enable gpu usage

Author: christinaexyou

src/trustyai/language/detoxify/tmarco.py

@@ -51,6 +51,7 @@ def __init__(
             tokenizer=None,
             max_length=150,
             model_type: str = "causal_lm",
+            device=None,
         ):
             if expert_weights is None:
                 expert_weights = [-0.5, 0.5]
@@ -94,6 +95,13 @@ def __init__(
                 )
             self.content_feature = "comment_text"
 
+            if isinstance(device, str):
+                self.device = torch.device(device)
+            else:
+                self.device = torch.device(
+                    "cuda" if torch.cuda.is_available() else "cpu"
+                )
+
         def load_models(self, experts: list, expert_weights: list = None):
             """Load expert models."""
             if expert_weights is not None:
@@ -102,7 +110,9 @@ def load_models(self, experts: list, expert_weights: list = None):
             for expert in experts:
                 if isinstance(expert, str):
                     expert = BartForConditionalGeneration.from_pretrained(
-                        expert, forced_bos_token_id=self.tokenizer.bos_token_id
+                        expert,
+                        forced_bos_token_id=self.tokenizer.bos_token_id,
+                        device_map="auto",
                     )
                 expert_models.append(expert)
             self.experts = expert_models
@@ -200,15 +210,21 @@ def train_models(
 
             if model_type is None:
                 gminus = BartForConditionalGeneration.from_pretrained(
-                    base_model, forced_bos_token_id=self.tokenizer.bos_token_id
+                    base_model,
+                    forced_bos_token_id=self.tokenizer.bos_token_id,
+                    device_map="auto",
                 )
             elif model_type == "causal_lm":
                 gminus = AutoModelForCausalLM.from_pretrained(
-                    base_model, forced_bos_token_id=self.tokenizer.bos_token_id
+                    base_model,
+                    forced_bos_token_id=self.tokenizer.bos_token_id,
+                    device_map="auto",
                 )
             elif model_type == "seq2seq_lm":
                 gminus = AutoModelForSeq2SeqLM.from_pretrained(
-                    base_model, forced_bos_token_id=self.tokenizer.bos_token_id
+                    base_model,
+                    forced_bos_token_id=self.tokenizer.bos_token_id,
+                    device_map="auto",
                 )
             else:
                 raise Exception(f"unsupported model type {model_type}")
@@ -254,15 +270,21 @@ def train_models(
 
             if model_type is None:
                 gplus = BartForConditionalGeneration.from_pretrained(
-                    base_model, forced_bos_token_id=self.tokenizer.bos_token_id
+                    base_model,
+                    forced_bos_token_id=self.tokenizer.bos_token_id,
+                    device_map="auto",
                 )
             elif model_type == "causal_lm":
                 gplus = AutoModelForCausalLM.from_pretrained(
-                    base_model, forced_bos_token_id=self.tokenizer.bos_token_id
+                    base_model,
+                    forced_bos_token_id=self.tokenizer.bos_token_id,
+                    device_map="auto",
                 )
             elif model_type == "seq2seq_lm":
                 gplus = AutoModelForSeq2SeqLM.from_pretrained(
-                    base_model, forced_bos_token_id=self.tokenizer.bos_token_id
+                    base_model,
+                    forced_bos_token_id=self.tokenizer.bos_token_id,
+                    device_map="auto",
                 )
             else:
                 raise Exception(f"unsupported model type {model_type}")
@@ -380,6 +402,7 @@ def rephrase(
                                     model=expert,
                                     tokenizer=self.tokenizer,
                                     top_k=self.tokenizer.vocab_size,
+                                    device=self.device,
                                 )
                             )
                     for idx in range(len(masked_sentence_tokens)):
@@ -477,9 +500,10 @@ def compute_mask_logits(
             self, model, sequence, verbose: bool = False, mask: bool = True
         ):
             """Compute mask logits."""
+            model.to(self.device)
             if verbose:
                 print(f"input sequence: {sequence}")
-            subseq_ids = self.tokenizer(sequence, return_tensors="pt")
+            subseq_ids = self.tokenizer(sequence, return_tensors="pt").to(self.device)
             if verbose:
                 raw_outputs = model.generate(**subseq_ids)
                 print(sequence)
@@ -502,9 +526,12 @@ def compute_mask_logits_multiple(
             self, model, sequences, verbose: bool = False, mask: bool = True
         ):
             """Compute mask logits multiple."""
+            model.to(self.device)
             if verbose:
                 print(f"input sequences: {sequences}")
-            subseq_ids = self.tokenizer(sequences, return_tensors="pt", padding=True)
+            subseq_ids = self.tokenizer(
+                sequences, return_tensors="pt", padding=True
+            ).to(self.device)
             if verbose:
                 raw_outputs = model.generate(**subseq_ids)
                 print(sequences)
@@ -554,6 +581,7 @@ def score(
                                 model=model,
                                 tokenizer=self.tokenizer,
                                 top_k=10,
+                                device=self.device,
                             )
                             for masked_sentence in masked_sentences:
                                 # approximated probabilities for top_k tokens
@@ -567,7 +595,9 @@ def score(
                     js_distances = []
                     for distr_pair in distr_pairs:
                         js_distance = jensenshannon(
-                            distr_pair[0], distr_pair[1], axis=1
+                            distr_pair[0].cpu().clone().numpy(),
+                            distr_pair[1].cpu().clone().numpy(),
+                            axis=1,
                         )
                         if normalize:
                             js_distance = js_distance / np.average(js_distance)
@@ -653,7 +683,10 @@ def reflect(
                 chat_tokenizer.chat_template = chat_template
 
             converse_pipeline = pipeline(
-                "conversational", model=chat_model, tokenizer=chat_tokenizer
+                "conversational",
+                model=chat_model,
+                tokenizer=chat_tokenizer,
+                device=self.device,
             )
 
             for text_id in range(len(texts)):
@@ -729,6 +762,7 @@ def reflect(
                         conversation_output = converse_pipeline(
                             formatted_messages,
                             pad_token_id=converse_pipeline.tokenizer.eos_token_id,
+                            device=self.device,
                         )
                         if verbose:
                             print(f"chat conversation:\n{conversation_output}")