mirror of
https://github.com/ROCm/composable_kernel.git
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CK Tile FA Training kernels (#1286)
* FA fwd dropout * FA bwd * epilogue reuse * CMakeLists update * [CK_TILE] support alibi (#1269) * add alibi support * fix code * update code based on comment * Support more hdim * fix fp8 bias * support seqlen_k=0 case * remove unused printf * fix format --------- Co-authored-by: rocking <ChunYu.Lai@amd.com> * now fwd/bwd can build * bwd alibi * add bwd validation stream_config * update generated filenames * update bwd kernel launch * CK_TILE_HOST_DEVICE in philox * Transpose -> transpose * format * format * format * Generate the instance for FA required * format * fix error in WarpGemm --------- Co-authored-by: danyao12 <danyao12> Co-authored-by: carlushuang <carlus.huang@amd.com> Co-authored-by: rocking <ChunYu.Lai@amd.com> Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com> Co-authored-by: Jing Zhang <jizhan@amd.com>
This commit is contained in:
Dan Yao
@@ -1,17 +1,29 @@
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# generate a list of kernels, but not actually emit files at config stage
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execute_process(
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COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
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--list_blobs ${CMAKE_CURRENT_BINARY_DIR}/blob_list.txt
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--direction fwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
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)
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# NOTE: for cmake, the FMHA_FWD_GEN_BLOBS files must be in the same directory
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execute_process(
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COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
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--direction bwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt
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)
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# NOTE: for cmake, the FMHA_FWD_GEN_BLOBS/FMHA_BWD_GEN_BLOBS files must be in the same directory
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# as current cmake list, otherwise will not figure out the dependency properly
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file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/blob_list.txt FMHA_FWD_GEN_BLOBS)
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file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt FMHA_FWD_GEN_BLOBS)
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file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt FMHA_BWD_GEN_BLOBS)
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add_custom_command(
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OUTPUT ${FMHA_FWD_GEN_BLOBS}
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COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
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--output_dir ${CMAKE_CURRENT_BINARY_DIR}
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--direction fwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
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)
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add_custom_command(
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OUTPUT ${FMHA_BWD_GEN_BLOBS}
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COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
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--direction bwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
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)
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set(EXAMPLE_FMHA_FWD "tile_example_fmha_fwd")
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@@ -22,6 +34,14 @@ add_executable(${EXAMPLE_FMHA_FWD} EXCLUDE_FROM_ALL fmha_fwd.cpp)
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target_include_directories(${EXAMPLE_FMHA_FWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
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target_sources(${EXAMPLE_FMHA_FWD} PRIVATE ${FMHA_FWD_GEN_BLOBS})
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set(EXAMPLE_FMHA_BWD "tile_example_fmha_bwd")
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# not using add_example_executable() to add this target, since we don't want this to have
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# to be included in "make all/install/check"
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message("adding example ${EXAMPLE_FMHA_BWD}")
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add_executable(${EXAMPLE_FMHA_BWD} EXCLUDE_FROM_ALL fmha_bwd.cpp)
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target_include_directories(${EXAMPLE_FMHA_BWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
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target_sources(${EXAMPLE_FMHA_BWD} PRIVATE ${FMHA_BWD_GEN_BLOBS})
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# NOTE: this is dangerous since will change the whole kernel to flush denormals
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# WIP with compiler team for an exp2 intrinsic..., then remove this
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if(NOT DEFINED FMHA_FWD_FAST_EXP2)
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@@ -29,16 +49,27 @@ if(NOT DEFINED FMHA_FWD_FAST_EXP2)
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endif()
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set(EXAMPLE_FMHA_FWD_COMPILE_OPTIONS)
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set(EXAMPLE_FMHA_BWD_COMPILE_OPTIONS)
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# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
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# ... because they are auto-generated
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if(FMHA_FWD_FAST_EXP2)
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list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero)
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list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero)
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else()
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list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0)
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list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0)
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endif()
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# Allow comparing floating points directly in order to check sentinel values
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list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-float-equal)
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list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-float-equal)
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target_compile_options(${EXAMPLE_FMHA_FWD} PRIVATE ${EXAMPLE_FMHA_FWD_COMPILE_OPTIONS})
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target_compile_options(${EXAMPLE_FMHA_BWD} PRIVATE ${EXAMPLE_FMHA_BWD_COMPILE_OPTIONS})
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# TODO: we have to turn off this global prop, otherwise the progress bar generated
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# by cmake will print too many files, execvp: /bin/sh: Argument list too long
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# however, this property may affect global
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# TODO: consider codegen a makefile by us
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set_property(GLOBAL PROPERTY RULE_MESSAGES OFF)
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932
example/ck_tile/01_fmha/fmha_bwd.cpp
Normal file
932
example/ck_tile/01_fmha/fmha_bwd.cpp
Normal file
@@ -0,0 +1,932 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#include "fmha_bwd.hpp"
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#include "ck_tile/host.hpp"
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#include "mask.hpp"
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#include "utils.hpp"
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#include <array>
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#include <cstring>
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#include <functional>
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#include <numeric>
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#include <ostream>
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#include <string>
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#include <tuple>
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#include <utility>
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#include <vector>
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template <typename T>
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std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
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{
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using size_type = typename std::vector<T>::size_type;
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os << "[";
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for(size_type idx = 0; idx < v.size(); ++idx)
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{
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if(0 < idx)
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{
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os << ", ";
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}
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os << v[idx];
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}
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return os << "]";
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}
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auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("v", "1", "weather do CPU validation or not")
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.insert("mode", "0", "kernel mode. 0:batch, 1:group")
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.insert("b", "2", "batch size")
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.insert("h", "8", "num of head, for q")
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.insert("h_k",
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"-1",
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"num of head, for k/v, -1 means equal to h\n"
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"if not equal to h, then this is GQA/MQA case")
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.insert("s",
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"3328",
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"seqlen_q. if group-mode, means the average value of seqlen_q\n"
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"total_seqlen_q = seqlen_q * batch, and seqlen_q per batch may vary")
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.insert("s_k", "-1", "seqlen_k, -1 means equal to s")
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.insert("d", "128", "head dim for q, k")
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.insert("d_v", "-1", "head dim for v, -1 means equal to d")
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.insert("scale", "0", "scale factor. 0 means equal to 1/sqrt(hdim)")
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.insert("iperm",
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"1",
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"permute input\n"
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"if true, will be b*h*s*d, else b*s*h*d")
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.insert("operm", "1", "permute output")
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.insert("bias",
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"n",
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"n or 0, no bias\n"
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"e(lementwise) or 1, elementwise bias with 1*1*s*s. e:1, 1*h*s*s. e:2, b*h*s*s\n"
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"a(libi) or 2, alibi with 1*h. a:1, b*h")
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.insert("dbias", "0", "output bias gradient or not")
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.insert("prec", "fp16", "data type. fp16 or bf16")
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.insert("mask",
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"0",
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"0: no mask, 1: top-left(same as 't'), 2:bottom-right(same as 'b')\n"
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"'t', top-left causal mask, 'b', bottom-r causal mask\n"
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"'t:l,r', top-left sliding window attn(swa) with FA style left right size\n"
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"'b:l,r', bottom-r sliding window attn(swa) with FA style left right size\n"
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"'xt:window_size', xformer style masking from top-left, window_size negative is "
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"causal, positive is swa\n"
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"'xb:window_size', xformer style masking from bottom-r, window_size negative is "
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"causal, positive is swa\n"
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"'g:y,x', generic attention mask coordinate with y/x size (only debug purpose for "
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"now)")
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.insert("kname", "0", "if set to 1 will print kernel name")
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.insert("init", "1", "init method. 0:random int, 1:random float, 2:trig float")
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.insert("seed",
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"11939",
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"random seed used for initializing input tensors. 0 for "
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"non-deterministic seed")
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.insert("p_drop", "0", "0~1 probability of dropout")
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.insert("drop_seed", "1", "seed for random number generator")
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.insert("drop_offset", "0", "offset for random number generator")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("warmup", "5", "number of iterations before benchmark the kernel")
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.insert("repeat", "20", "number of iterations to benchmark the kernel");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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// different threshold for different dtype
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template <typename DataType>
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auto get_elimit(int /*init_method*/)
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{
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double rtol = 1e-2;
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double atol = 1e-2;
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return ck_tile::make_tuple(rtol, atol);
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}
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template <typename DataType>
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bool run(const ck_tile::ArgParser& arg_parser)
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{
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std::string data_type = arg_parser.get_str("prec");
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int do_validation = arg_parser.get_int("v");
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auto mode = static_cast<mode_enum>(arg_parser.get_uint32("mode"));
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ck_tile::index_t batch = arg_parser.get_int("b");
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ck_tile::index_t nhead = arg_parser.get_int("h");
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ck_tile::index_t nhead_k = arg_parser.get_int("h_k");
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if(nhead_k < 0)
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nhead_k = nhead;
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if(nhead % nhead_k != 0)
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{
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std::cerr << "nhead:" << nhead << " must be multiple of nhead_k:" << nhead_k << std::endl;
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return false;
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}
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ck_tile::index_t seqlen_q = arg_parser.get_int("s");
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ck_tile::index_t seqlen_k = arg_parser.get_int("s_k");
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if(seqlen_k < 0)
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seqlen_k = seqlen_q;
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ck_tile::index_t hdim_q = arg_parser.get_int("d");
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ck_tile::index_t hdim_v = arg_parser.get_int("d_v");
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if(hdim_v < 0)
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hdim_v = hdim_q;
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if(hdim_q % 2 != 0 || hdim_v % 2 != 0)
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{
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std::cerr << "FMHA Bwd kernel currently only supports even headdim" << std::endl;
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return false;
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}
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bool i_perm = arg_parser.get_bool("iperm"); // if true, will be batch * nhead * seqlen * hdim
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bool o_perm = arg_parser.get_bool("operm"); // if false, will be batch * seqlen * nhead * hdim
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float scale = arg_parser.get_float("scale");
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if(scale == .0f)
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scale = 1.0 / ck_tile::sqrt(static_cast<float>(hdim_q));
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bias_info bias = bias_info::decode(arg_parser.get_str("bias"));
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bool use_dbias = arg_parser.get_bool("dbias");
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float p_drop = arg_parser.get_float("p_drop");
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uint64_t drop_seed = arg_parser.get_uint64("drop_seed");
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uint64_t drop_offset = arg_parser.get_uint64("drop_offset");
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if(use_dbias && bias.type != bias_enum::elementwise_bias)
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{
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std::cerr << "dbias only exists when bias type is elementwise" << std::endl;
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return false;
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}
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if(p_drop < 0.0f || p_drop > 1.0f)
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{
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std::cerr << "The value of p_drop should be 0~1" << std::endl;
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return false;
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}
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float p_undrop = 1.0 - p_drop;
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uint8_t p_undrop_in_uint8_t =
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uint8_t(std::floor(p_undrop * std::numeric_limits<uint8_t>::max()));
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float rp_undrop = 1.0 / p_undrop;
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bool s_randval = false;
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if(p_drop > 0.0f && do_validation)
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{
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s_randval = true;
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}
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mask_info mask = mask_info::decode(arg_parser.get_str("mask"), seqlen_q, seqlen_k);
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int init_method = arg_parser.get_int("init");
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std::optional<uint32_t> seed = arg_parser.get_uint32("seed");
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if(*seed == 0)
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{
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seed.reset();
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}
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int stream_warmup = arg_parser.get_int("warmup");
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int stream_repeat = arg_parser.get_int("repeat");
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bool kname = arg_parser.get_bool("kname");
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ck_tile::stream_config stream_config{nullptr,
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true,
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/* log_level = */ (kname ? 1 : 0),
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stream_warmup,
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stream_repeat,
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arg_parser.get_str("timer") == std::string("gpu")};
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const auto seqstart_q_host = generate_seqstarts(mode, batch, seqlen_q);
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const auto seqstart_k_host = generate_seqstarts(mode, batch, seqlen_k);
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using TypeConfig = FmhaBwdTypeConfig<DataType>;
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using QDataType = typename TypeConfig::QDataType;
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using KDataType = typename TypeConfig::KDataType;
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using VDataType = typename TypeConfig::VDataType;
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using GemmDataType = typename TypeConfig::GemmDataType;
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using BiasDataType = typename TypeConfig::BiasDataType;
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using LSEDataType = typename TypeConfig::LSEDataType;
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using AccDataType = typename TypeConfig::AccDataType;
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using DDataType = typename TypeConfig::DDataType;
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using RandValOutputDataType = typename TypeConfig::RandValOutputDataType;
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using ODataType = typename TypeConfig::ODataType;
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using OGradDataType = typename TypeConfig::OGradDataType;
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using QGradDataType = typename TypeConfig::QGradDataType;
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using KGradDataType = typename TypeConfig::KGradDataType;
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using VGradDataType = typename TypeConfig::VGradDataType;
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using BiasGradDataType = typename TypeConfig::BiasGradDataType;
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// accumulation numbers for performance evaluation
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std::size_t flop = 0, num_byte = 0;
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auto max_seqlen_q =
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std::numeric_limits<int32_t>::min(); // we will use max seqlen to decide grid size
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auto max_seqlen_k =
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std::numeric_limits<int32_t>::min(); // we will use max seqlen to decide grid size
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{
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for(ck_tile::index_t wb = 0; wb < batch; ++wb)
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{
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const int32_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
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const int32_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
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if(max_seqlen_q < real_seqlen_q)
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{
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max_seqlen_q = real_seqlen_q;
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}
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if(max_seqlen_k < real_seqlen_k)
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{
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max_seqlen_k = real_seqlen_k;
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}
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flop += nhead * (static_cast<std::size_t>(3) * static_cast<std::size_t>(2) *
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real_seqlen_q * real_seqlen_k * hdim_q + // Q@K/dS^T@Q^T/dS@K^T
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static_cast<std::size_t>(2) * static_cast<std::size_t>(2) *
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real_seqlen_q * real_seqlen_k * hdim_v); // dO@V/P^T@dO^T
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num_byte += nhead * (sizeof(QDataType) * real_seqlen_q * hdim_q +
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sizeof(KDataType) * real_seqlen_k * hdim_q +
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sizeof(VDataType) * real_seqlen_k * hdim_v +
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sizeof(ODataType) * real_seqlen_q * hdim_v +
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sizeof(OGradDataType) * real_seqlen_q * hdim_v +
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sizeof(QGradDataType) * real_seqlen_q * hdim_q +
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sizeof(KGradDataType) * real_seqlen_k * hdim_q +
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sizeof(VGradDataType) * real_seqlen_k * hdim_v +
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sizeof(LSEDataType) * real_seqlen_q);
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}
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}
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auto get_lengths = [&](bool permute,
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ck_tile::index_t b /*batch*/,
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ck_tile::index_t h /*nhead*/,
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ck_tile::index_t s /*seqlen*/,
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ck_tile::index_t d /*hdim*/) {
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if(permute)
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return std::array<ck_tile::index_t, 4>{b, h, s, d};
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else
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return std::array<ck_tile::index_t, 4>{b, s, h, d};
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};
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// host memory for storing all the tensor elements
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const ck_tile::index_t shape_batch = (mode == mode_enum::batch ? batch : 1);
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const ck_tile::index_t shape_seqlen_q =
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(mode == mode_enum::batch ? seqlen_q : seqstart_q_host.back());
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const ck_tile::index_t shape_seqlen_k =
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(mode == mode_enum::batch ? seqlen_k : seqstart_k_host.back());
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ck_tile::HostTensor<QDataType> q_host(
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get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, hdim_q));
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ck_tile::HostTensor<KDataType> k_host(
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get_lengths(i_perm, shape_batch, nhead_k, shape_seqlen_k, hdim_q));
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ck_tile::HostTensor<VDataType> v_host(
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get_lengths(i_perm, shape_batch, nhead_k, shape_seqlen_k, hdim_v));
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ck_tile::HostTensor<BiasDataType> bias_host(
|
||||
bias.type == bias_enum::elementwise_bias
|
||||
? get_lengths(i_perm, 1, 1, shape_seqlen_q, max_seqlen_k)
|
||||
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1} /* dummy shape for simplifying code */);
|
||||
ck_tile::HostTensor<AccDataType> alibi_slope_host(
|
||||
bias.type == bias_enum::alibi
|
||||
? (bias.rank_info == 0 ? std::array<ck_tile::index_t, 2>{1, nhead}
|
||||
: std::array<ck_tile::index_t, 2>{batch, nhead})
|
||||
: std::array<ck_tile::index_t, 2>{1, 1});
|
||||
ck_tile::HostTensor<ODataType> o_host(
|
||||
get_lengths(o_perm, shape_batch, nhead, shape_seqlen_q, hdim_v));
|
||||
ck_tile::HostTensor<LSEDataType> lse_host(
|
||||
std::array<ck_tile::index_t, 3>{batch, nhead, max_seqlen_q});
|
||||
ck_tile::HostTensor<DDataType> d_host(
|
||||
std::array<ck_tile::index_t, 3>{batch, nhead, max_seqlen_q});
|
||||
ck_tile::HostTensor<RandValOutputDataType> randval_host(
|
||||
p_drop > 0 ? get_lengths(true, shape_batch, nhead, shape_seqlen_q, max_seqlen_k)
|
||||
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
|
||||
ck_tile::HostTensor<QGradDataType> dq_host(
|
||||
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, hdim_q));
|
||||
ck_tile::HostTensor<KGradDataType> dk_host(
|
||||
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_k, hdim_q));
|
||||
ck_tile::HostTensor<VGradDataType> dv_host(
|
||||
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_k, hdim_v));
|
||||
ck_tile::HostTensor<OGradDataType> do_host(
|
||||
get_lengths(o_perm, shape_batch, nhead, shape_seqlen_q, hdim_v));
|
||||
ck_tile::HostTensor<BiasGradDataType> dbias_host(
|
||||
use_dbias
|
||||
? get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, max_seqlen_k)
|
||||
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1} /* dummy shape for simplifying code */);
|
||||
|
||||
if(init_method == 0)
|
||||
{
|
||||
ck_tile::FillUniformDistributionIntegerValue<QDataType>{-2.f, 2.f, seed}(q_host);
|
||||
ck_tile::FillUniformDistributionIntegerValue<KDataType>{-2.f, 2.f, seed}(k_host);
|
||||
ck_tile::FillUniformDistributionIntegerValue<VDataType>{-2.f, 2.f, seed}(v_host);
|
||||
ck_tile::FillUniformDistributionIntegerValue<BiasDataType>{-2.f, 2.f, seed}(bias_host);
|
||||
ck_tile::FillUniformDistributionIntegerValue<OGradDataType>{-2.f, 2.f, seed}(do_host);
|
||||
}
|
||||
else if(init_method == 1)
|
||||
{
|
||||
ck_tile::FillUniformDistribution<QDataType>{0.f, 1.f, seed}(q_host);
|
||||
ck_tile::FillUniformDistribution<KDataType>{0.f, 1.f, seed}(k_host);
|
||||
ck_tile::FillUniformDistribution<VDataType>{0.f, 1.f, seed}(v_host);
|
||||
ck_tile::FillUniformDistribution<BiasDataType>{0.f, 1.f, seed}(bias_host);
|
||||
ck_tile::FillUniformDistribution<OGradDataType>{0.f, 1.f, seed}(do_host);
|
||||
}
|
||||
else if(init_method == 2)
|
||||
{
|
||||
ck_tile::FillTrigValue<QDataType>{}(q_host);
|
||||
ck_tile::FillTrigValue<KDataType>{}(k_host);
|
||||
ck_tile::FillTrigValue<VDataType>{}(v_host);
|
||||
ck_tile::FillTrigValue<BiasDataType>{}(bias_host);
|
||||
ck_tile::FillTrigValue<OGradDataType>{}(do_host);
|
||||
}
|
||||
if(bias.type == bias_enum::alibi)
|
||||
{
|
||||
auto slopes = ck_tile::get_alibi_slopes<AccDataType>(nhead);
|
||||
assert(slopes.size() == nhead);
|
||||
if(bias.rank_info == 0)
|
||||
{
|
||||
// alibi in 1*h
|
||||
std::copy(slopes.begin(), slopes.end(), alibi_slope_host.begin());
|
||||
}
|
||||
else
|
||||
{
|
||||
// alibi in b*h
|
||||
for(auto i_b = 0; i_b < batch; i_b++)
|
||||
{
|
||||
std::copy(slopes.begin(), slopes.end(), alibi_slope_host.begin() + i_b * nhead);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
ck_tile::DeviceMem q_buf(q_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem k_buf(k_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem v_buf(v_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem bias_buf(bias_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem o_buf(o_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem lse_buf(lse_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem d_buf(d_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem randval_buf(randval_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem dq_buf(dq_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem dk_buf(dk_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem dv_buf(dv_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem do_buf(do_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem dbias_buf(dbias_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t));
|
||||
ck_tile::DeviceMem seqstart_k(seqstart_k_host.size() * sizeof(int32_t));
|
||||
ck_tile::DeviceMem alibi_slope_buf(alibi_slope_host.get_element_space_size_in_bytes());
|
||||
|
||||
q_buf.ToDevice(q_host.data());
|
||||
k_buf.ToDevice(k_host.data());
|
||||
v_buf.ToDevice(v_host.data());
|
||||
bias_buf.ToDevice(bias_host.data());
|
||||
do_buf.ToDevice(do_host.data());
|
||||
seqstart_q.ToDevice(seqstart_q_host.data());
|
||||
seqstart_k.ToDevice(seqstart_k_host.data());
|
||||
alibi_slope_buf.ToDevice(alibi_slope_host.data());
|
||||
|
||||
// clang-format off
|
||||
auto layout_str = [&](bool permute){
|
||||
if (permute) return std::string("bhsd");
|
||||
else return std::string("bshd");
|
||||
};
|
||||
auto io_layout = [&](bool iperm_, bool operm_) {
|
||||
if (iperm_ == operm_) return layout_str(iperm_);
|
||||
else return layout_str(iperm_) + std::string("-") + layout_str(operm_);
|
||||
};
|
||||
// clang-format on
|
||||
const std::string prec = arg_parser.get_str("prec");
|
||||
|
||||
std::cout << "[" << prec << "|" << mode << "|" << io_layout(i_perm, o_perm) << "] b:" << batch
|
||||
<< ", h:" << nhead << "/" << nhead_k << ", s:" << seqlen_q << "/" << seqlen_k
|
||||
<< ", d:" << hdim_q << "/" << hdim_v << ", scale:" << scale << ", bias:" << bias
|
||||
<< ", dbias:" << use_dbias << ", p_drop:" << p_drop << ", mask:" << mask
|
||||
<< std::flush;
|
||||
|
||||
auto fmha_traits = fmha_bwd_traits{hdim_q,
|
||||
hdim_v,
|
||||
data_type,
|
||||
mode == mode_enum::group,
|
||||
mask.type,
|
||||
bias.type,
|
||||
use_dbias,
|
||||
p_drop > 0.0f};
|
||||
auto fmha_args = [&]() {
|
||||
assert(nhead % nhead_k == 0);
|
||||
/// NOTE: we broadcast bias from [1, 1, seqlen_q, seqlen_k] to [batch, nhead, seqlen_q,
|
||||
/// seqlen_k] in this example, hence both the 'batch_stride_bias' &
|
||||
/// 'nhead_stride_bias' are 0.
|
||||
// setup stride_* arguments
|
||||
const ck_tile::index_t stride_q = (i_perm ? hdim_q : nhead * hdim_q);
|
||||
const ck_tile::index_t stride_k = (i_perm ? hdim_q : nhead_k * hdim_q);
|
||||
const ck_tile::index_t stride_v = (i_perm ? hdim_v : nhead_k * hdim_v);
|
||||
const ck_tile::index_t stride_bias = (max_seqlen_k);
|
||||
const ck_tile::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
|
||||
const ck_tile::index_t stride_randval = (max_seqlen_k);
|
||||
const ck_tile::index_t stride_do = (o_perm ? hdim_v : nhead * hdim_v);
|
||||
const ck_tile::index_t stride_dk = (i_perm ? hdim_q : nhead * hdim_q);
|
||||
const ck_tile::index_t stride_dv = (i_perm ? hdim_v : nhead * hdim_v);
|
||||
const ck_tile::index_t stride_dbias = (i_perm ? max_seqlen_k : nhead * max_seqlen_k);
|
||||
// setup nhead_stride_* arguments
|
||||
const ck_tile::index_t nhead_stride_q = (i_perm ? shape_seqlen_q * hdim_q : hdim_q);
|
||||
const ck_tile::index_t nhead_stride_k = (i_perm ? shape_seqlen_k * hdim_q : hdim_q);
|
||||
const ck_tile::index_t nhead_stride_v = (i_perm ? shape_seqlen_k * hdim_v : hdim_v);
|
||||
const ck_tile::index_t nhead_stride_bias = 0;
|
||||
const ck_tile::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
|
||||
const ck_tile::index_t nhead_stride_randval = (shape_seqlen_q * max_seqlen_k);
|
||||
const ck_tile::index_t nhead_stride_do = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
|
||||
const ck_tile::index_t nhead_stride_lsed = max_seqlen_q;
|
||||
const ck_tile::index_t nhead_stride_dbias =
|
||||
(i_perm ? shape_seqlen_q * max_seqlen_k : max_seqlen_k);
|
||||
// setup batch_stride_* arguments
|
||||
const ck_tile::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
|
||||
const ck_tile::index_t batch_stride_k = (nhead_k * shape_seqlen_k * hdim_q);
|
||||
const ck_tile::index_t batch_stride_v = (nhead_k * shape_seqlen_k * hdim_v);
|
||||
const ck_tile::index_t batch_stride_bias = 0;
|
||||
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
|
||||
const ck_tile::index_t batch_stride_randval = (nhead * shape_seqlen_q * max_seqlen_k);
|
||||
const ck_tile::index_t batch_stride_do = (nhead * shape_seqlen_q * hdim_v);
|
||||
const ck_tile::index_t batch_stride_lsed = (nhead * max_seqlen_q);
|
||||
const ck_tile::index_t batch_stride_dk = (nhead * shape_seqlen_k * hdim_q);
|
||||
const ck_tile::index_t batch_stride_dv = (nhead * shape_seqlen_k * hdim_v);
|
||||
const ck_tile::index_t batch_stride_dbias = (nhead * shape_seqlen_q * max_seqlen_k);
|
||||
|
||||
return fmha_bwd_args{q_buf.GetDeviceBuffer(),
|
||||
k_buf.GetDeviceBuffer(),
|
||||
v_buf.GetDeviceBuffer(),
|
||||
bias.type == bias_enum::alibi ? alibi_slope_buf.GetDeviceBuffer()
|
||||
: bias_buf.GetDeviceBuffer(),
|
||||
o_buf.GetDeviceBuffer(),
|
||||
lse_buf.GetDeviceBuffer(),
|
||||
do_buf.GetDeviceBuffer(),
|
||||
d_buf.GetDeviceBuffer(),
|
||||
randval_buf.GetDeviceBuffer(),
|
||||
dq_buf.GetDeviceBuffer(),
|
||||
dk_buf.GetDeviceBuffer(),
|
||||
dv_buf.GetDeviceBuffer(),
|
||||
dbias_buf.GetDeviceBuffer(),
|
||||
seqstart_q.GetDeviceBuffer(),
|
||||
seqstart_k.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
shape_seqlen_q,
|
||||
shape_seqlen_k,
|
||||
batch,
|
||||
max_seqlen_q,
|
||||
max_seqlen_k,
|
||||
hdim_q,
|
||||
hdim_v,
|
||||
nhead,
|
||||
nhead_k,
|
||||
scale,
|
||||
stride_q,
|
||||
stride_k,
|
||||
stride_v,
|
||||
bias.type == bias_enum::alibi ? (bias.rank_info == 0 ? 0 : nhead)
|
||||
: stride_bias,
|
||||
stride_o,
|
||||
stride_randval,
|
||||
stride_do,
|
||||
stride_dk,
|
||||
stride_dv,
|
||||
stride_dbias,
|
||||
nhead_stride_q,
|
||||
nhead_stride_k,
|
||||
nhead_stride_v,
|
||||
nhead_stride_bias,
|
||||
nhead_stride_o,
|
||||
nhead_stride_randval,
|
||||
nhead_stride_do,
|
||||
nhead_stride_lsed,
|
||||
nhead_stride_dbias,
|
||||
batch_stride_q,
|
||||
batch_stride_k,
|
||||
batch_stride_v,
|
||||
batch_stride_bias,
|
||||
batch_stride_o,
|
||||
batch_stride_randval,
|
||||
batch_stride_do,
|
||||
batch_stride_lsed,
|
||||
batch_stride_dk,
|
||||
batch_stride_dv,
|
||||
batch_stride_dbias,
|
||||
mask.left,
|
||||
mask.right,
|
||||
static_cast<ck_tile::index_t>(mask.type),
|
||||
p_drop,
|
||||
p_undrop,
|
||||
s_randval,
|
||||
{drop_seed, drop_offset}};
|
||||
}();
|
||||
|
||||
float ave_time = fmha_bwd(fmha_traits, fmha_args, stream_config);
|
||||
if(ave_time < 0)
|
||||
{
|
||||
std::cout << ", not supported yet" << std::flush << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_byte / 1.E6 / ave_time;
|
||||
|
||||
std::cout << std::fixed << ", " << std::setprecision(3) << ave_time << " ms, "
|
||||
<< std::setprecision(2) << tflops << " TFlops, " << std::setprecision(2) << gb_per_sec
|
||||
<< " GB/s" << std::flush;
|
||||
|
||||
if(!do_validation)
|
||||
{
|
||||
std::cout << std::flush << std::endl;
|
||||
return true;
|
||||
}
|
||||
|
||||
bool pass = true;
|
||||
|
||||
std::vector<ck_tile::HostTensor<QDataType>> q_host_refs;
|
||||
std::vector<ck_tile::HostTensor<KDataType>> k_host_refs;
|
||||
std::vector<ck_tile::HostTensor<VDataType>> v_host_refs;
|
||||
std::vector<ck_tile::HostTensor<ODataType>> o_host_refs;
|
||||
std::vector<ck_tile::HostTensor<RandValOutputDataType>> randval_host_refs;
|
||||
std::vector<ck_tile::HostTensor<AccDataType>> p_hp_host_refs;
|
||||
std::vector<ck_tile::HostTensor<GemmDataType>> p_lp_host_refs;
|
||||
|
||||
randval_buf.FromDevice(randval_host.data());
|
||||
|
||||
for(ck_tile::index_t wb = 0; wb < batch; ++wb)
|
||||
{
|
||||
const ck_tile::index_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
|
||||
const ck_tile::index_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
|
||||
|
||||
// adjust matrix index according to the mode
|
||||
const ck_tile::index_t b = (mode == mode_enum::batch ? wb : 0);
|
||||
const ck_tile::index_t query_offset = (mode == mode_enum::batch ? 0 : seqstart_q_host[wb]);
|
||||
const ck_tile::index_t key_offset = (mode == mode_enum::batch ? 0 : seqstart_k_host[wb]);
|
||||
|
||||
ck_tile::HostTensor<QDataType> q_host_ref({nhead, real_seqlen_q, hdim_q}); // q_g_m_k
|
||||
ck_tile::HostTensor<KDataType> k_host_ref({nhead, real_seqlen_k, hdim_q}); // k_g_n_k
|
||||
ck_tile::HostTensor<VDataType> v_host_ref({nhead, hdim_v, real_seqlen_k}); // v_g_o_n
|
||||
ck_tile::HostTensor<ODataType> o_host_ref({nhead, real_seqlen_q, hdim_v}); // o_g_m_o
|
||||
ck_tile::HostTensor<LSEDataType> lse_host_ref({nhead, real_seqlen_q}); // lse_g_m
|
||||
ck_tile::HostTensor<RandValOutputDataType> randval_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // randval_g_m_n
|
||||
ck_tile::HostTensor<AccDataType> s_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // s_g_m_n
|
||||
ck_tile::HostTensor<AccDataType> p_hp_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // p_hp_g_m_n high precision
|
||||
ck_tile::HostTensor<AccDataType> p_dropped_hp_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // p_dropped_hp_g_m_n high precision
|
||||
ck_tile::HostTensor<GemmDataType> p_lp_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // p_lp_g_m_n low precision
|
||||
|
||||
ck_tile::index_t nr = nhead / nhead_k;
|
||||
|
||||
// clang-format off
|
||||
// permute
|
||||
if(i_perm) q_host_ref.ForEach([&](auto& self, auto i) { self(i) = q_host(b, i[0], i[1] + query_offset, i[2]); });
|
||||
else q_host_ref.ForEach([&](auto& self, auto i) { self(i) = q_host(b, i[1] + query_offset, i[0], i[2]); });
|
||||
|
||||
if(i_perm) k_host_ref.ForEach([&](auto& self, auto i) { self(i) = k_host(b, i[0] / nr, i[1] + key_offset, i[2]); });
|
||||
else k_host_ref.ForEach([&](auto& self, auto i) { self(i) = k_host(b, i[1] + key_offset, i[0] / nr, i[2]); });
|
||||
|
||||
// v_host_ref: [nhead, hdim, seq], v_host: [b, h_k, s, d]
|
||||
if(i_perm) v_host_ref.ForEach([&](auto& self, auto i) { self(i) = v_host(b, i[0] / nr, i[2] + key_offset, i[1]); });
|
||||
// v_host_ref: [nhead, hdim, seq], v_host: [b, s, h_k, d]
|
||||
else v_host_ref.ForEach([&](auto& self, auto i) { self(i) = v_host(b, i[2] + key_offset, i[0] / nr, i[1]); });
|
||||
// clang-format on
|
||||
|
||||
// reference
|
||||
// S = scale * Q * K^T
|
||||
ck_tile::reference_batched_gemm<QDataType, KDataType, AccDataType, AccDataType>(
|
||||
q_host_ref,
|
||||
k_host_ref,
|
||||
s_host_ref,
|
||||
ck_tile::identity{},
|
||||
ck_tile::identity{},
|
||||
ck_tile::scales(scale)); // s_g_m_n = scale * q_g_m_k@k_g_n_k
|
||||
|
||||
if(bias.type == bias_enum::elementwise_bias)
|
||||
{
|
||||
// elementwise bias
|
||||
ck_tile::HostTensor<BiasDataType> bias_host_ref({1, real_seqlen_q, real_seqlen_k});
|
||||
// clang-format off
|
||||
if(i_perm)
|
||||
bias_host_ref.ForEach([&](auto& self, auto i) { self(i) = bias_host(0, 0, i[1] + query_offset, i[2]); });
|
||||
else
|
||||
bias_host_ref.ForEach([&](auto& self, auto i) { self(i) = bias_host(0, i[1] + query_offset, 0, i[2]); });
|
||||
// clang-format on
|
||||
|
||||
// broadcast from [1, real_seqlen_q, real_seqlen_k] to [nhead, real_seqlen_q,
|
||||
// real_seqlen_k]
|
||||
ck_tile::
|
||||
reference_batched_elementwise<AccDataType, BiasDataType, AccDataType, AccDataType>(
|
||||
s_host_ref, bias_host_ref, s_host_ref);
|
||||
}
|
||||
else if(bias.type == bias_enum::alibi)
|
||||
{
|
||||
// alibi construct elementwise bias to verify
|
||||
auto alibi_host = [&]() {
|
||||
if(mask.type != mask_enum::no_mask)
|
||||
{
|
||||
return ck_tile::make_alibi_from_lr_mask<AccDataType, false>(
|
||||
0,
|
||||
mask.left,
|
||||
mask.right,
|
||||
real_seqlen_q,
|
||||
real_seqlen_k,
|
||||
static_cast<ck_tile::GenericAttentionMaskEnum>(mask.type));
|
||||
}
|
||||
else
|
||||
{
|
||||
return ck_tile::Alibi<AccDataType, false>{
|
||||
0, real_seqlen_q, real_seqlen_k, ck_tile::AlibiMode::FROM_BOTTOM_RIGHT};
|
||||
}
|
||||
}();
|
||||
|
||||
ck_tile::HostTensor<AccDataType> alibi_bias_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k});
|
||||
auto i_b_slope = bias.rank_info == 0 ? 0 : wb;
|
||||
for(auto i_h = 0; i_h < nhead; i_h++)
|
||||
{
|
||||
AccDataType current_slope = alibi_slope_host(i_b_slope, i_h);
|
||||
alibi_host.slope = alibi_host.mode == ck_tile::AlibiMode::VERTICAL ? current_slope
|
||||
: -current_slope;
|
||||
for(auto i_r = 0; i_r < real_seqlen_q; i_r++)
|
||||
{
|
||||
for(auto i_c = 0; i_c < real_seqlen_k; i_c++)
|
||||
{
|
||||
AccDataType pixel = 0;
|
||||
alibi_host.update(pixel, i_r, i_c);
|
||||
alibi_bias_host_ref(i_h, i_r, i_c) = pixel;
|
||||
}
|
||||
}
|
||||
}
|
||||
// [nhead, real_seqlen_q, real_seqlen_k]
|
||||
ck_tile::
|
||||
reference_batched_elementwise<AccDataType, AccDataType, AccDataType, AccDataType>(
|
||||
s_host_ref, alibi_bias_host_ref, s_host_ref);
|
||||
}
|
||||
|
||||
if(mask.type == mask_enum::no_mask)
|
||||
{
|
||||
ck_tile::reference_batched_masking<AccDataType>(
|
||||
s_host_ref, FmhaMasks::NoMask{real_seqlen_q, real_seqlen_k});
|
||||
}
|
||||
else if(mask.type == mask_enum::window_generic)
|
||||
{
|
||||
ck_tile::reference_batched_masking<AccDataType>(
|
||||
s_host_ref,
|
||||
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
|
||||
mask.left, mask.right, real_seqlen_q, real_seqlen_k));
|
||||
}
|
||||
else
|
||||
{
|
||||
// if left window size is negative, means causal
|
||||
// else means generic (for current batch)
|
||||
if(mask.left < 0)
|
||||
ck_tile::reference_batched_masking<AccDataType>(
|
||||
s_host_ref,
|
||||
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::CausalMask>(
|
||||
mask.left,
|
||||
mask.right,
|
||||
real_seqlen_q,
|
||||
real_seqlen_k,
|
||||
mask.type == mask_enum::mask_top_left));
|
||||
else
|
||||
ck_tile::reference_batched_masking<AccDataType>(
|
||||
s_host_ref,
|
||||
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
|
||||
mask.left,
|
||||
mask.right,
|
||||
real_seqlen_q,
|
||||
real_seqlen_k,
|
||||
mask.type == mask_enum::mask_top_left));
|
||||
}
|
||||
ck_tile::reference_batched_softmax<AccDataType, LSEDataType, AccDataType>(
|
||||
s_host_ref, p_hp_host_ref, ck_tile::identity{}, lse_host_ref);
|
||||
|
||||
if(p_drop > 0)
|
||||
{
|
||||
p_hp_host_ref.ForEach(
|
||||
[&](auto& self, auto idx) { p_dropped_hp_host_ref(idx) = self(idx); });
|
||||
randval_host_ref.ForEach([&](auto& self, auto idx) {
|
||||
self(idx) = randval_host(b, idx[0], idx[1] + query_offset, idx[2]);
|
||||
});
|
||||
ck_tile::reference_batched_dropout(
|
||||
p_dropped_hp_host_ref, randval_host_ref, p_undrop_in_uint8_t, rp_undrop);
|
||||
p_dropped_hp_host_ref.ForEach([&](auto& self, auto idx) {
|
||||
p_lp_host_ref(idx) = ck_tile::type_convert<GemmDataType>(self(idx));
|
||||
});
|
||||
}
|
||||
else
|
||||
{
|
||||
p_hp_host_ref.ForEach([&](auto& self, auto idx) {
|
||||
p_lp_host_ref(idx) = ck_tile::type_convert<GemmDataType>(self(idx));
|
||||
});
|
||||
}
|
||||
|
||||
// O = P * V
|
||||
ck_tile::reference_batched_gemm<GemmDataType, VDataType, AccDataType, ODataType>(
|
||||
p_lp_host_ref, v_host_ref, o_host_ref); // o_g_m_o = p_lp_g_m_n@v_g_o_n
|
||||
|
||||
// clang-format off
|
||||
// permute
|
||||
if(o_perm) o_host_ref.ForEach([&](auto& self, auto idx) { o_host(b, idx[0], idx[1] + query_offset, idx[2]) = self(idx); });
|
||||
else o_host_ref.ForEach([&](auto& self, auto idx) { o_host(b, idx[1] + query_offset, idx[0], idx[2]) = self(idx); });
|
||||
|
||||
lse_host_ref.ForEach([&](auto& self, auto idx) { lse_host(wb, idx[0], idx[1]) = self(idx); });
|
||||
// clang-format on
|
||||
|
||||
q_host_refs.push_back(q_host_ref);
|
||||
k_host_refs.push_back(k_host_ref);
|
||||
v_host_refs.push_back(v_host_ref);
|
||||
o_host_refs.push_back(o_host_ref);
|
||||
p_hp_host_refs.push_back(p_hp_host_ref);
|
||||
p_lp_host_refs.push_back(p_lp_host_ref);
|
||||
if(p_drop > 0)
|
||||
{
|
||||
randval_host_refs.push_back(randval_host_ref);
|
||||
}
|
||||
}
|
||||
|
||||
o_buf.ToDevice(o_host.data());
|
||||
lse_buf.ToDevice(lse_host.data());
|
||||
dq_buf.SetZero();
|
||||
dbias_buf.SetZero();
|
||||
|
||||
ck_tile::stream_config stream_config_v{
|
||||
nullptr, true, 0, 0, 1, arg_parser.get_str("timer") == std::string("gpu")};
|
||||
fmha_bwd(fmha_traits, fmha_args, stream_config_v);
|
||||
|
||||
dq_buf.FromDevice(dq_host.data());
|
||||
dk_buf.FromDevice(dk_host.data());
|
||||
dv_buf.FromDevice(dv_host.data());
|
||||
dbias_buf.FromDevice(dbias_host.data());
|
||||
|
||||
for(ck_tile::index_t wb = 0; wb < batch; ++wb)
|
||||
{
|
||||
const ck_tile::index_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
|
||||
const ck_tile::index_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
|
||||
|
||||
// adjust matrix index according to the mode
|
||||
const ck_tile::index_t b = (mode == mode_enum::batch ? wb : 0);
|
||||
const ck_tile::index_t query_offset = (mode == mode_enum::batch ? 0 : seqstart_q_host[wb]);
|
||||
const ck_tile::index_t key_offset = (mode == mode_enum::batch ? 0 : seqstart_k_host[wb]);
|
||||
|
||||
ck_tile::HostTensor<OGradDataType> do_host_ref({nhead, real_seqlen_q, hdim_v}); // do_g_m_o
|
||||
ck_tile::HostTensor<AccDataType> ds_hp_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // ds_g_m_n high precision
|
||||
ck_tile::HostTensor<GemmDataType> ds_lp_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // ds_g_m_n low precision
|
||||
ck_tile::HostTensor<AccDataType> dp_hp_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // dp_g_m_n high precision
|
||||
ck_tile::HostTensor<BiasGradDataType> dbias_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // dbias_g_m_n
|
||||
ck_tile::HostTensor<QGradDataType> dq_host_ref({nhead, real_seqlen_q, hdim_q}); // dq_g_m_k
|
||||
ck_tile::HostTensor<KGradDataType> dk_host_ref({nhead, real_seqlen_k, hdim_q}); // dk_g_n_k
|
||||
ck_tile::HostTensor<VGradDataType> dv_host_ref({nhead, real_seqlen_k, hdim_v}); // dv_g_n_o
|
||||
|
||||
// clang-format off
|
||||
if(o_perm) do_host_ref.ForEach([&](auto& self, auto i) { self(i) = do_host(b, i[0], i[1] + query_offset, i[2]); });
|
||||
else do_host_ref.ForEach([&](auto& self, auto i) { self(i) = do_host(b, i[1] + query_offset, i[0], i[2]); });
|
||||
// clang-format on
|
||||
|
||||
// dP = dO@V x Z w/ dropout
|
||||
// dP = dO@V w/o dropout
|
||||
auto v_t_host_ref = v_host_refs[wb].transpose({0, 2, 1}); // v_g_o_n -> v_g_n_o
|
||||
ck_tile::reference_batched_gemm<OGradDataType, VDataType, AccDataType, AccDataType>(
|
||||
do_host_ref, v_t_host_ref, dp_hp_host_ref); // dp_g_m_n = do_g_m_o@v_g_n_o
|
||||
|
||||
if(p_drop > 0)
|
||||
{
|
||||
ck_tile::reference_batched_dropout(
|
||||
dp_hp_host_ref, randval_host_refs[wb], p_undrop_in_uint8_t, rp_undrop);
|
||||
}
|
||||
|
||||
// dS_i_j = P_i_j .* (dP_i_j - dO_i dot O_i)
|
||||
ds_hp_host_ref.ForEach([&](auto& self, auto idx_gmn) {
|
||||
AccDataType do_dot_o = 0;
|
||||
for(int o = 0; o < hdim_v; o++)
|
||||
{
|
||||
auto idx_gmo = idx_gmn;
|
||||
idx_gmo[2] = o;
|
||||
do_dot_o += ck_tile::type_convert<AccDataType>(do_host_ref(idx_gmo)) *
|
||||
ck_tile::type_convert<AccDataType>(o_host_refs[wb](idx_gmo));
|
||||
}
|
||||
self(idx_gmn) = ck_tile::type_convert<AccDataType>(
|
||||
p_hp_host_refs[wb](idx_gmn) * (dp_hp_host_ref(idx_gmn) - do_dot_o));
|
||||
});
|
||||
|
||||
if(use_dbias)
|
||||
{
|
||||
ds_hp_host_ref.ForEach([&](auto& self, auto idx) {
|
||||
dbias_host_ref(idx) = ck_tile::type_convert<BiasGradDataType>(self(idx));
|
||||
});
|
||||
}
|
||||
|
||||
ds_hp_host_ref.ForEach([&](auto& self, auto idx) {
|
||||
ds_lp_host_ref(idx) = ck_tile::type_convert<GemmDataType>(self(idx));
|
||||
});
|
||||
|
||||
// dV = P_drop^T@dO^T
|
||||
// dV = P^T@dO^T w/o dropout
|
||||
auto p_t_lp_host_ref = p_lp_host_refs[wb].transpose({0, 2, 1}); // p_lp_g_m_n -> p_lp_g_n_m
|
||||
auto do_t_host_ref = do_host_ref.transpose({0, 2, 1}); // do_g_m_o -> do_g_o_m
|
||||
ck_tile::reference_batched_gemm<GemmDataType, OGradDataType, AccDataType, VGradDataType>(
|
||||
p_t_lp_host_ref, do_t_host_ref, dv_host_ref); // dv_g_n_o = p_lp_g_n_m@do_g_o_m
|
||||
|
||||
// dQ = scale * dS@K^T
|
||||
auto k_t_host_ref = k_host_refs[wb].transpose({0, 2, 1}); // k_g_n_k -> k_g_k_n
|
||||
ck_tile::reference_batched_gemm<GemmDataType, KDataType, AccDataType, QGradDataType>(
|
||||
ds_lp_host_ref,
|
||||
k_t_host_ref,
|
||||
dq_host_ref,
|
||||
ck_tile::identity{},
|
||||
ck_tile::identity{},
|
||||
ck_tile::scales(scale)); // dq_g_m_k = ds_g_m_n@k_g_k_n
|
||||
|
||||
// dK = scale * dS^T@Q^T
|
||||
auto ds_t_lp_host_ref = ds_lp_host_ref.transpose({0, 2, 1}); // ds_g_m_n -> ds_g_n_m
|
||||
auto q_t_host_ref = q_host_refs[wb].transpose({0, 2, 1}); // q_g_m_k -> q_g_k_m
|
||||
ck_tile::reference_batched_gemm<GemmDataType, QDataType, AccDataType, KGradDataType>(
|
||||
ds_t_lp_host_ref,
|
||||
q_t_host_ref,
|
||||
dk_host_ref,
|
||||
ck_tile::identity{},
|
||||
ck_tile::identity{},
|
||||
ck_tile::scales(scale)); // dk_g_n_k = ds_g_n_m@q_g_k_m
|
||||
|
||||
ck_tile::HostTensor<QGradDataType> dq_host_result(
|
||||
{nhead, real_seqlen_q, hdim_q}); // dq_g_m_k
|
||||
ck_tile::HostTensor<KGradDataType> dk_host_result(
|
||||
{nhead, real_seqlen_k, hdim_q}); // dk_g_n_k
|
||||
ck_tile::HostTensor<VGradDataType> dv_host_result(
|
||||
{nhead, real_seqlen_k, hdim_v}); // dv_g_n_o
|
||||
ck_tile::HostTensor<BiasGradDataType> dbias_host_result(
|
||||
{nhead, real_seqlen_q, real_seqlen_k}); // dbias_g_m_n
|
||||
|
||||
// clang-format off
|
||||
// permute
|
||||
if(i_perm) dq_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dq_host(b, idx[0], idx[1] + query_offset, idx[2]); });
|
||||
else dq_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dq_host(b, idx[1] + query_offset, idx[0], idx[2]); });
|
||||
|
||||
if(i_perm) dk_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dk_host(b, idx[0], idx[1] + key_offset, idx[2]); });
|
||||
else dk_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dk_host(b, idx[1] + key_offset, idx[0], idx[2]); });
|
||||
|
||||
if(i_perm) dv_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dv_host(b, idx[0], idx[1] + key_offset, idx[2]); });
|
||||
else dv_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dv_host(b, idx[1] + key_offset, idx[0], idx[2]); });
|
||||
|
||||
if(use_dbias)
|
||||
{
|
||||
if(i_perm) dbias_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dbias_host(b, idx[0], idx[1] + query_offset, idx[2]); });
|
||||
else dbias_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dbias_host(b, idx[1] + query_offset, idx[0], idx[2]); });
|
||||
}
|
||||
// clang-format on
|
||||
|
||||
auto [rtol, atol] = get_elimit<DataType>(init_method);
|
||||
bool dq_cur_pass = ck_tile::check_err(dq_host_result,
|
||||
dq_host_ref,
|
||||
std::string("Error: QGrad Incorrect results!"),
|
||||
rtol,
|
||||
atol);
|
||||
bool dk_cur_pass = ck_tile::check_err(dk_host_result,
|
||||
dk_host_ref,
|
||||
std::string("Error: KGrad Incorrect results!"),
|
||||
rtol,
|
||||
atol);
|
||||
bool dv_cur_pass = ck_tile::check_err(dv_host_result,
|
||||
dv_host_ref,
|
||||
std::string("Error: VGrad Incorrect results!"),
|
||||
rtol,
|
||||
atol);
|
||||
|
||||
bool dbias_cur_pass = true;
|
||||
if(use_dbias)
|
||||
{
|
||||
dbias_cur_pass = ck_tile::check_err(dbias_host_result,
|
||||
dbias_host_ref,
|
||||
std::string("Error: BiasGrad Incorrect results!"),
|
||||
rtol,
|
||||
atol);
|
||||
}
|
||||
pass &= (dq_cur_pass & dk_cur_pass & dv_cur_pass & dbias_cur_pass);
|
||||
if(!(dq_cur_pass & dk_cur_pass & dv_cur_pass & dbias_cur_pass))
|
||||
{
|
||||
std::cerr << "mismatch found at batch: " << wb << std::endl
|
||||
<< "\tseqlen_q: " << real_seqlen_q << std::endl
|
||||
<< "\tseqlen_k: " << real_seqlen_k << std::endl
|
||||
<< "\tseqstart_q: " << seqstart_q_host << std::endl
|
||||
<< "\tseqstart_k: " << seqstart_k_host << std::endl;
|
||||
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
return -1;
|
||||
|
||||
const std::string data_type = arg_parser.get_str("prec");
|
||||
if(data_type == "fp16")
|
||||
{
|
||||
return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
|
||||
}
|
||||
else if(data_type == "bf16")
|
||||
{
|
||||
return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2;
|
||||
}
|
||||
|
||||
return -3;
|
||||
}
|
||||
359
example/ck_tile/01_fmha/fmha_bwd.hpp
Normal file
359
example/ck_tile/01_fmha/fmha_bwd.hpp
Normal file
@@ -0,0 +1,359 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck_tile/core.hpp"
|
||||
#include "ck_tile/host/kernel_launch.hpp"
|
||||
#include "ck_tile/ops/fmha.hpp"
|
||||
#include "ck_tile/ops/epilogue.hpp"
|
||||
#include "mask.hpp"
|
||||
#include "bias.hpp"
|
||||
#include <type_traits>
|
||||
|
||||
template <typename DataType>
|
||||
struct FmhaBwdTypeConfig;
|
||||
|
||||
template <>
|
||||
struct FmhaBwdTypeConfig<ck_tile::half_t>
|
||||
{
|
||||
using QDataType = ck_tile::half_t;
|
||||
using KDataType = ck_tile::half_t;
|
||||
using VDataType = ck_tile::half_t;
|
||||
using GemmDataType = ck_tile::half_t;
|
||||
using BiasDataType = ck_tile::half_t;
|
||||
using LSEDataType = float;
|
||||
using AccDataType = float; // data type for gemm accumulation
|
||||
using DDataType = float;
|
||||
using RandValOutputDataType = uint8_t;
|
||||
using ODataType = ck_tile::half_t;
|
||||
using OGradDataType = ck_tile::half_t;
|
||||
using QGradDataType = ck_tile::half_t;
|
||||
using KGradDataType = ck_tile::half_t;
|
||||
using VGradDataType = ck_tile::half_t;
|
||||
using BiasGradDataType = ck_tile::half_t;
|
||||
};
|
||||
|
||||
template <>
|
||||
struct FmhaBwdTypeConfig<ck_tile::bf16_t>
|
||||
{
|
||||
using QDataType = ck_tile::bf16_t;
|
||||
using KDataType = ck_tile::bf16_t;
|
||||
using VDataType = ck_tile::bf16_t;
|
||||
using GemmDataType = ck_tile::bf16_t;
|
||||
using BiasDataType = ck_tile::bf16_t;
|
||||
using LSEDataType = float;
|
||||
using AccDataType = float; // data type for gemm accumulation
|
||||
using DDataType = float;
|
||||
using RandValOutputDataType = uint8_t;
|
||||
using ODataType = ck_tile::bf16_t;
|
||||
using OGradDataType = ck_tile::bf16_t;
|
||||
using QGradDataType = ck_tile::bf16_t;
|
||||
using KGradDataType = ck_tile::bf16_t;
|
||||
using VGradDataType = ck_tile::bf16_t;
|
||||
using BiasGradDataType = ck_tile::bf16_t;
|
||||
};
|
||||
|
||||
struct FmhaMasks
|
||||
{
|
||||
using NoMask = ck_tile::GenericAttentionMask<false>;
|
||||
using GenericMask = ck_tile::GenericAttentionMask<true, true>;
|
||||
using CausalMask = ck_tile::GenericAttentionMask<true, false>;
|
||||
};
|
||||
|
||||
// runtime args, some will passed to karg, some will used to compute grids/blocks
|
||||
struct fmha_bwd_args
|
||||
{
|
||||
const void* q_ptr;
|
||||
const void* k_ptr;
|
||||
const void* v_ptr;
|
||||
const void* bias_ptr; // bias or alibi_slope pointer
|
||||
const void* o_ptr;
|
||||
const void* lse_ptr;
|
||||
const void* do_ptr;
|
||||
void* d_ptr;
|
||||
void* rand_val_ptr;
|
||||
void* dq_ptr;
|
||||
void* dk_ptr;
|
||||
void* dv_ptr;
|
||||
void* dbias_ptr;
|
||||
const void* seqstart_q_ptr;
|
||||
const void* seqstart_k_ptr;
|
||||
const void* seqlen_k_ptr;
|
||||
ck_tile::index_t seqlen_q;
|
||||
ck_tile::index_t seqlen_k;
|
||||
ck_tile::index_t batch;
|
||||
ck_tile::index_t max_seqlen_q;
|
||||
ck_tile::index_t max_seqlen_k;
|
||||
ck_tile::index_t hdim_q;
|
||||
ck_tile::index_t hdim_v;
|
||||
ck_tile::index_t nhead_q;
|
||||
ck_tile::index_t nhead_k;
|
||||
float scale;
|
||||
ck_tile::index_t stride_q;
|
||||
ck_tile::index_t stride_k;
|
||||
ck_tile::index_t stride_v;
|
||||
ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
|
||||
ck_tile::index_t stride_o;
|
||||
ck_tile::index_t stride_randval;
|
||||
ck_tile::index_t stride_do;
|
||||
ck_tile::index_t stride_dk;
|
||||
ck_tile::index_t stride_dv;
|
||||
ck_tile::index_t stride_dbias;
|
||||
ck_tile::index_t nhead_stride_q;
|
||||
ck_tile::index_t nhead_stride_k;
|
||||
ck_tile::index_t nhead_stride_v;
|
||||
ck_tile::index_t nhead_stride_bias;
|
||||
ck_tile::index_t nhead_stride_o;
|
||||
ck_tile::index_t nhead_stride_randval;
|
||||
ck_tile::index_t nhead_stride_do;
|
||||
ck_tile::index_t nhead_stride_lsed;
|
||||
ck_tile::index_t nhead_stride_dbias;
|
||||
ck_tile::index_t batch_stride_q;
|
||||
ck_tile::index_t batch_stride_k;
|
||||
ck_tile::index_t batch_stride_v;
|
||||
ck_tile::index_t batch_stride_bias;
|
||||
ck_tile::index_t batch_stride_o;
|
||||
ck_tile::index_t batch_stride_randval;
|
||||
ck_tile::index_t batch_stride_do;
|
||||
ck_tile::index_t batch_stride_lsed;
|
||||
ck_tile::index_t batch_stride_dk;
|
||||
ck_tile::index_t batch_stride_dv;
|
||||
ck_tile::index_t batch_stride_dbias;
|
||||
ck_tile::index_t window_size_left;
|
||||
ck_tile::index_t window_size_right;
|
||||
ck_tile::index_t mask_type;
|
||||
float p_drop;
|
||||
float p_undrop;
|
||||
bool s_randval;
|
||||
std::tuple<uint64_t, uint64_t> drop_seed_offset;
|
||||
};
|
||||
|
||||
template <typename FmhaBwdDQDKDVKernel>
|
||||
auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
|
||||
{
|
||||
assert(args.nhead_q % args.nhead_k == 0);
|
||||
auto kargs = [&] {
|
||||
// create group mode kernel arguments
|
||||
if constexpr(FmhaBwdDQDKDVKernel::kIsGroupMode)
|
||||
{
|
||||
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr,
|
||||
args.k_ptr,
|
||||
args.v_ptr,
|
||||
args.bias_ptr,
|
||||
args.lse_ptr,
|
||||
args.do_ptr,
|
||||
args.d_ptr,
|
||||
args.rand_val_ptr,
|
||||
args.dq_ptr,
|
||||
args.dk_ptr,
|
||||
args.dv_ptr,
|
||||
args.dbias_ptr,
|
||||
args.seqstart_q_ptr,
|
||||
args.seqstart_k_ptr,
|
||||
args.seqlen_k_ptr,
|
||||
args.hdim_q,
|
||||
args.hdim_v,
|
||||
args.nhead_q,
|
||||
args.nhead_q / args.nhead_k,
|
||||
args.scale,
|
||||
args.stride_q,
|
||||
args.stride_k,
|
||||
args.stride_v,
|
||||
args.stride_bias,
|
||||
args.stride_randval,
|
||||
args.stride_do,
|
||||
args.stride_dk,
|
||||
args.stride_dv,
|
||||
args.stride_dbias,
|
||||
args.nhead_stride_q,
|
||||
args.nhead_stride_k,
|
||||
args.nhead_stride_v,
|
||||
args.nhead_stride_bias,
|
||||
args.nhead_stride_randval,
|
||||
args.nhead_stride_do,
|
||||
args.nhead_stride_lsed,
|
||||
args.nhead_stride_dbias,
|
||||
args.batch_stride_lsed,
|
||||
args.window_size_left,
|
||||
args.window_size_right,
|
||||
args.mask_type,
|
||||
args.p_drop,
|
||||
args.s_randval,
|
||||
args.drop_seed_offset);
|
||||
}
|
||||
else
|
||||
{ // create batch mode kernel arguments
|
||||
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr,
|
||||
args.k_ptr,
|
||||
args.v_ptr,
|
||||
args.bias_ptr,
|
||||
args.lse_ptr,
|
||||
args.do_ptr,
|
||||
args.d_ptr,
|
||||
args.rand_val_ptr,
|
||||
args.dq_ptr,
|
||||
args.dk_ptr,
|
||||
args.dv_ptr,
|
||||
args.dbias_ptr,
|
||||
args.seqlen_q,
|
||||
args.seqlen_k,
|
||||
args.hdim_q,
|
||||
args.hdim_v,
|
||||
args.nhead_q,
|
||||
args.nhead_q / args.nhead_k,
|
||||
args.scale,
|
||||
args.stride_q,
|
||||
args.stride_k,
|
||||
args.stride_v,
|
||||
args.stride_bias,
|
||||
args.stride_randval,
|
||||
args.stride_do,
|
||||
args.stride_dk,
|
||||
args.stride_dv,
|
||||
args.stride_dbias,
|
||||
args.nhead_stride_q,
|
||||
args.nhead_stride_k,
|
||||
args.nhead_stride_v,
|
||||
args.nhead_stride_bias,
|
||||
args.nhead_stride_randval,
|
||||
args.nhead_stride_do,
|
||||
args.nhead_stride_lsed,
|
||||
args.nhead_stride_dbias,
|
||||
args.batch_stride_q,
|
||||
args.batch_stride_k,
|
||||
args.batch_stride_v,
|
||||
args.batch_stride_bias,
|
||||
args.batch_stride_randval,
|
||||
args.batch_stride_do,
|
||||
args.batch_stride_lsed,
|
||||
args.batch_stride_dk,
|
||||
args.batch_stride_dv,
|
||||
args.batch_stride_dbias,
|
||||
args.window_size_left,
|
||||
args.window_size_right,
|
||||
args.mask_type,
|
||||
args.p_drop,
|
||||
args.s_randval,
|
||||
args.drop_seed_offset);
|
||||
}
|
||||
}();
|
||||
|
||||
dim3 grids = FmhaBwdDQDKDVKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_k);
|
||||
return ck_tile::make_tuple(kargs, grids);
|
||||
}
|
||||
|
||||
template <typename FmhaBwdOGradDotOKernel>
|
||||
auto fmha_bwd_dot_do_o_create_kargs_and_grids(fmha_bwd_args args)
|
||||
{
|
||||
auto kargs = [&] {
|
||||
// create group mode kernel arguments
|
||||
if constexpr(FmhaBwdOGradDotOKernel::kIsGroupMode)
|
||||
{
|
||||
return FmhaBwdOGradDotOKernel::MakeKargs(args.o_ptr,
|
||||
args.do_ptr,
|
||||
args.d_ptr,
|
||||
args.p_undrop,
|
||||
args.seqstart_q_ptr,
|
||||
args.hdim_v,
|
||||
args.stride_do,
|
||||
args.stride_o,
|
||||
args.nhead_stride_do,
|
||||
args.nhead_stride_o,
|
||||
args.nhead_stride_lsed,
|
||||
args.batch_stride_lsed);
|
||||
}
|
||||
else
|
||||
{ // create batch mode kernel arguments
|
||||
return FmhaBwdOGradDotOKernel::MakeKargs(args.o_ptr,
|
||||
args.do_ptr,
|
||||
args.d_ptr,
|
||||
args.p_undrop,
|
||||
args.seqlen_q,
|
||||
args.hdim_v,
|
||||
args.stride_do,
|
||||
args.stride_o,
|
||||
args.nhead_stride_do,
|
||||
args.nhead_stride_o,
|
||||
args.nhead_stride_lsed,
|
||||
args.batch_stride_do,
|
||||
args.batch_stride_o,
|
||||
args.batch_stride_lsed);
|
||||
}
|
||||
}();
|
||||
|
||||
dim3 grids = FmhaBwdOGradDotOKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q);
|
||||
return ck_tile::make_tuple(kargs, grids);
|
||||
}
|
||||
|
||||
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
|
||||
template <ck_tile::index_t HDim_,
|
||||
typename DataType_,
|
||||
bool kIsGroupMode_,
|
||||
ck_tile::BlockFmhaBwdPipelineEnum FmhaBwdPipelineEnum_,
|
||||
typename FmhaMask_,
|
||||
ck_tile::BlockAttentionBiasEnum BiasEnum_,
|
||||
bool kHasBiasGrad_,
|
||||
bool kHasDropout_,
|
||||
bool kPadS_,
|
||||
bool kPadSK_,
|
||||
bool kPadD_,
|
||||
bool kPadDv_>
|
||||
struct fmha_bwd_dq_dk_dv_traits_
|
||||
{
|
||||
static constexpr ck_tile::index_t HDim = HDim_;
|
||||
using DataType = ck_tile::remove_cvref_t<DataType_>;
|
||||
static constexpr bool kIsGroupMode = kIsGroupMode_;
|
||||
static constexpr auto FmhaBwdPipelineEnum = FmhaBwdPipelineEnum_;
|
||||
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
|
||||
static constexpr auto BiasEnum = BiasEnum_;
|
||||
static constexpr bool kHasBiasGrad = kHasBiasGrad_;
|
||||
static constexpr bool kHasDropout = kHasDropout_;
|
||||
static constexpr bool kPadS = kPadS_;
|
||||
static constexpr bool kPadSK = kPadSK_;
|
||||
static constexpr bool kPadD = kPadD_;
|
||||
static constexpr bool kPadDv = kPadDv_;
|
||||
};
|
||||
|
||||
template <typename Traits_>
|
||||
float fmha_bwd_dq_dk_dv_(const ck_tile::stream_config&, fmha_bwd_args);
|
||||
|
||||
template <typename Traits_>
|
||||
void fmha_bwd_dq_dk_dv_oneshot_(const ck_tile::stream_config&, fmha_bwd_args);
|
||||
|
||||
template <typename Traits_>
|
||||
std::string fmha_bwd_dq_dk_dv_get_name_();
|
||||
|
||||
template <ck_tile::index_t HDim_, typename DataType_, bool kIsGroupMode_, bool kPadS_, bool kPadDv_>
|
||||
struct fmha_bwd_dot_do_o_traits_
|
||||
{
|
||||
static constexpr ck_tile::index_t HDim = HDim_;
|
||||
using DataType = ck_tile::remove_cvref_t<DataType_>;
|
||||
static constexpr bool kIsGroupMode = kIsGroupMode_;
|
||||
static constexpr bool kPadS = kPadS_;
|
||||
static constexpr bool kPadDv = kPadDv_;
|
||||
};
|
||||
|
||||
template <typename Traits_>
|
||||
float fmha_bwd_dot_do_o_(const ck_tile::stream_config&, fmha_bwd_args);
|
||||
|
||||
template <typename Traits_>
|
||||
void fmha_bwd_dot_do_o_oneshot_(const ck_tile::stream_config&, fmha_bwd_args);
|
||||
|
||||
template <typename Traits_>
|
||||
std::string fmha_bwd_dot_do_o_get_name_();
|
||||
|
||||
// This is the public API, will be generated by script
|
||||
struct fmha_bwd_traits
|
||||
{
|
||||
int hdim_q;
|
||||
int hdim_v;
|
||||
std::string data_type;
|
||||
bool is_group_mode;
|
||||
mask_enum mask_type;
|
||||
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
|
||||
bool has_dbias;
|
||||
bool has_dropout;
|
||||
// TODO: padding check is inside this api
|
||||
};
|
||||
float fmha_bwd(fmha_bwd_traits, fmha_bwd_args, const ck_tile::stream_config&);
|
||||
@@ -1,5 +1,5 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "fmha_fwd.hpp"
|
||||
#include "ck_tile/host.hpp"
|
||||
@@ -110,6 +110,9 @@ auto create_args(int argc, char* argv[])
|
||||
"11939",
|
||||
"random seed used for initializing input tensors. 0 for "
|
||||
"non-deterministic seed")
|
||||
.insert("p_drop", "0", "0~1 probability of dropout")
|
||||
.insert("drop_seed", "1", "seed for random number generator")
|
||||
.insert("drop_offset", "0", "offset for random number generator")
|
||||
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
|
||||
.insert("warmup", "5", "number of iterations before benchmark the kernel")
|
||||
.insert("repeat", "20", "number of iterations to benchmark the kernel");
|
||||
@@ -128,26 +131,11 @@ auto get_elimit(std::string /*init_method*/)
|
||||
}
|
||||
|
||||
template <>
|
||||
auto get_elimit<ck_tile::bf16_t>(std::string init_method)
|
||||
auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/)
|
||||
{
|
||||
if(init_method == "ui" || init_method == "ni")
|
||||
{
|
||||
double rtol = 1e-2;
|
||||
double atol = 1e-2;
|
||||
return ck_tile::make_tuple(rtol, atol);
|
||||
}
|
||||
else if(init_method == "nf")
|
||||
{
|
||||
double rtol = 1e-2;
|
||||
double atol = 1e-2;
|
||||
return ck_tile::make_tuple(rtol, atol);
|
||||
}
|
||||
else
|
||||
{
|
||||
double rtol = 3e-3;
|
||||
double atol = 3e-3;
|
||||
return ck_tile::make_tuple(rtol, atol);
|
||||
}
|
||||
double rtol = 1e-2;
|
||||
double atol = 1e-2;
|
||||
return ck_tile::make_tuple(rtol, atol);
|
||||
}
|
||||
|
||||
template <>
|
||||
@@ -250,6 +238,21 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
mask_info mask = mask_info::decode(
|
||||
arg_parser.get_str("mask"), seqlen_qs[0], seqlen_ks[0]); // TODO: we don't need x/y anymore
|
||||
|
||||
float p_drop = arg_parser.get_float("p_drop");
|
||||
uint64_t drop_seed = arg_parser.get_uint64("drop_seed");
|
||||
uint64_t drop_offset = arg_parser.get_uint64("drop_offset");
|
||||
if(p_drop < 0.0f || p_drop > 1.0f)
|
||||
{
|
||||
std::cerr << "The value of p_drop should be 0~1" << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
bool s_randval = false;
|
||||
if(p_drop > 0.0f && do_validation)
|
||||
{
|
||||
s_randval = true;
|
||||
}
|
||||
|
||||
std::string init_method = arg_parser.get_str("init");
|
||||
std::optional<uint32_t> seed = arg_parser.get_uint32("seed");
|
||||
if(*seed == 0)
|
||||
@@ -274,21 +277,23 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
|
||||
using TypeConfig = FmhaFwdTypeConfig<DataType>;
|
||||
|
||||
using QDataType = typename TypeConfig::QDataType;
|
||||
using KDataType = typename TypeConfig::KDataType;
|
||||
using VDataType = typename TypeConfig::VDataType;
|
||||
using BiasDataType = typename TypeConfig::BiasDataType;
|
||||
using LSEDataType = typename TypeConfig::LSEDataType;
|
||||
using SaccDataType = typename TypeConfig::SaccDataType;
|
||||
using SMPLComputeDataType = typename TypeConfig::SMPLComputeDataType;
|
||||
using PDataType = typename TypeConfig::PDataType;
|
||||
using OaccDataType = typename TypeConfig::OaccDataType;
|
||||
using ODataType = typename TypeConfig::ODataType;
|
||||
using QDataType = typename TypeConfig::QDataType;
|
||||
using KDataType = typename TypeConfig::KDataType;
|
||||
using VDataType = typename TypeConfig::VDataType;
|
||||
using BiasDataType = typename TypeConfig::BiasDataType;
|
||||
using RandValOutputDataType = typename TypeConfig::RandValOutputDataType;
|
||||
using LSEDataType = typename TypeConfig::LSEDataType;
|
||||
using SaccDataType = typename TypeConfig::SaccDataType;
|
||||
using SMPLComputeDataType = typename TypeConfig::SMPLComputeDataType;
|
||||
using PDataType = typename TypeConfig::PDataType;
|
||||
using OaccDataType = typename TypeConfig::OaccDataType;
|
||||
using ODataType = typename TypeConfig::ODataType;
|
||||
|
||||
// accumulation numbers for performance evaluation
|
||||
std::size_t flop = 0, num_byte = 0;
|
||||
auto max_seqlen_q =
|
||||
std::numeric_limits<int32_t>::min(); // we will use max seqlen to decide grid size
|
||||
auto max_seqlen_k = std::numeric_limits<int32_t>::min();
|
||||
{
|
||||
for(ck_tile::index_t wb = 0; wb < batch; ++wb)
|
||||
{
|
||||
@@ -300,6 +305,11 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
max_seqlen_q = real_seqlen_q;
|
||||
}
|
||||
|
||||
if(max_seqlen_k < real_seqlen_k)
|
||||
{
|
||||
max_seqlen_k = real_seqlen_k;
|
||||
}
|
||||
|
||||
flop += nhead * (static_cast<std::size_t>(2) * real_seqlen_q * real_seqlen_k * hdim_q +
|
||||
static_cast<std::size_t>(2) * real_seqlen_q * hdim_v * real_seqlen_k);
|
||||
|
||||
@@ -353,12 +363,16 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
|
||||
// self define lse data layout as [shape_batch, nhead, shape_seqlen_q]
|
||||
ck_tile::HostTensor<LSEDataType> lse_host(
|
||||
lse ? std::array<ck_tile::index_t, 3>{shape_batch, nhead, shape_seqlen_q}
|
||||
lse ? std::array<ck_tile::index_t, 3>{batch, nhead, max_seqlen_q}
|
||||
: std::array<ck_tile::index_t, 3>{1, 1, 1} /* dummy shape for simplifying code */);
|
||||
|
||||
ck_tile::HostTensor<ODataType> o_host(
|
||||
get_lengths(o_perm, shape_batch, nhead, shape_seqlen_q, hdim_v));
|
||||
|
||||
ck_tile::HostTensor<RandValOutputDataType> randval_host(
|
||||
p_drop > 0 ? get_lengths(true, shape_batch, nhead, shape_seqlen_q, max_seqlen_k)
|
||||
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
|
||||
|
||||
if(init_method == "ui" || init_method == "0")
|
||||
{
|
||||
ck_tile::FillUniformDistributionIntegerValue<QDataType>{-3.f, 3.f, seed}(q_host);
|
||||
@@ -434,6 +448,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
ck_tile::DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t));
|
||||
ck_tile::DeviceMem seqstart_k(seqstart_k_host.size() * sizeof(int32_t));
|
||||
ck_tile::DeviceMem seqlen_k_buf(seqlen_kpads[0] < 0 ? 0 : seqlen_ks.size() * sizeof(int32_t));
|
||||
ck_tile::DeviceMem randval_buf(randval_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem alibi_slope_buf(alibi_slope_host.get_element_space_size_in_bytes());
|
||||
|
||||
q_buf.ToDevice(q_host.data());
|
||||
@@ -463,8 +478,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
<< (seqlen_kpads[0] < 0 ? ""
|
||||
: (std::string("(") + std::to_string(seqlen_kpads[0]) + ")"))
|
||||
<< ", d:" << hdim_q << "/" << hdim_v << ", scale_s:" << scale_s << ", bias:" << bias
|
||||
<< ", lse:" << lse << ", squant:" << squant << ", mask:" << mask << ", v:" << vlayout
|
||||
<< std::flush;
|
||||
<< ", p_drop:" << p_drop << ", lse:" << lse << ", squant:" << squant
|
||||
<< ", mask:" << mask << ", v:" << vlayout << std::flush;
|
||||
|
||||
auto fmha_traits = fmha_fwd_traits{hdim_q,
|
||||
hdim_v,
|
||||
@@ -474,6 +489,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
mask.type,
|
||||
bias.type,
|
||||
lse,
|
||||
p_drop > 0.0f,
|
||||
squant};
|
||||
|
||||
auto p_compute_element_func = [&]() {
|
||||
@@ -505,8 +521,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
else
|
||||
return i_perm ? shape_seqlen_k : nhead_k * shape_seqlen_k;
|
||||
}();
|
||||
const ck_tile::index_t stride_bias = (i_perm ? shape_seqlen_k : 1 * shape_seqlen_k);
|
||||
const ck_tile::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
|
||||
const ck_tile::index_t stride_bias = (i_perm ? shape_seqlen_k : 1 * shape_seqlen_k);
|
||||
const ck_tile::index_t stride_randval = (max_seqlen_k);
|
||||
const ck_tile::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
|
||||
// setup nhead_stride_* arguments
|
||||
const ck_tile::index_t nhead_stride_q = (i_perm ? shape_seqlen_q * hdim_q : hdim_q);
|
||||
const ck_tile::index_t nhead_stride_k = (i_perm ? shape_seqlen_k * hdim_q : hdim_q);
|
||||
@@ -518,21 +535,24 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
}();
|
||||
const ck_tile::index_t nhead_stride_bias =
|
||||
(i_perm ? 0 * shape_seqlen_q * shape_seqlen_k : 0 * shape_seqlen_k);
|
||||
const ck_tile::index_t nhead_stride_lse = (shape_seqlen_q * 1);
|
||||
const ck_tile::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
|
||||
const ck_tile::index_t nhead_stride_randval = (shape_seqlen_q * max_seqlen_k);
|
||||
const ck_tile::index_t nhead_stride_lse = max_seqlen_q;
|
||||
const ck_tile::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
|
||||
// setup batch_stride_* arguments
|
||||
const ck_tile::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
|
||||
const ck_tile::index_t batch_stride_k = (nhead_k * shape_seqlen_k * hdim_q);
|
||||
const ck_tile::index_t batch_stride_v = (nhead_k * hdim_v * shape_seqlen_k);
|
||||
const ck_tile::index_t batch_stride_bias = (0 * nhead * shape_seqlen_q * shape_seqlen_k);
|
||||
const ck_tile::index_t batch_stride_lse = (nhead * shape_seqlen_q * 1);
|
||||
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
|
||||
const ck_tile::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
|
||||
const ck_tile::index_t batch_stride_k = (nhead_k * shape_seqlen_k * hdim_q);
|
||||
const ck_tile::index_t batch_stride_v = (nhead_k * hdim_v * shape_seqlen_k);
|
||||
const ck_tile::index_t batch_stride_bias = (0 * nhead * shape_seqlen_q * shape_seqlen_k);
|
||||
const ck_tile::index_t batch_stride_randval = (nhead * shape_seqlen_q * max_seqlen_k);
|
||||
const ck_tile::index_t batch_stride_lse = (nhead * max_seqlen_q);
|
||||
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
|
||||
|
||||
return fmha_fwd_args{q_buf.GetDeviceBuffer(),
|
||||
k_buf.GetDeviceBuffer(),
|
||||
v_buf.GetDeviceBuffer(),
|
||||
bias.type == bias_enum::alibi ? alibi_slope_buf.GetDeviceBuffer()
|
||||
: bias_buf.GetDeviceBuffer(),
|
||||
randval_buf.GetDeviceBuffer(),
|
||||
lse_buf.GetDeviceBuffer(),
|
||||
o_buf.GetDeviceBuffer(),
|
||||
seqstart_q.GetDeviceBuffer(),
|
||||
@@ -554,22 +574,28 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
stride_v,
|
||||
bias.type == bias_enum::alibi ? (bias.rank_info == 0 ? 0 : nhead)
|
||||
: stride_bias,
|
||||
stride_randval,
|
||||
stride_o,
|
||||
nhead_stride_q,
|
||||
nhead_stride_k,
|
||||
nhead_stride_v,
|
||||
nhead_stride_bias,
|
||||
nhead_stride_randval,
|
||||
nhead_stride_lse,
|
||||
nhead_stride_o,
|
||||
batch_stride_q,
|
||||
batch_stride_k,
|
||||
batch_stride_v,
|
||||
batch_stride_bias,
|
||||
batch_stride_randval,
|
||||
batch_stride_lse,
|
||||
batch_stride_o,
|
||||
mask.left,
|
||||
mask.right,
|
||||
static_cast<ck_tile::index_t>(mask.type)};
|
||||
static_cast<ck_tile::index_t>(mask.type),
|
||||
p_drop,
|
||||
s_randval,
|
||||
{drop_seed, drop_offset}};
|
||||
}();
|
||||
|
||||
float ave_time = fmha_fwd(fmha_traits, fmha_args, stream_config);
|
||||
@@ -596,6 +622,11 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
|
||||
o_buf.FromDevice(o_host.data());
|
||||
lse_buf.FromDevice(lse_host.data());
|
||||
randval_buf.FromDevice(randval_host.data());
|
||||
float p_undrop = 1.0 - p_drop;
|
||||
uint8_t p_undrop_in_uint8_t =
|
||||
uint8_t(std::floor(p_undrop * std::numeric_limits<uint8_t>::max()));
|
||||
float rp_undrop = 1.0 / p_undrop;
|
||||
|
||||
bool pass = true;
|
||||
|
||||
@@ -771,6 +802,17 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
s_host_ref, p_host_ref, p_compute_element_func);
|
||||
}
|
||||
|
||||
if(p_drop > 0)
|
||||
{
|
||||
ck_tile::HostTensor<RandValOutputDataType> randval_host_ref(
|
||||
{nhead, real_seqlen_q, real_seqlen_k});
|
||||
randval_host_ref.ForEach([&](auto& self, auto idx) {
|
||||
self(idx) = randval_host(b, idx[0], idx[1] + query_offset, idx[2]);
|
||||
});
|
||||
ck_tile::reference_batched_dropout(
|
||||
p_host_ref, randval_host_ref, p_undrop_in_uint8_t, rp_undrop);
|
||||
}
|
||||
|
||||
ck_tile::reference_batched_gemm<PDataType, VDataType, OaccDataType, ODataType>(
|
||||
p_host_ref,
|
||||
v_host_ref,
|
||||
@@ -804,9 +846,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
if(lse)
|
||||
{
|
||||
ck_tile::HostTensor<SMPLComputeDataType> lse_host_result({nhead, real_seqlen_q});
|
||||
lse_host_result.ForEach([&](auto& self, auto idx) {
|
||||
self(idx) = lse_host(b, idx[0], idx[1] + query_offset);
|
||||
});
|
||||
lse_host_result.ForEach(
|
||||
[&](auto& self, auto idx) { self(idx) = lse_host(wb, idx[0], idx[1]); });
|
||||
|
||||
bool lse_pass = ck_tile::check_err(lse_host_result,
|
||||
lse_host_ref,
|
||||
|
||||
@@ -17,61 +17,65 @@ struct FmhaFwdTypeConfig;
|
||||
template <>
|
||||
struct FmhaFwdTypeConfig<ck_tile::half_t>
|
||||
{
|
||||
using QDataType = ck_tile::half_t;
|
||||
using KDataType = ck_tile::half_t;
|
||||
using VDataType = ck_tile::half_t;
|
||||
using BiasDataType = ck_tile::half_t;
|
||||
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
|
||||
using SaccDataType = float; // data type for first gemm accumulation
|
||||
using SMPLComputeDataType = float; // data type for reduction, softmax
|
||||
using PDataType = ck_tile::half_t; // data type for A matrix of second gemm
|
||||
using OaccDataType = float; // data type for second gemm accumulation
|
||||
using ODataType = ck_tile::half_t;
|
||||
using QDataType = ck_tile::half_t;
|
||||
using KDataType = ck_tile::half_t;
|
||||
using VDataType = ck_tile::half_t;
|
||||
using BiasDataType = ck_tile::half_t;
|
||||
using RandValOutputDataType = uint8_t;
|
||||
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
|
||||
using SaccDataType = float; // data type for first gemm accumulation
|
||||
using SMPLComputeDataType = float; // data type for reduction, softmax
|
||||
using PDataType = ck_tile::half_t; // data type for A matrix of second gemm
|
||||
using OaccDataType = float; // data type for second gemm accumulation
|
||||
using ODataType = ck_tile::half_t;
|
||||
};
|
||||
|
||||
template <>
|
||||
struct FmhaFwdTypeConfig<ck_tile::bf16_t>
|
||||
{
|
||||
using QDataType = ck_tile::bf16_t;
|
||||
using KDataType = ck_tile::bf16_t;
|
||||
using VDataType = ck_tile::bf16_t;
|
||||
using BiasDataType = ck_tile::bf16_t;
|
||||
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
|
||||
using SaccDataType = float; // data type for first gemm accumulation
|
||||
using SMPLComputeDataType = float; // data type for reduction, softmax
|
||||
using PDataType = ck_tile::bf16_t; // data type for A matrix of second gemm
|
||||
using OaccDataType = float; // data type for second gemm accumulation
|
||||
using ODataType = ck_tile::bf16_t;
|
||||
using QDataType = ck_tile::bf16_t;
|
||||
using KDataType = ck_tile::bf16_t;
|
||||
using VDataType = ck_tile::bf16_t;
|
||||
using BiasDataType = ck_tile::bf16_t;
|
||||
using RandValOutputDataType = uint8_t;
|
||||
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
|
||||
using SaccDataType = float; // data type for first gemm accumulation
|
||||
using SMPLComputeDataType = float; // data type for reduction, softmax
|
||||
using PDataType = ck_tile::bf16_t; // data type for A matrix of second gemm
|
||||
using OaccDataType = float; // data type for second gemm accumulation
|
||||
using ODataType = ck_tile::bf16_t;
|
||||
};
|
||||
|
||||
template <>
|
||||
struct FmhaFwdTypeConfig<ck_tile::fp8_t>
|
||||
{
|
||||
using QDataType = ck_tile::fp8_t;
|
||||
using KDataType = ck_tile::fp8_t;
|
||||
using VDataType = ck_tile::fp8_t;
|
||||
using BiasDataType = float;
|
||||
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
|
||||
using SaccDataType = float; // data type for first gemm accumulation
|
||||
using SMPLComputeDataType = float; // data type for reduction, softmax
|
||||
using PDataType = ck_tile::fp8_t; // data type for A matrix of second gemm
|
||||
using OaccDataType = float; // data type for second gemm accumulation
|
||||
using ODataType = ck_tile::fp8_t;
|
||||
using QDataType = ck_tile::fp8_t;
|
||||
using KDataType = ck_tile::fp8_t;
|
||||
using VDataType = ck_tile::fp8_t;
|
||||
using BiasDataType = float;
|
||||
using RandValOutputDataType = uint8_t;
|
||||
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
|
||||
using SaccDataType = float; // data type for first gemm accumulation
|
||||
using SMPLComputeDataType = float; // data type for reduction, softmax
|
||||
using PDataType = ck_tile::fp8_t; // data type for A matrix of second gemm
|
||||
using OaccDataType = float; // data type for second gemm accumulation
|
||||
using ODataType = ck_tile::fp8_t;
|
||||
};
|
||||
|
||||
template <>
|
||||
struct FmhaFwdTypeConfig<ck_tile::bf8_t>
|
||||
{
|
||||
using QDataType = ck_tile::bf8_t;
|
||||
using KDataType = ck_tile::bf8_t;
|
||||
using VDataType = ck_tile::bf8_t;
|
||||
using BiasDataType = ck_tile::bf8_t;
|
||||
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
|
||||
using SaccDataType = float; // data type for first gemm accumulation
|
||||
using SMPLComputeDataType = float; // data type for reduction, softmax
|
||||
using PDataType = ck_tile::bf8_t; // data type for A matrix of second gemm
|
||||
using OaccDataType = float; // data type for second gemm accumulation
|
||||
using ODataType = ck_tile::bf8_t;
|
||||
using QDataType = ck_tile::bf8_t;
|
||||
using KDataType = ck_tile::bf8_t;
|
||||
using VDataType = ck_tile::bf8_t;
|
||||
using BiasDataType = ck_tile::bf8_t;
|
||||
using RandValOutputDataType = uint8_t;
|
||||
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
|
||||
using SaccDataType = float; // data type for first gemm accumulation
|
||||
using SMPLComputeDataType = float; // data type for reduction, softmax
|
||||
using PDataType = ck_tile::bf8_t; // data type for A matrix of second gemm
|
||||
using OaccDataType = float; // data type for second gemm accumulation
|
||||
using ODataType = ck_tile::bf8_t;
|
||||
};
|
||||
|
||||
struct FmhaMasks
|
||||
@@ -88,6 +92,7 @@ struct fmha_fwd_args
|
||||
const void* k_ptr;
|
||||
const void* v_ptr;
|
||||
const void* bias_ptr; // bias or alibi_slope pointer
|
||||
void* rand_val_ptr;
|
||||
void* lse_ptr;
|
||||
void* o_ptr;
|
||||
const void* seqstart_q_ptr;
|
||||
@@ -108,22 +113,28 @@ struct fmha_fwd_args
|
||||
ck_tile::index_t stride_k;
|
||||
ck_tile::index_t stride_v;
|
||||
ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
|
||||
ck_tile::index_t stride_randval;
|
||||
ck_tile::index_t stride_o;
|
||||
ck_tile::index_t nhead_stride_q;
|
||||
ck_tile::index_t nhead_stride_k;
|
||||
ck_tile::index_t nhead_stride_v;
|
||||
ck_tile::index_t nhead_stride_bias;
|
||||
ck_tile::index_t nhead_stride_randval;
|
||||
ck_tile::index_t nhead_stride_lse;
|
||||
ck_tile::index_t nhead_stride_o;
|
||||
ck_tile::index_t batch_stride_q;
|
||||
ck_tile::index_t batch_stride_k;
|
||||
ck_tile::index_t batch_stride_v;
|
||||
ck_tile::index_t batch_stride_bias;
|
||||
ck_tile::index_t batch_stride_randval;
|
||||
ck_tile::index_t batch_stride_lse;
|
||||
ck_tile::index_t batch_stride_o;
|
||||
ck_tile::index_t window_size_left;
|
||||
ck_tile::index_t window_size_right;
|
||||
ck_tile::index_t mask_type;
|
||||
float p_drop;
|
||||
bool s_randval;
|
||||
std::tuple<uint64_t, uint64_t> drop_seed_offset;
|
||||
};
|
||||
|
||||
template <typename FmhaKernel>
|
||||
@@ -138,6 +149,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
|
||||
args.k_ptr,
|
||||
args.v_ptr,
|
||||
args.bias_ptr,
|
||||
args.rand_val_ptr,
|
||||
args.lse_ptr,
|
||||
args.o_ptr,
|
||||
args.seqstart_q_ptr,
|
||||
@@ -145,6 +157,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
|
||||
args.seqlen_k_ptr,
|
||||
args.hdim_q,
|
||||
args.hdim_v,
|
||||
args.nhead_q,
|
||||
args.nhead_q / args.nhead_k,
|
||||
args.scale_s,
|
||||
args.scale_p,
|
||||
@@ -153,16 +166,22 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
|
||||
args.stride_k,
|
||||
args.stride_v,
|
||||
args.stride_bias,
|
||||
args.stride_randval,
|
||||
args.stride_o,
|
||||
args.nhead_stride_q,
|
||||
args.nhead_stride_k,
|
||||
args.nhead_stride_v,
|
||||
args.nhead_stride_bias,
|
||||
args.nhead_stride_randval,
|
||||
args.nhead_stride_lse,
|
||||
args.nhead_stride_o,
|
||||
args.batch_stride_lse,
|
||||
args.window_size_left,
|
||||
args.window_size_right,
|
||||
args.mask_type);
|
||||
args.mask_type,
|
||||
args.p_drop,
|
||||
args.s_randval,
|
||||
args.drop_seed_offset);
|
||||
}
|
||||
else
|
||||
{ // create batch mode kernel arguments
|
||||
@@ -170,12 +189,14 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
|
||||
args.k_ptr,
|
||||
args.v_ptr,
|
||||
args.bias_ptr,
|
||||
args.rand_val_ptr,
|
||||
args.lse_ptr,
|
||||
args.o_ptr,
|
||||
args.seqlen_q,
|
||||
args.seqlen_k,
|
||||
args.hdim_q,
|
||||
args.hdim_v,
|
||||
args.nhead_q,
|
||||
args.nhead_q / args.nhead_k,
|
||||
args.scale_s,
|
||||
args.scale_p,
|
||||
@@ -184,22 +205,28 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
|
||||
args.stride_k,
|
||||
args.stride_v,
|
||||
args.stride_bias,
|
||||
args.stride_randval,
|
||||
args.stride_o,
|
||||
args.nhead_stride_q,
|
||||
args.nhead_stride_k,
|
||||
args.nhead_stride_v,
|
||||
args.nhead_stride_bias,
|
||||
args.nhead_stride_randval,
|
||||
args.nhead_stride_lse,
|
||||
args.nhead_stride_o,
|
||||
args.batch_stride_q,
|
||||
args.batch_stride_k,
|
||||
args.batch_stride_v,
|
||||
args.batch_stride_bias,
|
||||
args.batch_stride_randval,
|
||||
args.batch_stride_lse,
|
||||
args.batch_stride_o,
|
||||
args.window_size_left,
|
||||
args.window_size_right,
|
||||
args.mask_type);
|
||||
args.mask_type,
|
||||
args.p_drop,
|
||||
args.s_randval,
|
||||
args.drop_seed_offset);
|
||||
}
|
||||
}();
|
||||
|
||||
@@ -222,6 +249,7 @@ template <ck_tile::index_t HDim_,
|
||||
typename FmhaMask_,
|
||||
ck_tile::BlockAttentionBiasEnum BiasEnum_,
|
||||
bool kStoreLse_,
|
||||
bool kHasDropout_,
|
||||
bool kDoFp8StaticQuant_,
|
||||
bool kPadS_,
|
||||
bool kPadSK_,
|
||||
@@ -243,6 +271,7 @@ struct fmha_fwd_traits_
|
||||
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
|
||||
static constexpr auto BiasEnum = BiasEnum_;
|
||||
static constexpr bool kStoreLse = kStoreLse_;
|
||||
static constexpr bool kHasDropout = kHasDropout_;
|
||||
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
|
||||
static constexpr bool kPadS = kPadS_;
|
||||
static constexpr bool kPadSK = kPadSK_;
|
||||
@@ -264,6 +293,7 @@ struct fmha_fwd_traits
|
||||
mask_enum mask_type;
|
||||
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
|
||||
bool has_lse;
|
||||
bool has_dropout;
|
||||
bool do_fp8_static_quant;
|
||||
// TODO: padding check is inside this api
|
||||
};
|
||||
|
||||
@@ -83,7 +83,6 @@ TILE_PARTITIONER_MAP = {
|
||||
"hbs" : "ck_tile::FmhaFwdTilePartitioner_HBS",
|
||||
}
|
||||
|
||||
DIRECTIONS = ["fwd"]
|
||||
GEN_DIR = "" # in Cmake, have to generate files in same folder
|
||||
|
||||
FMHA_FWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
|
||||
@@ -111,8 +110,10 @@ using fmha_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad},
|
||||
{F_dpad},
|
||||
{F_dvpad},
|
||||
{F_bias},
|
||||
false,
|
||||
{F_lse},
|
||||
{F_squant},
|
||||
{F_dropout},
|
||||
{F_squant},
|
||||
{F_occupancy}>;
|
||||
using fmha_mask_{F_idx} = {F_mask};
|
||||
|
||||
@@ -123,6 +124,7 @@ using fmha_pipeline_problem_{F_idx} = ck_tile::BlockFmhaPipelineProblem<
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::SaccDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::SMPLComputeDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::BiasDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::RandValOutputDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::LSEDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::PDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType,
|
||||
@@ -146,7 +148,7 @@ using fmha_kernel_{F_idx} =
|
||||
fmha_epilogue_{F_idx}>;
|
||||
|
||||
using trait_{F_idx} = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode},{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout},
|
||||
{F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
|
||||
{F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
|
||||
|
||||
#include <iostream>
|
||||
|
||||
@@ -191,9 +193,9 @@ MASK_SIMPLIFIED_CHECK_MAP = {
|
||||
"s_mask" : "t.mask_type != mask_enum::no_mask",
|
||||
}
|
||||
|
||||
FMHA_FWD_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.do_fp8_static_quant == {F_squant}) &&
|
||||
FMHA_FWD_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.has_dropout == {F_dropout}) && (t.do_fp8_static_quant == {F_squant}) &&
|
||||
({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
|
||||
using trait_ = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
|
||||
using trait_ = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
|
||||
return fmha_fwd_<trait_>(s, a);
|
||||
}}
|
||||
"""
|
||||
@@ -233,6 +235,7 @@ class FmhaFwdApiTrait:
|
||||
mask : str
|
||||
bias : str #
|
||||
lse : str #
|
||||
dropout : str
|
||||
squant : str #
|
||||
spad : str
|
||||
skpad : str
|
||||
@@ -242,7 +245,7 @@ class FmhaFwdApiTrait:
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return f'{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0blen}-'+\
|
||||
f'{self.vlayout}-{self.mask}-{self.bias}-{self.lse}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}'
|
||||
f'{self.vlayout}-{self.mask}-{self.bias}-{self.lse}-{self.dropout}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}'
|
||||
|
||||
@property
|
||||
def scheck(self) -> str:
|
||||
@@ -299,6 +302,7 @@ class FmhaFwdPipeline:
|
||||
F_dvpad : str #
|
||||
F_bias : str # true/false
|
||||
F_lse : str #
|
||||
F_dropout : str #
|
||||
F_squant : str #
|
||||
F_mask : str # value from MASK_MAP
|
||||
|
||||
@@ -321,6 +325,7 @@ class FmhaFwdPipeline:
|
||||
else:
|
||||
if self.F_mask != 'no' : n += f'_m{self.F_mask[0]}'
|
||||
if self.F_lse == 't' : n += '_lse'
|
||||
if self.F_dropout == 't' : n += '_dropout'
|
||||
if self.F_squant == 't' : n += '_squant'
|
||||
return n
|
||||
|
||||
@@ -351,7 +356,7 @@ class FmhaFwdApiPool:
|
||||
inners = inners + FMHA_FWD_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_vlayout=LAYOUT_MAP[trait.vlayout],
|
||||
F_pipeline_enum=PIPELINE_ENUM_MAP[trait.pipeline_tag], F_mask=get_mask_map(self.mask_impl)[trait.mask],
|
||||
F_mask_check=get_mask_check_map(self.mask_impl)[trait.mask], F_bias_check=BIAS_CHECK_MAP[trait.bias], F_bias=BIAS_MAP[trait.bias],
|
||||
F_lse=BOOL_MAP[trait.lse],
|
||||
F_lse=BOOL_MAP[trait.lse], F_dropout=BOOL_MAP[trait.dropout] ,
|
||||
F_squant=BOOL_MAP[trait.squant], F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
|
||||
F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
|
||||
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0blen=trait.bk0blen,
|
||||
@@ -365,7 +370,7 @@ class FmhaFwdApiPool:
|
||||
@dataclass
|
||||
class FmhaFwdTileSize:
|
||||
F_bm0 : int # tile size along q seqlen (block size)
|
||||
F_bn0 : int # tile size along qk seqlen
|
||||
F_bn0 : int # tile size along k seqlen
|
||||
F_bk0 : int # tile size along qk gemm unroll
|
||||
F_bn1 : int # tile size along v head_dim
|
||||
F_bk1 : int # tile size along kv gemm unroll
|
||||
@@ -424,9 +429,10 @@ class FmhaFwdKernel:
|
||||
F_spad = BOOL_MAP[self.F_pipeline.F_spad],
|
||||
F_skpad = BOOL_MAP[self.F_pipeline.F_skpad],
|
||||
F_dpad = BOOL_MAP[self.F_pipeline.F_dpad],
|
||||
F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad],
|
||||
F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad],
|
||||
F_bias = BIAS_MAP[self.F_pipeline.F_bias],
|
||||
F_lse = BOOL_MAP[self.F_pipeline.F_lse],
|
||||
F_dropout = BOOL_MAP[self.F_pipeline.F_dropout],
|
||||
F_squant = BOOL_MAP[self.F_pipeline.F_squant],
|
||||
F_occupancy = self.F_tile.F_occupancy,
|
||||
F_pipeline_enum = PIPELINE_ENUM_MAP[self.F_pipeline.tag],
|
||||
@@ -461,6 +467,7 @@ class FmhaFwdKernel:
|
||||
mask=self.F_pipeline.F_mask,
|
||||
bias=self.F_pipeline.F_bias,
|
||||
lse=self.F_pipeline.F_lse,
|
||||
dropout=self.F_pipeline.F_dropout,
|
||||
squant=self.F_pipeline.F_squant,
|
||||
spad=self.F_pipeline.F_spad,
|
||||
skpad=self.F_pipeline.F_skpad,
|
||||
@@ -489,7 +496,7 @@ def get_fmha_fwd_tile_dict_from_dtype(direction : str, dtype : str) -> Optional[
|
||||
else:
|
||||
return None
|
||||
|
||||
def get_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFwdApiPool, List[FmhaFwdKernel]]:
|
||||
def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFwdApiPool, List[FmhaFwdKernel]]:
|
||||
# TODO: we don't support tuning yet, so pick up one value for vlayout/pipeline/pad
|
||||
# support this in future
|
||||
def get_pipelines(dtype, hdim) -> List[FmhaFwdPipeline]:
|
||||
@@ -500,26 +507,26 @@ def get_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFw
|
||||
squant = 't' if dtype == 'fp8' else 'f'
|
||||
pipelines = []
|
||||
if dtype in ['fp16', 'bf16']:
|
||||
for mask, bias, lse in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"]):
|
||||
for mask, bias, lse, dropout in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"]):
|
||||
if hdim == 256:
|
||||
# if True:
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 'f', 'f', 'f', 'f', bias, lse, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, lse, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 'f', 'f', 'f', 'f', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, lse, dropout, squant, mask))
|
||||
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 't', 't', 't', 't', bias, lse, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
else:
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
if receipt == 1:
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, mask)) # TODO: cover arbitraty hdim
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, squant, mask)) # TODO: cover arbitraty hdim
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask)) # TODO: cover arbitraty hdim
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, dropout, squant, mask)) # TODO: cover arbitraty hdim
|
||||
elif dtype in ['fp8', 'bf8']:
|
||||
# no need lse kernels
|
||||
# no need lse/dropout kernels
|
||||
for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', 'f', squant, mask))
|
||||
else:
|
||||
assert False
|
||||
return pipelines
|
||||
@@ -527,7 +534,7 @@ def get_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFw
|
||||
gen = list()
|
||||
api_pool = FmhaFwdApiPool(mask_impl)
|
||||
|
||||
for direction, dtype in itertools.product(DIRECTIONS, DTYPE_MAP.keys()):
|
||||
for direction, dtype in itertools.product(["fwd"], DTYPE_MAP.keys()):
|
||||
d = get_fmha_fwd_tile_dict_from_dtype(direction, dtype)
|
||||
if d == None:
|
||||
continue
|
||||
@@ -551,44 +558,660 @@ def get_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFw
|
||||
if kernel_filter != None:
|
||||
if not fnmatch.fnmatch(k.name, kernel_filter):
|
||||
continue
|
||||
if receipt == 2:
|
||||
cond = dtype in ['fp16', 'bf16']
|
||||
cond &= pipeline.F_vlayout == 'row'
|
||||
cond &= pipeline.F_bias in ['no', 'alibi']
|
||||
cond &= pipeline.F_squant == 'f'
|
||||
if not cond:
|
||||
continue
|
||||
api_pool.register_traits(k.api_trait())
|
||||
gen.append(k)
|
||||
|
||||
return (api_pool, gen)
|
||||
|
||||
def write_single_kernel(kernel: FmhaFwdKernel, autogen_dir: Path) -> None:
|
||||
BWD_DQDKDV_PIPELINE_MAP = {
|
||||
"ks_kts_vr" : "ck_tile::BlockFmhaBwdDQDKDVPipelineKSKTSVR",
|
||||
"qs_ks_vr_dos" : "ck_tile::BlockFmhaBwdDQDKDVPipelineQSKSVROGradS",
|
||||
"ks_vr" : "ck_tile::BlockFmhaBwdDQDKDVPipelineKSVR",
|
||||
}
|
||||
|
||||
BWD_DQDKDV_PIPELINE_ENUM_MAP = {
|
||||
"ks_kts_vr" : "ck_tile::BlockFmhaBwdPipelineEnum::KSKTSVR",
|
||||
"qs_ks_vr_dos" : "ck_tile::BlockFmhaBwdPipelineEnum::QSKSVROGradS",
|
||||
"ks_vr" : "ck_tile::BlockFmhaBwdPipelineEnum::KSVR",
|
||||
}
|
||||
|
||||
FMHA_BWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.\n
|
||||
// auto generated by generate.py
|
||||
#include "fmha_bwd.hpp"
|
||||
"""
|
||||
|
||||
FMHA_BWD_DQ_DK_DV_KERNEL_BODY="""
|
||||
using fmha_dtype_{F_idx} = {F_dtype};
|
||||
|
||||
using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bk1}, {F_bk2}, {F_bk3}, {F_bk4}, {F_bhdq}, {F_bhdv}>;
|
||||
using fmha_block_warps0_{F_idx} = ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>;
|
||||
using fmha_block_warps1_{F_idx} = ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>;
|
||||
using fmha_block_warps2_{F_idx} = ck_tile::sequence<{F_rm2}, {F_rn2}, {F_rk2}>;
|
||||
using fmha_warp_tile_{F_idx} = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
|
||||
|
||||
// TODO: simplify Gemm0~4BlockWarps in TileFmhaBwdShape
|
||||
// G0&G2 -> GSdP
|
||||
// G1&G3 -> GdKV
|
||||
// G4 -> GdQ
|
||||
using fmha_bwd_shape_{F_idx} = ck_tile::TileFmhaBwdShape<fmha_block_tile_{F_idx},
|
||||
fmha_block_warps0_{F_idx},
|
||||
fmha_warp_tile_{F_idx},
|
||||
fmha_block_warps1_{F_idx},
|
||||
fmha_warp_tile_{F_idx},
|
||||
fmha_block_warps0_{F_idx},
|
||||
fmha_warp_tile_{F_idx},
|
||||
fmha_block_warps1_{F_idx},
|
||||
fmha_warp_tile_{F_idx},
|
||||
fmha_block_warps2_{F_idx},
|
||||
fmha_warp_tile_{F_idx}>;
|
||||
|
||||
using fmha_bwd_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad},
|
||||
{F_skpad},
|
||||
{F_dpad},
|
||||
{F_dvpad},
|
||||
{F_bias},
|
||||
{F_dbias},
|
||||
false,
|
||||
{F_dropout},
|
||||
false,
|
||||
{F_occupancy}>;
|
||||
using fmha_mask_{F_idx} = {F_mask};
|
||||
|
||||
using fmha_bwd_pipeline_problem_{F_idx} = ck_tile::BlockFmhaBwdPipelineProblem<
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::QDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::KDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::VDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::GemmDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::LSEDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::AccDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::DDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::BiasDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::RandValOutputDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::OGradDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::QGradDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::KGradDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::VGradDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::BiasGradDataType,
|
||||
fmha_bwd_shape_{F_idx},
|
||||
{F_mode},
|
||||
fmha_mask_{F_idx},
|
||||
fmha_bwd_trait_{F_idx}>;
|
||||
|
||||
using fmha_bwd_pipeline_{F_idx} = {F_pipeline}<
|
||||
fmha_bwd_pipeline_problem_{F_idx}>;
|
||||
|
||||
using fmha_bwd_dk_epilogue_{F_idx} =
|
||||
ck_tile::Default2DEpilogue<ck_tile::Default2DEpilogueProblem<typename FmhaBwdTypeConfig<{F_dtype}>::AccDataType,
|
||||
typename FmhaBwdTypeConfig<{F_dtype}>::KGradDataType,
|
||||
false, false>>;
|
||||
|
||||
using fmha_bwd_dv_epilogue_{F_idx} =
|
||||
ck_tile::Default2DEpilogue<ck_tile::Default2DEpilogueProblem<typename FmhaBwdTypeConfig<{F_dtype}>::AccDataType,
|
||||
typename FmhaBwdTypeConfig<{F_dtype}>::VGradDataType,
|
||||
false, false>>;
|
||||
|
||||
using fmha_bwd_dq_dk_dv_kernel_{F_idx} =
|
||||
ck_tile::FmhaBwdDQDKDVKernel<ck_tile::FmhaBwdTilePartitioner<fmha_bwd_shape_{F_idx}>,
|
||||
fmha_bwd_pipeline_{F_idx},
|
||||
fmha_bwd_dk_epilogue_{F_idx},
|
||||
fmha_bwd_dv_epilogue_{F_idx}>;
|
||||
|
||||
using dq_dk_dv_trait_{F_idx} = fmha_bwd_dq_dk_dv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_dbias}, {F_dropout}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
|
||||
|
||||
#include <iostream>
|
||||
|
||||
template<>
|
||||
float fmha_bwd_dq_dk_dv_<dq_dk_dv_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
|
||||
{{
|
||||
using k_ = fmha_bwd_dq_dk_dv_kernel_{F_idx};
|
||||
if(s.log_level_ > 0)
|
||||
std::cout << ", " << k_::GetName() << std::flush;
|
||||
auto [kargs, grids] = fmha_bwd_dq_dk_dv_create_kargs_and_grids<k_>(a);
|
||||
constexpr dim3 blocks = k_::BlockSize();
|
||||
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
|
||||
return ck_tile::launch_kernel(s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs));
|
||||
}}
|
||||
|
||||
template<>
|
||||
void fmha_bwd_dq_dk_dv_oneshot_<dq_dk_dv_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
|
||||
{{
|
||||
using k_ = fmha_bwd_dq_dk_dv_kernel_{F_idx};
|
||||
auto [kargs, grids] = fmha_bwd_dq_dk_dv_create_kargs_and_grids<k_>(a);
|
||||
constexpr dim3 blocks = k_::BlockSize();
|
||||
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
|
||||
ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs)(ck_tile::stream_config{{s.stream_id_}});
|
||||
}}
|
||||
|
||||
template<>
|
||||
std::string fmha_bwd_dq_dk_dv_get_name_<dq_dk_dv_trait_{F_idx}>()
|
||||
{{
|
||||
using k_ = fmha_bwd_dq_dk_dv_kernel_{F_idx};
|
||||
return k_::GetName();
|
||||
}}
|
||||
"""
|
||||
|
||||
FMHA_BWD_API_FILENAME="fmha_bwd_api.cpp"
|
||||
FMHA_BWD_API="""
|
||||
#include <iostream>
|
||||
|
||||
template<typename dot_do_o_trait_, typename dq_dk_dv_trait_>
|
||||
float fmha_bwd_(const ck_tile::stream_config& s, fmha_bwd_args a)
|
||||
{{
|
||||
if(s.log_level_ > 0)
|
||||
std::cout << ", " << fmha_bwd_dot_do_o_get_name_<dot_do_o_trait_>() << ", " << fmha_bwd_dq_dk_dv_get_name_<dq_dk_dv_trait_>() << std::flush;
|
||||
return ck_tile::launch_kernel(s,
|
||||
[=](const ck_tile::stream_config& s_){{ fmha_bwd_dot_do_o_oneshot_<dot_do_o_trait_>(s_, a); }},
|
||||
[=](const ck_tile::stream_config& s_){{ fmha_bwd_dq_dk_dv_oneshot_<dq_dk_dv_trait_>(s_, a); }}
|
||||
);
|
||||
}}
|
||||
|
||||
float fmha_bwd(fmha_bwd_traits t, fmha_bwd_args a, const ck_tile::stream_config& s){{
|
||||
float r = -1;
|
||||
{F_dispatch}
|
||||
return r;
|
||||
}}
|
||||
"""
|
||||
|
||||
FMHA_BWD_API_PER_DTYPE=""" {F_if}(t.data_type.compare(\"{F_dtype}\") == 0){{
|
||||
{F_hdim_case}
|
||||
}}
|
||||
"""
|
||||
FMHA_BWD_API_PER_HDIM_CASE=""" {F_if} (t.hdim_q <= {F_hdim} && t.hdim_v <= {F_hdim}) {{
|
||||
{F_inner_dispatch}
|
||||
}}
|
||||
"""
|
||||
|
||||
FMHA_BWD_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_dbias == {F_dbias}) && (t.has_dropout == {F_dropout}) &&
|
||||
({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
|
||||
using dq_dk_dv_trait_ = fmha_bwd_dq_dk_dv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_dbias}, {F_dropout}, {F_spad0}, {F_skpad}, {F_dpad}, {F_dvpad}>;
|
||||
using dot_do_o_trait_ = fmha_bwd_dot_do_o_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_spad1}, {F_dvpad}>;
|
||||
r = fmha_bwd_<dot_do_o_trait_, dq_dk_dv_trait_>(s, a);
|
||||
return r;
|
||||
}}
|
||||
"""
|
||||
|
||||
@dataclass
|
||||
class FmhaBwdDQDKDVApiTrait:
|
||||
pipeline : str
|
||||
# sync with fmha_bwd_traits<>, to generate fallback calls
|
||||
hdim : str
|
||||
dtype : str # data type
|
||||
mode : str # value from MODE_MAP
|
||||
bm0 : int # tile size along q seqlen (block size)
|
||||
bn0 : int # tile size along k seqlen
|
||||
bhdq : int # q head_dim
|
||||
bhdv : int # v head_dim
|
||||
mask : str
|
||||
bias : str
|
||||
dbias : str
|
||||
dropout : str
|
||||
spad : str
|
||||
skpad : str
|
||||
dpad : str
|
||||
dvpad : str
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return f'{self.pipeline}-{self.hdim}-{self.dtype}-{self.mode}-{self.mask}-{self.bias}-{self.dbias}-{self.dropout}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}'
|
||||
|
||||
def scheck(self, spad1 : str) -> str:
|
||||
if self.mode == 'group':
|
||||
return 'true' # always support
|
||||
elif self.spad == 't' and spad1 == 't':
|
||||
return f'a.seqlen_q % {self.bm0} != 0'
|
||||
elif self.spad == 'f' and spad1 == 't':
|
||||
return f'a.seqlen_q % {self.bm0} == 0 and a.seqlen_q % 256 != 0' # BlockSize
|
||||
else: # self.skpad == 'f' and skpad1 == 'f'
|
||||
return f'a.seqlen_q % 256 == 0' # BlockSize
|
||||
|
||||
@property
|
||||
def skcheck(self) -> str:
|
||||
if self.mode == 'group':
|
||||
return 'true' # always support
|
||||
elif self.skpad == 't':
|
||||
return f'a.seqlen_k % {self.bn0} != 0'
|
||||
else:
|
||||
return f'a.seqlen_k % {self.bn0} == 0'
|
||||
|
||||
@property
|
||||
def dcheck(self) -> str:
|
||||
if self.dpad == 't': return f'a.hdim_q % {self.bhdq} != 0'
|
||||
else : return f'a.hdim_q % {self.bhdq} == 0'
|
||||
|
||||
@property
|
||||
def dvcheck(self) -> str:
|
||||
if self.dvpad == 't': return f'a.hdim_v % {self.bhdv} != 0'
|
||||
else : return f'a.hdim_v % {self.bhdv} == 0'
|
||||
|
||||
class FmhaBwdApiPool:
|
||||
def __init__(self, mask_impl):
|
||||
self.dq_dk_dv_pool = dict()
|
||||
self.mask_impl = mask_impl
|
||||
|
||||
def register_dq_dk_dv_traits(self, trait : FmhaBwdDQDKDVApiTrait) -> None:
|
||||
# TODO: do we need to check duplication?
|
||||
if trait.dtype not in self.dq_dk_dv_pool.keys():
|
||||
self.dq_dk_dv_pool[trait.dtype] = dict()
|
||||
if trait.hdim not in self.dq_dk_dv_pool[trait.dtype].keys():
|
||||
self.dq_dk_dv_pool[trait.dtype][trait.hdim] = list()
|
||||
|
||||
self.dq_dk_dv_pool[trait.dtype][trait.hdim].append(copy.copy(trait))
|
||||
|
||||
@property
|
||||
def api(self) -> str:
|
||||
per_dtypes=str()
|
||||
for i, dtype in enumerate(self.dq_dk_dv_pool.keys()):
|
||||
per_hdim_case=str()
|
||||
for j, hdim in enumerate(self.dq_dk_dv_pool[dtype].keys()):
|
||||
traits=self.dq_dk_dv_pool[dtype][hdim]
|
||||
inners=str()
|
||||
for k, trait in enumerate(traits):
|
||||
if_k = 'if' if k == 0 else 'else if'
|
||||
for spad1 in ["t", "f"]:
|
||||
if ((spad1 == "f" and trait.spad == "t") or (trait.mode == "group" and spad1 == "f")):
|
||||
continue
|
||||
inners = inners + FMHA_BWD_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_mask=get_mask_map(self.mask_impl)[trait.mask], F_pipeline_enum=BWD_DQDKDV_PIPELINE_ENUM_MAP[trait.pipeline],
|
||||
F_mask_check=get_mask_check_map(self.mask_impl)[trait.mask], F_bias_check=BIAS_CHECK_MAP[trait.bias], F_bias=BIAS_MAP[trait.bias], F_dbias=BOOL_MAP[trait.dbias], F_dropout=BOOL_MAP[trait.dropout],
|
||||
F_scheck=trait.scheck(spad1=spad1), F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype],
|
||||
F_spad0=BOOL_MAP[trait.spad], F_spad1=BOOL_MAP[spad1], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad])
|
||||
|
||||
if_j = 'if' if j == 0 else 'else if'
|
||||
per_hdim_case = per_hdim_case + FMHA_BWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
|
||||
if_i = 'if' if i == 0 else 'else if'
|
||||
per_dtypes = per_dtypes + FMHA_BWD_API_PER_DTYPE.format(F_if=if_i, F_dtype=dtype, F_hdim_case=per_hdim_case)
|
||||
|
||||
return FMHA_BWD_KERNEL_HEADER + FMHA_BWD_API.format(F_dispatch = per_dtypes)
|
||||
|
||||
# GEMM0: Q@K=S^T
|
||||
# GEMM1: P^T@dO^T=dV(This was chosen as G1 to match fwd, but N1 must be equal to headdim_v)
|
||||
# GEMM2: dO@V=dP^T(This was chosen as G2 because of the calculation order)
|
||||
# GEMM3: dS^T@Q^T=dK(Similar to G1, but N3 must be equal to headdim_qk)
|
||||
# GEMM4: dS@K^T=dQ(N4 must be equal to headdim_qk)
|
||||
# Is it necessary to distinguish between K0~K4?
|
||||
@dataclass
|
||||
class FmhaBwdDQDKDVTileSize:
|
||||
F_bm0 : int # tile size along q seqlen (block size)
|
||||
F_bn0 : int # tile size along k seqlen
|
||||
F_bk0 : int # tile size along gemm0 unroll(F_bhdq)
|
||||
F_bk1 : int # tile size along gemm1 unroll(F_bm0)
|
||||
F_bk2 : int # tile size along gemm2 unroll(F_bhdv)
|
||||
F_bk3 : int # tile size along gemm3 unroll(F_bm0)
|
||||
F_bk4 : int # tile size along gemm4 unroll(F_bn0)
|
||||
F_bhdq : int # q head_dim
|
||||
F_bhdv : int # v head_dim
|
||||
F_rm0 : int # number of warps along q seqlen (block warps) in gemm0/gemm2
|
||||
F_rn0 : int # number of warps along k seqlen (block warps) in gemm0/gemm2
|
||||
F_rk0 : int # number of warps along gemm-k (not used) in gemm0/gemm2
|
||||
F_rm1 : int # number of warps along k seqlen (block warps) in gemm1/gemm3
|
||||
F_rn1 : int # number of warps along q seqlen (block warps) in gemm1/gemm3
|
||||
F_rk1 : int # number of warps along gemm-k (not used) in gemm1/gemm3
|
||||
F_rm2 : int # number of warps along k seqlen (block warps) in gemm4
|
||||
F_rn2 : int # number of warps along q seqlen (block warps) in gemm4
|
||||
F_rk2 : int # number of warps along gemm-k (not used) in gemm4
|
||||
F_wm : int # warp size along m (warp size)
|
||||
F_wn : int # warp size along n
|
||||
F_wk : int # warp size along k
|
||||
F_occupancy : int # occupancy
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return f"b{self.F_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bk1}x{self.F_bk2}x{self.F_bk3}x{self.F_bk4}x{self.F_bhdq}x{self.F_bhdv}" +\
|
||||
f"_r{self.F_rm0}x{self.F_rn0}x{self.F_rk0}_r{self.F_rm1}x{self.F_rn1}x{self.F_rk1}_r{self.F_rm2}x{self.F_rn2}x{self.F_rk2}" +\
|
||||
f"_w{self.F_wm}x{self.F_wn}x{self.F_wk}_o{self.F_occupancy}"
|
||||
|
||||
@dataclass
|
||||
class FmhaBwdDQDKDVKernel:
|
||||
direction : str
|
||||
F_idx : int # this is not a tunable, but a counter to differentiate symbol
|
||||
F_hdim : int # hdim
|
||||
F_dtype : str # data type
|
||||
F_tile : FmhaBwdDQDKDVTileSize
|
||||
F_spad : str # true/false
|
||||
F_skpad : str #
|
||||
F_dpad : str #
|
||||
F_dvpad : str #
|
||||
F_bias : str #
|
||||
F_dbias : str #
|
||||
F_dropout : str #
|
||||
F_mask : str # value from MASK_MAP
|
||||
F_mode : str # value from MODE_MAP
|
||||
F_pipeline : str
|
||||
mask_impl : str
|
||||
|
||||
@property
|
||||
def template(self) -> str:
|
||||
return FMHA_BWD_KERNEL_HEADER + \
|
||||
FMHA_BWD_DQ_DK_DV_KERNEL_BODY.format(
|
||||
F_idx = self.F_idx,
|
||||
F_hdim = self.F_hdim,
|
||||
F_dtype = DTYPE_MAP[self.F_dtype],
|
||||
F_bm0 = self.F_tile.F_bm0,
|
||||
F_bn0 = self.F_tile.F_bn0,
|
||||
F_bk0 = self.F_tile.F_bk0,
|
||||
F_bk1 = self.F_tile.F_bk1,
|
||||
F_bk2 = self.F_tile.F_bk2,
|
||||
F_bk3 = self.F_tile.F_bk3,
|
||||
F_bk4 = self.F_tile.F_bk4,
|
||||
F_bhdq = self.F_tile.F_bhdq,
|
||||
F_bhdv = self.F_tile.F_bhdv,
|
||||
F_rm0 = self.F_tile.F_rm0,
|
||||
F_rn0 = self.F_tile.F_rn0,
|
||||
F_rk0 = self.F_tile.F_rk0,
|
||||
F_rm1 = self.F_tile.F_rm1,
|
||||
F_rn1 = self.F_tile.F_rn1,
|
||||
F_rk1 = self.F_tile.F_rk1,
|
||||
F_rm2 = self.F_tile.F_rm2,
|
||||
F_rn2 = self.F_tile.F_rn2,
|
||||
F_rk2 = self.F_tile.F_rk2,
|
||||
F_wm = self.F_tile.F_wm,
|
||||
F_wn = self.F_tile.F_wn,
|
||||
F_wk = self.F_tile.F_wk,
|
||||
F_spad = BOOL_MAP[self.F_spad],
|
||||
F_skpad = BOOL_MAP[self.F_skpad],
|
||||
F_dpad = BOOL_MAP[self.F_dpad],
|
||||
F_dvpad = BOOL_MAP[self.F_dvpad],
|
||||
F_bias = BIAS_MAP[self.F_bias],
|
||||
F_dbias = BOOL_MAP[self.F_dbias],
|
||||
F_dropout = BOOL_MAP[self.F_dropout],
|
||||
F_occupancy = self.F_tile.F_occupancy,
|
||||
F_mask = get_mask_map(self.mask_impl)[self.F_mask],
|
||||
F_mode = MODE_MAP[self.F_mode],
|
||||
F_pipeline_enum = BWD_DQDKDV_PIPELINE_ENUM_MAP[self.F_pipeline],
|
||||
F_pipeline = BWD_DQDKDV_PIPELINE_MAP[self.F_pipeline])
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
def pad_name() -> str:
|
||||
n = ''
|
||||
if self.F_spad == 't': n += 's'
|
||||
if self.F_skpad == 't' : n += 'sk'
|
||||
if self.F_dpad == 't' : n += 'd'
|
||||
if self.F_dvpad == 't' : n += 'dv'
|
||||
if n != '' : n = 'p' + n
|
||||
return n
|
||||
pn = pad_name()
|
||||
n = f"fmha_{self.direction}_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_" + self.F_tile.name
|
||||
if pn != '' : n += f'_{pn}'
|
||||
if self.F_bias != 'no' : n += f'_{self.F_bias}'
|
||||
if self.F_dbias == 't' : n += '_dbias'
|
||||
if self.F_mask[0:2] == 's_':
|
||||
if self.F_mask == 's_mask': n += f'_mask'
|
||||
else:
|
||||
if self.F_mask != 'no' : n += f'_m{self.F_mask[0]}'
|
||||
if self.F_dropout == 't' : n += '_dropout'
|
||||
return n
|
||||
|
||||
@property
|
||||
def filename(self) -> str:
|
||||
return self.name + ".cpp"
|
||||
|
||||
def api_trait(self) -> FmhaBwdDQDKDVApiTrait:
|
||||
return FmhaBwdDQDKDVApiTrait(pipeline=self.F_pipeline,
|
||||
hdim=str(self.F_hdim),
|
||||
dtype=self.F_dtype,
|
||||
mode=self.F_mode,
|
||||
bm0=self.F_tile.F_bm0,
|
||||
bn0=self.F_tile.F_bn0,
|
||||
bhdq=self.F_tile.F_bhdq,
|
||||
bhdv=self.F_tile.F_bhdv,
|
||||
mask=self.F_mask,
|
||||
bias=self.F_bias,
|
||||
dbias=self.F_dbias,
|
||||
dropout=self.F_dropout,
|
||||
spad=self.F_spad,
|
||||
skpad=self.F_skpad,
|
||||
dpad=self.F_dpad,
|
||||
dvpad=self.F_dvpad)
|
||||
|
||||
# TODO: design a more practical way to do it
|
||||
# this is current supported tile size & pipeline.
|
||||
def get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(direction : str, dtype : str) -> Optional[dict]:
|
||||
if direction == 'bwd':
|
||||
if dtype == 'fp16' or dtype == 'bf16':
|
||||
return {
|
||||
'32' : [FmhaBwdDQDKDVTileSize(128, 128, 32, 32, 32, 32, 32, 32, 32, 1, 4, 1, 4, 1, 1, 4, 1, 1, 32, 32, 16, 1),
|
||||
"qs_ks_vr_dos"],
|
||||
'64' : [FmhaBwdDQDKDVTileSize( 64, 128, 32, 32, 32, 32, 32, 64, 64, 1, 4, 1, 4, 1, 1, 2, 2, 1, 32, 32, 16, 1),
|
||||
"qs_ks_vr_dos"],
|
||||
'128' : [FmhaBwdDQDKDVTileSize( 64, 128, 32, 32, 32, 32, 32, 128, 128, 1, 4, 1, 4, 1, 1, 2, 2, 1, 32, 32, 16, 1),
|
||||
"ks_vr"]
|
||||
}
|
||||
else:
|
||||
return None
|
||||
else:
|
||||
return None
|
||||
|
||||
def get_bwd_dq_dk_dv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaBwdApiPool, List[FmhaBwdDQDKDVKernel]]:
|
||||
# TODO: we don't support tuning yet, so pick up one value for pad
|
||||
# support this in future
|
||||
gen = list()
|
||||
api_pool = FmhaBwdApiPool(mask_impl)
|
||||
|
||||
for direction, dtype in itertools.product(["bwd"], DTYPE_MAP.keys()):
|
||||
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(direction, dtype)
|
||||
if d == None:
|
||||
continue
|
||||
for hdim_str, mode, mask, bias, dbias, dropout, spad, skpad, dpad, dvpad in itertools.product(d.keys(), MODE_MAP.keys(), get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"]):
|
||||
tile = d[hdim_str][0]
|
||||
ppl = d[hdim_str][1]
|
||||
hdim = int(hdim_str)
|
||||
if (mode == "group") and (spad == "f" or skpad == "f"):
|
||||
continue
|
||||
if ((bias == "no" or bias == "alibi") and dbias == "t"):
|
||||
continue
|
||||
k = FmhaBwdDQDKDVKernel(direction=direction, F_idx=0, F_hdim=hdim, F_dtype=dtype, F_tile=tile,
|
||||
F_spad=spad, F_skpad=skpad, F_dpad=dpad, F_dvpad=dvpad,
|
||||
F_bias=bias, F_dbias=dbias, F_dropout=dropout, F_mask=mask, F_mode=mode,
|
||||
F_pipeline=ppl, mask_impl=mask_impl)
|
||||
if kernel_filter != None:
|
||||
if not fnmatch.fnmatch(k.name, kernel_filter):
|
||||
continue
|
||||
if receipt == 2:
|
||||
cond = dtype in ['fp16', 'bf16']
|
||||
cond &= bias in ['no', 'alibi']
|
||||
if not cond:
|
||||
continue
|
||||
api_pool.register_dq_dk_dv_traits(k.api_trait())
|
||||
gen.append(k)
|
||||
|
||||
return (api_pool, gen)
|
||||
|
||||
FMHA_BWD_DOT_DO_O_KERNEL_BODY="""
|
||||
using fmha_dtype_{F_idx} = {F_dtype};
|
||||
|
||||
using fmha_bwd_dot_do_o_trait_{F_idx} = ck_tile::TileFmhaBwdOGradDotOTraits<{F_spad},
|
||||
{F_dvpad},
|
||||
{F_occupancy}>;
|
||||
|
||||
using fmha_bwd_dot_do_o_pipeline_problem_{F_idx} = ck_tile::BlockFmhaBwdOGradDotOPipelineProblem<
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::OGradDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::DDataType,
|
||||
/* BlockSize = */ 256,
|
||||
{F_hdim},
|
||||
{F_mode},
|
||||
fmha_bwd_dot_do_o_trait_{F_idx}>;
|
||||
|
||||
using fmha_bwd_dot_do_o_{F_idx} = typename ck_tile::BlockFmhaBwdOGradDotO<
|
||||
fmha_bwd_dot_do_o_pipeline_problem_{F_idx}>;
|
||||
|
||||
using fmha_bwd_dot_do_o_kernel_{F_idx} =
|
||||
ck_tile::FmhaBwdOGradDotOKernel<ck_tile::FmhaBwdOGradDotOTilePartitioner</* BlockSize = */ 256>,
|
||||
fmha_bwd_dot_do_o_{F_idx}>;
|
||||
|
||||
using dot_do_o_trait_{F_idx} = fmha_bwd_dot_do_o_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_spad}, {F_dvpad}>;
|
||||
|
||||
#include <iostream>
|
||||
|
||||
template<>
|
||||
float fmha_bwd_dot_do_o_<dot_do_o_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
|
||||
{{
|
||||
using k_ = fmha_bwd_dot_do_o_kernel_{F_idx};
|
||||
if(s.log_level_ > 0)
|
||||
std::cout << ", " << k_::GetName() << std::flush;
|
||||
auto [kargs, grids] = fmha_bwd_dot_do_o_create_kargs_and_grids<k_>(a);
|
||||
constexpr dim3 blocks = k_::BlockSize();
|
||||
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
|
||||
return ck_tile::launch_kernel(s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs));
|
||||
}}
|
||||
|
||||
template<>
|
||||
void fmha_bwd_dot_do_o_oneshot_<dot_do_o_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
|
||||
{{
|
||||
using k_ = fmha_bwd_dot_do_o_kernel_{F_idx};
|
||||
auto [kargs, grids] = fmha_bwd_dot_do_o_create_kargs_and_grids<k_>(a);
|
||||
constexpr dim3 blocks = k_::BlockSize();
|
||||
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
|
||||
ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs)(ck_tile::stream_config{{s.stream_id_}});
|
||||
}}
|
||||
|
||||
template<>
|
||||
std::string fmha_bwd_dot_do_o_get_name_<dot_do_o_trait_{F_idx}>()
|
||||
{{
|
||||
using k_ = fmha_bwd_dot_do_o_kernel_{F_idx};
|
||||
return k_::GetName();
|
||||
}}
|
||||
"""
|
||||
|
||||
@dataclass
|
||||
class FmhaBwdOGradDotOKernel:
|
||||
direction : str
|
||||
F_idx : int # this is not a tunable, but a counter to differentiate symbol
|
||||
F_hdim : int # hdim
|
||||
F_dtype : str # data type
|
||||
F_spad : str # true/false
|
||||
F_dvpad : str #
|
||||
F_mode : str # value from MODE_MAP
|
||||
F_occupancy : int
|
||||
|
||||
@property
|
||||
def template(self) -> str:
|
||||
return FMHA_BWD_KERNEL_HEADER + \
|
||||
FMHA_BWD_DOT_DO_O_KERNEL_BODY.format(
|
||||
F_idx = self.F_idx,
|
||||
F_hdim = self.F_hdim,
|
||||
F_dtype = DTYPE_MAP[self.F_dtype],
|
||||
F_spad = BOOL_MAP[self.F_spad],
|
||||
F_dvpad = BOOL_MAP[self.F_dvpad],
|
||||
F_mode = MODE_MAP[self.F_mode],
|
||||
F_occupancy = self.F_occupancy)
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
def pad_name() -> str:
|
||||
n = ''
|
||||
if self.F_spad == 't': n += 's'
|
||||
if self.F_dvpad == 't' : n += 'dv'
|
||||
if n != '' : n = 'p' + n
|
||||
return n
|
||||
pn = pad_name()
|
||||
n = f"fmha_{self.direction}_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_o{self.F_occupancy}"
|
||||
if pn != '' : n += f'_{pn}'
|
||||
return n
|
||||
|
||||
@property
|
||||
def filename(self) -> str:
|
||||
return self.name + ".cpp"
|
||||
|
||||
def get_bwd_dot_do_o_blobs() -> List[FmhaBwdOGradDotOKernel]:
|
||||
# TODO: we don't support tuning yet, so pick up one value for pad/occupancy
|
||||
# support this in future
|
||||
def get_occupancy(dtype, hdim):
|
||||
return 2
|
||||
|
||||
gen = list()
|
||||
|
||||
for direction, dtype in itertools.product(["bwd"], DTYPE_MAP.keys()):
|
||||
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(direction, dtype)
|
||||
if d == None:
|
||||
continue
|
||||
for hdim_str, mode, spad, dvpad in itertools.product(d.keys(), MODE_MAP.keys(), ["t", "f"], ["t", "f"]):
|
||||
hdim = int(hdim_str)
|
||||
if (mode == "group" and spad == "f"):
|
||||
continue
|
||||
k = FmhaBwdOGradDotOKernel(direction=direction+"_dot_do_o", F_idx=0, F_hdim=hdim, F_dtype=dtype,
|
||||
F_spad=spad, F_dvpad=dvpad, F_mode=mode,
|
||||
F_occupancy=get_occupancy(dtype, hdim))
|
||||
gen.append(k)
|
||||
|
||||
return gen
|
||||
|
||||
def write_single_fwd_kernel(kernel: FmhaFwdKernel, autogen_dir: Path) -> None:
|
||||
(autogen_dir / kernel.filename).write_text(kernel.template)
|
||||
|
||||
def write_api(api_pool : FmhaFwdApiPool, autogen_dir: Path) -> None:
|
||||
def write_fwd_api(api_pool : FmhaFwdApiPool, autogen_dir: Path) -> None:
|
||||
(autogen_dir / FMHA_FWD_API_FILENAME).write_text(api_pool.api)
|
||||
|
||||
def write_blobs(output_dir : Optional[str], kernel_filter : Optional[str], receipt, mask_impl) -> None:
|
||||
def write_single_bwd_dq_dk_dv_kernel(kernel: FmhaBwdDQDKDVKernel, autogen_dir: Path) -> None:
|
||||
(autogen_dir / kernel.filename).write_text(kernel.template)
|
||||
|
||||
def write_single_bwd_dot_do_o_kernel(kernel: FmhaBwdOGradDotOKernel, autogen_dir: Path) -> None:
|
||||
(autogen_dir / kernel.filename).write_text(kernel.template)
|
||||
|
||||
def write_bwd_api(api_pool : FmhaBwdApiPool, autogen_dir: Path) -> None:
|
||||
(autogen_dir / FMHA_BWD_API_FILENAME).write_text(api_pool.api)
|
||||
|
||||
def write_blobs(output_dir: Optional[str], direction: str, kernel_filter : Optional[str], receipt, mask_impl) -> None:
|
||||
if output_dir is None:
|
||||
output_dir = Path(__file__).parent
|
||||
else:
|
||||
output_dir = Path(output_dir) / GEN_DIR
|
||||
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
api_pool, kernels = get_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
write_single_kernel(kernel, output_dir)
|
||||
write_api(api_pool, output_dir)
|
||||
if direction == 'fwd':
|
||||
api_pool, kernels = get_fwd_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
write_single_fwd_kernel(kernel, output_dir)
|
||||
write_fwd_api(api_pool, output_dir)
|
||||
else:
|
||||
kernels = get_bwd_dot_do_o_blobs()
|
||||
for kernel in kernels:
|
||||
write_single_bwd_dot_do_o_kernel(kernel, output_dir)
|
||||
api_pool, kernels = get_bwd_dq_dk_dv_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
write_single_bwd_dq_dk_dv_kernel(kernel, output_dir)
|
||||
write_bwd_api(api_pool, output_dir)
|
||||
|
||||
# list all the files that will be generated
|
||||
def list_blobs(output_file : Optional[str], kernel_filter : Optional[str], receipt, mask_impl) -> None:
|
||||
def list_blobs(output_file : Optional[str], direction : str, kernel_filter : Optional[str], receipt, mask_impl) -> None:
|
||||
assert output_file is not None
|
||||
file_path = Path(output_file)
|
||||
with file_path.open('a') as f:
|
||||
_, kernels = get_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
|
||||
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_API_FILENAME) + "\n")
|
||||
if direction == 'fwd':
|
||||
_, kernels = get_fwd_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
|
||||
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_API_FILENAME) + "\n")
|
||||
else:
|
||||
kernels = get_bwd_dot_do_o_blobs()
|
||||
for kernel in kernels:
|
||||
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
|
||||
_, kernels = get_bwd_dq_dk_dv_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
|
||||
f.write(str(file_path.parent / GEN_DIR / FMHA_BWD_API_FILENAME) + "\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(
|
||||
prog="generate",
|
||||
description="gen api for CK fmha kernel",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--direction",
|
||||
default='fwd',
|
||||
choices=['fwd', 'bwd'],
|
||||
required=False,
|
||||
help="choose the direction of kernels(default: fwd)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-o",
|
||||
"--output_dir",
|
||||
@@ -623,11 +1246,12 @@ if __name__ == "__main__":
|
||||
default=0,
|
||||
required=False,
|
||||
help="codegen receipt. 0: generate only 8xhdim coverage\n" + \
|
||||
" 1: generate more instance to cover all hdim"
|
||||
" 1: generate more instance to cover all hdim\n" + \
|
||||
" 2: Only generate instance for Flash attention integration"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
if args.list_blobs is not None:
|
||||
list_blobs(args.list_blobs, args.filter, args.receipt, mask_impl=args.mask)
|
||||
list_blobs(args.list_blobs, args.direction, args.filter, int(args.receipt), mask_impl=args.mask)
|
||||
else:
|
||||
write_blobs(args.output_dir, args.filter, args.receipt, mask_impl=args.mask)
|
||||
write_blobs(args.output_dir, args.direction, args.filter, int(args.receipt), mask_impl=args.mask)
|
||||
|
||||
21
example/ck_tile/01_fmha/script/benchmark_bwd.sh
Normal file
21
example/ck_tile/01_fmha/script/benchmark_bwd.sh
Normal file
@@ -0,0 +1,21 @@
|
||||
#!/bin/sh
|
||||
# TODO: run this script from CK root
|
||||
BUILD=build
|
||||
EXE=$BUILD/bin/tile_example_fmha_bwd
|
||||
VALID=0
|
||||
|
||||
for prec in "fp16" "bf16" ; do
|
||||
for perm in 0 1 ; do
|
||||
for hdim in 32 64 128 ; do
|
||||
|
||||
nhead=$((2048 / $hdim)) # follow fav2 setup
|
||||
$EXE -prec=$prec -b=32 -h=$nhead -d=$hdim -s=512 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
|
||||
$EXE -prec=$prec -b=16 -h=$nhead -d=$hdim -s=1024 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
|
||||
$EXE -prec=$prec -b=8 -h=$nhead -d=$hdim -s=2048 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
|
||||
$EXE -prec=$prec -b=4 -h=$nhead -d=$hdim -s=4096 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
|
||||
$EXE -prec=$prec -b=2 -h=$nhead -d=$hdim -s=8192 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
|
||||
$EXE -prec=$prec -b=1 -h=$nhead -d=$hdim -s=16384 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
|
||||
|
||||
done
|
||||
done
|
||||
done
|
||||
33
example/ck_tile/01_fmha/script/smoke_test_bwd.sh
Normal file
33
example/ck_tile/01_fmha/script/smoke_test_bwd.sh
Normal file
@@ -0,0 +1,33 @@
|
||||
#!/bin/sh
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||||
# TODO: run this script from CK root
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||||
BUILD=build
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||||
EXE=$BUILD/bin/tile_example_fmha_bwd
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||||
KNAME=1
|
||||
|
||||
export CK_WARMUP=0
|
||||
export CK_REPEAT=1
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||||
|
||||
COMMON_ARGS='-v=1'
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||||
|
||||
for prec in "fp16" "bf16" ; do
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||||
for perm in 0 1 ; do
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||||
for hdim in 32 64 128 ; do
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||||
for mode in 0 1 ; do
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||||
for bias in "n" "e" "a"; do
|
||||
for dbias in 0 1 ; do
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||||
for p_drop in 0.0 0.2; do
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||||
|
||||
$EXE -prec=$prec -b=1 -h=4 -h_k=2 -d=$hdim -s=259 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -b=2 -h=2 -d=$hdim -s=516 -s_k=253 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -b=1 -h=4 -h_k=1 -d=$hdim -s=500 -s_k=251 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=1 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -b=1 -h=2 -d=$hdim -s=900 -s_k=258 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=2 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -b=2 -h=1 -d=$hdim -s=987 -s_k=219 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=t:128,30 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -b=2 -h=3 -h_k=1 -d=$hdim -s=244 -s_k=499 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=b:4,35 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
|
||||
|
||||
done
|
||||
done
|
||||
done
|
||||
done
|
||||
done
|
||||
done
|
||||
done
|
||||
@@ -17,17 +17,18 @@ for perm in 0 1 ; do
|
||||
for vlayout in "r" "c" ; do
|
||||
for hdim in 32 64 128 256 ; do
|
||||
for lse in 0 1 ; do
|
||||
for bias in "n" "e" "a"; do
|
||||
for bias in "n" "e" "a" ; do
|
||||
for p_drop in 0.0 0.2; do
|
||||
|
||||
# $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=2 -h=2 -h_k=1 -d=16, -d_v=$hdim -s=55 -s_k=256 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=1 -h=3 -d=$hdim -s=100 -s_k=51 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=16 -d_v=$hdim -s=99 -s_k=256 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=1 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1024 -s_k=256 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -d_v=24 -s=3 -s_k=99 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=3 -h=2 -h_k=1 -d=$hdim -s=200 -s_k=520 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=t:128,30 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -s=99 -s_k=32 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=b:4,35 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=33 -s_k=0 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
# $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=2 -h=2 -h_k=1 -d=16, -d_v=$hdim -s=55 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=1 -h=3 -d=$hdim -s=100 -s_k=51 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=16 -d_v=$hdim -s=99 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=1 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1024 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -d_v=24 -s=3 -s_k=99 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=3 -h=2 -h_k=1 -d=$hdim -s=200 -s_k=520 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=t:128,30 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -s=99 -s_k=32 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=b:4,35 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=33 -s_k=0 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1 -s_k=10 -s_kpad=32 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
|
||||
|
||||
done
|
||||
@@ -37,6 +38,7 @@ done
|
||||
done
|
||||
done
|
||||
done
|
||||
done
|
||||
|
||||
for perm in 0 1 ; do
|
||||
for bias in "n" "e" "a" ; do
|
||||
Reference in New Issue
Block a user