Skip to content

GitLab

"vscode:/vscode.git/clone" did not exist on "fcde67b016eb6bdc46afbd05e98d962d315b15d1"
Commit a7ae4f8e authored by Astha Rai's avatar Astha Rai
Browse files

Merge branch 'codegen_hiprtc' of github.com:ROCm/composable_kernel into codegen_hiprtc

parents a6055c3c 781005a5
Expand all Show whitespace changes
Inline Side-by-side
Showing with 560 additions and 929 deletions
example/ck_tile/15_fused_moe/instances/fused_moe_api.cpp
View file @ a7ae4f8e
......@@ -41,6 +41,7 @@ float fused_moe(fused_moe_traits t, fused_moe_args a, const ck_tile::stream_conf
t.prec_sq,
t.prec_kw,
t.block_m,
t.activation,
t.gate_only,
t.fused_quant};
auto a1 = fused_moegemm_args{
......
example/ck_tile/15_fused_moe/instances/fused_moegemm_api.cpp
View file @ a7ae4f8e
......@@ -17,15 +17,67 @@ float fused_moegemm(fused_moegemm_traits t, fused_moegemm_args a, const ck_tile:
// clang-format off
float r = -1;
if(t.prec_i == "bf16" && t.prec_w == "bf16" && t.prec_o == "bf16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1)
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1 && t.activation == 0)
{
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0>;
constexpr ck_tile::index_t act_ = 0;
constexpr ck_tile::index_t go_ = 1;
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "bf16" && t.prec_w == "bf16" && t.prec_o == "bf16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 0 && t.activation == 0)
{
constexpr ck_tile::index_t act_ = 0;
constexpr ck_tile::index_t go_ = 0;
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "fp16" && t.prec_w == "fp16" && t.prec_o == "fp16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1 && t.activation == 0)
{
constexpr ck_tile::index_t act_ = 0;
constexpr ck_tile::index_t go_ = 1;
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "fp16" && t.prec_w == "fp16" && t.prec_o == "fp16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 0 && t.activation == 0)
{
constexpr ck_tile::index_t act_ = 0;
constexpr ck_tile::index_t go_ = 0;
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "bf16" && t.prec_w == "bf16" && t.prec_o == "bf16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1 && t.activation == 1)
{
constexpr ck_tile::index_t act_ = 1;
constexpr ck_tile::index_t go_ = 1;
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "bf16" && t.prec_w == "bf16" && t.prec_o == "bf16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 0 && t.activation == 1)
{
constexpr ck_tile::index_t act_ = 1;
constexpr ck_tile::index_t go_ = 0;
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "fp16" && t.prec_w == "fp16" && t.prec_o == "fp16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1 && t.activation == 1)
{
constexpr ck_tile::index_t act_ = 1;
constexpr ck_tile::index_t go_ = 1;
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "fp16" && t.prec_w == "fp16" && t.prec_o == "fp16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1)
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 0 && t.activation == 1)
{
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0>;
constexpr ck_tile::index_t act_ = 1;
constexpr ck_tile::index_t go_ = 0;
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
// clang-format on
......
example/ck_tile/15_fused_moe/instances/fused_moegemm_api_internal.hpp
View file @ a7ae4f8e
......@@ -21,8 +21,18 @@ float fused_moegemm_(const ck_tile::stream_config& s, fused_moegemm_args a)
typename Ts_::BlockTile_1,
typename Ts_::WarpPerBlock_0,
typename Ts_::WarpTile_0>;
using f_problem =
ck_tile::FusedMoeGemmPipelineProblem<typename Ts_::ADataType,
constexpr auto get_activation_ = []() {
if constexpr(Ts_::Activation == 0)
{
return ck_tile::element_wise::FastGeluAsm{};
}
else
return ck_tile::element_wise::Silu{};
};
using f_act_ = ck_tile::remove_cvref_t<decltype(get_activation_())>;
using f_problem = ck_tile::FusedMoeGemmPipelineProblem<typename Ts_::ADataType,
typename Ts_::GDataType,
typename Ts_::DDataType,
typename Ts_::AccDataType,
......@@ -33,7 +43,7 @@ float fused_moegemm_(const ck_tile::stream_config& s, fused_moegemm_args a)
typename Ts_::YSmoothScaleDataType,
typename Ts_::TopkWeightDataType,
typename Ts_::IndexDataType,
ck_tile::element_wise::FastGeluAsm, // TODO: hardcoded
f_act_, // TODO: hardcoded
f_shape,
f_traits>;
......
example/ck_tile/15_fused_moe/instances/fused_moegemm_api_traits.hpp
View file @ a7ae4f8e
......@@ -16,6 +16,7 @@ template <typename I,
typename BlockTIle_, // seq<b_token, b_interm, b_hidden, b_down>
typename WarpPerBlock_,
typename WarpTile_, // seq<*,*,*>, used to select mfma
ck_tile::index_t Activation_ = 0, // 0: Gelu 1: Silu
ck_tile::index_t GateOnly_ = 0,
ck_tile::index_t FusedQuant_ = 0>
struct fmoe_ // traits, ugly name, only used for internal
......@@ -44,10 +45,11 @@ struct fmoe_ // traits, ugly name, only used for internal
using WarpPerBlock_0 = ck_tile::remove_cvref_t<WarpPerBlock_>;
using WarpTile_0 = ck_tile::remove_cvref_t<WarpTile_>;
using BlockTile_1 = ck_tile::sequence<BT_, BD_, BI_ / (GateOnly_ ? 1 : 2)>;
using BlockTile_1 = ck_tile::sequence<BT_, BD_, BI_>;
using WarpPerBlock_1 = ck_tile::remove_cvref_t<WarpPerBlock_>;
using WarpTile_1 = ck_tile::remove_cvref_t<WarpTile_>;
static constexpr ck_tile::index_t Activation = Activation_; // 0: Gelu 1: Silu
static constexpr ck_tile::index_t GateOnly = GateOnly_;
static constexpr ck_tile::index_t FusedQuant = FusedQuant_;
};
example/ck_tile/15_fused_moe/instances/fused_moegemm_bf16_m32.cpp
View file @ a7ae4f8e
......@@ -8,7 +8,18 @@
// clang-format off
template float fused_moegemm_<
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0>
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 0, 0, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 0, 1, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 1, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
// clang-format on
example/ck_tile/15_fused_moe/instances/fused_moegemm_fp16_m32.cpp
View file @ a7ae4f8e
......@@ -8,7 +8,19 @@
// clang-format off
template float fused_moegemm_<
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0>
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 0, 0, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 0, 1, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 1, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
// clang-format on
example/ck_tile/15_fused_moe/main.cpp
View file @ a7ae4f8e
......@@ -108,12 +108,14 @@ auto create_args(int argc, char* argv[])
.insert(
"gate_only", "1", "w0(gate/up) style, 0:gate+up will double interm size, 1:only gate")
.insert("api", "0", "benchmark api set: 0:fused-moe(moe-gemm+moe-sorting), 1:moe-gemm")
.insert("act", "0", "activation after first gemm. 0:gelu, 1:silu")
.insert("balance",
"0",
"if set to 1, will try balance the expert in topk-ids(convenient for testing)")
.insert("init",
"2",
"init method. 0:random stepped float(fast). 1: random uniform, 2:rand normalized"
"1",
"init method. 0:random stepped float(fast). 1: random uniform[-0.5, 0.5], 2:rand "
"normalized[0, 1]"
"normalized(slow)")
.insert("seed", "11939", "seed used to do random")
.insert("warmup", "5", "cold iter")
......@@ -135,6 +137,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::index_t intermediate_size = arg_parser.get_int("i");
ck_tile::index_t stride = arg_parser.get_int("stride");
ck_tile::index_t block_m = arg_parser.get_int("bm");
ck_tile::index_t activation = arg_parser.get_int("act");
if(stride < 0)
stride = hidden_size;
std::string prec_i = arg_parser.get_str("prec_i");
......@@ -194,11 +197,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
return std::string(", st:") + std::to_string(stride);
}();
std::cout << "[" << api_str << "|" << prec_str << "]"
std::cout
<< "[" << api_str << "|" << prec_str << "]"
<< " t:" << tokens << ", e:" << experts << ", k:" << topk << stride_str
<< ", hidden:" << hidden_size << ", interm:" << intermediate_size << ", tp:" << tp
<< ", shrd_interm:" << shared_intermediate_size_0 << "|" << shared_intermediate_size_1
<< ", go:" << gate_only << ", q:" << fused_quant << std::flush;
<< ", act:"
<< activation
// << ", shrd_interm:" << shared_intermediate_size_0 << "|" << shared_intermediate_size_1
<< (gate_only ? ", g1u0" : ", g1u1") << ", q:" << fused_quant << std::flush;
using TypeConfig = FusedMoeGemmTypeConfig<I, W, O, ST, SW, SQ, KW>;
using ADataType = typename TypeConfig::ADataType;
......@@ -370,6 +376,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
prec_sq,
prec_kw,
block_m,
activation,
gate_only,
fused_quant};
......@@ -389,7 +396,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
num_sorted_tiles_buf.GetDeviceBuffer(),
block_m,
hidden_size,
shared_intermediate_size_0,
intermediate_size / tp,
tokens,
experts,
topk,
......@@ -408,6 +415,28 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< cal_tbps(ave_time) << " TB/s" << std::flush;
bool pass = true;
#define CPU_FUSED_MOE(act_type_) \
ck_tile::reference_fused_moe<AccDataType, act_type_>(a_host, \
g_host, \
d_host, \
sa_host, \
sg_host, \
sd_host, \
sy_host, \
o_host, \
sorted_token_ids_host, \
sorted_weight_host, \
sorted_expert_ids_host, \
num_sorted_tiles_host, \
topk_ids_host, \
block_m, \
tokens, \
experts, \
hidden_size, \
intermediate_size / tp, \
topk, \
gate_only)
if(do_validation)
{
ck_tile::reference_moe_sorting<TopkWeightDataType, IndexDataType>(
......@@ -419,28 +448,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
num_sorted_tiles_host.mData[0],
experts,
block_m);
ck_tile::reference_fused_moe<AccDataType, ck_tile::element_wise::Gelu>(
a_host,
g_host,
d_host,
sa_host,
sg_host,
sd_host,
sy_host,
o_host,
sorted_token_ids_host,
sorted_weight_host,
sorted_expert_ids_host,
num_sorted_tiles_host,
topk_ids_host,
block_m,
tokens,
experts,
hidden_size,
shared_intermediate_size_0,
topk,
gate_only);
if(activation == 0)
{
CPU_FUSED_MOE(ck_tile::element_wise::Gelu);
}
else
{
CPU_FUSED_MOE(ck_tile::element_wise::Silu);
}
auto o_dev = o_buf.ToHost<ODataType>();
// o_dev.savetxt("gpu-out.txt", "float");
......@@ -491,6 +506,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
prec_sq,
prec_kw,
block_m,
activation,
gate_only,
fused_quant};
......@@ -507,7 +523,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
sorted_expert_ids_buf.GetDeviceBuffer(),
num_sorted_tiles_buf.GetDeviceBuffer(),
hidden_size,
shared_intermediate_size_0,
intermediate_size / tp,
tokens,
experts,
topk,
......@@ -529,27 +545,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(do_validation)
{
ck_tile::reference_fused_moe<AccDataType, ck_tile::element_wise::Gelu>(
a_host,
g_host,
d_host,
sa_host,
sg_host,
sd_host,
sy_host,
o_host,
sorted_token_ids_host,
sorted_weight_host,
sorted_expert_ids_host,
num_sorted_tiles_host,
topk_ids_host,
block_m,
tokens,
experts,
hidden_size,
shared_intermediate_size_0,
topk,
gate_only);
if(activation == 0)
{
CPU_FUSED_MOE(ck_tile::element_wise::Gelu);
}
else
{
CPU_FUSED_MOE(ck_tile::element_wise::Silu);
}
auto o_dev = o_buf.ToHost<ODataType>();
// o_dev.savetxt("gpu-out.txt", "float");
......
example/ck_tile/16_batched_gemm/batched_gemm.cpp
View file @ a7ae4f8e
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#include <hip/hip_runtime.h>
......@@ -51,7 +51,7 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
using TilePartitioner = ck_tile::GemmTilePartitioner<CodegenGemmShape>;
using TilePartitioner = ck_tile::GemmTile2DPartitioner<CodegenGemmShape>;
using GemmEpilogue = std::conditional_t<
CShuffleEpilogue,
......@@ -63,8 +63,8 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
kOutputRank,
1,
0,
TilePartitioner::kM,
TilePartitioner::kN>>,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock>>,
ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>>;
......@@ -72,9 +72,7 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
using CodegenPipelineProblem = ck_tile::
GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenGemmShape, CodegenGemmTraits>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem, CodegenGemmPolicy>;
using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
using Kernel = ck_tile::BatchedGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
......
example/ck_tile/16_batched_gemm/batched_gemm.hpp
View file @ a7ae4f8e
......@@ -39,7 +39,7 @@ auto create_args(int argc, char* argv[])
.insert("stride_b", "0", "Tensor B stride")
.insert("stride_c", "0", "Tensor C stride")
.insert("a_layout", "R", "A tensor data layout - Row by default")
.insert("b_layout", "R", "B tensor data layout - Row by default")
.insert("b_layout", "C", "B tensor data layout - Row by default")
.insert("c_layout", "R", "C tensor data layout - Row by default")
.insert("batch_stride_a", "32768", "Batch A stride")
.insert("batch_stride_b", "16384", "Batch B stride")
......
example/ck_tile/16_batched_gemm/run_batched_gemm_example.inc
View file @ a7ae4f8e
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
auto calculate_rtol_atol(const ck_tile::index_t K,
const ck_tile::index_t kbatch,
const float max_accumulated_value)
{
using ComputeType =
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
// Calculate thresholds
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
ck_tile::integer_divide_ceil(K, kbatch));
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
// Calculate error due to split_k accumulation
const auto rtol_split_k =
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
max_accumulated_value, kbatch);
// Use higher threshold
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}
template <typename ALayout, typename BLayout, typename CLayout>
float invoke_batched_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
ck_tile::DeviceMem& b_k_n_dev_buf,
......@@ -179,8 +199,18 @@ int run_batched_gemm_example_with_layouts(int argc,
ck_tile::reference_batched_gemm<ADataType, BDataType, AccDataType, CDataType>(
a_m_k, b_n_k, c_m_n_host_ref);
pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_host_ref);
const float max_accumulated_value =
*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol(K, kbatch, max_accumulated_value);
pass = ck_tile::check_err(c_m_n_dev_result,
c_m_n_host_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
}
......@@ -240,7 +270,18 @@ int run_batched_gemm_example_with_layouts(int argc,
ck_tile::hip_check_error(hipFree(d_C));
c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data());
pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_gpu_ref);
const float max_accumulated_value =
*std::max_element(c_m_n_gpu_ref.mData.begin(), c_m_n_gpu_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol(K, kbatch, max_accumulated_value);
pass = ck_tile::check_err(c_m_n_dev_result,
c_m_n_gpu_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The GPU verification result is: " << (pass ? "correct" : "fail") << std::endl;
}
......@@ -260,11 +301,11 @@ int run_batched_gemm_example(int argc, char* argv[])
std::string a_layout = arg_parser.get_str("a_layout");
std::string b_layout = arg_parser.get_str("b_layout");
if(a_layout == "R" && b_layout == "R")
{
return run_batched_gemm_example_with_layouts(argc, argv, Row{}, Row{}, Row{});
}
else if(a_layout == "R" && b_layout == "C")
// if(a_layout == "R" && b_layout == "R")
// {
// return run_batched_gemm_example_with_layouts(argc, argv, Row{}, Row{}, Row{});
// }
if(a_layout == "R" && b_layout == "C")
{
return run_batched_gemm_example_with_layouts(argc, argv, Row{}, Col{}, Row{});
}
......
example/ck_tile/17_grouped_gemm/grouped_gemm.cpp
View file @ a7ae4f8e
......@@ -15,7 +15,6 @@
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/host.hpp"
#include "grouped_gemm.hpp"
#include "utils.hpp"
namespace {
......@@ -89,12 +88,9 @@ using CodegenPipelineProblem =
CodegenGemmShape,
CodegenGemmTraits<ALayout, BLayout, CLayout>>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
template <typename ALayout, typename BLayout, typename CLayout>
using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem<ALayout, BLayout, CLayout>,
CodegenGemmPolicy>;
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem<ALayout, BLayout, CLayout>>;
template <typename ALayout, typename BLayout, typename CLayout>
using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner,
......@@ -102,7 +98,7 @@ using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner,
GemmEpilogue<CLayout>>;
}; // namespace
std::size_t GetWorkspaceSize(const std::vector<grouped_gemm_kargs>& gemm_descs)
std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs)
{
return ::Kernel<std::nullptr_t, std::nullptr_t, std::nullptr_t>::GetWorkSpaceSize(gemm_descs);
}
......
example/ck_tile/17_grouped_gemm/grouped_gemm.hpp
View file @ a7ae4f8e
......@@ -41,7 +41,7 @@ auto create_args(int argc, char* argv[])
.insert("stride_Bs", "", "Tensor B strides - it is empty by default.")
.insert("stride_Cs", "", "Tensor C strides - it is empty by default.")
.insert("a_layout", "R", "A tensor data layout - Row by default.")
.insert("b_layout", "R", "B tensor data layout - Row by default.")
.insert("b_layout", "C", "B tensor data layout - Row by default.")
.insert("c_layout", "R", "C tensor data layout - Row by default.")
.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
.insert("warmup", "10", "number of iterations before benchmark the kernel.")
......@@ -52,8 +52,8 @@ auto create_args(int argc, char* argv[])
return std::make_tuple(result, arg_parser);
}
std::size_t GetWorkspaceSize(const std::vector<grouped_gemm_kargs>& gemm_descs);
std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs);
float grouped_gemm_calc(const std::vector<grouped_gemm_kargs>& gemm_descs,
float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* p_workspace_);
example/ck_tile/17_grouped_gemm/utils.hpp deleted 100644 → 0
View file @ a6055c3c
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
template <typename TLayout>
constexpr auto
f_host_tensor_descriptor(std::size_t row, std::size_t col, std::size_t stride, TLayout layout)
{
using namespace ck_tile::literals;
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
}
}
template <typename TLayout>
constexpr auto
f_get_default_stride(std::size_t row, std::size_t col, std::size_t stride, TLayout layout)
{
if(stride == 0)
{
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return col;
}
else
{
return row;
}
}
else
return stride;
}
include/ck/ck.hpp
View file @ a7ae4f8e
......@@ -17,7 +17,9 @@ CK_DECLARE_ENV_VAR_BOOL(CK_LOGGING)
#endif
// to do: add various levels of logging with CK_LOG_LEVEL
#ifndef CK_TIME_KERNEL
#define CK_TIME_KERNEL 1
#endif
// constant address space for kernel parameter
// https://llvm.org/docs/AMDGPUUsage.html#address-spaces
......@@ -155,6 +157,9 @@ CK_DECLARE_ENV_VAR_BOOL(CK_LOGGING)
// LDS direct loads using inline assembly
#define CK_USE_AMD_LDS_DIRECT_LOAD_INLINE_ASM 0
// set rounding to nearest even as default for bf16 conversions
#define CK_USE_RNE_BF16_CONVERSION 1
// set rounding to nearest even as default for f8 conversions
#define CK_USE_SR_F8_CONVERSION 0
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp
View file @ a7ae4f8e
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2023-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -122,19 +122,6 @@ __global__ void
static_for<0, NumDTensor, 1>{}(
[&](auto i) { p_ds_grid_grp(i) = p_ds_grid[i] + ds_group_offset[i]; });
if constexpr(is_same_v<AElementwiseOperation, element_wise::DynamicUnaryOp>)
{
a_element_op.InitUnaryOpPtrOnDevice();
}
if constexpr(is_same_v<BElementwiseOperation, element_wise::DynamicUnaryOp>)
{
b_element_op.InitUnaryOpPtrOnDevice();
}
if constexpr(is_same_v<CDEElementwiseOperation, element_wise::DynamicUnaryOp>)
{
cde_element_op.InitUnaryOpPtrOnDevice();
}
if constexpr(isMultiA || isMultiB)
{
AsPointer p_as_grid_grp;
......
include/ck/utility/data_type.hpp
View file @ a7ae4f8e
......@@ -31,8 +31,6 @@ struct pk_i4_t
type data;
__host__ __device__ constexpr pk_i4_t() : data{type{}} {}
__host__ __device__ constexpr pk_i4_t(type init) : data{init} {}
__host__ __device__ constexpr operator float() const { return static_cast<int8_t>(data); }
};
inline constexpr auto next_pow2(uint32_t x)
......
include/ck/utility/dynamic_buffer.hpp
View file @ a7ae4f8e
This diff is collapsed.
include/ck/utility/type_convert.hpp
View file @ a7ae4f8e
This diff is collapsed.
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment