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Commit 41d4e92b authored by Khalique Ahmed's avatar Khalique Ahmed
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Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into develop

parents a8eb886b 32f6388c
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src/targets/gpu/include/migraphx/gpu/batch_norm_inference.hpp deleted 100644 → 0
View file @ a8eb886b
/*
* 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.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_BATCHNORM_HPP
#define MIGRAPHX_GUARD_RTGLIB_BATCHNORM_HPP
#include <migraphx/argument.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/reflect.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct context;
struct miopen_batch_norm_inference
{
op::batch_norm_inference op;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return migraphx::reflect(self.op, f);
}
std::string name() const { return "gpu::batch_norm_inference"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
{
return shapes.size() - 1;
}
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/targets/gpu/lowering.cpp
View file @ 41d4e92b
...@@ -37,7 +37,6 @@ ...@@ -37,7 +37,6 @@
#include <migraphx/op/quant_convolution.hpp> #include <migraphx/op/quant_convolution.hpp>
#include <migraphx/op/quant_dot.hpp> #include <migraphx/op/quant_dot.hpp>
#include <migraphx/gpu/batch_norm_inference.hpp>
#include <migraphx/gpu/context.hpp> #include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/convolution.hpp> #include <migraphx/gpu/convolution.hpp>
#include <migraphx/gpu/deconvolution.hpp> #include <migraphx/gpu/deconvolution.hpp>
...@@ -115,7 +114,6 @@ struct miopen_apply ...@@ -115,7 +114,6 @@ struct miopen_apply
add_extend_op("scatter_none"); add_extend_op("scatter_none");
add_extend_op("topk"); add_extend_op("topk");
add_batch_norm_inference_op();
add_convolution_op(); add_convolution_op();
add_deconvolution_op(); add_deconvolution_op();
add_gemm_op<op::dot>("dot"); add_gemm_op<op::dot>("dot");
...@@ -336,43 +334,6 @@ struct miopen_apply ...@@ -336,43 +334,6 @@ struct miopen_apply
}); });
} }
void add_batch_norm_inference_op()
{
apply_map.emplace("batch_norm_inference", [=](instruction_ref ins) {
auto&& op = any_cast<op::batch_norm_inference>(ins->get_operator());
auto output = insert_allocation(ins, ins->get_shape());
shape old_shape = ins->inputs().at(1)->get_shape();
auto input = ins->inputs()[0];
auto input_lens = input->get_shape().lens();
std::vector<int64_t> rsp_lens(input_lens.size(), 1);
// for per_activation case, also need to reshape input
if(op.bn_mode == op::batch_norm_inference::per_activation)
{
std::copy(input_lens.begin() + 1, input_lens.end(), rsp_lens.begin() + 1);
}
else
{
rsp_lens[1] = static_cast<int64_t>(old_shape.elements());
}
auto reshape_op = op::reshape{rsp_lens};
std::vector<instruction_ref> reshapes;
std::transform(ins->inputs().begin() + 1,
ins->inputs().end(),
std::back_inserter(reshapes),
[&](auto i) { return mod->insert_instruction(ins, reshape_op, i); });
return mod->replace_instruction(ins,
miopen_batch_norm_inference{op},
input,
reshapes[0],
reshapes[1],
reshapes[2],
reshapes[3],
output);
});
}
// use 0 - input to represent neg // use 0 - input to represent neg
void add_neg_op() void add_neg_op()
{ {
......
src/targets/gpu/target.cpp
View file @ 41d4e92b
...@@ -41,7 +41,6 @@ ...@@ -41,7 +41,6 @@
#include <migraphx/propagate_constant.hpp> #include <migraphx/propagate_constant.hpp>
#include <migraphx/register_target.hpp> #include <migraphx/register_target.hpp>
#include <migraphx/replace_allocate.hpp> #include <migraphx/replace_allocate.hpp>
#include <migraphx/rewrite_batchnorm.hpp>
#include <migraphx/rewrite_gelu.hpp> #include <migraphx/rewrite_gelu.hpp>
#include <migraphx/rewrite_pooling.hpp> #include <migraphx/rewrite_pooling.hpp>
#include <migraphx/rewrite_quantization.hpp> #include <migraphx/rewrite_quantization.hpp>
...@@ -110,8 +109,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti ...@@ -110,8 +109,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
dead_code_elimination{}, dead_code_elimination{},
insert_pad{}, insert_pad{},
dead_code_elimination{}, dead_code_elimination{},
rewrite_batchnorm{},
dead_code_elimination{},
rewrite_rnn{}, rewrite_rnn{},
dead_code_elimination{}, dead_code_elimination{},
inline_module{}, inline_module{},
......
src/targets/ref/lowering.cpp
View file @ 41d4e92b
...@@ -26,7 +26,6 @@ ...@@ -26,7 +26,6 @@
#include <migraphx/instruction.hpp> #include <migraphx/instruction.hpp>
#include <migraphx/dfor.hpp> #include <migraphx/dfor.hpp>
#include <migraphx/op/identity.hpp> #include <migraphx/op/identity.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/op/convolution.hpp> #include <migraphx/op/convolution.hpp>
#include <migraphx/op/deconvolution.hpp> #include <migraphx/op/deconvolution.hpp>
#include <migraphx/op/quant_convolution.hpp> #include <migraphx/op/quant_convolution.hpp>
...@@ -75,84 +74,6 @@ typename std::conditional_t<std::is_integral<T>{}, std::make_signed<T>, std::ena ...@@ -75,84 +74,6 @@ typename std::conditional_t<std::is_integral<T>{}, std::make_signed<T>, std::ena
return x; return x;
} }
//
// ref implemenataion of batch norm for inference
//
// inputs are:
// args[0] -> input data buffer
// args[1] -> mini batch mean
// args[2] -> mini batch variance
// args[3] -> gamma
// args[4] -> bias
//
// The equation to compute batch norm for inference is:
//
// output[i] = bias + gamma * (input[i] + mean) / sqrt(variance + epsilon)
//
// the input data format should be nchw
//
struct ref_batch_norm_inference
{
op::batch_norm_inference op;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return migraphx::reflect(self.op, f);
}
std::string name() const { return "ref::batch_norm_inference"; }
shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
argument compute(context&, const shape& output_shape, std::vector<argument> args) const
{
argument output{output_shape};
double epsilon = op.epsilon;
auto input = args[0];
auto arg_gamma = args[1];
auto arg_bias = args[2];
auto mini_batch_mean = args[3];
auto mini_batch_variance = args[4];
if(op.bn_mode == op::batch_norm_inference::spatial)
{
visit_all(output, input, mini_batch_mean, mini_batch_variance, arg_gamma, arg_bias)(
[&](auto result, auto buffer, auto mean, auto variance, auto gamma, auto bias) {
par_for(output_shape.elements(), [&](auto i) {
auto idx = output_shape.multi(i);
auto c = idx[1];
assert((variance[c] + epsilon) > 0);
result[i] =
gamma[c] * (buffer[i] - mean[c]) / std::sqrt(variance[c] + epsilon) +
bias[c];
});
});
}
if(op.bn_mode == op::batch_norm_inference::per_activation)
{
visit_all(output, input, mini_batch_mean, mini_batch_variance, arg_gamma, arg_bias)(
[&](auto result, auto buffer, auto mean, auto variance, auto gamma, auto bias) {
par_for(output_shape.elements(), [&](auto i) {
auto idx = output_shape.multi(i);
idx[0] = 0;
auto index = output_shape.index(idx);
assert((variance[index] + epsilon) > 0);
result[i] = gamma[index] * (buffer[i] - mean[index]) /
std::sqrt(variance[index] + epsilon) +
bias[index];
});
});
}
return output;
}
};
MIGRAPHX_REGISTER_OP(ref_batch_norm_inference)
struct ref_lrn struct ref_lrn
{ {
op::lrn op; op::lrn op;
...@@ -237,15 +158,16 @@ struct ref_convolution : auto_register_op<ref_convolution<Op>> ...@@ -237,15 +158,16 @@ struct ref_convolution : auto_register_op<ref_convolution<Op>>
argument compute(context&, shape output_shape, std::vector<argument> args) const argument compute(context&, shape output_shape, std::vector<argument> args) const
{ {
std::vector<std::size_t> padding; std::vector<std::size_t> padding;
if(op.use_dynamic_same_auto_pad) if(op.padding_mode != op::padding_mode_t::default_)
{ {
auto input_lens = args[0].get_shape().lens(); auto input_lens = args[0].get_shape().lens();
std::vector<std::size_t> img_lens{input_lens.begin() + 2, input_lens.end()};
auto weights_lens = args[1].get_shape().lens(); auto weights_lens = args[1].get_shape().lens();
std::vector<std::size_t> k_lens{weights_lens.begin() + 2, weights_lens.end()}; padding =
padding = calc_dyn_auto_pad(img_lens, k_lens, op.stride, op.dilation); op.padding_mode == op::same_upper
output_shape = ? calc_dyn_auto_pad(input_lens, weights_lens, op.stride, op.dilation, true)
compute_padded_shape({args.at(0).get_shape(), args.at(1).get_shape()}, padding); : calc_dyn_auto_pad(input_lens, weights_lens, op.stride, op.dilation, false);
output_shape = compute_padded_shape(
args[0].get_shape(), args[1].get_shape(), padding, op.stride, op.dilation);
} }
else else
{ {
...@@ -313,34 +235,6 @@ struct ref_convolution : auto_register_op<ref_convolution<Op>> ...@@ -313,34 +235,6 @@ struct ref_convolution : auto_register_op<ref_convolution<Op>>
}); });
return result; return result;
} }
private:
/*!
* Used for dynamic auto padding since padding needs to be computed at evaulation time.
* \param inputs two fixed shape inputs [input_tensor, weights]
* \param padding from auto_pad calculation
*/
shape compute_padded_shape(const std::vector<shape>& inputs,
const std::vector<std::size_t>& padding) const
{
const shape& input = inputs.at(0);
const shape& weights = inputs.at(1);
const size_t num_spatial_dims = input.lens().size() - 2;
std::vector<size_t> output_lens{input.lens()[0], weights.lens()[0]};
// calculate the output shape of the convolution: ((W - K + 2P) / S) + 1
for(size_t i = 0; i < num_spatial_dims; i++)
{
auto padding_factor = padding[i] + padding[i + num_spatial_dims];
output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[i + 2] - (1 + op.dilation[i] * (weights.lens()[i + 2] - 1)) +
padding_factor) /
op.stride[i] +
1)));
}
return inputs[0].with_lens(output_lens);
}
}; };
struct ref_im2col struct ref_im2col
...@@ -731,8 +625,6 @@ struct ref_apply ...@@ -731,8 +625,6 @@ struct ref_apply
void init() void init()
{ {
apply_map["batch_norm_inference"] =
extend_op<ref_batch_norm_inference, op::batch_norm_inference>();
apply_map["convolution"] = extend_op<ref_convolution<op::convolution>, op::convolution>(); apply_map["convolution"] = extend_op<ref_convolution<op::convolution>, op::convolution>();
apply_map["dot"] = extend_op<ref_gemm, op::dot>(); apply_map["dot"] = extend_op<ref_gemm, op::dot>();
apply_map["quant_dot"] = extend_op<ref_quant_gemm, op::quant_dot>(); apply_map["quant_dot"] = extend_op<ref_quant_gemm, op::quant_dot>();
......
src/tf/parse_batchnorm.cpp
View file @ 41d4e92b
...@@ -23,6 +23,7 @@ ...@@ -23,6 +23,7 @@
*/ */
#include <migraphx/tf/op_parser.hpp> #include <migraphx/tf/op_parser.hpp>
#include <migraphx/tf/tf_parser.hpp> #include <migraphx/tf/tf_parser.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/ranges.hpp> #include <migraphx/ranges.hpp>
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
...@@ -38,16 +39,37 @@ struct parse_batchnorm : op_parser<parse_batchnorm> ...@@ -38,16 +39,37 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
instruction_ref parse(const op_desc& /*opd*/, instruction_ref parse(const op_desc& /*opd*/,
const tf_parser& /*parser*/, const tf_parser& /*parser*/,
tf_parser::node_info info, tf_parser::node_info info,
const std::vector<instruction_ref>& args) const std::vector<instruction_ref> args) const
{ {
float epsilon = 1e-5f; // different default epsilon than from ONNX
float momentum = 0.9f; float epsilon = 1e-4f;
if(contains(info.attributes, "epsilon")) if(contains(info.attributes, "epsilon"))
{ {
epsilon = info.attributes.at("epsilon").f(); epsilon = info.attributes.at("epsilon").f();
} }
auto op = make_op("batch_norm_inference", {{"epsilon", epsilon}, {"momentum", momentum}});
return info.add_instruction(op, args); auto x_lens = args[0]->get_shape().lens();
auto x_type = args[0]->get_shape().type();
// unsqueeze tensors of shape (C) to broadcast correctly
auto rt = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {0.5}});
auto eps = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
auto scale_unsqueeze =
info.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), args[1]);
auto bias_unsqueeze =
info.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), args[2]);
auto mean_unsqueeze =
info.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), args[3]);
auto var_unsqueeze =
info.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), args[4]);
auto numer = info.add_broadcastable_binary_op("sub", args[0], mean_unsqueeze);
auto var_eps = info.add_broadcastable_binary_op("add", var_unsqueeze, eps);
auto denom = info.add_broadcastable_binary_op("pow", var_eps, rt);
auto div0 = info.add_broadcastable_binary_op("div", numer, denom);
auto r0 = info.add_broadcastable_binary_op("mul", div0, scale_unsqueeze);
return info.add_broadcastable_binary_op("add", r0, bias_unsqueeze);
} }
}; };
......
src/tf/parse_conv.cpp
View file @ 41d4e92b
...@@ -75,7 +75,6 @@ struct parse_conv : op_parser<parse_conv> ...@@ -75,7 +75,6 @@ struct parse_conv : op_parser<parse_conv>
const std::string& pad_mode = info.attributes.at("padding").s(); const std::string& pad_mode = info.attributes.at("padding").s();
if(pad_mode.find("SAME") != std::string::npos) if(pad_mode.find("SAME") != std::string::npos)
{ {
op.padding_mode = op::padding_mode_t::same;
std::vector<size_t> weight_dims = weights->get_shape().lens(); std::vector<size_t> weight_dims = weights->get_shape().lens();
size_t weight_h = weight_dims[2]; size_t weight_h = weight_dims[2];
size_t weight_w = weight_dims[3]; size_t weight_w = weight_dims[3];
...@@ -87,10 +86,6 @@ struct parse_conv : op_parser<parse_conv> ...@@ -87,10 +86,6 @@ struct parse_conv : op_parser<parse_conv>
op.padding = std::vector<size_t>(pads.begin(), pads.end()); op.padding = std::vector<size_t>(pads.begin(), pads.end());
} }
else if(pad_mode.find("VALID") != std::string::npos)
{
op.padding_mode = op::padding_mode_t::valid;
}
else if(pad_mode.find("EXPLICIT") != std::string::npos) else if(pad_mode.find("EXPLICIT") != std::string::npos)
{ {
std::vector<size_t> padding; std::vector<size_t> padding;
......
src/tf/parse_depthwiseconv.cpp
View file @ 41d4e92b
...@@ -80,7 +80,6 @@ struct parse_depthwiseconv : op_parser<parse_depthwiseconv> ...@@ -80,7 +80,6 @@ struct parse_depthwiseconv : op_parser<parse_depthwiseconv>
if(pad_mode.find("SAME") != std::string::npos) if(pad_mode.find("SAME") != std::string::npos)
{ {
op.padding_mode = op::padding_mode_t::same;
std::vector<size_t> weight_dims = weights->get_shape().lens(); std::vector<size_t> weight_dims = weights->get_shape().lens();
size_t weight_h = weight_dims[2]; size_t weight_h = weight_dims[2];
size_t weight_w = weight_dims[3]; size_t weight_w = weight_dims[3];
...@@ -101,10 +100,6 @@ struct parse_depthwiseconv : op_parser<parse_depthwiseconv> ...@@ -101,10 +100,6 @@ struct parse_depthwiseconv : op_parser<parse_depthwiseconv>
op.padding[1] = pads[1]; op.padding[1] = pads[1];
} }
} }
else if(pad_mode.find("VALID") != std::string::npos)
{
op.padding_mode = op::padding_mode_t::valid;
}
} }
std::vector<int64_t> new_weights_shape; std::vector<int64_t> new_weights_shape;
......
test/gpu/mlir.cpp
View file @ 41d4e92b
...@@ -141,7 +141,7 @@ TEST_CASE(conv) ...@@ -141,7 +141,7 @@ TEST_CASE(conv)
const std::string mlir_output = R"__migraphx__( const std::string mlir_output = R"__migraphx__(
module { module {
func.func @main(%arg0: tensor<2x8x3x3xf32>, %arg1: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {kernel = "mixr"} { func.func @main(%arg0: tensor<2x8x3x3xf32>, %arg1: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {kernel = "mixr"} {
%0 = migraphx.convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1], use_dynamic_same_auto_pad = 0 : i64} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32> %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> return %0 : tensor<1x2x2x2xf32>
} }
} }
...@@ -164,7 +164,7 @@ TEST_CASE(conv_add_relu) ...@@ -164,7 +164,7 @@ TEST_CASE(conv_add_relu)
const std::string mlir_output = R"__migraphx__( const std::string mlir_output = R"__migraphx__(
module { module {
func.func @main(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {kernel = "mixr"} { func.func @main(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {kernel = "mixr"} {
%0 = migraphx.convolution(%arg2, %arg1) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1], use_dynamic_same_auto_pad = 0 : i64} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32> %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> %1 = migraphx.add(%0, %arg0) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
%2 = migraphx.relu(%1) : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32> %2 = migraphx.relu(%1) : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
return %2 : tensor<1x2x2x2xf32> return %2 : tensor<1x2x2x2xf32>
......
test/onnx/onnx_test.cpp
View file @ 41d4e92b
...@@ -856,8 +856,7 @@ TEST_CASE(conv_autopad_same_test) ...@@ -856,8 +856,7 @@ TEST_CASE(conv_autopad_same_test)
auto l0 = mm->add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}}); auto l0 = mm->add_parameter("0", {migraphx::shape::float_type, {1, 3, 32, 32}});
auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}}); auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}});
migraphx::op::convolution op; migraphx::op::convolution op;
op.padding = {1, 1, 1, 1}; op.padding = {1, 1, 1, 1};
op.padding_mode = migraphx::op::padding_mode_t::same;
mm->add_instruction(op, l0, l1); mm->add_instruction(op, l0, l1);
auto prog = optimize_onnx("conv_autopad_same_test.onnx"); auto prog = optimize_onnx("conv_autopad_same_test.onnx");
...@@ -1034,15 +1033,11 @@ TEST_CASE(conv_dynamic_batch_same_upper) ...@@ -1034,15 +1033,11 @@ TEST_CASE(conv_dynamic_batch_same_upper)
auto l0 = mm->add_parameter( auto l0 = mm->add_parameter(
"0", {migraphx::shape::float_type, {{1, 10, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}}}); "0", {migraphx::shape::float_type, {{1, 10, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}}});
auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}}); auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}});
auto c0 = auto c0 = mm->add_instruction(
mm->add_instruction(migraphx::make_op("convolution", migraphx::make_op("convolution",
{{"padding", {1, 1, 1, 1}}, {{"padding", {1, 1, 1, 1}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
{"stride", {1, 1}}, l0,
{"dilation", {1, 1}}, l1);
{"padding_mode", migraphx::op::padding_mode_t::same},
{"use_dynamic_same_auto_pad", false}}),
l0,
l1);
mm->add_return({c0}); mm->add_return({c0});
migraphx::onnx_options options; migraphx::onnx_options options;
...@@ -1064,8 +1059,7 @@ TEST_CASE(conv_dynamic_img_same_upper) ...@@ -1064,8 +1059,7 @@ TEST_CASE(conv_dynamic_img_same_upper)
{{"padding", {0, 0}}, {{"padding", {0, 0}},
{"stride", {1, 1}}, {"stride", {1, 1}},
{"dilation", {1, 1}}, {"dilation", {1, 1}},
{"padding_mode", migraphx::op::padding_mode_t::same_upper}, {"padding_mode", migraphx::op::padding_mode_t::same_upper}}),
{"use_dynamic_same_auto_pad", true}}),
l0, l0,
l1); l1);
mm->add_return({c0}); mm->add_return({c0});
...@@ -1089,8 +1083,7 @@ TEST_CASE(conv_dynamic_kernel_same_lower) ...@@ -1089,8 +1083,7 @@ TEST_CASE(conv_dynamic_kernel_same_lower)
{{"padding", {0, 0}}, {{"padding", {0, 0}},
{"stride", {1, 1}}, {"stride", {1, 1}},
{"dilation", {1, 1}}, {"dilation", {1, 1}},
{"padding_mode", migraphx::op::padding_mode_t::same_lower}, {"padding_mode", migraphx::op::padding_mode_t::same_lower}}),
{"use_dynamic_same_auto_pad", true}}),
l0, l0,
l1); l1);
mm->add_return({c0}); mm->add_return({c0});
......
test/op_shape_test.cpp
View file @ 41d4e92b
...@@ -81,16 +81,6 @@ void throws_shape(const migraphx::shape&, Ts...) ...@@ -81,16 +81,6 @@ void throws_shape(const migraphx::shape&, Ts...)
"An expected shape should not be passed to throws_shape function"); "An expected shape should not be passed to throws_shape function");
} }
TEST_CASE(batch_norm_inference_shape)
{
const size_t channels = 3;
migraphx::shape s{migraphx::shape::float_type, {4, channels, 3, 3}};
migraphx::shape vars{migraphx::shape::float_type, {channels}};
expect_shape(s, migraphx::make_op("batch_norm_inference"), s, vars, vars, vars, vars);
throws_shape(migraphx::make_op("batch_norm_inference"), s);
throws_shape(migraphx::make_op("batch_norm_inference"), s, vars, vars, vars, vars, vars);
}
TEST_CASE(broadcast) TEST_CASE(broadcast)
{ {
{ {
...@@ -261,8 +251,7 @@ TEST_CASE(convolution_shape) ...@@ -261,8 +251,7 @@ TEST_CASE(convolution_shape)
migraphx::make_op("convolution", migraphx::make_op("convolution",
{{"stride", {1, 1}}, {{"stride", {1, 1}},
{"dilation", {1, 1}}, {"dilation", {1, 1}},
{"padding_mode", migraphx::op::padding_mode_t::same_upper}, {"padding_mode", migraphx::op::padding_mode_t::same_upper}}),
{"use_dynamic_same_auto_pad", true}}),
input_dyn_shape, input_dyn_shape,
weights_shape); weights_shape);
...@@ -275,8 +264,7 @@ TEST_CASE(convolution_shape) ...@@ -275,8 +264,7 @@ TEST_CASE(convolution_shape)
migraphx::make_op("convolution", migraphx::make_op("convolution",
{{"stride", {1, 1}}, {{"stride", {1, 1}},
{"dilation", {1, 1}}, {"dilation", {1, 1}},
{"padding_mode", migraphx::op::padding_mode_t::same_upper}, {"padding_mode", migraphx::op::padding_mode_t::same_upper}}),
{"use_dynamic_same_auto_pad", true}}),
input_dyn_shape, input_dyn_shape,
weights_shape); weights_shape);
...@@ -290,8 +278,7 @@ TEST_CASE(convolution_shape) ...@@ -290,8 +278,7 @@ TEST_CASE(convolution_shape)
migraphx::make_op("convolution", migraphx::make_op("convolution",
{{"stride", {1, 1}}, {{"stride", {1, 1}},
{"dilation", {1, 1}}, {"dilation", {1, 1}},
{"padding_mode", migraphx::op::padding_mode_t::same_lower}, {"padding_mode", migraphx::op::padding_mode_t::same_lower}}),
{"use_dynamic_same_auto_pad", true}}),
input_dyn_shape, input_dyn_shape,
weights_shape); weights_shape);
} }
......
test/ref_ops_test.cpp
View file @ 41d4e92b
...@@ -28,7 +28,6 @@ ...@@ -28,7 +28,6 @@
#include <limits> #include <limits>
#include <migraphx/literal.hpp> #include <migraphx/literal.hpp>
#include <migraphx/op/pooling.hpp> #include <migraphx/op/pooling.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/instruction.hpp> #include <migraphx/instruction.hpp>
#include <migraphx/quantization.hpp> #include <migraphx/quantization.hpp>
#include <migraphx/ref/target.hpp> #include <migraphx/ref/target.hpp>
...@@ -493,202 +492,6 @@ TEST_CASE(avgpool_test) ...@@ -493,202 +492,6 @@ TEST_CASE(avgpool_test)
} }
} }
TEST_CASE(batch_norm_1d_per_actv_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape x_shape{migraphx::shape::float_type, {2, 2, 4}};
migraphx::shape c_shape(migraphx::shape::float_type, {2, 4});
std::vector<float> x_data = {0.3547,
0.477,
-1.8575,
0.663,
-0.1881,
-0.5113,
-0.1803,
-0.5915,
-0.1552,
0.9821,
1.827,
0.0558,
-0.0417,
-1.0693,
1.9948,
-0.7448};
std::vector<float> scale_data = {
-0.3181, -0.3885, 1.655, 0.0704, -0.2565, -1.1761, -0.3751, 0.1057};
std::vector<float> bias_data = {
-1.2118, -2.1156, 0.0046, -0.1341, -0.2724, -1.0718, 0.5535, -0.889};
std::vector<float> mean_data = {
0.0997, 0.7295, -0.0153, 0.3594, -0.1149, -0.7903, 0.9073, -0.6681};
std::vector<float> variance_data = {
0.13, 0.1276, 6.7878, 0.1843, 0.0107, 0.1556, 2.3655, 0.0117};
auto x = mm->add_literal(migraphx::literal{x_shape, x_data});
auto scale = mm->add_literal(migraphx::literal{c_shape, scale_data});
auto bias = mm->add_literal(migraphx::literal{c_shape, bias_data});
auto mean = mm->add_literal(migraphx::literal{c_shape, mean_data});
auto variance = mm->add_literal(migraphx::literal{c_shape, variance_data});
mm->add_instruction(
migraphx::make_op(
"batch_norm_inference",
{{"epsilon", 1e-6},
{"momentum", 0.9},
{"bn_mode", migraphx::to_value(migraphx::op::batch_norm_inference::per_activation)}}),
x,
scale,
bias,
mean,
variance);
p.compile(migraphx::ref::target{});
auto result = p.eval({}).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {-1.43677,
-1.84098,
-1.16563,
-0.0843136,
-0.090896,
-1.90364,
0.81875,
-0.81415,
-0.986915,
-2.39032,
1.17489,
-0.183886,
-0.453904,
-0.239955,
0.288275,
-0.963948};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(batch_norm_1d_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape x_shape{migraphx::shape::float_type, {2, 3, 4}};
migraphx::shape c_shape(migraphx::shape::float_type, {3});
std::vector<float> x_data = {0.7253, -0.6356, 0.4606, -0.8689, -1.1932, 0.4538,
-1.0018, -0.365, -0.214, -0.9553, -0.7672, 0.2331,
-0.8416, -0.6142, 0.0814, 0.2498, -0.6706, 1.4872,
0.5112, -1.5212, -0.9126, 0.0735, 1.085, -0.3417};
std::vector<float> scale_data = {1.1, 1.2, 1.3};
std::vector<float> bias_data = {0.1, 0.2, 0.3};
std::vector<float> mean_data = {-0.1804, -0.2875, -0.2249};
std::vector<float> variance_data = {2.7914, 7.3424, 3.3287};
auto x = mm->add_literal(migraphx::literal{x_shape, x_data});
auto scale = mm->add_literal(migraphx::literal{c_shape, scale_data});
auto bias = mm->add_literal(migraphx::literal{c_shape, bias_data});
auto mean = mm->add_literal(migraphx::literal{c_shape, mean_data});
auto variance = mm->add_literal(migraphx::literal{c_shape, variance_data});
mm->add_instruction(migraphx::make_op("batch_norm_inference", {{"epsilon", 1e-5}}),
x,
scale,
bias,
mean,
variance);
p.compile(migraphx::ref::target{});
auto result = p.eval({}).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {0.696301, -0.199697, 0.522026, -0.353299, -0.201094, 0.528289,
-0.116332, 0.165679, 0.307767, -0.220435, -0.086407, 0.62634,
-0.335325, -0.185608, 0.272366, 0.383238, 0.0303421, 0.985936,
0.553709, -0.346351, -0.190009, 0.51262, 1.23335, 0.216776};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(batch_norm_3d_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape x_shape{migraphx::shape::float_type, {2, 2, 2, 2, 2}};
migraphx::shape c_shape(migraphx::shape::float_type, {2});
std::vector<float> x_data = {-1.0833, 1.9681, 1.2075, -0.723, -0.4076, -0.8738, 0.5853,
-0.5357, 1.734, 0.7904, 0.6953, -0.468, -0.425, 0.6895,
0.0096, 0.4205, -0.1749, 1.2821, 2.1453, -0.8538, 1.0687,
0.0906, 0.0714, -1.3079, -0.6376, 1.3023, 0.945, 0.0927,
-0.7421, -1.4341, -1.0309, 1.5153};
std::vector<float> scale_data = {1.1, 1.3};
std::vector<float> bias_data = {0.1, 0.2};
std::vector<float> mean_data = {0.1537, 0.2161};
std::vector<float> variance_data = {18.0805, 13.3906};
auto x = mm->add_literal(migraphx::literal{x_shape, x_data});
auto scale = mm->add_literal(migraphx::literal{c_shape, scale_data});
auto bias = mm->add_literal(migraphx::literal{c_shape, bias_data});
auto mean = mm->add_literal(migraphx::literal{c_shape, mean_data});
auto variance = mm->add_literal(migraphx::literal{c_shape, variance_data});
mm->add_instruction(migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
p.compile(migraphx::ref::target{});
auto result = p.eval({}).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {
-0.220005, 0.569376, 0.372612, -0.126798, -0.0452053, -0.165809, 0.211653, -0.0783441,
0.739245, 0.404024, 0.370239, -0.0430317, -0.0277556, 0.368179, 0.126639, 0.272615,
0.0149929, 0.391911, 0.615216, -0.160635, 0.336706, 0.0836764, 0.0787094, -0.278108,
-0.103283, 0.585881, 0.458947, 0.156161, -0.140408, -0.386246, -0.243006, 0.661551};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(batch_norm_inference_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
const size_t width = 2;
const size_t height = 2;
const size_t channels = 4;
const size_t batches = 2;
const float x_val = 8.0;
const float mean_val = 2.0;
const float variance_val = 4.0;
const float scale_val = 2.0f;
const float bias_val = 1.0f;
const float output_val = scale_val * (x_val - mean_val) / (std::sqrt(variance_val)) + bias_val;
migraphx::shape s{migraphx::shape::float_type, {batches, channels, height, width}};
migraphx::shape vars{migraphx::shape::float_type, {channels}};
std::vector<float> x_data(width * height * channels * batches);
std::vector<float> scale_data(channels);
std::vector<float> bias_data(channels);
std::vector<float> mean_data(channels);
std::vector<float> variance_data(channels);
std::fill(x_data.begin(), x_data.end(), x_val);
std::fill(mean_data.begin(), mean_data.end(), mean_val);
std::fill(variance_data.begin(), variance_data.end(), variance_val);
std::fill(scale_data.begin(), scale_data.end(), scale_val);
std::fill(bias_data.begin(), bias_data.end(), bias_val);
auto x = mm->add_literal(migraphx::literal{s, x_data});
auto scale = mm->add_literal(migraphx::literal{vars, scale_data});
auto bias = mm->add_literal(migraphx::literal{vars, bias_data});
auto mean = mm->add_literal(migraphx::literal{vars, mean_data});
auto variance = mm->add_literal(migraphx::literal{vars, variance_data});
mm->add_instruction(migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
p.compile(migraphx::ref::target{});
auto result = p.eval({}).back();
std::vector<float> result_vector(width * height * channels * batches);
std::vector<float> gold(width * height * channels * batches);
std::fill(gold.begin(), gold.end(), output_val);
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(broadcast_test) TEST_CASE(broadcast_test)
{ {
migraphx::program p; migraphx::program p;
...@@ -1168,10 +971,9 @@ TEST_CASE(conv_dynamic_img_same_upper_test) ...@@ -1168,10 +971,9 @@ TEST_CASE(conv_dynamic_img_same_upper_test)
auto input = mm->add_parameter("X", input_dyn_shape); auto input = mm->add_parameter("X", input_dyn_shape);
auto weights = mm->add_parameter("W", weights_shape); auto weights = mm->add_parameter("W", weights_shape);
mm->add_instruction( mm->add_instruction(
migraphx::make_op("convolution", migraphx::make_op(
{{"stride", {1, 1}}, "convolution",
{"padding_mode", migraphx::op::padding_mode_t::same_upper}, {{"stride", {1, 1}}, {"padding_mode", migraphx::op::padding_mode_t::same_upper}}),
{"use_dynamic_same_auto_pad", true}}),
input, input,
weights); weights);
...@@ -1228,7 +1030,7 @@ TEST_CASE(conv_dynamic_img_same_upper_test) ...@@ -1228,7 +1030,7 @@ TEST_CASE(conv_dynamic_img_same_upper_test)
EXPECT(migraphx::verify_range(results_vector, sol)); EXPECT(migraphx::verify_range(results_vector, sol));
} }
TEST_CASE(conv_dynamic_kernel_same_lower_test) TEST_CASE(conv_dynamic_kernel_same_upper_test)
{ {
migraphx::program p; migraphx::program p;
auto* mm = p.get_main_module(); auto* mm = p.get_main_module();
...@@ -1240,10 +1042,9 @@ TEST_CASE(conv_dynamic_kernel_same_lower_test) ...@@ -1240,10 +1042,9 @@ TEST_CASE(conv_dynamic_kernel_same_lower_test)
auto input = mm->add_parameter("X", input_shape); auto input = mm->add_parameter("X", input_shape);
auto weights = mm->add_parameter("W", weights_shape); auto weights = mm->add_parameter("W", weights_shape);
mm->add_instruction( mm->add_instruction(
migraphx::make_op("convolution", migraphx::make_op(
{{"stride", {1, 1}}, "convolution",
{"padding_mode", migraphx::op::padding_mode_t::same_lower}, {{"stride", {1, 1}}, {"padding_mode", migraphx::op::padding_mode_t::same_upper}}),
{"use_dynamic_same_auto_pad", true}}),
input, input,
weights); weights);
...@@ -1303,6 +1104,80 @@ TEST_CASE(conv_dynamic_kernel_same_lower_test) ...@@ -1303,6 +1104,80 @@ TEST_CASE(conv_dynamic_kernel_same_lower_test)
EXPECT(migraphx::verify_range(results_vector, sol)); EXPECT(migraphx::verify_range(results_vector, sol));
} }
TEST_CASE(conv_dynamic_kernel_same_lower_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape input_shape{migraphx::shape::float_type, {1, 3, 4, 4}};
migraphx::shape weights_shape{migraphx::shape::float_type,
{{1, 1, 0}, {3, 3, 0}, {2, 3, 0}, {2, 3, 0}}};
auto input = mm->add_parameter("X", input_shape);
auto weights = mm->add_parameter("W", weights_shape);
mm->add_instruction(
migraphx::make_op(
"convolution",
{{"stride", {1, 1}}, {"padding_mode", migraphx::op::padding_mode_t::same_lower}}),
input,
weights);
p.compile(migraphx::ref::target{});
std::vector<float> a = {0.63321185, 0.6466339, 0.8515352, 0.44240063, 0.5018913, 0.5068494,
0.75330657, 0.7383877, 0.15870683, 0.8171611, 0.56118083, 0.87004256,
0.24401724, 0.8815178, 0.4222333, 0.27191755,
0.41633207, 0.2460619, 0.32004243, 0.6962248, 0.12284133, 0.2620491,
0.96931046, 0.6030955, 0.7623861, 0.2395751, 0.61440414, 0.577285,
0.80087787, 0.12776066, 0.26566318, 0.46569306,
0.96701574, 0.3850145, 0.14165345, 0.5887347, 0.7152134, 0.5295342,
0.6303507, 0.4037548, 0.18556239, 0.79416305, 0.29107493, 0.18770285,
0.6870904, 0.30701008, 0.314684, 0.91075855};
std::vector<float> c = {2.8150102e-01,
3.3198616e-01,
9.5149356e-01,
7.4039467e-02,
9.6555042e-01,
2.8815505e-01,
2.5100240e-01,
5.2186239e-01,
2.3850012e-01,
8.2963020e-01,
3.0763101e-04,
6.7026985e-01};
std::vector<float> sol = {0.91231215,
1.1416453,
1.00216,
1.6813052,
1.7131033,
2.453681,
2.536207,
3.0187201,
1.3293691,
2.1738236,
2.9695358,
3.2319589,
1.3228729,
2.5953722,
2.50734,
2.7736917};
migraphx::shape weight_fixed_shape0{migraphx::shape::float_type, {1, 3, 2, 2}};
migraphx::parameter_map params0;
params0["X"] = migraphx::argument(input_shape, a.data());
params0["W"] = migraphx::argument(weight_fixed_shape0, c.data());
auto result = p.eval(params0).back();
std::vector<float> results_vector(16);
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify_range(results_vector, sol));
}
TEST_CASE(conv2d_padding_stride_test) TEST_CASE(conv2d_padding_stride_test)
{ {
migraphx::program p; migraphx::program p;
......
test/rewrite_batchnorm_test.cpp deleted 100644 → 0
View file @ a8eb886b
/*
* 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/rewrite_batchnorm.hpp>
#include <migraphx/program.hpp>
#include <migraphx/ref/target.hpp>
#include <migraphx/op/convolution.hpp>
#include <migraphx/op/reshape.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/ranges.hpp>
#include <test.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/serialize.hpp>
#include <migraphx/verify.hpp>
bool is_batch_norm(migraphx::instruction& ins) { return ins.name() == "batch_norm_inference"; }
TEST_CASE(fwd_conv_batchnorm_rewrite_test)
{
std::vector<float> xdata = {
0.26485917, 0.61703885, 0.32762103, 0.2503367, 0.6552712, 0.07947932, 0.95442678,
0.70892651, 0.890563, 0.80808088, 0.89540492, 0.52657048, 0.94614791, 0.64371508,
0.0971229, 0.2475562, 0.47405955, 0.85538928, 0.05428386, 0.993078, 0.72771973,
0.18312255, 0.3091522, 0.51396558, 0.35158192, 0.2419852, 0.83691474, 0.36355352,
0.04769134, 0.08312604, 0.61804092, 0.0508887, 0.30987137, 0.81307629, 0.16398955,
0.69886166, 0.02415926, 0.60608918, 0.81907569, 0.13208211, 0.48303735, 0.87533734,
0.92998813, 0.65553674, 0.73223327, 0.99401001, 0.09850688, 0.76972609, 0.11118327,
0.04392097, 0.39252306, 0.91129653, 0.89078693, 0.60571206, 0.98410397, 0.15290698,
0.86992609, 0.7575111, 0.80583525, 0.23649562, 0.7478029, 0.62888878, 0.39886601,
0.37066793, 0.72627947, 0.8745595, 0.13568234, 0.7413787, 0.5039495, 0.18945697,
0.87046838, 0.63970494, 0.01124038, 0.27459063, 0.65745586, 0.69182619, 0.80470603,
0.58039348, 0.36950583, 0.43634225, 0.01694425, 0.14099377, 0.77015849, 0.35809292,
0.40547674, 0.46538817, 0.65835358, 0.2266954, 0.39057646, 0.64642207, 0.84491134,
0.20998067, 0.41074121, 0.73055221, 0.26424874, 0.10612507, 0.24478521, 0.24091282,
0.52536754, 0.57292341, 0.82190903, 0.51858515, 0.17162996, 0.52048114, 0.96624787,
0.17527163, 0.56384485, 0.91991603};
std::vector<float> wdata = {
-1.12125056, 0.50228441, 1.12719446, -2.61705068, -0.2027315, -0.82199441, 0.05337102,
-0.62146691, -2.40572931, -1.47175612, 1.49654601, -1.07070376, -0.65908074, -0.28457694,
1.60046717, 0.20677642, -1.51844486, 0.41203847, -0.01285751, 0.07948031, -0.91507006,
-1.59481079, -0.12856238, 0.39970482, -1.89015158, 0.66969754, 0.10312618};
migraphx::shape xs{migraphx::shape::float_type, {1, 3, 6, 6}};
migraphx::shape ws{migraphx::shape::float_type, {1, 3, 3, 3}};
migraphx::shape vars{migraphx::shape::float_type, {1}};
auto create_program = [&]() {
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_literal(xs, xdata);
auto w = mm->add_literal(ws, wdata);
auto conv = mm->add_instruction(
migraphx::make_op("convolution",
{{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
x,
w);
auto scale = mm->add_literal(migraphx::literal{vars, {3.0f}});
auto bias = mm->add_literal(migraphx::literal{vars, {8.1f}});
auto mean = mm->add_literal(migraphx::literal{vars, {4.0f}});
auto variance = mm->add_literal(migraphx::literal{vars, {37.11f}});
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), conv, scale, bias, mean, variance);
return p;
};
migraphx::program p1 = create_program();
migraphx::program p2 = create_program();
migraphx::rewrite_batchnorm opt;
opt.apply(*p2.get_main_module());
p1.compile(migraphx::ref::target{});
p2.compile(migraphx::ref::target{});
auto result1 = p1.eval({}).back();
auto result2 = p2.eval({}).back();
std::vector<float> results_vector1;
std::vector<float> results_vector2;
result1.visit([&](auto output) { results_vector1.assign(output.begin(), output.end()); });
result2.visit([&](auto output) { results_vector2.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify_range(results_vector1, results_vector2));
}
TEST_CASE(non_literal)
{
migraphx::shape xs{migraphx::shape::float_type, {1, 3, 8, 8}};
migraphx::shape ws{migraphx::shape::float_type, {4, 3, 1, 1}};
migraphx::shape vars{migraphx::shape::float_type, {4}};
auto create_program = [&]() {
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter("x", xs);
auto w = mm->add_parameter("w", ws);
auto conv = mm->add_instruction(migraphx::make_op("convolution"), x, w);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), conv, scale, bias, mean, variance);
return p;
};
migraphx::program p1 = create_program();
migraphx::program p2 = create_program();
migraphx::rewrite_batchnorm opt;
opt.apply(*p2.get_main_module());
EXPECT(any_of(*p1.get_main_module(), &is_batch_norm));
EXPECT(none_of(*p2.get_main_module(), &is_batch_norm));
}
TEST_CASE(as_literal)
{
migraphx::shape xs{migraphx::shape::float_type, {1, 3, 8, 8}};
migraphx::shape ws{migraphx::shape::float_type, {4, 3, 1, 1}};
migraphx::shape vars{migraphx::shape::float_type, {4}};
auto create_program = [&]() {
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_literal(migraphx::generate_literal(xs, 1));
auto w = mm->add_literal(migraphx::generate_literal(ws, 1));
auto conv = mm->add_instruction(migraphx::make_op("convolution"), x, w);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), conv, scale, bias, mean, variance);
return p;
};
migraphx::program p1 = create_program();
migraphx::program p2 = create_program();
migraphx::rewrite_batchnorm opt;
opt.apply(*p2.get_main_module());
EXPECT(any_of(*p1.get_main_module(), &is_batch_norm));
EXPECT(none_of(*p2.get_main_module(), &is_batch_norm));
p1.compile(migraphx::ref::target{});
p2.compile(migraphx::ref::target{});
auto result1 = p1.eval({}).back();
auto result2 = p2.eval({}).back();
visit_all(result1, result2)([&](auto r1, auto r2) { EXPECT(migraphx::verify_range(r1, r2)); });
}
TEST_CASE(as_literal_1d)
{
migraphx::shape xs{migraphx::shape::float_type, {1, 3, 8}};
migraphx::shape ws{migraphx::shape::float_type, {4, 3, 1}};
migraphx::shape vars{migraphx::shape::float_type, {4}};
auto create_program = [&]() {
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_literal(migraphx::generate_literal(xs, 1));
auto w = mm->add_literal(migraphx::generate_literal(ws, 1));
auto conv = mm->add_instruction(
migraphx::make_op("convolution",
{{"padding", {0}}, {"stride", {1}}, {"dilation", {1}}}),
x,
w);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), conv, scale, bias, mean, variance);
return p;
};
migraphx::program p1 = create_program();
migraphx::program p2 = create_program();
migraphx::rewrite_batchnorm opt;
opt.apply(*p2.get_main_module());
EXPECT(any_of(*p1.get_main_module(), &is_batch_norm));
EXPECT(none_of(*p2.get_main_module(), &is_batch_norm));
p1.compile(migraphx::ref::target{});
p2.compile(migraphx::ref::target{});
auto result1 = p1.eval({}).back();
auto result2 = p2.eval({}).back();
visit_all(result1, result2)([&](auto r1, auto r2) { EXPECT(migraphx::verify_range(r1, r2)); });
}
TEST_CASE(as_literal_3d)
{
migraphx::shape xs{migraphx::shape::float_type, {1, 3, 2, 4, 8}};
migraphx::shape ws{migraphx::shape::float_type, {4, 3, 1, 1, 1}};
migraphx::shape vars{migraphx::shape::float_type, {4}};
auto create_program = [&]() {
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::op::convolution conv_op;
conv_op.padding = {0, 0, 0};
conv_op.stride = {1, 1, 1};
conv_op.dilation = {1, 1, 1};
auto x = mm->add_literal(migraphx::generate_literal(xs, 1));
auto w = mm->add_literal(migraphx::generate_literal(ws, 1));
auto conv = mm->add_instruction(conv_op, x, w);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), conv, scale, bias, mean, variance);
return p;
};
migraphx::program p1 = create_program();
migraphx::program p2 = create_program();
migraphx::rewrite_batchnorm opt;
opt.apply(*p2.get_main_module());
EXPECT(any_of(*p1.get_main_module(), &is_batch_norm));
EXPECT(none_of(*p2.get_main_module(), &is_batch_norm));
p1.compile(migraphx::ref::target{});
p2.compile(migraphx::ref::target{});
auto result1 = p1.eval({}).back();
auto result2 = p2.eval({}).back();
visit_all(result1, result2)([&](auto r1, auto r2) { EXPECT(migraphx::verify_range(r1, r2)); });
}
TEST_CASE(literal_reshape)
{
migraphx::shape xs{migraphx::shape::float_type, {1, 3, 8, 8}};
migraphx::shape ws{migraphx::shape::float_type, {4, 3, 1, 1}};
migraphx::shape vars{migraphx::shape::float_type, {4}};
auto create_program = [&]() {
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_literal(migraphx::generate_literal(xs, 1));
auto w = mm->add_literal(migraphx::generate_literal(ws, 1));
auto conv = mm->add_instruction(migraphx::make_op("convolution"), x, w);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), conv, scale, bias, mean, variance);
return p;
};
migraphx::program p1 = create_program();
migraphx::program p2 = create_program();
migraphx::rewrite_batchnorm opt;
opt.apply(*p2.get_main_module());
EXPECT(any_of(*p1.get_main_module(), &is_batch_norm));
EXPECT(none_of(*p2.get_main_module(), &is_batch_norm));
p1.compile(migraphx::ref::target{});
p2.compile(migraphx::ref::target{});
auto result1 = p1.eval({}).back();
auto result2 = p2.eval({}).back();
visit_all(result1, result2)([&](auto r1, auto r2) { EXPECT(migraphx::verify_range(r1, r2)); });
}
TEST_CASE(literal_reshape_per_actv)
{
migraphx::shape xs{migraphx::shape::float_type, {1, 3, 8, 7, 4}};
migraphx::shape ws{migraphx::shape::float_type, {4, 3, 1, 1, 1}};
migraphx::shape vars{migraphx::shape::float_type, {4, 8, 7, 4}};
auto create_program = [&]() {
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_literal(migraphx::generate_literal(xs, 1));
auto w = mm->add_literal(migraphx::generate_literal(ws, 1));
auto conv = mm->add_instruction(
migraphx::make_op(
"convolution",
{{"padding", {0, 0, 0}}, {"stride", {1, 1, 1}}, {"dilation", {1, 1, 1}}}),
x,
w);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op(
"batch_norm_inference",
{{"epsilon", 1.0e-5},
{"momentum", 0.88},
{"bn_mode",
migraphx::to_value(migraphx::op::batch_norm_inference::per_activation)}}),
conv,
scale,
bias,
mean,
variance);
return p;
};
migraphx::program p1 = create_program();
migraphx::program p2 = create_program();
migraphx::rewrite_batchnorm opt;
opt.apply(*p2.get_main_module());
EXPECT(any_of(*p1.get_main_module(), &is_batch_norm));
EXPECT(none_of(*p2.get_main_module(), &is_batch_norm));
p1.compile(migraphx::ref::target{});
p2.compile(migraphx::ref::target{});
auto result1 = p1.eval({}).back();
auto result2 = p2.eval({}).back();
visit_all(result1, result2)([&](auto r1, auto r2) { EXPECT(migraphx::verify_range(r1, r2)); });
}
int main(int argc, const char* argv[]) { test::run(argc, argv); }
test/tf/gen_tf_pb.py
View file @ 41d4e92b
...@@ -120,19 +120,45 @@ def batchnorm_test(g1): ...@@ -120,19 +120,45 @@ def batchnorm_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float32, g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 32), shape=(1, 16, 16, 32),
name='0') name='x')
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='scale')
g1_offset = tf.compat.v1.placeholder(tf.float32, shape=(32), name='2') g1_offset = tf.compat.v1.placeholder(tf.float32,
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='3') shape=(32),
name='bias')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='mean')
g1_variance = tf.compat.v1.placeholder(tf.float32, g1_variance = tf.compat.v1.placeholder(tf.float32,
shape=(32), shape=(32),
name='4') name='variance')
tf.compat.v1.nn.fused_batch_norm(x=g1_input, tf.compat.v1.nn.fused_batch_norm(x=g1_input,
scale=g1_scale, scale=g1_scale,
offset=g1_offset, offset=g1_offset,
mean=g1_mean, mean=g1_mean,
variance=g1_variance, variance=g1_variance,
epsilon=0.00001, epsilon=1e-4,
is_training=False,
name='batchnorm1')
@tf_test
def batchnorm_half_test(g1):
with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float16,
shape=(1, 16, 16, 32),
name='x')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='scale')
g1_offset = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='bias')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='mean')
g1_variance = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='variance')
tf.compat.v1.nn.fused_batch_norm(x=g1_input,
scale=g1_scale,
offset=g1_offset,
mean=g1_mean,
variance=g1_variance,
epsilon=1e-4,
is_training=False, is_training=False,
name='batchnorm1') name='batchnorm1')
...@@ -142,19 +168,21 @@ def batchnormv3_test(g1): ...@@ -142,19 +168,21 @@ def batchnormv3_test(g1):
with g1.as_default(): with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float32, g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 32), shape=(1, 16, 16, 32),
name='0') name='x')
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='scale')
g1_offset = tf.compat.v1.placeholder(tf.float32, shape=(32), name='2') g1_offset = tf.compat.v1.placeholder(tf.float32,
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='3') shape=(32),
name='bias')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='mean')
g1_variance = tf.compat.v1.placeholder(tf.float32, g1_variance = tf.compat.v1.placeholder(tf.float32,
shape=(32), shape=(32),
name='4') name='variance')
tf.raw_ops.FusedBatchNormV3(x=g1_input, tf.raw_ops.FusedBatchNormV3(x=g1_input,
scale=g1_scale, scale=g1_scale,
offset=g1_offset, offset=g1_offset,
mean=g1_mean, mean=g1_mean,
variance=g1_variance, variance=g1_variance,
epsilon=0.00001, epsilon=1e-6,
is_training=False, is_training=False,
name='batchnorm1') name='batchnorm1')
......
test/tf/tf_test.cpp
View file @ 41d4e92b
...@@ -24,6 +24,7 @@ ...@@ -24,6 +24,7 @@
#include <iostream> #include <iostream>
#include <vector> #include <vector>
#include <unordered_map> #include <unordered_map>
#include <migraphx/common.hpp>
#include <migraphx/literal.hpp> #include <migraphx/literal.hpp>
#include <migraphx/pass_manager.hpp> #include <migraphx/pass_manager.hpp>
#include <migraphx/simplify_reshapes.hpp> #include <migraphx/simplify_reshapes.hpp>
...@@ -33,7 +34,6 @@ ...@@ -33,7 +34,6 @@
#include <migraphx/instruction.hpp> #include <migraphx/instruction.hpp>
#include <migraphx/tf.hpp> #include <migraphx/tf.hpp>
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/op/convolution.hpp> #include <migraphx/op/convolution.hpp>
#include <migraphx/op/reduce_mean.hpp> #include <migraphx/op/reduce_mean.hpp>
#include <migraphx/op/pooling.hpp> #include <migraphx/op/pooling.hpp>
...@@ -186,50 +186,94 @@ TEST_CASE(batchmatmul_test) ...@@ -186,50 +186,94 @@ TEST_CASE(batchmatmul_test)
TEST_CASE(batchnorm_test) TEST_CASE(batchnorm_test)
{ {
float epsilon = 1.001e-5f;
float momentum = 0.9f;
migraphx::program p; migraphx::program p;
auto* mm = p.get_main_module(); auto* mm = p.get_main_module();
migraphx::op::batch_norm_inference op{
epsilon, momentum, migraphx::op::batch_norm_inference::spatial}; auto x = mm->add_parameter("x", {migraphx::shape::float_type, {1, 32, 16, 16}});
migraphx::shape s0{migraphx::shape::float_type, {32}}; auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {32}});
auto l0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 32, 16, 16}}); auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {32}});
std::vector<float> const_vals(32); auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {32}});
std::fill(const_vals.begin(), const_vals.end(), 1.0f);
std::vector<float> scale_data(32, 1.0);
auto l2 = mm->add_parameter("2", s0); auto scale = mm->add_literal(migraphx::shape{migraphx::shape::float_type, {32}}, scale_data);
auto l3 = mm->add_parameter("3", s0); auto rt = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto l4 = mm->add_parameter("4", s0); auto eps = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {1e-4f}});
auto l1 = mm->add_literal(migraphx::literal{s0, const_vals});
mm->add_instruction(op, l0, l1, l2, l3, l4); auto usq_scale = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), var);
auto numer = add_common_op(*mm, migraphx::make_op("sub"), {x, usq_mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(*mm, migraphx::make_op("mul"), {div0, usq_scale});
add_common_op(*mm, migraphx::make_op("add"), {r0, usq_bias});
auto prog = optimize_tf("batchnorm_test.pb", true); auto prog = optimize_tf("batchnorm_test.pb", true);
EXPECT(p == prog);
}
TEST_CASE(batchnorm_half_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter("x", {migraphx::shape::half_type, {1, 32, 16, 16}});
auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {32}});
auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {32}});
auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {32}});
std::vector<float> scale_data(32, 1.0);
auto scale = mm->add_literal(migraphx::shape{migraphx::shape::float_type, {32}}, scale_data);
auto rt = mm->add_literal(migraphx::literal{migraphx::shape::half_type, {0.5}});
auto eps = mm->add_literal(migraphx::literal{migraphx::shape::half_type, {1e-4f}});
auto usq_scale = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), var);
auto numer = add_common_op(*mm, migraphx::make_op("sub"), {x, usq_mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(*mm, migraphx::make_op("mul"), {div0, usq_scale});
add_common_op(*mm, migraphx::make_op("add"), {r0, usq_bias});
auto prog = optimize_tf("batchnorm_half_test.pb", true);
EXPECT(p == prog); EXPECT(p == prog);
} }
TEST_CASE(batchnormv3_test) TEST_CASE(batchnormv3_test)
{ {
float epsilon = 1.0e-5f;
float momentum = 0.9f;
migraphx::program p; migraphx::program p;
auto* mm = p.get_main_module(); auto* mm = p.get_main_module();
migraphx::op::batch_norm_inference op{
epsilon, momentum, migraphx::op::batch_norm_inference::spatial};
migraphx::shape s0{migraphx::shape::float_type, {32}};
auto l0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 32, 16, 16}});
std::vector<float> const_vals(32);
std::fill(const_vals.begin(), const_vals.end(), 1.0f);
auto l2 = mm->add_parameter("2", s0);
auto l3 = mm->add_parameter("3", s0);
auto l4 = mm->add_parameter("4", s0);
auto l1 = mm->add_literal(migraphx::literal{s0, const_vals});
mm->add_instruction(op, l0, l1, l2, l3, l4);
auto prog = optimize_tf("batchnormv3_test.pb", true);
auto x = mm->add_parameter("x", {migraphx::shape::float_type, {1, 32, 16, 16}});
auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {32}});
auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {32}});
auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {32}});
std::vector<float> scale_data(32, 1.0);
auto scale = mm->add_literal(migraphx::shape{migraphx::shape::float_type, {32}}, scale_data);
auto rt = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto eps = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {1e-6f}});
auto usq_scale = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), var);
auto numer = add_common_op(*mm, migraphx::make_op("sub"), {x, usq_mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(*mm, migraphx::make_op("mul"), {div0, usq_scale});
add_common_op(*mm, migraphx::make_op("add"), {r0, usq_bias});
auto prog = optimize_tf("batchnormv3_test.pb", true);
EXPECT(p == prog); EXPECT(p == prog);
} }
...@@ -327,10 +371,9 @@ migraphx::program create_conv() ...@@ -327,10 +371,9 @@ migraphx::program create_conv()
mm->add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 32}}, weight_data); mm->add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 32}}, weight_data);
migraphx::op::convolution op; migraphx::op::convolution op;
op.padding_mode = migraphx::op::padding_mode_t::same; op.padding = {1, 1, 1, 1};
op.padding = {1, 1, 1, 1}; op.stride = {1, 1};
op.stride = {1, 1}; op.dilation = {1, 1};
op.dilation = {1, 1};
auto l2 = auto l2 =
mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {3, 2, 0, 1}}}), l1); mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {3, 2, 0, 1}}}), l1);
mm->add_instruction(op, l0, l2); mm->add_instruction(op, l0, l2);
...@@ -406,11 +449,10 @@ TEST_CASE(depthwiseconv_test) ...@@ -406,11 +449,10 @@ TEST_CASE(depthwiseconv_test)
mm->add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 1}}, weight_data); mm->add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 1}}, weight_data);
migraphx::op::convolution op; migraphx::op::convolution op;
op.padding_mode = migraphx::op::padding_mode_t::same; op.padding = {1, 1};
op.padding = {1, 1}; op.stride = {1, 1};
op.stride = {1, 1}; op.dilation = {1, 1};
op.dilation = {1, 1}; op.group = 3;
op.group = 3;
auto l3 = auto l3 =
mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {3, 2, 0, 1}}}), l1); mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {3, 2, 0, 1}}}), l1);
auto l4 = mm->add_instruction(migraphx::make_op("contiguous"), l3); auto l4 = mm->add_instruction(migraphx::make_op("contiguous"), l3);
......
test/verify/quant_conv_default_mode.cpp
View file @ 41d4e92b
...@@ -37,10 +37,7 @@ struct quant_conv_default_mode : verify_program<quant_conv_default_mode> ...@@ -37,10 +37,7 @@ struct quant_conv_default_mode : verify_program<quant_conv_default_mode>
auto pa = mm->add_parameter("a", a_shape); auto pa = mm->add_parameter("a", a_shape);
migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3, 3}}; migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3, 3}};
auto pc = mm->add_parameter("c", c_shape); auto pc = mm->add_parameter("c", c_shape);
mm->add_instruction( mm->add_instruction(migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}}, pa, pc);
migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::same},
pa,
pc);
return p; return p;
} }
}; };
test/verify/quant_conv_int8x4_default.cpp
View file @ 41d4e92b
...@@ -37,10 +37,7 @@ struct quant_conv_int8x4_default : verify_program<quant_conv_int8x4_default> ...@@ -37,10 +37,7 @@ struct quant_conv_int8x4_default : verify_program<quant_conv_int8x4_default>
auto pa = mm->add_parameter("a", a_shape); auto pa = mm->add_parameter("a", a_shape);
migraphx::shape c_shape{migraphx::shape::int8_type, {16, 16, 3, 3}}; migraphx::shape c_shape{migraphx::shape::int8_type, {16, 16, 3, 3}};
auto pc = mm->add_parameter("c", c_shape); auto pc = mm->add_parameter("c", c_shape);
mm->add_instruction( mm->add_instruction(migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}}, pa, pc);
migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::same},
pa,
pc);
return p; return p;
} }
}; };
test/verify/quant_conv_valid_mode.cpp deleted 100644 → 0
View file @ a8eb886b
/*
* 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 "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/op/quant_convolution.hpp>
struct quant_conv_valid_mode : verify_program<quant_conv_valid_mode>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape a_shape{migraphx::shape::int8_type, {2, 3, 4, 4}};
auto pa = mm->add_parameter("a", a_shape);
migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3, 3}};
auto pc = mm->add_parameter("c", c_shape);
mm->add_instruction(
migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::valid},
pa,
pc);
return p;
}
};
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