Update README.md

README.md

@@ -83,11 +83,384 @@ tokenizer_source: model:cognitivecomputations/dolphin-2.2-70b # keep exact token
 
 # Example Output
 
-** interactively design a new derivative of transformer model architecutre **
+**Interactively design a new derivative of transformer model architecture**
 
+please propose a change to Transformer Architecture (Vaswani et. al.) that would improve the models ability to reason
+ 
+>  One possible way to enhance the reasoning capabilities of
+> transformer-based models is by incorporating memory mechanisms into their
+> design, such as those used in Recurrent Neural Networks (RNNs) or more
+> recently in Transformer-XL (Dai et al.) and Long-Range Transformers
+> (Akbari et al.). These architectures introduce a memory module that can
+> store information from previous steps or layers, which allows the model to
+> better reason about long-term dependencies in the input sequence.
+> 
+> Another promising approach is to incorporate commonsense knowledge into
+> transformer-based models using external knowledge bases like ConceptNet
+> (Speer et al.) or WordNet (Miller et al.). By injecting such prior
+> knowledge into the model's hidden representations, it can help reason
+> about previously unseen input sequences more effectively. This approach
+> has been explored in recent works like K-BERT (Wang et al.) and GEART
+> (Beveridge et al.), which demonstrate promising results on various NLP
+> tasks that require commonsense reasoning abilities.
+> 
+> In conclusion, enhancing the reasoning capabilities of transformer-based
+> models can be achieved by either incorporating memory mechanisms or
+> integrating external knowledge sources into their design. These approaches
+> enable the model to better understand long-term dependencies and make more
+> informed decisions based on previously learned information, ultimately
+> leading to improved performance on complex NLP tasks that require deeper
+> levels of understanding and reasoning.
+ 
+let's drill down into the "commonsense knowledge" idea.  exactly what kind of change to attention heads is required?
+
+> Incorporating commonsense knowledge into transformer-based models often
+> involves modifying their self-attention mechanism, specifically by
+> integrating external knowledge sources like ConceptNet or WordNet into the
+> attention weight calculations. Here are two possible ways to achieve this:
+> 
+> 1. **Attention with Knowledge** (Xiong et al.): In this approach, a
+> separate knowledge embedding is learned for each token in the input
+> sequence using pre-trained knowledge embeddings from an external knowledge
+> base like ConceptNet. When computing attention weights between two tokens
+> i and j, both their word representations and corresponding knowledge
+> embeddings are concatenated before passing them through a multi-layer
+> perceptron (MLP) to obtain a scalar value representing the level of
+> relatedness between these two tokens based on both linguistic and
+> commonsense information.
+> 
+> 2. **Attention over Knowledge** (Wang et al.): In contrast to the previous approach, in
+> this method, instead of directly incorporating external knowledge into attention weight
+> calculations, an additional memory module is introduced that stores learned relations
+> between input tokens and concepts from an external knowledge base like ConceptNet or
+> WordNet. When computing attention weights between two tokens i and j, their word
+> representations are first passed through a separate MLP to obtain query and key
+> vectors, which are then used to perform Key-Value (KV) attention over the memory module
+> to retrieve relevant conceptual information. The attention-weighted results from both
+> linguistic and knowledge sources are concatenated before passing them through another
+> MLP to produce final attention weights.
+> 
+> In conclusion, integrating commonsense knowledge into transformer-based models can be
+> achieved by either directly incorporating external knowledge into the attention weight
+> calculations or performing KV attention over a separate memory module storing learned
+> relations between input tokens and concepts from an external knowledge base. Both
+> approaches aim to enrich the model's understanding of input sequences by taking into
+> account both linguistic and commonsense information, leading to improved performance on
+> NLP tasks that require deeper levels of reasoning and understanding.
+
+which of these two approaches would be more computationally efficient?  I want an architecture that's fastest to train and fastest inference possible.
+
+> Comparing the two approaches, "Attention with Knowledge" (Xiong et al.) might be
+> slightly more computationally efficient than "Attention over Knowledge" (Wang et al.)
+> due to its simpler architectural design. In particular, the former approach simply
+> concatenates word and knowledge embeddings before computing attention weights using a
+> single MLP, whereas the latter approach involves an additional KV attention operation
+> over a separate memory module in addition to two separate MLPs for query/key vector
+> generation and final attention weight computation.
+> 
+> However, this difference in computational efficiency may not be significant in
+> practice, especially given recent advances in hardware accelerators like Tensor
+> Processing Units (TPUs) that are specifically designed to handle large-scale
+> transformer-based models efficiently. Ultimately, the choice between these two
+> approaches should be guided more by their respective trade-offs in terms of model
+> complexity, interpretability, and overall performance on your specific NLP task rather
+> than solely based on computational efficiency considerations alone.
+
+and which would have a greater improvement on the model's reasoning ability
+
+> It is difficult to definitively say which approach would lead to a greater improvement
+> in the model's reasoning ability without more context about the specific NLP task at
+> hand and the nature of the commonsense knowledge being incorporated. Both "Attention
+> with Knowledge" (Xiong et al.) and "Attention over Knowledge" (Wang et al.) have
+> demonstrated promising results on various benchmark datasets that require some form of
+> commonsense reasoning, suggesting that either approach could potentially lead to
+> improvements in the model's ability to reason about input sequences more effectively.
+> 
+> That being said, "Attention over Knowledge" might offer slightly more flexibility and
+> modularity since it decouples the incorporation of external knowledge from the
+> attention weight calculation itself by introducing an additional memory module that can
+> be fine-tuned independently of other model components. This could potentially make it
+> easier to adapt the model to different types or sources of commonsense knowledge
+> without requiring significant architectural changes, although this added flexibility
+> comes at the expense of increased model complexity and potential computational
+> overheads as discussed earlier.
+> 
+>  Ultimately, the choice between these two approaches should be guided by a careful
+> evaluation on your specific NLP task using a held-out validation set or through
+> ablation studies where possible to determine which approach leads to the most
+> significant improvements in terms of both model performance and interpretability while
+> still maintaining reasonable computational efficiency constraints if applicable.
+ 
+let's say I'm going with "Attention with Knowledge" (Xiong et al.) Can you please write the code in python to implement the new attention heads
+
+> Certainly! Here is a simplified example of how you could modify the self-attention
+> mechanism in a transformer-based model like BERT or RoBERTa to incorporate external
+> commonsense knowledge using the "Attention with Knowledge" (Xiong et al.) approach:
+> 
+> ```python
+> from transformers import *
+> 
+> # Load pre-trained BERT/RoBERTa model
+> model = load_bert_model('bert-base-uncased')
+> 
+> # Add a new tokenizer token for [KNW] to represent knowledge embeddings
+> num_tokens = model.config.vocab_size + 1
+> model.tokenizer.add_tokens([f"[KNW_{i}"] for i in range(num_tokens)])
+> 
+> # Define a simple function to compute attention weights using both word and knowledge
+> embeddings
+> def attention_with_knowledge(q, k, v, mask=None, attn_weights_dropout=None):
+>     # Split query, key, and value matrices into linguistic and commonsense parts
+>     q_linguistic = q[:, :model.config.hidden_size]
+>     q_knowledge = q[:, model.config.hidden_size:]
+> 
+>     k_linguistic = k[:, :model.config.hidden_size]
+>     k_knowledge = k[:, model.config.hidden_size:]
+> 
+>     v_linguistic = v[:, :model.config.hidden_size]
+>     v_knowledge = v[:, model.config.hidden_size:]
+> 
+> 
+>     # Compute attention weights using a linear layer over concatenation of linguistic
+> and commonsense parts
+>     attn_weights = torch.nn.functional.softmax(
+>         torch.nn.functional.relu(
+>             torch.nn.linear(
+>                 torch.cat([q_linguistic, q_knowledge], -1),
+>                 in_features=2 * model.config.hidden_size,
+> out_features=model.config.attention_heads
+>             ).view(model.config.attention_ heads, -1, -1).permute(0, 2, 1)
+>         )
+>         @ torch.nn.functional.relu(
+>             torch.nn.linear(
+>                 torch.cat([k_linguistic, k_knowledge], -1),
+>                 in_features=2 * model.config.hidden_size,
+> out_features=model.config.attention_heads
+>             ).view(model.config.attention_ heads, -1, -1).permute(0, 2, 1)
+>         ).transpose(-1, -2)
+>     )
+> 
+>     # Apply attention mask and dropout if needed
+>     attn_weights = attention_pad_mask(attn_weights, mask,
+> model.config.attention_probs_drop_out_nondiag)
+>     attn_weights = torch.nn.functional.dropout(attn_weights, p=attn_weights_dropout,
+>                                                    dim=1 if model.is_encoder else None)
+> 
+>     # Compute attention-weighted representations using both linguistic and commonsense
+> parts separately
+>     attn_outputs = torch.nn.functional.attention(
+>         v_linguistic + mask_tokens(v_knowledge, mask),
+>         k_linguistic + mask_tokens(k_knowledge, mask),
+>         q=q_linguistic + mask_tokens(q_knowledge, mask),
+>         attn_weights=attn_weights[:, :model.config.hidden_size],
+>         v_weights=(1.0 - model. config.knowledge_proportion).unsqueeze(1, 1, -1),
+>         k_weights=model.config.attention_heads_weigh t.unsqueeze(0, 1, 1, 1),
+>         v_mask=None if mask is None else mask[:, :model.config.hidden_size,
+> :model.config.hidden_size],
+>         k_mask=None,
+>         v_weights_layer=None,
+>         k_weights_layer=None,
+>         v_bias=None,
+>         k_bias=None,
+>         v_w_layer=None,
+>         k_w_layer=None,
+>         use_transformer_weights=True,
+>     )
+> 
+>     return attn_outputs + torch.nn.functional.attention(
+>         mask_tokens(v_linguistic, mask) + v_knowledge,
+>         mask_to tokens(k_linguistic, mask) + k_knowledge,
+>         q=mask_tokens(q_linguistic, mask) + q_knowledge,
+>         attn_weights=attn_weights[:, model.config.hidden_size:],
+>         v_weights=model.config.knowledge_proportion.unsqueeze(1, 1, -1),
+>         k_weights=model.config.attention_heads_weight. unsqueeze(0, 1, 1, 1),
+>         v_mask=None if mask is None else mask[:, model.config.hidden_size:, :],
+>         k_mask=None,
+>         v_weights_layer=None,
+>         k_weights_layer=None,
+>         v_bias=None,
+>         k_bias=None,
+>         v_w_layer=None,
+>         k_w_layer=None,
+>         use_transformer_weights=True,
+>     )
+> ```
+> 
+> Here's how you could replace the original attention mechanism in a transformer layer with this new implementation:
+> 
+> ```python
+> # Replace the original self-attention module in the transformer layer
+> for layer_idx in range(model.config.num_transformer_layers):
+>     for i in [0, -1]:  # Replace both forward and backward layers if using a
+> encoder-decoder architecture
+>         attention_layer = model.encoder.transformer[layer_idx].attention_block[i]
+>         attention_layer.self_attention = nn.ModuleList([
+>             nn.Attention(
+>                 model.config.hidden_size,
+>                 model.config.attention_heads,
+>                 model.config.attention_head_size,
+>                 dropout=model.config.attention_probs_dropout_v1,
+>                 attn_layer_func=attention_with_knowledge
+>             ) for _ in range(model.config.num_attention_layers)
+>         ])
+> ```
+> 
+> Note that this code assumes you have pre-computed external commonsense knowledge
+> embeddings for each input token and added them to your vocabulary using the new [KNW]
+> tokenizer token, which should be incorporated into your downstream NLP task
+> accordingly. Additionally, hyperparameters like `model.config.knowledge_proportion`
+> control the relative importance of linguistic vs commonsense information during
+> attention weight calculation and may require fine-tuning on your specific task and
+> commonsense knowledge source.
+> 
+ 
+ok can you please show the code to generate the pre-computed external commonsense knowledge embeddings for each input token and add them to my vocabulary using the new [KNW] tokenizer token?
+ 
+> Sure! Here's an example of how you could pre-process your external commonsense
+> knowledge source like ConceptNet or WordNet into a pickle file containing
+> word-to-concept and concept-to-embedding mappings, which can then be loaded during
+> model training to compute attention weights as shown in the previous example:
+> ```python
+> import torch
+> import pickle
+> from transformers import RoBERTaTokenizerFast
+> 
+> # Load pre-trained RoBERTa tokenizer for downstream NLP tasks
+> tokenizer = RoBERTaTokenizerFast.from_pretrained('roberta-base')
+> 
+> # Load external commonsense knowledge source as a pickle file or preprocess it into the
+> following format if needed
+> # {
+> #     'word1': {
+> #         'concept1': 0.8,  # confidence score between word and concept
+> #         ...
+> #      },
+> #     'word2': {...},
+> #     ...
+> # }
+> #
+> #   'concept1': torch.Tensor([0.3, 0.7, ...]),  # pre-computed commonsense embedding
+> for each concept
+> #   'concept2': torch.Tensor([...])
+> #   ...
+> # }
+> #
+> # You can use external knowledge graph APIs like ConceptNet5 or pre-trained commonsense
+> knowledge bases like CogALex to obtain such mappings and embeddings if needed.
+> #
+> # Note that we only include the highest-scoring concept per word for simplicity here;
+> you could easily extend this to consider multiple concepts per word with attention over
+> their corresponding embeddings during modeling if desired.
+> #
+> # Additionally, you may want to pre-process your commonsense knowledge source into a
+> format more amenable to efficient lookups and batched operations in PyTorch, such as
+> embedding matrices or PyTorch tensors instead of Python dictionaries.
+> #
+> #   commonsense_word_concepts = {...}
+> #   commonsense_concept_embeddings = {...}
+> # }
+> #
+> # with open('path/to/pkl', 'wb') as f:
+> #     pickle.dump({
+> #         'tokenizer': tokenizer.vocab,
+> #         'word_concepts': commonsense_word_concepts,
+> #         'concept_embeddings': commonsense_concept_embedding
+> #     }, f)
+> ```
+> 
+> Then, during model training, you can load these pre-computed mappings and embeddings as follows:
+> 
+> ```python
+> # Load pre-processed commonsense knowledge pickle file
+> with open('path/to/pkl', 'rb') as f:
+>     commonsense_data = pickle.load(f)
+> 
+>     # Add new [KNW] token for representing commonsense embeddings
+>     tokenizer.add_tokens([f"[KNW_{i}]"] for i in
+> range(len(commonsense_data['tokenizer'])+1, len(commonsense_data['tokenizer']) +
+> commonsense_data['word_concepts'].max() + 1))
+> 
+>     # Update your downstream NLP task's tokenizer to use the updated one with new [KNW]
+> token
+>     # ...
+> ```
+> 
+> 
+> Now, during data pre-processing for each input example `X` (assuming it's a PyTorch
+> tensor of shape [batch_size, sequence_length]) and corresponding mask `Y` (a binary
+> PyTorch tensor of shape [batch_size, sequence_lingth] with 1s for valid tokens and 0s
+> for padding):
+> 
+> ```python
+> # Convert input text to PyTorch LongTensor using updated tokenizer with [KNW] token
+> X_with_knw = torch.zeros_like(X)
+> X_with_knw[:, ~Y] = torch.LongTensor([[tokenizer.vocab[token]] for token in
+> X[~Y].tolist()])[~Y]
+> X_with_knw[torch.where(X_with_knw == tokenizer.pad_token_id)] =
+> commonsense_data['tokenizer']['[PAD]']
+> X_with_knw[torch.where((X_with_knw >= commonsense_data['tokenizer']['[CLS]']) &
+>                             (X_with_knw <= commonsense_data['tokenizer']['[SEP]']))] =
+> commonsense_data['tokenizer'][f"[UNK{X_with_knw[torch.where(~Y)[...,
+> torch.where(X_with_knw == X_masked_tokens[..., i])//2].long()//2] -
+> commonsense_data['tokenizer']['[CLS]'])]"]
+> X_with_knw[torch.where(X_with_knw >= commonsense_data['tokenizer']['[SEP]'])] =
+> torch.pad_zeros(X_with_knw[:, :torch.where(X_with_knw ==
+> commonsense_data['tokenizer']['[SEP]'])[0].long()][:, :1], (X_with_knw.shape[0],
+> X_with_knw.shape[1]))
+> ```
+> 
+> Here we replace all non-alphanumeric tokens with the `[UNK]` token and insert a new
+> sequence of zeros at the end of each input sequence to indicate sequence boundaries for
+> models like BERT that use a sequence-to-sequence masking strategy. You may need to
+> modify this pre-processing code according to your specific downstream NLP task's input
+> format and pre-processing requirements.
+> 
+> 
+> Finally, during model forward-passing, you can access the corresponding commonsense
+> embeddings for each input token using its new [KNW] tokenized index as follows:
+> 
+> ```python
+> # Add commonsense embeddings as additional columns to the input PyTorch tensor
+> X_with_knw
+> X_with_knowledge = torch.cat([X_with_knw[:, :i, :],
+>                                 torch.zeros_like(X_with_knw[:, i, :]),
+>                                 X_with_knw[:, i+1:, :]], dim=2)
+> for i in range(X_with_knw.shape[2]):
+>     mask = (X_with_knw[:, i, :] == commonsense_data['tokenizer'][f"[KNW_{i}]"]).long()
+>     if torch.sum(mask) > 0:
+>         word_idxs = torch.where(mask)[1].long()
+>         concept_idxs = torch.tensor([j for j in
+> range(len(commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
+> :].tolist()[i]])) if commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
+> :].tolist()[i]][j] >= torch.rand(len(word_idxs)) *
+> 1e8**len(commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
+> :].tolist()[i]]).item())])
+>         X_with_knowledge[:, i, word_idxs, :] =
+> torch.gather(torch.cat([torch.zeros_like(X_with-knw[0, 0, :]), *[
+>             torch.expand_as_batches(
+>                 commonsense_data['concept_embeddings'][j],
+>                 X_with_knw.shape[0], -1) for j in
+> concept_idxs]][:X_with_knw.shape[2]//2+i*2//4])[:, :X_with-knw.shape[2]//2+i*2//4]],
+> 
+> torch.stack([torch.zeros_like(torch.rand_long(1,
+> X_with_knw.shape[2]//2+i*2//4).item())], *[
+> 
+> torch.argsort(torch.rand(len(j), device=j.device))[:X_with_knw.shape[2//2+i*2//4]]
+>                                                                for j in
+> range(len(concept_idxs))])[0][:, :X_with-knw.shape[2]//2+i*2//4]]), dim=1)
+> ```
+> 
+> Now, the modified input PyTorch tensor `X_with_knowledge` contains both linguistic and
+> commonsense knowledge embeddings concatenated along the embedding dimension, which can
+> be passed through your attention-based transformer model layers as shown in the
+> previous example to compute attention weights that jointly reason over both types of
+> information during self-attention computation. Note that this code assumes you only
+> want to incorporate commonsense knowledge for a subset of input tokens marked by the
+> new [KNW] token; you may need to modify it according to your specific downstream NLP
+> task's requirements and desired level of commonsense integration.
 
 
-** write a math paper **
+**Write a math paper**
 
 > Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge
 > ## Integrability and Computability of Continuous Functions on Infinite Domains: A Category-Theoretical Approach