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Unverified Commit b73427c9 authored by Chris Austen's avatar Chris Austen Committed by GitHub
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Merge branch 'develop' into fix_for_multiconfig_generators

parents 55e635e5 4c059fa3
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Showing with 692 additions and 197 deletions
src/targets/gpu/kernels/include/migraphx/kernels/scatternd.hpp
View file @ b73427c9
......@@ -26,36 +26,10 @@
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/algorithm.hpp>
#include <migraphx/kernels/scatter_reduction_modes.hpp>
namespace migraphx {
struct assign_none
{
template <class T, class U>
MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
{
x = y;
}
};
struct assign_add
{
template <class T, class U>
MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
{
x += y;
}
};
struct assign_mul
{
template <class T, class U>
MIGRAPHX_DEVICE_CONSTEXPR void operator()(T& x, U y) const
{
x *= y;
}
};
template <class T, class U, class V, class F>
__device__ void scatternd(const T& indices_t, const U& updates_t, const V& output_t, F f)
{
......
src/targets/gpu/kernels/include/migraphx/kernels/softmax.hpp
View file @ b73427c9
......@@ -43,7 +43,7 @@ __device__ void softmax(Input input1, Output output)
auto exp_in = r.inner([&](auto x) { return migraphx::exp(x - c); })(input);
auto batch_sum =
r.reduce(op::sum{}, 0, [](auto x) { return migraphx::convert<float>(x); })(exp_in);
r.inner([&](auto& y, auto x) { y = x / batch_sum; })(output, exp_in);
r.inner([&](auto& y, auto x) { y = implicit_conversion(x / batch_sum); })(output, exp_in);
});
}
......
src/targets/gpu/kernels/include/migraphx/kernels/tensor_view.hpp
View file @ b73427c9
......@@ -27,6 +27,7 @@
#include <migraphx/kernels/shape.hpp>
#include <migraphx/kernels/debug.hpp>
#include <migraphx/kernels/iota_iterator.hpp>
#include <migraphx/kernels/float8.hpp>
namespace migraphx {
......
src/targets/gpu/kernels/include/migraphx/kernels/type_traits.hpp
View file @ b73427c9
......@@ -251,7 +251,7 @@ constexpr T numeric_max()
}
template <class T>
constexpr T numeric_lowest()
constexpr auto numeric_lowest() -> decltype(numeric_max<T>())
{
if constexpr(is_integral<T>{})
{
......
src/targets/gpu/kernels/include/migraphx/kernels/vec.hpp
View file @ b73427c9
......@@ -207,7 +207,7 @@ struct implicit_conversion_op
template <class U>
constexpr operator U() const
{
return x;
return static_cast<U>(x);
}
};
......
src/targets/gpu/mlir.cpp
View file @ b73427c9
......@@ -37,7 +37,7 @@
#include <mlir-c/Pass.h>
#include <mlir-c/Support.h>
#include <mutex>
#if !defined(MLIR_MIGRAPHX_DIALECT_API_VERSION) || MLIR_MIGRAPHX_DIALECT_API_VERSION != 3
#if !defined(MLIR_MIGRAPHX_DIALECT_API_VERSION) || MLIR_MIGRAPHX_DIALECT_API_VERSION != 4
#warning "Incompatible version of rocMLIR library used, disabling"
// Only undefine when not using cppcheck
#ifndef CPPCHECK
......@@ -73,6 +73,7 @@ namespace gpu {
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_MLIR);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNE_EXHAUSTIVE);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNE_LIMIT);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNING_DB);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNING_CFG);
......@@ -299,6 +300,8 @@ struct mlir_program
result = mlirF32TypeGet(ctx.get());
else if(as.type_enum() == shape::half_type)
result = mlirF16TypeGet(ctx.get());
else if(as.type_enum() == shape::fp8e4m3fnuz_type)
result = mlirFloat8E4M3FNUZTypeGet(ctx.get());
else if(as.type_enum() == shape::double_type)
result = mlirF64TypeGet(ctx.get());
else if(as.is_integral())
......@@ -318,31 +321,30 @@ struct mlir_program
return result;
}
MlirType make_tensor(const shape& s) const
MlirType make_mlir_shaped(const shape& s) const
{
if(not s.standard())
MIGRAPHX_THROW("MLIR expects all tensors to be in standard shape");
if(s.dynamic())
MIGRAPHX_THROW("MLIR does not support dynamic shapes");
std::vector<int64_t> lens(s.lens().begin(), s.lens().end());
return mlirRankedTensorTypeGet(
lens.size(), lens.data(), make_type(s.type()), mlirAttributeGetNull());
std::vector<int64_t> strides(s.strides().begin(), s.strides().end());
return rocmlirMIXRShapedTypeGet(
lens.size(), lens.data(), strides.data(), make_type(s.type()));
}
template <class Range>
std::vector<MlirType> make_tensors(const Range& r)
std::vector<MlirType> make_mlir_shapeds(const Range& r)
{
std::vector<MlirType> result;
std::transform(r.begin(), r.end(), std::back_inserter(result), [&](const auto& s) {
return make_tensor(s);
return make_mlir_shaped(s);
});
return result;
}
MlirType make_function_type(const std::vector<shape>& inputs, const std::vector<shape>& outputs)
{
auto in = make_tensors(inputs);
auto out = make_tensors(outputs);
auto in = make_mlir_shapeds(inputs);
auto out = make_mlir_shapeds(outputs);
return mlirFunctionTypeGet(ctx.get(), in.size(), in.data(), out.size(), out.data());
}
......@@ -504,11 +506,7 @@ struct mlir_program
mlir_operation_state& add_results(const std::vector<shape>& outputs)
{
std::vector<shape> reshaped(outputs.size());
std::transform(outputs.begin(), outputs.end(), reshaped.begin(), [](const shape& r) {
return shape{r.type(), r.lens()};
});
auto x = prog->make_tensors(reshaped);
auto x = prog->make_mlir_shapeds(outputs);
if(not x.empty())
{
mlirOperationStateAddResults(&op_state, x.size(), x.data());
......@@ -581,7 +579,7 @@ struct mlir_program
std::vector<shape> outputs = m.get_output_shapes();
std::vector<MlirLocation> arg_locs(inputs.size(), location);
auto body_inputs = make_tensors(inputs);
auto body_inputs = make_mlir_shapeds(inputs);
mlir_region region = mlirRegionCreate();
mlir_block fbody = mlirBlockCreate(body_inputs.size(), body_inputs.data(), arg_locs.data());
MlirBlock result = fbody.get();
......@@ -607,7 +605,7 @@ struct mlir_program
return "func.return";
if(ins->name() == "@literal")
{
return "tosa.const";
return "migraphx.literal";
}
return "migraphx." + ins->name();
}
......@@ -666,7 +664,8 @@ struct mlir_program
if(ins->name() == "@literal")
{
literal r = ins->get_literal();
MlirType tensor_type = make_tensor(ins->get_shape());
MlirType shaped_type = make_mlir_shaped(ins->get_shape());
MlirType tensor_type = rocmlirMIXRShapedTypeAsTensor(shaped_type);
MlirAttribute mlir_value_attr =
mlirDenseElementsAttrRawBufferGet(tensor_type, r.get_shape().bytes(), r.data());
ops.add_attributes({{"value", mlir_value_attr}});
......@@ -796,7 +795,9 @@ struct mlir_program
if(enabled(MIGRAPHX_MLIR_TUNE_EXHAUSTIVE{}))
tuning_mode = RocmlirTuningParamSetKindExhaustive;
mlir_tuning_space params{mlirRockTuningSpaceCreate(mmodule.get(), tuning_mode)};
for(auto i : range(mlirRockTuningGetNumParams(params.get())))
const auto limit =
value_of(MIGRAPHX_MLIR_TUNE_LIMIT{}, std::numeric_limits<std::size_t>::max());
for(auto i : range(std::min<std::size_t>(limit, mlirRockTuningGetNumParams(params.get()))))
{
mlir_tuning_param param{mlirRockTuningParamCreate()};
if(not mlirRockTuningParamGet(params.get(), i, param.get()))
......@@ -942,35 +943,7 @@ void adjust_param_shapes(module& m, const std::vector<shape>& inputs)
auto param = m.get_parameter(name);
if(input.standard())
continue;
auto lens = input.lens();
auto strides = input.strides();
std::vector<operation> ops;
if(input.transposed())
{
auto perm = find_permutation(input);
auto iperm = invert_permutation(perm);
lens = reorder_dims(lens, iperm);
strides = reorder_dims(strides, iperm);
ops.push_back(make_op("transpose", {{"permutation", perm}}));
}
if(input.broadcasted())
{
std::transform(lens.begin(),
lens.end(),
strides.begin(),
lens.begin(),
[](auto len, auto stride) -> std::size_t {
if(stride == 0)
return 1;
return len;
});
ops.push_back(make_op("multibroadcast", {{"out_lens", input.lens()}}));
}
auto new_param =
std::accumulate(ops.begin(),
ops.end(),
m.add_parameter(name + ".0", shape{input.type(), lens}),
[&](auto x, auto op) { return m.insert_instruction(param, op, x); });
auto new_param = m.add_parameter(name + ".0", input);
m.replace_instruction(param, new_param);
m.remove_instruction(param);
}
......@@ -1032,6 +1005,15 @@ tuning_config get_tuning_config_mlir(const context& migraphx_ctx,
mlir_program mp;
mp.set_gpu_properties(migraphx_ctx);
mp.parse(m);
const bool trace = enabled(MIGRAPHX_TRACE_MLIR{});
static std::mutex mutex;
if(trace)
{
const std::lock_guard<std::mutex> lock(mutex);
auto mod_op = mlirModuleGetOperation(mp.mmodule.get());
std::cout << mlir_print(&mlirOperationPrint, mod_op) << std::endl;
}
return mp.get_tuning_config(exhaustive);
}
......
src/targets/gpu/prefuse_ops.cpp
View file @ b73427c9
......@@ -31,6 +31,7 @@
#ifdef MIGRAPHX_USE_COMPOSABLEKERNEL
#include <migraphx/gpu/ck.hpp>
#endif
#include <migraphx/gpu/fuse_mlir.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -124,34 +125,55 @@ struct find_add_layernorm
}
};
#ifdef MIGRAPHX_USE_COMPOSABLEKERNEL
struct pre_gemm_softmax_gemm : gemm_softmax_gemm
{
std::string name() const { return "gpu::pre_gemm_softmax_gemm"; }
};
MIGRAPHX_REGISTER_OP(pre_gemm_softmax_gemm);
MIGRAPHX_PRED_MATCHER(is_ck_gemm, instruction_ref ins)
auto is_ck_gemm()
{
return match::make_basic_pred_matcher([=](instruction_ref ins) {
#ifdef MIGRAPHX_USE_COMPOSABLEKERNEL
if(not enabled(MIGRAPHX_ENABLE_CK{}))
return false;
if(ins->name() != "dot")
return false;
if(not pre_gemm_softmax_gemm::is_ck_supported_type(ins->get_shape().type()))
return false;
return true;
#else
(void)ins;
return false;
#endif
});
}
auto is_mlir_gemm()
{
return match::make_basic_pred_matcher([=](instruction_ref ins) {
if(not mlir_attention_enabled())
return false;
if(ins->name() != "dot")
return false;
return std::all_of(ins->inputs().begin(), ins->inputs().end(), [&](auto i) {
return pre_gemm_softmax_gemm::is_mlir_supported_type(i->get_shape().type());
});
});
}
struct find_gemm_softmax_gemm
{
auto matcher() const
{
auto gemm1 =
match::skip(match::name("contiguous"))(match::name("dot")(is_ck_gemm().bind("gemm1")));
auto gemm1 = match::skip(match::name("contiguous"))(
match::name("dot")(match::any_of(is_ck_gemm(), is_mlir_gemm()).bind("gemm1")));
auto mul = match::name("mul")(
match::nargs(2), match::either_arg(0, 1)(match::is_constant().bind("scale"), gemm1));
auto softmax = match::name("softmax")(match::arg(0)(mul)).bind("softmax");
return match::name("dot")(is_ck_gemm().bind("gemm2"))(match::arg(0)(softmax));
return match::name("dot")(match::any_of(is_ck_gemm(), is_mlir_gemm()).bind("gemm2"))(
match::arg(0)(softmax));
}
void apply(module_pass_manager& mpm, const match::matcher_result& r) const
......@@ -179,8 +201,6 @@ struct find_gemm_softmax_gemm
}
};
#endif
} // namespace
void prefuse_ops::apply(module_pass_manager& mpm) const
......@@ -188,10 +208,7 @@ void prefuse_ops::apply(module_pass_manager& mpm) const
match::find_matches(mpm.get_module(), find_layernorm{});
mpm.run_pass(dead_code_elimination{});
match::find_matches(mpm.get_module(), find_add_layernorm{});
#ifdef MIHRAPHX_USE_COMPOSABLEKERNEL
if(enabled(MIGRAPHX_ENABLE_CK{}))
match::find_matches(mpm, find_gemm_softmax_gemm{});
#endif
}
} // namespace gpu
......
src/targets/gpu/rocblas.cpp
View file @ b73427c9
......@@ -53,6 +53,15 @@ bool get_compute_fp32_flag()
return (starts_with(device_name, "gfx9") and device_name >= "gfx908");
}
bool rocblas_fp8_available()
{
#ifndef MIGRAPHX_USE_ROCBLAS_FP8_API
return false;
#else
return gfx_has_fp8_intrinsics();
#endif
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/target.cpp
View file @ b73427c9
......@@ -98,12 +98,36 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
ctx.set_exhaustive_tune_flag(options.exhaustive_tune);
std::set<shape::type_t> unsupported_types(shape::types().begin(), shape::types().end());
unsupported_types.erase(shape::type_t::float_type);
unsupported_types.erase(shape::type_t::fp8e4m3fnuz_type);
unsupported_types.erase(shape::type_t::half_type);
unsupported_types.erase(shape::type_t::bool_type);
unsupported_types.erase(shape::type_t::int8_type);
unsupported_types.erase(shape::type_t::uint8_type);
unsupported_types.erase(shape::type_t::int32_type);
unsupported_types.erase(shape::type_t::tuple_type);
// whiltelist supported Ops for the FP8
std::set<std::string> unsupported_fp8_ops = {};
if(not gpu::rocblas_fp8_available())
{
unsupported_fp8_ops.insert("dot");
}
// MIOpen doesn't have support for fp8 pooling yet.
unsupported_fp8_ops.insert("pooling");
if(not gpu::gfx_has_fp8_intrinsics())
{
unsupported_fp8_ops.insert("convolution");
unsupported_fp8_ops.insert("quant_convolution");
}
// add all device kernels
unsupported_fp8_ops.insert("logsoftmax");
unsupported_fp8_ops.insert("nonzero");
unsupported_fp8_ops.insert("prefix_scan_sum");
unsupported_fp8_ops.insert("scatter_none");
unsupported_fp8_ops.insert("topk");
unsupported_fp8_ops.insert("rnn_var_sl_shift_output");
unsupported_fp8_ops.insert("multinomial");
unsupported_fp8_ops.insert("argmax");
unsupported_fp8_ops.insert("argmin");
// clang-format off
return
{
......@@ -135,6 +159,8 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
prefuse_ops{},
dead_code_elimination{},
auto_contiguous{},
eliminate_data_type{{migraphx::shape::fp8e4m3fnuz_type}, shape::float_type, unsupported_fp8_ops},
dead_code_elimination{},
optimize_module{},
fuse_pointwise{},
dead_code_elimination{},
......
test/gpu/fuse_mlir.cpp
View file @ b73427c9
......@@ -144,10 +144,12 @@ TEST_CASE(int_quant_dot_tanh_fails)
auto tanh = add_pointwise(p1, "main:pointwise0", {dot}, single_pointwise("tanh"));
mm->add_return({tanh});
}
migraphx::program p2(p1);
// This pass should do nothing as int32_t tanh isn't supported.
// This pass should not fuse as int32_t tanh isn't supported.
run_pass(p1);
EXPECT(p1 == p2);
auto* mm = p1.get_main_module();
bool has_pointwise =
std::any_of(mm->begin(), mm->end(), [&](const auto& i) { return i.name() == "pointwise"; });
EXPECT(has_pointwise);
}
int main(int argc, const char* argv[])
......
test/gpu/jit.cpp
View file @ b73427c9
......@@ -139,7 +139,8 @@ const std::string math_template = R"__migraphx__(
#include <migraphx/kernels/pointwise.hpp>
#include <migraphx/kernels/math.hpp>
#include <migraphx/kernels/types.hpp>
using namespace migraphx;
namespace migraphx {
extern "C" {
__global__ void kernel(${type}* p)
{
......@@ -148,6 +149,7 @@ __global__ void kernel(${type}* p)
}
}
}
int main() {}
......@@ -348,19 +350,20 @@ TEST_CASE(compile_math)
auto vec_sizes = {2, 4, 6};
for(auto&& t : migraphx::shape::types())
{
if(contains({migraphx::shape::bool_type,
migraphx::shape::fp8e4m3fnuz_type,
migraphx::shape::tuple_type},
t))
if(contains({migraphx::shape::bool_type, migraphx::shape::tuple_type}, t))
continue;
auto name = migraphx::shape::cpp_type(t);
if(t == migraphx::shape::half_type)
name.insert(0, "migraphx::");
data_types.push_back(name);
// fp8 doesn't have vectorization support yet, therefore skip it for now.
if(t != migraphx::shape::fp8e4m3fnuz_type)
{
migraphx::transform(vec_sizes, std::back_inserter(data_types), [&](auto i) {
return "migraphx::vec<" + name + ", " + std::to_string(i) + ">";
});
}
}
migraphx::shape input{migraphx::shape::float_type, {5, 2}};
migraphx::gpu::hip_compile_options options;
options.global = 1024;
......@@ -429,7 +432,6 @@ TEST_CASE(assert_type_min_max)
min = std::to_string(as.min());
max = std::to_string(as.max());
}
auto src = migraphx::interpolate_string(assert_template,
{{"type", name}, {"max", max}, {"min", min}});
migraphx::shape input{migraphx::shape::float_type, {5, 2}};
......
test/gpu/mlir.cpp
View file @ b73427c9
......@@ -141,9 +141,9 @@ TEST_CASE(conv)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_convolution(%arg0: tensor<2x8x3x3xf32>, %arg1: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
return %0 : tensor<1x2x2x2xf32>
func.func @mlir_convolution(%arg0: !migraphx.shaped<2x8x3x3xf32, 72x9x3x1>, %arg1: !migraphx.shaped<1x8x4x4xf32, 128x16x4x1>) -> !migraphx.shaped<1x2x2x2xf32, 8x4x2x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.convolution %arg1, %arg0 {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : <1x8x4x4xf32, 128x16x4x1>, <2x8x3x3xf32, 72x9x3x1> -> <1x2x2x2xf32, 8x4x2x1>
return %0 : !migraphx.shaped<1x2x2x2xf32, 8x4x2x1>
}
}
)__migraphx__";
......@@ -160,15 +160,38 @@ module {
EXPECT(verify_mlir(m));
}
TEST_CASE(conv_nhwc)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_convolution(%arg0: !migraphx.shaped<2x8x3x3xf32, 72x1x24x8>, %arg1: !migraphx.shaped<1x8x4x4xf32, 128x1x32x8>) -> !migraphx.shaped<1x2x2x2xf32, 8x1x4x2> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.convolution %arg1, %arg0 {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : <1x8x4x4xf32, 128x1x32x8>, <2x8x3x3xf32, 72x1x24x8> -> <1x2x2x2xf32, 8x1x4x2>
return %0 : !migraphx.shaped<1x2x2x2xf32, 8x1x4x2>
}
}
)__migraphx__";
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::float_type, {1, 8, 4, 4}, {128, 1, 32, 8}});
auto w = m.add_parameter("w", {migraphx::shape::float_type, {2, 8, 3, 3}, {72, 1, 24, 8}});
auto conv = m.add_instruction(migraphx::make_op("convolution"), x, w);
m.add_return({conv});
auto s = migraphx::gpu::dump_mlir(m);
// Skip test if MLIR is not enabled
if(s.empty())
return;
CHECK(encode(s) == encode(mlir_output));
EXPECT(verify_mlir(m));
}
TEST_CASE(conv_add_relu)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_convolution_add_relu(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.convolution(%arg2, %arg1) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
%1 = migraphx.add(%0, %arg0) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
%2 = migraphx.relu(%1) : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
return %2 : tensor<1x2x2x2xf32>
func.func @mlir_convolution_add_relu(%arg0: !migraphx.shaped<1x2x2x2xf32, 8x4x2x1>, %arg1: !migraphx.shaped<2x8x3x3xf32, 72x9x3x1>, %arg2: !migraphx.shaped<1x8x4x4xf32, 128x16x4x1>) -> !migraphx.shaped<1x2x2x2xf32, 8x4x2x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.convolution %arg2, %arg1 {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : <1x8x4x4xf32, 128x16x4x1>, <2x8x3x3xf32, 72x9x3x1> -> <1x2x2x2xf32, 8x4x2x1>
%1 = migraphx.add %0, %arg0 : <1x2x2x2xf32, 8x4x2x1>, <1x2x2x2xf32, 8x4x2x1> -> <1x2x2x2xf32, 8x4x2x1>
%2 = migraphx.relu %1 : <1x2x2x2xf32, 8x4x2x1> -> <1x2x2x2xf32, 8x4x2x1>
return %2 : !migraphx.shaped<1x2x2x2xf32, 8x4x2x1>
}
}
)__migraphx__";
......@@ -192,10 +215,10 @@ TEST_CASE(quant_dot_add)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_quant_dot_add(%arg0: tensor<1x5x4xi8>, %arg1: tensor<1x4x3xi8>, %arg2: tensor<1x5x3xi32>) -> tensor<1x5x3xi32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.quant_dot(%arg0, %arg1) : (tensor<1x5x4xi8>, tensor<1x4x3xi8>) -> tensor<1x5x3xi32>
%1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xi32>, tensor<1x5x3xi32>) -> tensor<1x5x3xi32>
return %1 : tensor<1x5x3xi32>
func.func @mlir_quant_dot_add(%arg0: !migraphx.shaped<1x5x4xi8, 20x4x1>, %arg1: !migraphx.shaped<1x4x3xi8, 12x3x1>, %arg2: !migraphx.shaped<1x5x3xi32, 15x3x1>) -> !migraphx.shaped<1x5x3xi32, 15x3x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.quant_dot %arg0, %arg1 : <1x5x4xi8, 20x4x1>, <1x4x3xi8, 12x3x1> -> <1x5x3xi32, 15x3x1>
%1 = migraphx.add %0, %arg2 : <1x5x3xi32, 15x3x1>, <1x5x3xi32, 15x3x1> -> <1x5x3xi32, 15x3x1>
return %1 : !migraphx.shaped<1x5x3xi32, 15x3x1>
}
}
)__migraphx__";
......@@ -219,10 +242,10 @@ TEST_CASE(dot_add)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_dot_add(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
%1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
return %1 : tensor<1x5x3xf32>
func.func @mlir_dot_add(%arg0: !migraphx.shaped<1x5x4xf32, 20x4x1>, %arg1: !migraphx.shaped<1x4x3xf32, 12x3x1>, %arg2: !migraphx.shaped<1x5x3xf32, 15x3x1>) -> !migraphx.shaped<1x5x3xf32, 15x3x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.dot %arg0, %arg1 : <1x5x4xf32, 20x4x1>, <1x4x3xf32, 12x3x1> -> <1x5x3xf32, 15x3x1>
%1 = migraphx.add %0, %arg2 : <1x5x3xf32, 15x3x1>, <1x5x3xf32, 15x3x1> -> <1x5x3xf32, 15x3x1>
return %1 : !migraphx.shaped<1x5x3xf32, 15x3x1>
}
}
)__migraphx__";
......@@ -245,11 +268,11 @@ TEST_CASE(conv_int8_dequantize_quantize)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_quant_convolution_dequantizelinear_quantizelinear(%arg0: tensor<2x8x3x3xi8>, %arg1: tensor<1x8x4x4xi8>, %arg2: tensor<1x2x2x2xf32>, %arg3: tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.quant_convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xi8>, tensor<2x8x3x3xi8>) -> tensor<1x2x2x2xi32>
%1 = migraphx.dequantizelinear(%0, %arg2, %arg3) : (tensor<1x2x2x2xi32>, tensor<1x2x2x2xf32>, tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xf32>
%2 = migraphx.quantizelinear(%1, %arg2, %arg3) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>, tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32>
return %2 : tensor<1x2x2x2xi32>
func.func @mlir_quant_convolution_dequantizelinear_quantizelinear(%arg0: !migraphx.shaped<2x8x3x3xi8, 72x9x3x1>, %arg1: !migraphx.shaped<1x8x4x4xi8, 128x16x4x1>, %arg2: !migraphx.shaped<1x2x2x2xf32, 8x4x2x1>, %arg3: !migraphx.shaped<1x2x2x2xi32, 8x4x2x1>) -> !migraphx.shaped<1x2x2x2xi32, 8x4x2x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.quant_convolution %arg1, %arg0 {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : <1x8x4x4xi8, 128x16x4x1>, <2x8x3x3xi8, 72x9x3x1> -> <1x2x2x2xi32, 8x4x2x1>
%1 = migraphx.dequantizelinear %0, %arg2, %arg3 : <1x2x2x2xi32, 8x4x2x1>, <1x2x2x2xf32, 8x4x2x1>, !migraphx.shaped<1x2x2x2xi32, 8x4x2x1> -> <1x2x2x2xf32, 8x4x2x1>
%2 = migraphx.quantizelinear %1, %arg2, %arg3 : <1x2x2x2xf32, 8x4x2x1>, <1x2x2x2xf32, 8x4x2x1>, !migraphx.shaped<1x2x2x2xi32, 8x4x2x1> -> <1x2x2x2xi32, 8x4x2x1>
return %2 : !migraphx.shaped<1x2x2x2xi32, 8x4x2x1>
}
}
)__migraphx__";
......@@ -278,10 +301,10 @@ TEST_CASE(dot_convert)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_dot_convert(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>) -> tensor<1x5x3xf16> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
%1 = migraphx.convert(%0) {target_type = 1 : i64} : (tensor<1x5x3xf32>) -> tensor<1x5x3xf16>
return %1 : tensor<1x5x3xf16>
func.func @mlir_dot_convert(%arg0: !migraphx.shaped<1x5x4xf32, 20x4x1>, %arg1: !migraphx.shaped<1x4x3xf32, 12x3x1>) -> !migraphx.shaped<1x5x3xf16, 15x3x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.dot %arg0, %arg1 : <1x5x4xf32, 20x4x1>, <1x4x3xf32, 12x3x1> -> <1x5x3xf32, 15x3x1>
%1 = migraphx.convert %0 {target_type = 1 : i64} : <1x5x3xf32, 15x3x1> to <1x5x3xf16, 15x3x1>
return %1 : !migraphx.shaped<1x5x3xf16, 15x3x1>
}
}
)__migraphx__";
......@@ -304,10 +327,10 @@ TEST_CASE(dot_where)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_dot_where(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xi8>, %arg3: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
%1 = migraphx.where(%arg2, %0, %arg3) : (tensor<1x5x3xi8>, tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
return %1 : tensor<1x5x3xf32>
func.func @mlir_dot_where(%arg0: !migraphx.shaped<1x5x4xf32, 20x4x1>, %arg1: !migraphx.shaped<1x4x3xf32, 12x3x1>, %arg2: !migraphx.shaped<1x5x3xi8, 15x3x1>, %arg3: !migraphx.shaped<1x5x3xf32, 15x3x1>) -> !migraphx.shaped<1x5x3xf32, 15x3x1> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.dot %arg0, %arg1 : <1x5x4xf32, 20x4x1>, <1x4x3xf32, 12x3x1> -> <1x5x3xf32, 15x3x1>
%1 = migraphx.where %arg2, %0, %arg3 : <1x5x3xi8, 15x3x1>, <1x5x3xf32, 15x3x1>, <1x5x3xf32, 15x3x1> -> <1x5x3xf32, 15x3x1>
return %1 : !migraphx.shaped<1x5x3xf32, 15x3x1>
}
}
)__migraphx__";
......
test/onnx/.onnxrt-commit
View file @ b73427c9
a5537f2f563d4975c7e6121a7eb260bbbfd9455a
d69842226b47e5336568103541b071447caeb9bf
test/onnx/gen_onnx.py
View file @ b73427c9
......@@ -5994,6 +5994,263 @@ def qlinearadd_bcast_test():
[sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
@onnx_test()
def qlinearaveragepool_1d_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 32])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 3, 31])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[16])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2],
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 3, 3])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.015])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[16])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2],
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_ceil_test():
x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 1, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 1, 2, 2])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
[0])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[3, 3],
strides=[2, 2],
ceil_mode=True,
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_dilations_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 1, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 1, 2, 2])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.25])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[84])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2],
strides=[1, 1],
dilations=[2, 2],
ceil_mode=True,
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_pads_count_include_pad_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 6, 6])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.01])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[32])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
count_include_pad=1,
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_same_lower_test():
x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 3, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 3, 4, 4])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
[0])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2],
auto_pad="SAME_LOWER",
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_same_upper_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 4, 4])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[32])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 4, 4])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.25])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[0])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2],
auto_pad="SAME_UPPER",
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_2d_strides_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 8, 8])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 2, 2])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.05])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[8])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[5, 5],
strides=[2, 2],
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_3d_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 3, 3, 3, 3])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.05])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 3, 2, 2, 2])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.02])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[0])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[2, 2, 2],
)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_notset_test():
x = helper.make_tensor_value_info('x', TensorProto.INT8, [1, 1, 5, 5])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.INT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.INT8, [1, 1, 1, 1])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.INT8, [],
[10])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[6, 6],
strides=[2, 2],
pads=[0, 0, 1, 1],
channels_last=0,
auto_pad='NOTSET')
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearaveragepool_nt_cip_test():
x = helper.make_tensor_value_info('x', TensorProto.UINT8, [1, 1, 5, 5])
x_scale = helper.make_tensor('x_scale', TensorProto.FLOAT, [], [0.5])
x_zero_point = helper.make_tensor('x_zero_point', TensorProto.UINT8, [],
[0])
y = helper.make_tensor_value_info('y', TensorProto.UINT8, [1, 1, 1, 1])
y_scale = helper.make_tensor('y_scale', TensorProto.FLOAT, [], [0.5])
y_zero_point = helper.make_tensor('y_zero_point', TensorProto.UINT8, [],
[10])
node = onnx.helper.make_node(
'QLinearAveragePool',
inputs=['x', 'x_scale', 'x_zero_point', 'y_scale', 'y_zero_point'],
outputs=['y'],
kernel_shape=[6, 6],
strides=[2, 2],
pads=[0, 0, 1, 1],
channels_last=0,
auto_pad='NOTSET',
count_include_pad=1)
return ([node], [x], [y], [x_scale, x_zero_point, y_scale, y_zero_point])
@onnx_test()
def qlinearconv_test():
# https://xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__QLinearConv.html
......@@ -7455,8 +7712,7 @@ def scatter_none_test():
return ([node], [x, i, u], [y])
@onnx_test()
def scatternd_add_test():
def make_scatternd_test(reduction="none"):
data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
[2, 1, 2])
......@@ -7468,44 +7724,39 @@ def scatternd_add_test():
node = onnx.helper.make_node('ScatterND',
inputs=['data', 'indices', 'updates'],
outputs=['output'],
reduction="add")
reduction=reduction)
return ([node], [data, indices, updates], [output])
@onnx_test()
def scatternd_add_test():
return make_scatternd_test("add")
@onnx_test()
def scatternd_mul_test():
data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
[2, 1, 2])
updates = helper.make_tensor_value_info('updates', TensorProto.FLOAT,
[2, 1, 2])
output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
[2, 2, 2])
return make_scatternd_test("mul")
node = onnx.helper.make_node('ScatterND',
inputs=['data', 'indices', 'updates'],
outputs=['output'],
reduction="mul")
return ([node], [data, indices, updates], [output])
@onnx_test()
def scatternd_max_test():
return make_scatternd_test("max")
@onnx_test()
def scatternd_min_test():
return make_scatternd_test("min")
@onnx_test()
def scatternd_test():
data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
[2, 1, 2])
updates = helper.make_tensor_value_info('updates', TensorProto.FLOAT,
[2, 1, 2])
output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
[2, 2, 2])
return make_scatternd_test()
node = onnx.helper.make_node('ScatterND',
inputs=['data', 'indices', 'updates'],
outputs=['output'])
return ([node], [data, indices, updates], [output])
@onnx_test()
def scatternd_invalid_reduction_test():
return make_scatternd_test("invalid")
@onnx_test()
......@@ -9292,6 +9543,97 @@ def undefined_test():
return ([node], [x], [y])
@onnx_test()
def unique_dynamic_sorted_test():
x = helper.make_tensor_value_info('X', TensorProto.FLOAT, [6])
y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
[6])
count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
node = onnx.helper.make_node(
'Unique',
inputs=['X'],
outputs=['Y', 'indices', 'inverse_indices', 'counts'],
axis=0,
sorted=1)
return ([node], [x], [y, y_ind, x_ind, count])
@onnx_test()
def unique_dynamic_sorted_3D_test():
x = helper.make_tensor_value_info('X', TensorProto.INT64, [4, 4, 4])
y = helper.make_tensor_value_info('Y', TensorProto.INT64, [16])
y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [16])
x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
[64])
count = helper.make_tensor_value_info('counts', TensorProto.INT64, [16])
node = onnx.helper.make_node(
'Unique',
inputs=['X'],
outputs=['Y', 'indices', 'inverse_indices', 'counts'],
sorted=1)
return ([node], [x], [y, y_ind, x_ind, count])
@onnx_test()
def unique_dynamic_unsorted_test():
x = helper.make_tensor_value_info('X', TensorProto.FLOAT, [6])
y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
[6])
count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
node = onnx.helper.make_node(
'Unique',
inputs=['X'],
outputs=['Y', 'indices', 'inverse_indices', 'counts'],
axis=0,
sorted=0)
return ([node], [x], [y, y_ind, x_ind, count])
@onnx_test()
def unique_sorted_test():
x = helper.make_tensor('X', TensorProto.FLOAT, [6], [2, 1, 1, 3, 4, 3])
y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
[6])
count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
node = onnx.helper.make_node(
'Unique',
inputs=['X'],
outputs=['Y', 'indices', 'inverse_indices', 'counts'],
axis=0,
sorted=1)
return ([node], [], [y, y_ind, x_ind, count], [x])
@onnx_test()
def unique_unsorted_test():
x = helper.make_tensor('X', TensorProto.FLOAT, [6], [2, 1, 1, 3, 4, 3])
y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [4])
y_ind = helper.make_tensor_value_info('indices', TensorProto.INT64, [4])
x_ind = helper.make_tensor_value_info('inverse_indices', TensorProto.INT64,
[6])
count = helper.make_tensor_value_info('counts', TensorProto.INT64, [4])
node = onnx.helper.make_node(
'Unique',
inputs=['X'],
outputs=['Y', 'indices', 'inverse_indices', 'counts'],
axis=0,
sorted=0)
return ([node], [], [y, y_ind, x_ind, count], [x])
@onnx_test()
def unknown_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
......
test/onnx/onnx_test.cpp
View file @ b73427c9
......@@ -4826,8 +4826,9 @@ TEST_CASE(multinomial_test)
migraphx::shape s{migraphx::shape::float_type, {1}};
std::vector<float> seed_data = {seed};
auto seed_input = mm->add_literal(migraphx::literal(s, seed_data));
auto rand_dummy =
mm->add_literal(migraphx::literal{migraphx::shape::float_type, {batch_size * sample_size}});
auto rand_dummy = mm->add_literal(
migraphx::literal{migraphx::shape{migraphx::shape::float_type, {batch_size, sample_size}},
std::vector<float>(batch_size * sample_size)});
auto randoms = mm->add_instruction(migraphx::make_op("random_uniform"), seed_input, rand_dummy);
mm->add_instruction(migraphx::make_op("multinomial"), cdf, randoms);
......@@ -4978,8 +4979,9 @@ TEST_CASE(multinomial_int64_test)
auto seed_input = mm->add_literal(migraphx::literal(s, data));
// static size
auto rand_dummy =
mm->add_literal(migraphx::literal{migraphx::shape::float_type, {batch_size * sample_size}});
auto rand_dummy = mm->add_literal(
migraphx::literal{migraphx::shape{migraphx::shape::float_type, {batch_size, sample_size}},
std::vector<float>(batch_size * sample_size)});
auto randoms = mm->add_instruction(migraphx::make_op("random_uniform"), seed_input, rand_dummy);
mm->add_instruction(migraphx::make_op("multinomial", {{"dtype", dtype}}), cdf, randoms);
auto prog = optimize_onnx("multinomial_int64_test.onnx");
......@@ -5595,6 +5597,54 @@ TEST_CASE(qlinearadd_test)
EXPECT(p.sort() == prog.sort());
}
TEST_CASE(qlinearaveragepool_notset_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto sc_x = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto z_pt_x = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {0}});
auto sc_y = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto z_pt_y = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {10}});
auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::int8_type, {1, 1, 5, 5}});
auto scale_x_bcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 5, 5}}}), sc_x);
auto z_pt_x_bcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 5, 5}}}), z_pt_x);
auto fp_x =
mm->add_instruction(migraphx::make_op("dequantizelinear"), x, scale_x_bcast, z_pt_x_bcast);
auto fp_y =
mm->add_instruction(migraphx::make_op("pooling",
{{"mode", migraphx::op::pooling_mode::average},
{"padding", {2, 2, 2, 2}},
{"stride", {2, 2}},
{"lengths", {6, 6}}}),
fp_x);
fp_y = mm->add_instruction(
migraphx::make_op("slice", {{"axes", {2, 3}}, {"starts", {1, 1}}, {"ends", {2, 2}}}), fp_y);
auto scale_y_bcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 1, 1}}}), sc_y);
auto z_pt_y_bcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 1, 1}}}), z_pt_y);
auto y =
mm->add_instruction(migraphx::make_op("quantizelinear"), fp_y, scale_y_bcast, z_pt_y_bcast);
mm->add_return({y});
auto prog = migraphx::parse_onnx("qlinearaveragepool_notset_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(qlinearconv_test)
{
migraphx::program p;
......@@ -7227,20 +7277,35 @@ TEST_CASE(scatter_none_test)
EXPECT(p == prog);
}
TEST_CASE(scatternd_test)
void scatternd_test_base(const std::string& reduction, const std::string& onnx_file)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("data", migraphx::shape{migraphx::shape::float_type, {2, 2, 2}});
auto l1 = mm->add_parameter("indices", migraphx::shape{migraphx::shape::int64_type, {2, 1, 2}});
auto l2 = mm->add_parameter("updates", migraphx::shape{migraphx::shape::float_type, {2, 1, 2}});
auto r = mm->add_instruction(migraphx::make_op("scatternd_none"), l0, l1, l2);
auto r = mm->add_instruction(migraphx::make_op("scatternd_" + reduction), l0, l1, l2);
mm->add_return({r});
auto prog = migraphx::parse_onnx("scatternd_test.onnx");
auto prog = migraphx::parse_onnx(onnx_file);
EXPECT(p == prog);
}
TEST_CASE(scatternd_test) { scatternd_test_base("none", "scatternd_test.onnx"); }
TEST_CASE(scatternd_add_test) { scatternd_test_base("add", "scatternd_add_test.onnx"); }
TEST_CASE(scatternd_mul_test) { scatternd_test_base("mul", "scatternd_mul_test.onnx"); }
TEST_CASE(scatternd_max_test) { scatternd_test_base("max", "scatternd_max_test.onnx"); }
TEST_CASE(scatternd_min_test) { scatternd_test_base("min", "scatternd_min_test.onnx"); }
TEST_CASE(scatternd_invalid_reduction_test)
{
EXPECT(test::throws([&] { migraphx::parse_onnx("scatternd_invalid_reduction_test.onnx"); }));
}
TEST_CASE(scatternd_dyn_test)
{
// dynamic input.
......@@ -7264,34 +7329,6 @@ TEST_CASE(scatternd_dyn_test)
EXPECT(p == prog);
}
TEST_CASE(scatternd_add_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("data", migraphx::shape{migraphx::shape::float_type, {2, 2, 2}});
auto l1 = mm->add_parameter("indices", migraphx::shape{migraphx::shape::int64_type, {2, 1, 2}});
auto l2 = mm->add_parameter("updates", migraphx::shape{migraphx::shape::float_type, {2, 1, 2}});
auto r = mm->add_instruction(migraphx::make_op("scatternd_add"), l0, l1, l2);
mm->add_return({r});
auto prog = migraphx::parse_onnx("scatternd_add_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(scatternd_mul_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("data", migraphx::shape{migraphx::shape::float_type, {2, 2, 2}});
auto l1 = mm->add_parameter("indices", migraphx::shape{migraphx::shape::int64_type, {2, 1, 2}});
auto l2 = mm->add_parameter("updates", migraphx::shape{migraphx::shape::float_type, {2, 1, 2}});
auto r = mm->add_instruction(migraphx::make_op("scatternd_mul"), l0, l1, l2);
mm->add_return({r});
auto prog = migraphx::parse_onnx("scatternd_mul_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(selu_test)
{
migraphx::program p;
......@@ -8569,6 +8606,86 @@ TEST_CASE(undefined_test)
EXPECT(p == prog);
}
TEST_CASE(unique_dynamic_sorted_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {6}};
auto x = mm->add_parameter("X", s);
auto out = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}, {"axis", 0}}), x);
auto y = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
auto y_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
auto x_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
mm->add_return({y, y_ind, x_ind, count});
auto prog = migraphx::parse_onnx("unique_dynamic_sorted_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(unique_dynamic_sorted_3D_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::int64_type, {4, 4, 4}};
auto x = mm->add_parameter("X", s);
auto out = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}}), x);
auto y = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
auto y_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
auto x_ind = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
mm->add_return({y, y_ind, x_ind, count});
auto prog = migraphx::parse_onnx("unique_dynamic_sorted_3D_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(unique_sorted_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s_x{migraphx::shape::float_type, {6}};
std::vector<float> x_data = {2, 1, 1, 3, 4, 3};
auto x = mm->add_literal(migraphx::literal(s_x, x_data));
auto out = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 1}, {"axis", 0}}), x);
auto y = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
auto y_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
auto x_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
mm->add_return({y, y_idx, x_idx, count});
auto prog = migraphx::parse_onnx("unique_sorted_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(unique_unsorted_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s_x{migraphx::shape::float_type, {6}};
std::vector<float> x_data = {2, 1, 1, 3, 4, 3};
auto x = mm->add_literal(migraphx::literal(s_x, x_data));
auto out = mm->add_instruction(migraphx::make_op("unique", {{"sorted", 0}, {"axis", 0}}), x);
auto y = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 0}}), out);
auto y_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 1}}), out);
auto x_idx = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 2}}), out);
auto count = mm->add_instruction(migraphx::make_op("get_tuple_elem", {{"index", 3}}), out);
mm->add_return({y, y_idx, x_idx, count});
auto prog = migraphx::parse_onnx("unique_unsorted_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(unknown_test)
{
migraphx::program p;
......
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