working full training

convertinglinear.py

@@ -2,11 +2,12 @@ import torch
 
 
 class ConvertingLinear(torch.nn.Linear):
-    def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
+    def __init__(self, in_features, out_features, bias=True, device=None, dtype=None, output_dtype=None):
         super().__init__(in_features, out_features, bias, device, dtype)
+        self.output_dtype = output_dtype
 
     def forward(self, input: torch.Tensor):
-        output_dtype = input.dtype
+        output_dtype = input.dtype if self.output_dtype is None else self.output_dtype
         output_device = input.device
         if input.device != self.weight.device:
             input = input.to(self.weight.device)

train_dynamic.py

@@ -7,9 +7,11 @@ from torch.utils import tensorboard
 import os
 import shutil
 import math
+import collections
 from tqdm.auto import tqdm
 from random import randint
-from typing import Tuple
+import collections
+
 
 from arguments import DataArguments, ModelArguments, TrainingArguments
 from datamodules import create_data_module_s2s, create_data_module
@@ -49,9 +51,12 @@ def get_model(model_args: ModelArguments, cache_dir, gradient_checkpointing):
         attn_implementation="flash_attention_2"
     )
 
-    # for name, module in model.named_modules():
+    modules = dict(model.named_modules())
-    #     if 'norm' in name:
+    keys = find_all_linear_module_names(model)
-    #         module = module.to(torch.float32)
+    for key in keys:
+        modules[key].weight = modules[key].weight.to(dtype)
+        if modules[key].bias is not None:
+            modules[key].bias = modules[key].bias.to(dtype)
 
     return model
 
@@ -170,12 +175,23 @@ def save_model(model, global_step: int, output_dir: str, max_checkpoints: int =
 def get_optimizer(model, dynamic_module_names: list, static_module_names: list, lr: float, static_lr: float,
                   weight_decay: float, eps: float, adam8bit: bool):
 
+    all_keys = dynamic_module_names + static_module_names
+    duplicated = [k for k, v in collections.Counter(all_keys).items() if v > 1]
+    if len(duplicated) > 0:
+        print("duplicated items:")
+        for item in duplicated:
+            print(item)
+        raise ValueError("dynamic_module_names and or static_module_names contain duplicated paramters")
+
     parameters = list()
     modules = dict(model.named_modules())
     for key in dynamic_module_names:
         parameters.extend({'params': p} for p in modules[key].parameters() if p.requires_grad)
+    param_ids = set([id(p['params']) for p in parameters])
     for key in static_module_names:
-        parameters.extend({'params': p, 'lr': static_lr} for p in modules[key].parameters() if p.requires_grad)
+        parameters.extend({'params': p, 'lr': static_lr} for p in modules[key].parameters() if p.requires_grad and id(p) not in param_ids)
+        for p in modules[key].parameters():
+            param_ids.add(id(p))
 
     if not adam8bit:
         optimizer = torch.optim.AdamW(parameters, weight_decay=weight_decay, lr=lr, eps=training_args.adam_epsilon)
@@ -200,6 +216,7 @@ def compute_dynamic_parameter_ratio(model):
 
 def prepare(model_args: ModelArguments, data_args: DataArguments, training_args: TrainingArguments, primary_device: torch.device, secondary_device: torch.device) -> tuple:
     model = get_model(model_args, training_args.cache_dir, training_args.gradient_checkpointing).to(primary_device)
+
     tokenizer = get_tokenizer(model, training_args.cache_dir, model_args)
 
     if data_args.dataset.endswith("json"):