Add albert t5

graph_net/test/nlp_model_getter.py

@@ -107,3 +107,50 @@ def get_skep_model_and_inputs(model_name, text, dtype):
     tokenizer = TokenizerClass.from_pretrained(model_name)
     inputs = tokenizer(text, return_tensors="pd")
     return model, inputs
+
+
+def get_bart_model_and_inputs(model_name, text, dtype):
+    from paddlenlp.transformers import BartModel, BartTokenizer
+
+    model = BartModel.from_pretrained(model_name)
+    model.eval()
+
+    tokenizer = BartTokenizer.from_pretrained(model_name)
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pd",
+        padding=True,
+        truncation=True,
+        max_length=512,
+    )
+    inputs.pop("token_type_ids", None)
+
+    return model, inputs
+
+
+def get_xlnet_model_and_inputs(model_name, text, dtype):
+    import paddle
+    from paddlenlp.transformers import XLNetModel, XLNetTokenizer, XLNetConfig
+
+    config = XLNetConfig.from_pretrained(model_name)
+    model = XLNetModel(config)
+    if dtype == "float16":
+        model = model.astype(paddle.float16)
+    model.eval()
+
+    tokenizer = XLNetTokenizer.from_pretrained(model_name)
+
+    enc = tokenizer(
+        text,
+        return_tensors="pd",
+        padding=True,
+        truncation=True,
+        # max_length=512,
+    )
+    if "attention_mask" not in enc:
+        input_ids = enc["input_ids"]
+        pad_id = tokenizer.pad_token_id
+        enc["attention_mask"] = (input_ids != pad_id).astype("int64")
+
+    return model, enc

paddle_samples/PaddleNLP/albert-base-v1/graph_net.json

@@ -0,0 +1,6 @@
+{
+    "framework": "paddle",
+    "model_name": "albert-base-v1",
+    "num_devices_required": 1,
+    "num_nodes_required": 1
+}

paddle_samples/PaddleNLP/albert-base-v1/input_meta.py

@@ -0,0 +1,41 @@
+class Program_weight_tensor_data_0:
+    name = "data_0"
+    shape = [1, 21]
+    dtype = "int64"
+    data = [
+        2,
+        10975,
+        15,
+        51,
+        204,
+        25,
+        1909,
+        9,
+        31,
+        589,
+        2477,
+        88,
+        370,
+        816,
+        2761,
+        17,
+        66,
+        2607,
+        18,
+        9,
+        3,
+    ]
+
+
+class Program_weight_tensor_data_1:
+    name = "data_1"
+    shape = [1, 21]
+    dtype = "int64"
+    data = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+
+
+class Program_weight_tensor_data_2:
+    name = "data_2"
+    shape = [1, 21]
+    dtype = "int64"
+    data = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]