add llama_pp in blip2

paddlemix/examples/blip2/run_eval_caption.py

@@ -25,7 +25,6 @@
 from paddle.distributed import fleet
 from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
 from paddlenlp.trainer import PdArgumentParser, TrainingArguments, get_last_checkpoint
-from paddlenlp.transformers import AutoTokenizer
 
 from paddlemix.datasets import load_dataset
 from paddlemix.examples.blip2.utils import BlipCollator, create_tokenizer
@@ -217,7 +216,6 @@ def setdistenv(args):
     args.data_parallel_degree = args.dp_degree
     logger.info("args.dp_degree:{}".format(args.dp_degree))
     logger.info("args.sharding_parallel_degree):{}".format(args.sharding_parallel_degree))
-    # breakpoint()
     strategy.hybrid_configs = {
         "dp_degree": args.dp_degree,
         "mp_degree": args.tensor_parallel_degree,

paddlemix/examples/blip2/run_eval_vqav2_zeroshot.py

@@ -235,7 +235,6 @@ def setdistenv(args):
     args.data_parallel_degree = args.dp_degree
     logger.info("args.dp_degree:{}".format(args.dp_degree))
     logger.info("args.sharding_parallel_degree):{}".format(args.sharding_parallel_degree))
-    # breakpoint()
     strategy.hybrid_configs = {
         "dp_degree": args.dp_degree,
         "mp_degree": args.tensor_parallel_degree,

paddlemix/examples/blip2/run_predict.py

@@ -175,8 +175,6 @@ def setdistenv(args):
     args.data_parallel_degree = args.dp_degree
     logger.info("args.dp_degree:{}".format(args.dp_degree))
     logger.info("args.sharding_parallel_degree):{}".format(args.sharding_parallel_degree))
-    if args.sharding_parallel_degree > 1:
-        args.sharding = "stage1"
     strategy.hybrid_configs = {
         "dp_degree": args.dp_degree,
         "mp_degree": args.tensor_parallel_degree,

paddlemix/examples/blip2/run_pretrain_stage1.py

@@ -244,8 +244,6 @@ def setdistenv(args):
     args.data_parallel_degree = args.dp_degree
     logger.info("args.dp_degree:{}".format(args.dp_degree))
     logger.info("args.sharding_parallel_degree):{}".format(args.sharding_parallel_degree))
-    if args.sharding_parallel_degree > 1:
-        args.sharding = "stage1"
     strategy.hybrid_configs = {
         "dp_degree": args.dp_degree,
         "mp_degree": args.tensor_parallel_degree,

paddlemix/examples/blip2/run_pretrain_stage2.py

@@ -236,8 +236,6 @@ def setdistenv(args):
     args.data_parallel_degree = args.dp_degree
     logger.info("args.dp_degree:{}".format(args.dp_degree))
     logger.info("args.sharding_parallel_degree):{}".format(args.sharding_parallel_degree))
-    if args.sharding_parallel_degree > 1:
-        args.sharding = "stage1"
     strategy.hybrid_configs = {
         "dp_degree": args.dp_degree,
         "mp_degree": args.tensor_parallel_degree,

paddlemix/examples/blip2/utils.py

@@ -20,7 +20,7 @@
 import time
 
 import paddle
-from paddlenlp.transformers import AutoTokenizer, T5Tokenizer
+from paddlenlp.transformers import AutoTokenizer, LlamaTokenizer, T5Tokenizer
 from pycocoevalcap.eval import COCOEvalCap
 from pycocotools.coco import COCO
 
@@ -50,6 +50,9 @@ def create_tokenizer(text_model_name_or_path):
         tokenizer_class = AutoTokenizer.from_pretrained(text_model_name_or_path, use_fast=False)
     elif "t5" in text_model_name_or_path:
         tokenizer_class = T5Tokenizer.from_pretrained(text_model_name_or_path, use_fast=False)
+    elif "llama" in text_model_name_or_path:
+        tokenizer_class = LlamaTokenizer.from_pretrained(text_model_name_or_path)
+        tokenizer_class.pad_token = tokenizer_class.eos_token
     else:
         raise NotImplementedError
     return tokenizer_class
@@ -152,27 +155,10 @@ def load_model(args, model, optimizer=None, ckpt_dir="", load_language_model=Tru
 
     ckpt_dir = path
     if ckpt_dir and os.path.isfile(ckpt_dir):
-        # breakpoint()
         print("Try to load a whole checkpoint from %s " % ckpt_dir)
-        embedding_list = ["word_embeddings"]
-        collinear_list = [
-            "fc1",
-            "fc2",
-            "qkv",
-            "proj",
-            "query",
-            "key",
-            "value",
-            "qkv_proj",
-            "q_proj",
-            "k_proj",
-            "v_proj",
-            "linear1",
-            "linear2",
-            "project_in",
-            "project_out",
-        ]
-        rowlinear_list = ["out_proj"]
+        embedding_list = []
+        collinear_list = ["fc1", "qkv"]
+        rowlinear_list = []
         all_list = collinear_list + rowlinear_list + embedding_list
         skip_list = ["visual_encoder.patch_embed.proj.weight", "visual_encoder.patch_embed.proj.bias"]
 

paddlemix/models/blip2/modeling.py

@@ -20,6 +20,7 @@
 import paddle.distributed as dist
 import paddle.nn as nn
 from paddlenlp.transformers import AutoTokenizer
+from paddlenlp.transformers.llama.modeling import LlamaForCausalLM
 from paddlenlp.transformers.model_outputs import ModelOutput
 from paddlenlp.transformers.model_utils import PretrainedModel
 from paddlenlp.transformers.t5.modeling import T5ForConditionalGeneration
@@ -372,7 +373,9 @@ def __init__(
         from paddlemix.models.blip2.eva_vit import VisionTransformer
 
         self.visual_encoder = VisionTransformer.from_pretrained(
-            pretrained_model_name_or_path=config.vision_config, mp_degree=config.mp_degree
+            pretrained_model_name_or_path=config.vision_config,
+            mp_degree=config.mp_degree,
+            ignore_mismatched_sizes=True,
         )
         self.freeze_vit = config.freeze_vit
         self.train_stage1 = False
@@ -392,6 +395,7 @@ def __init__(
                 train_in_satge1=True,
                 tokenizer_length=len(self.tokenizer),
                 mp_degree=config.mp_degree,
+                ignore_mismatched_sizes=True,
             )
 
             state_dict = self.Qformer.state_dict()
@@ -408,8 +412,31 @@ def __init__(
             if config.use_decoder_only_language_model:
                 if "opt" in config.text_config:
                     language_model = OPTForCausalLM.from_pretrained(
-                        config.text_config, load_state_as_np=True, mp_degree=config.mp_degree
+                        config.text_config,
+                        load_state_as_np=True,
+                        mp_degree=config.mp_degree,
+                        ignore_mismatched_sizes=True,
                     )
+                elif "llama" in config.text_config:
+                    from paddlenlp.transformers.llama.configuration import LlamaConfig
+
+                    if config.mp_degree > 1:
+                        import paddle.distributed.fleet as fleet
+
+                        hcg = fleet.get_hybrid_communicate_group()
+                        language_model = LlamaForCausalLM.from_pretrained(
+                            config.text_config,
+                            tensor_parallel_degree=config.mp_degree,
+                            tensor_parallel_rank=hcg.get_model_parallel_rank(),
+                            tensor_parallel_output=False,
+                        )
+                    else:
+                        language_model = LlamaForCausalLM.from_pretrained(
+                            config.text_config,
+                            tensor_parallel_output=False,
+                        )
+                    language_model.hidden_size = LlamaConfig.from_pretrained(config.text_config).hidden_size
+                    language_model.pad_token_id = LlamaConfig.from_pretrained(config.text_config).pad_token_id
                 else:
                     raise NotImplementedError
             else:
@@ -418,8 +445,8 @@ def __init__(
                 t5_config = T5Config(config.text_config)
                 for key, value in config.text_config.items():
                     t5_config[key] = config.text_config[key]
-                language_model = T5ForConditionalGeneration(t5_config)
-                language_model.hidden_size = config.text_config["d_model"]
+                language_model = T5ForConditionalGeneration.from_pretrained(config.text_config, load_state_as_np=True)
+                language_model.hidden_size = t5_config["d_model"]
 
             self.language_model = language_model
             for name, param in self.language_model.named_parameters():
@@ -428,11 +455,11 @@ def __init__(
 
             self.Qformer = BertLMHeadModel.from_pretrained(
                 pretrained_model_name_or_path=config.qformer_config,
+                mp_degree=config.mp_degree,
                 encoder_width=self.visual_encoder.num_features,
                 train_in_satge1=False,
                 text_hidden_size=self.language_model.hidden_size,
                 ignore_mismatched_sizes=True,
-                mp_degree=config.mp_degree,
             )
             self.Qformer.cls = None
             self.Qformer.bert.embeddings.word_embeddings = None