GPT-2 Example (#353)

Author: 1vn

examples/text-generation/cortex.yaml

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+- kind: deployment
+  name: text-generation
+
+- kind: api
+  name: generator-124
+  model: s3://cortex-examples/gpt-2/124
+  request_handler: encoder.py
+
+- kind: api
+  name: generator-355
+  model: s3://cortex-examples/gpt-2/355
+  request_handler: encoder.py
+
+- kind: api
+  name: generator-774
+  model: s3://cortex-examples/gpt-2/774
+  request_handler: encoder.py

examples/text-generation/encoder.py

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+# This file includes code which was modified from https://github.com/openai/gpt-2
+
+import tensorflow as tf
+import os
+import json
+import regex as re
+from functools import lru_cache
+import requests
+import boto3
+
+
+@lru_cache()
+def bytes_to_unicode():
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2 ** 8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2 ** 8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+class Encoder:
+    def __init__(self, encoder, bpe_merges, errors="replace"):
+        self.encoder = encoder
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.errors = errors
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+        self.cache = {}
+        self.pat = re.compile(
+            r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
+        )
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except:
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        bpe_tokens = []
+        for token in re.findall(self.pat, text):
+            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
+            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = "".join([self.decoder[token] for token in tokens])
+        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+        return text
+
+
+def get_encoder():
+    s3 = boto3.client("s3")
+    encoder = json.load(
+        s3.get_object(Bucket="cortex-examples", Key="gpt-2/124M/encoder.json")["Body"]
+    )
+    bpe_data = (
+        s3.get_object(Bucket="cortex-examples", Key="gpt-2/124M/vocab.bpe")["Body"]
+        .read()
+        .decode("utf-8")
+    )
+    bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
+    return Encoder(encoder=encoder, bpe_merges=bpe_merges)
+
+
+encoder = get_encoder()
+
+
+def pre_inference(sample, metadata):
+    context = encoder.encode(sample["text"])
+    return {"context": [context]}
+
+
+def post_inference(prediction, metadata):
+    return {encoder.decode(prediction["response"]["sample"])}

examples/text-generation/requirements.txt

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+requests==2.21.0
+regex==2017.4.5

examples/text-generation/samples.json

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+{
+    "samples": [
+        {
+            "text": "Machine learning"
+        }
+    ]
+}