added new logs

src/bert_optica_koef.py

@@ -379,6 +379,8 @@ model = BertClassifier(
 ).to(device)
 
 print(f'Parameters model - {sum(p.numel() for p in model.parameters())}, parameters encoder - {sum(p.numel() for p in encoder.parameters())}')
+model_description = str(model) + f'\nParameters count - {sum(p.numel() for p in model.parameters())}, parameters encoder - {sum(p.numel() for p in encoder.parameters())}'
+writer.add_text('model', model_description, 0)
 optimizer = schedulefree.AdamWScheduleFree(list(model.parameters()) + list(encoder.parameters()))
 
 if checkpoint_file is not None:

src/bert_optica_koef_newf.py

@@ -387,6 +387,8 @@ model = BertClassifier(
 ).to(device)
 
 print(f'Parameters model - {sum(p.numel() for p in model.parameters())}, parameters encoder - {sum(p.numel() for p in encoder.parameters())}')
+model_description = str(model) + f'\nParameters count - {sum(p.numel() for p in model.parameters())}, parameters encoder - {sum(p.numel() for p in encoder.parameters())}'
+writer.add_text('model', model_description, 0)
 optimizer = schedulefree.AdamWScheduleFree(list(model.parameters()) + list(encoder.parameters()))
 
 if checkpoint_file is not None:

src/bert_optica_nokoef.py

@@ -377,6 +377,8 @@ model = BertClassifier(
 ).to(device)
 
 print(f'Parameters model - {sum(p.numel() for p in model.parameters())}, parameters encoder - {sum(p.numel() for p in encoder.parameters())}')
+model_description = str(model) + f'\nParameters count - {sum(p.numel() for p in model.parameters())}, parameters encoder - {sum(p.numel() for p in encoder.parameters())}'
+writer.add_text('model', model_description, 0)
 optimizer = schedulefree.AdamWScheduleFree(list(model.parameters()) + list(encoder.parameters()))
 
 if checkpoint_file is not None:

src/bert_optica_nokoef_newf.py

@@ -387,6 +387,8 @@ model = BertClassifier(
 ).to(device)
 
 print(f'Parameters model - {sum(p.numel() for p in model.parameters())}, parameters encoder - {sum(p.numel() for p in encoder.parameters())}')
+model_description = str(model) + f'\nParameters count - {sum(p.numel() for p in model.parameters())}, parameters encoder - {sum(p.numel() for p in encoder.parameters())}'
+writer.add_text('model', model_description, 0)
 optimizer = schedulefree.AdamWScheduleFree(list(model.parameters()) + list(encoder.parameters()))
 
 if checkpoint_file is not None:

src/optical_matrix_multiplication/config.py

@@ -274,8 +274,7 @@ class Config(ConfigOpticBase, ConfigModelBase):
                  wavelength: float = 532e-9,
                  distance: float = 0.03,
                  lens_pixel_size: float = 1.8e-6,
-                 lens_size: int = 8192,
-                 trainable_cylind_lens = False):
+                 lens_size: int = 8192):
         """
         Конструктор класса.
  
@@ -295,7 +294,6 @@ class Config(ConfigOpticBase, ConfigModelBase):
             distance: дистанция в метрах распространения светового поля между плоскостями.
             lens_pixel_size: размер пикселя в метрах скрещенных линз в оптической системе (нужен исключительно для моделирования).
             lens_size: размер скрещенных линз в метрах в оптической системе (нужен исключительно для моделирования).
-            trainable_cylind_lens: обучаемые диагональные матрицы, линза перед фурье плоскостью
         """
         ConfigOpticBase.__init__(self, wavelength, distance)
 
@@ -322,7 +320,6 @@ class Config(ConfigOpticBase, ConfigModelBase):
         self._input_vector_split_x: int = left_matrix_split_x
         self._input_vector_split_y: int = left_matrix_split_y
         self._result_vector_split: int = result_matrix_split
-        self._trainable_cylind_lens = trainable_cylind_lens
 
     @property
     def matrix_split_x(self) -> int:

src/optical_matrix_multiplication/optical_mul.py

@@ -371,7 +371,7 @@ class TrainableLensOpticalMul(_nn.Module):
         phase_row = phase_normalized.unsqueeze(0).unsqueeze(0)
         writer.add_image(f"{tag}/phase_row", phase_row, global_step, dataformats='CHW')
 
-        fig, ax = plt.subplots(figsize=(6, 4))
+        fig, ax = plt.subplots(figsize=(12, 4))
         ax.plot(wrapped_phase.detach().cpu().numpy(), label=f'Step {global_step}')
         ax.set_title(f"Cylindrical Lens Phase Profile (x) {tag}")
         ax.set_xlabel("Pixel Index")

src/optical_matrix_multiplication/propagator.py

@@ -111,7 +111,6 @@ class Propagator(_ABC, _nn.Module):
             Распределение комплексной амплитуды светового поля,
             после распространения.
         """
-
         if (resul_shape is not None):
             field_shape = field.shape[-2:]
             operator_Y_shape = self.operator_Y.shape[-2:]

src/train_gpt2.py

@@ -218,8 +218,9 @@ m = MODEL_CLASS(
   layers_num=layers_num
 )
 m = m.to(device)
-writer.add_text('model', str(m), 0)
-
+model_description = str(m) + f'\nParameters count - {sum(p.numel() for p in m.parameters())}'
+writer.add_text('model', model_description, 0)
+# TODO for all experiments
 optimizer = torch.optim.AdamW(m.parameters(), lr=learning_rate, betas=(0.90, 0.95), weight_decay=0.01)
 
 #################################### Checkpoint Function #########################################

src/train_optics_trainable_focal_dist_lens_64.py

@@ -329,7 +329,8 @@ m = MODEL_CLASS(
   layers_num=layers_num
 )
 m = m.to(device)
-writer.add_text('model', str(m), 0)
+model_description = str(m) + f'\nParameters count - {sum(p.numel() for p in m.parameters())}'
+writer.add_text('model', model_description, 0)
 
 #################################### Train #########################################
 

src/train_optics_trainable_lens_128.py

@@ -338,6 +338,8 @@ m = MODEL_CLASS(
 )
 m = m.to(device)
 writer.add_text('model', str(m), 0)
+model_description = str(m) + f'\nParameters count - {sum(p.numel() for p in m.parameters())}'
+writer.add_text('model', model_description, 0)
 
 #################################### Train #########################################
 
@@ -450,6 +452,7 @@ task_results = "\n".join([complete(m, encode(task_prompt), 32) for task_prompt i
 print(task_results)
 writer.add_text('completions/task', task_results, i+1)
 
+m.log_trainable_optic_params(writer, max_iters)
 # Save final checkpoint
 save_checkpoint(
     model=m,

src/train_optics_trainable_lens_256.py

@@ -238,7 +238,7 @@ class OpticGPT2TrainableScalarAndLens(nn.Module):
 ###################################################################################################
 
 batch_size = 50
-gradient_accumulation_steps = 5 # check this impl for correctness https://unsloth.ai/blog/gradient
+gradient_accumulation_steps = 1 # check this impl for correctness https://unsloth.ai/blog/gradient
 max_iters = int(4e4) #40000
 eval_interval = 300
 learning_rate = 1e-3
@@ -338,6 +338,8 @@ m = MODEL_CLASS(
 )
 m = m.to(device)
 writer.add_text('model', str(m), 0)
+model_description = str(m) + f'\nParameters count - {sum(p.numel() for p in m.parameters())}'
+writer.add_text('model', model_description, 0)
 
 #################################### Train #########################################
 
@@ -450,6 +452,8 @@ task_results = "\n".join([complete(m, encode(task_prompt), 32) for task_prompt i
 print(task_results)
 writer.add_text('completions/task', task_results, i+1)
 
+m.log_trainable_optic_params(writer, max_iters)
+
 # Save final checkpoint
 save_checkpoint(
     model=m,

src/train_optics_trainable_lens_512.py

@@ -238,7 +238,7 @@ class OpticGPT2TrainableScalarAndLens(nn.Module):
 ###################################################################################################
 
 batch_size = 50
-gradient_accumulation_steps = 10 # check this impl for correctness https://unsloth.ai/blog/gradient
+gradient_accumulation_steps = 1 # check this impl for correctness https://unsloth.ai/blog/gradient
 max_iters = int(4e4) #40000
 eval_interval = 300
 learning_rate = 1e-3
@@ -338,6 +338,8 @@ m = MODEL_CLASS(
 )
 m = m.to(device)
 writer.add_text('model', str(m), 0)
+model_description = str(m) + f'\nParameters count - {sum(p.numel() for p in m.parameters())}'
+writer.add_text('model', model_description, 0)
 
 #################################### Train #########################################
 
@@ -450,6 +452,8 @@ task_results = "\n".join([complete(m, encode(task_prompt), 32) for task_prompt i
 print(task_results)
 writer.add_text('completions/task', task_results, i+1)
 
+m.log_trainable_optic_params(writer, max_iters)
+
 # Save final checkpoint
 save_checkpoint(
     model=m,

src/train_optics_trainable_lens_64.py

@@ -337,7 +337,8 @@ m = MODEL_CLASS(
   layers_num=layers_num
 )
 m = m.to(device)
-writer.add_text('model', str(m), 0)
+model_description = str(m) + f'\nParameters count - {sum(p.numel() for p in m.parameters())}'
+writer.add_text('model', model_description, 0)
 
 #################################### Train #########################################
 
@@ -450,6 +451,7 @@ task_results = "\n".join([complete(m, encode(task_prompt), 32) for task_prompt i
 print(task_results)
 writer.add_text('completions/task', task_results, i+1)
 
+m.log_trainable_optic_params(writer, max_iters)
 # Save final checkpoint
 save_checkpoint(
     model=m,