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[CK_TILE]Moe update index (#1672)
* update MOCK_ID for moe-sorting * add moe-smoothquant * update a comment * fix format * hot fix * update topk in overflow case * update comments * update bf16 cvt --------- Co-authored-by: valarLip <340077269@qq.com>
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carlushuang
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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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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/ops/common.hpp"
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namespace ck_tile {
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// host side args
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struct MoeSmoothquantHostArgs
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{
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const void* p_x; // [tokens ,hidden_size], input, fp16/bf16
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const void* p_xscale; // [experts, hidden_size], input, columnwise scale, fp32
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const void* p_topk_ids; // [tokens, topk]
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void* p_yscale; // [topk * tokens, 1], output, rowwise quant scale
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void* p_qy; // [topk * tokens, hidden_size], output
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index_t tokens;
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index_t hidden_size;
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index_t experts;
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index_t topk;
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index_t x_stride; // input x row stride
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index_t y_stride; // output y stride(stride for topk)
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};
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// TODO: Extract some type to wrapper class
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template <typename Pipeline_>
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struct MoeSmoothquant
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{
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using Pipeline = remove_cvref_t<Pipeline_>;
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using Problem = typename Pipeline::Problem;
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using XDataType = remove_cvref_t<typename Problem::XDataType>;
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using XScaleDataType = remove_cvref_t<typename Problem::XScaleDataType>;
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using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
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using YScaleDataType = remove_cvref_t<typename Problem::YScaleDataType>;
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using QYDataType = remove_cvref_t<typename Problem::QYDataType>;
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static constexpr index_t Block_M = Problem::BlockShape::Block_M;
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static constexpr index_t Block_N = Problem::BlockShape::Block_N;
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static constexpr bool kPadM = false; // always no need to pad along M
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static constexpr bool kPadN = Problem::kPadN;
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static constexpr bool kTwoPass = Problem::kTwoPass;
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static constexpr index_t ThreadPerWarp_N = Problem::BlockShape::ThreadPerWarp_N;
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static constexpr index_t Vector_N = Problem::BlockShape::Vector_N;
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static constexpr index_t Repeat_N = Problem::BlockShape::Repeat_N;
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static constexpr auto I0 = number<0>{};
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static constexpr auto I1 = number<1>{};
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static_assert(Problem::BlockShape::Repeat_M == 1);
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struct Kargs
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{
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const void* p_x; // [tokens ,hidden_size], input, fp16/bf16
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const void* p_xscale; // [experts, hidden_size], input, columnwise scale, fp32
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const void* p_topk_ids; // [tokens, topk]
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void* p_yscale; // [topk, tokens, 1], output, rowwise quant scale
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void* p_qy; // [topk, tokens, hidden_size], output
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index_t tokens;
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index_t hidden_size;
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index_t experts;
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index_t topk;
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index_t x_stride; // input x row stride
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index_t y_stride; // output y stride(stride for topk)
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};
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using Hargs = MoeSmoothquantHostArgs;
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CK_TILE_HOST static constexpr Kargs MakeKargs(const Hargs& hargs)
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{
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return Kargs{hargs.p_x,
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hargs.p_xscale,
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hargs.p_topk_ids,
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hargs.p_yscale,
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hargs.p_qy,
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hargs.tokens,
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hargs.hidden_size,
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hargs.experts,
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hargs.topk,
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hargs.x_stride,
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hargs.y_stride};
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}
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CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs)
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{
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return dim3(hargs.topk, integer_divide_ceil(hargs.tokens, Block_M), 1);
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}
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CK_TILE_HOST static constexpr auto BlockSize() { return Problem::BlockShape::BlockSize; }
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// clang-format off
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template <typename T> struct t2s;
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template <> struct t2s<float> { static constexpr const char * name = "fp32"; };
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template <> struct t2s<ck_tile::fp16_t> { static constexpr const char * name = "fp16"; };
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template <> struct t2s<ck_tile::bf16_t> { static constexpr const char * name = "bf16"; };
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template <> struct t2s<ck_tile::fp8_t> { static constexpr const char * name = "fp8"; };
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template <> struct t2s<ck_tile::bf8_t> { static constexpr const char * name = "bf8"; };
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// clang-format on
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// in byte
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CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize() { return Pipeline::GetSmemSize(); }
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CK_TILE_HOST static std::string GetName()
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{
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// clang-format off
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using S_ = typename Problem::BlockShape;
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auto surfix = [&] () {
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std::string n;
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if (kPadN) n += "_pn";
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if (kTwoPass) n += "_2p";
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return n; }();
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#define _SS_ std::string
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#define _TS_ std::to_string
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return _SS_("moe_smoothquant_") + _SS_(t2s<XDataType>::name) + "_" +
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_TS_(S_::Block_M) + "x" + _TS_(S_::Block_N) + "_" + _TS_(S_::WarpPerBlock_M) + "x" + _TS_(S_::WarpPerBlock_N) + "_" +
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_TS_(S_::Warp_M) + "x" + _TS_(S_::Warp_N) + "_" + _TS_(S_::Vector_M) + "x" + _TS_(S_::Vector_N) + "_" +
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_SS_(Pipeline::name) + surfix;
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#undef _SS_
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#undef _TS_
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// clang-format on
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}
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CK_TILE_DEVICE void operator()(Kargs kargs) const
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{
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const index_t i_topk = blockIdx.x;
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const index_t i_token = blockIdx.y * Block_M;
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const index_t i_token_in_thrd =
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__builtin_amdgcn_readfirstlane(threadIdx.x / Problem::BlockShape::ThreadPerBlock_N);
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const index_t i_expert = reinterpret_cast<const index_t*>(
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kargs.p_topk_ids)[(i_token + i_token_in_thrd) * kargs.topk + i_topk];
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// [tokens ,hidden_size]
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const auto x_window = [&]() {
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const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
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static_cast<const XDataType*>(kargs.p_x),
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make_tuple(kargs.tokens, kargs.hidden_size),
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make_tuple(kargs.x_stride, 1),
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number<Vector_N>{},
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number<1>{});
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const auto tmp2_ = pad_tensor_view(
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tmp_, make_tuple(number<Block_M>{}, number<Block_N>{}), sequence<kPadM, kPadN>{});
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return make_tile_window(
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tmp2_, make_tuple(number<Block_M>{}, number<Block_N>{}), {i_token, 0});
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}();
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// [experts, hidden_size],
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const auto xscale_window = [&]() {
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const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
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static_cast<const XScaleDataType*>(kargs.p_xscale) + i_expert * kargs.hidden_size,
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make_tuple(kargs.hidden_size),
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make_tuple(1),
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number<Vector_N>{},
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number<1>{});
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const auto tmp2_ =
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pad_tensor_view(tmp_, make_tuple(number<Block_N>{}), sequence<kPadN>{});
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return make_tile_window(tmp2_, make_tuple(number<Block_N>{}), {0});
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}();
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// [topk, tokens]
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auto yscale_window = [&]() {
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const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
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static_cast<YScaleDataType*>(kargs.p_yscale) + i_topk * kargs.tokens,
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make_tuple(kargs.tokens),
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make_tuple(1),
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number<1>{});
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const auto tmp2_ =
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pad_tensor_view(tmp_, make_tuple(number<Block_M>{}), sequence<kPadM>{});
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return make_tile_window(tmp2_, make_tuple(number<Block_M>{}), {i_token});
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}();
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// [topk, tokens, hidden_size]
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auto qy_window = [&]() {
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auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
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static_cast<QYDataType*>(kargs.p_qy) + i_topk * kargs.tokens * kargs.y_stride,
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make_tuple(kargs.tokens, kargs.hidden_size),
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make_tuple(kargs.y_stride, 1),
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number<Vector_N>{},
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number<1>{});
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auto tmp2_ = pad_tensor_view(
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tmp_, make_tuple(number<Block_M>{}, number<Block_N>{}), sequence<kPadM, kPadN>{});
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return make_tile_window(
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tmp2_, make_tuple(number<Block_M>{}, number<Block_N>{}), {i_token, 0});
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}();
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__shared__ char smem[GetSmemSize()];
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Pipeline{}(x_window, xscale_window, yscale_window, qy_window, kargs.hidden_size, smem);
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}
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};
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} // namespace ck_tile
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