keep params in expendable mode

README.md

@@ -5,7 +5,7 @@
 
 This is a minimal implementation of Boris Dayma's [DALL·E Mini](https://github.com/borisdayma/dalle-mini).  It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch.  To run the torch model, the only third party dependencies are numpy and torch.  Flax is used to convert the weights (which can be saved with `torch.save` once the model is loaded), and wandb is only used to download the models.
 
-It currently takes **7.3 seconds** to generate an avocado armchair with DALL·E Mega in PyTorch on Colab
+It currently takes **7.3 seconds** to generate an avocado armchair with DALL·E Mega in PyTorch on Colab (with nonexpendable model in high RAM runtime)
 
 ### Setup
 

min_dalle/min_dalle_flax.py

@@ -28,7 +28,7 @@ class MinDalleFlax(MinDalleBase):
             text_token_count = self.config['max_text_length'],
             text_vocab_count = self.config['encoder_vocab_size'],
             layer_count = self.config['encoder_layers']
-        ).bind({'params': self.model_params.pop('encoder')})
+        ).bind({'params': self.model_params['encoder']})
 
 
     def init_decoder(self):
@@ -53,17 +53,13 @@ class MinDalleFlax(MinDalleBase):
         encoder_state = self.encoder(text_tokens)
         if self.is_expendable: del self.encoder
 
-        if self.is_expendable:
-            self.init_decoder()
-            params = self.model_params.pop('decoder')
-        else:
-            params = self.model_params['decoder']
+        if self.is_expendable: self.init_decoder()
         print("sampling image tokens")
         image_tokens = self.decoder.sample_image_tokens(
             text_tokens,
             encoder_state,
             jax.random.PRNGKey(seed),
-            params
+            self.model_params['decoder']
         )
         if self.is_expendable: del self.decoder
 

min_dalle/min_dalle_torch.py

@@ -40,13 +40,12 @@ class MinDalleTorch(MinDalleBase):
             glu_embed_count = self.config['encoder_ffn_dim']
         )
         params = convert_dalle_bart_torch_from_flax_params(
-            self.model_params.pop('encoder'), 
+            self.model_params['encoder'], 
             layer_count=self.config['encoder_layers'], 
             is_encoder=True
         )
         self.encoder.load_state_dict(params, strict=False)
         if torch.cuda.is_available(): self.encoder = self.encoder.cuda()
-        del params
 
 
     def init_decoder(self):
@@ -64,13 +63,12 @@ class MinDalleTorch(MinDalleBase):
             is_verbose = True
         )
         params = convert_dalle_bart_torch_from_flax_params(
-            self.model_params.pop('decoder'), 
+            self.model_params['decoder'], 
             layer_count=self.config['decoder_layers'],
             is_encoder=False
         )
         self.decoder.load_state_dict(params, strict=False)
         if torch.cuda.is_available(): self.decoder = self.decoder.cuda()
-        del params
 
     
     def init_detokenizer(self):