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tmp:fix bugs for vis-formula; test=develop #10528

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tmp:fix bugs for vis-formula; test=develop #10528

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1 change: 1 addition & 0 deletions lite/core/optimizer/mir/xpu_memory_optimize_pass.cc
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Expand Up @@ -54,6 +54,7 @@ void XPUMemoryOptimizePass::CollectLifeCycleByDevice(SSAGraph* graph) {
"scale",
"__xpu__resnet50",
"softmax",
"select_input",
};

auto insert_invalid_op_nodes_for_specific_target =
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5 changes: 5 additions & 0 deletions lite/kernels/host/cast_compute.cc
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Expand Up @@ -136,6 +136,11 @@ void CastCompute::Run() {
const int32_t* x_data_end = x_data_begin + param.X->numel();
bool* out_data = param.Out->mutable_data<bool>();
std::transform(x_data_begin, x_data_end, out_data, TransOp<int32_t, bool>);
} else if (param.in_dtype == 3 && param.out_dtype == 0) { // INT64 -> bool
const int64_t* x_data_begin = param.X->data<int64_t>();
const int64_t* x_data_end = x_data_begin + param.X->numel();
bool* out_data = param.Out->mutable_data<bool>();
std::transform(x_data_begin, x_data_end, out_data, TransOp<int64_t, bool>);
} else if (param.in_dtype == 2 && param.out_dtype == 2) { // INT32 -> INT32
const int32_t* x_data_begin = param.X->data<int32_t>();
const int32_t* x_data_end = x_data_begin + param.X->numel();
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1 change: 1 addition & 0 deletions lite/kernels/xpu/CMakeLists.txt
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Expand Up @@ -65,6 +65,7 @@ add_kernel(gather_nd_compute_xpu XPU extra SRCS gather_nd_compute.cc)
add_kernel(meshgrid_compute_xpu XPU basic SRCS meshgrid_compute.cc)
add_kernel(fetch_compute_xpu XPU basic SRCS fetch_compute.cc)
add_kernel(unbind_compute_xpu XPU basic SRCS unbind_compute.cc)
add_kernel(unbind_compute_xpu XPU basic SRCS unbind_compute.cc)

# extra
add_kernel(lookup_table_compute_xpu XPU extra SRCS lookup_table_compute.cc)
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8 changes: 4 additions & 4 deletions lite/kernels/xpu/activation_compute.cc
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Expand Up @@ -75,10 +75,10 @@ void SigmoidCompute<T, PType>::Run() {
auto& param = this->template Param<param_t>();
auto& ctx = this->ctx_->template As<XPUContext>();

int r = xdnn::fast_sigmoid(ctx.GetRawContext(),
param.X->template data<T>(),
param.Out->template mutable_data<T>(TARGET(kXPU)),
param.X->numel());
int r = xdnn::sigmoid(ctx.GetRawContext(),
param.X->template data<T>(),
param.Out->template mutable_data<T>(TARGET(kXPU)),
param.X->numel());
CHECK_EQ(r, 0);
}

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8 changes: 8 additions & 0 deletions lite/kernels/xpu/compare_compute.cc
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Expand Up @@ -82,6 +82,14 @@ void CompareCompute<PType, T, Functor>::Run() {
const auto* y = param.Y->template data<T>();

auto& ctx = this->ctx_->template As<XPUContext>();

if (x_dims.size() == 0) {
x_dims.ConstructFrom({1});
}
if (y_dims.size() == 0) {
y_dims.ConstructFrom({1});
}

Functor comp_func;
std::vector<int> xshape;
std::vector<int> yshape;
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8 changes: 8 additions & 0 deletions lite/kernels/xpu/concat_compute.cc
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Expand Up @@ -104,6 +104,8 @@ using concati64 =
paddle::lite::kernels::xpu::ConcatCompute<int64_t, PRECISION(kFloat)>;
using concati8 =
paddle::lite::kernels::xpu::ConcatCompute<int8_t, PRECISION(kInt8)>;
using concatbool =
paddle::lite::kernels::xpu::ConcatCompute<bool, PRECISION(kFloat)>;

REGISTER_LITE_KERNEL(concat, kXPU, kFloat, kNCHW, concatfp32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFloat))})
Expand Down Expand Up @@ -147,3 +149,9 @@ REGISTER_LITE_KERNEL(concat, kXPU, kInt8, kNCHW, concati8, concat_INT8)
{LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt8))})
.Finalize();
REGISTER_LITE_KERNEL(concat, kXPU, kFloat, kNCHW, concatbool, concat_BOOL)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kBool))})
.BindInput("AxisTensor",
{LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kBool))})
.Finalize();
35 changes: 31 additions & 4 deletions lite/kernels/xpu/linspace_compute.cc
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Expand Up @@ -21,6 +21,22 @@ namespace lite {
namespace kernels {
namespace xpu {

template <typename T>
T GetValueOfExpectedType(const lite::Tensor* x) {
switch (x->precision()) {
case PRECISION(kFloat):
return static_cast<T>(x->template data<float>()[0]);
case PRECISION(kInt32):
return static_cast<T>(x->template data<int32_t>()[0]);
case PRECISION(kInt64):
return static_cast<T>(x->template data<int64_t>()[0]);
default:
LOG(FATAL) << "data type is not supported: "
<< lite_api::PrecisionToStr(x->precision());
return static_cast<T>(0);
}
}

template <typename T, PrecisionType PType>
void LinspaceCompute<T, PType>::Run() {
auto& param = this->template Param<operators::LinspaceParam>();
Expand All @@ -31,20 +47,31 @@ void LinspaceCompute<T, PType>::Run() {
auto* out_tensor = param.Out;
int64_t num = static_cast<int64_t>(num_tensor->template data<int>()[0]);
int r = -1;

T start_val = GetValueOfExpectedType<T>(start_tensor);
T stop_val = GetValueOfExpectedType<T>(stop_tensor);
switch (param.Out->precision()) {
case PRECISION(kFloat):
r = xdnn::linspace<T>(ctx.GetRawContext(),
out_tensor->template mutable_data<T>(TARGET(kXPU)),
static_cast<T>(start_tensor->template data<T>()[0]),
static_cast<T>(stop_tensor->template data<T>()[0]),
start_val,
stop_val,
num);
CHECK_EQ(r, 0);
break;
case PRECISION(kInt32):
r = xdnn::linspace<T>(ctx.GetRawContext(),
out_tensor->template mutable_data<T>(TARGET(kXPU)),
static_cast<T>(start_tensor->template data<T>()[0]),
static_cast<T>(stop_tensor->template data<T>()[0]),
start_val,
stop_val,
num);
CHECK_EQ(r, 0);
break;
case PRECISION(kInt64):
r = xdnn::linspace<T>(ctx.GetRawContext(),
out_tensor->template mutable_data<T>(TARGET(kXPU)),
start_val,
stop_val,
num);
CHECK_EQ(r, 0);
break;
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2 changes: 1 addition & 1 deletion lite/kernels/xpu/scale_compute.cc
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Expand Up @@ -78,4 +78,4 @@ using XPUScale_Int64 =
REGISTER_LITE_KERNEL(scale, kXPU, kFloat, kNCHW, XPUScale_Int64, int64)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt64))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt64))})
.Finalize();
.Finalize();
4 changes: 3 additions & 1 deletion lite/kernels/xpu/topk_v2_compute.cc
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Expand Up @@ -54,7 +54,9 @@ void TopkV2Compute::Run() {
indices_int32_device,
m,
n,
k);
k,
true,
true);
CHECK_EQ(r, 0);

r = xdnn::cast_v2<int, int64_t>(
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2 changes: 1 addition & 1 deletion lite/kernels/xpu/unbind_compute.cc
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Expand Up @@ -58,4 +58,4 @@ using unbind_int64 =
REGISTER_LITE_KERNEL(unbind, kXPU, kFloat, kNCHW, unbind_int64, int64)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt64))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt64))})
.Finalize();
.Finalize();
2 changes: 1 addition & 1 deletion lite/kernels/xpu/unbind_compute.h
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Expand Up @@ -34,4 +34,4 @@ class UnbindCompute : public KernelLite<TARGET(kXPU), PType> {
} // namespace xpu
} // namespace kernels
} // namespace lite
} // namespace paddle
} // namespace paddle
2 changes: 1 addition & 1 deletion lite/operators/compare_op.cc
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Expand Up @@ -35,7 +35,7 @@ static void GetBroadcastDimsArrays(const DDim &x_dims,
};

CHECK_GE(axis, 0);
CHECK_LT(axis, max_dim);
CHECK_LE(axis, max_dim);
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这里为啥可以到 max_dim ?

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int max_dim = (std::max)(dim_x.size(), dim_y.size()); int axis = std::abs(static_cast<int>(dim_x.size() - dim_y.size()));
但是这里出现了dim_x.size() = 0 的情况，导致axis= 1 && max_dim =1,并且x还并不是一个0dim的scalar，暂时没有追到根因，这里先放开一下供业务临时使用，暂不合入

if (x_dims.size() > y_dims.size()) {
std::fill(y_dims_array, y_dims_array + axis, 1);
if (axis + y_dims.size() < max_dim) {
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