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Fix: Total number of tokens in each stop word should be 1 #1892

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merged 2 commits into from
Oct 16, 2023
Merged

Fix: Total number of tokens in each stop word should be 1 #1892

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56b1331
Fix: stop word should be 1
fzyxh Oct 11, 2023
c0d1464
Update: reformat huggingface_client
fzyxh Oct 12, 2023
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36 changes: 33 additions & 3 deletions src/helm/proxy/clients/huggingface_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,12 @@
import torch
from dataclasses import asdict
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.stopping_criteria import (
StoppingCriteria,
StoppingCriteriaList,
STOPPING_CRITERIA_INPUTS_DOCSTRING,
add_start_docstrings,
)
from typing import Any, Dict, List, Optional

from helm.common.cache import Cache, CacheConfig
Expand Down Expand Up @@ -35,6 +41,21 @@ def resolve_alias(model_name: str) -> str:
return _MODEL_NAME_ALIASES.get(model_name, model_name)


class StopAtSpecificTokenCriteria(StoppingCriteria):
def __init__(self, stop_sequence: List[int] = None):
super().__init__()
self.stop_sequence = stop_sequence

# @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
# Create a tensor from the stop_sequence
stop_sequence_tensor = torch.tensor(self.stop_sequence, device=input_ids.device, dtype=input_ids.dtype)
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This loads a tensor onto the model's device at every step, which seems slow; is there any way we can just load the tensor once in the constructor? Can you get the device and dtype from the model?

Separately, would you have an idea about how large the performance impact is?

cc @dlwh for PyTorch expertise - David would you have any advice here?

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Sorry I have not resolved the issue yet for poor knowledge on transformers library. In terms of efficiency, there are different reductions on different datasets and adapters. Usually it costs no more than 5 times the original time.


# Check if the current sequence ends with the stop_sequence
current_sequence = input_ids[:, -len(self.stop_sequence) :]
return torch.all(current_sequence == stop_sequence_tensor).item()


class HuggingFaceServer:
"""A thin wrapper around a Hugging Face AutoModelForCausalLM for HuggingFaceClient to call."""

Expand Down Expand Up @@ -72,9 +93,10 @@ def serve_request(self, raw_request: Dict[str, Any]):
raw_request["stop_sequences"], return_token_type_ids=False, add_special_tokens=False
)
assert len(stop_sequence_ids.input_ids) == 1, "Total number of stop words should be 1."
assert len(stop_sequence_ids.input_ids[0]) == 1, "Total number of tokens in each stop word should be 1."
# assert len(stop_sequence_ids.input_ids[0]) == 1, "Total number of tokens in each stop word should be 1."
if len(stop_sequence_ids.input_ids[0]) == 1:
raw_request["eos_token_id"] = stop_sequence_ids.input_ids[0][0]
del raw_request["stop_sequences"]
raw_request["eos_token_id"] = stop_sequence_ids.input_ids[0][0]

# Strip out irrelevant parameters
relevant_raw_request = {
Expand All @@ -83,8 +105,16 @@ def serve_request(self, raw_request: Dict[str, Any]):
if key not in ["engine", "prompt", "echo_prompt", "stop_sequences"]
}

stopping_criteria = StoppingCriteriaList()
if stop_sequence_ids != None:
stopping_criteria.append(StopAtSpecificTokenCriteria(stop_sequence=stop_sequence_ids.input_ids[0]))

# Use HuggingFace's `generate` method.
output = self.model.generate(**encoded_input, **relevant_raw_request)
output = self.model.generate(
**encoded_input,
**relevant_raw_request,
stopping_criteria=stopping_criteria if len(stop_sequence_ids.input_ids[0]) > 1 else None,
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However, the good news is that only few model have to use stopping_criteria.

)
sequences = output.sequences
scores = output.scores

Expand Down
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