Delete instruct_pipeline.py

instruct_pipeline.py

@@ -1,160 +0,0 @@
-import re
-
-import numpy as np
-from transformers import Pipeline, PreTrainedTokenizer
-
-
-INSTRUCTION_KEY = "### Instruction:"
-RESPONSE_KEY = "### Response:"
-END_KEY = "### End"
-INTRO_BLURB = (
-    "Below is an instruction that describes a task. Write a response that appropriately completes the request."
-)
-
-# This is the prompt that is used for generating responses using an already trained model.  It ends with the response
-# key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
-PROMPT_FOR_GENERATION_FORMAT = """{intro}
-
-{instruction_key}
-{instruction}
-
-{response_key}
-""".format(
-    intro=INTRO_BLURB,
-    instruction_key=INSTRUCTION_KEY,
-    instruction="{instruction}",
-    response_key=RESPONSE_KEY,
-)
-
-
-def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int:
-    """Gets the token ID for a given string that has been added to the tokenizer as a special token.
-
-    When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
-    treated specially and converted to a single, new token.  This retrieves the token ID each of these keys map to.
-
-    Args:
-        tokenizer (PreTrainedTokenizer): the tokenizer
-        key (str): the key to convert to a single token
-
-    Raises:
-        RuntimeError: if more than one ID was generated
-
-    Returns:
-        int: the token ID for the given key
-    """
-    token_ids = tokenizer.encode(key)
-    if len(token_ids) > 1:
-        raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
-    return token_ids[0]
-
-
-class InstructionTextGenerationPipeline(Pipeline):
-    def __init__(
-        self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs
-    ):
-        super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, **kwargs)
-
-    def _sanitize_parameters(self, return_instruction_text=False, **generate_kwargs):
-        preprocess_params = {}
-
-        # newer versions of the tokenizer configure the response key as a special token.  newer versions still may
-        # append a newline to yield a single token.  find whatever token is configured for the response key.
-        tokenizer_response_key = next(
-            (token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None
-        )
-
-        response_key_token_id = None
-        end_key_token_id = None
-        if tokenizer_response_key:
-            try:
-                response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key)
-                end_key_token_id = get_special_token_id(self.tokenizer, END_KEY)
-
-                # Ensure generation stops once it generates "### End"
-                generate_kwargs["eos_token_id"] = end_key_token_id
-            except ValueError:
-                pass
-
-        forward_params = generate_kwargs
-        postprocess_params = {
-            "response_key_token_id": response_key_token_id,
-            "end_key_token_id": end_key_token_id,
-            "return_instruction_text": return_instruction_text,
-        }
-
-        return preprocess_params, forward_params, postprocess_params
-
-    def preprocess(self, instruction_text, **generate_kwargs):
-        prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text)
-        inputs = self.tokenizer(
-            prompt_text,
-            return_tensors="pt",
-        )
-        inputs["prompt_text"] = prompt_text
-        inputs["instruction_text"] = instruction_text
-        return inputs
-
-    def _forward(self, model_inputs, **generate_kwargs):
-        input_ids = model_inputs["input_ids"]
-        attention_mask = model_inputs.get("attention_mask", None)
-        generated_sequence = self.model.generate(
-            input_ids=input_ids.to(self.model.device),
-            attention_mask=attention_mask,
-            pad_token_id=self.tokenizer.pad_token_id,
-            **generate_kwargs,
-        )[0].cpu()
-        instruction_text = model_inputs.pop("instruction_text")
-        return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}
-
-    def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_instruction_text):
-        sequence = model_outputs["generated_sequence"]
-        instruction_text = model_outputs["instruction_text"]
-
-        # The response will be set to this variable if we can identify it.
-        decoded = None
-
-        # If we have token IDs for the response and end, then we can find the tokens and only decode between them.
-        if response_key_token_id and end_key_token_id:
-            # Find where "### Response:" is first found in the generated tokens.  Considering this is part of the
-            # prompt, we should definitely find it.  We will return the tokens found after this token.
-            response_pos = None
-            response_positions = np.where(sequence == response_key_token_id)[0]
-            if len(response_positions) == 0:
-                pass
-            else:
-                response_pos = response_positions[0]
-
-            if response_pos:
-                # Next find where "### End" is located.  The model has been trained to end its responses with this
-                # sequence (or actually, the token ID it maps to, since it is a special token).  We may not find
-                # this token, as the response could be truncated.  If we don't find it then just return everything
-                # to the end.  Note that even though we set eos_token_id, we still see the this token at the end.
-                end_pos = None
-                end_positions = np.where(sequence == end_key_token_id)[0]
-                if len(end_positions) > 0:
-                    end_pos = end_positions[0]
-
-                decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
-        else:
-            # Otherwise we'll decode everything and use a regex to find the response and end.
-
-            fully_decoded = self.tokenizer.decode(sequence)
-
-            # The response appears after "### Response:".  The model has been trained to append "### End" at the
-            # end.
-            m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
-
-            if m:
-                decoded = m.group(1).strip()
-            else:
-                # The model might not generate the "### End" sequence before reaching the max tokens.  In this case,
-                # return everything after "### Response:".
-                m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
-                if m:
-                    decoded = m.group(1).strip()
-
-        if return_instruction_text:
-            return {"instruction_text": instruction_text, "generated_text": decoded}
-
-        return decoded