Skip to content

GitLab

Commit 9bb4ab41 authored by Junhao's avatar Junhao
Browse files

Merge branch 'junhzhan/fa-ifu-mqa' of... 

Merge branch 'junhzhan/fa-ifu-mqa' of https://github.com/ROCmSoftwarePlatform/composable_kernel into junhzhan/fa-ifu-mqa
parents 980b8835 5ff2d646
Expand all Show whitespace changes
Inline Side-by-side
Showing with 1785 additions and 866 deletions
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_backward_v2.cpp
View file @ 9bb4ab41
......@@ -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])
// 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 G1 = 6;
ck::index_t G1Q = 6; // h_q
ck::index_t G1KV = 6; // h_kv
bool input_permute = false;
bool output_permute = false;
......@@ -295,7 +296,7 @@ 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]);
......@@ -306,21 +307,22 @@ int run(int argc, char* argv[])
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
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 +543,10 @@ 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()),
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
nullptr, // p_acc0_bias;
nullptr, // p_acc1_bias;
nullptr,
nullptr,
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
k_gs_ns_ks_lengths,
......@@ -526,6 +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},
......@@ -558,8 +594,10 @@ 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()),
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
nullptr, // p_acc0_bias;
nullptr, // p_acc1_bias;
nullptr,
nullptr,
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
k_gs_ns_ks_lengths,
......@@ -571,6 +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},
......@@ -610,7 +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;
......@@ -630,10 +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());
......@@ -651,7 +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
......@@ -753,12 +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());
......@@ -767,25 +813,25 @@ 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& 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& 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& 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 @ 9bb4ab41
......@@ -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])
// 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 G1 = 6;
ck::index_t G1Q = 6; // h_q
ck::index_t G1KV = 6; // h_kv
bool input_permute = false;
bool output_permute = false;
......@@ -296,7 +297,7 @@ 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]);
......@@ -307,21 +308,22 @@ int run(int argc, char* argv[])
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
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());
......@@ -518,8 +548,10 @@ 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()),
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
nullptr, // p_acc0_bias;
nullptr, // p_acc1_bias;
nullptr,
nullptr,
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
k_gs_ns_ks_lengths,
......@@ -531,6 +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},
......@@ -564,8 +600,10 @@ 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()),
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
nullptr, // p_acc0_bias;
nullptr, // p_acc1_bias;
nullptr,
nullptr,
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
k_gs_ns_ks_lengths,
......@@ -577,6 +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},
......@@ -616,7 +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;
......@@ -636,10 +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());
......@@ -657,7 +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
......@@ -759,12 +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());
......@@ -773,25 +819,25 @@ 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& 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& 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& 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 @ 9bb4ab41
......@@ -75,7 +75,6 @@ static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecia
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;
#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/grouped_multihead_attention_backward_v2.cpp
View file @ 9bb4ab41
......@@ -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]);
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);
}
......@@ -372,44 +374,60 @@ int run(int argc, char* argv[])
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 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());
......@@ -604,6 +642,8 @@ int run(int argc, char* argv[])
p_vgrad,
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
{},
{},
problem_descs,
QKVElementOp{},
QKVElementOp{},
......@@ -650,6 +690,8 @@ int run(int argc, char* argv[])
p_vgrad,
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
{},
{},
problem_descs,
QKVElementOp{},
QKVElementOp{},
......@@ -670,11 +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],
......@@ -690,11 +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();
......@@ -707,13 +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});
......@@ -723,7 +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();
......@@ -766,17 +808,17 @@ 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());
......@@ -784,25 +826,25 @@ 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& 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& 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& 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 @ 9bb4ab41
......@@ -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]);
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);
}
......@@ -376,44 +378,60 @@ int run(int argc, char* argv[])
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 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());
......@@ -616,6 +654,8 @@ int run(int argc, char* argv[])
p_vgrad,
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
{},
{},
problem_descs,
QKVElementOp{},
QKVElementOp{},
......@@ -663,6 +703,8 @@ int run(int argc, char* argv[])
p_vgrad,
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
{},
{},
problem_descs,
QKVElementOp{},
QKVElementOp{},
......@@ -683,11 +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],
......@@ -703,11 +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();
......@@ -720,13 +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});
......@@ -736,7 +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();
......@@ -779,17 +821,17 @@ 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());
......@@ -797,25 +839,25 @@ 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& 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& 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& 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 @ 9bb4ab41
......@@ -75,7 +75,6 @@ static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecia
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;
#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/run_batched_multihead_attention_forward.inc
View file @ 9bb4ab41
......@@ -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])
// 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 G1 = 13;
ck::index_t G1Q = 12; // h_q
ck::index_t G1KV = 12; // h_kv
bool input_permute = false;
bool output_permute = true;
......@@ -37,7 +38,7 @@ 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]);
......@@ -48,21 +49,22 @@ int run(int argc, char* argv[])
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
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
......@@ -341,17 +351,17 @@ 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& 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& 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 @ 9bb4ab41
......@@ -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;
......@@ -88,43 +92,46 @@ int run(int argc, char* argv[])
int K = DIM;
int O = DIM;
int G0 = rand() % 3 + 1;
int G1 = rand() % 5 + 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
......@@ -409,17 +424,17 @@ 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& 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& 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/CMakeLists.txt
View file @ 9bb4ab41
add_example_executable(example_batched_multihead_attention_infer batched_gemm_multihead_attention_infer.cpp)
add_example_executable(example_batched_multihead_attention_bias_infer batched_gemm_multihead_attention_bias_infer.cpp)
add_example_executable(example_grouped_multihead_attention_bias_infer grouped_mutihead_attention_bias_infer.cpp)
add_example_executable(example_batched_multihead_attention_bias_forward_v2 batched_multihead_attention_bias_forward_v2.cpp)
add_example_executable(example_grouped_multihead_attention_bias_forward_v2 grouped_multihead_attention_bias_forward_v2.cpp)
......
example/52_flash_atten_bias/batched_gemm_multihead_attention_bias_infer.cpp 0 → 100644
View file @ 9bb4ab41
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
/*
Gemm + Softmax + Gemm fused operation. Computes C_g_m_o = Softmax(A_g_m_k * B0_g_k_n) * B1_g_n_o
|-----------------|
Gemm0
|-------------------------------------|
Gemm1
*/
#define DIM 128 // DIM should be a multiple of 8.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_mha_infer_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using AccDataType = F32;
using CShuffleDataType = F32;
using CDataType = F16;
using Acc0BiasDataType = F16;
using Acc1BiasDataType = void;
static constexpr ck::index_t NumDimG = 2;
static constexpr ck::index_t NumDimM = 1;
static constexpr ck::index_t NumDimN = 1;
static constexpr ck::index_t NumDimK = 1;
static constexpr ck::index_t NumDimO = 1;
using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = ck::tensor_operation::element_wise::Scale;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
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;
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionInfer_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
ADataType,
B0DataType,
B1DataType,
CDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecA,
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
DIM, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
DIM / 32, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
4,
S<16, 16, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
// Ref Gemm0: fp16 in, fp32 out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B0DataType,
AccDataType,
AccDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp>;
// Ref Softmax: fp32 in, fp16 out
using ReferenceSoftmaxInstance =
ck::tensor_operation::host::ReferenceSoftmax<AccDataType, ADataType, AccDataType>;
// Ref Gemm1: fp16 in, fp16 out
using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B1DataType,
CDataType,
AccDataType,
AElementOp,
B1ElementOp,
CElementOp>;
#include "run_batched_multihead_attention_bias_infer.inc"
int main(int argc, char* argv[]) { return run(argc, argv); }
example/52_flash_atten_bias/batched_gemm_multihead_attention_infer.cpp 0 → 100644
View file @ 9bb4ab41
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
/*
Gemm + Softmax + Gemm fused operation. Computes C_g_m_o = Softmax(A_g_m_k * B0_g_k_n) * B1_g_n_o
|-----------------|
Gemm0
|-------------------------------------|
Gemm1
*/
#define DIM 128 // DIM should be a multiple of 8.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_mha_infer_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using AccDataType = F32;
using CShuffleDataType = F32;
using CDataType = F16;
using Acc0BiasDataType = void;
using Acc1BiasDataType = void;
static constexpr ck::index_t NumDimG = 2;
static constexpr ck::index_t NumDimM = 1;
static constexpr ck::index_t NumDimN = 1;
static constexpr ck::index_t NumDimK = 1;
static constexpr ck::index_t NumDimO = 1;
using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = ck::tensor_operation::element_wise::Scale;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
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;
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionInfer_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
ADataType,
B0DataType,
B1DataType,
CDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecA,
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
DIM, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
DIM / 32, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
4,
S<16, 16, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
// Ref Gemm0: fp16 in, fp32 out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B0DataType,
AccDataType,
AccDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp>;
// Ref Softmax: fp32 in, fp16 out
using ReferenceSoftmaxInstance =
ck::tensor_operation::host::ReferenceSoftmax<AccDataType, ADataType, AccDataType>;
// Ref Gemm1: fp16 in, fp16 out
using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B1DataType,
CDataType,
AccDataType,
AElementOp,
B1ElementOp,
CElementOp>;
#include "run_batched_multihead_attention_infer.inc"
int main(int argc, char* argv[]) { return run(argc, argv); }
example/52_flash_atten_bias/batched_multihead_attention_bias_forward_v2.cpp
View file @ 9bb4ab41
......@@ -75,7 +75,6 @@ static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecia
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;
#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
......@@ -327,6 +323,6 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<
using ReferenceDropoutInstance =
ck::tensor_operation::host::ReferenceDropout<ZDataType, ADataType, ADataType>;
#include "run_batched_multihead_attention_bias_forward.inc"
#include "run_batched_multihead_attention_bias_forward_v2.inc"
int main(int argc, char* argv[]) { return run(argc, argv); }
example/52_flash_atten_bias/batched_multihead_attention_bias_forward_v2_zcheck.cpp
View file @ 9bb4ab41
......@@ -75,7 +75,6 @@ static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecia
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;
#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_forward_v2.cpp
View file @ 9bb4ab41
......@@ -75,7 +75,6 @@ static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecia
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;
#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
......@@ -327,6 +323,6 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<
using ReferenceDropoutInstance =
ck::tensor_operation::host::ReferenceDropout<ZDataType, ADataType, ADataType>;
#include "run_grouped_multihead_attention_bias_forward.inc"
#include "run_grouped_multihead_attention_bias_forward_v2.inc"
int main(int argc, char* argv[]) { return run(argc, argv); }
example/52_flash_atten_bias/grouped_mutihead_attention_bias_infer.cpp 0 → 100644
View file @ 9bb4ab41
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
/*
Gemm + Softmax + Gemm fused operation. Computes C_g_m_o = Softmax(A_g_m_k * B0_g_k_n) * B1_g_n_o
|-----------------|
Gemm0
|-------------------------------------|
Gemm1
*/
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_mha_infer_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using AccDataType = F32;
using CShuffleDataType = F32;
using CDataType = F16;
using Acc0BiasDataType = F16;
using Acc1BiasDataType = void;
static constexpr ck::index_t NumDimG = 2;
static constexpr ck::index_t NumDimM = 1;
static constexpr ck::index_t NumDimN = 1;
static constexpr ck::index_t NumDimK = 1;
static constexpr ck::index_t NumDimO = 1;
using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = ck::tensor_operation::element_wise::Scale;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
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;
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionInfer_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
ADataType,
B0DataType,
B1DataType,
CDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecA,
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
64, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
2, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
1,
S<16, 16, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
// Ref Gemm0: fp16 in, fp32 out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B0DataType,
AccDataType,
AccDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp>;
// Ref Softmax: fp32 in, fp16 out
using ReferenceSoftmaxInstance =
ck::tensor_operation::host::ReferenceSoftmax<AccDataType, ADataType, AccDataType>;
// Ref Gemm1: fp16 in, fp16 out
using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B1DataType,
CDataType,
AccDataType,
AElementOp,
B1ElementOp,
CElementOp>;
#include "run_grouped_multihead_attention_bias_infer.inc"
int main(int argc, char* argv[]) { return run(argc, argv); }
example/52_flash_atten_bias/run_batched_multihead_attention_bias_forward.inc → example/52_flash_atten_bias/run_batched_multihead_attention_bias_forward_v2.inc
View file @ 9bb4ab41
......@@ -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])
// 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 G1 = 13;
ck::index_t G1Q = 12; // h_q
ck::index_t G1KV = 12; // h_kv
bool input_permute = false;
bool output_permute = true;
......@@ -37,7 +38,7 @@ 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]);
......@@ -48,21 +49,22 @@ int run(int argc, char* argv[])
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
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});
......@@ -244,6 +246,18 @@ int run(int argc, char* argv[])
if(do_verification)
{
// data objects for hipGraph verification
hipGraph_t graph;
hipGraphExec_t g_instance;
hipStream_t stream;
std::cout << "verification with hipGraph capturing and replaying ... " << std::endl;
HIP_CHECK_ERROR(hipStreamCreate(&stream));
HIP_CHECK_ERROR(hipGraphCreate(&graph, 0));
HIP_CHECK_ERROR(hipStreamBeginCapture(stream, hipStreamCaptureModeGlobal));
// run for storing z tensor
argument =
gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
......@@ -277,9 +291,19 @@ int run(int argc, char* argv[])
p_drop, // dropout ratio
{seed, offset}); // dropout random seed and offset, offset should be
// at least the number of elements on a thread
c_device_buf.SetZero();
lse_device_buf.SetZero();
invoker.Run(argument, StreamConfig{nullptr, false});
HIP_CHECK_ERROR(hipMemsetAsync(
c_device_buf.GetDeviceBuffer(), 0, c_device_buf.GetBufferSize(), stream));
HIP_CHECK_ERROR(hipMemsetAsync(
lse_device_buf.GetDeviceBuffer(), 0, lse_device_buf.GetBufferSize(), stream));
invoker.Run(argument, StreamConfig{stream, false});
HIP_CHECK_ERROR(hipStreamEndCapture(stream, &graph));
HIP_CHECK_ERROR(hipGraphInstantiate(&g_instance, graph, nullptr, nullptr, 0));
HIP_CHECK_ERROR(hipGraphLaunch(g_instance, stream));
HIP_CHECK_ERROR(hipStreamSynchronize(stream));
c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data());
z_device_buf.FromDevice(z_gs_ms_ns.mData.data());
......@@ -290,29 +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
......@@ -363,17 +395,17 @@ 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& 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& 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]);
});
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment