Fix bugs in API tools (to fix #348) (#349)

* Use litellm.utils.get_max_tokens appropriatel * Set max_tokens appropriately * Use 16k model for HyDE * Use updated max_tokens logic in async call * Reduce context size needed for HyDE
neulab · Sep 11, 2023 · 5ca2858 · 5ca2858
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diff --git a/prompt2model/model_retriever/generate_hypothetical_document.py b/prompt2model/model_retriever/generate_hypothetical_document.py
@@ -7,7 +7,7 @@
 from prompt2model.prompt_parser import PromptSpec
 from prompt2model.utils import API_ERRORS, api_tools, handle_api_error
 
-PROMPT_PREFIX = '''HuggingFace contains models, which are each given a user-generated description. The first section of the description, delimited with two "---" lines, consists of a YAML description of the model. This may contain fields like "language" (supported by model), "datasets" (used to train the model), "tags" (e.g. tasks relevant to the model), and "metrics" (used to evaluate the model). Create a hypothetical HuggingFace model description that would satisfy a given user instruction. Here are some examples:
+PROMPT_PREFIX = """HuggingFace contains models, which are each given a user-generated description. The first section of the description, delimited with two "---" lines, consists of a YAML description of the model. This may contain fields like "language" (supported by model), "datasets" (used to train the model), "tags" (e.g. tasks relevant to the model), and "metrics" (used to evaluate the model). Create a hypothetical HuggingFace model description that would satisfy a given user instruction. Here are some examples:
 
 Instruction: "Give me some translation from English to Vietnamese. Input English and output Vietnamese."
 Hypothetical model description:
@@ -70,10 +70,6 @@
 
 - prepro:  normalization + SentencePiece (spm32k,spm32k)
 
-- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-vie/opus-2020-06-17.zip
-
-- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-vie/opus-2020-06-17.test.txt
-
 - src_alpha3: eng
 
 - tgt_alpha3: vie
@@ -102,14 +98,6 @@
 
 - long_pair: eng-vie
 
-- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
-
-- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
-
-- port_machine: brutasse
-
-- port_time: 2020-08-21-14:41
-
 
 Instruction: "I want to summarize things like news articles."
 Hypothetical model description:
@@ -144,58 +132,11 @@
       type: rouge
       value: 23.8845
       verified: true
-    - name: ROUGE-LSUM
-      type: rouge
-      value: 32.9017
-      verified: true
-    - name: loss
-      type: loss
-      value: 2.5757133960723877
-      verified: true
-    - name: gen_len
-      type: gen_len
-      value: 76.3984
-      verified: true
 ---
 
 ## Model description
 [PEGASUS](https://github.com/google-research/pegasus) fine-tuned for summarization
 
-## Install "sentencepiece" library required for tokenizer
-```
-pip install sentencepiece
-```
-
-## Model in Action 🚀
-```
-import torch
-from transformers import PegasusForConditionalGeneration, PegasusTokenizer
-model_name = 'tuner007/pegasus_summarizer'
-torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = PegasusTokenizer.from_pretrained(model_name)
-model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
-
-def get_response(input_text):
-  batch = tokenizer([input_text],truncation=True,padding='longest',max_length=1024, return_tensors="pt").to(torch_device)
-  gen_out = model.generate(**batch,max_length=128,num_beams=5, num_return_sequences=1, temperature=1.5)
-  output_text = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
-  return output_text
-```
-#### Example:
-context = """"
-India wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England on Wednesday. Pant revealed the incident made India skipper Virat Kohli "upset". "I think, somebody threw a ball inside, at Siraj, so he [Kohli] was upset," said Pant in a virtual press conference after the close of the first day\'s play."You can say whatever you want to chant, but don\'t throw things at the fielders and all those things. It is not good for cricket, I guess," he added.In the third session of the opening day of the third Test, a section of spectators seemed to have asked Siraj the score of the match to tease the pacer. The India pacer however came with a brilliant reply as he gestured 1-0 (India leading the Test series) towards the crowd.Earlier this month, during the second Test match, there was some bad crowd behaviour on a show as some unruly fans threw champagne corks at India batsman KL Rahul.Kohli also intervened and he was seen gesturing towards the opening batsman to know more about the incident. An over later, the TV visuals showed that many champagne corks were thrown inside the playing field, and the Indian players were visibly left frustrated.Coming back to the game, after bundling out India for 78, openers Rory Burns and Haseeb Hameed ensured that England took the honours on the opening day of the ongoing third Test.At stumps, England\'s score reads 120/0 and the hosts have extended their lead to 42 runs. For the Three Lions, Burns (52*) and Hameed (60*) are currently unbeaten at the crease.Talking about the pitch on opening day, Pant said, "They took the heavy roller, the wicket was much more settled down, and they batted nicely also," he said. "But when we batted, the wicket was slightly soft, and they bowled in good areas, but we could have applied [ourselves] much better."Both England batsmen managed to see off the final session and the hosts concluded the opening day with all ten wickets intact, extending the lead to 42.(ANI)
-"""
-
-```
-get_response(context)
-```
-#### Output:
-Team India wicketkeeper-batsman Rishabh Pant has said that Virat Kohli was "upset" after someone threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England. "You can say whatever you want to chant, but don't throw things at the fielders and all those things. It's not good for cricket, I guess," Pant added.'
-
-#### [Inshort](https://www.inshorts.com/) (60 words News summary app, rated 4.4 by 5,27,246+ users on android playstore) summary:
-India wicketkeeper-batsman Rishabh Pant has revealed that captain Virat Kohli was upset with the crowd during the first day of Leeds Test against England because someone threw a ball at pacer Mohammed Siraj. Pant added, "You can say whatever you want to chant, but don't throw things at the fielders and all those things. It is not good for cricket."
-
-
 > Created by [Arpit Rajauria](https://twitter.com/arpit_rajauria)
 [![Twitter icon](https://cdn0.iconfinder.com/data/icons/shift-logotypes/32/Twitter-32.png)](https://twitter.com/arpit_rajauria)
 
@@ -233,95 +174,6 @@ def get_response(input_text):
       name: Accuracy
       verified: true
       verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2YyOGMxYjY2Y2JhMjkxNjIzN2FmMjNiNmM2ZWViNGY3MTNmNWI2YzhiYjYxZTY0ZGUyN2M1NGIxZjRiMjQwZiIsInZlcnNpb24iOjF9.uui0srxV5ZHRhxbYN6082EZdwpnBgubPJ5R2-Wk8HTWqmxYE3QHidevR9LLAhidqGw6Ih93fK0goAXncld_gBg
-    - type: precision
-      value: 0.8978260869565218
-      name: Precision
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzgwYTYwYjA2MmM0ZTYwNDk0M2NmNTBkZmM2NGNhYzQ1OGEyN2NkNDQ3Mzc2NTQyMmZiNDJiNzBhNGVhZGUyOSIsInZlcnNpb24iOjF9.eHjLmw3K02OU69R2Au8eyuSqT3aBDHgZCn8jSzE3_urD6EUSSsLxUpiAYR4BGLD_U6-ZKcdxVo_A2rdXqvUJDA
-    - type: recall
-      value: 0.9301801801801802
-      name: Recall
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGIzM2E3MTI2Mzc2MDYwNmU3ZTVjYmZmZDBkNjY4ZTc5MGY0Y2FkNDU3NjY1MmVkNmE3Y2QzMzAwZDZhOWY1NiIsInZlcnNpb24iOjF9.PUZlqmct13-rJWBXdHm5tdkXgETL9F82GNbbSR4hI8MB-v39KrK59cqzFC2Ac7kJe_DtOeUyosj34O_mFt_1DQ
-    - type: auc
-      value: 0.9716626673402374
-      name: AUC
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDM0YWIwZmQ4YjUwOGZmMWU2MjI1YjIxZGQ2MzNjMzRmZmYxMzZkNGFjODhlMDcyZDM1Y2RkMWZlOWQ0MWYwNSIsInZlcnNpb24iOjF9.E7GRlAXmmpEkTHlXheVkuL1W4WNjv4JO3qY_WCVsTVKiO7bUu0UVjPIyQ6g-J1OxsfqZmW3Leli1wY8vPBNNCQ
-    - type: f1
-      value: 0.9137168141592922
-      name: F1
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGU4MjNmOGYwZjZjMDQ1ZTkyZTA4YTc1MWYwOTM0NDM4ZWY1ZGVkNDY5MzNhYTQyZGFlNzIyZmUwMDg3NDU0NyIsInZlcnNpb24iOjF9.mW5ftkq50Se58M-jm6a2Pu93QeKa3MfV7xcBwvG3PSB_KNJxZWTCpfMQp-Cmx_EMlmI2siKOyd8akYjJUrzJCA
-    - type: loss
-      value: 0.39013850688934326
-      name: loss
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTZiNzAyZDc0MzUzMmE1MGJiN2JlYzFiODE5ZTNlNGE4MmI4YzRiMTc2ODEzMTUwZmEzOTgxNzc4YjJjZTRmNiIsInZlcnNpb24iOjF9.VqIC7uYC-ZZ8ss9zQOlRV39YVOOLc5R36sIzCcVz8lolh61ux_5djm2XjpP6ARc6KqEnXC4ZtfNXsX2HZfrtCQ
-  - task:
-      type: text-classification
-      name: Text Classification
-    dataset:
-      name: sst2
-      type: sst2
-      config: default
-      split: train
-    metrics:
-    - type: accuracy
-      value: 0.9885521685548412
-      name: Accuracy
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2I3NzU3YzhmMDkxZTViY2M3OTY1NmI0ZTdmMDQxNjNjYzJiZmQxNzczM2E4YmExYTY5ODY0NDBkY2I4ZjNkOCIsInZlcnNpb24iOjF9.4Gtk3FeVc9sPWSqZIaeUXJ9oVlPzm-NmujnWpK2y5s1Vhp1l6Y1pK5_78wW0-NxSvQqV6qd5KQf_OAEpVAkQDA
-    - type: precision
-      value: 0.9881965062029833
-      name: Precision Macro
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDdlZDMzY2I3MTAwYTljNmM4MGMyMzU2YjAzZDg1NDYwN2ZmM2Y5OWZhMjUyMGJiNjY1YmZiMzFhMDI2ODFhNyIsInZlcnNpb24iOjF9.cqmv6yBxu4St2mykRWrZ07tDsiSLdtLTz2hbqQ7Gm1rMzq9tdlkZ8MyJRxtME_Y8UaOG9rs68pV-gKVUs8wABw
-    - type: precision
-      value: 0.9885521685548412
-      name: Precision Micro
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjFlYzAzNmE1YjljNjUwNzBjZjEzZDY0ZDQyMmY5ZWM2OTBhNzNjYjYzYTk1YWE1NjU3YTMxZDQwOTE1Y2FkNyIsInZlcnNpb24iOjF9.jnCHOkUHuAOZZ_ZMVOnetx__OVJCS6LOno4caWECAmfrUaIPnPNV9iJ6izRO3sqkHRmxYpWBb-27GJ4N3LU-BQ
-    - type: precision
-      value: 0.9885639626373408
-      name: Precision Weighted
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGUyODFjNjBlNTE2MTY3ZDAxOGU1N2U0YjUyY2NiZjhkOGVmYThjYjBkNGU3NTRkYzkzNDQ2MmMwMjkwMWNiMyIsInZlcnNpb24iOjF9.zTNabMwApiZyXdr76QUn7WgGB7D7lP-iqS3bn35piqVTNsv3wnKjZOaKFVLIUvtBXq4gKw7N2oWxvWc4OcSNDg
-    - type: recall
-      value: 0.9886145346602994
-      name: Recall Macro
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTU1YjlhODU3YTkyNTdiZDcwZGFlZDBiYjY0N2NjMGM2NTRiNjQ3MDNjNGMxOWY2ZGQ4NWU1YmMzY2UwZTI3YSIsInZlcnNpb24iOjF9.xaLPY7U-wHsJ3DDui1yyyM-xWjL0Jz5puRThy7fczal9x05eKEQ9s0a_WD-iLmapvJs0caXpV70hDe2NLcs-DA
-    - type: recall
-      value: 0.9885521685548412
-      name: Recall Micro
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODE0YTU0MDBlOGY4YzU0MjY5MzA3OTk2OGNhOGVkMmU5OGRjZmFiZWI2ZjY5ODEzZTQzMTI0N2NiOTVkNDliYiIsInZlcnNpb24iOjF9.SOt1baTBbuZRrsvGcak2sUwoTrQzmNCbyV2m1_yjGsU48SBH0NcKXicidNBSnJ6ihM5jf_Lv_B5_eOBkLfNWDQ
-    - type: recall
-      value: 0.9885521685548412
-      name: Recall Weighted
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWNkNmM0ZGRlNmYxYzIwNDk4OTI5MzIwZWU1NzZjZDVhMDcyNDFlMjBhNDQxODU5OWMwMWNhNGEzNjY3ZGUyOSIsInZlcnNpb24iOjF9.b15Fh70GwtlG3cSqPW-8VEZT2oy0CtgvgEOtWiYonOovjkIQ4RSLFVzVG-YfslaIyfg9RzMWzjhLnMY7Bpn2Aw
-    - type: f1
-      value: 0.9884019815052447
-      name: F1 Macro
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmM4NjQ5Yjk5ODRhYTU1MTY3MmRhZDBmODM1NTg3OTFiNWM4NDRmYjI0MzZkNmQ1MzE3MzcxODZlYzBkYTMyYSIsInZlcnNpb24iOjF9.74RaDK8nBVuGRl2Se_-hwQvP6c4lvVxGHpcCWB4uZUCf2_HoC9NT9u7P3pMJfH_tK2cpV7U3VWGgSDhQDi-UBQ
-    - type: f1
-      value: 0.9885521685548412
-      name: F1 Micro
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDRmYWRmMmQ0YjViZmQxMzhhYTUyOTE1MTc0ZDU1ZjQyZjFhMDYzYzMzZDE0NzZlYzQyOTBhMTBhNmM5NTlkMiIsInZlcnNpb24iOjF9.VMn_psdAHIZTlW6GbjERZDe8MHhwzJ0rbjV_VJyuMrsdOh5QDmko-wEvaBWNEdT0cEKsbggm-6jd3Gh81PfHAQ
-    - type: f1
-      value: 0.9885546181087554
-      name: F1 Weighted
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjUyZWFhZDZhMGQ3MzBmYmRiNDVmN2FkZDBjMjk3ODk0OTAxNGZkMWE0NzU5ZjI0NzE0NGZiNzM0N2Y2NDYyOSIsInZlcnNpb24iOjF9.YsXBhnzEEFEW6jw3mQlFUuIrW7Gabad2Ils-iunYJr-myg0heF8NEnEWABKFE1SnvCWt-69jkLza6SupeyLVCA
-    - type: loss
-      value: 0.040652573108673096
-      name: loss
-      verified: true
-      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTc3YjU3MjdjMzkxODA5MjU5NGUyY2NkMGVhZDg3ZWEzMmU1YWVjMmI0NmU2OWEyZTkzMTVjNDZiYTc0YjIyNCIsInZlcnNpb24iOjF9.lA90qXZVYiILHMFlr6t6H81Oe8a-4KmeX-vyCC1BDia2ofudegv6Vb46-4RzmbtuKeV6yy6YNNXxXxqVak1pAg
 ---
 
 # DistilBERT base uncased finetuned SST-2
@@ -345,26 +197,6 @@ def get_response(input_text):
     - [Model Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification)
     - [DistilBERT paper](https://arxiv.org/abs/1910.01108)
 
-## How to Get Started With the Model
-
-Example of single-label classification:
-
-```python
-import torch
-from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-
-tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
-model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
-
-inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-with torch.no_grad():
-    logits = model(**inputs).logits
-
-predicted_class_id = logits.argmax().item()
-model.config.id2label[predicted_class_id]
-
-```
-
 ## Uses
 
 #### Direct Use
@@ -379,13 +211,7 @@ def get_response(input_text):
 
 Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations.
 
-For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for the positive label depending on the country (0.89 if the country is France, but 0.08 if the country is Afghanistan) when nothing in the input indicates such a strong semantic shift. In this [colab](https://colab.research.google.com/gist/ageron/fb2f64fb145b4bc7c49efc97e5f114d3/biasmap.ipynb), [Aurélien Géron](https://twitter.com/aureliengeron) made an interesting map plotting these probabilities for each country.
-
-<img src="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/map.jpeg" alt="Map of positive probabilities per country." width="500"/>
-
-We strongly advise users to thoroughly probe these aspects on their use-cases in order to evaluate the risks of this model. We recommend looking at the following bias evaluation datasets as a place to start: [WinoBias](https://huggingface.co/datasets/wino_bias), [WinoGender](https://huggingface.co/datasets/super_glue), [Stereoset](https://huggingface.co/datasets/stereoset).
-
-
+For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for the positive label depending on the country (0.89 if the country is France, but 0.08 if the country is Afghanistan) when nothing in the input indicates such a strong semantic shift.
 
 # Training
 
@@ -394,19 +220,8 @@ def get_response(input_text):
 
 
 The authors use the following Stanford Sentiment Treebank([sst2](https://huggingface.co/datasets/sst2)) corpora for the model.
-
-#### Training Procedure
-
-###### Fine-tuning hyper-parameters
-
-
-- learning_rate = 1e-5
-- batch_size = 32
-- warmup = 600
-- max_seq_length = 128
-- num_train_epochs = 3.0
 ```
-:'''  # noqa: E501
+:"""  # noqa: E501
 
 
 def generate_hypothetical_model_description(

diff --git a/prompt2model/utils/api_tools.py b/prompt2model/utils/api_tools.py
@@ -58,6 +58,8 @@ def __init__(
         if max_tokens is None:
             try:
                 self.max_tokens = litellm.utils.get_max_tokens(model_name)
+                if isinstance(self.max_tokens, dict):
+                    self.max_tokens = self.max_tokens["max_tokens"]
             except Exception:
                 pass
 
@@ -86,7 +88,12 @@ def generate_one_completion(
             An OpenAI-like response object if there were no errors in generation.
             In case of API-specific error, Exception object is captured and returned.
         """
-        max_tokens = self.max_tokens or 4 * count_tokens_from_string(prompt)
+        num_prompt_tokens = count_tokens_from_string(prompt)
+        if self.max_tokens:
+            max_tokens = self.max_tokens - num_prompt_tokens
+        else:
+            max_tokens = 4 * num_prompt_tokens
+
         response = completion(  # completion gets the key from os.getenv
             model=self.model_name,
             messages=[
@@ -169,9 +176,12 @@ async def _throttled_completion_acreate(
                         await asyncio.sleep(10)
                 return {"choices": [{"message": {"content": ""}}]}
 
-        max_tokens = self.max_tokens or 4 * max(
-            count_tokens_from_string(prompt) for prompt in prompts
-        )
+        num_prompt_tokens = max(count_tokens_from_string(prompt) for prompt in prompts)
+        if self.max_tokens:
+            max_tokens = self.max_tokens - num_prompt_tokens
+        else:
+            max_tokens = 4 * num_prompt_tokens
+
         async_responses = [
             _throttled_completion_acreate(
                 model=self.model_name,