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@ -17,137 +17,91 @@ limitations under the License. */
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#include <string>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/clip_op.h"
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#include "paddle/fluid/operators/math/blas.h"
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#include "paddle/fluid/platform/transform.h"
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namespace paddle {
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namespace operators {
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using platform::Transform;
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template <typename DeviceContext, typename T>
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struct FindAbsMaxFunctor {
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void operator()(const DeviceContext& ctx, const T* in, const int num, T* out);
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};
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template <typename DeviceContext, typename T>
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class FakeQuantizeKernel : public framework::OpKernel<T> {
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struct ClipAndFakeQuantFunctor {
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void operator()(const DeviceContext& ctx, const framework::Tensor& in,
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const framework::Tensor& scale, const int bin_cnt,
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framework::Tensor* out);
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};
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template <typename DeviceContext, typename T>
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struct FindRangeAbsMaxFunctor {
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void operator()(const DeviceContext& ctx, const framework::Tensor& cur_scale,
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const framework::Tensor& last_scale,
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const framework::Tensor& iter, const int window_size,
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framework::Tensor* scales_arr, framework::Tensor* out_scale);
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};
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template <typename DeviceContext, typename T>
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class FakeQuantizeAbsMaxKernel : public framework::OpKernel<T> {
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public:
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T FindAbsMax(framework::Tensor* in, int n) const {
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T* p = in->mutable_data<T>(platform::CPUPlace());
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T abs_max = (T)0.00000001;
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for (int i = 0; i < n; i++) {
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T tmp = fabs(p[i]);
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if (tmp > abs_max) abs_max = tmp;
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}
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return T(abs_max);
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}
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T FindRangeAbsMax(framework::Tensor* scale_list, framework::Tensor* out_scale,
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const T& cur_scale, int window_size,
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int current_iter) const {
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T* sl = scale_list->mutable_data<T>(platform::CPUPlace());
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T remove_tmp = sl[current_iter];
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sl[current_iter] = cur_scale;
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T& max_scale = out_scale->mutable_data<T>(platform::CPUPlace())[0];
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if (max_scale < cur_scale) {
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max_scale = cur_scale;
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} else if (fabs(remove_tmp - max_scale) < 1e-6) {
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int size = (current_iter > window_size) ? window_size : current_iter;
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max_scale = T(FindAbsMax(scale_list, size));
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}
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return max_scale;
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}
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<framework::Tensor>("X");
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T FindMovingAverageAbsMmax(framework::Tensor* in_scale,
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framework::Tensor* out_scale,
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const T& cur_scale) const {
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T* ins = in_scale->mutable_data<T>(platform::CPUPlace());
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T* outs = out_scale->mutable_data<T>(platform::CPUPlace());
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outs[0] = 0.9 * cur_scale + 0.1 * ins[0];
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return T(outs[0]);
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auto* out = context.Output<framework::Tensor>("Out");
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auto* out_scale = context.Output<framework::Tensor>("OutScale");
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T* out_s = out_scale->mutable_data<T>(context.GetPlace());
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int bit_length = context.Attr<int>("bit_length");
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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auto& dev_ctx = context.template device_context<DeviceContext>();
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const T* in_data = in->data<T>();
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FindAbsMaxFunctor<DeviceContext, T>()(dev_ctx, in_data, in->numel(), out_s);
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ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, *in, *out_scale,
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bin_cnt, out);
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}
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};
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virtual void Compute(const framework::ExecutionContext& context) const {
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auto* tensor = context.Output<framework::Tensor>("Out");
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template <typename DeviceContext, typename T>
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class FakeQuantizeRangeAbsMaxKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<framework::Tensor>("X");
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const bool is_test = context.Attr<bool>("is_test");
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tensor->mutable_data<T>(in->place());
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auto* oms_tensor = context.Output<framework::Tensor>("OutMovingScale");
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oms_tensor->mutable_data<T>(in->place());
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auto quantize_type =
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static_cast<std::string>(context.Attr<std::string>("quantize_type"));
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if (quantize_type == std::string("range_abs_max")) {
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auto* oss_tensor = context.Output<framework::Tensor>("OutScales");
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oss_tensor->mutable_data<T>(
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context.Input<framework::Tensor>("InScales")->place());
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auto* oci_tensor = context.Output<framework::Tensor>("OutCurrentIter");
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oci_tensor->mutable_data<T>(
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context.Input<framework::Tensor>("InCurrentIter")->place());
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}
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auto* in_scale = context.Input<framework::Tensor>("InScale");
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T scale = static_cast<T>(1);
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int window_size = context.Attr<int>("window_size");
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auto* out = context.Output<framework::Tensor>("Out");
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out->mutable_data<T>(context.GetPlace());
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bool is_test = context.Attr<bool>("is_test");
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int bit_length = context.Attr<int>("bit_length");
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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auto& dev_ctx = context.template device_context<DeviceContext>();
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auto& dev =
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*context.template device_context<DeviceContext>().eigen_device();
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auto raw_in = framework::EigenVector<T>::Flatten(*in);
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if (quantize_type == std::string("abs_max")) {
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auto* saving_scale = context.Output<framework::Tensor>("OutMovingScale");
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auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
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scale_out.device(dev) = raw_in.abs().maximum();
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scale = scale_out(0);
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auto& device_ctx = context.template device_context<DeviceContext>();
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auto* scale_list = context.Output<framework::Tensor>("OutScales");
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math::SetConstant<DeviceContext, T> scalar;
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scale_list->mutable_data<T>(context.GetPlace());
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scalar(device_ctx, scale_list, static_cast<T>(0));
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auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
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iter->mutable_data<T>(context.GetPlace());
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scalar(device_ctx, iter, static_cast<T>(0));
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} else if (quantize_type == std::string("range_abs_max")) {
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auto* moving_scale = context.Input<framework::Tensor>("InMovingScale");
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if (is_test) {
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scale = moving_scale->data<T>()[0];
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} else {
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auto* it = context.Input<framework::Tensor>("InCurrentIter");
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auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
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const int* last_iter = it->data<int>();
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int* current_iter = iter->mutable_data<int>(platform::CPUPlace());
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auto* scale_list = context.Output<framework::Tensor>("OutScales");
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auto* saving_scale =
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context.Output<framework::Tensor>("OutMovingScale");
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auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
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scale_out.device(dev) = raw_in.abs().maximum();
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scale = saving_scale->mutable_data<T>(platform::CPUPlace())[0];
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scale = FindRangeAbsMax(scale_list, saving_scale, scale, window_size,
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current_iter[0]);
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saving_scale->mutable_data<T>(platform::CPUPlace())[0] = scale;
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(*current_iter) = (*last_iter) + 1;
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}
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} else if (quantize_type == std::string("moving_average_abs_max")) {
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auto* moving_scale = context.Input<framework::Tensor>("InMovingScale");
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if (is_test) {
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scale = moving_scale->data<T>()[0];
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} else {
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auto* saving_scale =
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context.Output<framework::Tensor>("OutMovingScale");
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auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
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scale_out.device(dev) = raw_in.abs().maximum();
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scale = saving_scale->mutable_data<T>(platform::CPUPlace())[0];
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scale = FindMovingAverageAbsMmax(
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const_cast<framework::Tensor*>(moving_scale), saving_scale, scale);
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saving_scale->mutable_data<T>(platform::CPUPlace())[0] = scale;
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}
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// testing
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if (is_test) {
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ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, *in, *in_scale,
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bin_cnt, out);
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return;
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}
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Transform<DeviceContext> trans;
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trans(context.template device_context<DeviceContext>(), in->data<T>(),
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in->data<T>() + in->numel(), tensor->mutable_data<T>(in->place()),
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ClipFunctor<T>(-scale, scale));
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auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
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auto eigen_in = framework::EigenVector<T>::Flatten(*tensor);
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eigen_out.device(dev) = (bin_cnt / scale * eigen_in).round();
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// training
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auto* out_scale = context.Output<framework::Tensor>("OutScale");
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auto* out_scales = context.Output<framework::Tensor>("OutScales");
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auto* iter = context.Input<framework::Tensor>("Iter");
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int window_size = context.Attr<int>("window_size");
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out_scale->mutable_data<T>(context.GetPlace());
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framework::Tensor cur_scale;
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T* cur_scale_data = cur_scale.mutable_data<T>({1}, context.GetPlace());
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FindAbsMaxFunctor<DeviceContext, T>()(dev_ctx, in->data<T>(), in->numel(),
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cur_scale_data);
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FindRangeAbsMaxFunctor<DeviceContext, T>()(dev_ctx, cur_scale, *in_scale,
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*iter, window_size, out_scales,
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out_scale);
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ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, *in, *out_scale,
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bin_cnt, out);
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}
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};
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tensor-tang
7 years ago