Merge 4883a1bc4c into 94e3dbebea

library/lumina_train_util.py

@@ -479,7 +479,6 @@ def time_shift(mu: float, sigma: float, t: torch.Tensor):
     # Since we adopt the reverse, the 1-t operations are needed
     t = 1 - t
     t = math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
-    t = 1 - t
     return t
 
 
@@ -1060,6 +1059,7 @@ def add_lumina_train_arguments(parser: argparse.ArgumentParser):
         default=1.0,
         help='Scale factor for sigmoid timestep sampling (only used when timestep-sampling is "sigmoid"). / sigmoidタイムステップサンプリングの倍率（timestep-samplingが"sigmoid"の場合のみ有効）。',
     )
+
     parser.add_argument(
         "--model_prediction_type",
         choices=["raw", "additive", "sigma_scaled"],

library/train_util.py

@@ -5525,6 +5525,9 @@ def prepare_accelerator(args: argparse.Namespace):
             if args.ddp_gradient_as_bucket_view or args.ddp_static_graph
             else None
         ),
+        (
+            DistributedDataParallelKwargs(find_unused_parameters=True)
+        ),
     ]
     kwargs_handlers = [i for i in kwargs_handlers if i is not None]
     deepspeed_plugin = deepspeed_utils.prepare_deepspeed_plugin(args)

lumina_train.py

@@ -361,70 +361,78 @@ def train(args):
     # 学習に必要なクラスを準備する
     accelerator.print("prepare optimizer, data loader etc.")
 
-    if args.blockwise_fused_optimizers:
-        # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html
-        # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each block of parameters.
-        # This balances memory usage and management complexity.
+    # if args.blockwise_fused_optimizers:
+    #     # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html
+    #     # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each block of parameters.
+    #     # This balances memory usage and management complexity.
 
-        # split params into groups. currently different learning rates are not supported
-        grouped_params = []
-        param_group = {}
-        for group in params_to_optimize:
-            named_parameters = list(nextdit.named_parameters())
-            assert len(named_parameters) == len(
-                group["params"]
-            ), "number of parameters does not match"
-            for p, np in zip(group["params"], named_parameters):
-                # determine target layer and block index for each parameter
-                block_type = "other"  # double, single or other
-                if np[0].startswith("double_blocks"):
-                    block_index = int(np[0].split(".")[1])
-                    block_type = "double"
-                elif np[0].startswith("single_blocks"):
-                    block_index = int(np[0].split(".")[1])
-                    block_type = "single"
-                else:
-                    block_index = -1
+    #     # split params into groups. currently different learning rates are not supported
+    #     grouped_params = []
+    #     param_group = {}
+    #     for group in params_to_optimize:
+    #         named_parameters = list(nextdit.named_parameters())
+    #         assert len(named_parameters) == len(
+    #             group["params"]
+    #         ), "number of parameters does not match"
+    #         for p, np in zip(group["params"], named_parameters):
+    #             # determine target layer and block index for each parameter
+    #             block_type = "other"  # double, single or other
+    #             if np[0].startswith("double_blocks"):
+    #                 block_index = int(np[0].split(".")[1])
+    #                 block_type = "double"
+    #             elif np[0].startswith("single_blocks"):
+    #                 block_index = int(np[0].split(".")[1])
+    #                 block_type = "single"
+    #             else:
+    #                 block_index = -1
 
-                param_group_key = (block_type, block_index)
-                if param_group_key not in param_group:
-                    param_group[param_group_key] = []
-                param_group[param_group_key].append(p)
+    #             param_group_key = (block_type, block_index)
+    #             if param_group_key not in param_group:
+    #                 param_group[param_group_key] = []
+    #             param_group[param_group_key].append(p)
 
-        block_types_and_indices = []
-        for param_group_key, param_group in param_group.items():
-            block_types_and_indices.append(param_group_key)
-            grouped_params.append({"params": param_group, "lr": args.learning_rate})
+    #     block_types_and_indices = []
+    #     for param_group_key, param_group in param_group.items():
+    #         block_types_and_indices.append(param_group_key)
+    #         grouped_params.append({"params": param_group, "lr": args.learning_rate})
 
-            num_params = 0
-            for p in param_group:
-                num_params += p.numel()
-            accelerator.print(f"block {param_group_key}: {num_params} parameters")
+    #         num_params = 0
+    #         for p in param_group:
+    #             num_params += p.numel()
+    #         accelerator.print(f"block {param_group_key}: {num_params} parameters")
 
-        # prepare optimizers for each group
-        optimizers = []
-        for group in grouped_params:
-            _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group])
-            optimizers.append(optimizer)
-        optimizer = optimizers[0]  # avoid error in the following code
+    #     # prepare optimizers for each group
+    #     optimizers = []
+    #     for group in grouped_params:
+    #         _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group])
+    #         optimizers.append(optimizer)
+    #     optimizer = optimizers[0]  # avoid error in the following code
 
-        logger.info(
-            f"using {len(optimizers)} optimizers for blockwise fused optimizers"
-        )
+    #     logger.info(
+    #         f"using {len(optimizers)} optimizers for blockwise fused optimizers"
+    #     )
 
-        if train_util.is_schedulefree_optimizer(optimizers[0], args):
-            raise ValueError(
-                "Schedule-free optimizer is not supported with blockwise fused optimizers"
-            )
-        optimizer_train_fn = lambda: None  # dummy function
-        optimizer_eval_fn = lambda: None  # dummy function
-    else:
-        _, _, optimizer = train_util.get_optimizer(
+    #     if train_util.is_schedulefree_optimizer(optimizers[0], args):
+    #         raise ValueError(
+    #             "Schedule-free optimizer is not supported with blockwise fused optimizers"
+    #         )
+    #     optimizer_train_fn = lambda: None  # dummy function
+    #     optimizer_eval_fn = lambda: None  # dummy function
+    # else:
+    #     _, _, optimizer = train_util.get_optimizer(
+    #         args, trainable_params=params_to_optimize
+    #     )
+    #     optimizer_train_fn, optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(
+    #         optimizer, args
+    #     )
+
+    #Currently when using blockwise_fused_optimizers the weight of model is not updated.
+    _, _, optimizer = train_util.get_optimizer(
             args, trainable_params=params_to_optimize
         )
-        optimizer_train_fn, optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(
-            optimizer, args
-        )
+    optimizer_train_fn, optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(
+        optimizer, args
+    )
 
     # prepare dataloader
     # strategies are set here because they cannot be referenced in another process. Copy them with the dataset
@@ -743,7 +751,7 @@ def train(args):
                     # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
                     model_pred = nextdit(
                         x=noisy_model_input,  # image latents (B, C, H, W)
-                        t=timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
+                        t= 1 - timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
                         cap_feats=gemma2_hidden_states,  # Gemma2的hidden states作为caption features
                         cap_mask=gemma2_attn_mask.to(
                             dtype=torch.int32

lumina_train_network.py

@@ -268,7 +268,7 @@ class LuminaNetworkTrainer(train_network.NetworkTrainer):
                 # NextDiT forward expects (x, t, cap_feats, cap_mask)
                 model_pred = dit(
                     x=img,  # image latents (B, C, H, W)
-                    t=timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
+                    t= 1 - timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
                     cap_feats=gemma2_hidden_states,  # Gemma2的hidden states作为caption features
                     cap_mask=gemma2_attn_mask.to(dtype=torch.int32),  # Gemma2的attention mask
                 )