add new t5 and albert models

Author: RbRe145

graph_net/test/nlp_model_getter.py

@@ -154,91 +154,3 @@ def get_xlnet_model_and_inputs(model_name, text, dtype):
         enc["attention_mask"] = (input_ids != pad_id).astype("int64")
 
     return model, enc
-
-
-def get_t5_model_and_inputs(model_name, text, dtype):
-    import paddle
-    from paddlenlp.transformers import T5ForConditionalGeneration, T5Tokenizer
-
-    # 1) 分词器（先建 tokenizer 方便取 pad/eos id）
-    tokenizer = T5Tokenizer.from_pretrained(model_name)
-
-    # 2) 编码输入（支持单条或批量 text）
-    enc = tokenizer(
-        text,
-        return_tensors="pd",
-        padding=True,
-        truncation=True,
-        max_length=512,
-    )
-
-    # 补 attention_mask（pad 处为 0，其他为 1）
-    if "attention_mask" not in enc:
-        input_ids = enc["input_ids"]
-        attn_mask = (input_ids != tokenizer.pad_token_id).astype("int64")
-        enc["attention_mask"] = attn_mask
-
-    # 构造 decoder_input_ids：
-    # T5 以 pad_token_id 作为 decoder_start_token_id
-    batch_size = enc["input_ids"].shape[0]
-    decoder_input_ids = paddle.full(
-        shape=[batch_size, 1],
-        fill_value=tokenizer.pad_token_id,
-        dtype="int64",
-    )
-
-    # 3) 加载模型
-    model = T5ForConditionalGeneration.from_pretrained(model_name)
-    if dtype == "float16":
-        model = model.astype(paddle.float16)
-    model.eval()
-
-    # 4) 组装喂给模型的输入
-    inputs = {
-        "input_ids": enc["input_ids"],
-        "attention_mask": enc["attention_mask"],
-        "decoder_input_ids": decoder_input_ids,
-    }
-    return model, inputs
-
-
-def get_albert_model_and_inputs(model_name, text, dtype):
-    """
-    加载 ALBERT backbone（AlbertModel）并构造输入。
-    - model_name 例如: "albert-base-v2", "albert-xxlarge-v1"（PaddleNLP 内置名称）
-    - dtype: "float32" 或 "float16"
-    返回: (model, inputs_dict)
-    """
-    import paddle
-    from paddlenlp.transformers import AlbertConfig, AlbertModel, AlbertTokenizer
-
-    # 1) 读取配置（不触发权重下载）
-    config = AlbertConfig.from_pretrained(model_name)
-
-    # 2) 模型
-    #    若你只需要网络结构，可改成: model = AlbertModel(config)
-    model = AlbertModel(config)
-    if dtype == "float16":
-        model = model.astype(paddle.float16)
-    model.eval()
-
-    # 3) 分词器
-    tokenizer = AlbertTokenizer.from_pretrained(model_name)
-
-    # 若无 pad_token，则回退到 unk_token（ALBERT 没有 eos_token，别设 pad=eos）
-    if tokenizer.pad_token is None:
-        tokenizer.pad_token = tokenizer.unk_token
-
-    enc = tokenizer(
-        text,
-        return_tensors="pd",
-        padding=True,
-        truncation=True,
-        max_length=512,
-    )
-
-    if "attention_mask" not in enc:
-        input_ids = enc["input_ids"]
-        enc["attention_mask"] = (input_ids != tokenizer.pad_token_id).astype("int64")
-
-    return model, enc

paddle_samples/PaddleNLP/albert-chinese-large/graph_net.json

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

paddle_samples/PaddleNLP/albert-chinese-large/input_meta.py

@@ -0,0 +1,19 @@
+class Program_weight_tensor_data_0:
+    name = "data_0"
+    shape = [1, 11]
+    dtype = "int64"
+    data = [101, 3614, 6816, 886, 4500, 4636, 2428, 7607, 3444, 106, 102]
+
+
+class Program_weight_tensor_data_1:
+    name = "data_1"
+    shape = [1, 11]
+    dtype = "int64"
+    data = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+
+
+class Program_weight_tensor_data_2:
+    name = "data_2"
+    shape = [1, 11]
+    dtype = "int64"
+    data = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]