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#include <caffe2/ideep/operators/conv_pool_base_op.h>
using namespace caffe2;
namespace {
class IDEEPInt8ConvOp : public IDEEPConvPoolOpBase {
public:
USE_IDEEP_DEF_ALIASES();
USE_IDEEP_CONV_POOL_BASE_FUNCTIONS();
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
IDEEPInt8ConvOp(const OperatorDef& operator_def, Workspace* ws)
: IDEEPConvPoolOpBase(operator_def, ws),
scale_(this->template GetSingleArgument<float>("Y_scale", 1.0)),
zero_point_(
this->template GetSingleArgument<int32_t>("Y_zero_point", 0)) {
OPERATOR_NEEDS_FEATURE(pad_l() == pad_r() && pad_t() == pad_b(),
"Uneven padding not supported.");
fusion_type_ = FUSION_UNKNOWN;
last_input_ = BIAS_OR_INPUT_S;
algo_ = ialgo::convolution_direct;
auto conv_algorithm = OperatorBase::GetSingleArgument<int>(
"conv_algorithm", CONV_ALGORITHM_AUTO);
if (conv_algorithm == CONV_ALGORITHM_WINOGRAD) {
algo_ = ialgo::convolution_winograd;
}
CAFFE_ENFORCE(zero_point_ == 128 || zero_point_ == 0);
Y_scales_ = ConvertScales({scale_});
}
// NOLINTNEXTLINE(modernize-use-override,modernize-use-equals-default)
virtual ~IDEEPInt8ConvOp() {}
bool RunOnDeviceWithOrderNCHW() override {
const auto &X = Input(INPUT_X);
const auto &filter = Input(FILTER);
auto *Y = Output(OUTPUT);
CAFFE_ENFORCE(X.has_scale());
CAFFE_ENFORCE(4 == X.ndims() && 4 == filter.ndims());
CAFFE_ENFORCE(X.get_data_type() == idtype::s8
|| X.get_data_type() == idtype::u8);
CAFFE_ENFORCE(filter.get_dim(2) == kernel_h());
CAFFE_ENFORCE(filter.get_dim(3) == kernel_w());
CAFFE_ENFORCE(
X.get_dim(1) == filter.get_dim(1) * group_,
"Convolution op: input channels does not match: # of input channels ",
X.get_dim(1), " is not equal to kernel channels * group:",
filter.get_dim(1), "*", group_);
bool input_changed = (cached_X_descriptor_ != X.get_descriptor());
if (input_changed) {
cached_X_descriptor_ = X.dup_descriptor();
}
bool weights_changed = (cached_weights_descriptor_ != filter.get_descriptor());
if (weights_changed) {
cached_weights_descriptor_ = filter.dup_descriptor();
CAFFE_ENFORCE(filter.get_data_type() == idtype::s8 && filter.has_scale());
auto X_dt = X.get_data_type();
lowp_kind_ = ilowp_kind::LOWP_U8S8;
if (X_dt == idtype::s8) {
lowp_kind_ = ilowp_kind::LOWP_S8S8;
}
auto expected_descriptor =
ideep::convolution_forward::expected_weights_desc(
filter.get_dims(),
idtype::s8,
{stride_.begin(), stride_.end()},
pad_tl(),
pad_br(),
{dilation_.begin(), dilation_.end()},
group_,
algo_,
iprop::forward_inference,
X_dt, X.get_dims());
if (filter.get_desc() != expected_descriptor) {
filter_.init(expected_descriptor);
filter_.set_scale(filter.get_scale());
filter_.feed_from(filter);
} else {
filter_ = filter;
}
if (InputSize() > last_input_) {
// NOTE: If the bias is shared by other operators in this module,
// The existing bias scale should not satisfy current operator.
// Thus, we have to requantize it by current input and filter scales.
auto bias = Input(BIAS_OR_INPUT_S);
bias_.init({bias.get_dims(), idtype::s32});
iscale bias_scales (filter_.get_scale());
for (auto &scale : bias_scales) { scale *= X.get_scale()[0]; }
bias_.set_scale(bias_scales);
bias_.feed_from(bias);
}
}
bool with_bias = InputSize() > last_input_;
if (input_changed || weights_changed) {
auto Y_dims = CalcOutputDims(X, filter.get_dim(0));
if (with_bias) {
ideep::convolution_forward::prepare(
conv_param,
X,
filter_,
bias_,
Y_dims,
*Y,
{stride_.begin(), stride_.end()},
{dilation_.begin(), dilation_.end()},
pad_tl(),
pad_br(),
group_,
iscale(),
iscale(),
Y_scales_,
attr_,
algo_,
iprop::forward_inference,
lowp_kind_);
} else {
ideep::convolution_forward::prepare(
conv_param,
X,
filter_,
Y_dims,
*Y,
{stride_.begin(), stride_.end()},
{dilation_.begin(), dilation_.end()},
pad_tl(),
pad_br(),
group_,
iscale(),
iscale(),
Y_scales_,
attr_,
algo_,
iprop::forward_inference,
lowp_kind_);
}
}
if (with_bias) {
ideep::convolution_forward::compute(conv_param, X, filter_, bias_, *Y);
} else {
ideep::convolution_forward::compute(conv_param, X, filter_, *Y);
}
if (fusion_type_ != FUSION_CONV_RELU && fusion_type_ != FUSION_UNKNOWN) {
CAFFE_ENFORCE(
Y == &(Input(InputSize() - 1)),
"Convolution fusion op: InPlace is enforced for sum fusion.");
}
return true;
}
protected:
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
iattr attr_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
ialgo algo_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
float scale_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
int last_input_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
int32_t zero_point_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
ilowp_kind lowp_kind_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
FusionType fusion_type_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
itensor filter_, bias_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
iscale Y_scales_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
itensor::descriptor cached_X_descriptor_, cached_weights_descriptor_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
ideep::convolution_forward_params conv_param;
INPUT_TAGS(INPUT_X, FILTER, BIAS_OR_INPUT_S, INPUT_S);
OUTPUT_TAGS(OUTPUT);
};
class IDEEPInt8ConvReluOp final : public IDEEPInt8ConvOp {
public:
USE_IDEEP_DEF_ALIASES();
USE_IDEEP_CONV_POOL_BASE_FUNCTIONS();
IDEEPInt8ConvReluOp(const OperatorDef& operator_def, Workspace* ws)
: IDEEPInt8ConvOp(operator_def, ws) {
CAFFE_ENFORCE(zero_point_ == 0);
last_input_ = BIAS_OR_INPUT_S;
attr_ = iattr::fuse_relu();
fusion_type_ = FUSION_CONV_RELU;
}
// NOLINTNEXTLINE(modernize-use-override,modernize-use-equals-default)
virtual ~IDEEPInt8ConvReluOp() {}
};
class IDEEPInt8ConvSumOp final : public IDEEPInt8ConvOp {
public:
USE_IDEEP_DEF_ALIASES();
USE_IDEEP_CONV_POOL_BASE_FUNCTIONS();
IDEEPInt8ConvSumOp(const OperatorDef& operator_def, Workspace* ws)
: IDEEPInt8ConvOp(operator_def, ws) {
last_input_ = INPUT_S;
attr_ = iattr::fuse_sum();
fusion_type_ = FUSION_CONV_SUM;
}
// NOLINTNEXTLINE(modernize-use-override,modernize-use-equals-default)
virtual ~IDEEPInt8ConvSumOp() {}
};
class IDEEPInt8ConvSumReluOp final : public IDEEPInt8ConvOp {
public:
USE_IDEEP_DEF_ALIASES();
USE_IDEEP_CONV_POOL_BASE_FUNCTIONS();
IDEEPInt8ConvSumReluOp(const OperatorDef& operator_def, Workspace* ws)
: IDEEPInt8ConvOp(operator_def, ws) {
last_input_ = INPUT_S;
attr_ = iattr::residual();
fusion_type_ = FUSION_CONV_SUM_RELU;
}
// NOLINTNEXTLINE(modernize-use-override,modernize-use-equals-default)
virtual ~IDEEPInt8ConvSumReluOp() {}
};
REGISTER_IDEEP_OPERATOR_WITH_ENGINE(Int8Conv, DNNLOWP, IDEEPInt8ConvOp);
REGISTER_IDEEP_OPERATOR_WITH_ENGINE(Int8ConvRelu, DNNLOWP, IDEEPInt8ConvReluOp);
REGISTER_IDEEP_OPERATOR_WITH_ENGINE(Int8ConvSum, DNNLOWP, IDEEPInt8ConvSumOp);
REGISTER_IDEEP_OPERATOR_WITH_ENGINE(Int8ConvSumRelu, DNNLOWP, IDEEPInt8ConvSumReluOp);
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,clang-diagnostic-unused-function)
OPERATOR_SCHEMA(Int8ConvSum)
.NumInputs(2, 4)
.NumOutputs(1)
.TensorInferenceFunction(ConvPoolOpBase<CPUContext>::TensorInferenceForConv)
.CostInferenceFunction(OpSchema::CostInferenceFunctionType(
ConvPoolOpBase<CPUContext>::CostInferenceForConv))
.AllowInplace({{2, 0}, {3, 0}});
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,clang-diagnostic-unused-function)
OPERATOR_SCHEMA(Int8ConvSumRelu)
.NumInputs(2, 4)
.NumOutputs(1)
.TensorInferenceFunction(ConvPoolOpBase<CPUContext>::TensorInferenceForConv)
.CostInferenceFunction(OpSchema::CostInferenceFunctionType(
ConvPoolOpBase<CPUContext>::CostInferenceForConv))
.AllowInplace({{2, 0}, {3, 0}});
} // namespace
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