Add application file

app.py

@@ -0,0 +1,177 @@
+import random
+from functools import partial
+
+import gradio as gr
+import numpy as np
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("adamcasson/ul2-tinystories")
+
+def mask_spans(
+    tokens,
+    mu,
+    r,
+    vocab_size,
+    eos_id,
+    prepend_id=None,
+    prefix_lm=False,
+):
+    masked_tokens = tokens[:]
+
+    encoder_inputs = [prepend_id] if prepend_id is not None else []
+    targets = []
+
+    # Original T5 code reused tokens at the end of vocab for sentinels
+    # https://github.com/google-research/text-to-text-transfer-transformer/blob/258fd30687e6c60d18b7204d009dc5c753142987/t5/data/preprocessors.py#L3106C6-L3106C6
+    sentinel_id = vocab_size - 1
+
+    if prefix_lm:
+        # n = 1
+        mu = max(1, int(len(tokens) * r))
+        start = max(
+            0, len(tokens) - random.randint(1, int(2 * mu))
+        )  # max to handle start < 0
+        encoder_inputs += tokens[:start] + [sentinel_id]
+        targets += tokens[start:]
+        for i in range(start, len(tokens)):
+            masked_tokens[i] = -1
+
+    else:
+        # n = ceil(len(tokens) / mu)
+        prev_span_unmasked = False
+        start = 0
+        end = 0
+        while start < len(tokens):
+            # uniform random span length
+            length = random.randint(1, int(2 * mu))
+            end = min(start + length, len(tokens))
+
+            # randomly decide if span should be masked
+            if np.random.binomial(1, p=r):
+                encoder_inputs.append(sentinel_id)
+                targets += tokens[start:end]
+                for i in range(start, end):
+                    masked_tokens[i] = -1
+                prev_span_unmasked = False
+                sentinel_id -= 1
+            else:
+                encoder_inputs += tokens[start:end]
+                # if previous span was also unmasked we don't need to keep adding the sentinel token
+                if not prev_span_unmasked:
+                    targets.append(sentinel_id)
+                    prev_span_unmasked = True
+            start = end
+
+    encoder_inputs.append(eos_id)
+    targets.append(eos_id)
+    decoder_inputs = (
+        [prepend_id] + targets[:-1]
+        if prepend_id is not None
+        else [eos_id] + targets[:-1]
+    )
+
+    return encoder_inputs, decoder_inputs, targets, masked_tokens
+
+# Create mixture-of-denoisers
+denoiser_map = {
+    "R (µ = 3, r = 0.15)": partial(
+        mask_spans,
+        mu=3,
+        r=0.15,
+        vocab_size=tokenizer.vocab_size,
+        eos_id=tokenizer.eos_token_id,
+        prepend_id=tokenizer.vocab["[R]"],
+    ),
+    "R (µ = 8, r = 0.15)": partial(
+        mask_spans,
+        mu=8,
+        r=0.15,
+        vocab_size=tokenizer.vocab_size,
+        eos_id=tokenizer.eos_token_id,
+        prepend_id=tokenizer.vocab["[R]"],
+    ),
+    "S (r = 0.25)": partial(
+        mask_spans,
+        mu=None,
+        r=0.25,
+        vocab_size=tokenizer.vocab_size,
+        eos_id=tokenizer.eos_token_id,
+        prefix_lm=True,
+        prepend_id=tokenizer.vocab["[S]"],
+    ),
+    "X (µ = 3, r = 0.5)": partial(
+        mask_spans,
+        mu=3,
+        r=0.5,
+        vocab_size=tokenizer.vocab_size,
+        eos_id=tokenizer.eos_token_id,
+        prepend_id=tokenizer.vocab["[X]"],
+    ),
+    "X (µ = 8, r = 0.5)": partial(
+        mask_spans,
+        mu=8,
+        r=0.5,
+        vocab_size=tokenizer.vocab_size,
+        eos_id=tokenizer.eos_token_id,
+        prepend_id=tokenizer.vocab["[X]"],
+    ),
+    "X (µ = 32, r = 0.15)": partial(
+        mask_spans,
+        mu=32,
+        r=0.15,
+        vocab_size=tokenizer.vocab_size,
+        eos_id=tokenizer.eos_token_id,
+        prepend_id=tokenizer.vocab["[X]"],
+    ),
+    "X (µ = 32, r = 0.5)": partial(
+        mask_spans,
+        mu=32,
+        r=0.5,
+        vocab_size=tokenizer.vocab_size,
+        eos_id=tokenizer.eos_token_id,
+        prepend_id=tokenizer.vocab["[X]"],
+    ),
+}
+
+def mask_viz(denoiser, text):
+    seq = tokenizer.encode(text)
+    tokens = tokenizer.tokenize(text)
+
+    out = denoiser_map[denoiser](seq)
+    
+    mask = out[-1]
+
+    highlight_tok = []
+    for tok, tok_mask in zip(tokens, mask):
+        highlight_tok.append((tok.replace("Ġ", " ").replace("Ċ", "\n"), "masked" if tok_mask == -1 else "unmasked"))
+
+    return highlight_tok
+
+iface = gr.Interface(
+    fn=mask_viz,
+    inputs=[
+        gr.Dropdown(
+            label="Denoiser",
+            choices=[
+                "R (µ = 3, r = 0.15)",
+                "R (µ = 8, r = 0.15)",
+                "S (r = 0.25)",
+                "X (µ = 3, r = 0.5)",
+                "X (µ = 8, r = 0.5)",
+                "X (µ = 32, r = 0.15)",
+                "X (µ = 32, r = 0.5)",
+            ],
+            value="R (µ = 3, r = 0.15)",
+        ),
+        gr.Textbox(
+            value='Once upon a time, there was a clever little dog named Max. Max loved to run and play with his friends in the park. One day, Max was running very fast when he fell and hurt his knee. Max went to his friend, the wise old owl, and said, "Owl, my knee hurts. What can I do?" The owl thought for a moment and said, "Max, you should test your knee. Try to walk slowly and see if it still hurts." So Max tested his knee by walking slowly. At first, it hurt a little, but soon Max felt better. He said, "Thank you, Owl, for your help. Now I can play with my friends again." Max was so happy that he could play with his friends without pain. He learned that sometimes, it was good to slow down and listen to his body. And Max and his friends played happily in the park ever after.'
+        ),
+    ],
+    outputs=gr.HighlightedText(
+        combine_adjacent=True,
+        show_legend=True,
+        color_map={"unmasked": "green", "masked": "red"}
+    )
+)
+
+iface.launch()