diff --git a/CMakeLists.txt b/CMakeLists.txt index 46f02038..771ebbbc 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -55,27 +55,9 @@ add_link_options(-s -Wl,-z,relro -Wl,-z,now) set(CMAKE_CPP_COMPILE_OPTIONS -xc++ "SHELL:-include stdint.h" "SHELL:-include stddef.h") -include_directories(${ASCEND_HOME_PATH}/include +include_directories(${ASCEND_CANN_PACKAGE_PATH}/include ${ASCEND_DRIVER_PATH}/kernel/inc) -if(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/tools/tikcpp/ascendc_kernel_cmake) - set(ASCENDC_CMAKE_DIR - ${ASCEND_CANN_PACKAGE_PATH}/tools/tikcpp/ascendc_kernel_cmake) -elseif(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake) - set(ASCENDC_CMAKE_DIR - ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake) -elseif(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/ascendc_devkit/tikcpp/samples/cmake) - set(ASCENDC_CMAKE_DIR - ${ASCEND_CANN_PACKAGE_PATH}/ascendc_devkit/tikcpp/samples/cmake) -else() - message( - FATAL_ERROR - "ascendc_kernel_cmake does not exist, please check whether the cann package is installed." - ) -endif() - -include(${ASCENDC_CMAKE_DIR}/ascendc.cmake) - include(FetchContent) # certain operations need newer pto-isa header, not CANN 8.5.0 default (pin @@ -108,21 +90,35 @@ message("* TORCH_NPU_PATH : ${TORCH_NPU_PATH}") message("* TORCH_LIBRARIES : ${TORCH_LIBRARIES}") message("***********************************************************") -ascendc_library( - no_workspace_kernel - SHARED - csrc/kernel/kernel_tri_inv_col_sweep.cpp - csrc/kernel/kernel_abs.cpp - csrc/kernel/kernel_csr_gather.cpp - csrc/kernel/kernel_simple_matmul.cpp - csrc/kernel/kernel_batch_matrix_square.cpp - csrc/kernel/kernel_tri_inv_rec_unroll.cpp - csrc/kernel/kernel_tri_inv_trick.cpp - csrc/kernel/kernel_swiglu.cpp) - -ascendc_include_directories( - no_workspace_kernel PRIVATE ${libpto_isa_headers_SOURCE_DIR}/include - ${libpto_isa_headers_SOURCE_DIR}/include/pto/common) +set(KERNEL_SOURCES + ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernel/kernel_tri_inv_col_sweep.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernel/kernel_abs.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernel/kernel_simple_matmul.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernel/kernel_batch_matrix_square.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernel/kernel_tri_inv_rec_unroll.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernel/kernel_tri_inv_trick.cpp + # ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernel/kernel_csr_gather.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/csrc/kernel/kernel_swiglu.cpp) + +set(NO_WORKSPACE_KERNEL_LIB + ${CMAKE_CURRENT_BINARY_DIR}/libno_workspace_kernel.so) + +add_custom_command( + OUTPUT ${NO_WORKSPACE_KERNEL_LIB} + COMMAND + bisheng -fPIC -shared -xcce -O0 -g -std=c++17 --npu-arch=dav-2201 + -I${libpto_isa_headers_SOURCE_DIR}/include ${KERNEL_SOURCES} -o + ${NO_WORKSPACE_KERNEL_LIB} + DEPENDS ${KERNEL_SOURCES} + COMMENT "Building no_workspace_kernel with bisheng compiler") + +add_custom_target(no_workspace_kernel_build ALL + DEPENDS ${NO_WORKSPACE_KERNEL_LIB}) + +add_library(no_workspace_kernel SHARED IMPORTED GLOBAL) +set_target_properties(no_workspace_kernel PROPERTIES IMPORTED_LOCATION + ${NO_WORKSPACE_KERNEL_LIB}) +add_dependencies(no_workspace_kernel no_workspace_kernel_build) pybind11_add_module(pto_kernels_ops csrc/host/pybind11.cpp) @@ -154,7 +150,7 @@ if(PIP_INSTALL) LINK_FLAGS "-Wl,-rpath,\${ORIGIN}/lib") # install dynamic libraries under site-packages/pto_kernels/libs - install(TARGETS no_workspace_kernel LIBRARY DESTINATION pto_kernels/lib) + install(FILES ${NO_WORKSPACE_KERNEL_LIB} DESTINATION pto_kernels/lib) else() message(STATUS ">>>======================================================") message(STATUS ">>> Ignoring dynamic libraries COPY inside Python wheel.") diff --git a/Makefile b/Makefile index 666f32b2..31b3a891 100644 --- a/Makefile +++ b/Makefile @@ -5,7 +5,10 @@ # https://github.com/huawei-csl/pto-kernels/ # for the full License text. # -------------------------------------------------------------------------------- -.PHONY: clean setup_once build_wheel install test +PTO_LIB_PATH ?= $(ASCEND_TOOLKIT_HOME) +CSRC_KERNEL_DIR := csrc/kernel + +.PHONY: clean setup_once build_cmake build_wheel install docs test test_tri_inv clean: rm -rf build/ dist/ extra-info/ *.egg-info/ kernel_meta/ @@ -20,6 +23,19 @@ build_cmake: clean build_wheel: export CMAKE_GENERATOR="Unix Makefiles" && pip wheel -v . --extra-index-url https://download.pytorch.org/whl/cpu + +# 'make compile_abs' compiles 'kernel_abs.cpp' into 'libkernel_abs.so' without building the whole wheel package. +# This is useful for development and debugging of individual kernels. +compile_%: + bisheng -fPIC -shared -xcce -DMEMORY_BASE -O2 -std=c++17 \ + -I$(CSRC_KERNEL_DIR) \ + -I$(PTO_LIB_PATH)/include \ + --npu-arch=dav-2201 \ + -Wno-ignored-attributes \ + $(CSRC_KERNEL_DIR)/kernel_$*.cpp \ + -o libkernel_$*.so + + install: python3 -m pip install --force-reinstall pto_kernels-*.whl diff --git a/csrc/host/pybind11.cpp b/csrc/host/pybind11.cpp index 134195b7..001cb3be 100644 --- a/csrc/host/pybind11.cpp +++ b/csrc/host/pybind11.cpp @@ -11,10 +11,10 @@ for the full License text. #include "torch_abs.h" #include "torch_batch_matrix_square.h" -#include "torch_csr_gather.h" +// #include "torch_csr_gather.h" #include "torch_simple_matmul.h" #include "torch_swiglu.h" -#include "torch_tri_inv.h" +#include "torch_tri_inv_col_sweep.h" #include "torch_tri_inv_rec_unroll.h" #include "torch_tri_inv_trick.h" @@ -37,7 +37,7 @@ PYBIND11_MODULE(pto_kernels_ops, m) { pybind11::arg("device_id") = 0); m.def("pto_abs", &pto_isa_ops::run_abs); m.def("pto_batch_matrix_square", &pto_isa_ops::run_batch_matrix_square); - m.def("pto_csr_gather", &pto_isa_ops::run_csr_gather); + // m.def("pto_csr_gather", &pto_isa_ops::run_csr_gather); m.def("pto_simple_matmul", &pto_isa_ops::run_simple_matmul); m.def("pto_swiglu", &pto_isa_ops::run_swiglu, py::arg("x"), py::arg("dim") = -1); diff --git a/csrc/host/torch_abs.h b/csrc/host/torch_abs.h index 3890c811..bd0d58b5 100644 --- a/csrc/host/torch_abs.h +++ b/csrc/host/torch_abs.h @@ -9,14 +9,18 @@ for the full License text. #pragma once #include +#include #include -#include "aclrtlaunch_vabs_fp16.h" -#include "aclrtlaunch_vabs_fp32.h" #include "utils.h" -namespace pto_isa_ops { +extern "C" void call_vabs_fp16(uint32_t blockDim, aclrtStream stream, void* x, + void* y, uint32_t num_elements); + +extern "C" void call_vabs_fp32(uint32_t blockDim, aclrtStream stream, void* x, + void* y, uint32_t num_elements); +namespace pto_isa_ops { /** * @brief Runs element-wise absolute value. * @@ -26,7 +30,7 @@ namespace pto_isa_ops { at::Tensor run_abs(const at::Tensor& x) { const auto dtype = x.options().dtype(); - at::Tensor z = at::empty_like(x); + const at::Tensor z = at::empty_like(x); // Define the number of blocks of vector core const uint32_t total_size = x.numel(); // FIXME: tile length is fixed to 128 for now @@ -34,18 +38,19 @@ at::Tensor run_abs(const at::Tensor& x) { // Persistent kernel launch parameter uint32_t total_tiles = (total_size + TILE_SIZE - 1) / TILE_SIZE; - uint32_t block_dim = GetNumVectorCores(); + uint32_t block_dim = GetNumCubeCores(); if (total_tiles < block_dim) { block_dim = total_tiles; } + auto acl_stream = c10_npu::getCurrentNPUStream().stream(true); if (dtype == at::kHalf) { - EXEC_KERNEL_CMD(vabs_fp16, block_dim, x, z, total_size); - + call_vabs_fp16(block_dim, acl_stream, ConvertType(x), ConvertType(z), + total_size); } else if (dtype == at::kFloat) { - EXEC_KERNEL_CMD(vabs_fp32, block_dim, x, z, total_size); - + call_vabs_fp32(block_dim, acl_stream, ConvertType(x), ConvertType(z), + total_size); } else { throw std::runtime_error("Unsupported dtype for `pto_abs` kernel"); } diff --git a/csrc/host/torch_batch_matrix_square.h b/csrc/host/torch_batch_matrix_square.h index 662d3cc2..55e72606 100644 --- a/csrc/host/torch_batch_matrix_square.h +++ b/csrc/host/torch_batch_matrix_square.h @@ -9,12 +9,18 @@ for the full License text. #pragma once #include +#include #include -#include "aclrtlaunch_batch_matrix_square_fp16.h" -#include "aclrtlaunch_batch_matrix_square_fp32.h" #include "utils.h" +extern "C" void call_batch_matrix_square_fp16(uint32_t blockDim, + aclrtStream stream, void* z, + void* x, uint32_t matrix_size); +extern "C" void call_batch_matrix_square_fp32(uint32_t blockDim, + aclrtStream stream, void* z, + void* x, uint32_t matrix_size); + namespace pto_isa_ops { /** @@ -46,10 +52,13 @@ at::Tensor run_batch_matrix_square(const at::Tensor& x) { at::zeros({block_dim, matrix_size, matrix_size}, at::TensorOptions().dtype(dtype_out).device(device)); + auto acl_stream = c10_npu::getCurrentNPUStream().stream(true); if (dtype == at::kHalf) { - EXEC_KERNEL_CMD(batch_matrix_square_fp16, block_dim, z, x, matrix_size); + call_batch_matrix_square_fp16(block_dim, acl_stream, ConvertType(z), + ConvertType(x), matrix_size); } else if (dtype == at::kFloat) { - EXEC_KERNEL_CMD(batch_matrix_square_fp32, block_dim, z, x, matrix_size); + call_batch_matrix_square_fp32(block_dim, acl_stream, ConvertType(z), + ConvertType(x), matrix_size); } return z; diff --git a/csrc/host/torch_simple_matmul.h b/csrc/host/torch_simple_matmul.h index bb517be9..13e78833 100644 --- a/csrc/host/torch_simple_matmul.h +++ b/csrc/host/torch_simple_matmul.h @@ -11,10 +11,15 @@ for the full License text. #include #include -#include "aclrtlaunch_simple_matmul_fp16.h" -#include "aclrtlaunch_simple_matmul_fp32.h" #include "utils.h" +extern "C" void call_simple_matmul_fp16(uint32_t blockDim, aclrtStream stream, + void* a, void* b, void* c, + uint32_t matrix_size); +extern "C" void call_simple_matmul_fp32(uint32_t blockDim, aclrtStream stream, + void* a, void* b, void* c, + uint32_t matrix_size); + namespace pto_isa_ops { /** @@ -45,10 +50,13 @@ at::Tensor run_simple_matmul(const at::Tensor& a, const at::Tensor& b) { at::ones({matrix_size, matrix_size}, at::TensorOptions().dtype(dtype_out).device(device)); + auto acl_stream = c10_npu::getCurrentNPUStream().stream(true); if (dtype == at::kHalf) { - EXEC_KERNEL_CMD(simple_matmul_fp16, block_dim, a, b, c, matrix_size); + call_simple_matmul_fp16(block_dim, acl_stream, ConvertType(a), + ConvertType(b), ConvertType(c), matrix_size); } else if (dtype == at::kFloat) { - EXEC_KERNEL_CMD(simple_matmul_fp32, block_dim, a, b, c, matrix_size); + call_simple_matmul_fp32(block_dim, acl_stream, ConvertType(a), + ConvertType(b), ConvertType(c), matrix_size); } return c; diff --git a/csrc/host/torch_swiglu.h b/csrc/host/torch_swiglu.h index acc7ba14..12eb6a56 100644 --- a/csrc/host/torch_swiglu.h +++ b/csrc/host/torch_swiglu.h @@ -13,7 +13,9 @@ for the full License text. #include -#include "aclrtlaunch_swiglu_fp16.h" +extern "C" uint32_t call_swiglu_fp16(uint32_t blockDim, aclrtStream stream, + void* x, void* y, uint32_t batch, + uint32_t input_n); #include "utils.h" namespace pto_isa_ops { @@ -62,8 +64,10 @@ at::Tensor run_swiglu(const at::Tensor& x, int64_t dim = -1) { const uint32_t input_n = static_cast(input_n_i64); const uint32_t block_dim = GetNumCubeCores(); + auto acl_stream = c10_npu::getCurrentNPUStream().stream(true); at::Tensor y = at::empty({batch_i64, output_n_i64}, x.options()); - EXEC_KERNEL_CMD(swiglu_fp16, block_dim, x, y, batch, input_n); + call_swiglu_fp16(block_dim, acl_stream, ConvertType(x), ConvertType(y), batch, + input_n); return y; } diff --git a/csrc/host/torch_tri_inv.h b/csrc/host/torch_tri_inv_col_sweep.h similarity index 58% rename from csrc/host/torch_tri_inv.h rename to csrc/host/torch_tri_inv_col_sweep.h index 951e8d4e..b15dbd3c 100644 --- a/csrc/host/torch_tri_inv.h +++ b/csrc/host/torch_tri_inv_col_sweep.h @@ -11,10 +11,18 @@ for the full License text. #include #include -#include "aclrtlaunch_triv_inv_col_sweep_fp16.h" -#include "aclrtlaunch_triv_inv_col_sweep_fp32.h" #include "utils.h" +extern "C" void call_triv_inv_col_sweep_fp16(uint32_t blockDim, + aclrtStream stream, void* M_inv, + void* M, uint32_t num_elems, + uint32_t matrix_size); + +extern "C" void call_triv_inv_col_sweep_fp32(uint32_t blockDim, + aclrtStream stream, void* M_inv, + void* M, uint32_t num_elems, + uint32_t matrix_size); + namespace pto_isa_ops { /** @@ -48,16 +56,16 @@ at::Tensor run_tri_inv(const at::Tensor& x) { const at::Tensor z = at::empty_like(x); + auto acl_stream = c10_npu::getCurrentNPUStream().stream(true); if (dtype == at::kHalf) { - EXEC_KERNEL_CMD(triv_inv_col_sweep_fp16, block_dim, x, z, num_elems, - matrix_size); - + call_triv_inv_col_sweep_fp16(block_dim, acl_stream, ConvertType(z), + ConvertType(x), num_elems, matrix_size); } else if (dtype == at::kFloat) { - EXEC_KERNEL_CMD(triv_inv_col_sweep_fp32, block_dim, x, z, num_elems, - matrix_size); - + call_triv_inv_col_sweep_fp32(block_dim, acl_stream, ConvertType(z), + ConvertType(x), num_elems, matrix_size); } else { - throw std::runtime_error("Unsupported dtype for `tri_inv` kernel"); + throw std::runtime_error( + "Unsupported dtype for `triv_inv_col_sweep` kernel"); } return z; diff --git a/csrc/host/torch_tri_inv_rec_unroll.h b/csrc/host/torch_tri_inv_rec_unroll.h index 0ec9e622..ca362a83 100644 --- a/csrc/host/torch_tri_inv_rec_unroll.h +++ b/csrc/host/torch_tri_inv_rec_unroll.h @@ -9,11 +9,16 @@ for the full License text. #pragma once #include +#include #include -#include "aclrtlaunch_tri_inv_rec_unroll_fp16.h" #include "utils.h" +extern "C" void call_tri_inv_rec_unroll_fp16( + uint32_t blockDim, aclrtStream stream, void* M_inv, void* M, void* I_neg, + uint32_t matrix_size, uint32_t num_matrices, uint32_t num_bsnd_heads, + void* cu_seqlens); + namespace pto_isa_ops { /** @@ -72,14 +77,19 @@ at::Tensor run_tri_inv_rec_unroll( at::TensorOptions().dtype(dtype).device(device)); I_neg.fill_diagonal_(-1); + auto acl_stream = c10_npu::getCurrentNPUStream().stream(true); if (dtype == at::kHalf) { if (cu_seqlens.numel() == 1) { void* void_null_ptr = nullptr; - EXEC_KERNEL_CMD(tri_inv_rec_unroll_fp16, block_dim, M_inv, M, I_neg, - matrix_size, total_tiles, num_bsnd_heads, void_null_ptr); + call_tri_inv_rec_unroll_fp16(block_dim, acl_stream, ConvertType(M_inv), + ConvertType(M), ConvertType(I_neg), + matrix_size, total_tiles, num_bsnd_heads, + void_null_ptr); } else { - EXEC_KERNEL_CMD(tri_inv_rec_unroll_fp16, block_dim, M_inv, M, I_neg, - matrix_size, total_tiles, num_bsnd_heads, cu_seqlens); + call_tri_inv_rec_unroll_fp16(block_dim, acl_stream, ConvertType(M_inv), + ConvertType(M), ConvertType(I_neg), + matrix_size, total_tiles, num_bsnd_heads, + cu_seqlens.data_ptr()); } } diff --git a/csrc/host/torch_tri_inv_trick.h b/csrc/host/torch_tri_inv_trick.h index 36f6ac2d..1e28af35 100644 --- a/csrc/host/torch_tri_inv_trick.h +++ b/csrc/host/torch_tri_inv_trick.h @@ -11,9 +11,13 @@ for the full License text. #include #include -#include "aclrtlaunch_tri_inv_trick_fp16.h" #include "utils.h" +extern "C" void call_tri_inv_trick_fp16(uint32_t blockDim, aclrtStream stream, + void* M_inv, void* M, void* I_neg, + uint32_t matrix_size, + uint32_t max_block_size); + namespace pto_isa_ops { /** @@ -48,9 +52,12 @@ at::Tensor run_tri_inv_trick(const at::Tensor& M) { at::zeros({matrix_size, matrix_size}, at::TensorOptions().dtype(dtype).device(device)); I_neg.fill_diagonal_(-1); + + auto acl_stream = c10_npu::getCurrentNPUStream().stream(true); if (dtype == at::kHalf) { - EXEC_KERNEL_CMD(tri_inv_trick_fp16, block_dim, M_inv, M, I_neg, matrix_size, - max_block_size); + call_tri_inv_trick_fp16(block_dim, acl_stream, ConvertType(M_inv), + ConvertType(M), ConvertType(I_neg), matrix_size, + max_block_size); } return M_inv; diff --git a/csrc/host/utils.h b/csrc/host/utils.h index db004410..681771a7 100644 --- a/csrc/host/utils.h +++ b/csrc/host/utils.h @@ -22,9 +22,6 @@ namespace pto_isa_ops { #define DEVICE_TYPE c10::DeviceType::PrivateUse1 -// Copied from tools/build/asc_rt/ascendc_runtime.h to avoid dependency on the -// header file. See -// https://gitcode.com/cann/asc-devkit/blob/v8.5.0/tools/build/asc_rt/ascendc_runtime.h #define ASSERT_RETVAL(exp, ret) \ do { \ if (!(exp)) { \ @@ -132,18 +129,18 @@ constexpr auto ConvertTypes(Ts&... args) { return std::make_tuple(ConvertType(args)...); } -#define EXEC_KERNEL_CMD(kernel_name, blockdim, ...) \ - do { \ - auto acl_stream = c10_npu::getCurrentNPUStream().stream(false); \ - auto converted_params = pto_isa_ops::ConvertTypes(__VA_ARGS__); \ - auto acl_call = [acl_stream, blockdim, converted_params]() -> int { \ - std::apply( \ - [&](auto&&... params) { \ - ACLRT_LAUNCH_KERNEL(kernel_name)(blockdim, acl_stream, params...); \ - }, \ - converted_params); \ - return 0; \ - }; \ - at_npu::native::OpCommand::RunOpApi(#kernel_name, acl_call); \ +#define EXEC_KERNEL_CMD(kernel_name, blockdim, ...) \ + do { \ + auto acl_stream = c10_npu::getCurrentNPUStream().stream(false); \ + auto converted_params = pto_isa_ops::ConvertTypes(__VA_ARGS__); \ + auto acl_call = [acl_stream, blockdim, converted_params]() -> int { \ + std::apply( \ + [&](auto&&... params) { \ + kernel_name(blockdim, acl_stream, params...); \ + }, \ + converted_params); \ + return 0; \ + }; \ + at_npu::native::OpCommand::RunOpApi(#kernel_name, acl_call); \ } while (false) } // namespace pto_isa_ops diff --git a/csrc/kernel/constants.h b/csrc/kernel/constants.h new file mode 100644 index 00000000..647c1f25 --- /dev/null +++ b/csrc/kernel/constants.h @@ -0,0 +1,307 @@ +/** +Copyright (c) 2026 Huawei Technologies Co., Ltd. +All rights reserved. + +See LICENSE in the root of the software repository: +https://github.com/huawei-csl/pto-kernels/ +for the full License text. +*/ + +/** + * Minus identity matrices statically allocated in the binary of the + * kernel. + * + * To generate these matrices below, you can run the following Python code and +copy-paste the output into this file. Each matrix is stored in row-major order, +and has a trailing comma after each row to facilitate copy-pasting.: + * + * +```python +import numpy as np +for s in [16, 32, 64, 128]: + I_minus_s = -np.eye(s).astype("int") + np.savetxt(f"matrix_{s}.csv", I_minus_s, fmt='%5.0f', delimiter=",") + !sed '$!s/$/,/' matrix_{s}_with_trailing_comma.csv > matrix_{s}_final.csv +``` + */ +#pragma once + +#ifndef MEMORY_BASE +#define MEMORY_BASE +#endif +#include + +#define CONST_HALF_TO_GM(x) \ + reinterpret_cast<__gm__ half*>(const_cast<__gm__ half*>((x))) + +// clang-format off +constexpr static __gm__ half minus_eye_fp16_16[256] = {-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1}; + +constexpr static __gm__ half minus_eye_fp16_32[1024] = {-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1}; + + +constexpr static __gm__ half minus_eye_fp16_64[4096] = {-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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CONST_HALF_TO_GM(minus_eye_fp16_16); + break; + + case 32: + return CONST_HALF_TO_GM(minus_eye_fp16_32); + break; + + case 64: + return CONST_HALF_TO_GM(minus_eye_fp16_64); + break; + + case 128: + return CONST_HALF_TO_GM(minus_eye_fp16_128); + break; + + default: + return nullptr; + break; + } +} diff --git a/csrc/kernel/kernel_abs.cpp b/csrc/kernel/kernel_abs.cpp index 5f09f7ff..d24e13ab 100644 --- a/csrc/kernel/kernel_abs.cpp +++ b/csrc/kernel/kernel_abs.cpp @@ -7,13 +7,12 @@ See LICENSE in the root of the software repository: for the full License text. */ -#if __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) - -#define MEMORY_BASE - -#include +#include "kernel_utils.h" +// clang-format off: so it does not get wrongfully flagged by linter +#ifndef GM_ADDR #define GM_ADDR __gm__ uint8_t* // To avoid #include "kernel_operator.h" +#endif using namespace pto; @@ -113,14 +112,30 @@ AICORE void runTAbs(__gm__ T* x, __gm__ T* z, uint32_t total_size) { extern "C" __global__ AICORE void vabs_fp16(GM_ADDR x, GM_ADDR z, uint32_t in_length) { +#if __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) constexpr uint32_t TILE_LEN = 128; runTAbs((__gm__ half*)x, (__gm__ half*)z, in_length); +#else + (void)x; + (void)z; + (void)in_length; +#endif } extern "C" __global__ AICORE void vabs_fp32(GM_ADDR x, GM_ADDR z, uint32_t in_length) { +#if __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) + constexpr uint32_t TILE_LEN = 128; runTAbs((__gm__ float*)x, (__gm__ float*)z, in_length); +#else + (void)x; + (void)z; + (void)in_length; +#endif } -#endif +extern "C" void call_vabs_fp16(uint32_t blockDim, void* stream, GM_ADDR x, + GM_ADDR y, uint32_t in_length) { + vabs_fp16<<>>(x, y, in_length); +} diff --git a/csrc/kernel/kernel_batch_matrix_square.cpp b/csrc/kernel/kernel_batch_matrix_square.cpp index 34827034..409b4006 100644 --- a/csrc/kernel/kernel_batch_matrix_square.cpp +++ b/csrc/kernel/kernel_batch_matrix_square.cpp @@ -16,9 +16,6 @@ using namespace pto; template AICORE void runKernelBatchMatrixSquare(__gm__ OutputT* z, __gm__ InputT* x) { -#if (__CHECK_FEATURE_AT_PRECOMPILE) || \ - (__CCE_AICORE__ == 220 && defined(__DAV_C220_CUBE__)) // Cube compilation - constexpr uint32_t TileLen = MatrixSize * MatrixSize; const uint32_t global_index = get_block_idx() * TileLen; @@ -79,9 +76,6 @@ AICORE void runKernelBatchMatrixSquare(__gm__ OutputT* z, __gm__ InputT* x) { wait_flag(PIPE_M, PIPE_FIX, EVENT_ID0); // FIX pipe waits for M pipe to set flag TSTORE(z_global_out, c_l0_tile); -#else -// Nothing to do on AIV -#endif } template @@ -108,13 +102,32 @@ AICORE void run_batch_matrix_square(__gm__ float* z, __gm__ InputT* x, } } -extern "C" __global__ AICORE void batch_matrix_square_fp16( - __gm__ void* z, __gm__ void* x, uint32_t matrix_size) { +__global__ AICORE void batch_matrix_square_fp16(__gm__ void* z, __gm__ void* x, + uint32_t matrix_size) { +#if (__CHECK_FEATURE_AT_PRECOMPILE) || \ + (__CCE_AICORE__ == 220 && defined(__DAV_C220_CUBE__)) // Cube compilation run_batch_matrix_square((__gm__ float*)z, (__gm__ half*)x, matrix_size); +#endif } -extern "C" __global__ AICORE void batch_matrix_square_fp32( - __gm__ void* z, __gm__ void* x, uint32_t matrix_size) { +__global__ AICORE void batch_matrix_square_fp32(__gm__ void* z, __gm__ void* x, + uint32_t matrix_size) { +#if (__CHECK_FEATURE_AT_PRECOMPILE) || \ + (__CCE_AICORE__ == 220 && defined(__DAV_C220_CUBE__)) // Cube compilation + run_batch_matrix_square((__gm__ float*)z, (__gm__ float*)x, matrix_size); +#endif +} + +extern "C" void call_batch_matrix_square_fp16(uint32_t block_dim, void* stream, + uint8_t* z, uint8_t* x, + uint32_t matrix_size) { + batch_matrix_square_fp16<<>>(z, x, matrix_size); +} + +extern "C" void call_batch_matrix_square_fp32(uint32_t block_dim, void* stream, + uint8_t* z, uint8_t* x, + uint32_t matrix_size) { + batch_matrix_square_fp32<<>>(z, x, matrix_size); } diff --git a/csrc/kernel/kernel_simple_matmul.cpp b/csrc/kernel/kernel_simple_matmul.cpp index 465ca488..c044d082 100644 --- a/csrc/kernel/kernel_simple_matmul.cpp +++ b/csrc/kernel/kernel_simple_matmul.cpp @@ -6,31 +6,10 @@ See LICENSE in the root of the software repository: https://github.com/huawei-csl/pto-kernels/ for the full License text. */ -#if defined __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) -// Placeholder for VEC compilation (the real kernel is CUBE-only). #define MEMORY_BASE -#include - -extern "C" __global__ AICORE void simple_matmul_fp16(__gm__ void* a, - __gm__ void* b, - __gm__ void* c, - uint32_t matrix_size) {} - -extern "C" __global__ AICORE void simple_matmul_fp32(__gm__ void* a, - __gm__ void* b, - __gm__ void* c, - uint32_t matrix_size) {} - -#elif (__CHECK_FEATURE_AT_PRECOMPILE) || \ - (__CCE_AICORE__ == 220 && defined(__DAV_C220_CUBE__)) - -#define MEMORY_BASE - #include -#define GM_ADDR __gm__ uint8_t* // To avoid #include "kernel_operator.h" - using namespace pto; constexpr unsigned NUM_BLOCKS = 20; // number of AICs @@ -124,6 +103,9 @@ AICORE void run_simple_matmul(__gm__ T* a, __gm__ T* b, __gm__ float* c, static_assert(std::is_same_v or std::is_same_v, "simple_matmul supports only fp16/fp32."); +#if (__CHECK_FEATURE_AT_PRECOMPILE) || \ + (__CCE_AICORE__ == 220 && defined(__DAV_C220_CUBE__)) // Cube compilation + switch (matrix_size) { case 16: runKernelSimpleMatMul(a, b, c); @@ -144,22 +126,31 @@ AICORE void run_simple_matmul(__gm__ T* a, __gm__ T* b, __gm__ float* c, runKernelSimpleMatMul(a, b, c); break; } +#endif } -extern "C" __global__ AICORE void simple_matmul_fp16(__gm__ void* a, - __gm__ void* b, - __gm__ void* c, - uint32_t matrix_size) { +__global__ AICORE void simple_matmul_fp16(__gm__ void* a, __gm__ void* b, + __gm__ void* c, + uint32_t matrix_size) { run_simple_matmul((__gm__ half*)a, (__gm__ half*)b, (__gm__ float*)c, matrix_size); } -extern "C" __global__ AICORE void simple_matmul_fp32(__gm__ void* a, - __gm__ void* b, - __gm__ void* c, - uint32_t matrix_size) { +__global__ AICORE void simple_matmul_fp32(__gm__ void* a, __gm__ void* b, + __gm__ void* c, + uint32_t matrix_size) { run_simple_matmul((__gm__ float*)a, (__gm__ float*)b, (__gm__ float*)c, matrix_size); } -#endif +extern "C" void call_simple_matmul_fp16(uint32_t block_dim, void* stream, + uint8_t* a, uint8_t* b, uint8_t* c, + uint32_t matrix_size) { + simple_matmul_fp16<<>>(a, b, c, matrix_size); +} + +extern "C" void call_simple_matmul_fp32(uint32_t block_dim, void* stream, + uint8_t* a, uint8_t* b, uint8_t* c, + uint32_t matrix_size) { + simple_matmul_fp32<<>>(a, b, c, matrix_size); +} diff --git a/csrc/kernel/kernel_swiglu.cpp b/csrc/kernel/kernel_swiglu.cpp index 234515a2..a7b1aa71 100644 --- a/csrc/kernel/kernel_swiglu.cpp +++ b/csrc/kernel/kernel_swiglu.cpp @@ -132,8 +132,8 @@ AICORE inline TileConfig makeTileConfig(uint32_t batch, uint32_t output_n, }; } -AICORE inline bool preferTileConfig(const TileConfig &cand, - const TileConfig &best) { +AICORE inline bool preferTileConfig(const TileConfig& cand, + const TileConfig& best) { if (cand.meets_target != best.meets_target) { return cand.meets_target; } @@ -187,8 +187,8 @@ AICORE inline TileWork makeTileWork(uint32_t global_tile_idx, } template -AICORE inline void computeSwiGLUTile(TileData &x0Tile, TileData &x1Tile, - TileData &yTile) { +AICORE inline void computeSwiGLUTile(TileData& x0Tile, TileData& x1Tile, + TileData& yTile) { TMULS(yTile, x0Tile, (T)-1); pipe_barrier(PIPE_V); TEXP(yTile, yTile); @@ -204,8 +204,8 @@ AICORE inline void computeSwiGLUTile(TileData &x0Tile, TileData &x1Tile, // col_count is padded for UB tile sizing; col_count_store is the actual GM // element count so tail columns never read past the tensor end. template -AICORE void issueTLoad(__gm__ T *x, uint32_t input_n, uint32_t output_n, - const TileWork &tile, unsigned x0_base, unsigned x1_base, +AICORE void issueTLoad(__gm__ T* x, uint32_t input_n, uint32_t output_n, + const TileWork& tile, unsigned x0_base, unsigned x1_base, event_t ev) { using TileShapeND = TileShape2D; using DynStrideND = Stride<1, 1, 1, DYNAMIC, 1>; @@ -235,7 +235,7 @@ AICORE void issueTLoad(__gm__ T *x, uint32_t input_n, uint32_t output_n, // col_count is padded for UB tile sizing; col_count_store is the actual GM // element count so tail columns never write past the tensor end. template -AICORE void issueTStore(__gm__ T *y, uint32_t output_n, const TileWork &tile, +AICORE void issueTStore(__gm__ T* y, uint32_t output_n, const TileWork& tile, unsigned y_base, event_t ev) { using TileShapeND = TileShape2D; using DynStrideND = Stride<1, 1, 1, DYNAMIC, 1>; @@ -259,7 +259,7 @@ AICORE void issueTStore(__gm__ T *y, uint32_t output_n, const TileWork &tile, } template -AICORE void runTSwiGLUTiled(__gm__ T *x, __gm__ T *y, uint32_t batch, +AICORE void runTSwiGLUTiled(__gm__ T* x, __gm__ T* y, uint32_t batch, uint32_t input_n, uint32_t num_cores, uint32_t vid, uint32_t row_tile_len) { #if __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) @@ -341,7 +341,7 @@ AICORE void runTSwiGLUTiled(__gm__ T *x, __gm__ T *y, uint32_t batch, } template -AICORE void runTSwiGLUMainTiled(__gm__ T *x, __gm__ T *y, uint32_t batch, +AICORE void runTSwiGLUMainTiled(__gm__ T* x, __gm__ T* y, uint32_t batch, uint32_t input_n, uint32_t num_cores, uint32_t vid) { #if __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) @@ -372,7 +372,7 @@ AICORE void runTSwiGLUMainTiled(__gm__ T *x, __gm__ T *y, uint32_t batch, } template -AICORE void runTSwiGLU(__gm__ T *x, __gm__ T *y, uint32_t batch, +AICORE void runTSwiGLU(__gm__ T* x, __gm__ T* y, uint32_t batch, uint32_t input_n, uint32_t num_cores, uint32_t vid) { #if __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) set_mask_norm(); @@ -400,14 +400,13 @@ AICORE void runTSwiGLU(__gm__ T *x, __gm__ T *y, uint32_t batch, #endif -extern "C" __global__ AICORE void swiglu_fp16(GM_ADDR x, GM_ADDR y, - uint32_t batch, - uint32_t input_n) { +__global__ AICORE void swiglu_fp16(GM_ADDR x, GM_ADDR y, uint32_t batch, + uint32_t input_n) { #if defined(__DAV_VEC__) const uint32_t num_cores = get_block_num() * get_subblockdim(); const uint32_t vid = get_block_idx() * get_subblockdim() + get_subblockid(); - runTSwiGLU((__gm__ half *)x, (__gm__ half *)y, batch, input_n, - num_cores, vid); + runTSwiGLU((__gm__ half*)x, (__gm__ half*)y, batch, input_n, num_cores, + vid); #else (void)x; (void)y; @@ -416,8 +415,7 @@ extern "C" __global__ AICORE void swiglu_fp16(GM_ADDR x, GM_ADDR y, #endif } -extern "C" void call_swiglu_kernel(uint32_t blockDim, void *stream, uint8_t *x, - uint8_t *y, uint32_t batch, - uint32_t input_n) { - swiglu_fp16<<>>(x, y, batch, input_n); +extern "C" void call_swiglu_fp16(uint32_t block_dim, void* stream, GM_ADDR x, + GM_ADDR y, uint32_t batch, uint32_t input_n) { + swiglu_fp16<<>>(x, y, batch, input_n); } diff --git a/csrc/kernel/kernel_tri_inv_col_sweep.cpp b/csrc/kernel/kernel_tri_inv_col_sweep.cpp index 062b2a63..250700c5 100644 --- a/csrc/kernel/kernel_tri_inv_col_sweep.cpp +++ b/csrc/kernel/kernel_tri_inv_col_sweep.cpp @@ -7,15 +7,12 @@ See LICENSE in the root of the software repository: for the full License text. */ -#if __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) - #define MEMORY_BASE - #include #include "kernel_utils.h" -#define GM_ADDR __gm__ uint8_t* // To avoid #include "kernel_operator.h" +#define GM_ADDR __gm__ uint8_t* using namespace pto; @@ -32,6 +29,8 @@ using namespace pto; template AICORE void runTTriInv(__gm__ T* vec_in, __gm__ T* vec_out, uint32_t total_length) { +#if __CCE_AICORE__ == 220 && defined(__DAV_C220_VEC__) + set_mask_norm(); set_vector_mask(-1, -1); @@ -159,10 +158,12 @@ AICORE void runTTriInv(__gm__ T* vec_in, __gm__ T* vec_out, } wait_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID0); wait_flag(PIPE_MTE3, PIPE_V, EVENT_ID0); +#endif } -extern "C" __global__ AICORE void triv_inv_col_sweep_fp16( - GM_ADDR x, GM_ADDR z, uint32_t in_length, uint32_t matrix_size) { +__global__ AICORE void triv_inv_col_sweep_fp16(GM_ADDR x, GM_ADDR z, + uint32_t in_length, + uint32_t matrix_size) { if (matrix_size == 16) { runTTriInv((__gm__ half*)x, (__gm__ half*)z, in_length); } else if (matrix_size == 32) { @@ -174,8 +175,9 @@ extern "C" __global__ AICORE void triv_inv_col_sweep_fp16( } } -extern "C" __global__ AICORE void triv_inv_col_sweep_fp32( - GM_ADDR x, GM_ADDR z, uint32_t in_length, uint32_t matrix_size) { +__global__ AICORE void triv_inv_col_sweep_fp32(GM_ADDR x, GM_ADDR z, + uint32_t in_length, + uint32_t matrix_size) { if (matrix_size == 16) { runTTriInv((__gm__ float*)x, (__gm__ float*)z, in_length); } else if (matrix_size == 32) { @@ -187,4 +189,20 @@ extern "C" __global__ AICORE void triv_inv_col_sweep_fp32( } } -#endif +extern "C" void call_triv_inv_col_sweep_fp16(uint32_t block_dim, void* stream, + uint8_t* tensor_out, + uint8_t* tensor_in, + uint32_t in_length, + uint32_t matrix_size) { + triv_inv_col_sweep_fp16<<>>( + tensor_in, tensor_out, in_length, matrix_size); +} + +extern "C" void call_triv_inv_col_sweep_fp32(uint32_t block_dim, void* stream, + uint8_t* tensor_out, + uint8_t* tensor_in, + uint32_t in_length, + uint32_t matrix_size) { + triv_inv_col_sweep_fp32<<>>( + tensor_in, tensor_out, in_length, matrix_size); +} diff --git a/csrc/kernel/kernel_tri_inv_rec_unroll.cpp b/csrc/kernel/kernel_tri_inv_rec_unroll.cpp index 8924aeee..1f27ae2a 100644 --- a/csrc/kernel/kernel_tri_inv_rec_unroll.cpp +++ b/csrc/kernel/kernel_tri_inv_rec_unroll.cpp @@ -816,3 +816,12 @@ extern "C" __global__ AICORE void tri_inv_rec_unroll_fp16( } } } + +extern "C" void call_tri_inv_rec_unroll_fp16( + uint32_t block_dim, void* stream, uint8_t* tensor_out, uint8_t* tensor_in, + uint8_t* minus_identity_in, uint32_t matrix_size, uint32_t num_matrices, + uint32_t num_bsnd_heads, uint8_t* cu_seqlens) { + tri_inv_rec_unroll_fp16<<>>( + tensor_out, tensor_in, minus_identity_in, matrix_size, num_matrices, + num_bsnd_heads, cu_seqlens); +} diff --git a/csrc/kernel/kernel_tri_inv_trick.cpp b/csrc/kernel/kernel_tri_inv_trick.cpp index 206a1618..0136f241 100644 --- a/csrc/kernel/kernel_tri_inv_trick.cpp +++ b/csrc/kernel/kernel_tri_inv_trick.cpp @@ -205,12 +205,21 @@ AICORE void run_tri_inv_trick(__gm__ float* tensor_out, } } -extern "C" __global__ AICORE void tri_inv_trick_fp16(__gm__ void* tensor_out, - __gm__ void* tensor_in, - __gm__ void* identity_in, - uint32_t matrix_size, - uint32_t max_block_size) { +__global__ AICORE void tri_inv_trick_fp16(__gm__ void* tensor_out, + __gm__ void* tensor_in, + __gm__ void* identity_in, + uint32_t matrix_size, + uint32_t max_block_size) { run_tri_inv_trick((__gm__ float*)tensor_out, (__gm__ half*)tensor_in, (__gm__ half*)identity_in, matrix_size, max_block_size); } + +extern "C" void call_tri_inv_trick_fp16(uint32_t block_dim, void* stream, + uint8_t* tensor_out, uint8_t* tensor_in, + uint8_t* identity_in, + uint32_t matrix_size, + uint32_t max_block_size) { + tri_inv_trick_fp16<<>>( + tensor_out, tensor_in, identity_in, matrix_size, max_block_size); +}