modify unittest of lstm forward, *test=kunlun (#41534)

* modify unittest of lstm forward, *test=kunlun

* modify unittest of lstm forward, *test=kunlun

cmake/external/xpu.cmake

@@ -36,7 +36,7 @@ ENDIF()
 
 if(NOT DEFINED XPU_BASE_URL)
   SET(XPU_BASE_URL_WITHOUT_DATE "https://baidu-kunlun-product.cdn.bcebos.com/KL-SDK/klsdk-dev")
-  SET(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20220402")
+  SET(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20220408")
 else()
   SET(XPU_BASE_URL "${XPU_BASE_URL}")
 endif()

paddle/fluid/operators/rnn_op_xpu.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 #include "paddle/fluid/platform/device/device_wrapper.h"
 #include "paddle/fluid/platform/device/xpu/xpu_header.h"
 #include "paddle/fluid/platform/device_context.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
 
 namespace paddle {
 namespace operators {
@@ -114,6 +115,9 @@ class RnnXPUKernel : public framework::OpKernel<T> {
       if (dropout_mask->numel() != output->numel()) dropout_mask->clear();
     }
     dropout_mask->mutable_data<uint8_t>(output->dims(), ctx.GetPlace());
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    phi::funcs::SetConstant<platform::XPUDeviceContext, uint8_t> ones;
+    ones(dev_ctx, dropout_mask, static_cast<uint8_t>(1));
 
     PADDLE_ENFORCE_EQ(
         mode, "LSTM",
@@ -190,7 +194,6 @@ class RnnXPUKernel : public framework::OpKernel<T> {
       seq_len_tensor = operators::GetDataFromTensor(sequence_length);
     }
 
-    auto& dev_ctx = ctx.template device_context<DeviceContext>();
     int state_offset = pre_state[0]->dims()[1] * pre_state[0]->dims()[2];
 
     for (int i = 0; i < num_layers; i++) {

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

@@ -46,8 +46,9 @@ class XPUTestRNNOp(XPUOpTestWrapper):
             self.init_dtype()
             self.op_type = "rnn"
             self.place = paddle.XPUPlace(0)
-            self.sequence_length = np.ones(
-                (self.batch_size, ), dtype=np.int32) * self.seq_length
+            self.sequence_length = np.array([12, 11, 10, 9, 8], dtype=np.int32)
+            self.num_layers = 1
+            self.is_bidirec = False
             self.set_attrs()
             self.mode = "LSTM"
             self.is_test = False
@@ -61,6 +62,10 @@ class XPUTestRNNOp(XPUOpTestWrapper):
                 high=0.1,
                 size=(self.seq_length, self.batch_size,
                       self.input_size)).astype(self.dtype)
+            input[11][1:][:] = 0
+            input[10][2:][:] = 0
+            input[9][3:][:] = 0
+            input[8][4:][:] = 0
 
             rnn1 = LSTM(
                 self.input_size,
@@ -126,10 +131,10 @@ class XPUTestRNNOp(XPUOpTestWrapper):
                 no_check_set=['Reserve', 'DropoutState'])
 
         def init_size(self):
-            self.seq_length = 1
-            self.batch_size = 1
-            self.input_size = 5
-            self.hidden_size = 16
+            self.seq_length = 12
+            self.batch_size = 5
+            self.input_size = 3
+            self.hidden_size = 2
 
         def get_weight_names(self):
             weight_names = []
@@ -142,38 +147,18 @@ class XPUTestRNNOp(XPUOpTestWrapper):
             return weight_names
 
         def set_attrs(self):
-            self.num_layers = 1
-            self.is_bidirec = False
+            pass
 
     class TestRNNOp1(TestRNNOp):
-        def init_size(self):
-            self.seq_length = 2
-            self.batch_size = 4
-            self.input_size = 10
-            self.hidden_size = 32
-
         def set_attrs(self):
-            self.num_layers = 1
-            self.is_bidirec = False
+            self.sequence_length = None
 
     class TestRNNOp2(TestRNNOp):
-        def init_size(self):
-            self.seq_length = 5
-            self.batch_size = 16
-            self.input_size = 30
-            self.hidden_size = 64
-
         def set_attrs(self):
             self.num_layers = 1
             self.is_bidirec = True
 
     class TestRNNOp3(TestRNNOp):
-        def init_size(self):
-            self.seq_length = 10
-            self.batch_size = 64
-            self.input_size = 50
-            self.hidden_size = 64
-
         def set_attrs(self):
             self.num_layers = 2
             self.is_bidirec = False
@@ -188,6 +173,17 @@ class XPUTestRNNOp(XPUOpTestWrapper):
             self.num_layers = 2
             self.is_bidirec = True
 
+    class TestRNNOp6(TestRNNOp):
+        def set_attrs(self):
+            self.num_layers = 2
+            self.is_bidirec = True
+            self.sequence_length = None
+
+    class TestRNNOp7(TestRNNOp):
+        def set_attrs(self):
+            self.num_layers = 3
+            self.is_bidirec = True
+
 
 support_types = get_xpu_op_support_types('rnn')
 for stype in support_types: