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[CK_TILE] Improve RMS/Layer Normalization 2 Pass Pipeline Performance #1861

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[CK_TILE] Improve RMS/Layer Normalization 2 Pass Pipeline Performance #1861

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df45a6b
50ms -> 28ms
ruanjm Jan 24, 2025
f41a43a
Fix bug in non fuse_add_store cases
ruanjm Jan 27, 2025
1532408
Fine tuned setting for 2 pass pipeline
ruanjm Jan 27, 2025
485e530
adjust workload
ruanjm Feb 5, 2025
b426b99
remove unnecessary change
ruanjm Feb 5, 2025
28f93c9
add layernorm
ruanjm Feb 6, 2025
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4 changes: 2 additions & 2 deletions example/ck_tile/02_layernorm2d/generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -564,9 +564,9 @@ def get_blobs(self, args):
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 512, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 8, 1,1024, 1, True, False, True, True, False, 0, 0, 0)],
'big' :[ h_traits('x', 'y', 'xs', 'ys', 1, 2, 1, 256, 8, True, False, True, True, True, 0, 0, 0),
'big' :[ h_traits('x', 'y', 'xs', 'ys', 1, 1, 1,1024, 8, True, False, True, True, True, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 4, True, False, True, True, True, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 1,1024, 2, True, False, True, True, True, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 12, 1, 256, 2, True, False, True, True, True, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 1, True, False, True, True, True, 0, 0, 0)]}
total_blob = list()
for hs_key in h_trait_dict:
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4 changes: 2 additions & 2 deletions example/ck_tile/10_rmsnorm2d/generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -535,9 +535,9 @@ def get_blobs(self):
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 512, 4, True, False, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 2, True, False, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 8, 1,1024, 1, True, False, False, 0, 0)],
'big' :[ h_traits('x', 'y', 'xs', 'ys', 1, 2, 1, 256, 8, True, False, True, 0, 0),
'big' :[ h_traits('x', 'y', 'xs', 'ys', 1, 1, 1,1024, 8, True, False, True, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 4, True, False, True, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 1,1024, 2, True, False, True, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 12, 1, 256, 2, True, False, True, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 1, True, False, True, 0, 0)]}
total_blob = list()
for hs_key in h_trait_dict:
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65 changes: 42 additions & 23 deletions include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_pipeline_two_pass.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -182,38 +182,60 @@ struct Layernorm2dFwdPipelineTwoPass
ck_tile::index_t stride_to_right_most_window =
row_size % Block_N == 0 ? row_size - Block_N : row_size - row_size % Block_N;

move_tile_window(x_window, {0, -Block_N});
move_tile_window(x_residual_window, {0, -Block_N});
move_tile_window(x_bias_window, {-Block_N});
if constexpr(kFusedAdd == Layernorm2dFusedAddEnum::PRE_ADD_STORE)
{
move_tile_window(y_residual_window, {0, -Block_N});
}
else
{
move_tile_window(x_window, {0, -Block_N});
move_tile_window(x_residual_window, {0, -Block_N});
move_tile_window(x_bias_window, {-Block_N});
}
move_tile_window(gamma_window, {stride_to_right_most_window});
move_tile_window(beta_window, {stride_to_right_most_window});
move_tile_window(y_window, {0, stride_to_right_most_window});

// layernorm computation
for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
{
auto x = load_tile(x_window);
auto x_resi = load_tile(x_residual_window);
const auto x_bias = load_tile(x_bias_window);
auto acc = cast_tile<ComputeDataType>(x);
auto acc = make_static_distributed_tensor<ComputeDataType>(
decltype(load_tile(x_window))::get_tile_distribution());

if constexpr(kXbias == Layernorm2dXBiasEnum::ADD_BIAS)
if constexpr(kFusedAdd == Layernorm2dFusedAddEnum::PRE_ADD_STORE)
{
sweep_tile(x, [&](auto idx) {
// compute x = bias + x
constexpr auto j_idx = make_tuple(idx[number<1>{}]);
acc(idx) = type_convert<ComputeDataType>(x_bias[j_idx]) + acc(idx);
});
acc = cast_tile<ComputeDataType>(load_tile(y_residual_window));
move_tile_window(y_residual_window, {0, -Block_N});
}

if constexpr(kFusedAdd == Layernorm2dFusedAddEnum::PRE_ADD_STORE ||
kFusedAdd == Layernorm2dFusedAddEnum::PRE_ADD)
else
{
sweep_tile(x_resi, [&](auto idx) {
// compute x = x_resi + x
acc(idx) = type_convert<ComputeDataType>(x_resi(idx)) + acc(idx);
});
acc = cast_tile<ComputeDataType>(load_tile(x_window));
move_tile_window(x_window, {0, -Block_N});

if constexpr(kXbias == Layernorm2dXBiasEnum::ADD_BIAS)
{
const auto x_bias = load_tile(x_bias_window);
move_tile_window(x_bias_window, {-Block_N});

sweep_tile(acc, [&](auto idx) {
// compute x = bias + x
constexpr auto j_idx = make_tuple(idx[number<1>{}]);
acc(idx) = type_convert<ComputeDataType>(x_bias[j_idx]) + acc(idx);
});
}

if constexpr(kFusedAdd == Layernorm2dFusedAddEnum::PRE_ADD)
{
auto x_resi = load_tile(x_residual_window);
move_tile_window(x_residual_window, {0, -Block_N});

sweep_tile(x_resi, [&](auto idx) {
// compute x = x_resi + x
acc(idx) = type_convert<ComputeDataType>(x_resi(idx)) + acc(idx);
});
}
}

// load gamma/beta (TODO: support no gamma/beta?)
const auto gamma = load_tile(gamma_window);
const auto beta = load_tile(beta_window);
Expand All @@ -235,9 +257,6 @@ struct Layernorm2dFwdPipelineTwoPass
static_assert(kFusedQuant != Layernorm2dFusedQuantEnum::DYNAMIC_QUANT);
Epilogue{}(y_window, ln);

move_tile_window(x_window, {0, -Block_N});
move_tile_window(x_residual_window, {0, -Block_N});
move_tile_window(x_bias_window, {-Block_N});
move_tile_window(gamma_window, {-Block_N});
move_tile_window(beta_window, {-Block_N});
move_tile_window(y_window, {0, -Block_N});
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45 changes: 31 additions & 14 deletions include/ck_tile/ops/rmsnorm2d/pipeline/rmsnorm2d_fwd_pipeline_two_pass.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -136,32 +136,51 @@ struct Rmsnorm2dFwdPipelineTwoPass
ck_tile::index_t stride_to_right_most_window =
row_size % Block_N == 0 ? row_size - Block_N : row_size - row_size % Block_N;

move_tile_window(x_window, {0, -Block_N});
move_tile_window(x_residual_window, {0, -Block_N});
if constexpr(kFusedAdd == Rmsnorm2dFusedAddEnum::PRE_ADD_STORE)
{
move_tile_window(y_residual_window, {0, -Block_N});
}
else
{
move_tile_window(x_window, {0, -Block_N});
move_tile_window(x_residual_window, {0, -Block_N});
}
move_tile_window(gamma_window, {stride_to_right_most_window});
move_tile_window(y_window, {0, stride_to_right_most_window});

// rmsnorm computation
for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
{
auto x = load_tile(x_window);
auto x_resi = load_tile(x_residual_window);
auto acc = cast_tile<ComputeDataType>(x);
auto acc = make_static_distributed_tensor<ComputeDataType>(
decltype(load_tile(x_window))::get_tile_distribution());

if constexpr(kFusedAdd == Rmsnorm2dFusedAddEnum::PRE_ADD_STORE ||
kFusedAdd == Rmsnorm2dFusedAddEnum::PRE_ADD)
if constexpr(kFusedAdd == Rmsnorm2dFusedAddEnum::PRE_ADD_STORE)
{
sweep_tile(x_resi, [&](auto idx) {
// compute x = x_resi + x
acc(idx) = type_convert<ComputeDataType>(x_resi(idx)) + acc(idx);
});
acc = cast_tile<ComputeDataType>(load_tile(y_residual_window));
move_tile_window(y_residual_window, {0, -Block_N});
}
else
{
acc = cast_tile<ComputeDataType>(load_tile(x_window));
move_tile_window(x_window, {0, -Block_N});

if constexpr(kFusedAdd == Rmsnorm2dFusedAddEnum::PRE_ADD)
{
auto x_resi = load_tile(x_residual_window);
sweep_tile(x_resi, [&](auto idx) {
// compute x = x_resi + x
acc(idx) = type_convert<ComputeDataType>(x_resi(idx)) + acc(idx);
});
move_tile_window(x_residual_window, {0, -Block_N});
}
}

// load gamma (TODO: support no gamma?)
const auto gamma = load_tile(gamma_window);

// rmsnorm computation
auto rmsn = make_static_distributed_tensor<ComputeDataType>(x.get_tile_distribution());
auto rmsn = make_static_distributed_tensor<ComputeDataType>(
decltype(load_tile(x_window))::get_tile_distribution());
sweep_tile(rmsn, [&, inv_rms_ = inv_rms](auto idx) {
constexpr auto i_idx = make_tuple(idx[number<0>{}]);
constexpr auto j_idx = make_tuple(idx[number<1>{}]);
Expand All @@ -176,8 +195,6 @@ struct Rmsnorm2dFwdPipelineTwoPass
static_assert(kFusedQuant == Rmsnorm2dFusedQuantEnum::NO_SWEEP);
Epilogue{}(y_window, rmsn);

move_tile_window(x_window, {0, -Block_N});
move_tile_window(x_residual_window, {0, -Block_N});
move_tile_window(gamma_window, {-Block_N});
move_tile_window(y_window, {0, -Block_N});
}
Expand Down