move the newer deep_gemm to fleety (PaddlePaddle#10860) (PaddlePaddle#11040)

Co-authored-by: wangxiangzhe <>

Author: Waynezee

ops/csrc/fp8/deep_gemm/include/deep_gemm/fp8_gemm.cuh

@@ -30,9 +30,10 @@ enum class GemmType {
 #pragma ide diagnostic ignored "cppcoreguidelines-pro-type-member-init"
 template <GemmType kGemmType,
           uint32_t SHAPE_N, uint32_t BLOCK_M, uint32_t BLOCK_N,
-          uint32_t kNumGroups, uint32_t kNumTMAMulticast,
+          uint32_t kNumGroups,
+          uint32_t kNumTMAMulticast, bool kIsTMAMulticastOnA,
           uint32_t kNumNBlocks = ceil_div(SHAPE_N, BLOCK_N),
-          uint32_t kNumNBlocksPerGroup = 16>
+          uint32_t kNum1DBlocksPerGroup = 16>
 struct Scheduler {
     int current_iter = -1;
     uint32_t num_aligned_m_blocks;
@@ -61,16 +62,27 @@ struct Scheduler {
     }
 
     __device__ __forceinline__ void get_swizzled_block_idx(const uint32_t num_m_blocks, int block_idx, uint32_t& m_block_idx, uint32_t& n_block_idx) {
-        DG_STATIC_ASSERT(kNumNBlocksPerGroup % kNumTMAMulticast == 0, "Invalid group size");
+        DG_STATIC_ASSERT(kNum1DBlocksPerGroup % kNumTMAMulticast == 0, "Invalid group size");
 
         // Swizzle for better L2 usages
-        auto num_blocks_per_group = num_m_blocks * kNumNBlocksPerGroup;
-        auto group_idx = block_idx / num_blocks_per_group;
-        auto first_n_block_idx = group_idx * kNumNBlocksPerGroup;
-        auto num_n_blocks_in_group = min(kNumNBlocksPerGroup, kNumNBlocks - first_n_block_idx);
-        auto in_group_idx = block_idx % num_blocks_per_group;
-        m_block_idx = in_group_idx / num_n_blocks_in_group;
-        n_block_idx = first_n_block_idx + in_group_idx % num_n_blocks_in_group;
+        // TODO: unify these 2 branches
+        if constexpr (kIsTMAMulticastOnA) {
+            auto num_blocks_per_group = num_m_blocks * kNum1DBlocksPerGroup;
+            auto group_idx = block_idx / num_blocks_per_group;
+            auto first_n_block_idx = group_idx * kNum1DBlocksPerGroup;
+            auto num_n_blocks_in_group = min(kNum1DBlocksPerGroup, kNumNBlocks - first_n_block_idx);
+            auto in_group_idx = block_idx % num_blocks_per_group;
+            m_block_idx = in_group_idx / num_n_blocks_in_group;
+            n_block_idx = first_n_block_idx + in_group_idx % num_n_blocks_in_group;
+        } else {
+            auto num_blocks_per_group = kNumNBlocks * kNum1DBlocksPerGroup;
+            auto group_idx = block_idx / num_blocks_per_group;
+            auto first_m_block_idx = group_idx * kNum1DBlocksPerGroup;
+            auto num_m_blocks_in_group = min(kNum1DBlocksPerGroup, num_m_blocks - first_m_block_idx);
+            auto in_group_idx = block_idx % num_blocks_per_group;
+            m_block_idx = first_m_block_idx + in_group_idx % num_m_blocks_in_group;
+            n_block_idx = in_group_idx / num_m_blocks_in_group;
+        }
     }
 
     template <bool kIgnoreGroupedForGroupedContiguous=true>
@@ -116,6 +128,7 @@ struct Scheduler {
         return true;
     }
 };
+
 #pragma clang diagnostic pop
 
 } // namespace deep_gemm

ops/csrc/fp8/deep_gemm/include/deep_gemm/mma_utils.cuh

@@ -58,20 +58,18 @@ constexpr CUtensorMapDataType get_CUtensorMapDataType() {
     }
 }
 
-PFN_cuTensorMapEncodeTiled get_cuTensorMapEncodeTiled() {
+inline PFN_cuTensorMapEncodeTiled get_cuTensorMapEncodeTiled() {
     // Get pointer to `cuTensorMapEncodeTiled`
     cudaDriverEntryPointQueryResult driver_status;
     void* cuTensorMapEncodeTiled_ptr = nullptr;
 
-/*
 #if CUDA_VERSION >= 12050
     cudaGetDriverEntryPointByVersion("cuTensorMapEncodeTiled", &cuTensorMapEncodeTiled_ptr, 12000,
                                      cudaEnableDefault, &driver_status);
 #else
-*/
     cudaGetDriverEntryPoint("cuTensorMapEncodeTiled", &cuTensorMapEncodeTiled_ptr,
                             cudaEnableDefault, &driver_status);
-//#endif
+#endif
 
     if (driver_status != cudaDriverEntryPointSuccess)
         throw std::runtime_error("driver_status != cudaDriverEntryPointSuccess");
@@ -83,16 +81,15 @@ CUtensorMap make_2d_tma_copy_desc(T* global_address, uint64_t gmem_dim[2],
                                   uint64_t stride_in_bytes, uint32_t smem_dim[2],
                                   CUtensorMapSwizzle swizzle_type,
                                   PFN_cuTensorMapEncodeTiled encode_func = nullptr) {
-    CUtensorMap tensor_map{};
-    constexpr uint32_t rank = 2;
-    uint64_t global_stride[rank - 1] = {stride_in_bytes};
-    uint32_t elem_strides[rank] = {1, 1};
+    CUtensorMap tensor_map = {};
+    uint64_t global_stride[1] = {stride_in_bytes};
+    uint32_t elem_strides[2] = {1, 1};
 
     if (encode_func == nullptr)
         encode_func = get_cuTensorMapEncodeTiled();
 
     auto result = encode_func(
-            &tensor_map, get_CUtensorMapDataType<typename std::remove_cv<T>::type>(), rank,
+            &tensor_map, get_CUtensorMapDataType<std::remove_cv_t<T>>(), 2,
             global_address, gmem_dim, global_stride, smem_dim, elem_strides,
             CUtensorMapInterleave::CU_TENSOR_MAP_INTERLEAVE_NONE, swizzle_type,
             CUtensorMapL2promotion::CU_TENSOR_MAP_L2_PROMOTION_L2_256B,

ops/csrc/fp8/deep_gemm/include/deep_gemm/scheduler.cuh

@@ -63,4 +63,9 @@ do {
 template <typename T>
 __device__ __host__ constexpr T ceil_div(T a, T b) {
     return (a + b - 1) / b;
+}
+
+template <typename T>
+__device__ __host__ constexpr T constexpr_gcd(T a, T b) {
+    return b == 0 ? a : constexpr_gcd(b, a % b);
 }

ops/csrc/fp8/deep_gemm/include/deep_gemm/tma_utils.cuh

@@ -16,6 +16,6 @@
 # Copyright (c) 2025 DeepSeek
 # Licensed under the MIT License - https://github.com/deepseek-ai/DeepEP/blob/main/LICENSE
 
-from .compiler import build, get_nvcc_compiler
-from .runtime import Runtime
+from .compiler import get_nvcc_compiler, build
 from .template import cpp_format, generate
+from .runtime import Runtime

ops/csrc/fp8/deep_gemm/include/deep_gemm/utils.cuh

@@ -16,8 +16,8 @@
 # Copyright (c) 2025 DeepSeek
 # Licensed under the MIT License - https://github.com/deepseek-ai/DeepEP/blob/main/LICENSE
 
-import functools
 import hashlib
+import functools
 import os
 import re
 import subprocess
@@ -75,9 +75,7 @@ def get_nvcc_compiler() -> Tuple[str, str]:
             match = version_pattern.search(os.popen(f"{path} --version").read())
             version = match.group(1)
             assert match, f"Cannot get the version of NVCC compiler {path}"
-            assert (
-                version >= least_version_required
-            ), f"NVCC {path} version {version} is lower than {least_version_required}"
+            assert version >= least_version_required, f"NVCC {path} version {version} is lower than {least_version_required}"
             return path, version
     raise RuntimeError("Cannot find any available NVCC compiler")
 
@@ -117,18 +115,13 @@ def put(path, data, is_binary=False):
 
 def build(name: str, arg_defs: tuple, code: str) -> Runtime:
     # Compiler flags
-    nvcc_flags = [
-        "-std=c++17",
-        "-shared",
-        "-O3",
-        "--expt-relaxed-constexpr",
-        "--expt-extended-lambda",
-        "-gencode=arch=compute_90a,code=sm_90a",
-        "--ptxas-options=--register-usage-level=10" + (",--verbose" if "DG_PTXAS_VERBOSE" in os.environ else ""),
-        # Suppress some unnecessary warnings, such as unused variables for certain `constexpr` branch cases
-        "--diag-suppress=177,174,940",
-    ]
-    cxx_flags = ["-fPIC", "-O3", "-Wno-deprecated-declarations", "-Wno-abi"]
+    cpp_standard = int(os.getenv("DG_NVCC_OVERRIDE_CPP_STANDARD", 20))
+    nvcc_flags = [f"-std=c++{cpp_standard}", "-shared", "-O3", "--expt-relaxed-constexpr", "--expt-extended-lambda",
+                  "-gencode=arch=compute_90a,code=sm_90a",
+                  "--ptxas-options=--register-usage-level=10" + (",--verbose" if "DG_PTXAS_VERBOSE" in os.environ else ""),
+                  # Suppress some unnecessary warnings, such as unused variables for certain `constexpr` branch cases
+                  "--diag-suppress=39,174,177,940"]
+    cxx_flags = ["-fPIC", "-O3", "-Wno-deprecated-declarations", "-Wno-abi", "-fconcepts"]
     flags = [*nvcc_flags, f'--compiler-options={",".join(cxx_flags)}']
     include_dirs = [get_jit_include_dir()]
 
@@ -155,8 +148,12 @@ def build(name: str, arg_defs: tuple, code: str) -> Runtime:
     # Compile into a temporary SO file
     so_path = f"{path}/kernel.so"
     tmp_so_path = f"{make_tmp_dir()}/nvcc.tmp.{str(uuid.uuid4())}.{hash_to_hex(so_path)}.so"
+
     # Compile
-    command = [get_nvcc_compiler()[0], src_path, "-o", tmp_so_path, *flags, *[f"-I{d}" for d in include_dirs]]
+    command = [get_nvcc_compiler()[0],
+               src_path, "-o", tmp_so_path,
+               *flags,
+               *[f"-I{d}" for d in include_dirs]]
     if os.getenv("DG_JIT_DEBUG", None) or os.getenv("DG_JIT_PRINT_NVCC_COMMAND", False):
         print(f"Compiling JIT runtime {name} with command {command}")
     return_code = subprocess.check_call(command)

ops/csrc/fp8/deep_gemm/jit/__init__.py

@@ -93,7 +93,7 @@ def parse_registers(line):
 
 
 def modify_segment(m, name, ffma_lines):
-    num_lines = len(ffma_lines)
+    num_lines = (len(ffma_lines) * 9 // 16) // 2 * 2
     assert num_lines % 2 == 0
 
     le_bytes, new_le_bytes = [], []

ops/csrc/fp8/deep_gemm/jit/compiler.py

@@ -23,8 +23,6 @@
 import paddle
 from paddle import Tensor
 
-from .template import map_ctype
-
 
 class Runtime:
     def __init__(self, path: str) -> None:
@@ -46,24 +44,52 @@ def is_path_valid(path: str) -> bool:
 
     def __call__(self, *args) -> int:
         # Load SO file
-        if self.lib is None or self.args is None:
+        if self.lib is None:
             self.lib = ctypes.CDLL(os.path.join(self.path, "kernel.so"))
-            with open(os.path.join(self.path, "kernel.args"), "r") as f:
-                self.args = eval(f.read())
-
-        # Check args and launch
-        assert len(args) == len(self.args), f"Expected {len(self.args)} arguments, got {len(args)}"
-        cargs = []
-        for arg, (name, dtype) in zip(args, self.args):
-            if isinstance(arg, Tensor):
-                assert arg.dtype == dtype, f"Expected tensor dtype `{dtype}` for `{name}`, got `{arg.dtype}`"
-            else:
-                assert isinstance(arg, dtype), f"Expected built-in type `{dtype}` for `{name}`, got `{type(arg)}`"
-            cargs.append(map_ctype(arg))
 
+        if len(args) == 9:
+            cargs = [
+                ctypes.c_void_p(args[0].data_ptr()),
+                ctypes.c_void_p(args[1].data_ptr()),
+                ctypes.c_void_p(args[2].data_ptr()),
+                ctypes.c_void_p(args[3].data_ptr()),
+                ctypes.c_void_p(args[4].data_ptr()),
+                ctypes.c_int(args[5]),
+                ctypes.c_void_p(args[6].cuda_stream),
+                ctypes.c_int(args[7]),
+                ctypes.c_int(args[8]),
+            ]
+        elif len(args) == 10:
+            cargs = [
+                ctypes.c_void_p(args[0].data_ptr()),
+                ctypes.c_void_p(args[1].data_ptr()),
+                ctypes.c_void_p(args[2].data_ptr()),
+                ctypes.c_void_p(args[3].data_ptr()),
+                ctypes.c_void_p(args[4].data_ptr()),
+                ctypes.c_void_p(args[5].data_ptr()),
+                ctypes.c_int(args[6]),
+                ctypes.c_void_p(args[7].cuda_stream),
+                ctypes.c_int(args[8]),
+                ctypes.c_int(args[9]),
+            ]
+        elif len(args) == 11:
+            cargs = [
+                ctypes.c_void_p(args[0].data_ptr()),
+                ctypes.c_void_p(args[1].data_ptr()),
+                ctypes.c_void_p(args[2].data_ptr()),
+                ctypes.c_void_p(args[3].data_ptr()),
+                ctypes.c_void_p(args[4].data_ptr()),
+                ctypes.c_void_p(args[5].data_ptr()),
+                ctypes.c_int(args[6]),
+                ctypes.c_int(args[7]),
+                ctypes.c_void_p(args[8].cuda_stream),
+                ctypes.c_int(args[9]),
+                ctypes.c_int(args[10]),
+            ]
+        else:
+            raise ValueError("Invalid number of arguments")
         return_code = ctypes.c_int(0)
         self.lib.launch(*cargs, ctypes.byref(return_code))
-        return return_code.value
 
 
 class RuntimeCache: