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Commit b76a9043 authored by charlie's avatar charlie
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Merge branch 'develop' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into refactor_dynamic_compute

parents 68c17b1b 66bbff1e
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Showing with 249 additions and 142 deletions
src/targets/gpu/kernels/include/migraphx/kernels/layernorm.hpp
View file @ b76a9043
......@@ -43,7 +43,7 @@ template <index_int Axis,
class Input2,
class... Inputs>
__device__ void generic_binary_layernorm(
F compute, BinOp op, Output output, Input1 input1, Input2 input2, Inputs... inputs)
F compute, BinOp op, float eps, Output output, Input1 input1, Input2 input2, Inputs... inputs)
{
using reduce_output = reduce::with_axis<Input1, Axis>;
reduce::block::run<reduce_output>([&](auto, auto r) {
......@@ -55,32 +55,34 @@ __device__ void generic_binary_layernorm(
return make_array(x, x * x) * vec_type<value_type>{1.0 / relements};
})(input1, input2);
auto mean_x = means[0];
auto mean_x2 = means[1];
auto variance = mean_x2 - (mean_x * mean_x);
auto mean_x = means[0];
auto mean_x2 = means[1];
auto variance = mean_x2 - (mean_x * mean_x);
value_type eps_val = eps; // implicit conversion for eps
r.inner([&](auto& y, auto x1, auto x2, auto... xs) {
auto x = op(x1, x2);
auto m = x - mean_x;
// m * rsqrt(mean(m ^ 2) + 1e-12)
y = compute(m * rsqrt(variance + value_type{1e-12}), xs...);
// m * rsqrt(mean(m ^ 2) + epsilon)
y = compute(m * rsqrt(variance + eps_val), xs...);
})(output, input1, input2, inputs...);
});
}
template <index_int Axis, class F, class Output, class Input, class... Inputs>
__device__ void layernorm(F compute, Output output, Input input, Inputs... inputs)
__device__ void layernorm(F compute, float eps, Output output, Input input, Inputs... inputs)
{
generic_binary_layernorm<Axis>(
compute, [](auto x, auto) { return x; }, output, input, input, inputs...);
compute, [](auto x, auto) { return x; }, eps, output, input, input, inputs...);
}
template <index_int Axis, class F, class Output, class Input1, class Input2, class... Inputs>
__device__ void
add_layernorm(F compute, Output output, Input1 input1, Input2 input2, Inputs... inputs)
add_layernorm(F compute, float eps, Output output, Input1 input1, Input2 input2, Inputs... inputs)
{
generic_binary_layernorm<Axis>(
compute, [](auto x1, auto x2) { return x1 + x2; }, output, input1, input2, inputs...);
compute, [](auto x1, auto x2) { return x1 + x2; }, eps, output, input1, input2, inputs...);
}
} // namespace migraphx
......
src/targets/gpu/lowering.cpp
View file @ b76a9043
......@@ -104,54 +104,10 @@ struct miopen_apply
offload_copy = (mod->name() == "main") ? pass->offload_copy : false;
add_generic_op("acos");
add_generic_op("acosh");
add_generic_op("add");
add_generic_op("asin");
add_generic_op("asinh");
add_generic_op("atan");
add_generic_op("atanh");
add_generic_op("ceil");
add_generic_op("contiguous");
add_generic_op("cos");
add_generic_op("cosh");
add_generic_op("div");
add_generic_op("equal");
add_generic_op("erf");
add_generic_op("exp");
add_generic_op("floor");
add_generic_op("greater");
add_generic_op("less");
add_generic_op("log");
add_generic_op("logical_and");
add_generic_op("logical_or");
add_generic_op("logical_xor");
add_generic_op("max");
add_generic_op("min");
add_generic_op("mul");
add_generic_op("not");
add_generic_op("pow");
add_generic_op("prelu");
add_generic_op("recip");
add_generic_op("relu");
add_generic_op("round");
add_generic_op("rsqrt");
add_generic_op("sigmoid");
add_generic_op("sign");
add_generic_op("sin");
add_generic_op("sinh");
add_generic_op("sqdiff");
add_generic_op("sqrt");
add_generic_op("sub");
add_generic_op("tan");
add_generic_op("tanh");
add_generic_op("where");
add_extend_op("abs");
add_extend_op("argmax");
add_extend_op("argmin");
add_extend_op("clip");
add_extend_op("convert");
add_extend_op("elu");
add_extend_op("gather");
add_extend_op("leaky_relu");
......
src/targets/gpu/prefuse_ops.cpp
View file @ b76a9043
......@@ -35,6 +35,12 @@ namespace {
template <class Derived, std::size_t N>
struct layernorm_base
{
float epsilon = 1e-12f;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.epsilon, "epsilon"));
}
shape compute_shape(std::vector<shape> inputs, std::vector<module_ref> mods) const
{
std::size_t nargs = 1;
......@@ -62,6 +68,7 @@ struct layernorm_base
struct layernorm : layernorm_base<layernorm, 0>
{
std::string name() const { return "gpu::prelayernorm"; }
};
MIGRAPHX_REGISTER_OP(layernorm);
......@@ -80,8 +87,9 @@ struct find_layernorm
{
auto ins = r.result;
auto x_ins = r.instructions["x"];
auto eps = r.instructions["eps"]->eval().at<float>();
m.replace_instruction(ins, layernorm{}, x_ins);
m.replace_instruction(ins, layernorm{eps}, x_ins);
}
};
......@@ -96,8 +104,9 @@ struct find_add_layernorm
{
auto ins = r.result;
auto add_ins = r.instructions["add"];
auto eps = r.instructions["eps"]->eval().at<float>();
m.replace_instruction(ins, add_layernorm{}, add_ins->inputs());
m.replace_instruction(ins, add_layernorm{eps}, add_ins->inputs());
}
};
} // namespace
......
src/targets/gpu/quant_convolution.cpp
View file @ b76a9043
......@@ -22,7 +22,6 @@
* THE SOFTWARE.
*/
#include <migraphx/gpu/quant_convolution.hpp>
#include <migraphx/gpu/device/convert.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/generate.hpp>
......
src/targets/gpu/softmax.cpp deleted 100644 → 0
View file @ 68c17b1b
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/gpu/softmax.hpp>
#include <migraphx/gpu/device/softmax.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/tune_axis.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape hip_softmax::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(2).standard();
return op.normalize_compute_shape({inputs.at(0)});
}
argument hip_softmax::compute(context& ctx, const shape&, const std::vector<argument>& args) const
{
auto n_dim = args.front().get_shape().lens().size();
auto tuned_axis = tune_axis(n_dim, op.axis, op.name());
device::softmax(ctx.get_stream().get(), args.back(), args.front(), tuned_axis);
return args.back();
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/tf/tf_parser.cpp
View file @ b76a9043
......@@ -347,7 +347,7 @@ void tf_parser::parse_node(const std::string& name)
// input was from a node with multiple outputs
if(contains(input_name, ':'))
{
input_name = input_name.substr(0, input.find(':'));
input_name.resize(input.find(':'));
}
else
{
......
test/gpu/adjust_allocation.cpp
View file @ b76a9043
......@@ -40,6 +40,10 @@
#include <migraphx/make_op.hpp>
#include <basic_ops.hpp>
#include <test.hpp>
#include "make_precompile_op.hpp"
// Treat some operators as compilable to enable lowering
MIGRAPHX_GPU_TEST_PRECOMPILE("add", "mul", "convert")
void run_lowering(migraphx::program& p, bool offload_copy = false)
{
......@@ -118,7 +122,7 @@ TEST_CASE(no_copy_dead_param)
auto xb = mm->add_instruction(migraphx::make_op("hip::allocate", {{"shape", to_value(s)}}));
auto gx = mm->add_instruction(migraphx::make_op("hip::copy_to_gpu"), x, xb);
auto ab = mm->add_instruction(migraphx::make_op("hip::allocate", {{"shape", to_value(s)}}));
auto sum = mm->add_instruction(migraphx::make_op("gpu::add"), gx, gx, ab);
auto sum = mm->add_instruction(make_precompile_op("add"), gx, gx, ab);
auto r = mm->add_instruction(migraphx::make_op("hip::copy_from_gpu"), sum);
mm->add_return({r});
......
src/targets/gpu/device/gelu.cpp → test/gpu/make_precompile_op.hpp
View file @ b76a9043
......@@ -21,63 +21,46 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/gpu/device/gelu.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
#include <cmath>
#ifndef MIGRAPHX_GUARD_TEST_GPU_MAKE_PRECOMPILE_OP_HPP
#define MIGRAPHX_GUARD_TEST_GPU_MAKE_PRECOMPILE_OP_HPP
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
#include <migraphx/operation.hpp>
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/make_op.hpp>
// x * 0.5 * (1.0 + erf(x / sqrt(2.0)))
template <class T>
auto gelu_fn(T x) __device__
{
return x * 0.5 * (1 + ::erf(x * M_SQRT1_2));
}
// NOLINTNEXTLINE
#define MIGRAPHX_GPU_TEST_PRECOMPILE(...) \
struct test_compiler : migraphx::gpu::compiler<test_compiler> \
{ \
std::vector<std::string> names() const { return {__VA_ARGS__}; } \
\
template <class... Ts> \
migraphx::operation compile_op(Ts&&...) const \
{ \
MIGRAPHX_THROW("Not compilable"); \
} \
\
template <class... Ts> \
migraphx::gpu::compiler_replace compile(Ts&&...) const \
{ \
MIGRAPHX_THROW("Not compilable"); \
} \
};
// 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * pow(x, 3))))
template <class T>
auto gelu_fn_new(T x) __device__
inline migraphx::operation make_precompile_op(migraphx::rank<0>, const migraphx::operation& op)
{
return 0.5 * x * (1 + tanh(sqrt(M_2_PI) * (x + 0.044715 * x * x * x)));
return migraphx::make_op("gpu::precompile_op", {{"op", migraphx::to_value(op)}});
}
void gelu(hipStream_t stream, const argument& result, const argument& arg)
inline migraphx::operation make_precompile_op(migraphx::rank<1>, const std::string& name)
{
nary(stream, result, arg)([](auto x) __device__ { return gelu_fn(to_hip_type(x)); });
return make_precompile_op(migraphx::rank<0>{}, migraphx::make_op(name));
}
void gelu_new(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) __device__ { return gelu_fn_new(to_hip_type(x)); });
}
void add_gelu(hipStream_t stream,
const argument& result,
const argument& arg1,
const argument& arg2)
{
nary(stream, result, arg1, arg2)([](auto x, auto y) __device__ {
auto sum = to_hip_type(x + y);
return gelu_fn(sum);
});
}
void add_gelu_new(hipStream_t stream,
const argument& result,
const argument& arg1,
const argument& arg2)
template <class T>
auto make_precompile_op(const T& x)
{
nary(stream, result, arg1, arg2)([](auto x, auto y) __device__ {
auto sum = to_hip_type(x + y);
return gelu_fn(sum);
});
return make_precompile_op(migraphx::rank<1>{}, x);
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif // MIGRAPHX_GUARD_TEST_GPU_MAKE_PRECOMPILE_OP_HPP
test/gpu/pack_int8_args.cpp
View file @ b76a9043
......@@ -38,6 +38,10 @@
#include <migraphx/pass_manager.hpp>
#include <migraphx/make_op.hpp>
#include <test.hpp>
#include "make_precompile_op.hpp"
// Treat some operators as compilable to enable lowering
MIGRAPHX_GPU_TEST_PRECOMPILE("add", "mul", "convert")
void run_passes(migraphx::module& m)
{
......@@ -116,9 +120,8 @@ TEST_CASE(quant_dot)
m.add_instruction(migraphx::make_op("gpu::contiguous"), beta_broadcast, beta_alloc);
auto mul_alloc = m.add_instruction(
migraphx::make_op("hip::allocate", {{"shape", migraphx::to_value(m3_shape)}}));
auto m3_beta =
m.add_instruction(migraphx::make_op("gpu::mul"), l3, beta_contiguous, mul_alloc);
auto gemm_add = m.add_instruction(migraphx::make_op("gpu::add"), gemm, m3_beta, output);
auto m3_beta = m.add_instruction(make_precompile_op("mul"), l3, beta_contiguous, mul_alloc);
auto gemm_add = m.add_instruction(make_precompile_op("add"), gemm, m3_beta, output);
m.add_return({gemm_add});
return m;
......@@ -187,21 +190,23 @@ TEST_CASE(quant_dot_trans)
// back result to int8
auto tl1_convert_alloc = m.add_instruction(migraphx::make_op(
"hip::allocate", {{"shape", migraphx::to_value(alpha_contiguous->get_shape())}}));
auto tl1_convert = m.add_instruction(
migraphx::make_op("gpu::convert", {{"target_type", alpha->get_shape().type()}}),
conta,
tl1_convert_alloc);
auto mul_alloc = m.add_instruction(migraphx::make_op(
auto tl1_convert =
m.add_instruction(make_precompile_op(migraphx::make_op(
"convert", {{"target_type", alpha->get_shape().type()}})),
conta,
tl1_convert_alloc);
auto mul_alloc = m.add_instruction(migraphx::make_op(
"hip::allocate", {{"shape", migraphx::to_value(tl1_convert->get_shape())}}));
auto tl1_alpha_int32 = m.add_instruction(
migraphx::make_op("gpu::mul"), alpha_contiguous, tl1_convert, mul_alloc);
auto tl1_alpha_int32 =
m.add_instruction(make_precompile_op("mul"), alpha_contiguous, tl1_convert, mul_alloc);
// convert mul_res to int8
auto tl1_alpha_int8_alloc = m.add_instruction(migraphx::make_op(
"hip::allocate", {{"shape", migraphx::to_value(conta->get_shape())}}));
auto tl1_alpha_int8 = m.add_instruction(
migraphx::make_op("gpu::convert", {{"target_type", conta->get_shape().type()}}),
tl1_alpha_int32,
tl1_alpha_int8_alloc);
auto tl1_alpha_int8 =
m.add_instruction(make_precompile_op(migraphx::make_op(
"convert", {{"target_type", conta->get_shape().type()}})),
tl1_alpha_int32,
tl1_alpha_int8_alloc);
auto packb = contb;
if(int8_x4)
......@@ -306,9 +311,8 @@ TEST_CASE(quant_dot_pad)
m.add_instruction(migraphx::make_op("gpu::contiguous"), beta_broadcast, beta_alloc);
auto mul_alloc = m.add_instruction(
migraphx::make_op("hip::allocate", {{"shape", migraphx::to_value(s3)}}));
auto m3_beta =
m.add_instruction(migraphx::make_op("gpu::mul"), l3, beta_contiguous, mul_alloc);
auto gemm_add = m.add_instruction(migraphx::make_op("gpu::add"), gemm, m3_beta, output);
auto m3_beta = m.add_instruction(make_precompile_op("mul"), l3, beta_contiguous, mul_alloc);
auto gemm_add = m.add_instruction(make_precompile_op("add"), gemm, m3_beta, output);
m.add_return({gemm_add});
return m;
};
......@@ -396,14 +400,15 @@ TEST_CASE(quant_dot_trans_pad)
// back result to int8
auto tl1_convert_alloc = m.add_instruction(migraphx::make_op(
"hip::allocate", {{"shape", migraphx::to_value(alpha_contiguous->get_shape())}}));
auto tl1_convert = m.add_instruction(
migraphx::make_op("gpu::convert", {{"target_type", alpha->get_shape().type()}}),
conta,
tl1_convert_alloc);
auto mul_alloc = m.add_instruction(migraphx::make_op(
auto tl1_convert =
m.add_instruction(make_precompile_op(migraphx::make_op(
"convert", {{"target_type", alpha->get_shape().type()}})),
conta,
tl1_convert_alloc);
auto mul_alloc = m.add_instruction(migraphx::make_op(
"hip::allocate", {{"shape", migraphx::to_value(tl1_convert->get_shape())}}));
auto tl1_alpha_int32 = m.add_instruction(
migraphx::make_op("gpu::mul"), alpha_contiguous, tl1_convert, mul_alloc);
auto tl1_alpha_int32 =
m.add_instruction(make_precompile_op("mul"), alpha_contiguous, tl1_convert, mul_alloc);
// convert mul_res to int8
auto tl1_alpha_int8_alloc = m.add_instruction(migraphx::make_op(
"hip::allocate", {{"shape", migraphx::to_value(conta->get_shape())}}));
......@@ -415,10 +420,11 @@ TEST_CASE(quant_dot_trans_pad)
migraphx::make_op("hip::allocate", {{"shape", migraphx::to_value(ps1)}}));
}
auto tl1_alpha_int8 = m.add_instruction(
migraphx::make_op("gpu::convert", {{"target_type", conta->get_shape().type()}}),
tl1_alpha_int32,
tl1_alpha_int8_alloc);
auto tl1_alpha_int8 =
m.add_instruction(make_precompile_op(migraphx::make_op(
"convert", {{"target_type", conta->get_shape().type()}})),
tl1_alpha_int32,
tl1_alpha_int8_alloc);
auto pa = tl1_alpha_int8;
if(int8_x4)
......
test/memory_coloring_test.cpp
View file @ b76a9043
......@@ -724,7 +724,7 @@ TEST_CASE(test39)
auto sub_modules = p.get_modules();
std::reverse(sub_modules.begin(), sub_modules.end());
for(auto& smod : sub_modules)
for(const auto& smod : sub_modules)
{
run_pass(*smod);
}
......
test/ref_ops_test.cpp
View file @ b76a9043
......@@ -3663,7 +3663,7 @@ TEST_CASE(multinomial_test)
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
std::vector<int> res_dist(5, 0);
for(auto& r : result_vec)
for(const auto& r : result_vec)
res_dist[r]++;
auto dist_sum = std::accumulate(dist.begin(), dist.end(), 0);
auto res_dist_sum = std::accumulate(res_dist.begin(), res_dist.end(), 0);
......
test/simplify_algebra_test.cpp
View file @ b76a9043
......@@ -236,6 +236,105 @@ TEST_CASE(simplify_mul_conv1)
EXPECT(new_conv->outputs().front()->name() != "mul");
}
TEST_CASE(simplify_mul_conv2)
{
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::int32_type, {1, 128, 28, 28}});
auto w =
m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256, 128, 3, 3}}));
auto conv = m.add_instruction(
migraphx::make_op("convolution",
{{"padding", {1, 1}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto a = m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256}}));
auto unsq_a = m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), a);
auto b = m.add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 256, 14, 14}}}), unsq_a);
auto mul = m.add_instruction(migraphx::make_op("mul"), conv, b);
m.add_instruction(pass_op{}, mul);
EXPECT(conv->outputs().front()->name() == "mul");
run_pass(m);
auto new_conv =
std::find_if(m.begin(), m.end(), [](auto&& ins) { return ins.name() == "convolution"; });
EXPECT(new_conv->outputs().front()->name() != "mul");
}
// len = 1 case
TEST_CASE(simplify_mul_conv3)
{
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::int32_type, {1, 128, 28, 28}});
auto w =
m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256, 128, 3, 3}}));
auto conv = m.add_instruction(
migraphx::make_op("convolution",
{{"padding", {1, 1}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto a = m.add_literal(
migraphx::generate_literal({migraphx::shape::int32_type, {256, 1, 1}, {1, 18, 1}}));
auto b =
m.add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 256, 14, 14}}}), a);
auto mul = m.add_instruction(migraphx::make_op("mul"), conv, b);
m.add_instruction(pass_op{}, mul);
EXPECT(conv->outputs().front()->name() == "mul");
run_pass(m);
auto new_conv =
std::find_if(m.begin(), m.end(), [](auto&& ins) { return ins.name() == "convolution"; });
EXPECT(new_conv->outputs().front()->name() != "mul");
}
// Previously broadcasted literal case, should skip
TEST_CASE(simplify_mul_conv_skip1)
{
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::int32_type, {1, 128, 28, 28}});
auto w =
m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256, 128, 3, 3}}));
auto conv = m.add_instruction(
migraphx::make_op("convolution",
{{"padding", {1, 1}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto a = m.add_literal(
migraphx::generate_literal({migraphx::shape::int32_type, {256, 14, 14}, {1, 0, 0}}));
auto b = m.add_instruction(
migraphx::make_op("broadcast", {{"axis", 1}, {"out_lens", {1, 256, 14, 14}}}), a);
auto mul = m.add_instruction(migraphx::make_op("mul"), conv, b);
m.add_instruction(pass_op{}, mul);
EXPECT(conv->outputs().front()->name() == "mul");
run_pass(m);
auto new_conv =
std::find_if(m.begin(), m.end(), [](auto&& ins) { return ins.name() == "convolution"; });
EXPECT(new_conv->outputs().front()->name() == "mul");
}
// Another previously broadcasted literal case, should skip
TEST_CASE(simplify_mul_conv_skip2)
{
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::int32_type, {1, 128, 28, 28}});
auto w =
m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256, 128, 3, 3}}));
auto conv = m.add_instruction(
migraphx::make_op("convolution",
{{"padding", {1, 1}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto a = m.add_literal(
migraphx::generate_literal({migraphx::shape::int32_type, {256, 14, 14}, {1, 0, 0}}));
auto b =
m.add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 256, 14, 14}}}), a);
auto mul = m.add_instruction(migraphx::make_op("mul"), conv, b);
m.add_instruction(pass_op{}, mul);
EXPECT(conv->outputs().front()->name() == "mul");
run_pass(m);
auto new_conv =
std::find_if(m.begin(), m.end(), [](auto&& ins) { return ins.name() == "convolution"; });
EXPECT(new_conv->outputs().front()->name() == "mul");
}
TEST_CASE(simplify_mul_slice_conv1)
{
migraphx::module m1;
......
test/verify/test_layernorm.cpp
View file @ b76a9043
......@@ -29,14 +29,16 @@
#include <migraphx/op/reduce_mean.hpp>
migraphx::instruction_ref
add_layernorm(migraphx::module& m, migraphx::instruction_ref x, std::vector<size_t> dims)
migraphx::instruction_ref add_layernorm(migraphx::module& m,
migraphx::instruction_ref x,
std::vector<size_t> dims,
float eps = 1e-12f)
{
auto scale =
m.add_parameter("scale", migraphx::shape{migraphx::shape::float_type, {dims.back()}});
auto bias =
m.add_parameter("bias", migraphx::shape{migraphx::shape::float_type, {dims.back()}});
auto epsilon = m.add_literal(1e-12f);
auto epsilon = m.add_literal(eps);
auto exponent = m.add_literal(2.0f);
auto mean = m.add_instruction(migraphx::op::reduce_mean({2}), x);
......@@ -88,6 +90,19 @@ struct test_layernorm2 : verify_program<test_layernorm2>
}
};
struct test_layernorm_eps : verify_program<test_layernorm_eps>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
std::vector<size_t> dims = {1, 2, 5};
auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, dims});
add_layernorm(*mm, x, dims, 1e-5f);
return p;
}
};
struct test_layernorm_triadd : verify_program<test_layernorm_triadd>
{
migraphx::program create_program() const
......
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