out_dtype,
+    accum_dtype,
+    num_stages,
+    threads,
+):
+    A_shape = (K, M) if trans_A else (M, K)
+    B_shape = (N, K) if trans_B else (K, N)
+    A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
+    B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
+
+    @T.prim_func
+    def main(
+            A: T.Tensor(A_shape, in_dtype),
+            B: T.Tensor(B_shape, in_dtype),
+            C: T.Tensor((M, N), out_dtype),
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
+            A_shared = T.alloc_shared(A_shared_shape, in_dtype)
+            B_shared = T.alloc_shared(B_shared_shape, in_dtype)
+            C_tmem = T.alloc_tmem([block_M, block_N], accum_dtype)
+            mbar = T.alloc_barrier(1)
+            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+            C_shared = T.alloc_shared((block_M, block_N), out_dtype)
+
+            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
+                T.copy(A[by * block_M, k * block_K], A_shared)
+                T.copy(B[bx * block_N, k * block_K], B_shared)
+                T.gemm_v2(
+                    A_shared,
+                    B_shared,
+                    C_tmem,
+                    trans_A,
+                    trans_B,
+                    mbar=mbar,
+                    wg_wait=-1,
+                    clear_accum=(k == 0),
+                )
+                T.mbarrier_wait_parity(mbar, k % 2)
+
+            T.copy(C_tmem, C_local)
+            T.copy(C_local, C_shared)
+
+            T.copy(C_shared, C[by * block_M, bx * block_N])
+
+    return main
+
+
+def calc_diff(x, y):
+    x, y = x.double(), y.double()
+    denominator = (x * x + y * y).sum()
+    sim = 2 * (x * y).sum() / denominator
+    return 1 - sim
+
+
+M, N, K = 4096, 4096, 8192
+block_M, block_N, block_K = 64, 256, 32
+trans_A, trans_B = False, True
+num_stages = 2
+threads = 256
+for tvm_fp8_dtype in ["float8_e4m3", "float8_e5m2"]:
+    for tvm_acc_dtype in ["float16", "float32"]:  # , torch.float16]:
+        torch_fp8_dtype = map_torch_type(tvm_fp8_dtype)
+        torch_acc_dtype = map_torch_type(tvm_acc_dtype)
+        print(f"running {tvm_fp8_dtype} -> {tvm_acc_dtype}")
+        in_dtype, out_dtype, accum_dtype = tvm_fp8_dtype, tvm_acc_dtype, tvm_acc_dtype
+
+        func = matmul(
+            M,
+            N,
+            K,
+            block_M,
+            block_N,
+            block_K,
+            trans_A,
+            trans_B,
+            in_dtype,
+            out_dtype,
+            accum_dtype,
+            num_stages,
+            threads,
+        )
+        jit_kernel = tilelang.compile(
+            func,
+            out_idx=[2],
+            target="cuda",
+            pass_configs={
+                tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
+                tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
+                tilelang.PassConfigKey.TL_ENABLE_PTXAS_VERBOSE_OUTPUT: True,
+            },
+        )
+        # jit_kernel.export_ptx("./dump.ptx")
+        # jit_kernel.export_sources("./dump.cu")
+
+        a = torch.randn(M, K, device="cuda", dtype=torch.float16).to(torch_fp8_dtype)
+        b = torch.randn(N, K, device="cuda", dtype=torch.float16).to(torch_fp8_dtype)
+
+        c = jit_kernel(a, b)
+        ref_c = (a.to(torch.half) @ b.T.to(torch.half)).float()
+        c = c.float()
+        diff = calc_diff(c, ref_c)
+        # assert diff < 1e-3, f"{diff}"
+        print(f"[{tvm_fp8_dtype} -> {tvm_acc_dtype}] diff = {diff}")
+
+        profiler = jit_kernel.get_profiler()
+        latency = profiler.do_bench()
+        print(f"[{tvm_fp8_dtype} -> {tvm_acc_dtype}] Latency: {latency} ms")
+        print(
+            f"[{tvm_fp8_dtype} -> {tvm_acc_dtype}] Flops: {2 * M * N * K / (latency / 1e3) / 1e12} TFLOPS"
+        )

src/op/copy.cc

@@ -1117,6 +1117,11 @@ Stmt CopyNode::LowerTmemCopy(const LowerArgs &T,
   bool is_ld = false; // tcgen05.ld (tensor memory -> register)
   bool is_st = false; // tcgen05.st (register -> tensor memory)
   bool is_cp = false; // tcgen05.cp (shared memory -> tensor memory)
+  bool src_needs_pack =
+      16 == src->dtype.bits(); // if needs .pack::16b when is_ld
+  bool dst_needs_unpack =
+      16 == dst->dtype.bits(); // if needs .unpack::16b when is_st
+
   if (src.scope() == "shared.tmem" && dst.scope() == "local.fragment") {
     is_ld = true;
   } else if (src.scope() == "local.fragment" && dst.scope() == "shared.tmem") {
@@ -1124,9 +1129,8 @@ Stmt CopyNode::LowerTmemCopy(const LowerArgs &T,
   } else if (src.scope() == "shared.dyn" && dst.scope() == "shared.tmem") {
     is_cp = true;
   } else {
-    ICHECK(0) << "Unsupported tensor memory copy: "
-              << "src scope = " << src.scope()
-              << ", dst scope = " << dst.scope();
+    ICHECK(0) << "Unsupported tensor memory copy: " << "src scope = "
+              << src.scope() << ", dst scope = " << dst.scope();
   }
   // Currently tcgen05.cp is not supported
   // TODO (mzw) Support tcgen05.cp
@@ -1246,8 +1250,10 @@ Stmt CopyNode::LowerTmemCopy(const LowerArgs &T,
               : relative_wg_idx * (num_chunks_each_wg * meta.width);
       have_succeeded = true;
       Array<PrimExpr> args;
+      const char *bool_str = src_needs_pack ? "true" : "false";
       args.push_back(StringImm(meta.intrinsics_name + "<" +
-                               std::to_string(num_chunks_each_wg) + ">"));
+                               std::to_string(num_chunks_each_wg) + ", " +
+                               bool_str + ">"));
       args.push_back(
           BufferLoad(src, {(int)logical_row_min,
                            (int)logical_col_min})); // Will be translated later

src/op/gemm_py.cc

@@ -428,6 +428,8 @@ TVM_FFI_STATIC_INIT_BLOCK() {
           result.push_back(Integer(meta.atom_m));
           result.push_back(Integer(meta.atom_n));
           result.push_back(Integer(meta.atom_k));
+          result.push_back(Integer(meta.enable_ws));
+          result.push_back(Integer(meta.enable_2cta));
         }
         return result;
       });

src/op/tcgen5_meta.h

@@ -15,16 +15,19 @@ using runtime::DataType;
 
 struct TCGEN5MMAMeta {
   int atom_m, atom_n, atom_k;
+  bool enable_ws, enable_2cta;
 };
 
 inline std::pair<bool, TCGEN5MMAMeta>
 GetTCGEN5MMAMeta(int M, int N, int K, DataType ab_dtype, DataType c_dtype) {
 // TODO (lei) Currently not all shapes / dtypes are supported for TCGEN5MMA.
 #define FAIL                                                                   \
-  return { false, TCGEN5MMAMeta{0, 0, 0} }
-#define SUCCESS(atom_m, atom_n, atom_k)                                        \
   return {                                                                     \
-    true, TCGEN5MMAMeta { atom_m, atom_n, atom_k }                             \
+    false, TCGEN5MMAMeta { 0, 0, 0, false, false }                             \
+  }
+#define SUCCESS(atom_m, atom_n, atom_k, use_ws, use_2cta)                      \
+  return {                                                                     \
+    true, TCGEN5MMAMeta { atom_m, atom_n, atom_k, use_ws, use_2cta }           \
   }
   std::vector<int> ws_valid_atom_ns = {256, 128, 64};
   if ((ab_dtype.is_bfloat16() || ab_dtype.is_float16()) &&
@@ -34,39 +37,52 @@ GetTCGEN5MMAMeta(int M, int N, int K, DataType ab_dtype, DataType c_dtype) {
     if (M % 128 == 0) {
       for (int atom_n = 256; atom_n >= 16; atom_n -= 16)
         if (N % atom_n == 0)
-          SUCCESS(128, atom_n, 16);
+          SUCCESS(128, atom_n, 16, false, false);
       FAIL;
     } else if (M % 64 == 0) {
       for (int atom_n : ws_valid_atom_ns)
         if (N % atom_n == 0)
-          SUCCESS(64, atom_n, 16);
+          SUCCESS(64, atom_n, 16, false, false);
       FAIL;
     } else if (M % 32 == 0) {
       for (int atom_n : ws_valid_atom_ns)
         if (N % atom_n == 0)
-          SUCCESS(32, atom_n, 16);
+          SUCCESS(32, atom_n, 16, false, false);
       FAIL;
     } else {
       FAIL;
     }
-  } else if ((ab_dtype.is_float8_e4m3fn() || ab_dtype.is_float8_e5m2()) &&
-             (c_dtype.is_float() && c_dtype.bits() == 32)) {
+  } else if ((ab_dtype.is_float8_e4m3fn() || ab_dtype.is_float8_e4m3() ||
+              ab_dtype.is_float8_e5m2() || ab_dtype.is_float8_e5m2fnuz() ||
+              ab_dtype.is_float6_e2m3fn() || ab_dtype.is_float6_e3m2fn() ||
+              ab_dtype.is_float4_e2m1fn()) &&
+             ((c_dtype.is_float() && c_dtype.bits() == 32) ||
+              (c_dtype.is_float16() && c_dtype.bits() == 16))) {
     if (K % 32 != 0)
       FAIL;
     if (M % 128 == 0) {
+      for (int atom_n : ws_valid_atom_ns)
+        if (N % atom_n == 0)
+          SUCCESS(128, atom_n, 32, true, false);
       for (int atom_n = 256; atom_n >= 16; atom_n -= 16)
         if (N % atom_n == 0)
-          SUCCESS(128, atom_n, 32);
+          SUCCESS(128, atom_n, 32, false, true);
+      for (int atom_n = 256; atom_n >= 8; atom_n -= 8)
+        if (N % atom_n == 0)
+          SUCCESS(128, atom_n, 32, false, false);
       FAIL;
     } else if (M % 64 == 0) {
       for (int atom_n : ws_valid_atom_ns)
         if (N % atom_n == 0)
-          SUCCESS(64, atom_n, 32);
+          SUCCESS(64, atom_n, 32, true, false);
+      for (int atom_n = 256; atom_n >= 8; atom_n -= 8)
+        if (N % atom_n == 0)
+          SUCCESS(128, atom_n, 32, false, false);
       FAIL;
     } else if (M % 32 == 0) {
       for (int atom_n : ws_valid_atom_ns)
         if (N % atom_n == 0)
-          SUCCESS(32, atom_n, 32);
+          SUCCESS(32, atom_n, 32, true, false);
       FAIL;
     } else {
       FAIL;

src/tl_templates/cuda/copy_sm100.h

@@ -51,6 +51,21 @@ __device__ __forceinline__ void st_global_256(fp8_e4_32_t *ptr,
                :
                : "l"(ptr), "l"(val.x), "l"(val.y), "l"(val.z), "l"(val.w));
 }
+__device__ __forceinline__ ulonglong4 ld_global_256(const fp8_e5_32_t *ptr) {
+  ulonglong4 ret;
+  asm volatile("ld.global.v4.u64 {%0, %1, %2, %3}, [%4];"
+               : "=l"(ret.x), "=l"(ret.y), "=l"(ret.z), "=l"(ret.w)
+               : "l"(ptr));
+  return ret;
+}
+
+__device__ __forceinline__ void st_global_256(fp8_e5_32_t *ptr,
+                                              fp8_e5_32_t &val8) {
+  ulonglong4 &val = *((ulonglong4 *)&val8);
+  asm volatile("st.global.v4.u64 [%0], {%1, %2, %3, %4};"
+               :
+               : "l"(ptr), "l"(val.x), "l"(val.y), "l"(val.z), "l"(val.w));
+}
 
 __device__ __forceinline__ unsigned long long
 pack_bfloat16x4(const bfloat16_t x, const bfloat16_t y, const bfloat16_t z,
@@ -95,38 +110,38 @@ __device__ __forceinline__ void tcgen05_ld_core(uint32_t const &tmem_start_col,
   }
 }
 
-template <int N, typename dst_t>
+template <int N, bool pack16, typename dst_t>
 __device__ __forceinline__ void
 tcgen05_ld_32dp32bNx(uint32_t const &tmem_start_col,
                      uint32_t const &tmem_col_offset, dst_t *dst_ptr) {
-  tcgen05_ld_core<tl::tmem_ld_32dp32bNx, 7, N>(tmem_start_col + tmem_col_offset,
-                                               dst_ptr);
+  tcgen05_ld_core<tl::tmem_ld_32dp32bNx<pack16>, 7, N>(
+      tmem_start_col + tmem_col_offset, dst_ptr);
   tl::fence_view_async_tmem_load();
 }
 
-template <int N, typename dst_t>
+template <int N, bool pack16, typename dst_t>
 __device__ __forceinline__ void
 tcgen05_ld_32dp64bNx(uint32_t const &tmem_start_col,
                      uint32_t const &tmem_col_offset, dst_t *dst_ptr) {
-  tcgen05_ld_core<tl::tmem_ld_32dp64bNx, 7, N>(tmem_start_col + tmem_col_offset,
-                                               dst_ptr);
+  tcgen05_ld_core<tl::tmem_ld_32dp64bNx<pack16>, 7, N>(
+      tmem_start_col + tmem_col_offset, dst_ptr);
   tl::fence_view_async_tmem_load();
 }
 
-template <int N, typename dst_t>
+template <int N, bool pack16, typename dst_t>
 __device__ __forceinline__ void
 tcgen05_ld_32dp128bNx(uint32_t const &tmem_start_col,
                       uint32_t const &tmem_col_offset, dst_t *dst_ptr) {
-  tcgen05_ld_core<tl::tmem_ld_32dp128bNx, 6, N>(
+  tcgen05_ld_core<tl::tmem_ld_32dp128bNx<pack16>, 6, N>(
       tmem_start_col + tmem_col_offset, dst_ptr);
   tl::fence_view_async_tmem_load();
 }
 
-template <int N, typename dst_t>
+template <int N, bool pack16, typename dst_t>
 __device__ __forceinline__ void
 tcgen05_ld_32dp256bNx(uint32_t const &tmem_start_col,
                       uint32_t const &tmem_col_offset, dst_t *dst_ptr) {
-  tcgen05_ld_core<tl::tmem_ld_32dp256bNx, 5, N>(
+  tcgen05_ld_core<tl::tmem_ld_32dp256bNx<pack16>, 5, N>(
       tmem_start_col + tmem_col_offset, dst_ptr);
   tl::fence_view_async_tmem_load();
 }

src/tl_templates/cuda/gemm_sm100.h

@@ -243,46 +243,96 @@ struct DispatchInstruction<half_t, half_t, float, M, N, K, a_major, b_major,
 };
 
 template <int M, int N, int K, UMMA::Major a_major, UMMA::Major b_major>
-struct DispatchInstruction<fp8_e4_t, fp8_e4_t, float, M, N, K, a_major, b_major,
+struct DispatchInstruction<cute::float_e4m3_t, cute::float_e4m3_t, float, M, N,
+                           K, a_major, b_major,
                            std::enable_if_t<M == 128 && K == 32>> {
-  using MMA = MMA_Traits<SM100_MMA_F8F6F4_SS, fp8_e4_t, fp8_e4_t, float, Int<M>,
-                         Int<N>, integral_constant<UMMA::Major, a_major>,
+  using MMA =
+      MMA_Traits<SM100_MMA_F8F6F4_SS, cute::float_e4m3_t, cute::float_e4m3_t,
+                 float, Int<M>, Int<N>, integral_constant<UMMA::Major, a_major>,
+                 integral_constant<UMMA::Major, b_major>,
+                 integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>,
+                 integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>>;
+};
+template <int M, int N, int K, UMMA::Major a_major, UMMA::Major b_major>
+struct DispatchInstruction<cute::float_e4m3_t, cute::float_e4m3_t, half_t, M, N,
+                           K, a_major, b_major,
+                           std::enable_if_t<M == 128 && K == 32>> {
+  using MMA = MMA_Traits<SM100_MMA_F8F6F4_SS, cute::float_e4m3_t,
+                         cute::float_e4m3_t, half_t, Int<M>, Int<N>,
+                         integral_constant<UMMA::Major, a_major>,
                          integral_constant<UMMA::Major, b_major>,
                          integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>,
                          integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>>;
 };
 
 template <int M, int N, int K, UMMA::Major a_major, UMMA::Major b_major>
-struct DispatchInstruction<fp8_e4_t, fp8_e4_t, float, M, N, K, a_major, b_major,
-                           std::enable_if_t<(M == 64 || M == 32) && K == 32>> {
+struct DispatchInstruction<cute::float_e4m3_t, cute::float_e4m3_t, float, M, N,
+                           K, a_major, b_major,
+                           std::enable_if_t<M == 64 && K == 32>> {
   using MMA =
-      MMA_Traits<SM100_MMA_F8F6F4_WS_SS, fp8_e4_t, fp8_e4_t, float, Int<M>,
-                 Int<N>, integral_constant<UMMA::Major, a_major>,
+      MMA_Traits<SM100_MMA_F8F6F4_WS_SS, cute::float_e4m3_t, cute::float_e4m3_t,
+                 float, Int<M>, Int<N>, integral_constant<UMMA::Major, a_major>,
                  integral_constant<UMMA::Major, b_major>,
                  integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>,
                  integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>>;
 };
+template <int M, int N, int K, UMMA::Major a_major, UMMA::Major b_major>
+struct DispatchInstruction<cute::float_e4m3_t, cute::float_e4m3_t, half_t, M, N,
+                           K, a_major, b_major,
+                           std::enable_if_t<M == 64 && K == 32>> {
+  using MMA = MMA_Traits<SM100_MMA_F8F6F4_WS_SS, cute::float_e4m3_t,
+                         cute::float_e4m3_t, half_t, Int<M>, Int<N>,
+                         integral_constant<UMMA::Major, a_major>,
+                         integral_constant<UMMA::Major, b_major>,
+                         integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>,
+                         integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>>;
+};
 
 template <int M, int N, int K, UMMA::Major a_major, UMMA::Major b_major>
-struct DispatchInstruction<fp8_e5_t, fp8_e5_t, float, M, N, K, a_major, b_major,
+struct DispatchInstruction<cute::float_e5m2_t, cute::float_e5m2_t, float, M, N,
+                           K, a_major, b_major,
                            std::enable_if_t<M == 128 && K == 32>> {
-  using MMA = MMA_Traits<SM100_MMA_F8F6F4_SS, fp8_e5_t, fp8_e5_t, float, Int<M>,
-                         Int<N>, integral_constant<UMMA::Major, a_major>,
+  using MMA =
+      MMA_Traits<SM100_MMA_F8F6F4_SS, cute::float_e5m2_t, cute::float_e5m2_t,
+                 float, Int<M>, Int<N>, integral_constant<UMMA::Major, a_major>,
+                 integral_constant<UMMA::Major, b_major>,
+                 integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>,
+                 integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>>;
+};
+template <int M, int N, int K, UMMA::Major a_major, UMMA::Major b_major>
+struct DispatchInstruction<cute::float_e5m2_t, cute::float_e5m2_t, half_t, M, N,
+                           K, a_major, b_major,
+                           std::enable_if_t<M == 128 && K == 32>> {
+  using MMA = MMA_Traits<SM100_MMA_F8F6F4_SS, cute::float_e5m2_t,
+                         cute::float_e5m2_t, half_t, Int<M>, Int<N>,
+                         integral_constant<UMMA::Major, a_major>,
                          integral_constant<UMMA::Major, b_major>,
                          integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>,
                          integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>>;
 };
 
 template <int M, int N, int K, UMMA::Major a_major, UMMA::Major b_major>
-struct DispatchInstruction<fp8_e5_t, fp8_e5_t, float, M, N, K, a_major, b_major,
+struct DispatchInstruction<cute::float_e5m2_t, cute::float_e5m2_t, float, M, N,
+                           K, a_major, b_major,
                            std::enable_if_t<(M == 64 || M == 32) && K == 32>> {
   using MMA =
-      MMA_Traits<SM100_MMA_F8F6F4_WS_SS, fp8_e5_t, fp8_e5_t, float, Int<M>,
-                 Int<N>, integral_constant<UMMA::Major, a_major>,
+      MMA_Traits<SM100_MMA_F8F6F4_WS_SS, cute::float_e5m2_t, cute::float_e5m2_t,
+                 float, Int<M>, Int<N>, integral_constant<UMMA::Major, a_major>,
                  integral_constant<UMMA::Major, b_major>,
                  integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>,
                  integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>>;
 };
+template <int M, int N, int K, UMMA::Major a_major, UMMA::Major b_major>
+struct DispatchInstruction<cute::float_e5m2_t, cute::float_e5m2_t, half_t, M, N,
+                           K, a_major, b_major,
+                           std::enable_if_t<(M == 64 || M == 32) && K == 32>> {
+  using MMA = MMA_Traits<SM100_MMA_F8F6F4_WS_SS, cute::float_e5m2_t,
+                         cute::float_e5m2_t, half_t, Int<M>, Int<N>,
+                         integral_constant<UMMA::Major, a_major>,
+                         integral_constant<UMMA::Major, b_major>,
+                         integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>,
+                         integral_constant<UMMA::ScaleIn, UMMA::ScaleIn::One>>;
+};
 
 template <int M, int N, int K, int AtomM, int AtomN, int AtomK, bool trans_A,
           bool trans_B, typename A_type_raw, typename B_type_raw,

tilelang/intrinsics/mma_macro_generator.py

@@ -45,7 +45,10 @@ class TensorCoreIntrinEmitter:
         "int8": "int8",
         "int32": "int32",
         "float8_e4m3": "e4m3",
+        "float8_e4m3fn": "e4m3",
+        "float8_e4m3fnuz": "e4m3",
         "float8_e5m2": "e5m2",
+        "float8_e5m2fnuz": "e5m2",
     }
 
     # Represent the thread binding in the form of (tx, warp_n, warp_m)

tilelang/intrinsics/tcgen05_macro_generator.py

@@ -169,12 +169,11 @@ class TensorCoreIntrinEmitter(MMAIntrinEmitter):
         accum_dtype_in_bits = DataType(accum_dtype).bits
 
         meta = self.get_tcgen5_mma_meta(m_dim, n_dim, k_dim)
-        if len(meta) != 3:
+        if len(meta) != 5:
             raise ValueError(
                 f"Unsupported TCGEN5MMA configuration for desc generation: M={m_dim}, N={n_dim}, "
                 f"K={k_dim}, A dtype={self.a_dtype}, accum dtype={self.accum_dtype}")
-        atom_m, atom_n, atom_k = (int(x) for x in meta)
-        enable_ws = atom_m != 128
+        atom_m, atom_n, atom_k, enable_ws, enable_2cta = (int(x) for x in meta)
 
         # by default, we utilize non-swizzle layout offset
         a_leading_byte_offset = (8 * 8 * elems_in_bytes) if a_is_k_major else (8 * m_dim *
@@ -382,10 +381,10 @@ class TensorCoreIntrinEmitter(MMAIntrinEmitter):
         k = int(self.chunk)
 
         meta = self.get_tcgen5_mma_meta(m, n, k)
-        if len(meta) != 3:
+        if len(meta) != 5:
             raise ValueError(f"Unsupported TCGEN5MMA configuration: M={m}, N={n}, K={k}, "
                              f"A dtype={self.a_dtype}, accum dtype={self.accum_dtype}")
-        atom_m, atom_n, _ = (int(x) for x in meta)
+        atom_m, atom_n, _, _, _ = (int(x) for x in meta)
 
         if m % atom_m != 0 or n % atom_n != 0:
             raise ValueError(

tilelang/jit/adapter/wrapper.py

@@ -144,6 +144,7 @@ class TLCUDASourceWrapper:
         "float16": "half_t",
         "bfloat16": "bfloat16_t",
         "float8_e4m3": "fp8_e4_t",
+        "float8_e4m3fn": "fp8_e4_t",
         "float8_e5m2": "fp8_e5_t",
         "float64": "double",
         "int64": "int64_t",

tilelang/tileop/gemm/gemm_tcgen05.py

@@ -85,6 +85,9 @@ class GemmTCGEN5(GemmBase):
             raise ValueError(f"TCGEN5MMA currently only supports gemm_ss, got "
                              f"A scope {self.A.scope()}, B scope {self.B.scope()}")
 
+        atom_m, atom_n, atom_k, enable_ws, enable_2cta = mma_emitter.get_tcgen5_mma_meta(
+            self.M, self.N, self.K)
+
         if self.A.scope() not in {"shared", "shared.dyn", "shared.tmem"}:
             raise ValueError(f"Unsupported A scope for TCGEN5MMA: {self.A.scope()}")
         if self.B.scope() not in {"shared", "shared.dyn"}:
@@ -103,7 +106,7 @@ class GemmTCGEN5(GemmBase):
             raise ValueError("TCGEN5MMA expects 2D coordinates for C buffer access")
 
         accum_dtype = str(self.C.dtype)
-        if accum_dtype != "float32":
+        if accum_dtype not in ["float32", 'float16']:
             raise ValueError(f"Unsupported accumulator dtype for TCGEN5MMA: {accum_dtype}")
 
         A_shared = self.ARegion