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Commit 34150e61 authored by Khalique's avatar Khalique
Browse files

add decorators

parents 8ded3a31 62cb3441
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Showing with 570 additions and 573 deletions
src/onnx/onnx.cpp
View file @ 34150e61
...@@ -206,6 +206,16 @@ struct onnx_parser ...@@ -206,6 +206,16 @@ struct onnx_parser
return out_lens; return out_lens;
} }
instruction_ref make_contiguous(instruction_ref ins)
{
if(ins->get_shape().standard())
{
return ins;
}
return prog.add_instruction(op::contiguous{}, ins);
}
template <class T> template <class T>
instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x) instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x)
{ {
...@@ -441,12 +451,7 @@ struct onnx_parser ...@@ -441,12 +451,7 @@ struct onnx_parser
s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); }); s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
} }
if(!args[0]->get_shape().standard()) return prog.add_instruction(op, make_contiguous(args[0]));
{
args[0] = prog.add_instruction(op::contiguous{}, args[0]);
}
return prog.add_instruction(op, args[0]);
} }
instruction_ref instruction_ref
...@@ -494,23 +499,41 @@ struct onnx_parser ...@@ -494,23 +499,41 @@ struct onnx_parser
{ {
axis = parse_value(attributes.at("axis")).at<int>(); axis = parse_value(attributes.at("axis")).at<int>();
} }
op::gather op{axis}; op::gather op{axis};
return prog.add_instruction(op, std::move(args)); return prog.add_instruction(op, make_contiguous(args[0]), make_contiguous(args[1]));
} }
instruction_ref instruction_ref
parse_slice(const std::string&, attribute_map attributes, std::vector<instruction_ref> args) parse_slice(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
{ {
op::slice op; op::slice op;
std::vector<size_t> dims = args[0]->get_shape().lens();
size_t num_dims = dims.size();
if(contains(attributes, "axes")) if(contains(attributes, "axes"))
{ {
literal s = parse_value(attributes.at("axes")); literal s = parse_value(attributes.at("axes"));
s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); }); s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
} }
else
{
op.axes = std::vector<int64_t>(num_dims);
std::iota(op.axes.begin(), op.axes.end(), 0);
}
if(contains(attributes, "ends"))
{ {
literal s = parse_value(attributes.at("ends")); literal s = parse_value(attributes.at("ends"));
s.visit([&](auto v) { copy(v, std::back_inserter(op.ends)); }); s.visit([&](auto v) { copy(v, std::back_inserter(op.ends)); });
for(size_t i = 0; i < num_dims; i++)
{
if(static_cast<size_t>(op.ends[i]) > dims[i])
{
op.ends[i] = dims[i];
}
}
} }
if(contains(attributes, "starts"))
{ {
literal s = parse_value(attributes.at("starts")); literal s = parse_value(attributes.at("starts"));
s.visit([&](auto v) { copy(v, std::back_inserter(op.starts)); }); s.visit([&](auto v) { copy(v, std::back_inserter(op.starts)); });
......
src/quantization.cpp
View file @ 34150e61
...@@ -74,7 +74,8 @@ void quantize(program& prog, const std::vector<std::string>& ins_names) ...@@ -74,7 +74,8 @@ void quantize(program& prog, const std::vector<std::string>& ins_names)
// if the input is a convert operator, uses its input // if the input is a convert operator, uses its input
// as its current input // as its current input
instruction_ref input_fp16{}; instruction_ref input_fp16{};
if(input->name() == "convert") if(input->name() == "convert" and
input->inputs().front()->get_shape().type() == shape::half_type)
{ {
input_fp16 = input->inputs().front(); input_fp16 = input->inputs().front();
} }
......
src/targets/gpu/quant_gemm.cpp
View file @ 34150e61
...@@ -8,51 +8,6 @@ namespace migraphx { ...@@ -8,51 +8,6 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
namespace gpu { namespace gpu {
template <class... Ts>
rocblas_status generic_rocblas_gemm_ex(Ts&&... xs)
{
return rocblas_gemm_ex(std::forward<Ts>(xs)...);
}
template <class... Ts>
rocblas_status generic_rocblas_batched_gemm_ex(Ts&&... xs)
{
return rocblas_gemm_strided_batched_ex(std::forward<Ts>(xs)...);
}
template <class T>
struct compute_rocblas_type
{
using type = T;
};
template <class T>
struct compute_rocblas_type<const T>
{
using type = const typename compute_rocblas_type<T>::type;
};
template <>
struct compute_rocblas_type<half>
{
using type = rocblas_half;
};
template <class T>
using rb_type = typename compute_rocblas_type<T>::type;
template <class T>
rb_type<T> to_rocblas_type(T x)
{
return reinterpret_cast<const rb_type<T>&>(x);
}
template <class T>
rb_type<T>* to_rocblas_type(T* x)
{
return reinterpret_cast<rb_type<T>*>(x);
}
shape rocblas_quant_gemm::compute_shape(const std::vector<shape>& inputs) const shape rocblas_quant_gemm::compute_shape(const std::vector<shape>& inputs) const
{ {
std::vector<shape> in_shapes(inputs); std::vector<shape> in_shapes(inputs);
...@@ -102,13 +57,13 @@ argument rocblas_quant_gemm::compute(context& ctx, ...@@ -102,13 +57,13 @@ argument rocblas_quant_gemm::compute(context& ctx,
auto a_lens = args[0].get_shape().lens(); auto a_lens = args[0].get_shape().lens();
auto b_lens = args[1].get_shape().lens(); auto b_lens = args[1].get_shape().lens();
output_shape.visit_type([&](auto as) { output_shape.visit_type([&](auto as) {
auto alpha_r = to_rocblas_type(as(op.alpha)); auto alpha_r = as(op.alpha);
auto beta_r = to_rocblas_type(as(beta)); auto beta_r = as(beta);
auto out_lens = output_shape.lens(); auto out_lens = output_shape.lens();
rocblas_int m = out_lens[dim_0]; rocblas_int m = out_lens[dim_0];
rocblas_int n = out_lens[dim_1]; rocblas_int n = out_lens[dim_1];
rocblas_int k = args[0].get_shape().lens()[dim_1]; rocblas_int k = args[0].get_shape().lens()[dim_1];
auto to_pointer = [&](auto&& arg) { return to_rocblas_type(as.from(arg.data())); }; auto to_pointer = [&](auto&& arg) { return as.from(arg.data()); };
assert(k % 4 == 0); assert(k % 4 == 0);
auto num_matrices = std::accumulate( auto num_matrices = std::accumulate(
...@@ -119,36 +74,36 @@ argument rocblas_quant_gemm::compute(context& ctx, ...@@ -119,36 +74,36 @@ argument rocblas_quant_gemm::compute(context& ctx,
// column-major format. When doing a C = A * B, we actually do // column-major format. When doing a C = A * B, we actually do
// C^T = (B^T) * (A^T). That is the reason we input args[1] as // C^T = (B^T) * (A^T). That is the reason we input args[1] as
// A and args[0] as B in calling the rocblas_gemm. // A and args[0] as B in calling the rocblas_gemm.
generic_rocblas_gemm_ex(ctx.get_stream().get_rocblas(), rocblas_gemm_ex(ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none, transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none, transa ? rocblas_operation_transpose : rocblas_operation_none,
n, n,
m, m,
k, k,
&alpha_r, &alpha_r,
to_pointer(args.at(1)), to_pointer(args.at(1)),
rocblas_datatype_i8_r, rocblas_datatype_i8_r,
ldb, ldb,
to_pointer(args.at(0)), to_pointer(args.at(0)),
rocblas_datatype_i8_r, rocblas_datatype_i8_r,
lda, lda,
&beta_r, &beta_r,
to_pointer(args[2]), to_pointer(args[2]),
rocblas_datatype_i32_r, rocblas_datatype_i32_r,
ldc, ldc,
is_3inputs ? to_pointer(args[3]) : to_pointer(args[2]), is_3inputs ? to_pointer(args[3]) : to_pointer(args[2]),
rocblas_datatype_i32_r, rocblas_datatype_i32_r,
ldc, ldc,
rocblas_datatype_i32_r, rocblas_datatype_i32_r,
rocblas_gemm_algo_standard, rocblas_gemm_algo_standard,
0, 0,
0, 0,
nullptr, nullptr,
nullptr); nullptr);
} }
else else
{ {
generic_rocblas_batched_gemm_ex( rocblas_gemm_strided_batched_ex(
ctx.get_stream().get_rocblas(), ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none, transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none, transa ? rocblas_operation_transpose : rocblas_operation_none,
......
src/tf/tf.cpp
View file @ 34150e61
...@@ -26,7 +26,6 @@ struct tf_parser ...@@ -26,7 +26,6 @@ struct tf_parser
{ {
using attribute_map = std::unordered_map<std::string, tensorflow::AttrValue>; using attribute_map = std::unordered_map<std::string, tensorflow::AttrValue>;
using node_map = std::map<std::string, tensorflow::NodeDef>; using node_map = std::map<std::string, tensorflow::NodeDef>;
// using input_node_map = std::unordered_map<std::string, std::unordered_set<std::string>>;
using op_func = std::function<instruction_ref(attribute_map, std::vector<instruction_ref>)>; using op_func = std::function<instruction_ref(attribute_map, std::vector<instruction_ref>)>;
node_map nodes; node_map nodes;
...@@ -149,9 +148,26 @@ struct tf_parser ...@@ -149,9 +148,26 @@ struct tf_parser
return axes; return axes;
} }
std::vector<int64_t> get_axes_from_mask(const size_t num_axes, const uint32_t mask)
{
uint32_t bitwise_compare = 1;
std::vector<int64_t> axes;
for(size_t i = 0; i < num_axes; i++)
{
// the LSB corresponds to axis 0 when determining which axes to begin
if(((mask >> i) & bitwise_compare) == 1)
axes.push_back(1);
else
axes.push_back(0);
}
return axes;
}
tf_parser() tf_parser()
{ {
add_generic_op("All", op::identity{});
add_generic_op("Identity", op::identity{}); add_generic_op("Identity", op::identity{});
add_generic_op("LessEqual", op::identity{});
add_generic_op("Relu", op::relu{}); add_generic_op("Relu", op::relu{});
add_generic_op("Relu6", op::clip{6.0, 0.0}); add_generic_op("Relu6", op::clip{6.0, 0.0});
add_generic_op("Rsqrt", op::rsqrt{}); add_generic_op("Rsqrt", op::rsqrt{});
...@@ -166,6 +182,7 @@ struct tf_parser ...@@ -166,6 +182,7 @@ struct tf_parser
add_mem_op("AvgPool", &tf_parser::parse_pooling); add_mem_op("AvgPool", &tf_parser::parse_pooling);
add_mem_op("BatchMatMul", &tf_parser::parse_matmul, false); add_mem_op("BatchMatMul", &tf_parser::parse_matmul, false);
add_mem_op("BatchMatMulV2", &tf_parser::parse_matmul, false);
add_mem_op("BiasAdd", &tf_parser::parse_biasadd); add_mem_op("BiasAdd", &tf_parser::parse_biasadd);
add_mem_op("Cast", &tf_parser::parse_cast, false); add_mem_op("Cast", &tf_parser::parse_cast, false);
add_mem_op("ConcatV2", &tf_parser::parse_concat, false); add_mem_op("ConcatV2", &tf_parser::parse_concat, false);
...@@ -177,14 +194,15 @@ struct tf_parser ...@@ -177,14 +194,15 @@ struct tf_parser
add_mem_op("GatherV2", &tf_parser::parse_gather, false); add_mem_op("GatherV2", &tf_parser::parse_gather, false);
add_mem_op("MatMul", &tf_parser::parse_matmul, false); add_mem_op("MatMul", &tf_parser::parse_matmul, false);
add_mem_op("MaxPool", &tf_parser::parse_pooling); add_mem_op("MaxPool", &tf_parser::parse_pooling);
add_mem_op("Mean", &tf_parser::parse_mean); add_mem_op("Mean", &tf_parser::parse_mean, false);
add_mem_op("OneHot", &tf_parser::parse_onehot, false);
add_mem_op("Pack", &tf_parser::parse_pack, false); add_mem_op("Pack", &tf_parser::parse_pack, false);
add_mem_op("Pad", &tf_parser::parse_pad); add_mem_op("Pad", &tf_parser::parse_pad);
add_mem_op("Reshape", &tf_parser::parse_reshape, false); add_mem_op("Reshape", &tf_parser::parse_reshape, false);
add_mem_op("Slice", &tf_parser::parse_slice, false); add_mem_op("Slice", &tf_parser::parse_slice, false);
add_mem_op("Softmax", &tf_parser::parse_softmax<op::softmax>); add_mem_op("Softmax", &tf_parser::parse_softmax<op::softmax>, false);
add_mem_op("Squeeze", &tf_parser::parse_squeeze, false); add_mem_op("Squeeze", &tf_parser::parse_squeeze, false);
add_mem_op("StridedSlice", &tf_parser::parse_stridedslice); add_mem_op("StridedSlice", &tf_parser::parse_stridedslice, false);
add_mem_op("Transpose", &tf_parser::parse_transpose, false); add_mem_op("Transpose", &tf_parser::parse_transpose, false);
} }
...@@ -547,7 +565,7 @@ struct tf_parser ...@@ -547,7 +565,7 @@ struct tf_parser
} }
if(contains(attributes, "transpose_b")) if(contains(attributes, "transpose_b"))
{ {
transb = attributes.at("transpose_a").b(); transb = attributes.at("transpose_b").b();
} }
if(contains(attributes, "adj_x")) if(contains(attributes, "adj_x"))
...@@ -574,8 +592,7 @@ struct tf_parser ...@@ -574,8 +592,7 @@ struct tf_parser
parse_mean(const std::string&, attribute_map attributes, std::vector<instruction_ref> args) parse_mean(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
{ {
bool keep_dims = attributes.at("keep_dims").b(); bool keep_dims = attributes.at("keep_dims").b();
auto lens = args[0]->get_shape().lens(); auto axes = args[1]->eval().get<int32_t>().to_vector<int64_t>();
auto axes = parse_axes(args[1]->eval().get<int32_t>().to_vector<int64_t>(), lens.size());
if(keep_dims) if(keep_dims)
{ {
...@@ -588,6 +605,32 @@ struct tf_parser ...@@ -588,6 +605,32 @@ struct tf_parser
} }
} }
instruction_ref
parse_onehot(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
{
size_t depth = static_cast<size_t>(args[1]->eval().at<int32_t>());
int64_t axis = -1;
float on_value = args[2]->eval().at<float>();
float off_value = args[3]->eval().at<float>();
std::vector<float> depth_input(depth * depth, off_value);
for(int i = 0; i < depth; i++)
{
depth_input[depth * i + i] = on_value;
}
if(contains(attributes, "axis"))
axis = attributes.at("axis").i();
if(axis == -1)
{
shape s{shape::float_type, {depth, depth}};
auto l0 = prog.add_literal({s, depth_input});
return prog.add_instruction(op::gather{0}, {l0, args[0]});
}
MIGRAPHX_THROW("MIGraphX does not support axis != -1");
}
instruction_ref parse_pack(const std::string&, instruction_ref parse_pack(const std::string&,
const attribute_map& attributes, const attribute_map& attributes,
std::vector<instruction_ref> args) std::vector<instruction_ref> args)
...@@ -799,21 +842,50 @@ struct tf_parser ...@@ -799,21 +842,50 @@ struct tf_parser
std::vector<instruction_ref> args) std::vector<instruction_ref> args)
{ {
op::slice op; op::slice op;
auto starts = args[1]->eval().get<int32_t>().to_vector(); auto starts = args[1]->eval().get<int32_t>().to_vector();
auto ends = args[2]->eval().get<int32_t>().to_vector(); auto ends = args[2]->eval().get<int32_t>().to_vector();
size_t num_axes = args[0]->get_shape().lens().size(); auto l0 = args[0];
size_t num_axes = l0->get_shape().lens().size();
std::vector<size_t> axes = l0->get_shape().lens();
op.starts = std::vector<int64_t>(starts.begin(), starts.end()); op.starts = std::vector<int64_t>(starts.begin(), starts.end());
op.ends = std::vector<int64_t>(ends.begin(), ends.end()); op.ends = std::vector<int64_t>(ends.begin(), ends.end());
op.axes = std::vector<int64_t>(num_axes); op.axes = std::vector<int64_t>(num_axes);
std::iota(op.axes.begin(), op.axes.end(), 0); std::iota(op.axes.begin(), op.axes.end(), 0);
uint32_t begin_mask = 0;
uint32_t end_mask = 0;
uint32_t shrink_axis_mask = 0; uint32_t shrink_axis_mask = 0;
uint32_t bitwise_compare = 1; uint32_t bitwise_compare = 1;
std::vector<int64_t> squeeze_axes; std::vector<int64_t> squeeze_axes;
if(contains(attributes, "begin_mask"))
begin_mask = static_cast<uint32_t>(attributes.at("begin_mask").i());
if(contains(attributes, "end_mask"))
end_mask = static_cast<uint32_t>(attributes.at("end_mask").i());
if(contains(attributes, "shrink_axis_mask")) if(contains(attributes, "shrink_axis_mask"))
shrink_axis_mask = static_cast<uint32_t>(attributes.at("shrink_axis_mask").i()); shrink_axis_mask = static_cast<uint32_t>(attributes.at("shrink_axis_mask").i());
std::vector<int64_t> begin_axes = get_axes_from_mask(num_axes, begin_mask);
std::vector<int64_t> end_axes = get_axes_from_mask(num_axes, end_mask);
for(size_t i = 0; i < num_axes; i++)
{
if(begin_axes.at(i) == 1)
{
op.starts.at(i) = 0;
}
if(end_axes.at(i) == 1)
{
op.ends.at(i) = axes.at(i);
}
}
auto l1 = prog.add_instruction(op, l0);
if(shrink_axis_mask == 0)
return l1;
for(size_t i = 0; i < num_axes; i++) for(size_t i = 0; i < num_axes; i++)
{ {
// the LSB corresponds to axis 0 when determining which axes to squeeze // the LSB corresponds to axis 0 when determining which axes to squeeze
...@@ -821,8 +893,7 @@ struct tf_parser ...@@ -821,8 +893,7 @@ struct tf_parser
squeeze_axes.push_back(i); squeeze_axes.push_back(i);
} }
auto l0 = prog.add_instruction(op, make_contiguous(args[0])); return prog.add_instruction(op::squeeze{squeeze_axes}, l1);
return to_nhwc(prog.add_instruction(op::squeeze{squeeze_axes}, l0));
} }
instruction_ref instruction_ref
...@@ -862,10 +933,16 @@ struct tf_parser ...@@ -862,10 +933,16 @@ struct tf_parser
if(instructions.count(name) == 0) if(instructions.count(name) == 0)
{ {
auto&& node = nodes.at(name); auto&& node = nodes.at(name);
// assert ops ignored
if(node.op() == "Assert" or contains(name, "Assert"))
return;
std::vector<instruction_ref> args; std::vector<instruction_ref> args;
for(auto&& input : node.input()) for(auto&& input : node.input())
{ {
// control dependencies (signified by ^ before the name) are ignored
if(contains(input, "^"))
continue;
if(nodes.count(input) > 0) if(nodes.count(input) > 0)
{ {
auto&& iname = get_name(nodes.at(input)); auto&& iname = get_name(nodes.at(input));
......
test/onnx/gen_onnx.py
View file @ 34150e61
This diff is collapsed.
test/onnx/onnx_test.cpp
View file @ 34150e61
...@@ -4,6 +4,7 @@ ...@@ -4,6 +4,7 @@
#include <migraphx/operators.hpp> #include <migraphx/operators.hpp>
#include <migraphx/program.hpp> #include <migraphx/program.hpp>
#include <migraphx/instruction.hpp> #include <migraphx/instruction.hpp>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/onnx.hpp> #include <migraphx/onnx.hpp>
#include "test.hpp" #include "test.hpp"
...@@ -1015,6 +1016,32 @@ TEST_CASE(transpose_test) ...@@ -1015,6 +1016,32 @@ TEST_CASE(transpose_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(transpose_gather_test)
{
migraphx::program p;
auto make_contiguous = [&p](migraphx::instruction_ref ins) {
if(ins->get_shape().standard())
{
return ins;
}
return p.add_instruction(migraphx::op::contiguous{}, ins);
};
auto data = p.add_parameter("data", migraphx::shape{migraphx::shape::float_type, {3, 5, 4, 6}});
auto ind =
p.add_parameter("indices", migraphx::shape{migraphx::shape::int32_type, {2, 4, 3, 5}});
auto tr_data = p.add_instruction(migraphx::op::transpose{{0, 2, 1, 3}}, data);
auto tr_ind = p.add_instruction(migraphx::op::transpose{{0, 2, 1, 3}}, ind);
int axis = 1;
p.add_instruction(
migraphx::op::gather{axis}, make_contiguous(tr_data), make_contiguous(tr_ind));
auto prog = migraphx::parse_onnx("transpose_gather_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(unknown_test) TEST_CASE(unknown_test)
{ {
migraphx::program p; migraphx::program p;
......
test/tf/gen_tf_pb.py
View file @ 34150e61
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
def tf_test(op_test):
def run_test():
g1 = tf.Graph()
op_test(g1)
tf.io.write_graph(g1, '.', '{}.pb'.format(op_test.__name__), as_text=False)
return run_test
def add_test(g1=tf.Graph()): @tf_test
def add_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1') g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.add(g1_input, g2_input, name = 'add1') tf.add(g1_input, g2_input, name = 'add1')
tf.train.write_graph(g1, '.', 'add_test.pb', as_text=False) @tf_test
def add_bcast_test(g1):
def add_bcast_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2,1), name = '1') g2_input = tf.placeholder(tf.float32, shape=(2,1), name = '1')
tf.math.add(g1_input, g2_input, name = 'add_bcast1') tf.math.add(g1_input, g2_input, name = 'add_bcast1')
tf.train.write_graph(g1, '.', 'add_bcast_test.pb', as_text=False) @tf_test
def assert_less_equal_test(g1):
def assert_less_equal_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2,3), name = '1') g2_input = tf.placeholder(tf.float32, shape=(2,3), name = '1')
with tf.control_dependencies([tf.assert_less_equal(g1_input, g2_input)]): with tf.control_dependencies([tf.assert_less_equal(g1_input, g2_input)]):
tf.add(g1_input, g2_input, name = 'add1') tf.add(g1_input, g2_input, name = 'add1')
tf.train.write_graph(g1, '.', 'assert_less_equal_test.pb', as_text=False)
def batchmatmul_test(g1=tf.Graph()): @tf_test
def batchmatmul_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,8,4), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,2,8,4), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,4,8), name = '1') g2_input = tf.placeholder(tf.float32, shape=(1,2,4,8), name = '1')
tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='batchmatmul1') tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='batchmatmul1')
tf.train.write_graph(g1, '.', 'batchmatmul_test.pb', as_text=False) @tf_test
def batchnorm_test(g1):
def batchnorm_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 32), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 32), name = '0')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name = '1') g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name = '1')
...@@ -43,271 +46,241 @@ def batchnorm_test(g1=tf.Graph()): ...@@ -43,271 +46,241 @@ def batchnorm_test(g1=tf.Graph()):
g1_mean = tf.placeholder(tf.float32, shape=(32), name = '3') g1_mean = tf.placeholder(tf.float32, shape=(32), name = '3')
g1_variance = tf.placeholder(tf.float32, shape=(32), name = '4') g1_variance = tf.placeholder(tf.float32, shape=(32), name = '4')
tf.nn.fused_batch_norm( tf.nn.fused_batch_norm(
g1_input, g1_scale, g1_offset, g1_mean, g1_variance, g1_input, g1_scale, g1_offset, g1_mean, g1_variance,
epsilon=0.00001, is_training=False, name='batchnorm1') epsilon=0.00001, is_training=False, name='batchnorm1'
)
tf.train.write_graph(g1, '.', 'batchnorm_test.pb', as_text=False) @tf_test
def biasadd_test(g1):
def biasadd_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,500), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,1,1,500), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(500), name = '1') g2_input = tf.placeholder(tf.float32, shape=(500), name = '1')
tf.nn.bias_add(g1_input, g2_input, name = 'bias_add1') tf.nn.bias_add(g1_input, g2_input, name = 'bias_add1')
tf.train.write_graph(g1, '.', 'biasadd_test.pb', as_text=False) @tf_test
def cast_test(g1):
def cast_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.cast(g1_input, dtype=tf.int32, name='cast1') tf.cast(g1_input, dtype=tf.int32, name='cast1')
tf.train.write_graph(g1, '.', 'cast_test.pb', as_text=False) @tf_test
def concat_test(g1=tf.Graph()): def concat_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(4,7,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(4,7,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(4,2,3), name = '1') g2_input = tf.placeholder(tf.float32, shape=(4,2,3), name = '1')
tf.concat([g1_input, g2_input], axis=1, name = 'concat1') tf.concat([g1_input, g2_input], axis=1, name = 'concat1')
tf.train.write_graph(g1, '.', 'concat_test.pb', as_text=False) @tf_test
def const_test(g1):
def const_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
tf.constant(1.0, dtype=tf.float32 ,name='constant1') tf.constant(1.0, dtype=tf.float32 ,name='constant1')
tf.train.write_graph(g1, '.', 'constant_test.pb', as_text=False) @tf_test
def conv_test(g1):
def conv_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,32), name = '1') g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,32), name = '1')
tf.nn.conv2d(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'conv1') tf.nn.conv2d(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'conv1')
tf.train.write_graph(g1, '.', 'conv_test.pb', as_text=False) @tf_test
def depthwiseconv_test(g1):
def depthwiseconv_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,1), name = '1') g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,1), name = '1')
tf.nn.depthwise_conv2d_native(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'depthwiseconv1') tf.nn.depthwise_conv2d_native(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'depthwiseconv1')
tf.train.write_graph(g1, '.', 'depthwise_conv_test.pb', as_text=False) @tf_test
def expanddims_test(g1):
def expanddims_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,3,4), name = '0') g1_input = tf.placeholder(tf.float32, shape=(2,3,4), name = '0')
tf.expand_dims(g1_input, axis=-1, name='expanddims_neg') tf.expand_dims(g1_input, axis=-1, name='expanddims_neg')
tf.train.write_graph(g1, '.', 'expanddims_neg_test.pb', as_text=False) @tf_test
def gather_test(g1):
def gather_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2,4), name = '0') g1_input = tf.placeholder(tf.float32, shape=(2,4), name = '0')
tf.gather(g1_input, [1,1], axis=1, name='gather1') tf.gather(g1_input, [1,1], axis=1, name='gather1')
tf.train.write_graph(g1, '.', 'gather_test.pb', as_text=False) @tf_test
def identity_test(g1):
def identity_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.identity(g1_input, 'identity') tf.identity(g1_input, 'identity')
tf.train.write_graph(g1, '.', 'identity_test.pb', as_text=False) @tf_test
def matmul_test(g1):
def matmul_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(8,4), name = '0') g1_input = tf.placeholder(tf.float32, shape=(8,4), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(4,8), name = '1') g2_input = tf.placeholder(tf.float32, shape=(4,8), name = '1')
tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='matmul1') tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='matmul1')
tf.train.write_graph(g1, '.', 'matmul_test.pb', as_text=False) @tf_test
def mean_test(g1):
def mean_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.math.reduce_mean( tf.math.reduce_mean(
g1_input, g1_input,
axis=(2,3), axis=(2,3),
keepdims=True, keepdims=True,
name='mean1' name='mean1'
) )
tf.math.reduce_mean( tf.math.reduce_mean(
g1_input, g1_input,
axis=(2,3), axis=(2,3),
keepdims=False, keepdims=False,
name='mean2' name='mean2'
) )
tf.train.write_graph(g1, '.', 'mean_test.pb', as_text=False) @tf_test
def mean_test_nhwc(g1):
def mean_test_nhwc(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
tf.math.reduce_mean( tf.math.reduce_mean(
g1_input, g1_input,
axis=(1,2), axis=(1,2),
keepdims=True, keepdims=True,
name='mean1' name='mean1'
) )
tf.math.reduce_mean( tf.math.reduce_mean(
g1_input, g1_input,
axis=(1,2), axis=(1,2),
keepdims=False, keepdims=False,
name='mean2' name='mean2'
) )
tf.train.write_graph(g1, '.', 'mean_test_nhwc.pb', as_text=False)
def mul_test(g1=tf.Graph()): @tf_test
def mul_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '1') g2_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '1')
tf.multiply(g1_input, g2_input, name='mul1') tf.multiply(g1_input, g2_input, name='mul1')
tf.train.write_graph(g1, '.', 'mul_test.pb', as_text=False)
def pack_test(g1=tf.Graph()): @tf_test
def pack_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(2), name = '0') g1_input = tf.placeholder(tf.float32, shape=(2), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(2), name = '1') g2_input = tf.placeholder(tf.float32, shape=(2), name = '1')
g3_input = tf.placeholder(tf.float32, shape=(2), name = '2') g3_input = tf.placeholder(tf.float32, shape=(2), name = '2')
tf.stack([g1_input, g2_input, g3_input], axis=1, name = 'pack1') tf.stack([g1_input, g2_input, g3_input], axis=1, name = 'pack1')
tf.train.write_graph(g1, '.', 'pack_test.pb', as_text=False) @tf_test
def pack_test_nhwc(g1):
def pack_test_nhwc(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '1') g2_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '1')
g3_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '2') g3_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '2')
tf.stack([g1_input, g2_input, g3_input], axis=3, name = 'pack1') tf.stack([g1_input, g2_input, g3_input], axis=3, name = 'pack1')
tf.train.write_graph(g1, '.', 'pack_test_nhwc.pb', as_text=False) @tf_test
def pooling_test(g1):
def pooling_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
tf.nn.avg_pool( tf.nn.avg_pool(
value=g1_input, value=g1_input,
ksize=(1,2,2,1), ksize=(1,2,2,1),
strides=(1,2,2,1), strides=(1,2,2,1),
padding='VALID', padding='VALID',
data_format='NHWC', data_format='NHWC',
name='avg_pooling' name='avg_pooling'
) )
tf.nn.max_pool( tf.nn.max_pool(
value=g1_input, value=g1_input,
ksize=(1,2,2,1), ksize=(1,2,2,1),
strides=(1,2,2,1), strides=(1,2,2,1),
padding='VALID', padding='VALID',
data_format='NHWC', data_format='NHWC',
name='max_pooling' name='max_pooling'
) )
tf.train.write_graph(g1, '.', 'pooling_test.pb', as_text=False) @tf_test
def pow_test(g1):
def pow_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1') g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.pow(g1_input, g2_input, name = 'pow1') tf.pow(g1_input, g2_input, name = 'pow1')
tf.train.write_graph(g1, '.', 'pow_test.pb', as_text=False) @tf_test
def relu_test(g1):
def relu_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.nn.relu(g1_input, 'relu') tf.nn.relu(g1_input, 'relu')
tf.train.write_graph(g1, '.', 'relu_test.pb', as_text=False) @tf_test
def relu6_test(g1):
def relu6_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.nn.relu6(g1_input, 'relu6') tf.nn.relu6(g1_input, 'relu6')
tf.train.write_graph(g1, '.', 'relu6_test.pb', as_text=False) @tf_test
def reshape_test(g1):
def reshape_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(16), name = '0')
tf.reshape(g1_input, (1,1,1,16), 'reshape') tf.reshape(g1_input, (1,1,1,16), 'reshape')
tf.train.write_graph(g1, '.', 'reshape_test.pb', as_text=False) @tf_test
def rsqrt_test(g1):
def rsqrt_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.math.rsqrt(g1_input, 'rsqrt') tf.math.rsqrt(g1_input, 'rsqrt')
tf.train.write_graph(g1, '.', 'rsqrt_test.pb', as_text=False) @tf_test
def slice_test(g1):
def slice_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(5,10), name = '0') g1_input = tf.placeholder(tf.float32, shape=(5,10), name = '0')
tf.slice(g1_input, [1, 0], [2, -1], name = 'slice1') tf.slice(g1_input, [1, 0], [2, -1], name = 'slice1')
tf.train.write_graph(g1, '.', 'slice_test.pb', as_text=False) @tf_test
def softmax_test(g1):
def softmax_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3), name = '0')
tf.nn.softmax(g1_input, name='softmax') tf.nn.softmax(g1_input, name='softmax')
tf.train.write_graph(g1, '.', 'softmax_test.pb', as_text=False) @tf_test
def sqdiff_test(g1):
def sqdiff_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1') g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.squared_difference(g1_input, g2_input, name = 'sqdiff') tf.squared_difference(g1_input, g2_input, name = 'sqdiff')
tf.train.write_graph(g1, '.', 'sqdiff_test.pb', as_text=False) @tf_test
def squeeze_test(g1):
def squeeze_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,3,1), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,2,3,1), name = '0')
tf.squeeze(g1_input, name='squeeze') tf.squeeze(g1_input, name='squeeze')
tf.train.write_graph(g1, '.', 'squeeze_test.pb', as_text=False) @tf_test
def stopgradient_test(g1):
def stopgradient_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.stop_gradient(g1_input, 'stopgradient') tf.stop_gradient(g1_input, 'stopgradient')
tf.train.write_graph(g1, '.', 'stopgradient_test.pb', as_text=False) @tf_test
def stridedslice_test(g1):
def stridedslice_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,1,1,10), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,1,1,10), name = '0')
tf.strided_slice(g1_input, [0, 0, 0, 0], [1, 1, 1, 5], [1,1,1,1], shrink_axis_mask=2, name = 'stridedslice1') tf.strided_slice(g1_input, [0, 0, 0, 0], [1, 1, 1, 5], [1,1,1,1], shrink_axis_mask=2, name = 'stridedslice1')
tf.train.write_graph(g1, '.', 'stridedslice_test.pb', as_text=False) @tf_test
def stridedslice_masks_test(g1):
def stridedslice_masks_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,3,10), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,3,10), name = '0')
tf.strided_slice(g1_input, [0, 1, 1, 0], [0, 0, 0, 0], [1,1,1,1], begin_mask=9, end_mask=15, name = 'stridedslice1') tf.strided_slice(g1_input, [0, 1, 1, 0], [0, 0, 0, 0], [1,1,1,1], begin_mask=9, end_mask=15, name = 'stridedslice1')
tf.train.write_graph(g1, '.', 'stridedslice_masks_test.pb', as_text=False) @tf_test
def sub_test(g1):
def sub_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1') g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
tf.subtract(g1_input, g2_input, name = 'sub1') tf.subtract(g1_input, g2_input, name = 'sub1')
tf.train.write_graph(g1, '.', 'sub_test.pb', as_text=False) @tf_test
def tanh_test(g1):
def tanh_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.tanh(g1_input, 'tanh') tf.tanh(g1_input, 'tanh')
tf.train.write_graph(g1, '.', 'tanh_test.pb', as_text=False) @tf_test
def transpose_test(g1):
def transpose_test(g1=tf.Graph()):
with g1.as_default(): with g1.as_default():
g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0') g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
tf.transpose(g1_input, perm=[0,2,3,1], name = 'transpose') tf.transpose(g1_input, perm=[0,2,3,1], name = 'transpose')
tf.train.write_graph(g1, '.', 'transpose_test.pb', as_text=False)
test/tf/tf_test.cpp
View file @ 34150e61
...@@ -48,6 +48,22 @@ TEST_CASE(add_bcast_test) ...@@ -48,6 +48,22 @@ TEST_CASE(add_bcast_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(assert_less_equal_test)
{
migraphx::program p;
migraphx::shape s0{migraphx::shape::float_type, {2, 3}};
auto l0 = p.add_parameter("0", s0);
auto l1 = p.add_parameter("1", s0);
migraphx::literal l{migraphx::shape{migraphx::shape::int32_type, {2}}, {0, 1}};
auto l2 = p.add_literal(l);
p.add_instruction(migraphx::op::add{}, l0, l1);
auto l3 = p.add_instruction(migraphx::op::identity{}, l0, l1);
p.add_instruction(migraphx::op::identity{}, l3, l2);
auto prog = optimize_tf("assert_less_equal_test.pb", false);
EXPECT(p == prog);
}
TEST_CASE(batchmatmul_test) TEST_CASE(batchmatmul_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -100,6 +116,16 @@ TEST_CASE(biasadd_test) ...@@ -100,6 +116,16 @@ TEST_CASE(biasadd_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(cast_test)
{
migraphx::program p;
auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
p.add_instruction(migraphx::op::convert{migraphx::shape::int32_type}, l0);
auto prog = optimize_tf("cast_test.pb", false);
EXPECT(p == prog);
}
TEST_CASE(concat_test) TEST_CASE(concat_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -118,16 +144,6 @@ TEST_CASE(concat_test) ...@@ -118,16 +144,6 @@ TEST_CASE(concat_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(cast_test)
{
migraphx::program p;
auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
p.add_instruction(migraphx::op::convert{migraphx::shape::int32_type}, l0);
auto prog = optimize_tf("cast_test.pb", false);
EXPECT(p == prog);
}
TEST_CASE(const_test) TEST_CASE(const_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -271,9 +287,10 @@ TEST_CASE(mean_test_nhwc) ...@@ -271,9 +287,10 @@ TEST_CASE(mean_test_nhwc)
migraphx::program p; migraphx::program p;
migraphx::literal l{migraphx::shape{migraphx::shape::int32_type, {2}}, {1, 2}}; migraphx::literal l{migraphx::shape{migraphx::shape::int32_type, {2}}, {1, 2}};
auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}}); auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
migraphx::op::reduce_mean op{{2, 3}}; auto l1 = p.add_instruction(migraphx::op::transpose{{0, 2, 3, 1}}, l0);
auto l3 = p.add_instruction(op, l0); migraphx::op::reduce_mean op{{1, 2}};
p.add_instruction(migraphx::op::squeeze{{2, 3}}, l3); auto l2 = p.add_instruction(op, l1);
p.add_instruction(migraphx::op::squeeze{{1, 2}}, l2);
auto prog = optimize_tf("mean_test_nhwc.pb", true); auto prog = optimize_tf("mean_test_nhwc.pb", true);
EXPECT(p == prog); EXPECT(p == prog);
...@@ -291,6 +308,23 @@ TEST_CASE(mul_test) ...@@ -291,6 +308,23 @@ TEST_CASE(mul_test)
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(onehot_test)
{
migraphx::program p;
auto l0 = p.add_literal(
migraphx::literal{migraphx::shape{migraphx::shape::int32_type, {5}}, {1, 1, 1, 1, 1}});
p.add_literal(2);
p.add_literal(1.0f);
p.add_literal(0.0f);
auto l1 = p.add_literal(
migraphx::literal{migraphx::shape{migraphx::shape::float_type, {2, 2}}, {1, 0, 0, 1}});
int axis = 0;
p.add_instruction(migraphx::op::gather{axis}, l1, l0);
auto prog = optimize_tf("onehot_test.pb", false);
EXPECT(p == prog);
}
TEST_CASE(pack_test) TEST_CASE(pack_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -475,20 +509,44 @@ TEST_CASE(stridedslice_test) ...@@ -475,20 +509,44 @@ TEST_CASE(stridedslice_test)
{ {
migraphx::program p; migraphx::program p;
auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 10, 1, 1}}); auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 10, 1, 1}});
auto l1 = p.add_instruction(migraphx::op::transpose{{0, 2, 3, 1}}, l0);
std::size_t num_axes = 4; std::size_t num_axes = 4;
migraphx::op::slice op; migraphx::op::slice op;
op.starts = {0, 0, 0, 0}; op.starts = {0, 0, 0, 0};
op.ends = {1, 1, 1, 5}; op.ends = {1, 1, 1, 5};
op.axes = std::vector<int64_t>(num_axes); op.axes = std::vector<int64_t>(num_axes);
std::iota(op.axes.begin(), op.axes.end(), 0); std::iota(op.axes.begin(), op.axes.end(), 0);
auto l1 = p.add_instruction(op, l0); auto l2 = p.add_instruction(op, l1);
auto shrink_axis = 1; auto shrink_axis = 1;
p.add_instruction(migraphx::op::squeeze{{shrink_axis}}, l1); p.add_instruction(migraphx::op::squeeze{{shrink_axis}}, l2);
auto prog = optimize_tf("stridedslice_test.pb", true); auto prog = optimize_tf("stridedslice_test.pb", true);
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(stridedslice_masks_test)
{
migraphx::program p;
auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 10, 3, 3}});
std::size_t num_axes = 4;
migraphx::op::slice op;
op.starts = {0, 1, 1, 0};
op.ends = {1, 3, 3, 10};
op.axes = std::vector<int64_t>(num_axes);
std::iota(op.axes.begin(), op.axes.end(), 0);
// add literals for starts, ends, and strides in tf (NHWC format)
p.add_literal(migraphx::shape{migraphx::shape::int32_type, {4}}, std::vector<int>{0, 1, 1, 0});
p.add_literal(migraphx::shape{migraphx::shape::int32_type, {4}}, std::vector<int>{0, 0, 0, 0});
p.add_literal(migraphx::shape{migraphx::shape::int32_type, {4}}, std::vector<int>{1, 1, 1, 1});
auto l1 = p.add_instruction(migraphx::op::transpose{{0, 2, 3, 1}}, l0);
auto l2 = p.add_instruction(op, l1);
p.add_instruction(migraphx::op::transpose{{0, 3, 1, 2}}, l2);
auto prog = migraphx::parse_tf("stridedslice_masks_test.pb", true);
EXPECT(p == prog);
}
TEST_CASE(sub_test) TEST_CASE(sub_test)
{ {
migraphx::program p; migraphx::program p;
......
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