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Commit e87ddb0e authored by letaoqin's avatar letaoqin
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Merge branch 'mha-train-develop' into mha-train-develop-bias-shfl

parents 13129772 5ff2d646
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Showing with 1537 additions and 918 deletions
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_backward_v2.cpp
View file @ e87ddb0e
......@@ -269,14 +269,15 @@ int run(int argc, char* argv[])
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t M = 512;
ck::index_t N = 512;
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4;
ck::index_t G1 = 6;
// y_g0_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
// y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
ck::index_t M = 512;
ck::index_t N = 512;
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4;
ck::index_t G1Q = 6; // h_q
ck::index_t G1KV = 6; // h_kv
bool input_permute = false;
bool output_permute = false;
......@@ -295,32 +296,33 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 13)
else if(argc == 14)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1Q = std::stoi(argv[9]);
G1KV = std::stoi(argv[10]);
p_drop = std::stof(argv[10]);
p_drop = std::stof(argv[11]);
input_permute = std::stoi(argv[11]);
output_permute = std::stoi(argv[12]);
input_permute = std::stoi(argv[12]);
output_permute = std::stoi(argv[13]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4 to 10: M, N, K, O, G0, G1Q, G1KV\n");
printf("arg11: p_drop\n");
printf("arg12 to 13: input / output permute\n");
exit(0);
}
......@@ -337,7 +339,8 @@ int run(int argc, char* argv[])
std::cout << "K: " << K << std::endl;
std::cout << "O: " << O << std::endl;
std::cout << "G0: " << G0 << std::endl;
std::cout << "G1: " << G1 << std::endl;
std::cout << "G1Q: " << G1Q << std::endl;
std::cout << "G1KV: " << G1KV << std::endl;
std::cout << "alpha: " << alpha << std::endl;
std::cout << "input_permute: " << input_permute << std::endl;
std::cout << "output_permute: " << output_permute << std::endl;
......@@ -345,45 +348,57 @@ int run(int argc, char* argv[])
std::cout << "seed: " << seed << std::endl;
std::cout << "offset: " << offset << std::endl;
const ck::index_t BatchCount = G0 * G1;
const ck::index_t BatchCount = G0 * G1Q;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // Q layout [G0, G1Q, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1} // K layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O} // V layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // Y layout [G0, G1Q, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1} // KGrad layout [G0, N, G1Q, K]
: std::vector<ck::index_t>{G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]
std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
std::vector<ck::index_t> vgrad_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O} // VGrad layout [G0, N, G1Q, O]
: std::vector<ck::index_t>{G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward pass
// Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
Tensor<InputDataType> q_gs_ms_ks(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<InputDataType> k_gs_ns_ks(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
......@@ -392,6 +407,8 @@ int run(int argc, char* argv[])
Tensor<InputDataType> y_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks(kgrad_gs_ns_ks_lengths, kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns(vgrad_gs_os_ns_lengths, vgrad_gs_os_ns_strides);
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
std::cout << "k_gs_ns_ks: " << k_gs_ns_ks.mDesc << std::endl;
......@@ -399,6 +416,8 @@ int run(int argc, char* argv[])
std::cout << "v_gs_os_ns: " << v_gs_os_ns.mDesc << std::endl;
std::cout << "y_gs_ms_os: " << y_gs_ms_os.mDesc << std::endl;
std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
std::cout << "kgrad_gs_ns_ks: " << kgrad_gs_ns_ks.mDesc << std::endl;
std::cout << "vgrad_gs_os_ns: " << vgrad_gs_os_ns.mDesc << std::endl;
z_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
switch(init_method)
......@@ -432,14 +451,14 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1q, m, o]
// dO dot O = [0; 1; 2; ...]
break;
case 6:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -452,7 +471,8 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -474,11 +494,21 @@ int run(int argc, char* argv[])
Tensor<LSEDataType> lse_g_m({BatchCount, M});
q_gs_ms_ks.ForEach(
[&](auto& self, auto idx) { q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
k_gs_ns_ks.ForEach(
[&](auto& self, auto idx) { k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
v_gs_os_ns.ForEach(
[&](auto& self, auto idx) { v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx); });
[&](auto& self, auto idx) { q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx); });
k_g_n_k.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
});
v_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
// qkv gradients have the same descriptor as with qkv
DeviceMem q_device_buf(sizeof(InputDataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
......@@ -488,8 +518,8 @@ int run(int argc, char* argv[])
DeviceMem y_device_buf(sizeof(InputDataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
DeviceMem lse_device_buf(sizeof(LSEDataType) * lse_gs_ms.mDesc.GetElementSpaceSize());
DeviceMem qgrad_device_buf(sizeof(OutputDataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem kgrad_device_buf(sizeof(OutputDataType) * k_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem vgrad_device_buf(sizeof(OutputDataType) * v_gs_os_ns.mDesc.GetElementSpaceSize());
DeviceMem kgrad_device_buf(sizeof(OutputDataType) * kgrad_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem vgrad_device_buf(sizeof(OutputDataType) * vgrad_gs_os_ns.mDesc.GetElementSpaceSize());
DeviceMem ygrad_device_buf(sizeof(InputDataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
q_device_buf.ToDevice(q_gs_ms_ks.mData.data());
......@@ -513,8 +543,8 @@ int run(int argc, char* argv[])
static_cast<OutputDataType*>(qgrad_device_buf.GetDeviceBuffer()),
static_cast<OutputDataType*>(kgrad_device_buf.GetDeviceBuffer()),
static_cast<OutputDataType*>(vgrad_device_buf.GetDeviceBuffer()),
nullptr, // p_acc0_bias;
nullptr, // p_acc1_bias;
nullptr, // p_acc0_bias;
nullptr, // p_acc1_bias;
nullptr,
nullptr,
q_gs_ms_ks_lengths,
......@@ -528,6 +558,10 @@ int run(int argc, char* argv[])
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
......@@ -560,8 +594,8 @@ int run(int argc, char* argv[])
static_cast<OutputDataType*>(qgrad_device_buf.GetDeviceBuffer()),
static_cast<OutputDataType*>(kgrad_device_buf.GetDeviceBuffer()),
static_cast<OutputDataType*>(vgrad_device_buf.GetDeviceBuffer()),
nullptr, // p_acc0_bias;
nullptr, // p_acc1_bias;
nullptr, // p_acc0_bias;
nullptr, // p_acc1_bias;
nullptr,
nullptr,
q_gs_ms_ks_lengths,
......@@ -575,6 +609,10 @@ int run(int argc, char* argv[])
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
......@@ -614,7 +652,7 @@ int run(int argc, char* argv[])
// copy z matirx data form device
z_device_buf.FromDevice(z_gs_ms_ns.mData.data());
z_gs_ms_ns.ForEach(
[&](auto& self, auto idx) { z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
[&](auto& self, auto idx) { z_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx); });
// std::cout << "z_g_m_n ref:\n" << z_g_m_n;
bool pass = true;
......@@ -634,10 +672,10 @@ int run(int argc, char* argv[])
p_dropout_in_uint8_t,
rp_dropout);
y_gs_ms_os.ForEach([&](auto& self, auto idx) {
self(idx) = y_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]);
self(idx) = y_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]);
});
lse_gs_ms.ForEach(
[&](auto& self, auto idx) { self(idx) = lse_g_m(idx[0] * G1 + idx[1], idx[2]); });
[&](auto& self, auto idx) { self(idx) = lse_g_m(idx[0] * G1Q + idx[1], idx[2]); });
y_device_buf.ToDevice(y_gs_ms_os.mData.data());
lse_device_buf.ToDevice(lse_gs_ms.mData.data());
......@@ -655,7 +693,7 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_dot_y_g_m({BatchCount, M});
ygrad_gs_ms_os.ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
#if PRINT_HOST
......@@ -757,12 +795,16 @@ int run(int argc, char* argv[])
#endif
Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides);
Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides);
qgrad_device_buf.FromDevice(qgrad_gs_ms_ks_device_result.mData.data());
kgrad_device_buf.FromDevice(kgrad_gs_ns_ks_device_result.mData.data());
......@@ -770,26 +812,26 @@ int run(int argc, char* argv[])
// permute
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
......
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_backward_v3.cpp
View file @ e87ddb0e
......@@ -270,14 +270,15 @@ int run(int argc, char* argv[])
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t M = 512;
ck::index_t N = 512;
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4;
ck::index_t G1 = 6;
// y_g0_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
// y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
ck::index_t M = 512;
ck::index_t N = 512;
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4;
ck::index_t G1Q = 6; // h_q
ck::index_t G1KV = 6; // h_kv
bool input_permute = false;
bool output_permute = false;
......@@ -296,32 +297,33 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 13)
else if(argc == 14)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1Q = std::stoi(argv[9]);
G1KV = std::stoi(argv[10]);
p_drop = std::stof(argv[10]);
p_drop = std::stof(argv[11]);
input_permute = std::stoi(argv[11]);
output_permute = std::stoi(argv[12]);
input_permute = std::stoi(argv[12]);
output_permute = std::stoi(argv[13]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4 to 10: M, N, K, O, G0, G1Q, G1KV\n");
printf("arg11: p_drop\n");
printf("arg12 to 13: input / output permute\n");
exit(0);
}
......@@ -338,7 +340,8 @@ int run(int argc, char* argv[])
std::cout << "K: " << K << std::endl;
std::cout << "O: " << O << std::endl;
std::cout << "G0: " << G0 << std::endl;
std::cout << "G1: " << G1 << std::endl;
std::cout << "G1Q: " << G1Q << std::endl;
std::cout << "G1KV: " << G1KV << std::endl;
std::cout << "alpha: " << alpha << std::endl;
std::cout << "input_permute: " << input_permute << std::endl;
std::cout << "output_permute: " << output_permute << std::endl;
......@@ -346,45 +349,57 @@ int run(int argc, char* argv[])
std::cout << "seed: " << seed << std::endl;
std::cout << "offset: " << offset << std::endl;
const ck::index_t BatchCount = G0 * G1;
const ck::index_t BatchCount = G0 * G1Q;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // Q layout [G0, G1Q, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1} // K layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O} // V layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // Y layout [G0, G1Q, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1} // KGrad layout [G0, N, G1Q, K]
: std::vector<ck::index_t>{G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]
std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
std::vector<ck::index_t> vgrad_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O} // VGrad layout [G0, N, G1Q, O]
: std::vector<ck::index_t>{G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward pass
// Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
Tensor<InputDataType> q_gs_ms_ks(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<InputDataType> k_gs_ns_ks(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
......@@ -394,6 +409,8 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<DDataType> d_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks(kgrad_gs_ns_ks_lengths, kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns(vgrad_gs_os_ns_lengths, vgrad_gs_os_ns_strides);
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
std::cout << "k_gs_ns_ks: " << k_gs_ns_ks.mDesc << std::endl;
......@@ -402,6 +419,8 @@ int run(int argc, char* argv[])
std::cout << "y_gs_ms_os: " << y_gs_ms_os.mDesc << std::endl;
std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
std::cout << "d_gs_ms_os: " << d_gs_ms.mDesc << std::endl;
std::cout << "kgrad_gs_ns_ks: " << kgrad_gs_ns_ks.mDesc << std::endl;
std::cout << "vgrad_gs_os_ns: " << vgrad_gs_os_ns.mDesc << std::endl;
z_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
switch(init_method)
......@@ -435,14 +454,14 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1q, m, o]
// dO dot O = [0; 1; 2; ...]
break;
case 6:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -455,7 +474,8 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -477,11 +497,21 @@ int run(int argc, char* argv[])
Tensor<LSEDataType> lse_g_m({BatchCount, M});
q_gs_ms_ks.ForEach(
[&](auto& self, auto idx) { q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
k_gs_ns_ks.ForEach(
[&](auto& self, auto idx) { k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
v_gs_os_ns.ForEach(
[&](auto& self, auto idx) { v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx); });
[&](auto& self, auto idx) { q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx); });
k_g_n_k.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
});
v_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
// qkv gradients have the same descriptor as with qkv
DeviceMem q_device_buf(sizeof(InputDataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
......@@ -491,8 +521,8 @@ int run(int argc, char* argv[])
DeviceMem y_device_buf(sizeof(InputDataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
DeviceMem lse_device_buf(sizeof(LSEDataType) * lse_gs_ms.mDesc.GetElementSpaceSize());
DeviceMem qgrad_device_buf(sizeof(OutputDataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem kgrad_device_buf(sizeof(OutputDataType) * k_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem vgrad_device_buf(sizeof(OutputDataType) * v_gs_os_ns.mDesc.GetElementSpaceSize());
DeviceMem kgrad_device_buf(sizeof(OutputDataType) * kgrad_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem vgrad_device_buf(sizeof(OutputDataType) * vgrad_gs_os_ns.mDesc.GetElementSpaceSize());
DeviceMem ygrad_device_buf(sizeof(InputDataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DDataType) * d_gs_ms.mDesc.GetElementSpaceSize());
......@@ -533,6 +563,10 @@ int run(int argc, char* argv[])
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
......@@ -581,6 +615,10 @@ int run(int argc, char* argv[])
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
......@@ -620,7 +658,7 @@ int run(int argc, char* argv[])
// copy z matirx data form device
z_device_buf.FromDevice(z_gs_ms_ns.mData.data());
z_gs_ms_ns.ForEach(
[&](auto& self, auto idx) { z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
[&](auto& self, auto idx) { z_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx); });
// std::cout << "z_g_m_n ref:\n" << z_g_m_n;
bool pass = true;
......@@ -640,10 +678,10 @@ int run(int argc, char* argv[])
p_dropout_in_uint8_t,
rp_dropout);
y_gs_ms_os.ForEach([&](auto& self, auto idx) {
self(idx) = y_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]);
self(idx) = y_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]);
});
lse_gs_ms.ForEach(
[&](auto& self, auto idx) { self(idx) = lse_g_m(idx[0] * G1 + idx[1], idx[2]); });
[&](auto& self, auto idx) { self(idx) = lse_g_m(idx[0] * G1Q + idx[1], idx[2]); });
y_device_buf.ToDevice(y_gs_ms_os.mData.data());
lse_device_buf.ToDevice(lse_gs_ms.mData.data());
......@@ -661,7 +699,7 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_dot_y_g_m({BatchCount, M});
ygrad_gs_ms_os.ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
#if PRINT_HOST
......@@ -763,12 +801,16 @@ int run(int argc, char* argv[])
#endif
Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides);
Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides);
qgrad_device_buf.FromDevice(qgrad_gs_ms_ks_device_result.mData.data());
kgrad_device_buf.FromDevice(kgrad_gs_ns_ks_device_result.mData.data());
......@@ -776,26 +818,26 @@ int run(int argc, char* argv[])
// permute
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
......
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_forward_v2.cpp
View file @ e87ddb0e
......@@ -71,11 +71,10 @@ static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecializatio
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr bool Deterministic = false;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
#if(DIM <= 32)
using DeviceGemmInstance =
......@@ -149,8 +148,7 @@ using DeviceGemmInstance =
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 64)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -223,8 +221,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 128)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -297,8 +294,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#endif
// Ref Gemm0: DataType in, AccDataType out
......
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_train_v2.cpp
View file @ e87ddb0e
......@@ -113,11 +113,11 @@ static constexpr bool Deterministic = false;
#if(DIM <= 32)
// clang-format off
using DeviceGemmInstanceFWD =
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec| Deterministic|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| | |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| | |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 32, 32, 8, 8, 2, 32, 32, 1, 4, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 4, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 2, false, 1, 1, S<1, 64, 1, 4>, 8, 4, MaskingSpec, Deterministic>;
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 32, 32, 8, 8, 2, 32, 32, 1, 4, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 4, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 2, false, 1, 1, S<1, 64, 1, 4>, 8, 4, MaskingSpec>;
using DeviceGemmInstanceBWD =
// ########################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
......@@ -129,11 +129,11 @@ using DeviceGemmInstanceBWD =
#elif(DIM <= 64)
// clang-format off
using DeviceGemmInstanceFWD =
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec| Deterministic|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| | |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| | |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 64, 32, 8, 8, 2, 32, 32, 1, 4, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 4, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 2, S<1, 32, 1, 8>, 8, 4, MaskingSpec, Deterministic>;
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 64, 32, 8, 8, 2, 32, 32, 1, 4, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 4, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 2, S<1, 32, 1, 8>, 8, 4, MaskingSpec>;
using DeviceGemmInstanceBWD =
// ########################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
......@@ -153,11 +153,11 @@ using DeviceGemmInstanceBWD =
#elif(DIM <= 128)
// clang-format off
using DeviceGemmInstanceFWD =
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec| Deterministic|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| | |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| | |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 64, 32, 8, 8, 2, 32, 32, 1, 4, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 4, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 2, S<1, 32, 1, 8>, 8, 4, MaskingSpec, Deterministic>;
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 64, 32, 8, 8, 2, 32, 32, 1, 4, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 4, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 2, S<1, 32, 1, 8>, 8, 4, MaskingSpec>;
using DeviceGemmInstanceBWD =
// ########################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
......@@ -299,14 +299,15 @@ int run(int argc, char* argv[])
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t N = 500; // 512
ck::index_t M = 500; // 512
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4; // 54
ck::index_t G1 = 6; // 16
// y_g0_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
// y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
ck::index_t N = 500; // 512
ck::index_t M = 500; // 512
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4;
ck::index_t G1Q = 6; // h_q
ck::index_t G1KV = 6; // h_kv
bool input_permute = false;
bool output_permute = false;
......@@ -325,32 +326,33 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 13)
else if(argc == 14)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1Q = std::stoi(argv[9]);
G1KV = std::stoi(argv[10]);
p_drop = std::stof(argv[10]);
p_drop = std::stof(argv[11]);
input_permute = std::stoi(argv[11]);
output_permute = std::stoi(argv[12]);
input_permute = std::stoi(argv[12]);
output_permute = std::stoi(argv[13]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4 to 10: M, N, K, O, G0, G1Q, G1KV\n");
printf("arg11: p_drop\n");
printf("arg12 to 13: input / output permute\n");
exit(0);
}
......@@ -367,7 +369,8 @@ int run(int argc, char* argv[])
std::cout << "K: " << K << std::endl;
std::cout << "O: " << O << std::endl;
std::cout << "G0: " << G0 << std::endl;
std::cout << "G1: " << G1 << std::endl;
std::cout << "G1Q: " << G1Q << std::endl;
std::cout << "G1KV: " << G1KV << std::endl;
std::cout << "alpha: " << alpha << std::endl;
std::cout << "input_permute: " << input_permute << std::endl;
std::cout << "output_permute: " << output_permute << std::endl;
......@@ -375,45 +378,57 @@ int run(int argc, char* argv[])
std::cout << "seed: " << seed << std::endl;
std::cout << "offset: " << offset << std::endl;
const ck::index_t BatchCount = G0 * G1;
const ck::index_t BatchCount = G0 * G1Q;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // Q layout [G0, G1Q, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1} // K layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O} // V layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // Y layout [G0, G1Q, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1} // KGrad layout [G0, N, G1Q, K]
: std::vector<ck::index_t>{G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]
std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
std::vector<ck::index_t> vgrad_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O} // VGrad layout [G0, N, G1Q, O]
: std::vector<ck::index_t>{G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward pass
// Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
Tensor<InputDataType> q_gs_ms_ks(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<InputDataType> k_gs_ns_ks(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
......@@ -424,8 +439,10 @@ int run(int argc, char* argv[])
Tensor<InputDataType> y_gs_ms_os_device_result(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms_device_result(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides);
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
std::cout << "k_gs_ns_ks: " << k_gs_ns_ks.mDesc << std::endl;
......@@ -467,14 +484,14 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1q, m, o]
// dO dot O = [0; 1; 2; ...]
break;
case 6:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -487,7 +504,8 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -612,6 +630,10 @@ int run(int argc, char* argv[])
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
......@@ -656,8 +678,10 @@ int run(int argc, char* argv[])
Tensor<InputDataType> y_gs_ms_os_host_result(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms_host_result(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides);
Tensor<InputDataType> q_g_m_k({BatchCount, M, K});
Tensor<InputDataType> k_g_n_k({BatchCount, N, K});
......@@ -760,6 +784,10 @@ int run(int argc, char* argv[])
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
......@@ -793,16 +821,24 @@ int run(int argc, char* argv[])
}
q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
k_gs_ns_ks.ForEach([&](auto& self, auto idx) {
k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
k_g_n_k.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
});
v_gs_os_ns.ForEach([&](auto& self, auto idx) {
v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
v_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
z_fwd_gs_ms_ns.ForEach([&](auto& self, auto idx) {
z_fwd_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_fwd_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
run_attention_fwd_host(q_g_m_k,
......@@ -819,10 +855,10 @@ int run(int argc, char* argv[])
rp_dropout);
ygrad_gs_ms_os.ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
z_bwd_gs_ms_ns.ForEach([&](auto& self, auto idx) {
z_bwd_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_bwd_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
#if PRINT_HOST
......@@ -925,42 +961,42 @@ int run(int argc, char* argv[])
// permute
y_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = y_g_m_o(g, idx[2], idx[3]);
});
lse_gs_ms_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = lse_g_m(g, idx[2]);
});
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
......
example/32_batched_gemm_scale_softmax_gemm/grouped_multihead_attention_backward_v2.cpp
View file @ e87ddb0e
......@@ -268,10 +268,11 @@ int run(int argc, char* argv[])
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
// y_g0_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
// y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
float alpha = 1.f / std::sqrt(DIM);
float p_drop = 0.0;
int h_ratio = 1; // G1Q / G1KV
bool input_permute = true;
bool output_permute = true;
......@@ -289,25 +290,26 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 7)
else if(argc == 8)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
p_drop = std::stof(argv[4]);
p_drop = std::stof(argv[4]);
h_ratio = std::stof(argv[5]);
input_permute = std::stoi(argv[5]);
output_permute = std::stoi(argv[6]);
input_permute = std::stoi(argv[6]);
output_permute = std::stoi(argv[7]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4: p_drop\n");
printf("arg5: h_ratio\n");
printf("arg6 to 7: input / output permute\n");
exit(0);
}
......@@ -367,49 +369,65 @@ int run(int argc, char* argv[])
std::size_t flop = 0, num_byte = 0;
for(std::size_t i = 0; i < group_count; i++)
{
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 4 + 1;
int G1 = rand() % 4 + 1;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 4 + 1;
int G1KV = rand() % 4 + 1;
int G1Q = G1KV * h_ratio;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // Q layout [G0, G1Q, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1}
// K layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O}
// V layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // Y layout [G0, G1Q, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
input_permute ? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1}
// KGrad layout [G0, N, G1Q, K]
: std::vector<ck::index_t>{
G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]
std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
std::vector<ck::index_t> vgrad_gs_os_ns_strides =
input_permute ? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O}
// VGrad layout [G0, N, G1Q, O]
: std::vector<ck::index_t>{
G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward
// pass Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
problem_descs.push_back({
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
......@@ -423,13 +441,17 @@ int run(int argc, char* argv[])
y_gs_ms_os_strides,
lse_gs_ms_lengths,
lse_gs_ms_strides,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_strides},
});
int BatchCount = G0 * G1;
int BatchCount = G0 * G1Q;
flop += (size_t(3) * M * N * K + size_t(2) * M * N * O) * 2 * BatchCount;
// Q/K/V/Y, dQ/dK/dV/dY, LSE
num_byte += (sizeof(InputDataType) * M * K + sizeof(InputDataType) * K * N +
......@@ -446,6 +468,8 @@ int run(int argc, char* argv[])
Tensor<InputDataType> y_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks(kgrad_gs_ns_ks_lengths, kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns(vgrad_gs_os_ns_lengths, vgrad_gs_os_ns_strides);
if(i < 4)
{
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
......@@ -454,6 +478,8 @@ int run(int argc, char* argv[])
std::cout << "v_gs_os_ns: " << v_gs_os_ns.mDesc << std::endl;
std::cout << "y_gs_ms_os: " << y_gs_ms_os.mDesc << std::endl;
std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
std::cout << "kgrad_gs_ns_ks: " << kgrad_gs_ns_ks.mDesc << std::endl;
std::cout << "vgrad_gs_os_ns: " << vgrad_gs_os_ns.mDesc << std::endl;
}
z_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
switch(init_method)
......@@ -487,14 +513,16 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_Sequential<2>{}); // dy[g0, g1q, m, o]
// dO dot O = [0; 1; 2; ...]
break;
case 6:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_Sequential<3>{}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -508,7 +536,7 @@ int run(int argc, char* argv[])
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1, m, o]
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -529,13 +557,21 @@ int run(int argc, char* argv[])
Tensor<InputDataType> p_drop_g_m_n({BatchCount, M, N});
q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
k_gs_ns_ks.ForEach([&](auto& self, auto idx) {
k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
k_g_n_k.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
});
v_gs_os_ns.ForEach([&](auto& self, auto idx) {
v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
v_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
q_g_m_ks.push_back(q_g_m_k);
......@@ -554,6 +590,8 @@ int run(int argc, char* argv[])
z_tensors.push_back(z_gs_ms_ns);
lse_tensors.push_back(lse_gs_ms);
ygrad_tensors.push_back(ygrad_gs_ms_os);
kgrad_tensors.push_back(kgrad_gs_ns_ks);
vgrad_tensors.push_back(vgrad_gs_os_ns);
q_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(InputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
k_tensors_device.emplace_back(
......@@ -568,10 +606,10 @@ int run(int argc, char* argv[])
std::make_unique<DeviceMem>(sizeof(LSEDataType) * lse_gs_ms.GetElementSpaceSize()));
qgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
kgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * k_gs_ns_ks.GetElementSpaceSize()));
vgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * v_gs_os_ns.GetElementSpaceSize()));
kgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(OutputDataType) * kgrad_gs_ns_ks.GetElementSpaceSize()));
vgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(OutputDataType) * vgrad_gs_os_ns.GetElementSpaceSize()));
ygrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(InputDataType) * y_gs_ms_os.GetElementSpaceSize()));
q_tensors_device.back()->ToDevice(q_gs_ms_ks.data());
......@@ -674,11 +712,11 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int G1 = v_tensors[i].GetLengths()[1];
int G1Q = q_tensors[i].GetLengths()[1];
// copy z matirx data form device
z_tensors_device[i]->FromDevice(z_tensors[i].mData.data());
z_tensors[i].ForEach([&](auto& self, auto idx) {
z_g_m_ns[i](idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_g_m_ns[i](idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
run_attention_fwd_host(q_g_m_ks[i],
k_g_n_ks[i],
......@@ -694,11 +732,11 @@ int run(int argc, char* argv[])
rp_dropout);
y_tensors[i].ForEach([&](auto& self, auto idx) {
self(idx) = y_g_m_os[i](idx[0] * G1 + idx[1], idx[2], idx[3]);
self(idx) = y_g_m_os[i](idx[0] * G1Q + idx[1], idx[2], idx[3]);
});
y_tensors_device[i]->ToDevice(y_tensors[i].data());
lse_tensors[i].ForEach([&](auto& self, auto idx) {
self(idx) = lse_g_ms[i](idx[0] * G1 + idx[1], idx[2]);
self(idx) = lse_g_ms[i](idx[0] * G1Q + idx[1], idx[2]);
});
lse_tensors_device[i]->ToDevice(lse_tensors[i].data());
qgrad_tensors_device[i]->SetZero();
......@@ -711,13 +749,13 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int G0 = v_tensors[i].GetLengths()[0];
int G1 = v_tensors[i].GetLengths()[1];
int G0 = q_tensors[i].GetLengths()[0];
int G1Q = q_tensors[i].GetLengths()[1];
int O = v_tensors[i].GetLengths()[2];
int N = v_tensors[i].GetLengths()[3];
int M = q_tensors[i].GetLengths()[2];
int K = q_tensors[i].GetLengths()[3];
int BatchCount = G0 * G1;
int BatchCount = G0 * G1Q;
Tensor<OutputDataType> qgrad_g_m_k({BatchCount, M, K});
Tensor<OutputDataType> kgrad_g_n_k({BatchCount, N, K});
Tensor<OutputDataType> vgrad_g_n_o({BatchCount, N, O});
......@@ -727,7 +765,7 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_g_m_o({BatchCount, M, O});
ygrad_tensors[i].ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
auto ref_gemm0_grad = ReferenceGemm0GradInstance{};
auto ref_gemm0_grad_invoker = ref_gemm0_grad.MakeInvoker();
......@@ -770,43 +808,43 @@ int run(int argc, char* argv[])
Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_tensors[i].GetLengths(),
q_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_tensors[i].GetLengths(),
k_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_tensors[i].GetLengths(),
v_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_tensors[i].GetLengths(),
kgrad_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_tensors[i].GetLengths(),
vgrad_tensors[i].GetStrides());
Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_tensors[i].GetLengths(),
q_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_tensors[i].GetLengths(),
k_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_tensors[i].GetLengths(),
v_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_tensors[i].GetLengths(),
kgrad_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_tensors[i].GetLengths(),
vgrad_tensors[i].GetStrides());
qgrad_tensors_device[i]->FromDevice(qgrad_gs_ms_ks_device_result.data());
kgrad_tensors_device[i]->FromDevice(kgrad_gs_ns_ks_device_result.data());
vgrad_tensors_device[i]->FromDevice(vgrad_gs_os_ns_device_result.data());
// permute
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
......
example/32_batched_gemm_scale_softmax_gemm/grouped_multihead_attention_backward_v3.cpp
View file @ e87ddb0e
......@@ -24,7 +24,7 @@ Kernel outputs:
*/
#define USING_MASK 0
#define DIM 32 // DIM should be a multiple of 8.
#define DIM 128 // DIM should be a multiple of 8.
#include <iostream>
#include <numeric>
......@@ -269,10 +269,11 @@ int run(int argc, char* argv[])
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
// y_g0_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
// y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
float alpha = 1.f / std::sqrt(DIM);
float p_drop = 0.0;
int h_ratio = 1; // G1Q / G1KV
bool input_permute = true;
bool output_permute = true;
......@@ -290,25 +291,26 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 7)
else if(argc == 8)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
p_drop = std::stof(argv[4]);
p_drop = std::stof(argv[4]);
h_ratio = std::stof(argv[5]);
input_permute = std::stoi(argv[5]);
output_permute = std::stoi(argv[6]);
input_permute = std::stoi(argv[6]);
output_permute = std::stoi(argv[7]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4: p_drop\n");
printf("arg5: h_ratio\n");
printf("arg6 to 7: input / output permute\n");
exit(0);
}
......@@ -371,49 +373,65 @@ int run(int argc, char* argv[])
std::size_t flop = 0, num_byte = 0;
for(std::size_t i = 0; i < group_count; i++)
{
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 4 + 1;
int G1 = rand() % 4 + 1;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 4 + 1;
int G1KV = rand() % 4 + 1;
int G1Q = G1KV * h_ratio;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // Q layout [G0, G1Q, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1}
// K layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O}
// V layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // Y layout [G0, G1Q, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
input_permute ? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1}
// KGrad layout [G0, N, G1Q, K]
: std::vector<ck::index_t>{
G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]
std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
std::vector<ck::index_t> vgrad_gs_os_ns_strides =
input_permute ? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O}
// VGrad layout [G0, N, G1Q, O]
: std::vector<ck::index_t>{
G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward
// pass Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
problem_descs.push_back({
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
......@@ -427,13 +445,17 @@ int run(int argc, char* argv[])
y_gs_ms_os_strides,
lse_gs_ms_lengths,
lse_gs_ms_strides,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_strides},
});
int BatchCount = G0 * G1;
int BatchCount = G0 * G1Q;
flop += (size_t(3) * M * N * K + size_t(2) * M * N * O) * 2 * BatchCount;
// Q/K/V/Y, dQ/dK/dV/dY, LSE
num_byte += (sizeof(InputDataType) * M * K + sizeof(InputDataType) * K * N +
......@@ -451,6 +473,8 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<DDataType> d_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks(kgrad_gs_ns_ks_lengths, kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns(vgrad_gs_os_ns_lengths, vgrad_gs_os_ns_strides);
if(i < 4)
{
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
......@@ -460,6 +484,8 @@ int run(int argc, char* argv[])
std::cout << "y_gs_ms_os: " << y_gs_ms_os.mDesc << std::endl;
std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
std::cout << "d_gs_ms_os: " << d_gs_ms.mDesc << std::endl;
std::cout << "kgrad_gs_ns_ks: " << kgrad_gs_ns_ks.mDesc << std::endl;
std::cout << "vgrad_gs_os_ns: " << vgrad_gs_os_ns.mDesc << std::endl;
}
z_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
switch(init_method)
......@@ -493,14 +519,16 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_Sequential<2>{}); // dy[g0, g1q, m, o]
// dO dot O = [0; 1; 2; ...]
break;
case 6:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_Sequential<3>{}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -514,7 +542,7 @@ int run(int argc, char* argv[])
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1, m, o]
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -536,13 +564,21 @@ int run(int argc, char* argv[])
Tensor<InputDataType> p_drop_g_m_n({BatchCount, M, N});
q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
k_gs_ns_ks.ForEach([&](auto& self, auto idx) {
k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
k_g_n_k.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
});
v_gs_os_ns.ForEach([&](auto& self, auto idx) {
v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
v_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
q_g_m_ks.push_back(q_g_m_k);
......@@ -562,6 +598,8 @@ int run(int argc, char* argv[])
z_tensors.push_back(z_gs_ms_ns);
lse_tensors.push_back(lse_gs_ms);
ygrad_tensors.push_back(ygrad_gs_ms_os);
kgrad_tensors.push_back(kgrad_gs_ns_ks);
vgrad_tensors.push_back(vgrad_gs_os_ns);
q_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(InputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
k_tensors_device.emplace_back(
......@@ -578,10 +616,10 @@ int run(int argc, char* argv[])
std::make_unique<DeviceMem>(sizeof(DDataType) * d_gs_ms.GetElementSpaceSize()));
qgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
kgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * k_gs_ns_ks.GetElementSpaceSize()));
vgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * v_gs_os_ns.GetElementSpaceSize()));
kgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(OutputDataType) * kgrad_gs_ns_ks.GetElementSpaceSize()));
vgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(OutputDataType) * vgrad_gs_os_ns.GetElementSpaceSize()));
ygrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(InputDataType) * y_gs_ms_os.GetElementSpaceSize()));
q_tensors_device.back()->ToDevice(q_gs_ms_ks.data());
......@@ -687,11 +725,11 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int G1 = v_tensors[i].GetLengths()[1];
int G1Q = q_tensors[i].GetLengths()[1];
// copy z matirx data form device
z_tensors_device[i]->FromDevice(z_tensors[i].mData.data());
z_tensors[i].ForEach([&](auto& self, auto idx) {
z_g_m_ns[i](idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_g_m_ns[i](idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
run_attention_fwd_host(q_g_m_ks[i],
k_g_n_ks[i],
......@@ -707,11 +745,11 @@ int run(int argc, char* argv[])
rp_dropout);
y_tensors[i].ForEach([&](auto& self, auto idx) {
self(idx) = y_g_m_os[i](idx[0] * G1 + idx[1], idx[2], idx[3]);
self(idx) = y_g_m_os[i](idx[0] * G1Q + idx[1], idx[2], idx[3]);
});
y_tensors_device[i]->ToDevice(y_tensors[i].data());
lse_tensors[i].ForEach([&](auto& self, auto idx) {
self(idx) = lse_g_ms[i](idx[0] * G1 + idx[1], idx[2]);
self(idx) = lse_g_ms[i](idx[0] * G1Q + idx[1], idx[2]);
});
lse_tensors_device[i]->ToDevice(lse_tensors[i].data());
qgrad_tensors_device[i]->SetZero();
......@@ -724,13 +762,13 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int G0 = v_tensors[i].GetLengths()[0];
int G1 = v_tensors[i].GetLengths()[1];
int G0 = q_tensors[i].GetLengths()[0];
int G1Q = q_tensors[i].GetLengths()[1];
int O = v_tensors[i].GetLengths()[2];
int N = v_tensors[i].GetLengths()[3];
int M = q_tensors[i].GetLengths()[2];
int K = q_tensors[i].GetLengths()[3];
int BatchCount = G0 * G1;
int BatchCount = G0 * G1Q;
Tensor<OutputDataType> qgrad_g_m_k({BatchCount, M, K});
Tensor<OutputDataType> kgrad_g_n_k({BatchCount, N, K});
Tensor<OutputDataType> vgrad_g_n_o({BatchCount, N, O});
......@@ -740,7 +778,7 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_g_m_o({BatchCount, M, O});
ygrad_tensors[i].ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
auto ref_gemm0_grad = ReferenceGemm0GradInstance{};
auto ref_gemm0_grad_invoker = ref_gemm0_grad.MakeInvoker();
......@@ -783,43 +821,43 @@ int run(int argc, char* argv[])
Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_tensors[i].GetLengths(),
q_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_tensors[i].GetLengths(),
k_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_tensors[i].GetLengths(),
v_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_tensors[i].GetLengths(),
kgrad_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_tensors[i].GetLengths(),
vgrad_tensors[i].GetStrides());
Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_tensors[i].GetLengths(),
q_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_tensors[i].GetLengths(),
k_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_tensors[i].GetLengths(),
v_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_tensors[i].GetLengths(),
kgrad_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_tensors[i].GetLengths(),
vgrad_tensors[i].GetStrides());
qgrad_tensors_device[i]->FromDevice(qgrad_gs_ms_ks_device_result.data());
kgrad_tensors_device[i]->FromDevice(kgrad_gs_ns_ks_device_result.data());
vgrad_tensors_device[i]->FromDevice(vgrad_gs_os_ns_device_result.data());
// permute
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
......
example/32_batched_gemm_scale_softmax_gemm/grouped_multihead_attention_forward_v2.cpp
View file @ e87ddb0e
......@@ -71,11 +71,10 @@ static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecializatio
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr bool Deterministic = true;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
#if(DIM <= 32)
using DeviceGemmInstance =
......@@ -149,8 +148,7 @@ using DeviceGemmInstance =
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
1,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 64)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -223,8 +221,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
1,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 128)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -297,8 +294,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
1,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#endif
// Ref Gemm0: DataType in, AccDataType out
......
example/32_batched_gemm_scale_softmax_gemm/grouped_multihead_attention_train_v2.cpp
View file @ e87ddb0e
......@@ -112,11 +112,11 @@ static constexpr bool Deterministic = false;
#if(DIM <= 32)
// clang-format off
using DeviceGemmInstanceFWD =
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec| Deterministic|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| | |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| | |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 32, 32, 8, 8, 2, 32, 32, 1, 4, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 2, false, 1, 1, S<1, 64, 1, 4>, 8, 1, MaskingSpec, Deterministic>;
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 32, 32, 8, 8, 2, 32, 32, 1, 4, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 2, false, 1, 1, S<1, 64, 1, 4>, 8, 1, MaskingSpec>;
using DeviceGemmInstanceBWD =
// ########################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
......@@ -128,11 +128,11 @@ using DeviceGemmInstanceBWD =
#elif(DIM <= 64)
// clang-format off
using DeviceGemmInstanceFWD =
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec| Deterministic|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| | |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| | |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 64, 32, 8, 8, 2, 32, 32, 1, 4, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 2, S<1, 32, 1, 8>, 8, 1, MaskingSpec, Deterministic>;
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 64, 32, 8, 8, 2, 32, 32, 1, 4, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 2, S<1, 32, 1, 8>, 8, 1, MaskingSpec>;
using DeviceGemmInstanceBWD =
// ########################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
......@@ -152,11 +152,11 @@ using DeviceGemmInstanceBWD =
#elif(DIM <= 128)
// clang-format off
using DeviceGemmInstanceFWD =
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec| Deterministic|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| | |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| | |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 64, 32, 8, 8, 2, 32, 32, 1, 4, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 2, S<1, 32, 1, 8>, 8, 1, MaskingSpec, Deterministic>;
// #################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| ADataType| BDataType| B1DataType| CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec|
// #################################################################################| | | | | | | | | | | | | | | DataType| DataType| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Specialization| | | | | Prefetch| Size| MPer| NPer| KPer| NPer| KPer| | | | XDL| XDL| MXdl| NXdl| NXdl| Step| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| _NPerBlock| SrcScalar| |
// #################################################################################| | | | | | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | | | | Stage| | Block| Block| Block| Block| Block| | | | | | Per| Per| Per| | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| | PerVector| |
// #################################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Wave| Wave| Wave| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2< NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType, InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType, void, void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp, Scale, QKVElementOp, YElementOp, GemmSpec, TensorSpecQ, TensorSpecK, TensorSpecV, TensorSpecY, 1, 256, 128, 128, 32, 64, 32, 8, 8, 2, 32, 32, 1, 4, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, false, 1, 2, S<1, 32, 1, 8>, 8, 1, MaskingSpec>;
using DeviceGemmInstanceBWD =
// ########################################################################################| NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType| GemmAcc| CShuffle| A| B| Acc| B1| C| GEMM| ATensorSpec| B0TensorSpec| B1TensorSpec| CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
......@@ -298,10 +298,11 @@ int run(int argc, char* argv[])
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
// y_g0_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
// y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
float alpha = 1.f / std::sqrt(DIM);
float p_drop = 0.2;
int h_ratio = 1; // G1Q / G1KV
bool input_permute = true;
bool output_permute = true;
......@@ -319,25 +320,26 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 7)
else if(argc == 8)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
p_drop = std::stof(argv[4]);
p_drop = std::stof(argv[4]);
h_ratio = std::stof(argv[5]);
input_permute = std::stoi(argv[5]);
output_permute = std::stoi(argv[6]);
input_permute = std::stoi(argv[6]);
output_permute = std::stoi(argv[7]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4: p_drop\n");
printf("arg5: h_ratio\n");
printf("arg6 to 7: input / output permute\n");
exit(0);
}
......@@ -407,49 +409,65 @@ int run(int argc, char* argv[])
std::size_t flop_bwd = 0, num_byte_bwd = 0;
for(std::size_t i = 0; i < group_count; i++)
{
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 4 + 1;
int G1 = rand() % 4 + 1;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 4 + 1;
int G1KV = rand() % 4 + 1;
int G1Q = G1KV * h_ratio;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // Q layout [G0, G1Q, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1}
// K layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O}
// V layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // Y layout [G0, G1Q, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
input_permute ? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1}
// KGrad layout [G0, N, G1Q, K]
: std::vector<ck::index_t>{
G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]
std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
std::vector<ck::index_t> vgrad_gs_os_ns_strides =
input_permute ? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O}
// VGrad layout [G0, N, G1Q, O]
: std::vector<ck::index_t>{
G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward
// pass Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
problem_descs_fwd.push_back({
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
......@@ -481,13 +499,17 @@ int run(int argc, char* argv[])
y_gs_ms_os_strides,
lse_gs_ms_lengths,
lse_gs_ms_strides,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_strides},
});
int BatchCount = G0 * G1;
int BatchCount = G0 * G1Q;
flop_fwd += (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
num_byte_fwd += (sizeof(InputDataType) * M * K + sizeof(InputDataType) * K * N +
sizeof(InputDataType) * N * O + sizeof(InputDataType) * M * O) *
......@@ -510,6 +532,8 @@ int run(int argc, char* argv[])
Tensor<InputDataType> y_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks(kgrad_gs_ns_ks_lengths, kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns(vgrad_gs_os_ns_lengths, vgrad_gs_os_ns_strides);
if(i < 4)
{
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
......@@ -518,6 +542,8 @@ int run(int argc, char* argv[])
std::cout << "v_gs_os_ns: " << v_gs_os_ns.mDesc << std::endl;
std::cout << "y_gs_ms_os: " << y_gs_ms_os.mDesc << std::endl;
std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
std::cout << "kgrad_gs_ns_ks: " << kgrad_gs_ns_ks.mDesc << std::endl;
std::cout << "vgrad_gs_os_ns: " << vgrad_gs_os_ns.mDesc << std::endl;
}
z_fwd_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
z_bwd_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
......@@ -552,14 +578,16 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_Sequential<2>{}); // dy[g0, g1q, m, o]
// dO dot O = [0; 1; 2; ...]
break;
case 6:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_Sequential<3>{}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -573,7 +601,7 @@ int run(int argc, char* argv[])
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1, m, o]
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1q, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
......@@ -596,13 +624,21 @@ int run(int argc, char* argv[])
Tensor<InputDataType> p_drop_g_m_n({BatchCount, M, N});
q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
k_gs_ns_ks.ForEach([&](auto& self, auto idx) {
k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
k_g_n_k.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
});
v_gs_os_ns.ForEach([&](auto& self, auto idx) {
v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
v_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
q_g_m_ks.push_back(q_g_m_k);
......@@ -624,6 +660,8 @@ int run(int argc, char* argv[])
z_bwd_tensors.push_back(z_bwd_gs_ms_ns);
lse_tensors.push_back(lse_gs_ms);
ygrad_tensors.push_back(ygrad_gs_ms_os);
kgrad_tensors.push_back(kgrad_gs_ns_ks);
vgrad_tensors.push_back(vgrad_gs_os_ns);
q_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(InputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
......@@ -641,10 +679,10 @@ int run(int argc, char* argv[])
std::make_unique<DeviceMem>(sizeof(LSEDataType) * lse_gs_ms.GetElementSpaceSize()));
qgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
kgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * k_gs_ns_ks.GetElementSpaceSize()));
vgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * v_gs_os_ns.GetElementSpaceSize()));
kgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(OutputDataType) * kgrad_gs_ns_ks.GetElementSpaceSize()));
vgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(OutputDataType) * vgrad_gs_os_ns.GetElementSpaceSize()));
ygrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(InputDataType) * y_gs_ms_os.GetElementSpaceSize()));
......@@ -840,15 +878,15 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int G1 = v_tensors[i].GetLengths()[1];
int G1Q = q_tensors[i].GetLengths()[1];
// copy z matirx data form device
z_fwd_tensors_device[i]->FromDevice(z_fwd_tensors[i].mData.data());
z_fwd_tensors[i].ForEach([&](auto& self, auto idx) {
z_fwd_g_m_ns[i](idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_fwd_g_m_ns[i](idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
z_bwd_tensors_device[i]->FromDevice(z_bwd_tensors[i].mData.data());
z_bwd_tensors[i].ForEach([&](auto& self, auto idx) {
z_bwd_g_m_ns[i](idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_bwd_g_m_ns[i](idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
run_attention_fwd_host(q_g_m_ks[i],
k_g_n_ks[i],
......@@ -863,12 +901,12 @@ int run(int argc, char* argv[])
p_dropout_in_uint8_t,
rp_dropout);
int G0 = v_tensors[i].GetLengths()[0];
int G0 = q_tensors[i].GetLengths()[0];
int O = v_tensors[i].GetLengths()[2];
int N = v_tensors[i].GetLengths()[3];
int M = q_tensors[i].GetLengths()[2];
int K = q_tensors[i].GetLengths()[3];
int BatchCount = G0 * G1;
int BatchCount = G0 * G1Q;
Tensor<OutputDataType> qgrad_g_m_k({BatchCount, M, K});
Tensor<OutputDataType> kgrad_g_n_k({BatchCount, N, K});
Tensor<OutputDataType> vgrad_g_n_o({BatchCount, N, O});
......@@ -878,7 +916,7 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_g_m_o({BatchCount, M, O});
ygrad_tensors[i].ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
auto ref_gemm0_grad = ReferenceGemm0GradInstance{};
auto ref_gemm0_grad_invoker = ref_gemm0_grad.MakeInvoker();
......@@ -921,10 +959,10 @@ int run(int argc, char* argv[])
Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_tensors[i].GetLengths(),
q_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_tensors[i].GetLengths(),
k_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_tensors[i].GetLengths(),
v_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_tensors[i].GetLengths(),
kgrad_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_tensors[i].GetLengths(),
vgrad_tensors[i].GetStrides());
Tensor<InputDataType> y_gs_ms_os_host_result(y_tensors[i].GetLengths(),
y_tensors[i].GetStrides());
Tensor<LSEDataType> lse_gs_ms_host_result(lse_tensors[i].GetLengths(),
......@@ -932,10 +970,10 @@ int run(int argc, char* argv[])
Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_tensors[i].GetLengths(),
q_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_tensors[i].GetLengths(),
k_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_tensors[i].GetLengths(),
v_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_tensors[i].GetLengths(),
kgrad_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_tensors[i].GetLengths(),
vgrad_tensors[i].GetStrides());
Tensor<InputDataType> y_gs_ms_os_device_result(y_tensors[i].GetLengths(),
y_tensors[i].GetStrides());
Tensor<LSEDataType> lse_gs_ms_device_result(lse_tensors[i].GetLengths(),
......@@ -949,42 +987,42 @@ int run(int argc, char* argv[])
// permute
y_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = y_g_m_os[i](g, idx[2], idx[3]);
});
lse_gs_ms_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = lse_g_ms[i](g, idx[2]);
});
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
......
example/32_batched_gemm_scale_softmax_gemm/run_batched_multihead_attention_forward.inc
View file @ e87ddb0e
......@@ -14,11 +14,12 @@ int run(int argc, char* argv[])
ck::index_t K = DIM;
ck::index_t O = DIM;
// Output shape C[G0, M, G1, O]. Batch dim, outer dim, inner dim must match GEMM shape
// C_g0_g1_m_o = reshape(C_g_m_o, [g0, g1, m, o])
// C_g0_m_g1_o = permute(C_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t G0 = 7;
ck::index_t G1 = 13;
// Output shape C[G0, M, G1Q, O]. Batch dim, outer dim, inner dim must match GEMM shape
// C_g0_g1q_m_o = reshape(C_g_m_o, [g0, g1q, m, o])
// C_g0_m_g1q_o = permute(C_g0_g1q_m_o, [0, 2, 1, 3])
ck::index_t G0 = 7;
ck::index_t G1Q = 12; // h_q
ck::index_t G1KV = 12; // h_kv
bool input_permute = false;
bool output_permute = true;
......@@ -37,32 +38,33 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 13)
else if(argc == 14)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1Q = std::stoi(argv[9]);
G1KV = std::stoi(argv[10]);
p_drop = std::stof(argv[10]);
p_drop = std::stof(argv[11]);
input_permute = std::stoi(argv[11]);
output_permute = std::stoi(argv[12]);
input_permute = std::stoi(argv[12]);
output_permute = std::stoi(argv[13]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4 to 10: M, N, K, O, G0, G1Q, G1KV\n");
printf("arg11: p_drop\n");
printf("arg12 to 13: input / output permute\n");
exit(0);
}
......@@ -71,39 +73,39 @@ int run(int argc, char* argv[])
float rp_dropout = 1.0 / p_dropout;
float alpha = 1.f / std::sqrt(K);
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> a_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // A layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // A layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // A layout [G0, G1Q, M, K]
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // B0 layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1} // B0 layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // B0 layout [G0, G1KV, N, K]
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> b1_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // B1 layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O} // B1 layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // B1 layout [G0, G1KV, N, O]
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> c_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // C layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // C layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // C layout [G0, G1Q, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides =
std::vector<ck::index_t>{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t>{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
Tensor<B0DataType> b0_gs_ns_ks(b0_gs_ns_ks_lengths, b0_gs_ns_ks_strides);
......@@ -211,7 +213,7 @@ int run(int argc, char* argv[])
return 0;
}
ck::index_t BatchCount = G0 * G1;
ck::index_t BatchCount = G0 * G1Q;
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
......@@ -276,24 +278,32 @@ int run(int argc, char* argv[])
Tensor<B1DataType> b1_g_n_o({BatchCount, N, O});
Tensor<AccDataType> acc0_g_m_n({BatchCount, M, N}); // scratch object after gemm0
Tensor<ADataType> a1_g_m_n({BatchCount, M, N}); // scratch object after softmax
Tensor<ADataType> a1_g_m_n_drop({G0 * G1, M, N});
Tensor<ADataType> a1_g_m_n_drop({BatchCount, M, N});
Tensor<LSEDataType> lse_g_m_host_result(
{BatchCount, M}); // scratch object after max + ln(sum)
Tensor<ZDataType> z_g_m_n({G0 * G1, M, N});
Tensor<ZDataType> z_g_m_n({BatchCount, M, N});
Tensor<CDataType> c_g_m_o_host_result({BatchCount, M, O}); // scratch object after gemm1
// permute
a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
a_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
a_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
b0_g_k_n(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
b0_g_k_n.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = b0_gs_ns_ks(g0, g1kv, idx[2], idx[1]);
});
b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
b1_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
b1_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = b1_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
z_gs_ms_ns.ForEach([&](auto& self, auto idx) {
z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
// gemm 0
......@@ -340,18 +350,18 @@ int run(int argc, char* argv[])
// permute
c_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = c_g_m_o_host_result(g, idx[2], idx[3]);
});
lse_gs_ms_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = lse_g_m_host_result(g, idx[2]);
});
......
example/32_batched_gemm_scale_softmax_gemm/run_grouped_multihead_attention_forward.inc
View file @ e87ddb0e
......@@ -11,6 +11,7 @@ int run(int argc, char* argv[])
bool output_permute = true;
float p_drop = 0.2;
int h_ratio = 1; // G1Q / G1KV
const unsigned long long seed = 1;
const unsigned long long offset = 0;
......@@ -24,22 +25,25 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 7)
else if(argc == 8)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
p_drop = std::stoi(argv[4]);
input_permute = std::stoi(argv[5]);
output_permute = std::stoi(argv[6]);
h_ratio = std::stof(argv[5]);
input_permute = std::stoi(argv[6]);
output_permute = std::stoi(argv[7]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 5: input / output permute\n");
printf("arg4: p_drop\n");
printf("arg5: h_ratio\n");
printf("arg6 to 7: input / output permute\n");
exit(0);
}
......@@ -60,7 +64,7 @@ int run(int argc, char* argv[])
std::vector<void*> p_z; // for result verification
std::vector<void*> p_z_nullptr; // for time test
std::vector<void*> p_lse;
std::vector<std::vector<int>> g0_g1_m_n_k_o;
std::vector<std::vector<int>> g0_g1q_m_n_k_o;
std::vector<Tensor<ADataType>> a_tensors;
std::vector<Tensor<B0DataType>> b0_tensors;
......@@ -83,48 +87,51 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 3 + 1;
int G1 = rand() % 5 + 1;
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 3 + 1;
int G1KV = rand() % 5 + 1;
int G1Q = G1KV * h_ratio;
g0_g1_m_n_k_o.push_back({G0, G1, M, N, K, O});
g0_g1q_m_n_k_o.push_back({G0, G1Q, M, N, K, O});
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> a_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // A layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // A layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // A layout [G0, G1Q, M, K]
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // B0 layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1}
// B0 layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // B0 layout [G0, G1KV, N, K]
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> b1_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // B1 layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O}
// B1 layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // B1 layout [G0, G1KV, N, O]
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> c_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // C layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // C layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // C layout [G0, G1Q, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides =
std::vector<ck::index_t>{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t>{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
problem_descs.push_back({a_gs_ms_ks_lengths,
a_gs_ms_ks_strides,
......@@ -151,7 +158,7 @@ int run(int argc, char* argv[])
Tensor<ZDataType> z_gs_ms_ns(z_gs_ms_ns_lengths, z_gs_ms_ns_strides);
Tensor<LSEDataType> lse_gs_ms_device_result(lse_gs_ms_lengths, lse_gs_ms_strides);
int Batch = G0 * G1;
int Batch = G0 * G1Q;
flop += (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * Batch;
num_byte += (sizeof(ADataType) * M * K + sizeof(B0DataType) * K * N +
sizeof(B1DataType) * N * O + sizeof(CDataType) * M * O) *
......@@ -308,12 +315,12 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
const int& G0 = g0_g1_m_n_k_o[i][0];
const int& G1 = g0_g1_m_n_k_o[i][1];
const int& M = g0_g1_m_n_k_o[i][2];
const int& N = g0_g1_m_n_k_o[i][3];
const int& K = g0_g1_m_n_k_o[i][4];
const int& O = g0_g1_m_n_k_o[i][5];
const int& G0 = g0_g1q_m_n_k_o[i][0];
const int& G1Q = g0_g1q_m_n_k_o[i][1];
const int& M = g0_g1q_m_n_k_o[i][2];
const int& N = g0_g1q_m_n_k_o[i][3];
const int& K = g0_g1q_m_n_k_o[i][4];
const int& O = g0_g1q_m_n_k_o[i][5];
const auto& c_gs_ms_os_lengths = problem_descs[i].c_gs_ms_os_lengths;
const auto& c_gs_ms_os_strides = problem_descs[i].c_gs_ms_os_strides;
......@@ -334,31 +341,39 @@ int run(int argc, char* argv[])
z_gs_ms_ns_device_buf.FromDevice(z_gs_ms_ns_device_result.mData.data());
lse_gs_ms_device_buf.FromDevice(lse_gs_ms_device_result.mData.data());
Tensor<ADataType> a_g_m_k({G0 * G1, M, K});
Tensor<B0DataType> b0_g_k_n({G0 * G1, K, N});
Tensor<B1DataType> b1_g_n_o({G0 * G1, N, O});
Tensor<AccDataType> acc0_g_m_n({G0 * G1, M, N}); // scratch object after gemm0
Tensor<ADataType> a1_g_m_n({G0 * G1, M, N}); // scratch object after softmax
Tensor<ADataType> a1_g_m_n_drop({G0 * G1, M, N}); // scratch object after softmax
Tensor<CDataType> c_g_m_o_host_result({G0 * G1, M, O}); // scratch object after gemm1
Tensor<ADataType> a_g_m_k({G0 * G1Q, M, K});
Tensor<B0DataType> b0_g_k_n({G0 * G1Q, K, N});
Tensor<B1DataType> b1_g_n_o({G0 * G1Q, N, O});
Tensor<AccDataType> acc0_g_m_n({G0 * G1Q, M, N}); // scratch object after gemm0
Tensor<ADataType> a1_g_m_n({G0 * G1Q, M, N}); // scratch object after softmax
Tensor<ADataType> a1_g_m_n_drop({G0 * G1Q, M, N}); // scratch object after softmax
Tensor<CDataType> c_g_m_o_host_result({G0 * G1Q, M, O}); // scratch object after gemm1
Tensor<CDataType> c_gs_ms_os_host_result(c_gs_ms_os_lengths, c_gs_ms_os_strides);
Tensor<ZDataType> z_g_m_n({G0 * G1, M, N});
Tensor<LSEDataType> lse_g_m_host_result({G0 * G1, M}); // scratch object after gemm1
Tensor<ZDataType> z_g_m_n({G0 * G1Q, M, N});
Tensor<LSEDataType> lse_g_m_host_result({G0 * G1Q, M}); // scratch object after gemm1
Tensor<LSEDataType> lse_gs_ms_host_result(lse_gs_ms_lengths, lse_gs_ms_strides);
// permute
a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
a_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
a_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
b0_g_k_n(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
b0_g_k_n.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = b0_gs_ns_ks(g0, g1kv, idx[2], idx[1]);
});
b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
b1_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
b1_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = b1_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
z_gs_ms_ns_device_result.ForEach([&](auto& self, auto idx) {
z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
// gemm 0
......@@ -408,18 +423,18 @@ int run(int argc, char* argv[])
// permute
c_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = c_g_m_o_host_result(g, idx[2], idx[3]);
});
lse_gs_ms_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = lse_g_m_host_result(g, idx[2]);
});
......
example/52_flash_atten_bias/batched_multihead_attention_bias_backward_v2.cpp
View file @ e87ddb0e
......@@ -273,14 +273,15 @@ int run(int argc, char* argv[])
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t M = 512;
ck::index_t N = 512;
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4;
ck::index_t G1 = 6;
// y_g0_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
// y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
ck::index_t M = 512;
ck::index_t N = 512;
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4;
ck::index_t G1Q = 6; // h_q
ck::index_t G1KV = 6; // h_kv
bool input_permute = false;
bool output_permute = false;
......@@ -299,32 +300,33 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 13)
else if(argc == 14)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1Q = std::stoi(argv[9]);
G1KV = std::stoi(argv[10]);
p_drop = std::stof(argv[10]);
p_drop = std::stof(argv[11]);
input_permute = std::stoi(argv[11]);
output_permute = std::stoi(argv[12]);
input_permute = std::stoi(argv[12]);
output_permute = std::stoi(argv[13]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4 to 10: M, N, K, O, G0, G1Q, G1KV\n");
printf("arg11: p_drop\n");
printf("arg12 to 13: input / output permute\n");
exit(0);
}
......@@ -341,7 +343,8 @@ int run(int argc, char* argv[])
std::cout << "K: " << K << std::endl;
std::cout << "O: " << O << std::endl;
std::cout << "G0: " << G0 << std::endl;
std::cout << "G1: " << G1 << std::endl;
std::cout << "G1Q: " << G1Q << std::endl;
std::cout << "G1KV: " << G1KV << std::endl;
std::cout << "alpha: " << alpha << std::endl;
std::cout << "input_permute: " << input_permute << std::endl;
std::cout << "output_permute: " << output_permute << std::endl;
......@@ -349,51 +352,63 @@ int run(int argc, char* argv[])
std::cout << "seed: " << seed << std::endl;
std::cout << "offset: " << offset << std::endl;
const ck::index_t BatchCount = G0 * G1;
const ck::index_t BatchCount = G0 * G1Q;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // Q layout [G0, G1Q, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1} // K layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O} // V layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // Y layout [G0, G1Q, M, O]
std::vector<ck::index_t> d0_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> d0_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> d0_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // D layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // D layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // D layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // D layout [G0, G1Q, M, N]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1} // KGrad layout [G0, N, G1Q, K]
: std::vector<ck::index_t>{G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]
std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
std::vector<ck::index_t> vgrad_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O} // VGrad layout [G0, N, G1Q, O]
: std::vector<ck::index_t>{G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward pass
// Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
Tensor<InputDataType> q_gs_ms_ks(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<InputDataType> k_gs_ns_ks(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
......@@ -403,6 +418,8 @@ int run(int argc, char* argv[])
Tensor<InputDataType> y_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks(kgrad_gs_ns_ks_lengths, kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns(vgrad_gs_os_ns_lengths, vgrad_gs_os_ns_strides);
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
std::cout << "k_gs_ns_ks: " << k_gs_ns_ks.mDesc << std::endl;
......@@ -411,6 +428,8 @@ int run(int argc, char* argv[])
std::cout << "v_gs_os_ns: " << v_gs_os_ns.mDesc << std::endl;
std::cout << "y_gs_ms_os: " << y_gs_ms_os.mDesc << std::endl;
std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
std::cout << "kgrad_gs_ns_ks: " << kgrad_gs_ns_ks.mDesc << std::endl;
std::cout << "vgrad_gs_os_ns: " << vgrad_gs_os_ns.mDesc << std::endl;
z_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
switch(init_method)
......@@ -448,7 +467,7 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1q, m, o]
d0_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
// dO dot O = [0; 1; 2; ...]
break;
......@@ -456,7 +475,7 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1q, m, o]
d0_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
......@@ -470,7 +489,8 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1}); // dy[g0,g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1q, m, o]
d0_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
......@@ -491,8 +511,8 @@ int run(int argc, char* argv[])
DeviceMem y_device_buf(sizeof(InputDataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
DeviceMem lse_device_buf(sizeof(LSEDataType) * lse_gs_ms.mDesc.GetElementSpaceSize());
DeviceMem qgrad_device_buf(sizeof(OutputDataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem kgrad_device_buf(sizeof(OutputDataType) * k_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem vgrad_device_buf(sizeof(OutputDataType) * v_gs_os_ns.mDesc.GetElementSpaceSize());
DeviceMem kgrad_device_buf(sizeof(OutputDataType) * kgrad_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem vgrad_device_buf(sizeof(OutputDataType) * vgrad_gs_os_ns.mDesc.GetElementSpaceSize());
DeviceMem ygrad_device_buf(sizeof(InputDataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
DeviceMem d0grad_device_buf(sizeof(Acc0BiasDataType) * d0_gs_ms_ns.mDesc.GetElementSpaceSize());
......@@ -533,6 +553,10 @@ int run(int argc, char* argv[])
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
d0_gs_ms_ns_lengths, // acc0_bias_gs_ms_ns_lengths
d0_gs_ms_ns_strides, // acc0_bias_gs_ms_ns_strides
{}, // acc1_bias_gs_ms_os_lengths,
......@@ -580,6 +604,10 @@ int run(int argc, char* argv[])
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
d0_gs_ms_ns_lengths, // acc0_bias_gs_ms_ns_lengths
d0_gs_ms_ns_strides, // acc0_bias_gs_ms_ns_strides
{}, // acc1_bias_gs_ms_os_lengths,
......@@ -624,7 +652,7 @@ int run(int argc, char* argv[])
Tensor<InputDataType> q_g_m_k({BatchCount, M, K});
Tensor<InputDataType> k_g_n_k({BatchCount, N, K});
Tensor<Acc0BiasDataType> d0_g_m_n({G0 * G1, M, N});
Tensor<Acc0BiasDataType> d0_g_m_n({BatchCount, M, N});
Tensor<ZDataType> z_g_m_n({BatchCount, M, N});
Tensor<InputDataType> v_g_n_o({BatchCount, N, O});
Tensor<AccDataType> s_g_m_n({BatchCount, M, N});
......@@ -635,19 +663,27 @@ int run(int argc, char* argv[])
z_device_buf.FromDevice(z_gs_ms_ns.mData.data());
z_gs_ms_ns.ForEach([&](auto& self, auto idx) {
z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
k_gs_ns_ks.ForEach([&](auto& self, auto idx) {
k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
k_g_n_k.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
});
v_gs_os_ns.ForEach([&](auto& self, auto idx) {
v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
v_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
d0_gs_ms_ns.ForEach([&](auto& self, auto idx) {
d0_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
d0_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
// run fwd again for y, cause z_g_m_n update
run_attention_fwd_host(q_g_m_k,
......@@ -664,10 +700,10 @@ int run(int argc, char* argv[])
p_dropout_in_uint8_t,
rp_dropout);
y_gs_ms_os.ForEach([&](auto& self, auto idx) {
self(idx) = y_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]);
self(idx) = y_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]);
});
lse_gs_ms.ForEach(
[&](auto& self, auto idx) { self(idx) = lse_g_m(idx[0] * G1 + idx[1], idx[2]); });
[&](auto& self, auto idx) { self(idx) = lse_g_m(idx[0] * G1Q + idx[1], idx[2]); });
y_device_buf.ToDevice(y_gs_ms_os.mData.data());
lse_device_buf.ToDevice(lse_gs_ms.mData.data());
......@@ -685,7 +721,7 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_dot_y_g_m({BatchCount, M});
ygrad_gs_ms_os.ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
#if PRINT_HOST
......@@ -787,14 +823,18 @@ int run(int argc, char* argv[])
#endif
Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides);
Tensor<Acc0BiasDataType> d0grad_gs_ms_ns_host_result(d0_gs_ms_ns_lengths,
d0_gs_ms_ns_strides);
Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides);
Tensor<Acc0BiasDataType> d0grad_gs_ms_ns_device_result(d0_gs_ms_ns_lengths,
d0_gs_ms_ns_strides);
......@@ -805,35 +845,35 @@ int run(int argc, char* argv[])
// permute
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
d0grad_gs_ms_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = sgrad_g_m_n(g, idx[2], idx[3]);
});
......
example/52_flash_atten_bias/batched_multihead_attention_bias_forward_v2.cpp
View file @ e87ddb0e
......@@ -71,11 +71,10 @@ static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecializatio
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr bool Deterministic = false;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
#if(DIM <= 32)
using DeviceGemmInstance =
......@@ -149,8 +148,7 @@ using DeviceGemmInstance =
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 64)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -223,8 +221,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 128)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -297,8 +294,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#endif
// Ref Gemm0: DataType in, AccDataType out
......
example/52_flash_atten_bias/batched_multihead_attention_bias_forward_v2_zcheck.cpp
View file @ e87ddb0e
......@@ -71,11 +71,10 @@ static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecializatio
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr bool Deterministic = false;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
#if(DIM <= 32)
using DeviceGemmInstance =
......@@ -149,8 +148,7 @@ using DeviceGemmInstance =
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 64)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -223,8 +221,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 128)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -297,8 +294,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#endif
using DeviceDropoutInstance = ck::tensor_operation::device::DeviceBatchedDropout<NumDimG,
......
example/52_flash_atten_bias/grouped_multihead_attention_bias_backward_v2.cpp
View file @ e87ddb0e
......@@ -24,7 +24,7 @@ Kernel outputs:
*/
#define USING_MASK 0
#define DIM 128 // DIM should be a multiple of 8.
#define DIM 64 // DIM should be a multiple of 8.
#include <iostream>
#include <numeric>
......@@ -271,10 +271,11 @@ int run(int argc, char* argv[])
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
// y_g0_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
// y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
float alpha = 1.f / std::sqrt(DIM);
float p_drop = 0.0;
int h_ratio = 1; // G1Q / G1KV
bool input_permute = true;
bool output_permute = true;
......@@ -292,25 +293,26 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 7)
else if(argc == 8)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
p_drop = std::stof(argv[4]);
p_drop = std::stof(argv[4]);
h_ratio = std::stof(argv[5]);
input_permute = std::stoi(argv[5]);
output_permute = std::stoi(argv[6]);
input_permute = std::stoi(argv[6]);
output_permute = std::stoi(argv[7]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4: p_drop\n");
printf("arg5: h_ratio\n");
printf("arg6 to 7: input / output permute\n");
exit(0);
}
......@@ -377,55 +379,71 @@ int run(int argc, char* argv[])
std::size_t flop = 0, num_byte = 0;
for(std::size_t i = 0; i < group_count; i++)
{
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 4 + 1;
int G1 = rand() % 4 + 1;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 4 + 1;
int G1KV = rand() % 4 + 1;
int G1Q = G1KV * h_ratio;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // Q layout [G0, G1Q, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1}
// K layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O}
// V layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // Y layout [G0, G1Q, M, O]
std::vector<ck::index_t> d0_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> d0_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> d0_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // d0 layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // d0 layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // d0 layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // d0 layout [G0, G1Q, M, N]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
input_permute ? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1}
// KGrad layout [G0, N, G1Q, K]
: std::vector<ck::index_t>{
G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]
std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
std::vector<ck::index_t> vgrad_gs_os_ns_strides =
input_permute ? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O}
// VGrad layout [G0, N, G1Q, O]
: std::vector<ck::index_t>{
G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward
// pass Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
problem_descs.push_back({
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
......@@ -439,13 +457,17 @@ int run(int argc, char* argv[])
y_gs_ms_os_strides,
lse_gs_ms_lengths,
lse_gs_ms_strides,
kgrad_gs_ns_ks_lengths,
kgrad_gs_ns_ks_strides,
vgrad_gs_os_ns_lengths,
vgrad_gs_os_ns_strides,
d0_gs_ms_ns_lengths,
d0_gs_ms_ns_strides,
{}, // acc1_bias_gs_ms_os_lengths,
{}, // acc1_bias_gs_ms_os_strides,
});
int BatchCount = G0 * G1;
int BatchCount = G0 * G1Q;
flop += (size_t(3) * M * N * K + size_t(2) * M * N * O) * 2 * BatchCount;
// Q/K/V/Y, dQ/dK/dV/dY, LSE
num_byte +=
......@@ -464,6 +486,8 @@ int run(int argc, char* argv[])
Tensor<InputDataType> y_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
Tensor<OutputDataType> kgrad_gs_ns_ks(kgrad_gs_ns_ks_lengths, kgrad_gs_ns_ks_strides);
Tensor<OutputDataType> vgrad_gs_os_ns(vgrad_gs_os_ns_lengths, vgrad_gs_os_ns_strides);
if(i < 4)
{
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
......@@ -473,6 +497,8 @@ int run(int argc, char* argv[])
std::cout << "v_gs_os_ns: " << v_gs_os_ns.mDesc << std::endl;
std::cout << "y_gs_ms_os: " << y_gs_ms_os.mDesc << std::endl;
std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
std::cout << "kgrad_gs_ns_ks: " << kgrad_gs_ns_ks.mDesc << std::endl;
std::cout << "vgrad_gs_os_ns: " << vgrad_gs_os_ns.mDesc << std::endl;
}
z_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
switch(init_method)
......@@ -510,7 +536,8 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_Sequential<2>{}); // dy[g0, g1q, m, o]
d0_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
// dO dot O = [0; 1; 2; ...]
break;
......@@ -518,7 +545,8 @@ int run(int argc, char* argv[])
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_Sequential<3>{}); // dy[g0, g1q, m, o]
d0_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
......@@ -533,7 +561,7 @@ int run(int argc, char* argv[])
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
ygrad_gs_ms_os.GenerateTensorValue(
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1, m, o]
GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1q, m, o]
d0_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
......@@ -556,16 +584,24 @@ int run(int argc, char* argv[])
Tensor<InputDataType> p_drop_g_m_n({BatchCount, M, N});
q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
k_gs_ns_ks.ForEach([&](auto& self, auto idx) {
k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
k_g_n_k.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
});
d0_gs_ms_ns.ForEach([&](auto& self, auto idx) {
d0_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
d0_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
v_gs_os_ns.ForEach([&](auto& self, auto idx) {
v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
v_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
q_g_m_ks.push_back(q_g_m_k);
......@@ -586,6 +622,8 @@ int run(int argc, char* argv[])
z_tensors.push_back(z_gs_ms_ns);
lse_tensors.push_back(lse_gs_ms);
ygrad_tensors.push_back(ygrad_gs_ms_os);
kgrad_tensors.push_back(kgrad_gs_ns_ks);
vgrad_tensors.push_back(vgrad_gs_os_ns);
q_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(InputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
k_tensors_device.emplace_back(
......@@ -602,12 +640,12 @@ int run(int argc, char* argv[])
std::make_unique<DeviceMem>(sizeof(LSEDataType) * lse_gs_ms.GetElementSpaceSize()));
qgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
kgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * k_gs_ns_ks.GetElementSpaceSize()));
kgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(OutputDataType) * kgrad_gs_ns_ks.GetElementSpaceSize()));
d0grad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(Acc0BiasDataType) * d0_gs_ms_ns.GetElementSpaceSize()));
vgrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(OutputDataType) * v_gs_os_ns.GetElementSpaceSize()));
vgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(OutputDataType) * vgrad_gs_os_ns.GetElementSpaceSize()));
ygrad_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(InputDataType) * y_gs_ms_os.GetElementSpaceSize()));
q_tensors_device.back()->ToDevice(q_gs_ms_ks.data());
......@@ -713,11 +751,11 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int G1 = v_tensors[i].GetLengths()[1];
int G1Q = q_tensors[i].GetLengths()[1];
// copy z matirx data form device
z_tensors_device[i]->FromDevice(z_tensors[i].mData.data());
z_tensors[i].ForEach([&](auto& self, auto idx) {
z_g_m_ns[i](idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_g_m_ns[i](idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
run_attention_fwd_host(q_g_m_ks[i],
k_g_n_ks[i],
......@@ -734,11 +772,11 @@ int run(int argc, char* argv[])
rp_dropout);
y_tensors[i].ForEach([&](auto& self, auto idx) {
self(idx) = y_g_m_os[i](idx[0] * G1 + idx[1], idx[2], idx[3]);
self(idx) = y_g_m_os[i](idx[0] * G1Q + idx[1], idx[2], idx[3]);
});
y_tensors_device[i]->ToDevice(y_tensors[i].data());
lse_tensors[i].ForEach([&](auto& self, auto idx) {
self(idx) = lse_g_ms[i](idx[0] * G1 + idx[1], idx[2]);
self(idx) = lse_g_ms[i](idx[0] * G1Q + idx[1], idx[2]);
});
lse_tensors_device[i]->ToDevice(lse_tensors[i].data());
qgrad_tensors_device[i]->SetZero();
......@@ -752,13 +790,13 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int G0 = v_tensors[i].GetLengths()[0];
int G1 = v_tensors[i].GetLengths()[1];
int G0 = q_tensors[i].GetLengths()[0];
int G1Q = q_tensors[i].GetLengths()[1];
int O = v_tensors[i].GetLengths()[2];
int N = v_tensors[i].GetLengths()[3];
int M = q_tensors[i].GetLengths()[2];
int K = q_tensors[i].GetLengths()[3];
int BatchCount = G0 * G1;
int BatchCount = G0 * G1Q;
Tensor<OutputDataType> qgrad_g_m_k({BatchCount, M, K});
Tensor<OutputDataType> kgrad_g_n_k({BatchCount, N, K});
Tensor<OutputDataType> vgrad_g_n_o({BatchCount, N, O});
......@@ -768,7 +806,7 @@ int run(int argc, char* argv[])
Tensor<InputDataType> ygrad_g_m_o({BatchCount, M, O});
ygrad_tensors[i].ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
auto ref_gemm0_grad = ReferenceGemm0GradInstance{};
auto ref_gemm0_grad_invoker = ref_gemm0_grad.MakeInvoker();
......@@ -814,21 +852,21 @@ int run(int argc, char* argv[])
Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_tensors[i].GetLengths(),
q_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_tensors[i].GetLengths(),
k_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_tensors[i].GetLengths(),
kgrad_tensors[i].GetStrides());
Tensor<Acc0BiasDataType> d0grad_gs_ms_ns_host_result(d0_tensors[i].GetLengths(),
d0_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_tensors[i].GetLengths(),
v_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_tensors[i].GetLengths(),
vgrad_tensors[i].GetStrides());
Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_tensors[i].GetLengths(),
q_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_tensors[i].GetLengths(),
k_tensors[i].GetStrides());
Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_tensors[i].GetLengths(),
kgrad_tensors[i].GetStrides());
Tensor<Acc0BiasDataType> d0grad_gs_ms_ns_device_result(d0_tensors[i].GetLengths(),
d0_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_tensors[i].GetLengths(),
v_tensors[i].GetStrides());
Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_tensors[i].GetLengths(),
vgrad_tensors[i].GetStrides());
qgrad_tensors_device[i]->FromDevice(qgrad_gs_ms_ks_device_result.data());
kgrad_tensors_device[i]->FromDevice(kgrad_gs_ns_ks_device_result.data());
......@@ -836,34 +874,34 @@ int run(int argc, char* argv[])
vgrad_tensors_device[i]->FromDevice(vgrad_gs_os_ns_device_result.data());
// permute
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
d0grad_gs_ms_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = sgrad_g_m_n(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
......
example/52_flash_atten_bias/grouped_multihead_attention_bias_forward_v2.cpp
View file @ e87ddb0e
......@@ -71,11 +71,10 @@ static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecializatio
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr bool Deterministic = false;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
#if(DIM <= 32)
using DeviceGemmInstance =
......@@ -149,8 +148,7 @@ using DeviceGemmInstance =
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
1,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 64)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -223,8 +221,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
1,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#elif(DIM <= 128)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2<
......@@ -297,8 +294,7 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
1,
MaskingSpec, // MaskingSpecialization
Deterministic>;
MaskingSpec>; // MaskingSpecialization
#endif
// Ref Gemm0: DataType in, AccDataType out
......
example/52_flash_atten_bias/run_batched_multihead_attention_bias_forward_v2.inc
View file @ e87ddb0e
......@@ -14,11 +14,12 @@ int run(int argc, char* argv[])
ck::index_t K = DIM;
ck::index_t O = DIM;
// Output shape C[G0, M, G1, O]. Batch dim, outer dim, inner dim must match GEMM shape
// C_g0_g1_m_o = reshape(C_g_m_o, [g0, g1, m, o])
// C_g0_m_g1_o = permute(C_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t G0 = 7;
ck::index_t G1 = 13;
// Output shape C[G0, M, G1Q, O]. Batch dim, outer dim, inner dim must match GEMM shape
// C_g0_g1q_m_o = reshape(C_g_m_o, [g0, g1q, m, o])
// C_g0_m_g1q_o = permute(C_g0_g1q_m_o, [0, 2, 1, 3])
ck::index_t G0 = 7;
ck::index_t G1Q = 12; // h_q
ck::index_t G1KV = 12; // h_kv
bool input_permute = false;
bool output_permute = true;
......@@ -37,32 +38,33 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 13)
else if(argc == 14)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1Q = std::stoi(argv[9]);
G1KV = std::stoi(argv[10]);
p_drop = std::stof(argv[10]);
p_drop = std::stof(argv[11]);
input_permute = std::stoi(argv[11]);
output_permute = std::stoi(argv[12]);
input_permute = std::stoi(argv[12]);
output_permute = std::stoi(argv[13]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
printf("arg4 to 10: M, N, K, O, G0, G1Q, G1KV\n");
printf("arg11: p_drop\n");
printf("arg12 to 13: input / output permute\n");
exit(0);
}
......@@ -71,45 +73,45 @@ int run(int argc, char* argv[])
float rp_dropout = 1.0 / p_dropout;
float alpha = 1.f / std::sqrt(K);
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> a_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // A layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // A layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // A layout [G0, G1Q, M, K]
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // B0 layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1} // B0 layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // B0 layout [G0, G1KV, N, K]
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> b1_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // B1 layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O} // B1 layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // B1 layout [G0, G1KV, N, O]
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> c_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // C layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // C layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // C layout [G0, G1Q, M, O]
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> d_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // D layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // D layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // D layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // D layout [G0, G1Q, M, N]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides =
std::vector<ck::index_t>{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t>{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
Tensor<B0DataType> b0_gs_ns_ks(b0_gs_ns_ks_lengths, b0_gs_ns_ks_strides);
......@@ -224,7 +226,7 @@ int run(int argc, char* argv[])
return 0;
}
ck::index_t BatchCount = G0 * G1;
ck::index_t BatchCount = G0 * G1Q;
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
......@@ -312,29 +314,37 @@ int run(int argc, char* argv[])
Tensor<B1DataType> b1_g_n_o({BatchCount, N, O});
Tensor<AccDataType> acc0_g_m_n({BatchCount, M, N}); // scratch object after gemm0
Tensor<ADataType> a1_g_m_n({BatchCount, M, N}); // scratch object after softmax
Tensor<ADataType> a1_g_m_n_drop({G0 * G1, M, N});
Tensor<ADataType> a1_g_m_n_drop({BatchCount, M, N});
Tensor<LSEDataType> lse_g_m_host_result(
{BatchCount, M}); // scratch object after max + ln(sum)
Tensor<Acc0BiasDataType> d_g_m_n({G0 * G1, M, N});
Tensor<ZDataType> z_g_m_n({G0 * G1, M, N});
Tensor<Acc0BiasDataType> d_g_m_n({BatchCount, M, N});
Tensor<ZDataType> z_g_m_n({BatchCount, M, N});
Tensor<CDataType> c_g_m_o_host_result({BatchCount, M, O}); // scratch object after gemm1
// permute
a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
a_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
a_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
b0_g_k_n(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
b0_g_k_n.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = b0_gs_ns_ks(g0, g1kv, idx[2], idx[1]);
});
b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
b1_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
b1_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / (G1Q / G1KV);
self(idx) = b1_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
d_gs_ms_ns.ForEach([&](auto& self, auto idx) {
d_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
d_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
z_gs_ms_ns.ForEach([&](auto& self, auto idx) {
z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
// gemm 0
......@@ -384,18 +394,18 @@ int run(int argc, char* argv[])
// permute
c_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = c_g_m_o_host_result(g, idx[2], idx[3]);
});
lse_gs_ms_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = lse_g_m_host_result(g, idx[2]);
});
......
example/52_flash_atten_bias/run_grouped_multihead_attention_bias_forward_v2.inc
View file @ e87ddb0e
......@@ -11,6 +11,7 @@ int run(int argc, char* argv[])
bool output_permute = true;
float p_drop = 0.2;
int h_ratio = 1; // G1Q / G1KV
const unsigned long long seed = 1;
const unsigned long long offset = 0;
......@@ -24,22 +25,25 @@ int run(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 7)
else if(argc == 8)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
p_drop = std::stoi(argv[4]);
input_permute = std::stoi(argv[5]);
output_permute = std::stoi(argv[6]);
h_ratio = std::stof(argv[5]);
input_permute = std::stoi(argv[6]);
output_permute = std::stoi(argv[7]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 5: input / output permute\n");
printf("arg4: p_drop\n");
printf("arg5: h_ratio\n");
printf("arg6 to 7: input / output permute\n");
exit(0);
}
......@@ -61,7 +65,7 @@ int run(int argc, char* argv[])
std::vector<void*> p_z; // for result verification
std::vector<void*> p_z_nullptr; // for time test
std::vector<void*> p_lse;
std::vector<std::vector<int>> g0_g1_m_n_k_o;
std::vector<std::vector<int>> g0_g1q_m_n_k_o;
std::vector<Tensor<ADataType>> a_tensors;
std::vector<Tensor<B0DataType>> b0_tensors;
......@@ -86,54 +90,57 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 3 + 1;
int G1 = rand() % 5 + 1;
int M = 128 * (rand() % 8) + (rand() % 128);
int N = 128 * (rand() % 8) + (rand() % 128);
int K = DIM;
int O = DIM;
int G0 = rand() % 3 + 1;
int G1KV = rand() % 5 + 1;
int G1Q = G1KV * h_ratio;
g0_g1_m_n_k_o.push_back({G0, G1, M, N, K, O});
g0_g1q_m_n_k_o.push_back({G0, G1Q, M, N, K, O});
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1Q, M, K};
std::vector<ck::index_t> a_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // A layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]
? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // A layout [G0, M, G1Q, K]
: std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1}; // A layout [G0, G1Q, M, K]
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1KV, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // B0 layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]
? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1}
// B0 layout [G0, N, G1KV, K]
: std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // B0 layout [G0, G1KV, N, K]
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1KV, O, N};
std::vector<ck::index_t> b1_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // B1 layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]
? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O}
// B1 layout [G0, N, G1KV, O]
: std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // B1 layout [G0, G1KV, N, O]
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1Q, M, O};
std::vector<ck::index_t> c_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // C layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]
? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // C layout [G0, M, G1Q, O]
: std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1}; // C layout [G0, G1Q, M, O]
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> d_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // D layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // D layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // D layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // D layout [G0, G1Q, M, N]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
: std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1}; // Z layout [G0, G1Q, M, N]
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1Q, M};
std::vector<ck::index_t> lse_gs_ms_strides =
std::vector<ck::index_t>{G1 * M, M, 1}; // LSE layout [G0, G1, M]
std::vector<ck::index_t>{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
problem_descs.push_back({a_gs_ms_ks_lengths,
a_gs_ms_ks_strides,
......@@ -161,7 +168,7 @@ int run(int argc, char* argv[])
Tensor<ZDataType> z_gs_ms_ns(z_gs_ms_ns_lengths, z_gs_ms_ns_strides);
Tensor<LSEDataType> lse_gs_ms_device_result(lse_gs_ms_lengths, lse_gs_ms_strides);
int Batch = G0 * G1;
int Batch = G0 * G1Q;
flop += (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * Batch;
num_byte +=
(sizeof(ADataType) * M * K + sizeof(B0DataType) * K * N + sizeof(B1DataType) * N * O +
......@@ -351,12 +358,12 @@ int run(int argc, char* argv[])
for(std::size_t i = 0; i < group_count; i++)
{
const int& G0 = g0_g1_m_n_k_o[i][0];
const int& G1 = g0_g1_m_n_k_o[i][1];
const int& M = g0_g1_m_n_k_o[i][2];
const int& N = g0_g1_m_n_k_o[i][3];
const int& K = g0_g1_m_n_k_o[i][4];
const int& O = g0_g1_m_n_k_o[i][5];
const int& G0 = g0_g1q_m_n_k_o[i][0];
const int& G1Q = g0_g1q_m_n_k_o[i][1];
const int& M = g0_g1q_m_n_k_o[i][2];
const int& N = g0_g1q_m_n_k_o[i][3];
const int& K = g0_g1q_m_n_k_o[i][4];
const int& O = g0_g1q_m_n_k_o[i][5];
const auto& c_gs_ms_os_lengths = problem_descs[i].c_gs_ms_os_lengths;
const auto& c_gs_ms_os_strides = problem_descs[i].c_gs_ms_os_strides;
......@@ -378,36 +385,43 @@ int run(int argc, char* argv[])
z_gs_ms_ns_device_buf.FromDevice(z_gs_ms_ns_device_result.mData.data());
lse_gs_ms_device_buf.FromDevice(lse_gs_ms_device_result.mData.data());
Tensor<ADataType> a_g_m_k({G0 * G1, M, K});
Tensor<B0DataType> b0_g_k_n({G0 * G1, K, N});
Tensor<B1DataType> b1_g_n_o({G0 * G1, N, O});
Tensor<AccDataType> acc0_g_m_n({G0 * G1, M, N}); // scratch object after gemm0
Tensor<Acc0BiasDataType> d_g_m_n({G0 * G1, M, N});
Tensor<ADataType> a1_g_m_n({G0 * G1, M, N}); // scratch object after softmax
Tensor<ADataType> a1_g_m_n_drop({G0 * G1, M, N}); // scratch object after softmax
Tensor<CDataType> c_g_m_o_host_result({G0 * G1, M, O}); // scratch object after gemm1
Tensor<ADataType> a_g_m_k({G0 * G1Q, M, K});
Tensor<B0DataType> b0_g_k_n({G0 * G1Q, K, N});
Tensor<B1DataType> b1_g_n_o({G0 * G1Q, N, O});
Tensor<AccDataType> acc0_g_m_n({G0 * G1Q, M, N}); // scratch object after gemm0
Tensor<Acc0BiasDataType> d_g_m_n({G0 * G1Q, M, N});
Tensor<ADataType> a1_g_m_n({G0 * G1Q, M, N}); // scratch object after softmax
Tensor<ADataType> a1_g_m_n_drop({G0 * G1Q, M, N}); // scratch object after softmax
Tensor<CDataType> c_g_m_o_host_result({G0 * G1Q, M, O}); // scratch object after gemm1
Tensor<CDataType> c_gs_ms_os_host_result(c_gs_ms_os_lengths, c_gs_ms_os_strides);
Tensor<ZDataType> z_g_m_n({G0 * G1, M, N});
Tensor<LSEDataType> lse_g_m_host_result({G0 * G1, M}); // scratch object after gemm1
Tensor<ZDataType> z_g_m_n({G0 * G1Q, M, N});
Tensor<LSEDataType> lse_g_m_host_result({G0 * G1Q, M}); // scratch object after gemm1
Tensor<LSEDataType> lse_gs_ms_host_result(lse_gs_ms_lengths, lse_gs_ms_strides);
// permute
a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
a_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
a_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
b0_g_k_n(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
});
b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
b1_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
b0_g_k_n.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = b0_gs_ns_ks(g0, g1kv, idx[2], idx[1]);
});
b1_g_n_o.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0] / G1Q;
const size_t& g1q = idx[0] % G1Q;
const size_t& g1kv = g1q / h_ratio;
self(idx) = b1_gs_os_ns(g0, g1kv, idx[2], idx[1]);
});
d_gs_ms_ns.ForEach([&](auto& self, auto idx) {
d_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
d_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
z_gs_ms_ns_device_result.ForEach([&](auto& self, auto idx) {
z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
z_g_m_n(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
});
// gemm 0
......@@ -461,18 +475,18 @@ int run(int argc, char* argv[])
// permute
c_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = c_g_m_o_host_result(g, idx[2], idx[3]);
});
lse_gs_ms_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t& g0 = idx[0];
const size_t& g1q = idx[1];
const size_t g = g0 * G1 + g1;
const size_t g = g0 * G1Q + g1q;
self(idx) = lse_g_m_host_result(g, idx[2]);
});
......
include/ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_light_v1.hpp
View file @ e87ddb0e
......@@ -132,14 +132,17 @@ __global__ void
const CElementwiseOperation c_element_op,
const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
const BGridDesc_BK0_N_BK1 bgrad_grid_desc_bk0_n_bk1,
const D0GridDescriptor_M0_N0_M1_M2_N1_M3 d0_grid_desc_m0_n0_m1_m2_n1_m3,
const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
const B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1,
const B1GridDesc_BK0_N_BK1 b1grad_grid_desc_bk0_n_bk1,
const LSEGridDescriptor_M lse_grid_desc_m,
const YGradGridDesc_O0_M_O1 ygrad_grid_desc_o0_m_o1,
const Block2CTileMap block_2_ctile_map,
const index_t batch_count,
const index_t h_ratio,
const index_t nblock,
const ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch,
const C0MatrixMask c0_matrix_mask,
......@@ -155,21 +158,26 @@ __global__ void
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const index_t gkv_idx = __builtin_amdgcn_readfirstlane(g_idx / h_ratio);
// NOTE: assumes QKVY has the same layout as dQ/dK/dV/dY therefore being able to reuse batch
// offsets
const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(g_idx)));
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(gkv_idx)));
const long_index_t z_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetZBasePtr(g_idx)));
const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(g_idx)));
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(gkv_idx)));
const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));
const long_index_t lse_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetLSEBasePtr(g_idx)));
const long_index_t bgrad_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBGradBasePtr(g_idx)));
const long_index_t b1grad_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1GradBasePtr(g_idx)));
ck::philox ph(seed, 0, offset);
ZDataType* z_matrix_ptr = (p_z_grid == nullptr ? nullptr : p_z_grid + z_batch_offset);
......@@ -205,9 +213,9 @@ __global__ void
p_d_grid + lse_batch_offset,
p_ygrad_grid + c_batch_offset,
p_qgrad_grid + a_batch_offset,
p_kgrad_grid + b_batch_offset,
p_kgrad_grid + bgrad_batch_offset,
tmp_p_d0grad_grid,
p_vgrad_grid + b1_batch_offset,
p_vgrad_grid + b1grad_batch_offset,
p_shared,
a_element_op,
b_element_op,
......@@ -216,9 +224,11 @@ __global__ void
c_element_op,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
bgrad_grid_desc_bk0_n_bk1,
d0_grid_desc_m0_n0_m1_m2_n1_m3,
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
b1_grid_desc_bk0_n_bk1,
b1grad_grid_desc_bk0_n_bk1,
lse_grid_desc_m,
ygrad_grid_desc_o0_m_o1,
block_2_ctile_map,
......@@ -242,9 +252,9 @@ __global__ void
p_d_grid + lse_batch_offset,
p_ygrad_grid + c_batch_offset,
p_qgrad_grid + a_batch_offset,
p_kgrad_grid + b_batch_offset,
p_kgrad_grid + bgrad_batch_offset,
tmp_p_d0grad_grid,
p_vgrad_grid + b1_batch_offset,
p_vgrad_grid + b1grad_batch_offset,
p_shared,
a_element_op,
b_element_op,
......@@ -253,9 +263,11 @@ __global__ void
c_element_op,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
bgrad_grid_desc_bk0_n_bk1,
d0_grid_desc_m0_n0_m1_m2_n1_m3,
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
b1_grid_desc_bk0_n_bk1,
b1grad_grid_desc_bk0_n_bk1,
lse_grid_desc_m,
ygrad_grid_desc_o0_m_o1,
block_2_ctile_map,
......@@ -286,13 +298,16 @@ __global__ void
ignore = c_element_op;
ignore = a_grid_desc_ak0_m_ak1;
ignore = b_grid_desc_bk0_n_bk1;
ignore = bgrad_grid_desc_bk0_n_bk1;
ignore = d0_grid_desc_m0_n0_m1_m2_n1_m3;
ignore = c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3;
ignore = b1_grid_desc_bk0_n_bk1;
ignore = b1grad_grid_desc_bk0_n_bk1;
ignore = lse_grid_desc_m;
ignore = ygrad_grid_desc_o0_m_o1;
ignore = block_2_ctile_map;
ignore = batch_count;
ignore = h_ratio;
ignore = nblock;
ignore = compute_base_ptr_of_batch;
ignore = c0_matrix_mask;
......@@ -695,6 +710,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
const ZGridDesc_G_M_N& z_grid_desc_g_m_n,
const B1GridDesc_G_N_K& b1_grid_desc_g_n_k,
const CGridDesc_G_M_N& c_grid_desc_g_m_n,
const BGridDesc_G_N_K& bgrad_grid_desc_g_n_k,
const B1GridDesc_G_N_K& b1grad_grid_desc_g_n_k,
index_t BatchStrideLSE)
: a_grid_desc_g_m_k_(a_grid_desc_g_m_k),
b_grid_desc_g_n_k_(b_grid_desc_g_n_k),
......@@ -702,6 +719,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
z_grid_desc_g_m_n_(z_grid_desc_g_m_n),
b1_grid_desc_g_n_k_(b1_grid_desc_g_n_k),
c_grid_desc_g_m_n_(c_grid_desc_g_m_n),
bgrad_grid_desc_g_n_k_(bgrad_grid_desc_g_n_k),
b1grad_grid_desc_g_n_k_(b1grad_grid_desc_g_n_k),
BatchStrideLSE_(BatchStrideLSE)
{
}
......@@ -741,6 +760,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
return g_idx * static_cast<long_index_t>(BatchStrideLSE_);
}
__host__ __device__ constexpr long_index_t GetBGradBasePtr(index_t g_idx) const
{
return bgrad_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
}
__host__ __device__ constexpr long_index_t GetB1GradBasePtr(index_t g_idx) const
{
return b1grad_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
}
private:
AGridDesc_G_M_K a_grid_desc_g_m_k_;
BGridDesc_G_N_K b_grid_desc_g_n_k_;
......@@ -748,6 +777,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
ZGridDesc_G_M_N z_grid_desc_g_m_n_;
B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
CGridDesc_G_M_N c_grid_desc_g_m_n_;
BGridDesc_G_N_K bgrad_grid_desc_g_n_k_;
B1GridDesc_G_N_K b1grad_grid_desc_g_n_k_;
index_t BatchStrideLSE_;
};
......@@ -858,6 +889,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -888,9 +923,13 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
DeviceOp::MakeAGridDescriptor_AK0_M_AK1(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
b_grid_desc_bk0_n_bk1_{
DeviceOp::MakeBGridDescriptor_BK0_N_BK1(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
bgrad_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(
bgrad_gs_ns_ks_lengths, bgrad_gs_ns_ks_strides)},
z_grid_desc_m_n_{MakeZGridDescriptor_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
b1_grid_desc_bk0_n_bk1_{DeviceOp::MakeVGridDescriptor_O0_N_O1(
b1_gs_gemm1ns_gemm1ks_lengths, b1_gs_gemm1ns_gemm1ks_strides)},
b1grad_grid_desc_bk0_n_bk1_{DeviceOp::MakeVGridDescriptor_O0_N_O1(
b1grad_gs_gemm1ns_gemm1ks_lengths, b1grad_gs_gemm1ns_gemm1ks_strides)},
y_grid_desc_m_o_{Transform::MakeCGridDescriptor_M_N(c_gs_ms_gemm1ns_lengths,
c_gs_ms_gemm1ns_strides)},
d_y_grid_desc_m_o_{DTransform::MakeCGridDescriptor_M_N(c_gs_ms_gemm1ns_lengths,
......@@ -912,6 +951,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
c_gs_ms_gemm1ns_strides)},
z_grid_desc_g_m_n_{
Transform::MakeC0GridDescriptor_G_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
bgrad_grid_desc_g_n_k_{Transform::MakeB0GridDescriptor_G_N_K(bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides)},
b1grad_grid_desc_g_n_k_{Transform::MakeB1GridDescriptor_G_N_K(
b1grad_gs_gemm1ns_gemm1ks_lengths, b1grad_gs_gemm1ns_gemm1ks_strides)},
block_2_ctile_map_{GridwiseGemm::MakeDefaultBlock2CTileMap(k_grid_desc_n_k_)},
d_block_2_ctile_map_{
GridwiseYDotYGrad::MakeDefaultBlock2CTileMap(d_y_grid_desc_m_o_)},
......@@ -935,6 +978,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
c_mz_gemm1nz_strides_{c_gs_ms_gemm1ns_strides[NumDimG + NumDimM - 1],
c_gs_ms_gemm1ns_strides[NumDimG + NumDimM + NumDimO - 1]},
batch_count_{c_grid_desc_g_m_n_.GetLength(I0)},
h_ratio_{c_grid_desc_g_m_n_.GetLength(I0) / b_grid_desc_g_n_k_.GetLength(I0)},
p_drop_{p_drop}
{
// TODO: implement bias addition
......@@ -964,6 +1008,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
z_grid_desc_g_m_n_,
b1_grid_desc_g_n_k_,
c_grid_desc_g_m_n_,
bgrad_grid_desc_g_n_k_,
b1grad_grid_desc_g_n_k_,
type_convert<index_t>(lse_grid_desc_m_.GetElementSpaceSize()));
seed_ = std::get<0>(seeds);
......@@ -990,7 +1036,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
<< b_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b_grid_desc_g_n_k_.Print();
std::cout << "b1_grid_desc_g_o_n_: " << b1_grid_desc_g_n_k_.GetLength(I0) << ", "
std::cout << "b1_grid_desc_g_n_k_: " << b1_grid_desc_g_n_k_.GetLength(I0) << ", "
<< b1_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b1_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b1_grid_desc_g_n_k_.Print();
......@@ -1003,6 +1049,17 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
std::cout << "ygrad_grid_desc_o0_m_o1_: " << ygrad_grid_desc_o0_m_o1_.GetLength(I0)
<< ", " << ygrad_grid_desc_o0_m_o1_.GetLength(I1) << ", "
<< ygrad_grid_desc_o0_m_o1_.GetLength(I2) << '\n';
std::cout << "d0_grid_desc_g_m_n_: " << d0_grid_desc_g_m_n_.GetLength(I0) << ", "
<< d0_grid_desc_g_m_n_.GetLength(I1) << ", "
<< d0_grid_desc_g_m_n_.GetLength(I2) << '\n';
std::cout << "bgrad_grid_desc_g_n_k_: " << bgrad_grid_desc_g_n_k_.GetLength(I0) << ", "
<< bgrad_grid_desc_g_n_k_.GetLength(I1) << ", "
<< bgrad_grid_desc_g_n_k_.GetLength(I2) << '\n';
// bgrad_grid_desc_g_n_k_.Print();
std::cout << "b1grad_grid_desc_g_n_k_: " << b1grad_grid_desc_g_n_k_.GetLength(I0)
<< ", " << b1grad_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b1grad_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b1grad_grid_desc_g_n_k_.Print();
}
// pointers
......@@ -1023,9 +1080,11 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
// tensor descriptor
AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
BGridDesc_BK0_N_BK1 bgrad_grid_desc_bk0_n_bk1_;
typename GridwiseGemm::D0GridDescriptor_M0_N0_M1_M2_N1_M3 d0_grid_desc_m0_n0_m1_m2_n1_m3_;
ZGridDesc_M_N z_grid_desc_m_n_;
B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1_;
B1GridDesc_BK0_N_BK1 b1grad_grid_desc_bk0_n_bk1_;
YGridDesc_M_O y_grid_desc_m_o_;
DYGridDesc_M_O d_y_grid_desc_m_o_;
LSEGridDesc_M lse_grid_desc_m_;
......@@ -1040,6 +1099,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
CGridDesc_G_M_N c_grid_desc_g_m_n_;
ZGridDesc_G_M_N z_grid_desc_g_m_n_;
BGridDesc_G_N_K bgrad_grid_desc_g_n_k_;
B1GridDesc_G_N_K b1grad_grid_desc_g_n_k_;
typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_;
......@@ -1068,6 +1129,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
std::vector<index_t> c_mz_gemm1nz_strides_;
index_t batch_count_;
index_t h_ratio_;
ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch_;
float p_drop_;
......@@ -1189,13 +1251,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.bgrad_grid_desc_bk0_n_bk1_,
arg.d0_grid_desc_m0_n0_m1_m2_n1_m3_,
arg.c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_,
arg.b1_grid_desc_bk0_n_bk1_,
arg.b1grad_grid_desc_bk0_n_bk1_,
arg.lse_grid_desc_m_,
arg.ygrad_grid_desc_o0_m_o1_,
arg.block_2_ctile_map_,
arg.batch_count_,
arg.h_ratio_,
arg.block_2_ctile_map_.CalculateGridSize(arg.k_grid_desc_n_k_),
arg.compute_base_ptr_of_batch_,
arg.c0_matrix_mask_,
......@@ -1249,13 +1314,14 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
// Check if C permute dimension matches GEMM + GEMM shape
const index_t c_g = arg.c_grid_desc_g_m_n_.GetLength(I0); // unpadded
const index_t b_g = arg.b_grid_desc_g_n_k_.GetLength(I0);
const index_t c_m = arg.y_grid_desc_m_o_.GetLength(I0);
const index_t c_gemm1n = arg.y_grid_desc_m_o_.GetLength(I1);
const index_t a_m = arg.a_grid_desc_ak0_m_ak1_.GetLength(I1);
const index_t b1_gemm1n =
arg.b1_grid_desc_bk0_n_bk1_.GetLength(I0) * arg.b1_grid_desc_bk0_n_bk1_.GetLength(I2);
if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n))
if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n && c_g % b_g == 0))
{
return false;
}
......@@ -1293,6 +1359,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
return false;
}
// saving dQ data with atomic_add instruction, so KzRaw must be a multiple of 2
if constexpr(is_same<OutputDataType, half_t>::value ||
is_same<OutputDataType, bhalf_t>::value)
{
if(KzRaw % 2 != 0)
{
std::cout << "K_q must be a multiple of 2" << std::endl;
return false;
}
}
// Check vector load/store requirement
const auto a_stride_lowest =
ABlockTransferSrcVectorDim == 2 ? arg.a_mz_kz_strides_[1] : arg.a_mz_kz_strides_[0];
......@@ -1345,6 +1421,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -1385,6 +1465,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
lse_gs_ms_lengths,
bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides,
b1grad_gs_gemm1ns_gemm1ks_lengths,
b1grad_gs_gemm1ns_gemm1ks_strides,
acc0_bias_gs_ms_ns_lengths,
acc0_bias_gs_ms_ns_strides,
acc1_bias_gs_ms_gemm1ns_lengths, // acc1_bias_gs_ms_os_lengths
......@@ -1414,8 +1498,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
void* p_qgrad_grid,
void* p_kgrad_grid,
void* p_vgrad_grid,
const D0DataType* p_acc0_bias,
const D1DataType* p_acc1_bias,
const void* p_acc0_bias,
const void* p_acc1_bias,
void* p_d0grad_grid,
void* p_d1grad_grid,
const std::vector<index_t>& a_gs_ms_ks_lengths,
......@@ -1429,6 +1513,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -1470,6 +1558,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V1
c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
lse_gs_ms_lengths,
bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides,
b1grad_gs_gemm1ns_gemm1ks_lengths,
b1grad_gs_gemm1ns_gemm1ks_strides,
acc0_bias_gs_ms_ns_lengths,
acc0_bias_gs_ms_ns_strides,
acc1_bias_gs_ms_gemm1ns_lengths,
......
include/ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_light_v2.hpp
View file @ e87ddb0e
......@@ -132,14 +132,17 @@ __global__ void
const CElementwiseOperation c_element_op,
const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
const BGridDesc_BK0_N_BK1 bgrad_grid_desc_bk0_n_bk1,
const D0GridDescriptor_M0_N0_M1_M2_N1_M3 d0_grid_desc_m0_n0_m1_m2_n1_m3,
const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
const B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1,
const B1GridDesc_BK0_N_BK1 b1grad_grid_desc_bk0_n_bk1,
const LSEGridDescriptor_M lse_grid_desc_m,
const YGradGridDesc_M0_O_M1 ygrad_grid_desc_m0_o_m1,
const Block2CTileMap block_2_ctile_map,
const index_t batch_count,
const index_t h_ratio,
const index_t nblock,
const ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch,
const C0MatrixMask c0_matrix_mask,
......@@ -155,21 +158,26 @@ __global__ void
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const index_t gkv_idx = __builtin_amdgcn_readfirstlane(g_idx / h_ratio);
// NOTE: assumes QKVY has the same layout as dQ/dK/dV/dY therefore being able to reuse batch
// offsets
const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(g_idx)));
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(gkv_idx)));
const long_index_t z_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetZBasePtr(g_idx)));
const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(g_idx)));
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(gkv_idx)));
const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));
const long_index_t lse_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetLSEBasePtr(g_idx)));
const long_index_t bgrad_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBGradBasePtr(g_idx)));
const long_index_t b1grad_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1GradBasePtr(g_idx)));
ck::philox ph(seed, 0, offset);
ZDataType* z_matrix_ptr = (p_z_grid == nullptr ? nullptr : p_z_grid + z_batch_offset);
......@@ -206,9 +214,9 @@ __global__ void
p_d_grid + lse_batch_offset,
p_ygrad_grid + c_batch_offset,
p_qgrad_grid + a_batch_offset,
p_kgrad_grid + b_batch_offset,
p_kgrad_grid + bgrad_batch_offset,
tmp_p_d0grad_grid,
p_vgrad_grid + b1_batch_offset,
p_vgrad_grid + b1grad_batch_offset,
p_shared,
a_element_op,
b_element_op,
......@@ -217,9 +225,11 @@ __global__ void
c_element_op,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
bgrad_grid_desc_bk0_n_bk1,
d0_grid_desc_m0_n0_m1_m2_n1_m3,
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
b1_grid_desc_bk0_n_bk1,
b1grad_grid_desc_bk0_n_bk1,
lse_grid_desc_m,
ygrad_grid_desc_m0_o_m1,
block_2_ctile_map,
......@@ -243,9 +253,9 @@ __global__ void
p_d_grid + lse_batch_offset,
p_ygrad_grid + c_batch_offset,
p_qgrad_grid + a_batch_offset,
p_kgrad_grid + b_batch_offset,
p_kgrad_grid + bgrad_batch_offset,
tmp_p_d0grad_grid,
p_vgrad_grid + b1_batch_offset,
p_vgrad_grid + b1grad_batch_offset,
p_shared,
a_element_op,
b_element_op,
......@@ -254,9 +264,11 @@ __global__ void
c_element_op,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
bgrad_grid_desc_bk0_n_bk1,
d0_grid_desc_m0_n0_m1_m2_n1_m3,
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
b1_grid_desc_bk0_n_bk1,
b1grad_grid_desc_bk0_n_bk1,
lse_grid_desc_m,
ygrad_grid_desc_m0_o_m1,
block_2_ctile_map,
......@@ -287,13 +299,16 @@ __global__ void
ignore = c_element_op;
ignore = a_grid_desc_ak0_m_ak1;
ignore = b_grid_desc_bk0_n_bk1;
ignore = bgrad_grid_desc_bk0_n_bk1;
ignore = d0_grid_desc_m0_n0_m1_m2_n1_m3;
ignore = c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3;
ignore = b1_grid_desc_bk0_n_bk1;
ignore = b1grad_grid_desc_bk0_n_bk1;
ignore = lse_grid_desc_m;
ignore = ygrad_grid_desc_m0_o_m1;
ignore = block_2_ctile_map;
ignore = batch_count;
ignore = h_ratio;
ignore = nblock;
ignore = compute_base_ptr_of_batch;
ignore = c0_matrix_mask;
......@@ -704,6 +719,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
const ZGridDesc_G_M_N& z_grid_desc_g_m_n,
const B1GridDesc_G_N_K& b1_grid_desc_g_n_k,
const CGridDesc_G_M_N& c_grid_desc_g_m_n,
const BGridDesc_G_N_K& bgrad_grid_desc_g_n_k,
const B1GridDesc_G_N_K& b1grad_grid_desc_g_n_k,
index_t BatchStrideLSE)
: a_grid_desc_g_m_k_(a_grid_desc_g_m_k),
b_grid_desc_g_n_k_(b_grid_desc_g_n_k),
......@@ -711,6 +728,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
z_grid_desc_g_m_n_(z_grid_desc_g_m_n),
b1_grid_desc_g_n_k_(b1_grid_desc_g_n_k),
c_grid_desc_g_m_n_(c_grid_desc_g_m_n),
bgrad_grid_desc_g_n_k_(bgrad_grid_desc_g_n_k),
b1grad_grid_desc_g_n_k_(b1grad_grid_desc_g_n_k),
BatchStrideLSE_(BatchStrideLSE)
{
}
......@@ -729,6 +748,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
{
return d0_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
}
__host__ __device__ constexpr long_index_t GetZBasePtr(index_t g_idx) const
{
return z_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
......@@ -749,6 +769,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
return g_idx * static_cast<long_index_t>(BatchStrideLSE_);
}
__host__ __device__ constexpr long_index_t GetBGradBasePtr(index_t g_idx) const
{
return bgrad_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
}
__host__ __device__ constexpr long_index_t GetB1GradBasePtr(index_t g_idx) const
{
return b1grad_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
}
private:
AGridDesc_G_M_K a_grid_desc_g_m_k_;
BGridDesc_G_N_K b_grid_desc_g_n_k_;
......@@ -756,6 +786,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
ZGridDesc_G_M_N z_grid_desc_g_m_n_;
B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
CGridDesc_G_M_N c_grid_desc_g_m_n_;
BGridDesc_G_N_K bgrad_grid_desc_g_n_k_;
B1GridDesc_G_N_K b1grad_grid_desc_g_n_k_;
index_t BatchStrideLSE_;
};
......@@ -874,6 +906,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -904,9 +940,13 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
DeviceOp::MakeAGridDescriptor_AK0_M_AK1(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
b_grid_desc_bk0_n_bk1_{
DeviceOp::MakeBGridDescriptor_BK0_N_BK1(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
bgrad_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(
bgrad_gs_ns_ks_lengths, bgrad_gs_ns_ks_strides)},
z_grid_desc_m_n_{MakeZGridDescriptor_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
b1_grid_desc_bk0_n_bk1_{DeviceOp::MakeVGridDescriptor_O0_N_O1(
b1_gs_gemm1ns_gemm1ks_lengths, b1_gs_gemm1ns_gemm1ks_strides)},
b1grad_grid_desc_bk0_n_bk1_{DeviceOp::MakeVGridDescriptor_O0_N_O1(
b1grad_gs_gemm1ns_gemm1ks_lengths, b1grad_gs_gemm1ns_gemm1ks_strides)},
y_grid_desc_m_o_{Transform::MakeCGridDescriptor_M_N(c_gs_ms_gemm1ns_lengths,
c_gs_ms_gemm1ns_strides)},
d_y_grid_desc_m_o_{DTransform::MakeCGridDescriptor_M_N(c_gs_ms_gemm1ns_lengths,
......@@ -927,6 +967,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
c_gs_ms_gemm1ns_strides)},
z_grid_desc_g_m_n_{
Transform::MakeC0GridDescriptor_G_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
bgrad_grid_desc_g_n_k_{Transform::MakeB0GridDescriptor_G_N_K(bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides)},
b1grad_grid_desc_g_n_k_{Transform::MakeB1GridDescriptor_G_N_K(
b1grad_gs_gemm1ns_gemm1ks_lengths, b1grad_gs_gemm1ns_gemm1ks_strides)},
block_2_ctile_map_{GridwiseGemm::MakeDefaultBlock2CTileMap(k_grid_desc_n_k_)},
d_block_2_ctile_map_{
GridwiseYDotYGrad::MakeDefaultBlock2CTileMap(d_y_grid_desc_m_o_)},
......@@ -950,6 +994,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
c_mz_gemm1nz_strides_{c_gs_ms_gemm1ns_strides[NumDimG + NumDimM - 1],
c_gs_ms_gemm1ns_strides[NumDimG + NumDimM + NumDimO - 1]},
batch_count_{c_grid_desc_g_m_n_.GetLength(I0)},
h_ratio_{c_grid_desc_g_m_n_.GetLength(I0) / b_grid_desc_g_n_k_.GetLength(I0)},
p_drop_{p_drop}
{
// TODO: implement bias addition
......@@ -979,6 +1024,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
z_grid_desc_g_m_n_,
b1_grid_desc_g_n_k_,
c_grid_desc_g_m_n_,
bgrad_grid_desc_g_n_k_,
b1grad_grid_desc_g_n_k_,
type_convert<index_t>(lse_grid_desc_m_.GetElementSpaceSize()));
seed_ = std::get<0>(seeds);
......@@ -1005,7 +1052,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
<< b_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b_grid_desc_g_n_k_.Print();
std::cout << "b1_grid_desc_g_o_n_: " << b1_grid_desc_g_n_k_.GetLength(I0) << ", "
std::cout << "b1_grid_desc_g_n_k_: " << b1_grid_desc_g_n_k_.GetLength(I0) << ", "
<< b1_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b1_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b1_grid_desc_g_n_k_.Print();
......@@ -1018,6 +1065,17 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
std::cout << "ygrad_grid_desc_m0_o_m1_: " << ygrad_grid_desc_m0_o_m1_.GetLength(I0)
<< ", " << ygrad_grid_desc_m0_o_m1_.GetLength(I1) << ", "
<< ygrad_grid_desc_m0_o_m1_.GetLength(I2) << '\n';
std::cout << "d0_grid_desc_g_m_n_: " << d0_grid_desc_g_m_n_.GetLength(I0) << ", "
<< d0_grid_desc_g_m_n_.GetLength(I1) << ", "
<< d0_grid_desc_g_m_n_.GetLength(I2) << '\n';
std::cout << "bgrad_grid_desc_g_n_k_: " << bgrad_grid_desc_g_n_k_.GetLength(I0) << ", "
<< bgrad_grid_desc_g_n_k_.GetLength(I1) << ", "
<< bgrad_grid_desc_g_n_k_.GetLength(I2) << '\n';
// bgrad_grid_desc_g_n_k_.Print();
std::cout << "b1grad_grid_desc_g_n_k_: " << b1grad_grid_desc_g_n_k_.GetLength(I0)
<< ", " << b1grad_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b1grad_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b1grad_grid_desc_g_n_k_.Print();
}
// pointers
......@@ -1038,9 +1096,11 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
// tensor descriptor
AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
BGridDesc_BK0_N_BK1 bgrad_grid_desc_bk0_n_bk1_;
typename GridwiseGemm::D0GridDescriptor_M0_N0_M1_M2_N1_M3 d0_grid_desc_m0_n0_m1_m2_n1_m3_;
ZGridDesc_M_N z_grid_desc_m_n_;
B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1_;
B1GridDesc_BK0_N_BK1 b1grad_grid_desc_bk0_n_bk1_;
YGridDesc_M_O y_grid_desc_m_o_;
DYGridDesc_M_O d_y_grid_desc_m_o_;
LSEGridDesc_M lse_grid_desc_m_;
......@@ -1055,6 +1115,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
CGridDesc_G_M_N c_grid_desc_g_m_n_;
ZGridDesc_G_M_N z_grid_desc_g_m_n_;
BGridDesc_G_N_K bgrad_grid_desc_g_n_k_;
B1GridDesc_G_N_K b1grad_grid_desc_g_n_k_;
typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_;
......@@ -1083,6 +1145,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
std::vector<index_t> c_mz_gemm1nz_strides_;
index_t batch_count_;
index_t h_ratio_;
ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch_;
float p_drop_;
......@@ -1208,13 +1271,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.bgrad_grid_desc_bk0_n_bk1_,
arg.d0_grid_desc_m0_n0_m1_m2_n1_m3_,
arg.c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_,
arg.b1_grid_desc_bk0_n_bk1_,
arg.b1grad_grid_desc_bk0_n_bk1_,
arg.lse_grid_desc_m_,
arg.ygrad_grid_desc_m0_o_m1_,
arg.block_2_ctile_map_,
arg.batch_count_,
arg.h_ratio_,
arg.block_2_ctile_map_.CalculateGridSize(arg.k_grid_desc_n_k_),
arg.compute_base_ptr_of_batch_,
arg.c0_matrix_mask_,
......@@ -1280,13 +1346,14 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
// Check if C permute dimension matches GEMM + GEMM shape
const index_t c_g = arg.c_grid_desc_g_m_n_.GetLength(I0); // unpadded
const index_t b_g = arg.b_grid_desc_g_n_k_.GetLength(I0);
const index_t c_m = arg.y_grid_desc_m_o_.GetLength(I0);
const index_t c_gemm1n = arg.y_grid_desc_m_o_.GetLength(I1);
const index_t a_m = arg.a_grid_desc_ak0_m_ak1_.GetLength(I1);
const index_t b1_gemm1n =
arg.b1_grid_desc_bk0_n_bk1_.GetLength(I0) * arg.b1_grid_desc_bk0_n_bk1_.GetLength(I2);
if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n))
if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n && c_g % b_g == 0))
{
return false;
}
......@@ -1325,6 +1392,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
return false;
}
// saving dQ data with atomic_add instruction, so KzRaw must be a multiple of 2
if constexpr(is_same<OutputDataType, half_t>::value ||
is_same<OutputDataType, bhalf_t>::value)
{
if(KzRaw % 2 != 0)
{
std::cout << "K_q must be a multiple of 2" << std::endl;
return false;
}
}
// Check vector load/store requirement
const auto a_stride_lowest =
ABlockTransferSrcVectorDim == 2 ? arg.a_mz_kz_strides_[1] : arg.a_mz_kz_strides_[0];
......@@ -1380,6 +1457,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -1420,6 +1501,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
lse_gs_ms_lengths,
bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides,
b1grad_gs_gemm1ns_gemm1ks_lengths,
b1grad_gs_gemm1ns_gemm1ks_strides,
acc0_bias_gs_ms_ns_lengths,
acc0_bias_gs_ms_ns_strides,
acc1_bias_gs_ms_gemm1ns_lengths, // acc1_bias_gs_ms_os_lengths
......@@ -1464,6 +1549,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -1505,6 +1594,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_Light_V2
c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
lse_gs_ms_lengths,
bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides,
b1grad_gs_gemm1ns_gemm1ks_lengths,
b1grad_gs_gemm1ns_gemm1ks_strides,
acc0_bias_gs_ms_ns_lengths,
acc0_bias_gs_ms_ns_strides,
acc1_bias_gs_ms_gemm1ns_lengths,
......
include/ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_v1.hpp
View file @ e87ddb0e
......@@ -74,16 +74,19 @@ __global__ void
const CElementwiseOperation c_element_op,
const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
const BGridDesc_BK0_N_BK1 bgrad_grid_desc_bk0_n_bk1,
const D0GridDescriptor_M0_N0_M1_M2_N1_M3 d0_grid_desc_m0_n0_m1_m2_n1_m3,
const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
const B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1,
const B1GridDesc_BK0_N_BK1 b1grad_grid_desc_bk0_n_bk1,
const YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock
c_grid_desc_mblock_mperblock_nblock_nperblock,
const LSEGridDescriptor_M lse_grid_desc_m,
const YGradGridDesc_O0_M_O1 ygrad_grid_desc_o0_m_o1,
const Block2CTileMap block_2_ctile_map,
const index_t batch_count,
const index_t h_ratio,
const index_t nblock,
const ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch,
const C0MatrixMask c0_matrix_mask,
......@@ -99,21 +102,26 @@ __global__ void
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const index_t gkv_idx = __builtin_amdgcn_readfirstlane(g_idx / h_ratio);
// NOTE: assumes QKVY has the same layout as dQ/dK/dV/dY therefore being able to reuse batch
// offsets
const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(g_idx)));
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(gkv_idx)));
const long_index_t z_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetZBasePtr(g_idx)));
const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(g_idx)));
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(gkv_idx)));
const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));
const long_index_t lse_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetLSEBasePtr(g_idx)));
const long_index_t bgrad_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetBGradBasePtr(g_idx)));
const long_index_t b1grad_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1GradBasePtr(g_idx)));
ck::philox ph(seed, 0, offset);
ZDataType* z_matrix_ptr = (p_z_grid == nullptr ? nullptr : p_z_grid + z_batch_offset);
......@@ -149,9 +157,9 @@ __global__ void
p_lse_grid + lse_batch_offset,
p_ygrad_grid + c_batch_offset,
p_qgrad_grid + a_batch_offset,
p_kgrad_grid + b_batch_offset,
p_kgrad_grid + bgrad_batch_offset,
tmp_p_d0grad_grid,
p_vgrad_grid + b1_batch_offset,
p_vgrad_grid + b1grad_batch_offset,
p_shared,
a_element_op,
b_element_op,
......@@ -160,9 +168,11 @@ __global__ void
c_element_op,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
bgrad_grid_desc_bk0_n_bk1,
d0_grid_desc_m0_n0_m1_m2_n1_m3,
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
b1_grid_desc_bk0_n_bk1,
b1grad_grid_desc_bk0_n_bk1,
c_grid_desc_mblock_mperblock_nblock_nperblock,
lse_grid_desc_m,
ygrad_grid_desc_o0_m_o1,
......@@ -187,9 +197,9 @@ __global__ void
p_lse_grid + lse_batch_offset,
p_ygrad_grid + c_batch_offset,
p_qgrad_grid + a_batch_offset,
p_kgrad_grid + b_batch_offset,
p_kgrad_grid + bgrad_batch_offset,
tmp_p_d0grad_grid,
p_vgrad_grid + b1_batch_offset,
p_vgrad_grid + b1grad_batch_offset,
p_shared,
a_element_op,
b_element_op,
......@@ -198,9 +208,11 @@ __global__ void
c_element_op,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
bgrad_grid_desc_bk0_n_bk1,
d0_grid_desc_m0_n0_m1_m2_n1_m3,
c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
b1_grid_desc_bk0_n_bk1,
b1grad_grid_desc_bk0_n_bk1,
c_grid_desc_mblock_mperblock_nblock_nperblock,
lse_grid_desc_m,
ygrad_grid_desc_o0_m_o1,
......@@ -232,14 +244,17 @@ __global__ void
ignore = c_element_op;
ignore = a_grid_desc_ak0_m_ak1;
ignore = b_grid_desc_bk0_n_bk1;
ignore = bgrad_grid_desc_bk0_n_bk1;
ignore = d0_grid_desc_m0_n0_m1_m2_n1_m3;
ignore = c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3;
ignore = b1_grid_desc_bk0_n_bk1;
ignore = b1grad_grid_desc_bk0_n_bk1;
ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
ignore = lse_grid_desc_m;
ignore = ygrad_grid_desc_o0_m_o1;
ignore = block_2_ctile_map;
ignore = batch_count;
ignore = h_ratio;
ignore = nblock;
ignore = compute_base_ptr_of_batch;
ignore = c0_matrix_mask;
......@@ -603,6 +618,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
const ZGridDesc_G_M_N& z_grid_desc_g_m_n,
const B1GridDesc_G_N_K& b1_grid_desc_g_n_k,
const CGridDesc_G_M_N& c_grid_desc_g_m_n,
const BGridDesc_G_N_K& bgrad_grid_desc_g_n_k,
const B1GridDesc_G_N_K& b1grad_grid_desc_g_n_k,
index_t BatchStrideLSE)
: a_grid_desc_g_m_k_(a_grid_desc_g_m_k),
b_grid_desc_g_n_k_(b_grid_desc_g_n_k),
......@@ -610,6 +627,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
z_grid_desc_g_m_n_(z_grid_desc_g_m_n),
b1_grid_desc_g_n_k_(b1_grid_desc_g_n_k),
c_grid_desc_g_m_n_(c_grid_desc_g_m_n),
bgrad_grid_desc_g_n_k_(bgrad_grid_desc_g_n_k),
b1grad_grid_desc_g_n_k_(b1grad_grid_desc_g_n_k),
BatchStrideLSE_(BatchStrideLSE)
{
}
......@@ -649,6 +668,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
return g_idx * static_cast<long_index_t>(BatchStrideLSE_);
}
__host__ __device__ constexpr long_index_t GetBGradBasePtr(index_t g_idx) const
{
return bgrad_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
}
__host__ __device__ constexpr long_index_t GetB1GradBasePtr(index_t g_idx) const
{
return b1grad_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
}
private:
AGridDesc_G_M_K a_grid_desc_g_m_k_;
BGridDesc_G_N_K b_grid_desc_g_n_k_;
......@@ -656,6 +685,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
ZGridDesc_G_M_N z_grid_desc_g_m_n_;
B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
CGridDesc_G_M_N c_grid_desc_g_m_n_;
BGridDesc_G_N_K bgrad_grid_desc_g_n_k_;
B1GridDesc_G_N_K b1grad_grid_desc_g_n_k_;
index_t BatchStrideLSE_;
};
......@@ -755,6 +786,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -784,9 +819,13 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
DeviceOp::MakeAGridDescriptor_AK0_M_AK1(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
b_grid_desc_bk0_n_bk1_{
DeviceOp::MakeBGridDescriptor_BK0_N_BK1(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
bgrad_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(
bgrad_gs_ns_ks_lengths, bgrad_gs_ns_ks_strides)},
z_grid_desc_m_n_{MakeZGridDescriptor_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
b1_grid_desc_bk0_n_bk1_{DeviceOp::MakeVGridDescriptor_O0_N_O1(
b1_gs_gemm1ns_gemm1ks_lengths, b1_gs_gemm1ns_gemm1ks_strides)},
b1grad_grid_desc_bk0_n_bk1_{DeviceOp::MakeVGridDescriptor_O0_N_O1(
b1grad_gs_gemm1ns_gemm1ks_lengths, b1grad_gs_gemm1ns_gemm1ks_strides)},
y_grid_desc_m_o_{Transform::MakeCGridDescriptor_M_N(c_gs_ms_gemm1ns_lengths,
c_gs_ms_gemm1ns_strides)},
lse_grid_desc_m_{DeviceOp::MakeLSEGridDescriptor_M(lse_gs_ms_lengths[NumDimG])},
......@@ -805,6 +844,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
c_gs_ms_gemm1ns_strides)},
z_grid_desc_g_m_n_{
Transform::MakeC0GridDescriptor_G_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
bgrad_grid_desc_g_n_k_{Transform::MakeB0GridDescriptor_G_N_K(bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides)},
b1grad_grid_desc_g_n_k_{Transform::MakeB1GridDescriptor_G_N_K(
b1grad_gs_gemm1ns_gemm1ks_lengths, b1grad_gs_gemm1ns_gemm1ks_strides)},
y_grid_desc_mblock_mperblock_oblock_operblock_{},
block_2_ctile_map_{GridwiseGemm::MakeDefaultBlock2CTileMap(k_grid_desc_n_k_)},
a_element_op_{a_element_op},
......@@ -826,6 +869,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
c_mz_gemm1nz_strides_{c_gs_ms_gemm1ns_strides[NumDimG + NumDimM - 1],
c_gs_ms_gemm1ns_strides[NumDimG + NumDimM + NumDimO - 1]},
batch_count_{c_grid_desc_g_m_n_.GetLength(I0)},
h_ratio_{c_grid_desc_g_m_n_.GetLength(I0) / b_grid_desc_g_n_k_.GetLength(I0)},
p_drop_{p_drop}
{
// TODO: implement bias addition
......@@ -864,6 +908,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
z_grid_desc_g_m_n_,
b1_grid_desc_g_n_k_,
c_grid_desc_g_m_n_,
bgrad_grid_desc_g_n_k_,
b1grad_grid_desc_g_n_k_,
type_convert<index_t>(lse_grid_desc_m_.GetElementSpaceSize()));
seed_ = std::get<0>(seeds);
......@@ -887,7 +933,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
<< b_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b_grid_desc_g_n_k_.Print();
std::cout << "b1_grid_desc_g_o_n_: " << b1_grid_desc_g_n_k_.GetLength(I0) << ", "
std::cout << "b1_grid_desc_g_n_k_: " << b1_grid_desc_g_n_k_.GetLength(I0) << ", "
<< b1_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b1_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b1_grid_desc_g_n_k_.Print();
......@@ -900,6 +946,17 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
std::cout << "ygrad_grid_desc_o0_m_o1_: " << ygrad_grid_desc_o0_m_o1_.GetLength(I0)
<< ", " << ygrad_grid_desc_o0_m_o1_.GetLength(I1) << ", "
<< ygrad_grid_desc_o0_m_o1_.GetLength(I2) << '\n';
std::cout << "d0_grid_desc_g_m_n_: " << d0_grid_desc_g_m_n_.GetLength(I0) << ", "
<< d0_grid_desc_g_m_n_.GetLength(I1) << ", "
<< d0_grid_desc_g_m_n_.GetLength(I2) << '\n';
std::cout << "bgrad_grid_desc_g_n_k_: " << bgrad_grid_desc_g_n_k_.GetLength(I0) << ", "
<< bgrad_grid_desc_g_n_k_.GetLength(I1) << ", "
<< bgrad_grid_desc_g_n_k_.GetLength(I2) << '\n';
// bgrad_grid_desc_g_n_k_.Print();
std::cout << "b1grad_grid_desc_g_n_k_: " << b1grad_grid_desc_g_n_k_.GetLength(I0)
<< ", " << b1grad_grid_desc_g_n_k_.GetLength(I1) << ", "
<< b1grad_grid_desc_g_n_k_.GetLength(I2) << '\n';
// b1grad_grid_desc_g_n_k_.Print();
}
// pointers
......@@ -919,9 +976,11 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
// tensor descriptor
AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
BGridDesc_BK0_N_BK1 bgrad_grid_desc_bk0_n_bk1_;
typename GridwiseGemm::D0GridDescriptor_M0_N0_M1_M2_N1_M3 d0_grid_desc_m0_n0_m1_m2_n1_m3_;
ZGridDesc_M_N z_grid_desc_m_n_;
B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1_;
B1GridDesc_BK0_N_BK1 b1grad_grid_desc_bk0_n_bk1_;
YGridDesc_M_O y_grid_desc_m_o_;
LSEGridDesc_M lse_grid_desc_m_;
KGridDesc_N_K k_grid_desc_n_k_;
......@@ -934,6 +993,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
CGridDesc_G_M_N c_grid_desc_g_m_n_;
ZGridDesc_G_M_N z_grid_desc_g_m_n_;
BGridDesc_G_N_K bgrad_grid_desc_g_n_k_;
B1GridDesc_G_N_K b1grad_grid_desc_g_n_k_;
typename GridwiseGemm::YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock
y_grid_desc_mblock_mperblock_oblock_operblock_;
......@@ -961,6 +1022,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
std::vector<index_t> c_mz_gemm1nz_strides_;
index_t batch_count_;
index_t h_ratio_;
ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch_;
float p_drop_;
......@@ -1047,14 +1109,17 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
arg.c_element_op_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.bgrad_grid_desc_bk0_n_bk1_,
arg.d0_grid_desc_m0_n0_m1_m2_n1_m3_,
arg.c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_,
arg.b1_grid_desc_bk0_n_bk1_,
arg.b1grad_grid_desc_bk0_n_bk1_,
arg.y_grid_desc_mblock_mperblock_oblock_operblock_,
arg.lse_grid_desc_m_,
arg.ygrad_grid_desc_o0_m_o1_,
arg.block_2_ctile_map_,
arg.batch_count_,
arg.h_ratio_,
arg.block_2_ctile_map_.CalculateGridSize(arg.k_grid_desc_n_k_),
arg.compute_base_ptr_of_batch_,
arg.c0_matrix_mask_,
......@@ -1108,13 +1173,14 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
// Check if C permute dimension matches GEMM + GEMM shape
const index_t c_g = arg.c_grid_desc_g_m_n_.GetLength(I0); // unpadded
const index_t b_g = arg.b_grid_desc_g_n_k_.GetLength(I0);
const index_t c_m = arg.y_grid_desc_m_o_.GetLength(I0);
const index_t c_gemm1n = arg.y_grid_desc_m_o_.GetLength(I1);
const index_t a_m = arg.a_grid_desc_ak0_m_ak1_.GetLength(I1);
const index_t b1_gemm1n =
arg.b1_grid_desc_bk0_n_bk1_.GetLength(I0) * arg.b1_grid_desc_bk0_n_bk1_.GetLength(I2);
if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n))
if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n && c_g % b_g == 0))
{
return false;
}
......@@ -1152,6 +1218,17 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
return false;
}
// saving dQ data with atomic_add instruction, so KzRaw must be a multiple of 2
if constexpr(is_same<OutputDataType, half_t>::value ||
is_same<OutputDataType, bhalf_t>::value)
{
if(KzRaw % 2 != 0)
{
std::cout << "K_q must be a multiple of 2" << std::endl;
return false;
}
}
// Check vector load/store requirement
const auto a_stride_lowest =
ABlockTransferSrcVectorDim == 2 ? arg.a_mz_kz_strides_[1] : arg.a_mz_kz_strides_[0];
......@@ -1203,6 +1280,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -1242,6 +1323,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
lse_gs_ms_lengths,
bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides,
b1grad_gs_gemm1ns_gemm1ks_lengths,
b1grad_gs_gemm1ns_gemm1ks_strides,
acc0_bias_gs_ms_ns_lengths,
acc0_bias_gs_ms_ns_strides,
acc1_bias_gs_ms_gemm1ns_lengths, // acc1_bias_gs_ms_os_lengths
......@@ -1270,10 +1355,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
void* p_qgrad_grid,
void* p_kgrad_grid,
void* p_vgrad_grid,
const D0DataType* p_acc0_bias,
const D1DataType* p_acc1_bias,
D0DataType* p_d0grad_grid,
D1DataType* p_d1grad_grid,
const void* p_acc0_bias,
const void* p_acc1_bias,
void* p_d0grad_grid,
void* p_d1grad_grid,
const std::vector<index_t>& a_gs_ms_ks_lengths,
const std::vector<index_t>& a_gs_ms_ks_strides,
const std::vector<index_t>& b_gs_ns_ks_lengths,
......@@ -1285,6 +1370,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::vector<index_t>& lse_gs_ms_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
const std::vector<index_t>& bgrad_gs_ns_ks_strides,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
const std::vector<ck::index_t>&
......@@ -1312,8 +1401,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
static_cast<OutputDataType*>(p_vgrad_grid),
static_cast<const D0DataType*>(p_acc0_bias), // cast in struct Argument
static_cast<const D1DataType*>(p_acc1_bias), // cast in struct Argument
static_cast<const D0DataType*>(p_d0grad_grid),
static_cast<const D1DataType*>(p_d1grad_grid),
static_cast<D0DataType*>(p_d0grad_grid),
static_cast<D1DataType*>(p_d1grad_grid),
a_gs_ms_ks_lengths,
a_gs_ms_ks_strides,
b_gs_ns_ks_lengths,
......@@ -1325,6 +1414,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V1
c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
lse_gs_ms_lengths,
bgrad_gs_ns_ks_lengths,
bgrad_gs_ns_ks_strides,
b1grad_gs_gemm1ns_gemm1ks_lengths,
b1grad_gs_gemm1ns_gemm1ks_strides,
acc0_bias_gs_ms_ns_lengths,
acc0_bias_gs_ms_ns_strides,
acc1_bias_gs_ms_gemm1ns_lengths,
......
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