Update modeling_text_encoder.py

modeling_text_encoder.py

@@ -1,15 +1,13 @@
-now cam you optimise this file
-
 import torch
 import torch.nn as nn
 import os
 
 
 from transformers import (
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    T5EncoderModel,
-    T5TokenizerFast,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5TokenizerFast,
 )
 
 
@@ -18,155 +16,155 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 
 class SD3TextEncoderWithMask(nn.Module):
-    def __init__(self, model_path, torch_dtype):
-        super().__init__()
+    def __init__(self, model_path, torch_dtype):
+        super().__init__()
 
         self.device_0 = torch.device('cuda:0')  # GPU 0 for text encoder
         self.device_1 = torch.device('cuda:1')  # GPU 1 for other tasks
 
-        # CLIP-L
-        self.tokenizer = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer'))
-        self.tokenizer_max_length = self.tokenizer.model_max_length
-        self.text_encoder = CLIPTextModelWithProjection.from_pretrained(os.path.join(model_path, 'text_encoder'), torch_dtype=torch_dtype)
+        # CLIP-L
+        self.tokenizer = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer'))
+        self.tokenizer_max_length = self.tokenizer.model_max_length
+        self.text_encoder = CLIPTextModelWithProjection.from_pretrained(os.path.join(model_path, 'text_encoder'), torch_dtype=torch_dtype)
 
 
-        # CLIP-G
-        self.tokenizer_2 = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer_2'))
-        self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(os.path.join(model_path, 'text_encoder_2'), torch_dtype=torch_dtype)
+        # CLIP-G
+        self.tokenizer_2 = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer_2'))
+        self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(os.path.join(model_path, 'text_encoder_2'), torch_dtype=torch_dtype)
 
 
-        # T5
-        self.tokenizer_3 = T5TokenizerFast.from_pretrained(os.path.join(model_path, 'tokenizer_3'))
-        self.text_encoder_3 = T5EncoderModel.from_pretrained(os.path.join(model_path, 'text_encoder_3'), torch_dtype=torch_dtype).to(self.device_1)
-    
-        self._freeze()
+        # T5
+        self.tokenizer_3 = T5TokenizerFast.from_pretrained(os.path.join(model_path, 'tokenizer_3'))
+        self.text_encoder_3 = T5EncoderModel.from_pretrained(os.path.join(model_path, 'text_encoder_3'), torch_dtype=torch_dtype).to(self.device_1)
+    
+        self._freeze()
 
 
-    def _freeze(self):
-        for param in self.parameters():
-            param.requires_grad = False
+    def _freeze(self):
+        for param in self.parameters():
+            param.requires_grad = False
 
 
-    def _get_t5_prompt_embeds(
-        self,
-        prompt: Union[str, List[str]] = None,
-        num_images_per_prompt: int = 1,
-        device: Optional[torch.device] = None,
-        max_sequence_length: int = 128,
-    ):
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        batch_size = len(prompt)
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 128,
+    ):
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
 
 
-        text_inputs = self.tokenizer_3(
-            prompt,
-            padding="max_length",
-            max_length=max_sequence_length,
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        prompt_attention_mask = text_inputs.attention_mask
-        prompt_attention_mask = prompt_attention_mask.to(device)
-        prompt_embeds = self.text_encoder_3(text_input_ids.to(device), attention_mask=prompt_attention_mask)[0]
-        dtype = self.text_encoder_3.dtype
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        text_inputs = self.tokenizer_3(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        prompt_attention_mask = text_inputs.attention_mask
+        prompt_attention_mask = prompt_attention_mask.to(device)
+        prompt_embeds = self.text_encoder_3(text_input_ids.to(device), attention_mask=prompt_attention_mask)[0]
+        dtype = self.text_encoder_3.dtype
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
 
 
-        _, seq_len, _ = prompt_embeds.shape
+        _, seq_len, _ = prompt_embeds.shape
 
 
-        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-        prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
-        prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
+        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+        prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
+        prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
 
 
-        return prompt_embeds, prompt_attention_mask
+        return prompt_embeds, prompt_attention_mask
 
 
-    def _get_clip_prompt_embeds(
-        self,
-        prompt: Union[str, List[str]],
-        num_images_per_prompt: int = 1,
-        device: Optional[torch.device] = None,
-        clip_skip: Optional[int] = None,
-        clip_model_index: int = 0,
-    ):
+    def _get_clip_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]],
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        clip_skip: Optional[int] = None,
+        clip_model_index: int = 0,
+    ):
 
 
-        clip_tokenizers = [self.tokenizer, self.tokenizer_2]
-        clip_text_encoders = [self.text_encoder, self.text_encoder_2]
+        clip_tokenizers = [self.tokenizer, self.tokenizer_2]
+        clip_text_encoders = [self.text_encoder, self.text_encoder_2]
 
 
-        tokenizer = clip_tokenizers[clip_model_index]
-        text_encoder = clip_text_encoders[clip_model_index]
+        tokenizer = clip_tokenizers[clip_model_index]
+        text_encoder = clip_text_encoders[clip_model_index]
 
 
-        batch_size = len(prompt)
+        batch_size = len(prompt)
 
 
-        text_inputs = tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
 
 
-        text_input_ids = text_inputs.input_ids
-        prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
-        pooled_prompt_embeds = prompt_embeds[0]
-        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
+        text_input_ids = text_inputs.input_ids
+        prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+        pooled_prompt_embeds = prompt_embeds[0]
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
 
 
-        return pooled_prompt_embeds
+        return pooled_prompt_embeds
 
 
-    def encode_prompt(self, 
-        prompt, 
-        num_images_per_prompt=1, 
-        clip_skip: Optional[int] = None,
-        device=None,
-    ):
-        prompt = [prompt] if isinstance(prompt, str) else prompt
+    def encode_prompt(self, 
+        prompt, 
+        num_images_per_prompt=1, 
+        clip_skip: Optional[int] = None,
+        device=None,
+    ):
+        prompt = [prompt] if isinstance(prompt, str) else prompt
 
 
-        pooled_prompt_embed = self._get_clip_prompt_embeds(
-            prompt=prompt,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            clip_skip=clip_skip,
-            clip_model_index=0,
-        )
-        pooled_prompt_2_embed = self._get_clip_prompt_embeds(
-            prompt=prompt,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            clip_skip=clip_skip,
-            clip_model_index=1,
-        )
-        pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)
+        pooled_prompt_embed = self._get_clip_prompt_embeds(
+            prompt=prompt,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            clip_skip=clip_skip,
+            clip_model_index=0,
+        )
+        pooled_prompt_2_embed = self._get_clip_prompt_embeds(
+            prompt=prompt,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            clip_skip=clip_skip,
+            clip_model_index=1,
+        )
+        pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)
 
 
-        prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(
-            prompt=prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            device=device,
-        )
-        return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds
+        prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(
+            prompt=prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+        )
+        return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds
 
 
-    def forward(self, input_prompts, device):
-        with torch.no_grad():
-            prompt_embeds, prompt_attention_mask, pooled_prompt_embeds = self.encode_prompt(input_prompts, 1, clip_skip=None, device=device)
+    def forward(self, input_prompts, device):
+        with torch.no_grad():
+            prompt_embeds, prompt_attention_mask, pooled_prompt_embeds = self.encode_prompt(input_prompts, 1, clip_skip=None, device=device)
 
 
-        return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds
+        return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds
 
 
 implement cpu offload