fix gpu outofrange (#29238) (#29348)

* fix gpu emb out of range

Change-Id: I5794ac73bd634d5ea069a6fbbd914274b6d6b7bf

* fix doc

Change-Id: I5a3350b2930a9ab2f52116c192b087307faf8fdf

paddle/fluid/operators/lookup_table_v2_op.cu

@@ -31,16 +31,6 @@ __global__ void LookupTableV2(T *output, const T *table, const int64_t *ids,
 
   while (idy < K) {
     int64_t id = ids[idy];
-    PADDLE_ENFORCE(
-        id >= 0,
-        "Variable value (input) of OP(fluid.layers.embedding) "
-        "expected >= 0 and < %ld, but got %ld. Please check input value.",
-        N, id);
-    PADDLE_ENFORCE(
-        id < N,
-        "Variable value (input) of OP(fluid.layers.embedding) "
-        "expected >= 0 and < %ld, but got %ld. Please check input value.",
-        N, id);
     T *out = output + idy * D;
     const T *tab = table + id * D;
     for (int i = idx; i < D; i += BlockDimX) {
@@ -66,16 +56,6 @@ __global__ void LookupTableV2Grad(T *table, const T *output, const int64_t *ids,
 
   while (idy < K) {
     int64_t id = ids[idy];
-    PADDLE_ENFORCE(
-        id >= 0,
-        "Variable value (input) of OP(fluid.layers.embedding) "
-        "expected >= 0 and < %ld, but got %ld. Please check input value.",
-        N, id);
-    PADDLE_ENFORCE(
-        id < N,
-        "Variable value (input) of OP(fluid.layers.embedding) "
-        "expected >= 0 and < %ld, but got %ld. Please check input value.",
-        N, id);
     const T *out = output + idy * D;
     T *tab = table + id * D;
     for (int i = idx; i < D; i += BlockDimX) {
@@ -127,6 +107,21 @@ class LookupTableV2CUDAKernel : public framework::OpKernel<T> {
       ids_p = ids_t->data<int64_t>();
     }
 
+    for (int64_t i = 0; i < K; ++i) {
+      PADDLE_ENFORCE_GE(
+          ids[i], 0,
+          platform::errors::InvalidArgument(
+              "Variable value (input) of OP(paddle.nn.embedding) "
+              "expected >= 0 and < %ld, but got %ld. Please check input value.",
+              N, ids[i]));
+      PADDLE_ENFORCE_LT(
+          ids[i], N,
+          platform::errors::InvalidArgument(
+              "Variable value (input) of OP(paddle.nn.embedding) "
+              "expected >= 0 and < %ld, but got %ld. Please check input value.",
+              N, ids[i]));
+    }
+
     auto *table = table_t->data<T>();
     auto *output = output_t->mutable_data<T>(context.GetPlace());
 

python/paddle/fluid/input.py

@@ -197,10 +197,7 @@ def embedding(input,
             indicates the size of the dictionary of embeddings and the size of each embedding vector respectively.
         is_sparse(bool): The flag indicating whether to use sparse update. This parameter only
             affects the performance of the backwards gradient update. It is recommended to set 
-            True because sparse update is faster. But some optimizer does not support sparse update,
-            such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` , 
-            :ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` ,
-            :ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` .
+            True because sparse update is faster. But some optimizer does not support sparse update
             In these case, is_sparse must be False. Default: False.
         is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used
             in multi-machine distributed CPU training. Default: False.
@@ -210,11 +207,10 @@ def embedding(input,
             encounters :math:`padding\_idx` in id. And the padding data will not be updated while training.
             If set None, it makes no effect to output. Default: None.
         param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the
-            default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition,
+            default weight parameter property is used. In addition,
             user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter. 
             The local word vector needs to be transformed into numpy format, and the shape of local word
-            vector should be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer`
-            is used to load custom or pre-trained word vectors. See code example 2 for details.
+            vector should be consistent with :attr:`size` .
         dtype(str|core.VarDesc.VarType): It refers to the data type of output Tensor.
             It must be float32 or float64. Default: float32.
 
@@ -267,9 +263,7 @@ def embedding(input,
 
             import paddle
             import numpy as np
-            
-            paddle.disable_static()
-            
+
             x_data = np.arange(3, 6).reshape((3, 1)).astype(np.int64)
             
             # x is a Tensor.

python/paddle/nn/functional/input.py

@@ -168,28 +168,25 @@ def embedding(x, weight, padding_idx=None, sparse=False, name=None):
 
         .. code-block:: python
 
+            import numpy as np
             import paddle
             import paddle.nn as nn
 
-            weight = prog.global_block().create_parameter(
-                attr=self._param_attr,
-                shape=param_shape,
-                dtype=self._dtype,
-                default_initializer=Constant(1.0))
+            x0 = np.arange(3, 6).reshape((3, 1)).astype(np.int64)
+            w0 = np.full(shape=(10, 3), fill_value=2).astype(np.float32)
 
-            prog = paddle.static.Program()
+            # x.data = [[3], [4], [5]]
+            # x.shape = [3, 1]
+            x = paddle.to_tensor(x0, stop_gradient=False)
 
-            weight = prog.global_block().create_parameter(
-                    (128, 100), dtype="float32", default_initializer=Constant(1.0))
+            # w.data = [[2. 2. 2.] ... [2. 2. 2.]]
+            # w.shape = [10, 3]
+            w = paddle.to_tensor(w0, stop_gradient=False)
 
-            label = paddle.static.data(
-                    name="label",
-                    shape=[4],
-                    append_batch_size=False,
-                    dtype="int64")
-
-            emb = nn.embedding(
-                    x=label, weight=weight, sparse=True, name="embedding")
+            # emb.data = [[[2., 2., 2.]], [[2., 2., 2.]], [[2., 2., 2.]]]
+            # emb.shape = [3, 1, 3]
+            emb = nn.functional.embedding(
+                    x=x, weight=w, sparse=True, name="embedding")
 
     """
     padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (

python/paddle/nn/layer/common.py

@@ -1216,7 +1216,7 @@ class Embedding(layers.Layer):
 
             x_data = np.arange(3, 6).reshape((3, 1)).astype(np.int64)
             y_data = np.arange(6, 12).reshape((3, 2)).astype(np.float32)
-            paddle.disable_static(paddle.CPUPlace())
+
             x = paddle.to_tensor(x_data, stop_gradient=False)
             y = paddle.to_tensor(y_data, stop_gradient=False)