Making Conversation possible to create directly a full conversation (#9434)
* Cleaning up conversation tests. * Adding tests that don't require downloading models + conversation can be fully created from static state. * Making tests non flaky (by fixing generation length) * Bumping isort version. * Doc cleanup. * Remove unused test in this PR. * Torch import guard for TF. * Missing torch guard. * Small mistake in doc. * Actual uses `_history` and `_index` cache. + remove dead enumerate + improve warning message. * Update src/transformers/pipelines/conversational.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines/conversational.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines/conversational.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Adding comments and cleaner code to address history copy. * Improving pipeline name in tests. * Change tokenizer to a real one (still created at runtime with no external dependency) * Simplify DummyTok, reverse changes on tokenization. * Removing DummyTok. Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Nicolas Patry
GitHub
parent
4fbcf8ea49
commit
02e05fb0a5
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@ -33,6 +33,14 @@ class Conversation:
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conversation_id (:obj:`uuid.UUID`, `optional`):
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Unique identifier for the conversation. If not provided, a random UUID4 id will be assigned to the
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conversation.
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past_user_inputs (:obj:`List[str]`, `optional`):
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Eventual past history of the conversation of the user. You don't need to pass it manually if you use the
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pipeline interactively but if you want to recreate history you need to set both :obj:`past_user_inputs` and
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:obj:`generated_responses` with equal length lists of strings
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generated_responses (:obj:`List[str]`, `optional`):
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Eventual past history of the conversation of the model. You don't need to pass it manually if you use the
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pipeline interactively but if you want to recreate history you need to set both :obj:`past_user_inputs` and
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:obj:`generated_responses` with equal length lists of strings
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Usage::
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@ -47,14 +55,33 @@ class Conversation:
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conversation.add_user_input("Is it good?")
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"""
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def __init__(self, text: str = None, conversation_id: uuid.UUID = None):
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def __init__(
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self, text: str = None, conversation_id: uuid.UUID = None, past_user_inputs=None, generated_responses=None
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):
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if not conversation_id:
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conversation_id = uuid.uuid4()
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if past_user_inputs is None:
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past_user_inputs = []
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if generated_responses is None:
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generated_responses = []
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self.uuid: uuid.UUID = conversation_id
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self.past_user_inputs: List[str] = []
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self.generated_responses: List[str] = []
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self.history: List[int] = []
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self.past_user_inputs: List[str] = past_user_inputs
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self.generated_responses: List[str] = generated_responses
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self.new_user_input: Optional[str] = text
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self._index: int = 0
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self._history: List[int] = []
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def __eq__(self, other):
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if not isinstance(other, Conversation):
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return False
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if self.uuid == other.uuid:
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return True
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return (
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self.new_user_input == other.new_user_input
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and self.past_user_inputs == other.past_user_inputs
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and self.generated_responses == other.generated_responses
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)
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def add_user_input(self, text: str, overwrite: bool = False):
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"""
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@ -100,16 +127,6 @@ class Conversation:
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"""
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self.generated_responses.append(response)
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def set_history(self, history: List[int]):
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"""
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Updates the value of the history of the conversation. The history is represented by a list of :obj:`token_ids`.
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The history is used by the model to generate responses based on the previous conversation turns.
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Args:
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history (:obj:`List[int]`): History of tokens provided and generated for this conversation.
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"""
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self.history = history
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def __repr__(self):
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"""
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Generates a string representation of the conversation.
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@ -167,12 +184,40 @@ class ConversationalPipeline(Pipeline):
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super().__init__(*args, **kwargs)
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# We need at least an eos_token
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assert self.tokenizer.eos_token_id is not None, "DialoguePipeline tokenizer should have an EOS token set"
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assert self.tokenizer.eos_token_id is not None, "ConversationalPipeline tokenizer should have an EOS token set"
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if self.tokenizer.pad_token_id is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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self.min_length_for_response = min_length_for_response
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def _get_history(self, conversation):
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"""
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Private function (subject to change) that simply tokenizes and concatenates past inputs. Also saves that
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tokenization into the conversation state.
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Args:
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conversation (:class:`~transformers.Conversation`)
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Returns:
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:obj:`List[int]`: The list of tokens for the past input of that conversation.
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"""
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# Make a copy to prevent messing cache up if there's an error
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# within this function
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history = conversation._history.copy()
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index = conversation._index
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new_index = index
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for i, (past_user_input, generated_response) in enumerate(
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zip(conversation.past_user_inputs[index:], conversation.generated_responses[index:])
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):
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for el in (past_user_input, generated_response):
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new_history = self._parse_and_tokenize([el])[0]
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history.extend(new_history)
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new_index = i + index + 1
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conversation._index = new_index
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conversation._history = history
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# Hand back a copy to caller so they can't accidently modify our cache.
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return history.copy()
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def __call__(
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self,
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conversations: Union[Conversation, List[Conversation]],
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@ -220,7 +265,7 @@ class ConversationalPipeline(Pipeline):
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with self.device_placement():
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inputs = self._parse_and_tokenize([conversation.new_user_input for conversation in conversations])
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histories = [conversation.history for conversation in conversations]
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histories = [self._get_history(conversation) for conversation in conversations]
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max_length = generate_kwargs.get("max_length", self.model.config.max_length)
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inputs = self._concat_inputs_history(inputs, histories, max_length)
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@ -266,7 +311,6 @@ class ConversationalPipeline(Pipeline):
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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)
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)
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conversation.set_history(history[conversation_index])
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output.append(conversation)
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if len(output) == 1:
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return output[0]
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@ -333,7 +377,9 @@ class ConversationalPipeline(Pipeline):
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if cutoff_eos_index == 0 or cutoff_eos_index == len(new_input) - 1:
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break
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else:
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new_input = new_input[cutoff_eos_index + 1 :]
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logger.warning(
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f"Cutting history off because it's too long ({len(new_input)} > {max_length - self.min_length_for_response}) for underlying model"
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)
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outputs.append(new_input)
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padded_outputs = self.tokenizer.pad(
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{"input_ids": outputs}, padding="longest", return_attention_mask=True, return_tensors=self.framework
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@ -14,15 +14,177 @@
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import unittest
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, Conversation, ConversationalPipeline, pipeline
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from transformers import (
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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Conversation,
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ConversationalPipeline,
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is_torch_available,
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pipeline,
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)
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from transformers.testing_utils import require_torch, slow, torch_device
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from .test_pipelines_common import MonoInputPipelineCommonMixin
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if is_torch_available():
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import torch
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from transformers.models.gpt2 import GPT2Config, GPT2LMHeadModel
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DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
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class SimpleConversationPipelineTests(unittest.TestCase):
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def get_pipeline(self):
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# When
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config = GPT2Config(
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vocab_size=263,
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n_ctx=128,
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max_length=128,
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n_embd=64,
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n_layer=1,
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n_head=8,
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bos_token_id=256,
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eos_token_id=257,
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)
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model = GPT2LMHeadModel(config)
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# Force model output to be L
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V, D = model.lm_head.weight.shape
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bias = torch.zeros(V, requires_grad=True)
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bias[76] = 1
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model.lm_head.bias = torch.nn.Parameter(bias)
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# # Created with:
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# import tempfile
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# from tokenizers import Tokenizer, models
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# from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
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# vocab = [(chr(i), i) for i in range(256)]
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# tokenizer = Tokenizer(models.Unigram(vocab))
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# with tempfile.NamedTemporaryFile() as f:
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# tokenizer.save(f.name)
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# real_tokenizer = PreTrainedTokenizerFast(tokenizer_file=f.name, eos_token="<eos>", bos_token="<bos>")
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# real_tokenizer._tokenizer.save("dummy.json")
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# Special tokens are automatically added at load time.
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tokenizer = AutoTokenizer.from_pretrained("Narsil/small_conversational_test")
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conversation_agent = pipeline(
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task="conversational", device=DEFAULT_DEVICE_NUM, model=model, tokenizer=tokenizer
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)
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return conversation_agent
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@require_torch
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def test_integration_torch_conversation(self):
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conversation_agent = self.get_pipeline()
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conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
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conversation_2 = Conversation("What's the last book you have read?")
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self.assertEqual(len(conversation_1.past_user_inputs), 0)
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self.assertEqual(len(conversation_2.past_user_inputs), 0)
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with self.assertLogs("transformers", level="WARNING") as log:
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result = conversation_agent([conversation_1, conversation_2], max_length=48)
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self.assertEqual(len(log.output), 2)
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self.assertIn("You might consider trimming the early phase of the conversation", log.output[0])
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self.assertIn("Setting `pad_token_id`", log.output[1])
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# Two conversations in one pass
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self.assertEqual(result, [conversation_1, conversation_2])
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self.assertEqual(
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result,
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[
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Conversation(
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None,
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past_user_inputs=["Going to the movies tonight - any suggestions?"],
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generated_responses=["L"],
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),
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Conversation(
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None, past_user_inputs=["What's the last book you have read?"], generated_responses=["L"]
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),
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],
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)
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# One conversation with history
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conversation_2.add_user_input("Why do you recommend it?")
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with self.assertLogs("transformers", level="WARNING") as log:
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result = conversation_agent(conversation_2, max_length=64)
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self.assertEqual(len(log.output), 3)
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self.assertIn("Cutting history off because it's too long", log.output[0])
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self.assertIn("You might consider trimming the early phase of the conversation", log.output[1])
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self.assertIn("Setting `pad_token_id`", log.output[2])
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self.assertEqual(result, conversation_2)
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self.assertEqual(
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result,
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Conversation(
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None,
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past_user_inputs=["What's the last book you have read?", "Why do you recommend it?"],
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generated_responses=["L", "L"],
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),
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)
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@require_torch
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def test_history_cache(self):
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conversation_agent = self.get_pipeline()
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conversation = Conversation(
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"Why do you recommend it?",
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past_user_inputs=["What's the last book you have read?"],
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generated_responses=["b"],
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)
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with self.assertLogs("transformers", level="WARNING") as log:
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_ = conversation_agent(conversation, max_length=60)
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self.assertEqual(len(log.output), 3)
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self.assertIn("Cutting history off because it's too long (63 > 28) for underlying model", log.output[0])
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self.assertIn("63 is bigger than 0.9 * max_length: 60", log.output[1])
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self.assertIn("Setting `pad_token_id`", log.output[2])
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self.assertEqual(conversation._index, 1)
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self.assertEqual(
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conversation._history,
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[
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87,
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104,
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97,
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116,
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39,
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115,
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32,
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116,
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104,
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101,
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32,
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108,
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97,
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115,
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116,
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32,
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98,
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111,
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111,
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107,
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32,
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121,
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111,
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117,
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32,
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104,
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97,
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118,
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101,
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32,
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114,
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101,
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97,
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100,
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63,
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259, # EOS
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98, # b
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259, # EOS
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],
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)
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class ConversationalPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "conversational"
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small_models = [] # Models tested without the @slow decorator
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