Commit 43708207 authored by charlie's avatar charlie
Browse files

Merge branch 'dyn_unsqueeze' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_onnx_matmul

parents 4ea977e3 c3e62f5f
......@@ -42,6 +42,13 @@ static bool try_compute_shape(instruction_ref ins,
try
{
shape new_shape = ins->get_operator().compute_shape(inputs, mods);
// Cannot tell if a dynamic shape will need to be made contiguous
if(new_shape.dynamic())
{
return false;
}
// If the output shape is a standard shape, no need to try its output
if(new_shape.standard())
{
......@@ -133,14 +140,20 @@ static void remove_contiguous(const std::string& op_name, module& m, F f)
}
}
// Perform evaluations in parallel
// Perform static contiguous evaluations in parallel
std::vector<argument> literals(const_instructions.size());
par_for(const_instructions.size(), 1, [&](const auto i) {
auto c = op::contiguous{};
auto prev = const_instructions[i]->inputs().front();
literals[i] = c.compute(c.compute_shape({prev->get_shape()}), {prev->eval()});
auto c = op::contiguous{};
auto prev = const_instructions[i]->inputs().front();
// compute the output contiguous shape from the previous instruction shape
shape computed_shape = c.compute_shape({prev->get_shape()});
const std::vector<argument>& prev_eval = {prev->eval()};
// prev_eval should not be used in make_compute_output_shape() as computed_shape is static
auto co_shape = make_compute_output_shape(pack(c, computed_shape, prev_eval));
literals[i] = c.compute(co_shape, prev_eval);
});
// Replace static contiguous operations with a literal
for(size_t i = 0; i < const_instructions.size(); i++)
{
auto l = m.add_literal(literals[i].get_shape(), literals[i].data());
......
......@@ -45,7 +45,16 @@ static literal get_scalar(instruction_ref ins)
return {};
auto e = ins->eval();
literal r{};
e.visit_at([&](auto x) { r = literal{x}; });
// needed for bool as visit_at invokes as() which promotes bool to int8
// Without this we'll break type checks for logical ops that are fused.
if(e.get_shape().type() == shape::bool_type)
{
r = literal{e.at<bool>()};
}
else
{
e.visit_at([&](auto x) { r = literal{x}; });
}
return r;
}
......
......@@ -28,6 +28,7 @@
#include <migraphx/argument.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <migraphx/dyn_output.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -42,19 +43,27 @@ namespace op {
struct contiguous
{
std::string name() const { return "contiguous"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
if(inputs.front().standard())
return inputs.front();
auto lens = inputs.at(0).lens();
auto t = inputs.at(0).type();
return {t, lens};
check_shapes{inputs, *this, true}.has(1);
auto s0 = inputs.front();
if(s0.dynamic() or s0.standard())
{
return s0;
}
else
{
const auto& lens = s0.lens();
auto t = s0.type();
return {t, lens};
}
}
argument compute(const shape& output_shape, std::vector<argument> args) const
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
assert(output_shape.standard());
argument result{output_shape};
assert(dyn_out.computed_shape.standard());
argument result{dyn_out.computed_shape};
visit_all(result, args[0])([&](auto output, auto input) {
shape_for_each(output.get_shape(), [&](const auto& idx) {
output(idx.begin(), idx.end()) = input(idx.begin(), idx.end());
......
......@@ -29,6 +29,7 @@
#include <migraphx/config.hpp>
#include <migraphx/value.hpp>
#include <migraphx/op/normalize_attribute.hpp>
#include <migraphx/dyn_output.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -54,52 +55,90 @@ struct squeeze
std::string name() const { return "squeeze"; }
shape normalize_compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
check_shapes{inputs, *this, true}.has(1);
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
auto old_strides = input_shape.strides();
if(std::any_of(axes.begin(), axes.end(), [&](auto axis) { return old_lens[axis] != 1; }))
if(input_shape.dynamic())
{
MIGRAPHX_THROW("squeeze axis dimension should be equal to 1");
}
std::vector<std::size_t> new_lens;
std::vector<std::size_t> new_strides;
if(axes.empty())
{
for(auto i : range(old_lens.size()))
shape::dynamic_dimension one_dyn_dim{1, 1, 0};
if(std::any_of(axes.begin(), axes.end(), [&](auto axis) {
return input_shape.dyn_dims()[axis] != one_dyn_dim;
}))
{
MIGRAPHX_THROW(
"SQUEEZE: dynamic axis dimension should be equal to {1, 1, 0} or {1, 1, 1}");
}
std::vector<shape::dynamic_dimension> dyn_dims = {};
if(axes.empty())
{
if(old_lens[i] != 1)
for(auto i : range(input_shape.ndim()))
{
new_lens.push_back(old_lens[i]);
new_strides.push_back(old_strides[i]);
auto dd = input_shape.dyn_dims()[i];
if(dd != one_dyn_dim)
{
dyn_dims.push_back(dd);
}
}
}
}
else
{
for(auto i : range(old_lens.size()))
else
{
if(std::find(axes.begin(), axes.end(), i) == axes.end())
for(auto i : range(input_shape.ndim()))
{
new_lens.push_back(old_lens[i]);
new_strides.push_back(old_strides[i]);
if(std::find(axes.begin(), axes.end(), i) == axes.end())
{
dyn_dims.push_back(input_shape.dyn_dims()[i]);
}
}
}
}
if(new_lens.empty())
{
return shape{type};
return {input_shape.type(), dyn_dims};
}
else
{
return shape{type, new_lens, new_strides};
auto type = input_shape.type();
auto old_lens = input_shape.lens();
auto old_strides = input_shape.strides();
if(std::any_of(
axes.begin(), axes.end(), [&](auto axis) { return old_lens[axis] != 1; }))
{
MIGRAPHX_THROW("SQUEEZE: static axis dimension should be equal to 1");
}
std::vector<std::size_t> new_lens;
std::vector<std::size_t> new_strides;
if(axes.empty())
{
for(auto i : range(old_lens.size()))
{
if(old_lens[i] != 1)
{
new_lens.push_back(old_lens[i]);
new_strides.push_back(old_strides[i]);
}
}
}
else
{
for(auto i : range(old_lens.size()))
{
if(std::find(axes.begin(), axes.end(), i) == axes.end())
{
new_lens.push_back(old_lens[i]);
new_strides.push_back(old_strides[i]);
}
}
}
if(new_lens.empty())
{
return shape{type};
}
else
{
return shape{type, new_lens, new_strides};
}
}
}
argument compute(shape output_shape, std::vector<argument> args) const
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
return args[0].reshape(output_shape);
return args[0].reshape(dyn_out.computed_shape);
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
......
......@@ -29,11 +29,20 @@
#include <migraphx/config.hpp>
#include <migraphx/value.hpp>
#include <migraphx/op/normalize_attribute.hpp>
#include <migraphx/dyn_output.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
/**
* Adds dimensions to a tensor based on the axes attribute.
* `axes` are based on the number of output shape dimensions and should not contain duplicates.
* `steps` are for modifying dimensions added to the middle of the original shape.
* Each step must be a factor of the original dimension.
* ex: unsqueeze(shape = [3, 4, 10], axes = [2, 4, 5], steps = [2]) -> shape = [3, 4, 2, 5, 1, 1]
* Dynamic shape version does not handle `steps`.
*/
struct unsqueeze
{
std::vector<int64_t> axes;
......@@ -56,63 +65,89 @@ struct unsqueeze
std::string name() const { return "unsqueeze"; }
shape normalize_compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
check_shapes{inputs, *this, true}.has(1);
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
auto old_strides = input_shape.strides();
if(input_shape.scalar())
if(input_shape.dynamic())
{
if(old_lens.size() == 1 and old_lens.front() == 1)
return shape{type, old_lens};
else
MIGRAPHX_THROW("UNSQUEEZE: Input must be a scalar");
if(not steps.empty())
{
MIGRAPHX_THROW("UNSQUEEZE_dyn: nonempty steps attribute");
}
std::vector<shape::dynamic_dimension> dyn_dims = {};
auto new_ndim = input_shape.ndim() + axes.size();
std::size_t k = 0;
for(auto i : range(new_ndim))
{
if(std::find(axes.begin(), axes.end(), i) != axes.end())
{
dyn_dims.push_back({1, 1, 0});
}
else
{
dyn_dims.push_back(input_shape.dyn_dims().at(k++));
}
}
return {input_shape.type(), dyn_dims};
}
else
{
auto type = input_shape.type();
auto old_lens = input_shape.lens();
auto old_strides = input_shape.strides();
if(input_shape.scalar())
{
if(old_lens.size() == 1 and old_lens.front() == 1)
return shape{type, old_lens};
else
MIGRAPHX_THROW("UNSQUEEZE: Input must be a scalar");
}
if(steps.size() > axes.size())
MIGRAPHX_THROW("UNSQUEEZE: Steps provided with no axis");
if(steps.size() > axes.size())
MIGRAPHX_THROW("UNSQUEEZE: Steps provided with no axis");
std::size_t new_size = old_lens.size() + axes.size();
std::size_t new_size = old_lens.size() + axes.size();
std::vector<std::size_t> new_lens(new_size);
std::vector<std::size_t> new_strides(new_size);
std::size_t p = 0;
for(auto i : range(new_size))
{
auto axis_idx = std::find(axes.begin(), axes.end(), i) - axes.begin();
if(axis_idx < axes.size())
std::vector<std::size_t> new_lens(new_size);
std::vector<std::size_t> new_strides(new_size);
std::size_t p = 0;
for(auto i : range(new_size))
{
std::int64_t step = 1;
if(axis_idx < steps.size())
step = steps[axis_idx];
if(step == 0)
MIGRAPHX_THROW("UNSQUEEZE: step must be non-zero");
new_lens[i] = step;
if(p < old_strides.size())
auto axis_idx = std::find(axes.begin(), axes.end(), i) - axes.begin();
if(axis_idx < axes.size())
{
if((old_lens[p] % step) != 0)
MIGRAPHX_THROW("UNSQUEEZE: Axis dimenstion is not divisible by step");
old_lens[p] /= step;
new_strides[i] = old_strides[p] * old_lens[p];
std::int64_t step = 1;
if(axis_idx < steps.size())
step = steps[axis_idx];
if(step == 0)
MIGRAPHX_THROW("UNSQUEEZE: step must be non-zero");
new_lens[i] = step;
if(p < old_strides.size())
{
if((old_lens[p] % step) != 0)
MIGRAPHX_THROW("UNSQUEEZE: Axis dimenstion is not divisible by step");
old_lens[p] /= step;
new_strides[i] = old_strides[p] * old_lens[p];
}
else
{
if(step != 1)
MIGRAPHX_THROW("UNSQUEEZE: Step must be 1 for extra axes");
new_strides[i] = 1;
}
}
else
{
if(step != 1)
MIGRAPHX_THROW("UNSQUEEZE: Step must be 1 for extra axes");
new_strides[i] = 1;
new_lens[i] = old_lens[p];
new_strides[i] = old_strides[p++];
}
}
else
{
new_lens[i] = old_lens[p];
new_strides[i] = old_strides[p++];
}
return shape{type, new_lens, new_strides};
}
return shape{type, new_lens, new_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
return args[0].reshape(output_shape);
return args[0].reshape(dyn_out.computed_shape);
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
......
......@@ -233,11 +233,14 @@ get_target_property(MIOPEN_LOCATION MIOpen LOCATION)
check_library_exists(MIOpen "miopenHiddenSetConvolutionFindMode" "${MIOPEN_LOCATION}" HAS_FIND_MODE_API)
check_library_exists(MIOpen "miopenFindSolutions" "${MIOPEN_LOCATION}" HAS_FIND_2_API)
if(HAS_FIND_2_API)
# TODO: Set default to HAS_FIND_2_API
set(MIGRAPHX_USE_FIND_2_API OFF CACHE BOOL "")
if(MIGRAPHX_USE_FIND_2_API)
target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API)
message(STATUS "MIGraphx is using Find-2.0 API of MIOpen")
else()
message(STATUS "MIOpen does not have Find-2.0 API")
message(STATUS "MIGraphx is using legacy Find API in MIOpen")
endif()
if(HAS_FIND_MODE_API)
......
......@@ -272,6 +272,35 @@ TEST_CASE(contiguous_input)
EXPECT(p1 == p2);
}
TEST_CASE(contiguous_boolean_input)
{
migraphx::shape s{migraphx::shape::bool_type, {2, 3}};
migraphx::shape s_lit{migraphx::shape::bool_type, {1}, {0}};
migraphx::program p1;
{
auto* mm = p1.get_main_module();
auto x = mm->add_parameter("x", s);
auto one = mm->add_literal(migraphx::literal(s_lit, {1.0}));
auto yb =
mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), one);
auto y = mm->add_instruction(migraphx::make_op("contiguous"), yb);
auto xor1 = mm->add_instruction(migraphx::make_op("logical_xor"), x, y);
mm->add_return({xor1});
}
run_pass(p1);
migraphx::program p2;
{
auto* mm = p2.get_main_module();
auto x = mm->add_parameter("x", s);
auto xor1 = add_pointwise(p2, "main:pointwise0", {x}, [=](auto* pm, const auto& inputs) {
auto y = pm->add_literal(migraphx::literal(s_lit, {1}));
return pm->add_instruction(migraphx::make_op("logical_xor"), inputs[0], y);
});
mm->add_return({xor1});
}
}
TEST_CASE(all_scalar_input)
{
migraphx::shape s{migraphx::shape::float_type};
......
......@@ -5984,6 +5984,26 @@ def squeeze_unsqueeze_test():
return ([node, node2], [x], [y])
@onnx_test
def squeeze_unsqueeze_dyn_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
[1, None, 1, 1, None, 1])
y = helper.make_tensor_value_info('2', TensorProto.FLOAT,
[1, 1, None, 1, None, 1])
node = onnx.helper.make_node('Squeeze',
inputs=['0'],
axes=[0, 2, 3, 5],
outputs=['1'])
node2 = onnx.helper.make_node('Unsqueeze',
inputs=['1'],
axes=[0, 1, 3, 5],
outputs=['2'])
return ([node, node2], [x], [y])
@onnx_test
def sub_bcast_test():
arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
......
......@@ -5776,6 +5776,29 @@ TEST_CASE(squeeze_unsqueeze_test)
EXPECT(p == prog);
}
TEST_CASE(squeeze_unsqueeze_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
std::vector<int64_t> squeeze_axes{0, 2, 3, 5};
std::vector<int64_t> unsqueeze_axes{0, 1, 3, 5};
auto l0 = mm->add_parameter(
"0",
migraphx::shape{migraphx::shape::float_type,
{{1, 1, 0}, {1, 4, 0}, {1, 1, 0}, {1, 1, 0}, {1, 4, 0}, {1, 1, 0}}});
auto c0 = mm->add_instruction(migraphx::make_op("contiguous"), l0);
auto l1 = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", squeeze_axes}}), c0);
auto c1 = mm->add_instruction(migraphx::make_op("contiguous"), l1);
auto ret = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", unsqueeze_axes}}), c1);
mm->add_return({ret});
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4, 0};
auto prog = parse_onnx("squeeze_unsqueeze_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(squeeze_axes_input_test)
{
migraphx::program p;
......
......@@ -365,6 +365,12 @@ TEST_CASE(contiguous_shape)
expect_shape(single, migraphx::make_op("contiguous"), single);
}
TEST_CASE(contiguous_dyn_shape)
{
migraphx::shape s0{migraphx::shape::float_type, {{1, 4, 0}, {2, 2, 2}}};
expect_shape(s0, migraphx::make_op("contiguous"), s0);
}
TEST_CASE(contiguous_shape_scalar)
{
migraphx::shape output{migraphx::shape::float_type};
......@@ -2089,6 +2095,30 @@ TEST_CASE(test_squeeze_all)
expect_shape(s2, migraphx::make_op("squeeze", {{"axes", {0}}}), s1);
}
TEST_CASE(test_squeeze_dyn)
{
migraphx::shape s1{migraphx::shape::float_type,
{{1, 4, 0}, {1, 1, 0}, {3, 3, 0}, {1, 1, 0}, {3, 3, 0}}};
migraphx::shape s2{migraphx::shape::float_type, {{1, 4, 0}, {1, 1, 0}, {3, 3, 0}, {3, 3, 0}}};
expect_shape(s2, migraphx::make_op("squeeze", {{"axes", {3}}}), s1);
migraphx::shape s3{migraphx::shape::float_type, {{1, 4, 0}, {3, 3, 0}, {3, 3, 0}}};
expect_shape(s3, migraphx::make_op("squeeze"), s1);
throws_shape(migraphx::make_op("squeeze", {{"axes", {0}}}), s1);
}
TEST_CASE(test_squeeze_dyn_neg_axes)
{
migraphx::shape s1{migraphx::shape::float_type,
{{1, 4, 0}, {1, 1, 0}, {3, 3, 0}, {1, 1, 0}, {3, 3, 0}}};
migraphx::shape s2{migraphx::shape::float_type, {{1, 4, 0}, {1, 1, 0}, {3, 3, 0}, {3, 3, 0}}};
expect_shape(s2, migraphx::make_op("squeeze", {{"axes", {-2}}}), s1);
migraphx::shape s3{migraphx::shape::float_type, {{1, 4, 0}, {3, 3, 0}, {3, 3, 0}}};
expect_shape(s3, migraphx::make_op("squeeze", {{"axes", {-2, -4}}}), s1);
}
TEST_CASE(test_squeeze_transpose)
{
migraphx::shape s1{migraphx::shape::float_type, {4, 4, 1}, {4, 1, 4}};
......@@ -2130,6 +2160,30 @@ TEST_CASE(test_unsqueeze)
expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2}}}), s1);
}
TEST_CASE(test_unsqueeze_dyn)
{
migraphx::shape s1{migraphx::shape::float_type, {{1, 4, 3}, {2, 5, 0}, {3, 3, 0}}};
migraphx::shape s2{migraphx::shape::float_type, {{1, 4, 3}, {2, 5, 0}, {1, 1, 0}, {3, 3, 0}}};
expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2}}}), s1);
migraphx::shape s3{migraphx::shape::float_type,
{{1, 4, 3}, {2, 5, 0}, {1, 1, 0}, {3, 3, 0}, {1, 1, 0}}};
expect_shape(s3, migraphx::make_op("unsqueeze", {{"axes", {2, 4}}}), s1);
throws_shape(migraphx::make_op("unsqueeze", {{"axes", {2, 4}}, {"steps", {2}}}), s1);
}
TEST_CASE(test_unsqueeze_dyn_neg_axes)
{
migraphx::shape s1{migraphx::shape::float_type, {{1, 4, 3}, {2, 5, 0}, {3, 3, 0}}};
migraphx::shape s2{migraphx::shape::float_type, {{1, 4, 3}, {2, 5, 0}, {1, 1, 0}, {3, 3, 0}}};
expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {-2}}}), s1);
migraphx::shape s3{migraphx::shape::float_type,
{{1, 4, 3}, {2, 5, 0}, {1, 1, 0}, {3, 3, 0}, {1, 1, 0}}};
expect_shape(s3, migraphx::make_op("unsqueeze", {{"axes", {-1, -3}}}), s1);
}
TEST_CASE(test_unsqueeze_step)
{
migraphx::shape s1{migraphx::shape::float_type, {4, 5, 12}};
......@@ -2161,13 +2215,27 @@ TEST_CASE(test_unsqueeze_mismatch_step_axis)
throws_shape(migraphx::make_op("unsqueeze", {{"axes", {2}}, {"steps", {2, 3}}}), s1);
}
TEST_CASE(test_unsqueeze_negative_axis)
TEST_CASE(test_unsqueeze_negative_axis1)
{
migraphx::shape s1{migraphx::shape::float_type, {4, 5, 3}};
migraphx::shape s2{migraphx::shape::float_type, {4, 5, 1, 3}};
expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {-2}}}), s1);
}
TEST_CASE(test_unsqueeze_negative_axis2)
{
migraphx::shape s1{migraphx::shape::float_type, {4, 5, 3}};
migraphx::shape s2{migraphx::shape::float_type, {4, 5, 3, 1}};
expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {-1}}}), s1);
}
TEST_CASE(test_unsqueeze_negative_axis3)
{
migraphx::shape s1{migraphx::shape::float_type, {4, 5, 3}};
migraphx::shape s2{migraphx::shape::float_type, {4, 1, 5, 3}};
expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {-3}}}), s1);
}
TEST_CASE(test_unsqueeze_scalar)
{
migraphx::shape s1{migraphx::shape::float_type, {1}, {0}};
......
......@@ -925,6 +925,33 @@ TEST_CASE(contiguous_test)
EXPECT(migraphx::verify_range(results_vector, data));
}
TEST_CASE(contiguous_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape dyn_shape{migraphx::shape::float_type,
{{1, 1, 0}, {2, 6, 0}, {2, 2, 0}, {2, 2, 0}}};
auto input = mm->add_parameter("X", dyn_shape);
mm->add_instruction(migraphx::make_op("contiguous"), input);
p.compile(migraphx::ref::target{});
migraphx::shape static_shape{migraphx::shape::float_type, {1, 3, 2, 2}, {12, 1, 6, 3}};
std::vector<float> data(12);
std::iota(data.begin(), data.end(), 0);
migraphx::parameter_map params;
params["X"] = migraphx::argument(static_shape, data.data());
auto result = p.eval(params).back();
std::vector<size_t> new_strides = {12, 4, 2, 1};
EXPECT(result.get_shape().strides() == new_strides);
std::vector<float> results_vector(12);
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
EXPECT(migraphx::verify_range(results_vector, gold));
}
TEST_CASE(conv_dynamic_batch_test)
{
migraphx::program p;
......@@ -7084,6 +7111,25 @@ TEST_CASE(squeeze_test)
}
}
TEST_CASE(squeeze_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s1{migraphx::shape::float_type,
{{1, 4, 0}, {1, 1, 0}, {3, 3, 0}, {1, 1, 0}, {3, 3, 0}}};
auto p0 = mm->add_parameter("x", s1);
mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {1}}}), p0);
p.compile(migraphx::ref::target{});
std::vector<float> input_data(4 * 3 * 3);
migraphx::parameter_map params0;
migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {4, 1, 3, 1, 3}};
params0["x"] = migraphx::argument(input_fixed_shape0, input_data.data());
auto result = p.eval(params0).back();
migraphx::shape s2{migraphx::shape::float_type, {4, 3, 1, 3}};
EXPECT(result.get_shape() == s2);
}
TEST_CASE(step_test)
{
{
......@@ -7361,6 +7407,25 @@ TEST_CASE(unsqueeze_test)
}
}
TEST_CASE(unsqueeze_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s1{migraphx::shape::float_type, {{1, 4, 0}, {3, 3, 0}, {3, 3, 0}}};
auto p0 = mm->add_parameter("x", s1);
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), p0);
p.compile(migraphx::ref::target{});
std::vector<float> input_data(4 * 3 * 3);
migraphx::parameter_map params0;
migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {4, 3, 3}};
params0["x"] = migraphx::argument(input_fixed_shape0, input_data.data());
auto result = p.eval(params0).back();
migraphx::shape s2{migraphx::shape::float_type, {4, 1, 3, 3}};
EXPECT(result.get_shape() == s2);
}
TEST_CASE(where_test)
{
migraphx::program p;
......
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