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/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2024 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#include "graphapi_mha_cpp_common.hpp"
namespace mha_graph_test {
class GPU_MhaBwdGraphTest_FP32 : public MhaGraphTestBase
{
tensor<float> mSoftMax;
public:
void createMhaGraph(size_t n, size_t h, size_t s, size_t d) override
{
mGraphBuilder = std::make_unique<gr::OpGraphBuilder>();
std::vector<size_t> nhsd = {n, h, s, d};
std::vector<size_t> nhds = {n, h, d, s};
std::vector<size_t> nhss = {n, h, s, s};
std::vector<size_t> nhs1 = {n, h, s, 1};
std::vector<size_t> all1s = {1, 1, 1, 1};
MAKE_TENSOR_F(Q, nhsd, false);
MAKE_TENSOR_F(K, nhsd, false);
MAKE_TENSOR_F(V, nhsd, false);
MAKE_TENSOR_F(dO, nhsd, false);
MAKE_TENSOR_F(O, nhsd, false);
MAKE_TENSOR_F(K_T, nhds, true);
MAKE_TENSOR_F(T_MM_0, nhss, true);
addNode("OP_RESHAPE", {K}, {K_T});
addNode("OP_MATMUL", {Q, K_T}, {T_MM_0});
MAKE_TENSOR_F(T_SCL_0, nhss, true);
// MAKE_TENSOR_F(ATN_SCL, all1s, false);
// addNode("OP_POINTWISE:MUL", {T_MM_0, ATN_SCL}, {T_SCL_0});
// NOTE(Amber): Replacing the code above with a hacky solution to pass
// attention scale as a scalar param instead of a tensor. This is because
// Find 2.0 for MHA expects it as a scalar param set on MHA Problem
// Descriptor.
auto* pw_id = makePointWiseDesc(MIOPEN_POINTWISE_IDENTITY);
addUnaryPointwiseNode(pw_id, {T_MM_0}, {T_SCL_0}, mAttentionScale);
MAKE_TENSOR_F(T_SCL_1, nhss, true);
MAKE_TENSOR_F(DSCL_Q, all1s, false);
addNode("OP_POINTWISE:MUL", {T_SCL_0, DSCL_Q}, {T_SCL_1});
MAKE_TENSOR_F(T_SCL_2, nhss, true);
MAKE_TENSOR_F(DSCL_K, all1s, false);
addNode("OP_POINTWISE:MUL", {T_SCL_1, DSCL_K}, {T_SCL_2});
MAKE_TENSOR_F(M, nhs1, false);
MAKE_TENSOR_F(T_SUB_0, nhss, true);
addNode("OP_POINTWISE:SUB", {T_SCL_2, M}, {T_SUB_0});
MAKE_TENSOR_F(T_EXP, nhss, true);
addNode("OP_POINTWISE:EXP", {T_SUB_0}, {T_EXP});
MAKE_TENSOR_F(Z_INV, nhs1, false);
MAKE_TENSOR_F(T_MUL_0, nhss, true);
addNode("OP_POINTWISE:MUL", {T_EXP, Z_INV}, {T_MUL_0});
MAKE_TENSOR_I(RND_SD, all1s, false);
MAKE_TENSOR_I(RND_OFF, all1s, false);
MAKE_TENSOR_F(T_RND, nhss, true);
addNode("OP_RNG", {RND_SD, RND_OFF}, {T_RND});
MAKE_TENSOR_F(T_MUL_1, nhss, true);
addNode("OP_POINTWISE:MUL", {T_MUL_0, T_RND}, {T_MUL_1});
MAKE_TENSOR_F(RND_PRB, all1s, false); // TODO(Amber): revisit
MAKE_TENSOR_F(T_SCL_3, nhss, true);
// T_SCL_3 feeds into the middle column of the graph
addNode("OP_POINTWISE:MUL", {T_MUL_1, RND_PRB}, {T_SCL_3});
MAKE_TENSOR_F(T_SCL_4, nhss, true);
MAKE_TENSOR_F(SCL_S, all1s, false);
addNode("OP_POINTWISE:MUL", {T_SCL_3, SCL_S}, {T_SCL_4});
MAKE_TENSOR_F(SCL_4T, nhss, true);
addNode("OP_RESHAPE", {T_SCL_4}, {SCL_4T});
MAKE_TENSOR_F(T_MM_1, nhsd, true);
addNode("OP_MATMUL", {SCL_4T, dO}, {T_MM_1});
MAKE_TENSOR_F(T_SCL_5, nhsd, true);
MAKE_TENSOR_F(DSCL_S, all1s, false);
addNode("OP_POINTWISE:MUL", {T_MM_1, DSCL_S}, {T_SCL_5});
MAKE_TENSOR_F(T_SCL_6, nhsd, true);
MAKE_TENSOR_F(DSCL_dO, all1s, false);
addNode("OP_POINTWISE:MUL", {T_SCL_5, DSCL_dO}, {T_SCL_6});
MAKE_TENSOR_F(dV, nhsd, false);
MAKE_TENSOR_F(SCL_dV, all1s, false);
addNode("OP_POINTWISE:MUL", {T_SCL_6, SCL_dV}, {dV});
MAKE_TENSOR_F(AMax_dV, all1s, false);
addNode("OP_REDUCTION:MAX", {T_SCL_6}, {AMax_dV});
////////////////// Center-top //////////////////////////////////
MAKE_TENSOR_F(V_T, nhds, true);
addNode("OP_RESHAPE", {V}, {V_T});
MAKE_TENSOR_F(T_MM_2, nhss, true);
addNode("OP_MATMUL", {dO, V_T}, {T_MM_2});
MAKE_TENSOR_F(T_SCL_7, nhss, true);
addNode("OP_POINTWISE:MUL", {T_MM_2, DSCL_dO}, {T_SCL_7});
MAKE_TENSOR_F(T_SCL_8, nhss, true);
MAKE_TENSOR_F(DSCL_V, all1s, false);
addNode("OP_POINTWISE:MUL", {T_SCL_7, DSCL_V}, {T_SCL_8});
MAKE_TENSOR_F(T_SCL_9, nhss, true);
addNode("OP_POINTWISE:MUL", {T_SCL_8, T_RND}, {T_SCL_9});
MAKE_TENSOR_F(T_SCL_10, nhss, true);
addNode("OP_POINTWISE:MUL", {T_SCL_9, RND_PRB}, {T_SCL_10});
////////////////// Right-top //////////////////////////////////
MAKE_TENSOR_F(T_SCL_11, nhsd, true);
addNode("OP_POINTWISE:MUL", {dO, DSCL_dO}, {T_SCL_11});
MAKE_TENSOR_F(DSCL_O, all1s, false);
MAKE_TENSOR_F(T_SCL_12, nhsd, true);
addNode("OP_POINTWISE:MUL", {O, DSCL_O}, {T_SCL_12});
MAKE_TENSOR_F(T_MUL_2, nhsd, true);
addNode("OP_POINTWISE:MUL", {T_SCL_11, T_SCL_12}, {T_MUL_2});
MAKE_TENSOR_F(T_SCL_13, nhsd, true);
addNode("OP_POINTWISE:MUL", {T_MUL_2, RND_PRB}, {T_SCL_13});
MAKE_TENSOR_F(T_SUM_0, nhs1, true);
addNode("OP_REDUCTION:ADD", {T_SCL_13}, {T_SUM_0});
////////////////// Center Part //////////////////////////////////
MAKE_TENSOR_F(T_SUB_1, nhss, true);
addNode("OP_POINTWISE:SUB", {T_SCL_10, T_SUM_0}, {T_SUB_1});
MAKE_TENSOR_F(T_SCL_14, nhss, true);
addUnaryPointwiseNode(pw_id, {T_SUB_1}, {T_SCL_14}, mAttentionScale);
MAKE_TENSOR_F(T_MUL_3, nhss, true);
addNode("OP_POINTWISE:MUL", {T_SCL_14, T_SCL_3}, {T_MUL_3});
MAKE_TENSOR_F(T_SCL_15, nhss, true);
MAKE_TENSOR_F(SCL_dS, all1s, false);
addNode("OP_POINTWISE:MUL", {T_MUL_3, SCL_dS}, {T_SCL_15});
MAKE_TENSOR_F(AMax_dS, all1s, false);
addNode("OP_REDUCTION:MAX", {T_MUL_3}, {AMax_dS});
////////////////// Center Bottom //////////////////////////////////
MAKE_TENSOR_F(T_MM_3, nhsd, true);
addNode("OP_MATMUL", {T_SCL_15, K}, {T_MM_3});
MAKE_TENSOR_F(T_SCL_16, nhsd, true);
MAKE_TENSOR_F(DSCL_dS, all1s, false);
addNode("OP_POINTWISE:MUL", {T_MM_3, DSCL_dS}, {T_SCL_16});
MAKE_TENSOR_F(T_SCL_17, nhsd, true);
addNode("OP_POINTWISE:MUL", {T_SCL_16, DSCL_K}, {T_SCL_17});
MAKE_TENSOR_F(dQ, nhsd, false);
MAKE_TENSOR_F(SCL_dQ, all1s, false);
addNode("OP_POINTWISE:MUL", {T_SCL_17, SCL_dQ}, {dQ});
MAKE_TENSOR_F(AMax_dQ, all1s, false);
addNode("OP_REDUCTION:MAX", {T_SCL_17}, {AMax_dQ});
////////////////// Right Bottom //////////////////////////////////
MAKE_TENSOR_F(SCL_15T, nhss, true);
addNode("OP_RESHAPE", {T_SCL_15}, {SCL_15T});
MAKE_TENSOR_F(T_MM_4, nhsd, true);
addNode("OP_MATMUL", {SCL_15T, Q}, {T_MM_4});
MAKE_TENSOR_F(T_SCL_18, nhsd, true);
addNode("OP_POINTWISE:MUL", {T_MM_4, DSCL_dS}, {T_SCL_18});
MAKE_TENSOR_F(T_SCL_19, nhsd, true);
addNode("OP_POINTWISE:MUL", {T_SCL_18, DSCL_Q}, {T_SCL_19});
MAKE_TENSOR_F(dK, nhsd, false);
MAKE_TENSOR_F(SCL_dK, all1s, false);
addNode("OP_POINTWISE:MUL", {T_SCL_19, SCL_dK}, {dK});
MAKE_TENSOR_F(AMax_dK, all1s, false);
addNode("OP_REDUCTION:MAX", {T_SCL_19}, {AMax_dK});
// create tensor SCL_O for running cpu fwd mha
MAKE_TENSOR_F(SCL_O, all1s, false);
std::ignore = SCL_O;
}
void initInputs(size_t n, size_t h, size_t s, size_t d) override
{
using namespace test::cpu;
auto Q = GenScaledTensorBackward<float>(n, h, s, d);
auto K = GenScaledTensorBackward<float>(n, h, s, d);
auto V = GenScaledTensorBackward<float>(n, h, s, d);
auto dO = GenScaledTensorBackward<float>(n, h, s, d);
for(auto& [k, v] : mFilledTensors)
{
if(k == "Q")
{
v.init(std::move(Q.mTensor));
}
else if(k == "DSCL_Q")
{
v.init(Q.mDescale);
}
else if(k == "K")
{
v.init(std::move(K.mTensor));
}
else if(k == "DSCL_K")
{
v.init(K.mDescale);
}
else if(k == "V")
{
v.init(std::move(V.mTensor));
}
else if(k == "DSCL_V")
{
v.init(V.mDescale);
}
else if(k == "dO")
{
v.init(std::move(dO.mTensor));
}
else if(k == "DSCL_dO")
{
v.init(dO.mDescale);
}
else if(k == "SCL_O" || k == "SCL_S" || k == "DSCL_O" || k == "DSCL_S" ||
k == "ATN_SCL" || k == "SCL_dS" || k == "DSCL_dS" || k == "SCL_dV" ||
k == "SCL_dQ" || k == "SCL_dK")
{
v.init(1.0f);
}
else if(k == "RND_PRB")
{
v.init(mProbDropout);
}
else if(k == "RND_SD" || k == "RND_OFF")
{
v.init(0ll);
}
else if(k == "dV" || k == "dQ" || k == "dK" || k == "AMax_dV" || k == "AMax_dQ" ||
k == "AMax_dK" || k == "AMax_dS")
{
// these are outputs
v.init(0.0f);
}
else if(k == "M" || k == "Z_INV" || k == "O")
{
// init later below
}
else
{
FAIL() << "Uninitialized input or output: " << k;
}
}
CpuMhaFwdOut out = runCpuMhaFWd(n, h, s, d);
lookup("M").init(std::move(out.mM));
lookup("Z_INV").init(std::move(out.mZinv));
lookup("O").init(std::move(out.mO));
// softmax needed for calling cpu backward mha
mSoftMax = std::move(out.mSoftMax);
// Remove "SCL_O" here so that it doesn't pollute the variant pack used for
// execution
size_t cnt = mFilledTensors.erase("SCL_O");
ASSERT_EQ(cnt, 1);
}
void runCpuVerify(size_t n, size_t h, size_t s, size_t d) override
{
/// \todo remove virtual once backward mha is ready to execute
static constexpr bool disable_verification = true; // TODO
auto dQ_ref = tensor<float>{n, h, s, d};
auto dK_ref = tensor<float>{n, h, s, d};
auto dV_ref = tensor<float>{n, h, s, d};
float amax_dS_ref = 0;
float amax_dQ_ref = 0;
float amax_dK_ref = 0;
float amax_dV_ref = 0;
test::cpu::MultiHeadAttentionBackwardDataf8(lookup_f("Q"),
lookup_f("K"),
lookup_f("V"),
lookup_f("O"),
lookup_f("dO"),
mSoftMax,
lookup_f("DSCL_Q")[0],
lookup_f("DSCL_K")[0],
lookup_f("DSCL_V")[0],
lookup_f("SCL_dQ")[0],
lookup_f("SCL_dK")[0],
lookup_f("SCL_dV")[0],
lookup_f("SCL_S")[0],
lookup_f("DSCL_S")[0],
lookup_f("SCL_dS")[0],
lookup_f("DSCL_dS")[0],
lookup_f("DSCL_O")[0],
lookup_f("DSCL_dO")[0],
amax_dS_ref,
amax_dQ_ref,
amax_dK_ref,
amax_dV_ref,
dQ_ref,
dK_ref,
dV_ref);
/// \todo remove once backward mha is ready to execute
if(disable_verification)
return;
checkAmax("AMax_dS", amax_dS_ref);
checkAmax("AMax_dQ", amax_dQ_ref);
checkAmax("AMax_dK", amax_dK_ref);
checkAmax("AMax_dV", amax_dV_ref);
checkTensor("dQ", dQ_ref);
checkTensor("dK", dK_ref);
checkTensor("dV", dV_ref);
}
};
} // end namespace mha_graph_test
//
using namespace mha_graph_test;
TEST_P(GPU_MhaBwdGraphTest_FP32, MhaBwdGraph) { Run(MhaDir::Bwd); }
INSTANTIATE_TEST_SUITE_P(Smoke,
GPU_MhaBwdGraphTest_FP32,
testing::Combine(testing::ValuesIn(std::vector<std::size_t>{2}), // n
testing::ValuesIn(std::vector<std::size_t>{8}), // h
testing::ValuesIn(std::vector<std::size_t>{4, 64}), // s
testing::ValuesIn(std::vector<std::size_t>{16}), // d
testing::ValuesIn({0.0f, 0.5f}) // mProbDropout
));
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