error INFO 04-18 15:38:45 selector.py:51] Cannot use FlashAttention because the package is not found. Please install it for better performance. INFO 04-18 15:38:45 selector.py:25] Using XFormers backend. INFO 04-18 15:38:47 model_runner.py:104] Loading model weights took 3.9080 ...
It calculates the Key-Query-Value vectors of the single input token and append the Key-Values to the KV$ It processes only the single token through all layers of LM but calculate the causal attention of the single token with all the Key-Value vectors in KV$. ...
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Sadly, there’s also no way to strictly disable Meta AI here, which can be doubly frustrating, since I’m probably more likely to search for “how to peel an onion” or “how to cut an avocado” here than on Facebook proper. Just pay attention to the icon next to your suggested sea...
While it may seem intuitive to input prompts in natural language, it actually requires some adjustment of the prompt to achieve the desired output from an LLM. This adjustment process is known as prompt engineering. Once you have a good prompt, you may want to use it as a template for ...
However, when most shows have flash-forwards they only feature one in the premiere and practically have no build-up to it, HTGAWM does the opposite, it features a flash-forward in every one of the first 8 episodes of a season and plays with our mind. By the time you have watched the...
For convolution, we use the standard implementation in PyTorch, which separately performs FFTs on the inputs and the filters, multiply them in frequency domain, then performs an inverse FFT to obtain the result. The theoretical complexity is O(L log(L)) for sequence length L. For attention,...
I expected the batch size to be 8 during the decoding process, such as when executing the paged attention v2 kernel. However, when I added print statements in the file csrc/attention/attention_kernels.cu as blow: void paged_attention_v2( torch::Tensor& out, // [num_seqs, num_heads, he...
this would be done before passing to cross-attention layers. However, this results in less-than-optimal performance gains. The optimized implementation we went with reduces compute and memory by taking advantage of the fact that the repeated tensors are identical, allowing for expansion to ...
other is that FlashAttention2 does not work well on the backward pass still. It is coming, but there are architectural differences that make it tough. AMD’s L1 cache is doubled, but the LDS is still the same size. This is still tougher to make FA2 work versus Nvidia’s larger ...