feat: support check_nan_inf for kunlun/xpu device (#29694)

* feat: support check_nan_inf for kunlun device

* support kunlun stack

* minor

paddle/fluid/framework/details/nan_inf_utils_detail.cc

@@ -333,6 +333,33 @@ void CheckVarHasNanOrInf(const std::string& op_type,
     PADDLE_THROW(platform::errors::PreconditionNotMet(
         "Tensor[%s] use gpu place. PaddlePaddle must compile with GPU.",
         var_name));
+#endif
+    return;
+  } else if (platform::is_xpu_place(tensor->place())) {
+#ifdef PADDLE_WITH_XPU
+    if (tensor->type() != proto::VarType::FP32) {
+      return;
+    }
+
+    float* cpu_data = new float[tensor->numel()];
+    xpu_memcpy(cpu_data, tensor->data<float>(), tensor->numel() * sizeof(float),
+               XPU_DEVICE_TO_HOST);
+    bool flag = false;
+    for (int i = 0; i < tensor->numel(); i++) {
+      if (isnan(cpu_data[i]) || isinf(cpu_data[i])) {
+        flag = true;
+        break;
+      }
+    }
+    delete[] cpu_data;
+    PADDLE_ENFORCE_NE(
+        flag, true,
+        platform::errors::Fatal("Operator %s output Tensor %s contains Inf.",
+                                op_type, var_name));
+#else
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "Tensor[%s] use xpu place. PaddlePaddle must compile with XPU.",
+        var_name));
 #endif
     return;
   }

paddle/fluid/operators/stack_op_xpu.cc

@@ -28,50 +28,34 @@ class StackXPUKernel : public framework::OpKernel<T> {
     auto* y = ctx.Output<Tensor>("Y");
     int axis = ctx.Attr<int>("axis");
     if (axis < 0) {
-      axis += (x[0]->dims().size() + 1);
+      axis += x[0]->dims().size() + 1;
     }
-    int n = static_cast<int>(x.size());
-    PADDLE_ENFORCE_LE(n, 24,
-                      platform::errors::InvalidArgument(
-                          "XPU only surpport at most 24 tensors for now"));
     auto* y_data = y->mutable_data<T>(ctx.GetPlace());
-    int pre = 1, post = 1;
+
     auto& dim = x[0]->dims();
-    for (auto i = 0; i < axis; ++i) {
-      pre *= dim[i];
+    std::vector<int> xdims;
+    for (auto i = 0; i < dim.size(); ++i) {
+      xdims.push_back(dim[i]);
     }
-    for (auto i = axis; i < dim.size(); ++i) {
-      post *= dim[i];
+    xdims.push_back(1);
+    std::vector<std::vector<int>> xdims_list;
+    int n = static_cast<int>(x.size());
+    for (int i = 0; i < n; i++) {
+      xdims_list.push_back(xdims);
     }
-    auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    void* x_datas_host = std::malloc(n * sizeof(void*));
-    void* x_datas_device = nullptr;
-    PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void**>(&x_datas_device),
-                                 n * sizeof(void*)),
-                      XPU_SUCCESS,
-                      platform::errors::ResourceExhausted(
-                          "\n\nOut of memory error on XPU, Cannot"
-                          "allocate %s memory on XPU. \n\nPlease "
-                          "check whether there is any other process "
-                          "using XPU.\n",
-                          string::HumanReadableSize(n * sizeof(void*))));
-    for (auto i = 0; i < n; ++i) {
-      ((const void**)x_datas_host)[i] = x[i]->data<T>();
+
+    std::vector<const T*> x_list;
+    for (int i = 0; i < n; i++) {
+      x_list.push_back(x[i]->data<T>());
     }
-    memory::Copy(BOOST_GET_CONST(platform::XPUPlace, ctx.GetPlace()),
-                 x_datas_device, platform::CPUPlace(), x_datas_host,
-                 n * sizeof(void*));
-    int r = xpu::stack_forward<float>(dev_ctx.x_context(), pre, post, n,
-                                      x_datas_device, y_data);
-    PADDLE_ENFORCE_EQ(
-        r, xpu::Error_t::SUCCESS,
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    int r =
+        xpu::concat<T>(dev_ctx.x_context(), x_list, y_data, xdims_list, axis);
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                       platform::errors::External(
-            "The stack XPU API return wrong value[%d], please check "
-            "where Baidu Kunlun Card is properly installed.",
-            r));
-    dev_ctx.Wait();
-    std::free(x_datas_host);
-    xpu_free(x_datas_device);
+                          "The stack XPU API return wrong value[%d %s]", r,
+                          XPUAPIErrorMsg[r]));
   }
 };
 

python/paddle/fluid/tests/unittests/xpu/test_stack_op_xpu.py

@@ -19,18 +19,19 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from op_test import OpTest, skip_check_grad_ci
+from op_test_xpu import XPUOpTest
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
 
 
 @skip_check_grad_ci(reason="There is no grad kernel for stack_xpu op.")
-class TestStackOpBase(OpTest):
+class TestStackOpBase(XPUOpTest):
     def initDefaultParameters(self):
         self.num_inputs = 4
         self.input_dim = (5, 6, 7)
         self.axis = 0
-        self.dtype = 'float64'
+        self.dtype = 'float32'
 
     def initParameters(self):
         pass
@@ -73,7 +74,7 @@ class TestStackOp1(TestStackOpBase):
 
 class TestStackOp2(TestStackOpBase):
     def initParameters(self):
-        self.num_inputs = 20
+        self.num_inputs = 30
 
 
 class TestStackOp3(TestStackOpBase):