Added possibility to generate base text on GPU for text evaluation. (#1945)

Convert input device type according to device type of model for text
evaluator.

Author: andreyanufr

tools/who_what_benchmark/whowhatbench/text_evaluator.py

@@ -189,29 +189,24 @@ def worst_examples(self, top_k: int = 5, metric="similarity"):
     def _generate_data(self, model, gen_answer_fn=None, generation_config=None):
         def default_gen_answer(model, tokenizer, prompt, max_new_tokens, crop_question, use_chat_template=False):
             is_awq = getattr(model, "is_awq", None) is not None
+            device = "cpu"
+            if hasattr(model, "device"):
+                device = model.device
 
             if use_chat_template:
                 message = [{"role": "user", "content": prompt}]
-                inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors="pt")
-                if is_awq:
-                    with patch_awq_for_inference(is_awq):
-                        tokens = model.generate(inputs, do_sample=False, max_new_tokens=max_new_tokens)
-                else:
-                    tokens = model.generate(inputs, do_sample=False, max_new_tokens=max_new_tokens)
-                if crop_question:
-                    tokens = tokens[:, inputs.shape[-1]:]
-                res = self.tokenizer.decode(tokens[0], skip_special_tokens=True)
-                return res
+                inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(device)
             else:
-                inputs = self.tokenizer(prompt, return_tensors="pt")
-                if is_awq:
-                    with patch_awq_for_inference(is_awq):
-                        tokens = model.generate(**inputs, do_sample=False, max_new_tokens=max_new_tokens)
-                else:
+                inputs = self.tokenizer(prompt, return_tensors="pt").to(device)
+
+            if is_awq:
+                with patch_awq_for_inference(is_awq):
                     tokens = model.generate(**inputs, do_sample=False, max_new_tokens=max_new_tokens)
-                if crop_question:
-                    tokens = tokens[:, inputs["input_ids"].shape[-1] :]
-                return self.tokenizer.batch_decode(tokens, skip_special_tokens=True)[0]
+            else:
+                tokens = model.generate(**inputs, do_sample=False, max_new_tokens=max_new_tokens)
+            if crop_question:
+                tokens = tokens[:, inputs["input_ids"].shape[-1] :]
+            return self.tokenizer.batch_decode(tokens, skip_special_tokens=True)[0]
 
         gen_answer_fn = gen_answer_fn or default_gen_answer