How you can Construct Reminiscence-Environment friendly Transformers with xFormers Utilizing Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Consideration
print(“n” + “=”*70 + “n4. Variable-length packed batch — no padding wasten” + “=”*70) seqlens = [37, 120, 8, 200] whole = sum(seqlens) H, Ok = 8, 64 q = torch.randn(1, whole, H, Ok, machine=machine, dtype=torch.float16) ok = torch.randn(1, whole, H, Ok, machine=machine, dtype=torch.float16) v = torch.randn(1, whole, H, Ok, machine=machine, dtype=torch.float16) attempt: bias =…
