[example] Palm adding gemini, still has bugs (#2221)

examples/language/palm/palm_config.py

@@ -0,0 +1,6 @@
+SEQ_LENGTH = 1024
+BATCH_SIZE = 4
+NUM_EPOCHS = 4
+TPDEGREE = 2
+USE_SHARD_INIT = False
+placement = 'cpu'

examples/language/palm/palm_pytorch/palm_pytorch.py

@@ -47,7 +47,9 @@ class RotaryEmbedding(nn.Module):
     def forward(self, max_seq_len, *, device):
         seq = torch.arange(max_seq_len, device=device)
         #freqs = einsum("i , j -> i j", seq.type_as(self.inv_freq), self.inv_freq)
-        freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
+        #freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
+        i, j = len(seq.type_as(self.inv_freq)), len(self.inv_freq)
+        freqs = matmul(seq.type_as(self.inv_freq).reshape(i, 1), self.inv_freq.reshape(1, j))
         return torch.cat((freqs, freqs), dim=-1)
 
 

examples/language/palm/run.sh

@@ -0,0 +1 @@
+env OMP_NUM_THREADS=12 torchrun  --nproc_per_node 8  --master_port 29501  train.py --config palm_config.py

examples/language/palm/train.py

@@ -9,6 +9,16 @@ from palm_pytorch import PaLM
 from palm_pytorch.autoregressive_wrapper import AutoregressiveWrapper
 from torch.nn import functional as F
 from torch.utils.data import DataLoader, Dataset
+from packaging import version
+
+import colossalai
+from colossalai.utils.model.colo_init_context import ColoInitContext
+from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec
+from colossalai.utils import MultiTimer, get_current_device
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
+from colossalai.nn.parallel import GeminiDDP
+from colossalai.logging import disable_existing_loggers, get_dist_logger
 
 # constants
 
@@ -20,6 +30,9 @@ VALIDATE_EVERY = 100
 GENERATE_EVERY = 500
 GENERATE_LENGTH = 512
 SEQ_LEN = 1024
+TPDEGREE = 2
+USE_SHARD_INIT = False
+placement = 'cpu'
 
 # helpers
 
@@ -37,16 +50,55 @@ def decode_token(token):
 def decode_tokens(tokens):
     return "".join(list(map(decode_token, tokens)))
 
+# Gemini + ZeRO DDP
+def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"):
+    cai_version = colossalai.__version__
+    if version.parse(cai_version) > version.parse("0.1.10"):
+        from colossalai.nn.parallel import GeminiDDP
+        model = GeminiDDP(model,
+                          device=get_current_device(),
+                          placement_policy=placememt_policy,
+                          pin_memory=True,
+                          search_range_mb=32)
+    elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
+        from colossalai.gemini import ChunkManager, GeminiManager
+        chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
+        gemini_manager = GeminiManager(placememt_policy, chunk_manager)
+        chunk_manager = ChunkManager(chunk_size,
+                                     pg,
+                                     enable_distributed_storage=True,
+                                     init_device=GeminiManager.get_default_device(placememt_policy))
+        model = ZeroDDP(model, gemini_manager)
+    else:
+        raise NotImplemented(f"CAI version {cai_version} is not supported")
+    return model
+    
+# instantiate GPT-like decoder model
+
+parser = colossalai.get_default_parser()
+args = parser.parse_args()
+disable_existing_loggers()
+colossalai.launch_from_torch(config=args.config, seed=42)
+
 
 # instantiate GPT-like decoder model
 
-model = PaLM(num_tokens=256, dim=512, depth=8)
+default_pg = ProcessGroup(tp_degree=TPDEGREE)
+default_dist_spec = ShardSpec([-1], [TPDEGREE]) if USE_SHARD_INIT else None
+ctx = ColoInitContext(device='cpu', default_dist_spec=default_dist_spec, default_pg=default_pg)
+with ctx:
+        model = PaLM(num_tokens=256,dim=512,depth=8)
+        model = AutoregressiveWrapper(model, max_seq_len=SEQ_LEN)
+        model.cuda()
 
-model = AutoregressiveWrapper(model, max_seq_len=2048)
-model.cuda()
 
 # prepare enwik8 data
 
+# model = PaLM(num_tokens=256, dim=512, depth=8)
+
+# model = AutoregressiveWrapper(model, max_seq_len=SEQ_LEN)
+# model.cuda()
+
 with gzip.open("./data/enwik8.gz") as file:
     X = np.fromstring(file.read(int(95e6)), dtype=np.uint8)
     trX, vaX = np.split(X, [int(90e6)])
@@ -74,9 +126,20 @@ val_dataset = TextSamplerDataset(data_val, SEQ_LEN)
 train_loader = cycle(DataLoader(train_dataset, batch_size=BATCH_SIZE))
 val_loader = cycle(DataLoader(val_dataset, batch_size=BATCH_SIZE))
 
-# optimizer
+#tensor_parallelize(model, pg)
 
-optim = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
+pg = default_pg
+# model = GeminiDDP(model,
+#                             device=get_current_device(),
+#                           placement_policy="auto",
+#                           pin_memory=True,
+#                           search_range_mb=32)
+model = gemini_zero_dpp(model, pg, placement)
+
+#optimizer
+
+optimizer = GeminiAdamOptimizer(model, lr=1e-7, initial_scale=2**5)
+#optim = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
 
 # training
 
@@ -89,8 +152,10 @@ for i in tqdm.tqdm(range(NUM_BATCHES), mininterval=10.0, desc="training"):
 
     print(f"training loss: {loss.item()}")
     torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
-    optim.step()
-    optim.zero_grad()
+    # optim.step()
+    # optim.zero_grad()
+    optimizer.step()
+    optimizer.zero_grad()
 
     if i % VALIDATE_EVERY == 0:
         model.eval()