fix vt

lm_eval/tasks/ruler/vt_utils.py

@@ -24,17 +24,17 @@ from tqdm import tqdm
 
 from lm_eval.tasks.ruler.common_utils import DEFAULT_SEQ_LENGTHS, get_tokenizer
 
+
 TASKS = {
     "variable_tracking": {
         "tokens_to_generate": 30,
         "template": """Memorize and track the chain(s) of variable assignment hidden in the following text.\n\n{context}\nQuestion: Find all variables that are assigned the value {query} in the text above.""",
-        "answer_prefix": """Answer: According to the chain(s) of variable assignment in the text above, {num_v} variables are assigned the value {query}, they are:""",
+        "answer_prefix": """ Answer: According to the chain(s) of variable assignment in the text above, {num_v} variables are assgined the value {query}, they are: """,
     },
 }
 
 TEMPLATE = (
-    """Memorize and track the chain(s) of variable assignment hidden in the following text.\n\n{context}\nQuestion: Find all variables that are assigned the value {query} in the text above."""
-    + TASKS["variable_tracking"]["answer_prefix"]
+    TASKS["variable_tracking"]["template"] + TASKS["variable_tracking"]["answer_prefix"]
 )
 
 
@@ -63,9 +63,7 @@ def generate_chains(
     return vars_ret, chains_ret
 
 
-def generate_input_output(
-    num_noises: int, num_chains: int, num_hops: int, is_icl: bool = False
-) -> tuple[str, list[str]]:
+def generate_input_output(num_noises, num_chains, num_hops, is_icl=False):
     vars, chains = generate_chains(num_chains, num_hops, is_icl=is_icl)
 
     noise = "The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\n"
@@ -81,8 +79,7 @@ def generate_input_output(
             assert len(sentences) > len(chains[0]), (
                 "Noises too short, unable to generate data"
             )
-        # ruff: noqa
-        except:
+        except:  # noqa: E722
             print("reduces chain length for not enough noises")
             chains = [chain[: len(sentences) - 1] for chain in chains]
     # sample random positions to insert variable assignment
@@ -136,41 +133,37 @@ def sys_vartrack_w_noise_random(
     tokens_to_generate=30,
     icl_example: dict = None,
     remove_newline_tab=False,
-) -> list[dict]:
+):
     write_jsons = []
+    tokens_to_generate = tokens_to_generate
+
     # Find the perfect num_noises
     num_noises = incremental
-    TOKENIZER = tokenizer
 
     total_tokens = 0  # Track the total tokens generated for this example
     example_tokens = 0
     if add_fewshot and (icl_example is not None):
         icl_example_out = " ".join(icl_example["outputs"])
-        prefix = icl_example["gen_prefix"]
-        icl_example = (
-            icl_example["input"] + " " + prefix + " " + icl_example_out + "\n\n"
-        )
-        example_tokens = len(TOKENIZER(icl_example).input_ids)
+        icl_example = icl_example["input"] + " " + icl_example_out + "\n\n"
+        example_tokens = len(tokenizer(icl_example).input_ids)
 
     while total_tokens + tokens_to_generate + example_tokens < max_seq_length:
         input_text, answer = generate_input_output(
             num_noises, num_chains, num_hops, is_icl=add_fewshot & (icl_example is None)
         )
         # Calculate the number of tokens in the example
-        total_tokens = len(TOKENIZER(input_text + f" {answer}").input_ids)
-        # print(
-        #     f"Max length {max_seq_length} | Current length {total_tokens + tokens_to_generate + example_tokens} | Noises: {num_noises}"
-        # )
+        total_tokens = len(tokenizer(input_text + f" {answer}").input_ids)
+        print(
+            f"Max length {max_seq_length} | Current length {total_tokens + tokens_to_generate + example_tokens} | Noises: {num_noises}"
+        )
         if total_tokens + tokens_to_generate + example_tokens > max_seq_length:
             num_noises -= incremental
             break
         num_noises += incremental
-    # print("Num noises:", num_noises)
+    print("Num noises:", num_noises)
 
     # Generate samples
-    for index in tqdm(
-        range(num_samples), desc=f"Generating VT Samples| {max_seq_length}"
-    ):
+    for index in tqdm(range(num_samples)):
         used_noises = num_noises
         while True:
             try:
@@ -181,14 +174,13 @@ def sys_vartrack_w_noise_random(
                     is_icl=add_fewshot & (icl_example is None),
                 )
                 length = (
-                    len(TOKENIZER(input_text).input_ids)
+                    len(tokenizer(input_text).input_ids)
                     + tokens_to_generate
                     + example_tokens
                 )
                 assert length <= max_seq_length, f"{length} exceeds max_seq_length."
                 break
-            # ruff: noqa
-            except:
+            except:  # noqa: E722
                 if used_noises > incremental:
                     used_noises -= incremental
 
@@ -206,8 +198,12 @@ def sys_vartrack_w_noise_random(
                 input_text.replace("\n", " ").replace("\t", " ").strip().split()
             )
 
-        gen_prefix_index = input_text.rfind(" Answer")
+        gen_prefix_index = input_text.rfind(
+            " Answer: According to the chain(s) of variable assignment"
+        )
         gen_prefix = input_text[gen_prefix_index:].strip()
+        # This condition is to check if we are generating the few-shot.
+        if icl_example is not None:
             input_text = input_text[:gen_prefix_index]
         formatted_output = {
             "index": index,
@@ -225,7 +221,10 @@ def sys_vartrack_w_noise_random(
 def get_dataset(pretrained, seq=None, **kwargs) -> list[dict]:
     tokenizer = get_tokenizer(pretrained)
     icl_example = sys_vartrack_w_noise_random(
-        tokenizer=tokenizer, num_samples=1, max_seq_length=500, incremental=5
+        tokenizer=tokenizer,
+        num_samples=1,
+        max_seq_length=500,
+        incremental=5,
     )[0]
     write_jsons = sys_vartrack_w_noise_random(
         tokenizer=tokenizer,