just need filter stats and fine-tuning logic

README.md

@@ -76,7 +76,20 @@ toolformer = Toolformer(
 
 data_with_api_calls = toolformer.generate_data_with_api_calls(data)
 
-# complete the filtering and fine tuning step
+filtered_data, filtered_data_with_api_calls = toolformer.filter_and_keep_only_first_api_call(data, data_with_api_calls)
+
+data_with_api_responses = toolformer.make_api_calls(filtered_data_with_api_calls)
+
+filtered_results = toolformer.filter_by_api_responses(
+    filtered_data,
+    filtered_data_with_api_calls,
+    data_with_api_responses
+)
+
+# then finetune with token ids at
+# -> filtered_results.filtered_tokens_without_api_response
+# complete this with toolformer.finetune(filtered_results)
+
 ```
 
 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.
@@ -162,7 +175,7 @@ invoke_tools(function_registry, text)
 - [ ] 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
+    - [x] 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

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'toolformer-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.0.19',
+  version = '0.0.20',
   license='MIT',
   description = 'Toolformer - Pytorch',
   author = 'Phil Wang',

toolformer_pytorch/toolformer_pytorch.py

@@ -16,7 +16,7 @@
 from toolformer_pytorch.prompts import DEFAULT_PROMPT_INPUT_TAG
 
 from beartype import beartype
-from beartype.typing import Callable, Optional, Union, List
+from beartype.typing import Callable, Optional, Union, List, Tuple
 
 from tqdm import tqdm
 from x_clip.tokenizer import tokenizer
@@ -142,7 +142,7 @@ def replace_fn(
 
     # return original text with the output delimiter and the stringified output
 
-    return f'{text_without_end_api_token} {delimiter} {str(out)}{end_api_token}'
+    return f'{text_without_end_api_token} {delimiter} {str(out)} {end_api_token}'
 
 # main function, which takes a registry of functions, the text in question, and makes all the appropriate api calls and append the output
 
@@ -451,11 +451,16 @@ def filter_tokens_with_api_response(
     assert all_contains_id(tokens_with_api_response, api_start_token_id)
     assert all_contains_id(tokens_with_api_response, api_end_token_id)
 
+    # auto set devices
+
+    device = next(model.parameters()).device
+    tokens, tokens_without_api_response, tokens_with_api_response = map(lambda t: t.to(device), (tokens, tokens_without_api_response, tokens_with_api_response))
+
     # get all the logits
 
     with torch.no_grad():
         model.eval()
-        logits, logits_without_api_response, logits_with_api_response = map(model, (tokens, tokens_with_api_response, tokens_without_api_response))
+        logits, logits_without_api_response, logits_with_api_response = map(model, (tokens, tokens_without_api_response, tokens_with_api_response))
 
     # derive all predicted prob of the actual next token id in sequence
 
@@ -472,8 +477,10 @@ def filter_tokens_with_api_response(
 
     # deriving the weighting for the original passage is more tricky
     # would need to start counting up from <api> start token location
+    # this would also assume that the language model perfectly copied the passage over and that both token ids are aligned except for the inserted API call - but this can be done with the custom filtering functions eventually
 
     weight = weight_and_mask_fn(tokens_without_api_response[:, 1:], api_start_token_id) # shift to the left by one since <api> does not exist in the original sequence
+    weight = weight[:, :probs.shape[-1]]
 
     # get the loss L for all three types of sequences
 
@@ -561,9 +568,11 @@ def __init__(
         tool: Callable,
         api_start_str = ' [',
         api_stop_str = ']',
+        api_response_delimiter = '→',
         api_start_id = None,
         api_stop_id = None,
         teach_tool_prompt: str,
+        filter_threshold = 1.,
         pad_id = 0,
         prompt_batch_size = 4,
         model_seq_len = 2048,
@@ -582,12 +591,28 @@ def __init__(
 
         self.tokenizer_encode = tokenizer_encode
         self.tokenizer_decode = tokenizer_decode
+        self.tokenizer_encode_to_tensor = lambda s: torch.tensor(tokenizer_encode(s)).long()
+
+        self.filter_threshold = filter_threshold
 
         self.api_start_str = api_start_str
         self.api_stop_str = api_stop_str
+        self.api_response_delimiter = api_response_delimiter
+
+        if not exists(api_start_id):
+            api_start_id = tokenizer_encode(api_start_str)
+            assert len(api_start_id) == 1
+            api_start_id = api_start_id[0]
 
         self.api_start_id = api_start_id
+
+        if not exists(api_stop_id):
+            api_stop_id = tokenizer_encode(api_stop_str)
+            assert len(api_stop_id) == 1
+            api_stop_id = api_stop_id[0]
+
         self.api_stop_id = api_stop_id
+
         self.pad_id = pad_id
 
         self.tool_id = tool_id
@@ -638,9 +663,85 @@ def generate_data_with_api_calls(
 
         return prompted_outputs
 
+    def filter_and_keep_only_first_api_call(
+        self,
+        data,
+        data_with_api_calls: List[str],
+        return_excluded = False
+    ):
+        included = []
+        excluded = []
+
+        api_start_stop_kwargs = dict(api_start = self.api_start_str, api_stop = self.api_stop_str)
+
+        has_api_calls_ = partial(has_api_calls, **api_start_stop_kwargs)
+        replace_all_but_first_ = partial(replace_all_but_first, **api_start_stop_kwargs)
+
+        for datum, data_with_api_call in zip(data, data_with_api_calls):
+            if has_api_calls_(data_with_api_call):
+                data_with_api_call = replace_all_but_first_(data_with_api_call)
+                included.append((datum, data_with_api_call))
+            else:
+                excluded.append((datum, data_with_api_call))
+
+        included = tuple(map(list, zip(*included)))
+
+        if not return_excluded:
+            return included
+
+        excluded = tuple(map(list, zip(*excluded)))
+        return included, excluded
+
+    def make_api_calls(
+        self,
+        filtered_data_with_api_calls: List[str]
+    ):
+        invoke_tools_ = partial(
+            invoke_tools,
+            self.registry,
+            api_start = self.api_start_str,
+            api_stop = self.api_stop_str, delimiter = self.api_response_delimiter
+        )
+
+        data_with_api_responses = []
+        for data in filtered_data_with_api_calls:
+            output = invoke_tools_(data)
+            data_with_api_responses.append(output)
+
+        return data_with_api_responses
+
+    def filter_by_api_responses(
+        self,
+        data: List[str],
+        data_with_api_calls: List[str],
+        data_with_api_responses: List[str]
+    ) -> FilteredResults:
+
+        to_token_ids = lambda l: pad_sequence([*map(self.tokenizer_encode_to_tensor, l)], batch_first = True, padding_value = self.pad_id)
+
+        tokens, tokens_without_api_response, tokens_with_api_response = map(to_token_ids, (data, data_with_api_calls, data_with_api_responses))
+
+        filtered_results = filter_tokens_with_api_response(
+            model = self.model,
+            tokens = tokens,
+            tokens_with_api_response = tokens_with_api_response,
+            tokens_without_api_response = tokens_without_api_response,
+            filter_threshold = self.filter_threshold,
+            api_start_token_id = self.api_start_id,
+            api_end_token_id = self.api_stop_id
+        )
+
+        return filtered_results
+
     def forward(
         self,
         data: List[str]
     ):
         data_with_api_calls = self.generate_data_with_api_calls(data)
-        return data_with_api_calls
+        filtered_data_with_api_calls = self.filter_and_keep_only_first_api_call(data_with_api_calls)
+
+        assert len(filtered_data_with_api_calls) > 0, 'your model failed to follow instructions and make API calls. please try a better model or do some better prompt engineering'
+
+        data_with_responses = self.make_api_calls(filtered_data_with_api_calls)
+
+        return data_with_responses