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layernorm2d forward (#1339)

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* Add layernorm2d forward

* Refind file path

* clang format

* Exclude ck_tile op from all

* use add_executable instead

* refactor layernorm2d_fwd example

---------

Co-authored-by: carlushuang <carlus.huang@amd.com>
This commit is contained in:
rocking
2024-06-24 08:45:52 +08:00
committed by GitHub
parent 05b10e0e5a
commit cb13839425
17 changed files with 975 additions and 10 deletions
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154
include/ck_tile/ops/welford/warp/warp_welford.hpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
namespace ck_tile {
template <typename ComputeDataType_, bool BroadcastLane = true, bool GetActualVariance = true>
struct WarpMergeWelford
{
using ComputeDataType = remove_cvref_t<ComputeDataType_>;
template <typename T>
CK_TILE_DEVICE static void
Merge(T& mean_a, T& var_a, int& count_a, T mean_b, T var_b, int count_b)
{
int count = count_a + count_b;
T count_ = type_convert<T>(count);
T count_a_ = type_convert<T>(count_a);
T count_b_ = type_convert<T>(count_b);
T count_b_over_count = count == 0 ? type_convert<T>(0) : count_b_ / count_;
T delta = mean_b - mean_a;
mean_a += delta * count_b_over_count;
var_a += var_b + delta * delta * count_a_ * count_b_over_count;
count_a = count;
}
template <typename MeanDistributedTensor_, typename VarDistributedTensor_>
CK_TILE_DEVICE void
operator()(MeanDistributedTensor_& mean_tensor, VarDistributedTensor_& var_tensor, int& count)
{
using Dstr = typename MeanDistributedTensor_::StaticTileDistribution;
using DstrEncode = typename Dstr::DstrEncode;
using DstrEncodeDetail = typename DstrEncode::detail;
static_assert(std::is_same_v<Dstr, typename VarDistributedTensor_::StaticTileDistribution>,
"wrong!");
constexpr index_t NDimP = Dstr::get_num_of_dimension_p();
constexpr index_t NDimR = Dstr::get_num_of_dimension_r();
constexpr index_t idim_p_lane = NDimP - 1;
const auto ps_idx = make_array<index_t>(get_warp_id(), get_lane_id());
const auto rs_idx =
mean_tensor.get_tile_distribution().calculate_rs_index_from_ps_index(ps_idx);
constexpr index_t thread_buf_size = MeanDistributedTensor_::get_thread_buffer_size();
static_assert(thread_buf_size == VarDistributedTensor_::get_thread_buffer_size());
const int original_count = count;
// loop over thread data
static_for<0, thread_buf_size, 1>{}([&](auto i) {
auto v_local_mean = mean_tensor.get_thread_buffer()[i];
auto v_local_var = var_tensor.get_thread_buffer()[i];
auto v_local_count = original_count;
// cross-lane reduce for replication
// only reduce on R dimension correspond to lane
// (lane id maps to this R dimension)
static_for<0, NDimR, 1>{}([&](auto idim_r) {
// FIXME: nasty to use does_p_own_r_
if constexpr(DstrEncodeDetail::does_p_own_r_[idim_p_lane][idim_r])
{
constexpr index_t r_length = DstrEncode::rs_lengths_[idim_r];
constexpr index_t lid_over_rid_derivative =
DstrEncodeDetail::ps_over_rs_derivative_[idim_p_lane][idim_r];
static_assert(is_power_of_two_integer(r_length),
"wrong! only support power of 2 reduction");
constexpr index_t nstage = integer_log2_floor(r_length);
// reduction sweep forward
static_for<0, nstage, 1>{}([&](auto istage) {
constexpr index_t lid_delta =
lid_over_rid_derivative * (1 << (nstage - istage - 1));
// pull data from remote lane
const auto v_remote_mean = warp_shuffle_down(v_local_mean, lid_delta);
const auto v_remote_var = warp_shuffle_down(v_local_var, lid_delta);
const auto v_remote_count = warp_shuffle_down(v_local_count, lid_delta);
// welford merge
Merge(v_local_mean,
v_local_var,
v_local_count,
v_remote_mean,
v_remote_var,
v_remote_count);
});
}
});
// cross-lane broadcast for replication
// only broadcast on R dimension correspond to lane
// (lane id maps to this R dimension)
if constexpr(BroadcastLane)
{
static_for<0, NDimR, 1>{}([&](auto idim_r) {
// FIXME: nasty to use does_p_own_r_
if constexpr(DstrEncodeDetail::does_p_own_r_[idim_p_lane][idim_r])
{
const index_t r_id = rs_idx[idim_r];
constexpr index_t r_length = DstrEncode::rs_lengths_[idim_r];
constexpr index_t lid_over_rid_derivative =
DstrEncodeDetail::ps_over_rs_derivative_[NDimP - 1][idim_r];
static_assert(is_power_of_two_integer(r_length),
"wrong! only support power of 2 reduction");
constexpr index_t nstage = integer_log2_floor(r_length);
// broadcast sweep backward
static_for<0, nstage, 1>{}([&](auto istage) {
// do I hold reduced data?
const bool do_i_hold_reduced_data = r_id < (1 << istage);
constexpr index_t lid_delta = lid_over_rid_derivative * (1 << istage);
// pull data from remote lane
const auto v_remote_mean = warp_shuffle_up(v_local_mean, lid_delta);
const auto v_remote_var = warp_shuffle_up(v_local_var, lid_delta);
const auto v_remote_count = warp_shuffle_up(v_local_count, lid_delta);
// decide whether to update local data with remote data
v_local_mean = do_i_hold_reduced_data ? v_local_mean : v_remote_mean;
v_local_var = do_i_hold_reduced_data ? v_local_var : v_remote_var;
v_local_count = do_i_hold_reduced_data ? v_local_count : v_remote_count;
});
}
});
}
mean_tensor.get_thread_buffer()(i) = v_local_mean;
if constexpr(GetActualVariance)
var_tensor.get_thread_buffer()(i) = v_local_var / v_local_count;
else
var_tensor.get_thread_buffer()(i) = v_local_var;
count = v_local_count;
});
}
};
} // namespace ck_tile