Skip to content

GitLab

Commit 673ca71c authored by Paul's avatar Paul
Browse files

Merge branch 'develop' into conv-add

parents 4bfe1662 91cc7242
Hide whitespace changes
Inline Side-by-side
Showing with 1045 additions and 67 deletions
test/onnx/gemm_test.onnx
View file @ 673ca71c
No preview for this file type
test/onnx/gen_onnx.py
View file @ 673ca71c
...@@ -1121,6 +1121,24 @@ def conv_dynamic_batch_test(): ...@@ -1121,6 +1121,24 @@ def conv_dynamic_batch_test():
return ([node], [x, y], [out]) return ([node], [x, y], [out])
@onnx_test()
def conv_dynamic_bias_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[None, 3, 32, 32])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
out = helper.make_tensor_value_info('3', TensorProto.FLOAT,
[None, 2, 28, 28])
node = onnx.helper.make_node('Conv',
inputs=['0', '1', '2'],
outputs=['3'],
dilations=[1, 1],
strides=[1, 1])
return ([node], [x, y, z], [out])
@onnx_test() @onnx_test()
def conv_dynamic_img_test(): def conv_dynamic_img_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
...@@ -2035,6 +2053,40 @@ def gather_test(): ...@@ -2035,6 +2053,40 @@ def gather_test():
return ([node], [x, i], [y]) return ([node], [x, i], [y])
@onnx_test()
def gather_scalar_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
i = helper.make_tensor_value_info('indices', TensorProto.INT32, [])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 5, 6])
node = onnx.helper.make_node(
'Gather',
inputs=['data', 'indices'],
outputs=['y'],
axis=1,
)
return ([node], [x, i], [y])
@onnx_test()
def gather_dyn_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT,
[None, 4, 5, 6])
i = helper.make_tensor_value_info('indices', TensorProto.INT32,
[None, 3, 4, 5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])
node = onnx.helper.make_node(
'Gather',
inputs=['data', 'indices'],
outputs=['y'],
axis=1,
)
return ([node], [x, i], [y])
@onnx_test() @onnx_test()
def gather_elements_axis0_test(): def gather_elements_axis0_test():
x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4]) x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4])
...@@ -2098,71 +2150,136 @@ def gathernd_batch_dims_test(): ...@@ -2098,71 +2150,136 @@ def gathernd_batch_dims_test():
@onnx_test() @onnx_test()
def gemm_test(): def gemm_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 7]) A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [8, 6])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [11, 5]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [8, 7])
z = helper.make_tensor_value_info('2', TensorProto.FLOAT, []) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [6, 7])
a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [7, 11]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [6, 7])
node = onnx.helper.make_node('Gemm', node = onnx.helper.make_node('Gemm',
inputs=['0', '1', '2'], inputs=['A', 'B', 'C'],
outputs=['3'], outputs=['Y'],
alpha=0.5,
beta=0.8,
transA=1)
return ([node], [A, B, C], [Y])
@onnx_test()
def gemm_no_C_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [5, 7])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [11, 5])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [7, 11])
node = onnx.helper.make_node('Gemm',
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=2.0, alpha=2.0,
beta=2.0, beta=2.0,
transA=1, transA=1,
transB=1) transB=1)
return ([node], [x, y, z], [a]) return ([node], [A, B, C], [Y])
@onnx_test() @onnx_test()
def gemm_ex_test(): def gemm_brcst_C_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 8, 6]) A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [5, 6])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 8, 7]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [5, 7])
m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 7]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [6, 1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [6, 7])
node = onnx.helper.make_node('Gemm', node = onnx.helper.make_node('Gemm',
inputs=['1', '2', '3'], inputs=['A', 'B', 'C'],
outputs=['y'], outputs=['Y'],
alpha=0.5, alpha=0.5,
beta=0.8, beta=0.8,
transA=1) transA=1)
return ([node], [m1, m2, m3], [y]) return ([node], [A, B, C], [Y])
@onnx_test() @onnx_test()
def gemm_ex_brcst_test(): def gemm_half_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6]) A = helper.make_tensor_value_info('A', TensorProto.FLOAT16, [8, 6])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT16, [8, 7])
m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 1]) C = helper.make_tensor_value_info('C', TensorProto.FLOAT16, [6, 1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT16, [6, 7])
node = onnx.helper.make_node('Gemm', node = onnx.helper.make_node('Gemm',
inputs=['1', '2', '3'], inputs=['A', 'B', 'C'],
outputs=['y'], outputs=['Y'],
alpha=0.5, alpha=0.5,
beta=0.8, beta=0.8,
transA=1) transA=1)
return ([node], [m1, m2, m3], [y]) return ([node], [A, B, C], [Y])
@onnx_test() @onnx_test()
def gemm_half_test(): def gemm_dyn_inner_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1, 1, 8, 6]) A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [None, 6])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1, 1, 8, 7]) B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [None, 7])
m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT16, [1, 1, 6, 1]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [6, 7])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [1, 1, 6, 7])
node = onnx.helper.make_node('Gemm', node = onnx.helper.make_node('Gemm',
inputs=['1', '2', '3'], inputs=['A', 'B'],
outputs=['y'], outputs=['Y'],
alpha=0.5,
transA=1)
return ([node], [A, B], [Y])
@onnx_test()
def gemm_dyn_outer_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [5, None])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [11, 5])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [None, 11])
node = onnx.helper.make_node('Gemm',
inputs=['A', 'B'],
outputs=['Y'],
alpha=2.0,
transA=1,
transB=1)
return ([node], [A, B], [Y])
@onnx_test()
def gemm_dyn_bias_test():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [8, None])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [8, 7])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [1, 7])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [None, 7])
node = onnx.helper.make_node('Gemm',
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=1.0,
beta=1.0,
transA=1)
return ([node], [A, B, C], [Y])
@onnx_test()
def gemm_rank_error():
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [4, 1, 8, 6])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [4, 1, 8, 7])
C = helper.make_tensor_value_info('C', TensorProto.FLOAT, [6, 7])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4, 1, 6, 7])
node = onnx.helper.make_node('Gemm',
inputs=['A', 'B', 'C'],
outputs=['Y'],
alpha=0.5, alpha=0.5,
beta=0.8, beta=0.8,
transA=1) transA=1)
return ([node], [m1, m2, m3], [y]) return ([node], [A, B, C], [Y])
@onnx_test() @onnx_test()
...@@ -3545,6 +3662,81 @@ def matmul_vv_test(): ...@@ -3545,6 +3662,81 @@ def matmul_vv_test():
return ([node], [m1, m2], [y]) return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_mm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, None])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_mv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_vm_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, None])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_vv_test():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test()
def matmul_dyn_broadcast_error():
m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, None])
node = onnx.helper.make_node(
'MatMul',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test() @onnx_test()
def matmulinteger_test(): def matmulinteger_test():
m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [3, 6, 16]) m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [3, 6, 16])
...@@ -3560,6 +3752,21 @@ def matmulinteger_test(): ...@@ -3560,6 +3752,21 @@ def matmulinteger_test():
return ([node], [m1, m2], [y]) return ([node], [m1, m2], [y])
@onnx_test()
def matmulinteger_dyn_error():
m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [None, 6, 16])
m2 = helper.make_tensor_value_info('2', TensorProto.INT8, [None, 16, 8])
y = helper.make_tensor_value_info('y', TensorProto.INT32, [None, 6, 8])
node = onnx.helper.make_node(
'MatMulInteger',
inputs=['1', '2'],
outputs=['y'],
)
return ([node], [m1, m2], [y])
@onnx_test() @onnx_test()
def max_test(): def max_test():
a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3]) a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
...@@ -4198,6 +4405,53 @@ def pad_reflect_multiaxis_test(): ...@@ -4198,6 +4405,53 @@ def pad_reflect_multiaxis_test():
return ([arg_pad, node], [x], [y]) return ([arg_pad, node], [x], [y])
@onnx_test()
def pad_attr_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
node = onnx.helper.make_node('Pad',
inputs=['0'],
pads=[1, 1, 1, 1],
outputs=['1'])
return ([node], [x], [y])
@onnx_test()
def pad_cnst_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
sizes = np.array([0, 2, 0, 1])
pad_tensor = helper.make_tensor(name='pad_size',
data_type=TensorProto.INT32,
dims=sizes.shape,
vals=sizes.astype(int))
arg_pad = onnx.helper.make_node('Constant',
inputs=[],
outputs=['arg_pad'],
value=pad_tensor)
node = onnx.helper.make_node('Pad', inputs=['0', 'arg_pad'], outputs=['1'])
return ([arg_pad, node], [x], [y])
@onnx_test()
def pad_dyn_reflect_error():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
node = onnx.helper.make_node('Pad',
mode='reflect',
inputs=['0'],
pads=[0, 2, 0, 1],
outputs=['1'])
return ([node], [x], [y])
@onnx_test() @onnx_test()
def pow_test(): def pow_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
...@@ -6551,6 +6805,92 @@ def transpose_gather_test(): ...@@ -6551,6 +6805,92 @@ def transpose_gather_test():
return ([td, ti, node], [x, i], [y]) return ([td, ti, node], [x, i], [y])
@onnx_test()
def trilu_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
node = onnx.helper.make_node(
'Trilu',
inputs=['x'],
outputs=['y'],
)
return ([node], [x], [y])
@onnx_test()
def trilu_batch_diff_k_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3])
k = np.array([2])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3])
k_tensor = helper.make_tensor(name='k',
data_type=TensorProto.INT64,
dims=k.shape,
vals=k.astype(np.int64))
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
return ([node], [x], [y], [k_tensor])
@onnx_test()
def trilu_lower_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
node = onnx.helper.make_node('Trilu', inputs=['x'], outputs=['y'], upper=0)
return ([node], [x], [y])
@onnx_test()
def trilu_neg_k_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
k = np.array([-1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
k_tensor = helper.make_tensor(name='k',
data_type=TensorProto.INT64,
dims=k.shape,
vals=k.astype(np.int64))
node = onnx.helper.make_node('Trilu', inputs=['x', 'k'], outputs=['y'])
return ([node], [x], [y], [k_tensor])
@onnx_test()
def trilu_out_k_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
k = np.array([5])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
k_tensor = helper.make_tensor(name='k',
data_type=TensorProto.INT64,
dims=k.shape,
vals=k.astype(np.int64))
node = onnx.helper.make_node('Trilu', inputs=['x', 'k'], outputs=['y'])
return ([node], [x], [y], [k_tensor])
@onnx_test()
def trilu_row_one_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4])
k = np.array([1])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4])
k_tensor = helper.make_tensor(name='k',
data_type=TensorProto.INT64,
dims=k.shape,
vals=k.astype(np.int64))
node = onnx.helper.make_node(
'Trilu',
inputs=['x', 'k'],
outputs=['y'],
)
return ([node], [x], [y], [k_tensor])
@onnx_test() @onnx_test()
def undefined_test(): def undefined_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5]) x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
......
test/onnx/onnx_test.cpp
View file @ 673ca71c
...@@ -1118,6 +1118,25 @@ TEST_CASE(conv_dynamic_batch_test) ...@@ -1118,6 +1118,25 @@ TEST_CASE(conv_dynamic_batch_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(conv_dynamic_bias_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x0 = mm->add_parameter(
"0", {migraphx::shape::float_type, {{1, 6, 0}, {3, 3, 0}, {32, 32, 0}, {32, 32, 0}}});
auto x1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
auto x2 = mm->add_parameter("2", {migraphx::shape::float_type, {1}});
auto x3 = mm->add_instruction(migraphx::make_op("convolution"), x0, x1);
auto x4 = mm->add_instruction(migraphx::make_op("broadcast", {{"axis", 1}}), x2, x3);
auto x5 = mm->add_instruction(migraphx::make_op("add"), x3, x4);
mm->add_return({x5});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 6, 0};
auto prog = migraphx::parse_onnx("conv_dynamic_bias_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(conv_dynamic_img_test) TEST_CASE(conv_dynamic_img_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -2029,6 +2048,46 @@ TEST_CASE(gather_test) ...@@ -2029,6 +2048,46 @@ TEST_CASE(gather_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(gather_scalar_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("data", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
std::vector<size_t> idims{1};
auto l1 =
mm->add_parameter("indices", migraphx::shape{migraphx::shape::int32_type, idims, {0}});
int axis = 1;
mm->add_instruction(migraphx::make_op("gather", {{"axis", axis}}), l0, l1);
auto prog = optimize_onnx("gather_scalar_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(gather_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter(
"data",
migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {4, 4, 0}, {5, 5, 0}, {6, 6, 0}}});
auto l1 = mm->add_parameter(
"indices",
migraphx::shape{migraphx::shape::int32_type, {{1, 4, 0}, {3, 3, 0}, {4, 4, 0}, {5, 5, 0}}});
auto cont_l0 = mm->add_instruction(migraphx::make_op("contiguous"), l0);
auto cont_l1 = mm->add_instruction(migraphx::make_op("contiguous"), l1);
int axis = 1;
auto gather_op = migraphx::make_op("gather", {{"axis", axis}});
auto ret = mm->add_instruction(gather_op, cont_l0, cont_l1);
mm->add_return({ret});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
auto prog = parse_onnx("gather_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(gather_elements_axis0_test) TEST_CASE(gather_elements_axis0_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -2116,64 +2175,64 @@ TEST_CASE(gemm_test) ...@@ -2116,64 +2175,64 @@ TEST_CASE(gemm_test)
{ {
migraphx::program p; migraphx::program p;
auto* mm = p.get_main_module(); auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {5, 7}}); auto l0 = mm->add_parameter("A", migraphx::shape{migraphx::shape::float_type, {8, 6}});
auto l1 = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {11, 5}}); auto l1 = mm->add_parameter("B", migraphx::shape{migraphx::shape::float_type, {8, 7}});
auto l2 = mm->add_parameter("2", migraphx::shape{migraphx::shape::float_type}); auto l2 = mm->add_parameter("C", migraphx::shape{migraphx::shape::float_type, {6, 7}});
auto alpha = 2.f; auto alpha = 0.5f;
auto beta = 2.0f; auto beta = 0.8f;
auto a_l = mm->add_literal(alpha); auto a_l = mm->add_literal(alpha);
auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0}); auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0});
t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), t_a); t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), t_a);
auto t1 = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), l1); auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, l1}, migraphx::make_op("dot"), 1.0f, 0.0f);
auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, t1}, migraphx::make_op("dot"), 1.0f, 0.0f);
auto b_l = mm->add_literal(beta); auto b_l = mm->add_literal(beta);
auto l2_b =
mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {7, 11}}}), l2);
auto b_b = mm->add_instruction( auto b_b = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", l2_b->get_shape().lens()}}), b_l); migraphx::make_op("multibroadcast", {{"out_lens", l2->get_shape().lens()}}), b_l);
auto l2_bb = mm->add_instruction(migraphx::make_op("mul"), l2_b, b_b); auto l2_b = mm->add_instruction(migraphx::make_op("mul"), l2, b_b);
mm->add_instruction(migraphx::make_op("add"), dot, l2_bb); mm->add_instruction(migraphx::make_op("add"), dot, l2_b);
auto prog = optimize_onnx("gemm_test.onnx"); auto prog = optimize_onnx("gemm_test.onnx");
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(gemm_ex_test) TEST_CASE(gemm_no_C_test)
{ {
migraphx::program p; migraphx::program p;
auto* mm = p.get_main_module(); auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 1, 8, 6}}); auto l0 = mm->add_parameter("A", migraphx::shape{migraphx::shape::float_type, {5, 7}});
auto l1 = mm->add_parameter("2", migraphx::shape{migraphx::shape::float_type, {1, 1, 8, 7}}); auto l1 = mm->add_parameter("B", migraphx::shape{migraphx::shape::float_type, {11, 5}});
auto l2 = mm->add_parameter("3", migraphx::shape{migraphx::shape::float_type, {1, 1, 6, 7}}); auto l2 = mm->add_parameter("C", migraphx::shape{migraphx::shape::float_type});
auto alpha = 0.5f; auto alpha = 2.f;
auto beta = 0.8f; auto beta = 2.0f;
auto a_l = mm->add_literal(alpha); auto a_l = mm->add_literal(alpha);
auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0}); auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0});
t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 1, 3, 2}}}), t_a); t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), t_a);
auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, l1}, migraphx::make_op("dot"), 1.0f, 0.0f); auto t1 = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), l1);
auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, t1}, migraphx::make_op("dot"), 1.0f, 0.0f);
auto b_l = mm->add_literal(beta); auto b_l = mm->add_literal(beta);
auto l2_b =
mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {7, 11}}}), l2);
auto b_b = mm->add_instruction( auto b_b = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", l2->get_shape().lens()}}), b_l); migraphx::make_op("multibroadcast", {{"out_lens", l2_b->get_shape().lens()}}), b_l);
auto l2_b = mm->add_instruction(migraphx::make_op("mul"), l2, b_b); auto l2_bb = mm->add_instruction(migraphx::make_op("mul"), l2_b, b_b);
mm->add_instruction(migraphx::make_op("add"), dot, l2_b); mm->add_instruction(migraphx::make_op("add"), dot, l2_bb);
auto prog = optimize_onnx("gemm_ex_test.onnx"); auto prog = optimize_onnx("gemm_no_C_test.onnx");
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(gemm_ex_brcst_test) TEST_CASE(gemm_brcst_C_test)
{ {
migraphx::program p; migraphx::program p;
auto* mm = p.get_main_module(); auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 6}}); auto l0 = mm->add_parameter("A", migraphx::shape{migraphx::shape::float_type, {5, 6}});
auto l1 = mm->add_parameter("2", migraphx::shape{migraphx::shape::float_type, {1, 1, 5, 7}}); auto l1 = mm->add_parameter("B", migraphx::shape{migraphx::shape::float_type, {5, 7}});
auto l2 = mm->add_parameter("3", migraphx::shape{migraphx::shape::float_type, {1, 1, 6, 1}}); auto l2 = mm->add_parameter("C", migraphx::shape{migraphx::shape::float_type, {6, 1}});
std::vector<std::size_t> out_lens{1, 1, 6, 7}; std::vector<std::size_t> out_lens{6, 7};
auto alpha = 0.5f; auto alpha = 0.5f;
auto beta = 0.8f; auto beta = 0.8f;
auto a_l = mm->add_literal(alpha); auto a_l = mm->add_literal(alpha);
auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0}); auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0});
t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 1, 3, 2}}}), t_a); t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), t_a);
auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, l1}, migraphx::make_op("dot"), 1.0f, 0.0f); auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, l1}, migraphx::make_op("dot"), 1.0f, 0.0f);
auto b_l = mm->add_literal(beta); auto b_l = mm->add_literal(beta);
auto l2_b = auto l2_b =
...@@ -2183,7 +2242,7 @@ TEST_CASE(gemm_ex_brcst_test) ...@@ -2183,7 +2242,7 @@ TEST_CASE(gemm_ex_brcst_test)
auto l2_bb = mm->add_instruction(migraphx::make_op("mul"), l2_b, b_b); auto l2_bb = mm->add_instruction(migraphx::make_op("mul"), l2_b, b_b);
mm->add_instruction(migraphx::make_op("add"), dot, l2_bb); mm->add_instruction(migraphx::make_op("add"), dot, l2_bb);
auto prog = optimize_onnx("gemm_ex_brcst_test.onnx"); auto prog = optimize_onnx("gemm_brcst_C_test.onnx");
EXPECT(p == prog); EXPECT(p == prog);
} }
...@@ -2191,17 +2250,17 @@ TEST_CASE(gemm_half_test) ...@@ -2191,17 +2250,17 @@ TEST_CASE(gemm_half_test)
{ {
migraphx::program p; migraphx::program p;
auto* mm = p.get_main_module(); auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("1", migraphx::shape{migraphx::shape::half_type, {1, 1, 8, 6}}); auto l0 = mm->add_parameter("A", migraphx::shape{migraphx::shape::half_type, {8, 6}});
auto l1 = mm->add_parameter("2", migraphx::shape{migraphx::shape::half_type, {1, 1, 8, 7}}); auto l1 = mm->add_parameter("B", migraphx::shape{migraphx::shape::half_type, {8, 7}});
auto l2 = mm->add_parameter("3", migraphx::shape{migraphx::shape::half_type, {1, 1, 6, 1}}); auto l2 = mm->add_parameter("C", migraphx::shape{migraphx::shape::half_type, {6, 1}});
auto alpha = 0.5f; auto alpha = 0.5f;
auto beta = 0.8f; auto beta = 0.8f;
auto a_l = mm->add_literal(alpha); auto a_l = mm->add_literal(alpha);
auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0}); auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0});
t_a = mm->add_instruction( t_a = mm->add_instruction(
migraphx::make_op("convert", {{"target_type", migraphx::shape::half_type}}), t_a); migraphx::make_op("convert", {{"target_type", migraphx::shape::half_type}}), t_a);
t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 1, 3, 2}}}), t_a); t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), t_a);
std::vector<std::size_t> lens = {1, 1, 6, 7}; std::vector<std::size_t> lens = {6, 7};
auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, l1}, migraphx::make_op("dot"), 1.0f, 0.0f); auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, l1}, migraphx::make_op("dot"), 1.0f, 0.0f);
l2 = mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", lens}}), l2); l2 = mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", lens}}), l2);
l2 = mm->add_instruction( l2 = mm->add_instruction(
...@@ -2217,6 +2276,73 @@ TEST_CASE(gemm_half_test) ...@@ -2217,6 +2276,73 @@ TEST_CASE(gemm_half_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(gemm_dyn_inner_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter(
"A", migraphx::shape{migraphx::shape::float_type, {{1, 10, 8}, {6, 6, 0}}});
auto l1 = mm->add_parameter(
"B", migraphx::shape{migraphx::shape::float_type, {{1, 10, 8}, {7, 7, 0}}});
auto alpha = 0.5f;
auto a_l = mm->add_literal(alpha);
auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0});
t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), t_a);
auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, l1}, migraphx::make_op("dot"), 1.0f, 0.0f);
mm->add_return({dot});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 10, 8};
auto prog = migraphx::parse_onnx("gemm_dyn_inner_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(gemm_dyn_outer_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter(
"A", migraphx::shape{migraphx::shape::float_type, {{5, 5, 0}, {5, 10, 7}}});
auto l1 = mm->add_parameter("B", migraphx::shape{migraphx::shape::float_type, {11, 5}});
auto alpha = 2.f;
auto a_l = mm->add_literal(alpha);
auto t_a = add_common_op(*mm, migraphx::make_op("mul"), {a_l, l0});
t_a = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), t_a);
auto t1 = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), l1);
auto dot = migraphx::add_apply_alpha_beta(*mm, {t_a, t1}, migraphx::make_op("dot"), 1.0f, 0.0f);
mm->add_return({dot});
migraphx::onnx_options options;
options.default_dyn_dim_value = {5, 10, 7};
auto prog = migraphx::parse_onnx("gemm_dyn_outer_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(gemm_dyn_bias_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x0 =
mm->add_parameter("A", migraphx::shape{migraphx::shape::float_type, {{8, 8}, {1, 10}}});
auto x1 = mm->add_parameter("B", migraphx::shape{migraphx::shape::float_type, {8, 7}});
auto x2 = mm->add_parameter("C", migraphx::shape{migraphx::shape::float_type, {1, 7}});
auto x0_t = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {1, 0}}}), x0);
auto dot = mm->add_instruction(migraphx::make_op("dot"), x0_t, x1);
auto x2_b = mm->add_instruction(migraphx::make_op("multibroadcast"), x2, dot);
auto ret = mm->add_instruction(migraphx::make_op("add"), dot, x2_b);
mm->add_return({ret});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 10};
auto prog = parse_onnx("gemm_dyn_bias_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(gemm_rank_error)
{
EXPECT(test::throws([&] { migraphx::parse_onnx("gemm_rank_error.onnx"); }));
}
TEST_CASE(globalavgpool_test) TEST_CASE(globalavgpool_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -3413,6 +3539,92 @@ TEST_CASE(matmul_vv_test) ...@@ -3413,6 +3539,92 @@ TEST_CASE(matmul_vv_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(matmul_dyn_mm_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter(
"1", migraphx::shape{migraphx::shape::float_type, {{4, 8, 6}, {7, 7, 0}}});
auto l1 = mm->add_parameter(
"2", migraphx::shape{migraphx::shape::float_type, {{7, 7, 0}, {1, 5, 3}}});
auto ret = migraphx::add_apply_alpha_beta(*mm, {l0, l1}, migraphx::make_op("dot"), 1.0f, 0.0f);
mm->add_return({ret});
migraphx::onnx_options options;
options.map_dyn_input_dims["1"] = {{4, 8, 6}, {7, 7, 0}};
options.map_dyn_input_dims["2"] = {{7, 7, 0}, {1, 5, 3}};
auto prog = parse_onnx("matmul_dyn_mm_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(matmul_dyn_mv_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter(
"1", migraphx::shape{migraphx::shape::float_type, {{4, 8, 6}, {7, 7, 0}}});
auto l1 = mm->add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7}});
auto sl1 = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), l1);
auto res = migraphx::add_apply_alpha_beta(*mm, {l0, sl1}, migraphx::make_op("dot"), 1.0f, 0.0f);
auto ret = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {1}}}), res);
mm->add_return({ret});
migraphx::onnx_options options;
options.map_dyn_input_dims["1"] = {{4, 8, 6}, {7, 7, 0}};
auto prog = parse_onnx("matmul_dyn_mv_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(matmul_dyn_vm_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7}});
auto l1 = mm->add_parameter(
"2", migraphx::shape{migraphx::shape::float_type, {{7, 7, 0}, {4, 10, 8}}});
auto sl0 = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {0}}}), l0);
auto res = migraphx::add_apply_alpha_beta(*mm, {sl0, l1}, migraphx::make_op("dot"), 1.0f, 0.0f);
auto ret = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0}}}), res);
mm->add_return({ret});
migraphx::onnx_options options;
options.map_dyn_input_dims["2"] = {{7, 7, 0}, {4, 10, 8}};
auto prog = parse_onnx("matmul_dyn_vm_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(matmul_dyn_vv_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape::dynamic_dimension dd{5, 8, 7};
auto l0 = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {dd}});
auto l1 = mm->add_parameter("2", migraphx::shape{migraphx::shape::float_type, {dd}});
auto sl0 = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {0}}}), l0);
auto sl1 = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), l1);
auto res =
migraphx::add_apply_alpha_beta(*mm, {sl0, sl1}, migraphx::make_op("dot"), 1.0f, 0.0f);
auto sr0 = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0}}}), res);
auto ret = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0}}}), sr0);
mm->add_return({ret});
migraphx::onnx_options options;
options.default_dyn_dim_value = dd;
auto prog = parse_onnx("matmul_dyn_vv_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(matmul_dyn_broadcast_error)
{
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
EXPECT(test::throws([&] { migraphx::parse_onnx("matmul_dyn_broadcast_error.onnx", options); }));
}
TEST_CASE(matmulinteger_test) TEST_CASE(matmulinteger_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -3426,6 +3638,13 @@ TEST_CASE(matmulinteger_test) ...@@ -3426,6 +3638,13 @@ TEST_CASE(matmulinteger_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(matmulinteger_dyn_error)
{
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
EXPECT(test::throws([&] { migraphx::parse_onnx("matmulinteger_dyn_error.onnx", options); }));
}
TEST_CASE(max_test) TEST_CASE(max_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -4016,6 +4235,44 @@ TEST_CASE(pad_3arg_test) ...@@ -4016,6 +4235,44 @@ TEST_CASE(pad_3arg_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(pad_attr_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter(
"0", migraphx::shape{migraphx::shape::float_type, {{2, 4, 2}, {2, 4, 2}}});
auto ret = mm->add_instruction(migraphx::make_op("pad", {{"pads", {1, 1, 1, 1}}}), x);
mm->add_return({ret});
migraphx::onnx_options options;
options.map_dyn_input_dims["0"] = {{2, 4, 2}, {2, 4, 2}};
auto prog = parse_onnx("pad_attr_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(pad_cnst_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter(
"0", migraphx::shape{migraphx::shape::float_type, {{2, 4, 2}, {2, 4, 2}}});
mm->add_literal({migraphx::shape{migraphx::shape::int32_type, {4}}, {0, 2, 0, 1}});
auto ret = mm->add_instruction(migraphx::make_op("pad", {{"pads", {0, 2, 0, 1}}}), x);
mm->add_return({ret});
migraphx::onnx_options options;
options.map_dyn_input_dims["0"] = {{2, 4, 2}, {2, 4, 2}};
auto prog = parse_onnx("pad_cnst_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(pad_dyn_reflect_error)
{
migraphx::onnx_options options;
options.default_dyn_dim_value = {2, 4, 2};
EXPECT(test::throws([&] { migraphx::parse_onnx("pad_dyn_reflect_error.onnx", options); }));
}
TEST_CASE(pad_reflect_test) TEST_CASE(pad_reflect_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -6335,6 +6592,11 @@ TEST_CASE(transpose_gather_test) ...@@ -6335,6 +6592,11 @@ TEST_CASE(transpose_gather_test)
EXPECT(p.sort() == prog.sort()); EXPECT(p.sort() == prog.sort());
} }
TEST_CASE(trilu_neg_k_test)
{
EXPECT(test::throws([&] { migraphx::parse_onnx("trilu_neg_k_test.onnx"); }));
}
TEST_CASE(undefined_test) TEST_CASE(undefined_test)
{ {
migraphx::program p; migraphx::program p;
......
test/onnx/trilu_batch_diff_k_test.onnx 0 → 100644
View file @ 673ca71c
trilu_batch_diff_k_test:i

x
ky"Trilutrilu_batch_diff_k_test*
:BkZ
x



b
y



B
\ No newline at end of file
test/onnx/trilu_neg_k_test.onnx 0 → 100644
View file @ 673ca71c
trilu_neg_k_test:c

x
ky"Trilutrilu_neg_k_test*:
BkZ
x


b
y


B
\ No newline at end of file
test/onnx/trilu_out_k_test.onnx 0 → 100644
View file @ 673ca71c
trilu_out_k_test:Z

x
ky"Trilutrilu_out_k_test*
:BkZ
x


b
y


B
\ No newline at end of file
test/onnx/trilu_row_one_test.onnx 0 → 100644
View file @ 673ca71c
trilu_row_one_test:\

x
ky"Trilutrilu_row_one_test*
:BkZ
x


b
y


B
\ No newline at end of file
test/onnx/trilu_test.onnx 0 → 100644
View file @ 673ca71c

trilu_test:E
xy"Trilu
trilu_testZ
x


b
y


B
\ No newline at end of file
test/onnx/verify_onnx.cpp
View file @ 673ca71c
...@@ -451,6 +451,94 @@ TEST_CASE(gather_elements) ...@@ -451,6 +451,94 @@ TEST_CASE(gather_elements)
EXPECT(migraphx::verify_range(result_vector, gold)); EXPECT(migraphx::verify_range(result_vector, gold));
} }
TEST_CASE(gemm_test)
{
migraphx::program p = migraphx::parse_onnx("gemm_brcst_C_test.onnx");
p.compile(migraphx::ref::target{});
migraphx::shape a_shape{migraphx::shape::float_type, {5, 6}};
std::vector<float> a_data = {0.26472837, 0.8525864, 0.41929847, 0.14151508, 0.43216065,
0.67468566, 0.42488748, 0.82021785, 0.9782456, 0.5794279,
0.6627283, 0.4790396, 0.9237051, 0.7340607, 0.67379653,
0.87168175, 0.37324256, 0.33278653, 0.42736676, 0.024699844,
0.75851107, 0.48719302, 0.5834426, 0.6938476, 0.43747696,
0.24054702, 0.26912406, 0.6760658, 0.5419149, 0.89949054};
migraphx::shape b_shape{migraphx::shape::float_type, {5, 7}};
std::vector<float> b_data = {
0.65727437, 0.54262096, 0.14126152, 0.8994123, 0.21831702, 0.81191784, 0.9371278,
0.3438551, 0.7121373, 0.90316695, 0.26614252, 0.80144906, 0.80301756, 0.49930334,
0.0719704, 0.63484156, 0.7343097, 0.32130218, 0.7094916, 0.6116475, 0.74144083,
0.021210382, 0.38724765, 0.44830495, 0.62347615, 0.022489505, 0.23316588, 0.76540905,
0.895689, 0.81540287, 0.223875, 0.9275573, 0.4621397, 0.70785195, 0.5658555};
migraphx::shape c_shape{migraphx::shape::float_type, {6, 1}};
std::vector<float> c_data = {
0.07358502, 0.13792239, 0.8574055, 0.40553397, 0.38205826, 0.62062204};
migraphx::parameter_map params;
params["A"] = migraphx::argument(a_shape, a_data.data());
params["B"] = migraphx::argument(b_shape, b_data.data());
params["C"] = migraphx::argument(c_shape, c_data.data());
auto result = p.eval(params).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {
0.45261115, 0.83629227, 0.7533463, 0.7189715, 0.69160205, 0.824082, 0.9187499,
0.6659525, 0.96956736, 0.84293026, 0.8400868, 0.84835225, 1.0982862, 1.0642393,
1.1447254, 1.6184721, 1.6048342, 1.4741788, 1.4334437, 1.638659, 1.7428316,
0.8098607, 1.2157929, 1.1010075, 1.0706307, 1.0429881, 1.1771785, 1.2362702,
0.8239243, 1.1112559, 0.9639262, 1.0813537, 0.8825792, 1.121141, 1.1885703,
1.2227502, 1.4568202, 1.1388762, 1.55058, 1.0958102, 1.4637487, 1.5756242};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(gemm_half_test)
{
migraphx::program p = migraphx::parse_onnx("gemm_half_test.onnx");
p.compile(migraphx::ref::target{});
migraphx::shape a_shape{migraphx::shape::half_type, {8, 6}};
std::vector tmp = {0.2646, 0.8525, 0.4192, 0.1415, 0.4321, 0.675, 0.4248, 0.8203,
0.978, 0.5796, 0.6626, 0.479, 0.924, 0.734, 0.674, 0.8716,
0.3733, 0.3328, 0.4272, 0.0247, 0.7583, 0.4873, 0.5835, 0.694,
0.4375, 0.2406, 0.269, 0.6763, 0.542, 0.8994, 0.657, 0.5425,
0.1412, 0.8994, 0.2183, 0.812, 0.937, 0.3438, 0.712, 0.9033,
0.266, 0.8013, 0.803, 0.4993, 0.07196, 0.635, 0.7344, 0.3213};
std::vector<migraphx::half> a_data{tmp.cbegin(), tmp.cend()};
migraphx::shape b_shape{migraphx::shape::half_type, {8, 7}};
tmp = {0.7095, 0.612, 0.741, 0.02121, 0.3872, 0.4482, 0.6235, 0.02249, 0.2332, 0.7656,
0.8955, 0.8154, 0.2239, 0.9277, 0.4622, 0.708, 0.566, 0.0736, 0.138, 0.8574,
0.4055, 0.382, 0.6206, 0.424, 0.3674, 0.435, 0.998, 0.3594, 0.701, 0.6216,
0.01826, 0.6313, 0.514, 0.1095, 0.3203, 0.01636, 0.537, 0.01952, 0.4502, 0.8965,
0.5415, 0.7456, 0.793, 0.756, 0.9, 0.5264, 0.05368, 0.4214, 0.276, 0.1517,
0.08453, 0.83, 0.417, 0.1682, 0.845, 0.1729};
std::vector<migraphx::half> b_data{tmp.cbegin(), tmp.cend()};
migraphx::shape c_shape{migraphx::shape::half_type, {6, 1}};
tmp = {0.10846, 0.672, 0.527, 0.94, 0.429, 0.2291};
std::vector<migraphx::half> c_data{tmp.cbegin(), tmp.cend()};
migraphx::parameter_map params;
params["A"] = migraphx::argument(a_shape, a_data.data());
params["B"] = migraphx::argument(b_shape, b_data.data());
params["C"] = migraphx::argument(c_shape, c_data.data());
auto result = p.eval(params).back();
std::vector<migraphx::half> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
tmp = {1.071, 1.378, 1.465, 1.093, 0.968, 1.542, 1.145, 1.287, 1.533, 1.75, 1.338,
1.449, 1.592, 1.668, 1.265, 1.531, 1.656, 1.348, 1.2705, 1.525, 1.479, 1.754,
2.143, 2.062, 1.921, 1.836, 2.203, 1.952, 1.055, 1.225, 1.418, 1.209, 1.155,
1.42, 1.234, 1.302, 1.593, 1.368, 1.289, 1.327, 1.451, 1.394};
std::vector<migraphx::half> gold{tmp.cbegin(), tmp.cend()};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(greaterorequal_test) TEST_CASE(greaterorequal_test)
{ {
migraphx::program p = migraphx::parse_onnx("greaterorequal_test.onnx"); migraphx::program p = migraphx::parse_onnx("greaterorequal_test.onnx");
...@@ -1370,4 +1458,73 @@ TEST_CASE(where_test) ...@@ -1370,4 +1458,73 @@ TEST_CASE(where_test)
EXPECT(migraphx::verify_range(result_vector, gold)); EXPECT(migraphx::verify_range(result_vector, gold));
} }
std::vector<float> gen_trilu_test(const migraphx::shape& s, const migraphx::program& p)
{
// input data filled with values 1 to nelements
std::vector<float> x_data(s.elements());
std::iota(x_data.begin(), x_data.end(), 1);
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s, x_data.data());
auto result = p.eval(pp).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
return result_vector;
}
TEST_CASE(trilu_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
std::vector<float> gold = {1, 2, 3, 4, 0, 6, 7, 8, 0, 0, 11, 12};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(trilu_batch_diff_k_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_batch_diff_k_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {2, 2, 3}}, p);
std::vector<float> gold = {0, 0, 3, 0, 0, 0, 0, 0, 9, 0, 0, 0};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(trilu_lower_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_lower_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
std::vector<float> gold = {0, 0, 0, 0, 5, 0, 0, 0, 9, 10, 0, 0};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(trilu_out_k_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_out_k_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
std::vector<float> gold(12, 0);
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(trilu_row_one_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_row_one_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {1, 4}}, p);
std::vector<float> gold = {0, 2, 3, 4};
EXPECT(migraphx::verify_range(result_vector, gold));
}
int main(int argc, const char* argv[]) { test::run(argc, argv); } int main(int argc, const char* argv[]) { test::run(argc, argv); }
test/op_shape_test.cpp
View file @ 673ca71c
...@@ -831,6 +831,77 @@ TEST_CASE(gather) ...@@ -831,6 +831,77 @@ TEST_CASE(gather)
} }
} }
TEST_CASE(gather_dyn0)
{
// Insert dynamic index into dynamic shape
migraphx::shape input{migraphx::shape::float_type,
{{2, 3, 2}, {3, 4, 3}, {6, 9, 7}, {12, 14, 13}}};
migraphx::shape indices{migraphx::shape::int32_type, {{2, 7, 3}, {3, 3, 0}}};
int axis = 1;
expect_shape(migraphx::shape{migraphx::shape::float_type,
{{2, 3, 2}, {2, 7, 3}, {3, 3, 0}, {6, 9, 7}, {12, 14, 13}}},
migraphx::make_op("gather", {{"axis", axis}}),
input,
indices);
}
TEST_CASE(gather_dyn1)
{
// Insert static index into dynamic shape
migraphx::shape input{migraphx::shape::float_type,
{{2, 3, 2}, {3, 4, 3}, {6, 9, 7}, {12, 14, 13}}};
migraphx::shape indices{migraphx::shape::int32_type, {2, 3}};
int axis = 1;
expect_shape(migraphx::shape{migraphx::shape::float_type,
{{2, 3, 2}, {2, 2, 0}, {3, 3, 0}, {6, 9, 7}, {12, 14, 13}}},
migraphx::make_op("gather", {{"axis", axis}}),
input,
indices);
}
TEST_CASE(gather_dyn2)
{
// Insert scalar (static) index into dynamic shape
migraphx::shape input{migraphx::shape::float_type,
{{2, 3, 2}, {3, 4, 3}, {6, 9, 7}, {12, 14, 13}}};
std::vector<std::size_t> mins;
std::vector<std::size_t> maxes;
std::vector<std::size_t> opts;
migraphx::shape indices{migraphx::shape::int32_type, mins, maxes, opts};
int axis = 1;
expect_shape(migraphx::shape{migraphx::shape::float_type, {{2, 3, 2}, {6, 9, 7}, {12, 14, 13}}},
migraphx::make_op("gather", {{"axis", axis}}),
input,
indices);
}
TEST_CASE(gather_dyn3)
{
// Insert dynamic index into static shape, axis 1
migraphx::shape input{migraphx::shape::float_type, {2, 3, 6, 12}};
migraphx::shape indices{migraphx::shape::int32_type, {{2, 3, 2}, {3, 4, 3}}};
int axis = 1;
expect_shape(migraphx::shape{migraphx::shape::float_type,
{{2, 2, 0}, {2, 3, 2}, {3, 4, 3}, {6, 6, 0}, {12, 12, 0}}},
migraphx::make_op("gather", {{"axis", axis}}),
input,
indices);
}
TEST_CASE(gather_dyn4)
{
// Insert dynamic index into static shape, axis 0
migraphx::shape input{migraphx::shape::float_type, {2, 3, 6, 12}};
migraphx::shape indices{migraphx::shape::int32_type, {{2, 3, 2}, {3, 4, 3}}};
int axis = 0;
expect_shape(migraphx::shape{migraphx::shape::float_type,
{{2, 3, 2}, {3, 4, 3}, {3, 3, 0}, {6, 6, 0}, {12, 12, 0}}},
migraphx::make_op("gather", {{"axis", axis}}),
input,
indices);
}
TEST_CASE(get_tuple_elem_test) TEST_CASE(get_tuple_elem_test)
{ {
migraphx::shape s0{migraphx::shape::bool_type, {1, 1}}; migraphx::shape s0{migraphx::shape::bool_type, {1, 1}};
...@@ -1716,6 +1787,38 @@ TEST_CASE(nms_shape) ...@@ -1716,6 +1787,38 @@ TEST_CASE(nms_shape)
score_thres_s); score_thres_s);
} }
TEST_CASE(pad_shape0)
{
migraphx::shape input{migraphx::shape::float_type, {2, 3, 3, 3}};
migraphx::shape output{migraphx::shape::float_type, {2, 3, 5, 5}};
expect_shape(output, migraphx::make_op("pad", {{"pads", {0, 0, 1, 1, 0, 0, 1, 1}}}), input);
}
TEST_CASE(pad_shape1)
{
migraphx::shape input{migraphx::shape::float_type, {2, 3, 3, 3}};
migraphx::shape output{migraphx::shape::float_type, {2, 3, 6, 6}};
expect_shape(output, migraphx::make_op("pad", {{"pads", {0, 0, 2, 2, 0, 0, 1, 1}}}), input);
}
TEST_CASE(pad_dyn_shape0)
{
migraphx::shape input{migraphx::shape::float_type,
{{1, 4, 2}, {3, 3, 0}, {3, 5, 0}, {3, 5, 0}}};
migraphx::shape output{migraphx::shape::float_type,
{{1, 4, 2}, {3, 3, 0}, {5, 7, 0}, {5, 7, 0}}};
expect_shape(output, migraphx::make_op("pad", {{"pads", {0, 0, 1, 1, 0, 0, 1, 1}}}), input);
}
TEST_CASE(pad_dyn_shape1)
{
migraphx::shape input{migraphx::shape::float_type,
{{1, 4, 2}, {3, 3, 0}, {3, 5, 5}, {3, 5, 5}}};
migraphx::shape output{migraphx::shape::float_type,
{{1, 4, 2}, {3, 3, 0}, {5, 7, 7}, {5, 7, 7}}};
expect_shape(output, migraphx::make_op("pad", {{"pads", {0, 0, 1, 1, 0, 0, 1, 1}}}), input);
}
TEST_CASE(pooling_shape0) TEST_CASE(pooling_shape0)
{ {
migraphx::shape input{migraphx::shape::float_type, {4, 3, 3, 3}}; migraphx::shape input{migraphx::shape::float_type, {4, 3, 3, 3}};
...@@ -2026,6 +2129,55 @@ TEST_CASE(reshape_shape) ...@@ -2026,6 +2129,55 @@ TEST_CASE(reshape_shape)
} }
} }
TEST_CASE(reshape_dyn_shape)
{
migraphx::shape input{migraphx::shape::float_type,
{{1, 4, 0}, {24, 24, 0}, {1, 1, 0}, {1, 1, 0}}};
for(auto&& new_shape : std::vector<std::vector<int64_t>>{
{-1, 1, 1, 24}, {0, 8, 3, 1}, {-1, 3, 4, 2}, {0, 2, 4, 3}})
{
std::vector<migraphx::shape::dynamic_dimension> out_dyn_dims{};
for(std::size_t i = 0; i < new_shape.size(); ++i)
{
if(new_shape[i] == 0 or new_shape[i] == -1)
{
out_dyn_dims.push_back(input.dyn_dims().at(i));
}
else
{
std::size_t d = new_shape[i];
out_dyn_dims.push_back({d, d, 0});
}
}
migraphx::shape output{migraphx::shape::float_type, out_dyn_dims};
expect_shape(output, migraphx::make_op("reshape", {{"dims", new_shape}}), input);
}
}
TEST_CASE(reshape_multiple_non_fixed_error)
{
migraphx::shape input{migraphx::shape::float_type,
{{1, 4, 0}, {24, 24, 0}, {10, 20, 0}, {1, 1, 0}}};
std::vector<int64_t> new_shape = {0, 1, 0, 24};
throws_shape(migraphx::make_op("reshape", {{"dims", new_shape}}), input);
}
TEST_CASE(reshape_fixed_ele_not_matching_error)
{
migraphx::shape input{migraphx::shape::float_type,
{{1, 4, 0}, {24, 24, 0}, {10, 10, 0}, {1, 1, 0}}};
std::vector<int64_t> new_shape = {0, 1, 5, 24};
throws_shape(migraphx::make_op("reshape", {{"dims", new_shape}}), input);
}
TEST_CASE(reshape_non_fixed_not_matching_error)
{
migraphx::shape input{migraphx::shape::float_type,
{{1, 4, 0}, {24, 24, 0}, {1, 1, 0}, {1, 1, 0}}};
std::vector<int64_t> new_shape = {2, 1, 1, 24};
throws_shape(migraphx::make_op("reshape", {{"dims", new_shape}}), input);
}
TEST_CASE(rnn) TEST_CASE(rnn)
{ {
{ {
......
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