take the first steps towards an end-to-end solution

README.md

@@ -20,6 +20,65 @@ $ pip install toolformer-pytorch
 
 ## Usage
 
+Example usage with the example of giving language models awareness of current date and time.
+
+```python
+import torch
+from toolformer_pytorch import Toolformer, PaLM
+
+# simple calendar api call - function that returns a string
+
+def Calendar():
+    import datetime
+    from calendar import day_name, month_name
+    now = datetime.datetime.now()
+    return f'Today is {day_name[now.weekday()]}, {month_name[now.month]} {now.day}, {now.year}.'
+
+# prompt for teaching it to use the Calendar function from above
+
+prompt = f"""
+Your task is to add calls to a Calendar API to a piece of text.
+The API calls should help you get information required to complete the text.
+You can call the API by writing "[Calendar()]"
+Here are some examples of API calls:
+Input: Today is the first Friday of the year.
+Output: Today is the first [Calendar()] Friday of the year.
+Input: The president of the United States is Joe Biden.
+Output: The president of the United States is [Calendar()] Joe Biden.
+Input: [input]
+Output: 
+"""
+
+data = [
+    "The store is never open on the weekend, so today it is closed.",
+    "The number of days from now until Christmas is 30",
+    "The current day of the week is Wednesday."
+]
+
+# model - here using PaLM, but any nn.Module that returns logits in the shape (batch, seq, num_tokens) is fine
+
+model = PaLM(
+    dim = 512,
+    depth = 2,
+    heads = 8,
+    dim_head = 64
+).cuda()
+
+# toolformer
+
+toolformer = Toolformer(
+    model = model,
+    model_seq_len = 256,
+    teach_tool_prompt = prompt,
+    tool_id = 'Calendar',
+    tool = Calendar
+)
+
+data_with_api_calls = toolformer.generate_data_with_api_calls(data)
+
+# complete the filtering and fine tuning step
+```
+
 The main novelty of the paper is defining a fitness score for the outputs from a transformer instructed to insert API calls. The score is used to filter the sampled outputs for finetuning the transformer to make API calls that decreases perplexity of the text that follows it.
 
 ```python
@@ -98,10 +157,14 @@ invoke_tools(function_registry, text)
 
 - [x] create custom generate function for palm that can do external API calls
     - [x] allow for generating tokens at different cursor indices
+    - [x] api token (which was left and right brackets in paper) needs to be customizable
     - [ ] allow for customizing how to fine handling errors in function name, parameters, or execution and output
-    - [ ] api token (which was left and right brackets in paper) needs to be customizable
 - [ ] Toolformer should eventually calculate all statistics (how many properly sampled, filtered out by different criterias, the distribution of scores as well as how many were rejected) before the final fine-tuning
 - [ ] do end-to-end training in `Toolformer`
+    - [x] doing the prompting and bootstrapping the data
+    - [ ] prefiltering of bootstrapped data followed by api calls and then another round of filtering
+        - [ ] keep track of all stats
+    - [ ] take care of fine-tuning, with the interleaving of datasets + optimizer hyperparams
 - [ ] hook up gpt-j
 - [ ] test for a simple calculator eval dataset
 

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'toolformer-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.0.18',
+  version = '0.0.19',
   license='MIT',
   description = 'Toolformer - Pytorch',
   author = 'Phil Wang',
@@ -21,7 +21,8 @@
     'beartype',
     'einops>=0.4',
     'torch>=1.6',
-    'tqdm'
+    'tqdm',
+    'x-clip'
   ],
   classifiers=[
     'Development Status :: 4 - Beta',

toolformer_pytorch/palm.py

@@ -2,6 +2,8 @@
 from torch import nn, einsum
 from einops import rearrange
 
+from x_clip.tokenizer import tokenizer
+
 # helpers
 
 def exists(val):
@@ -162,7 +164,7 @@ def __init__(
         depth, 
         heads, 
         dim_head, 
-        ff_mult=4,
+        ff_mult = 4,
     ):
         super().__init__()
         self.layers = nn.ModuleList([])
@@ -184,8 +186,8 @@ class PaLM(nn.Module):
     def __init__(
         self, 
         dim, 
-        num_tokens, 
         depth, 
+        num_tokens=tokenizer.vocab_size,
         dim_head=64, 
         heads=8, 
         ff_mult=4,

toolformer_pytorch/toolformer_pytorch.py

@@ -7,6 +7,7 @@
 import torch.nn.functional as F
 from torch import nn, einsum
 from torch.utils.data import Dataset, DataLoader
+from torch.nn.utils.rnn import pad_sequence
 
 from einops import rearrange, reduce
 
@@ -18,6 +19,7 @@
 from beartype.typing import Callable, Optional, Union, List
 
 from tqdm import tqdm
+from x_clip.tokenizer import tokenizer
 
 # helpers
 
@@ -233,6 +235,7 @@ def sample(
     select_api_start_id_top_k = 10,
 ):
     device = next(model.parameters()).device
+    positions = positions.clone()
     max_seq_len = seq_len + 1
 
     # validate
@@ -262,7 +265,7 @@ def sample(
     # lengthen the prime to the entire sequence length
 
     remain_iterations = seq_len - prime_length
-    output = F.pad(prime, (max_seq_len - prime_length, 0), value = 0.)
+    output = F.pad(prime, (0, max_seq_len - prime_length), value = 0.)
 
     batch_indices = torch.arange(batch_size, device = device)
     batch_indices = rearrange(batch_indices, 'b -> b 1')
@@ -337,7 +340,6 @@ def create_api_token_mask(num_tokens, api_start_token_id):
     # remove the last token in output (use as noop placeholder)
 
     output = output[:, :-1]
-
     return output
 
 @beartype
@@ -511,6 +513,42 @@ def loss_fn(weight, probs):
 
     return ret
 
+# datasets and dataloaders
+
+# for bootstrapping the initial datasets with api calls
+# as well as for the final finetuning
+
+@beartype
+class PromptDataset(Dataset):
+    def __init__(
+        self,
+        prompt: str,
+        prompt_input_tag: str,
+        data: List[str],
+        tokenizer_encode: Callable
+    ):
+        self.data = data
+        self.prompt = prompt
+        self.prompt_input_tag_regex = re.escape(prompt_input_tag)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        data_string = self.data[idx]
+        data_with_prompt = re.sub(self.prompt_input_tag_regex, data_string, self.prompt)
+        token_ids = tokenizer.encode(data_with_prompt)
+        return torch.tensor(token_ids).long(), torch.tensor(len(token_ids)).long()
+
+def prompt_collate_fn(data, padding_value = 0):
+    prompts, prompt_lengths = zip(*data)
+    prompts = pad_sequence(prompts, batch_first = True, padding_value = padding_value)
+    return prompts, torch.stack(prompt_lengths)
+
+def PromptDataloader(ds: Dataset, *args, padding_value = 0, **kwargs):
+    collate_fn = partial(prompt_collate_fn, padding_value = padding_value)
+    return DataLoader(ds, *args, collate_fn = collate_fn, **kwargs)
+
 # classes
 
 @beartype
@@ -521,12 +559,36 @@ def __init__(
         *,
         tool_id: str,
         tool: Callable,
+        api_start_str = ' [',
+        api_stop_str = ']',
+        api_start_id = None,
+        api_stop_id = None,
         teach_tool_prompt: str,
+        pad_id = 0,
+        prompt_batch_size = 4,
+        model_seq_len = 2048,
+        tokenizer_encode: Callable = tokenizer.encode,
+        tokenizer_decode: Callable = tokenizer.decode,
         prompt_input_tag: str = DEFAULT_PROMPT_INPUT_TAG,
         exclude_filters: dict[str, Callable[[str], bool]] = dict()
     ):
         super().__init__()
         self.model = model
+        self.model_seq_len = model_seq_len
+
+        self.teach_tool_prompt = teach_tool_prompt
+        self.prompt_batch_size = prompt_batch_size
+        self.prompt_input_tag = prompt_input_tag
+
+        self.tokenizer_encode = tokenizer_encode
+        self.tokenizer_decode = tokenizer_decode
+
+        self.api_start_str = api_start_str
+        self.api_stop_str = api_stop_str
+
+        self.api_start_id = api_start_id
+        self.api_stop_id = api_stop_id
+        self.pad_id = pad_id
 
         self.tool_id = tool_id
         self.tool = tool
@@ -537,8 +599,48 @@ def __init__(
         self.teach_tool_prompt = teach_tool_prompt
         self.exclude_filters = exclude_filters
 
+    def generate_data_with_api_calls(
+        self,
+        data: List[str],
+        temperature: float = 0.9
+    ) -> List[str]:
+
+        dataset = PromptDataset(
+            data = data,
+            prompt_input_tag = self.prompt_input_tag,
+            prompt = self.teach_tool_prompt,
+            tokenizer_encode = self.tokenizer_encode
+        )
+
+        dl = PromptDataloader(
+            dataset,
+            batch_size = self.prompt_batch_size
+        )
+
+        prompted_outputs = []
+
+        for prime, positions in dl:
+
+            sampled_outputs = sample(
+                model = self.model,
+                prime = prime,
+                positions = positions,
+                seq_len = self.model_seq_len,
+                pad_id = self.pad_id,
+                temperature = temperature
+            )
+
+            for sample_output, position in zip(sampled_outputs, positions):
+                start_position = position.item()
+
+                prompted_output = self.tokenizer_decode(sample_output[start_position:])
+                prompted_outputs.append(prompted_output)
+
+        return prompted_outputs
+
     def forward(
         self,
         data: List[str]
     ):
-        raise NotImplementedError
+        data_with_api_calls = self.generate_data_with_api_calls(data)
+        return data_with_api_calls