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Commit 792bb51a authored by Shucai Xiao's avatar Shucai Xiao
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parent e1876299
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Showing with 246 additions and 78 deletions
test/cpu_ops_test.cpp
View file @ 792bb51a
......@@ -1349,17 +1349,44 @@ TEST_CASE(min_test)
TEST_CASE(rnn_forward)
{
std::size_t batch_size = 2;
std::size_t seq_len = 2;
std::size_t batch_size = 2;
std::size_t seq_len = 2;
std::size_t hidden_size = 4;
std::size_t input_size = 3;
std::size_t num_dirct = 1;
std::vector<float> wf_data{0.4691, 0.3185, -0.2227 ,0.4423, -0.0609, -0.2803, 0.1744, 0.3146, 0.4049, -0.3973, -0.0890, -0.1636};
std::vector<float> rf_data{-0.0456, 0.1061, 0.1574, -0.4928, -0.4300, -0.1909, -0.0225, -0.2668, 0.1840, -0.4453, -0.4896, 0.1302, -0.0929, 0.3545, -0.4981, 0.0616};
std::vector<float> biasf_data{-0.4938, 0.4355, -0.3186, 0.2094, 0.1037, -0.1071, 0.4504, -0.3990};
std::size_t input_size = 3;
std::size_t num_dirct = 1;
std::vector<float> wf_data{0.4691,
0.3185,
-0.2227,
0.4423,
-0.0609,
-0.2803,
0.1744,
0.3146,
0.4049,
-0.3973,
-0.0890,
-0.1636};
std::vector<float> rf_data{-0.0456,
0.1061,
0.1574,
-0.4928,
-0.4300,
-0.1909,
-0.0225,
-0.2668,
0.1840,
-0.4453,
-0.4896,
0.1302,
-0.0929,
0.3545,
-0.4981,
0.0616};
std::vector<float> biasf_data{
-0.4938, 0.4355, -0.3186, 0.2094, 0.1037, -0.1071, 0.4504, -0.3990};
std::vector<float> input(seq_len * batch_size * input_size, 0);
input[0] = input[1] = 1.0;
float clip = 0.0f;
float clip = 0.0f;
{
std::vector<float> ih_data(num_dirct * batch_size * hidden_size, 0);
......@@ -1378,21 +1405,35 @@ TEST_CASE(rnn_forward)
auto bias = p.add_literal(migraphx::literal{b_shape, biasf_data});
p.add_instruction(migraphx::op::rnn{hidden_size,
{migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn::forward,
clip},
seq,
w,
r,
bias,
ih);
{migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn::forward,
clip},
seq,
w,
r,
bias,
ih);
p.compile(migraphx::cpu::target{});
auto hs_concat = p.eval({});
std::vector<float> hs_data;
hs_concat.visit([&](auto output) { hs_data.assign(output.begin(), output.end()); });
std::vector<float> hs_data_gold{0.37780784, 0.61055139, 0.55168478, -0.5888475, -0.37144644, 0.31708236, 0.13104209, -0.18736027,
0.03445704, 0.19167931, -0.3946827, -0.30889652, -0.22276389, 0.44193283, -0.16477929, -0.11893477};
std::vector<float> hs_data_gold{0.37780784,
0.61055139,
0.55168478,
-0.5888475,
-0.37144644,
0.31708236,
0.13104209,
-0.18736027,
0.03445704,
0.19167931,
-0.3946827,
-0.30889652,
-0.22276389,
0.44193283,
-0.16477929,
-0.11893477};
EXPECT(migraphx::verify_range(hs_data, hs_data_gold));
}
......@@ -1413,15 +1454,16 @@ TEST_CASE(rnn_forward)
migraphx::shape b_shape{migraphx::shape::float_type, {num_dirct, 2 * hidden_size}};
auto bias = p.add_literal(migraphx::literal{b_shape, biasf_data});
auto out_hs = p.add_instruction(migraphx::op::rnn{hidden_size,
auto out_hs =
p.add_instruction(migraphx::op::rnn{hidden_size,
{migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn::forward,
clip},
seq,
w,
r,
bias,
ih);
seq,
w,
r,
bias,
ih);
p.add_instruction(migraphx::op::rnn_last_output{}, out_hs);
p.compile(migraphx::cpu::target{});
......@@ -1430,24 +1472,57 @@ TEST_CASE(rnn_forward)
std::vector<float> last_output_data;
last_output.visit([&](auto out) { last_output_data.assign(out.begin(), out.end()); });
std::vector<float> last_output_data_gold{0.03445704, 0.19167931, -0.3946827, -0.30889652, -0.22276389, 0.44193283, -0.16477929, -0.11893477};
std::vector<float> last_output_data_gold{0.03445704,
0.19167931,
-0.3946827,
-0.30889652,
-0.22276389,
0.44193283,
-0.16477929,
-0.11893477};
EXPECT(migraphx::verify_range(last_output_data, last_output_data_gold));
}
}
TEST_CASE(rnn_reverse)
{
std::size_t batch_size = 2;
std::size_t seq_len = 2;
std::size_t batch_size = 2;
std::size_t seq_len = 2;
std::size_t hidden_size = 4;
std::size_t input_size = 3;
std::size_t num_dirct = 1;
std::vector<float> wr_data{-0.0296, -0.1341, 0.1761, -0.2325, -0.0717, 0.1852, 0.2720, 0.1471, -0.1097, 0.3363, -0.0587, -0.2302};
std::vector<float> rr_data{ 0.2528, -0.2333, 0.3973, 0.1593, -0.0388, 0.1702, 0.3829, -0.0712, -0.1668, 0.3074, -0.2854, 0.4049, -0.3737, -0.1051, 0.4482, -0.2841};
std::vector<float> biasr_data{-0.3188, 0.1341, -0.4446, 0.1389, 0.3117, 0.3664, 0.2352, 0.2552};
std::size_t input_size = 3;
std::size_t num_dirct = 1;
std::vector<float> wr_data{-0.0296,
-0.1341,
0.1761,
-0.2325,
-0.0717,
0.1852,
0.2720,
0.1471,
-0.1097,
0.3363,
-0.0587,
-0.2302};
std::vector<float> rr_data{0.2528,
-0.2333,
0.3973,
0.1593,
-0.0388,
0.1702,
0.3829,
-0.0712,
-0.1668,
0.3074,
-0.2854,
0.4049,
-0.3737,
-0.1051,
0.4482,
-0.2841};
std::vector<float> biasr_data{-0.3188, 0.1341, -0.4446, 0.1389, 0.3117, 0.3664, 0.2352, 0.2552};
std::vector<float> input(seq_len * batch_size * input_size, 0);
input[0] = input[1] = 1.0;
float clip = 0.0f;
float clip = 0.0f;
{
std::vector<float> ih_data(num_dirct * batch_size * hidden_size, 0);
......@@ -1469,18 +1544,32 @@ TEST_CASE(rnn_reverse)
{migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn::reverse,
clip},
seq,
w,
r,
bias,
ih);
seq,
w,
r,
bias,
ih);
p.compile(migraphx::cpu::target{});
auto hs_concat = p.eval({});
std::vector<float> hs_data;
hs_concat.visit([&](auto output) { hs_data.assign(output.begin(), output.end()); });
std::vector<float> hs_data_gold{-0.29385301, 0.16796815, 0.51075965, 0.40258689, -0.13818839, 0.44124447, 0.14365635, 0.14803654,
-0.0070999, 0.46251031, -0.20639211, 0.37488942, -0.0070999, 0.46251031, -0.20639211, 0.37488942};
std::vector<float> hs_data_gold{-0.29385301,
0.16796815,
0.51075965,
0.40258689,
-0.13818839,
0.44124447,
0.14365635,
0.14803654,
-0.0070999,
0.46251031,
-0.20639211,
0.37488942,
-0.0070999,
0.46251031,
-0.20639211,
0.37488942};
EXPECT(migraphx::verify_range(hs_data, hs_data_gold));
}
......@@ -1501,15 +1590,16 @@ TEST_CASE(rnn_reverse)
migraphx::shape b_shape{migraphx::shape::float_type, {num_dirct, 2 * hidden_size}};
auto bias = p.add_literal(migraphx::literal{b_shape, biasr_data});
auto out_hs = p.add_instruction(migraphx::op::rnn{hidden_size,
auto out_hs =
p.add_instruction(migraphx::op::rnn{hidden_size,
{migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn::reverse,
clip},
seq,
w,
r,
bias,
ih);
seq,
w,
r,
bias,
ih);
p.add_instruction(migraphx::op::rnn_last_output{}, out_hs);
p.compile(migraphx::cpu::target{});
......@@ -1518,27 +1608,87 @@ TEST_CASE(rnn_reverse)
std::vector<float> last_output_data;
last_output.visit([&](auto out) { last_output_data.assign(out.begin(), out.end()); });
std::vector<float> last_output_data_gold{-0.29385301, 0.16796815, 0.51075965, 0.40258689, -0.13818839, 0.44124447, 0.14365635, 0.14803654};
std::vector<float> last_output_data_gold{-0.29385301,
0.16796815,
0.51075965,
0.40258689,
-0.13818839,
0.44124447,
0.14365635,
0.14803654};
EXPECT(migraphx::verify_range(last_output_data, last_output_data_gold));
}
}
TEST_CASE(rnn_bidirectional)
{
std::size_t batch_size = 2;
std::size_t seq_len = 2;
std::size_t batch_size = 2;
std::size_t seq_len = 2;
std::size_t hidden_size = 4;
std::size_t input_size = 3;
std::size_t num_dirct = 2;
std::vector<float> wf_data{0.4691, 0.3185, -0.2227 ,0.4423, -0.0609, -0.2803, 0.1744, 0.3146, 0.4049, -0.3973, -0.0890, -0.1636};
std::vector<float> wr_data{-0.0296, -0.1341, 0.1761, -0.2325, -0.0717, 0.1852, 0.2720, 0.1471, -0.1097, 0.3363, -0.0587, -0.2302};
std::vector<float> rf_data{-0.0456, 0.1061, 0.1574, -0.4928, -0.4300, -0.1909, -0.0225, -0.2668, 0.1840, -0.4453, -0.4896, 0.1302, -0.0929, 0.3545, -0.4981, 0.0616};
std::vector<float> rr_data{ 0.2528, -0.2333, 0.3973, 0.1593, -0.0388, 0.1702, 0.3829, -0.0712, -0.1668, 0.3074, -0.2854, 0.4049, -0.3737, -0.1051, 0.4482, -0.2841};
std::vector<float> biasf_data{-0.4938, 0.4355, -0.3186, 0.2094, 0.1037, -0.1071, 0.4504, -0.3990};
std::vector<float> biasr_data{-0.3188, 0.1341, -0.4446, 0.1389, 0.3117, 0.3664, 0.2352, 0.2552};
std::size_t input_size = 3;
std::size_t num_dirct = 2;
std::vector<float> wf_data{0.4691,
0.3185,
-0.2227,
0.4423,
-0.0609,
-0.2803,
0.1744,
0.3146,
0.4049,
-0.3973,
-0.0890,
-0.1636};
std::vector<float> wr_data{-0.0296,
-0.1341,
0.1761,
-0.2325,
-0.0717,
0.1852,
0.2720,
0.1471,
-0.1097,
0.3363,
-0.0587,
-0.2302};
std::vector<float> rf_data{-0.0456,
0.1061,
0.1574,
-0.4928,
-0.4300,
-0.1909,
-0.0225,
-0.2668,
0.1840,
-0.4453,
-0.4896,
0.1302,
-0.0929,
0.3545,
-0.4981,
0.0616};
std::vector<float> rr_data{0.2528,
-0.2333,
0.3973,
0.1593,
-0.0388,
0.1702,
0.3829,
-0.0712,
-0.1668,
0.3074,
-0.2854,
0.4049,
-0.3737,
-0.1051,
0.4482,
-0.2841};
std::vector<float> biasf_data{
-0.4938, 0.4355, -0.3186, 0.2094, 0.1037, -0.1071, 0.4504, -0.3990};
std::vector<float> biasr_data{-0.3188, 0.1341, -0.4446, 0.1389, 0.3117, 0.3664, 0.2352, 0.2552};
std::vector<float> input(seq_len * batch_size * input_size, 0);
input[0] = input[1] = 1.0;
float clip = 0.0f;
float clip = 0.0f;
{
std::vector<float> ih_data(num_dirct * batch_size * hidden_size, 0);
......@@ -1565,23 +1715,26 @@ TEST_CASE(rnn_bidirectional)
auto bias = p.add_literal(migraphx::literal{b_shape, bias_data});
p.add_instruction(migraphx::op::rnn{hidden_size,
{migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn::bidirectional,
clip},
seq,
w,
r,
bias,
ih);
{migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn::bidirectional,
clip},
seq,
w,
r,
bias,
ih);
p.compile(migraphx::cpu::target{});
auto hs_concat = p.eval({});
std::vector<float> hs_data;
hs_concat.visit([&](auto output) { hs_data.assign(output.begin(), output.end()); });
std::vector<float> hs_data_gold{0.37780784, 0.61055139, 0.55168478, -0.5888475, -0.37144644, 0.31708236, 0.13104209, -0.18736027,
-0.29385301, 0.16796815, 0.51075965, 0.40258689, -0.13818839, 0.44124447, 0.14365635, 0.14803654,
0.03445704, 0.19167931, -0.3946827, -0.30889652, -0.22276389, 0.44193283, -0.16477929, -0.11893477,
-0.0070999, 0.46251031, -0.20639211, 0.37488942, -0.0070999, 0.46251031, -0.20639211, 0.37488942};
std::vector<float> hs_data_gold{
0.37780784, 0.61055139, 0.55168478, -0.5888475, -0.37144644, 0.31708236,
0.13104209, -0.18736027, -0.29385301, 0.16796815, 0.51075965, 0.40258689,
-0.13818839, 0.44124447, 0.14365635, 0.14803654, 0.03445704, 0.19167931,
-0.3946827, -0.30889652, -0.22276389, 0.44193283, -0.16477929, -0.11893477,
-0.0070999, 0.46251031, -0.20639211, 0.37488942, -0.0070999, 0.46251031,
-0.20639211, 0.37488942};
EXPECT(migraphx::verify_range(hs_data, hs_data_gold));
}
......@@ -1610,15 +1763,16 @@ TEST_CASE(rnn_bidirectional)
migraphx::shape b_shape{migraphx::shape::float_type, {num_dirct, 2 * hidden_size}};
auto bias = p.add_literal(migraphx::literal{b_shape, bias_data});
auto out_hs = p.add_instruction(migraphx::op::rnn{hidden_size,
auto out_hs =
p.add_instruction(migraphx::op::rnn{hidden_size,
{migraphx::op::tanh{}, migraphx::op::tanh{}},
migraphx::op::rnn::bidirectional,
clip},
seq,
w,
r,
bias,
ih);
seq,
w,
r,
bias,
ih);
p.add_instruction(migraphx::op::rnn_last_output{}, out_hs);
p.compile(migraphx::cpu::target{});
......@@ -1627,8 +1781,22 @@ TEST_CASE(rnn_bidirectional)
std::vector<float> last_output_data;
last_output.visit([&](auto out) { last_output_data.assign(out.begin(), out.end()); });
std::vector<float> last_output_data_gold{0.03445704, 0.19167931, -0.3946827, -0.30889652, -0.22276389, 0.44193283, -0.16477929, -0.11893477,
-0.29385301, 0.16796815, 0.51075965, 0.40258689, -0.13818839, 0.44124447, 0.14365635, 0.14803654};
std::vector<float> last_output_data_gold{0.03445704,
0.19167931,
-0.3946827,
-0.30889652,
-0.22276389,
0.44193283,
-0.16477929,
-0.11893477,
-0.29385301,
0.16796815,
0.51075965,
0.40258689,
-0.13818839,
0.44124447,
0.14365635,
0.14803654};
EXPECT(migraphx::verify_range(last_output_data, last_output_data_gold));
}
......
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