chore(): add py pipeline

instruct_pipeline.py

@@ -0,0 +1,212 @@
+import logging
+import re
+from typing import List
+
+import numpy as np
+from transformers import Pipeline, PreTrainedTokenizer
+
+from transformers.utils import is_tf_available
+
+if is_tf_available():
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+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
+    ):
+        """Initialize the pipeline
+
+        Args:
+            do_sample (bool, optional): Whether or not to use sampling. Defaults to True.
+            max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.
+            top_p (float, optional): If set to float < 1, only the smallest set of most probable tokens with
+                probabilities that add up to top_p or higher are kept for generation. Defaults to 0.92.
+            top_k (int, optional): The number of highest probability vocabulary tokens to keep for top-k-filtering.
+                Defaults to 0.
+        """
+        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_full_text: bool = None,
+                             **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
+        }
+
+        if return_full_text is not None:
+            postprocess_params["return_full_text"] = return_full_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)
+
+        if input_ids.shape[1] == 0:
+            input_ids = None
+            attention_mask = None
+            in_b = 1
+        else:
+            in_b = input_ids.shape[0]
+
+        generated_sequence = self.model.generate(
+            input_ids=input_ids.to(self.model.device),
+            attention_mask=attention_mask.to(self.model.device) if attention_mask is not None else None,
+            pad_token_id=self.tokenizer.pad_token_id,
+            **generate_kwargs,
+        )
+
+        out_b = generated_sequence.shape[0]
+        if self.framework == "pt":
+            generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:])
+        elif self.framework == "tf":
+            generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
+
+        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_full_text: bool = False):
+
+        generated_sequence = model_outputs["generated_sequence"][0]
+        instruction_text = model_outputs["instruction_text"]
+
+        generated_sequence: List[List[int]] = generated_sequence.numpy().tolist()
+        records = []
+        for sequence in generated_sequence:
+
+            # 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.
+                try:
+                    response_pos = sequence.index(response_key_token_id)
+                except ValueError:
+                    logger.warn(f"Could not find response key {response_key_token_id} in: {sequence}")
+                    response_pos = None
+
+                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.
+                    try:
+                        end_pos = sequence.index(end_key_token_id)
+                    except ValueError:
+                        end_pos = None
+
+                    decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
+
+            if not decoded:
+                # 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()
+                    else:
+                        logger.warn(f"Failed to find response in:\n{fully_decoded}")
+
+            # If the full text is requested, then append the decoded text to the original instruction.
+            # This technically isn't the full text, as we format the instruction in the prompt the model has been
+            # trained on, but to the client it will appear to be the full text.
+            if return_full_text:
+                decoded = f"{instruction_text}\n{decoded}"
+
+            rec = {"generated_text": decoded}
+
+            records.append(rec)
+
+        return records