Merge pull request #71 from EleutherAI/uyhcire-normalize-loglikelihoods

Fix eval script to normalize loglikelihoods

batch_eval/main.py

@@ -4,6 +4,7 @@ import time
 
 import click
 import torch
+import torch.nn.functional as F
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
 
 
@@ -53,18 +54,18 @@ def evaluate_examples(model_runner, examples):
         for prompt, example in zip(prompts, examples)
     ]
 
-    average_token_logits_with_sentence_1 = (
-        model_runner.compute_average_token_logits_on_batch(inputs_for_sentence_1)
+    average_token_loglikelihoods_with_sentence_1 = (
+        model_runner.compute_average_token_loglikelihoods_on_batch(inputs_for_sentence_1)
     )
-    average_token_logits_with_sentence_2 = (
-        model_runner.compute_average_token_logits_on_batch(inputs_for_sentence_2)
+    average_token_loglikelihoods_with_sentence_2 = (
+        model_runner.compute_average_token_loglikelihoods_on_batch(inputs_for_sentence_2)
     )
 
     evaluation_results = []
     for i in range(len(examples)):
         if (
-            average_token_logits_with_sentence_1[i]
-            > average_token_logits_with_sentence_2[i]
+            average_token_loglikelihoods_with_sentence_1[i]
+            > average_token_loglikelihoods_with_sentence_2[i]
         ):
             model_answer = examples[i]["RandomFifthSentenceQuiz1"]
             model_answer_code = "1"
@@ -96,15 +97,15 @@ class ModelRunner:
 
         model_runner.model = AutoModelForCausalLM.from_pretrained(
             # 117M
-            pretrained_model_name_or_path="gpt2",
+            pretrained_model_name_or_path="gpt2-large",
             config=AutoConfig.from_pretrained(
-                "gpt2",
+                "gpt2-large",
                 # <|endoftext|>
                 pad_token_id=50256,
             ),
         ).to("cuda")
         model_runner.model = model_runner.model.eval()
-        model_runner.tokenizer = AutoTokenizer.from_pretrained("gpt2")
+        model_runner.tokenizer = AutoTokenizer.from_pretrained("gpt2-large")
         model_runner.tokenizer.pad_token = "<|endoftext|>"
 
         prompt = "The quick brown fox jumps over"
@@ -126,11 +127,11 @@ class ModelRunner:
 
         return model_runner
 
-    def compute_average_token_logits_on_batch(self, input_texts):
+    def compute_average_token_loglikelihoods_on_batch(self, input_texts):
         """
-        For each input text in the batch, compute the average logit (log-likelihood) over all tokens.
+        For each input text in the batch, compute the average log-likelihood over all tokens.
 
-        For example, if an input sequence is 3 tokens long, and the token logits are [-1, -2, -3], the "average token logit" is -2.
+        For example, if an input sequence is 3 tokens long, and the token loglikelihoods are [-1, -2, -3], the "average token loglikelihood" is -2.
         """
         # The ModelRunner can take a big batch on input_texts, and it can be as large as the caller wants.
         # But to prevent the GPU from running out of memory, we need to subdivide the overall batch
@@ -138,16 +139,16 @@ class ModelRunner:
         # For GPT-2-117M, a GPU can process a batch of roughly 10 or so inputs before the inference latency starts to increase.
         gpu_batch_size = 20
 
-        average_token_logits = []
+        average_token_loglikelihoods = []
         for i in range(0, len(input_texts), gpu_batch_size):
-            average_token_logits.extend(
-                self._average_token_logits_on_gpu_batch(
+            average_token_loglikelihoods.extend(
+                self._average_token_loglikelihoods_on_gpu_batch(
                     input_texts[i : i + gpu_batch_size]
                 )
             )
-        return average_token_logits
+        return average_token_loglikelihoods
 
-    def _average_token_logits_on_gpu_batch(self, input_texts):
+    def _average_token_loglikelihoods_on_gpu_batch(self, input_texts):
         tokenized_inputs = self.tokenizer(
             input_texts,
             add_special_tokens=False,
@@ -164,42 +165,49 @@ class ModelRunner:
         output_logits = self.model(tokenized_inputs).logits
         self.num_inferences += 1
 
-        # Align the output logits to the input tokens.
-        logits_for_input_positions = output_logits[
+        # Normalize probabilities - at each position, the token likelihoods should add up to 1
+        output_loglikelihoods = F.log_softmax(
+            output_logits,
+            # The embedding dimension
+            dim=-1,
+        )
+
+        # Align the output loglikelihoods to the input tokens.
+        loglikelihoods_for_input_positions = output_loglikelihoods[
             # The batch dimension
             :,
             # The position dimension
-            # The last logit needs to be dropped, because it's predicting the "next token", and it doesn't correspond to any input token
+            # The last loglikelihood needs to be dropped, because it's predicting the "next token", and it doesn't correspond to any input token
             :-1,
             # The embedding dimension
             :,
         ]
-        input_tokens_at_positions_with_logits = tokenized_inputs[
+        input_tokens_at_positions_with_loglikelihoods = tokenized_inputs[
             # The batch dimension
             :,
             # The position dimension
             # The model does not predict the first input token, so the first token needs to be dropped.
             1:,
         ]
-        # At each position, the model outputs ~50k logits, one for every possible token.
-        # To get the logits of the tokens that were actually provided, we need to select the right logit at each position.
-        logits_for_provided_tokens = torch.gather(
-            logits_for_input_positions,
+        # At each position, the model outputs ~50k loglikelihoods, one for every possible token.
+        # To get the loglikelihoods of the tokens that were actually provided, we need to select the right loglikelihood at each position.
+        loglikelihoods_for_provided_tokens = torch.gather(
+            loglikelihoods_for_input_positions,
             2,
-            input_tokens_at_positions_with_logits.unsqueeze(2),
+            input_tokens_at_positions_with_loglikelihoods.unsqueeze(2),
         ).squeeze(2)
 
-        mask_for_non_padded_positions = input_tokens_at_positions_with_logits != 50256
-        average_token_logits = (
-            logits_for_provided_tokens * mask_for_non_padded_positions
+        mask_for_non_padded_positions = input_tokens_at_positions_with_loglikelihoods != 50256
+        average_token_loglikelihoods = (
+            loglikelihoods_for_provided_tokens * mask_for_non_padded_positions
         ).sum(1) / mask_for_non_padded_positions.sum(1)
-        average_token_logits = average_token_logits.tolist()
+        average_token_loglikelihoods = average_token_loglikelihoods.tolist()
 
         end_time = time.time()
         print(
             f"Time to evaluate once (inference #{self.num_inferences}): {end_time - start_time}"
         )
-        return average_token_logits
+        return average_token_loglikelihoods
 
 
 if __name__ == "__main__":