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> implemented tensor slicing by using .slice() in fully stride-aware, views share memory while adjusting offset, shape, and strides for efficient sub-tensors. > added transpose logic similar to pytorch, no data copy, just stride permutation for fast and memory-efficient…
> implemented tensor slicing by using .slice() in fully stride-aware, views share memory while adjusting offset, shape, and strides for efficient sub-tensors. > added transpose logic similar to pytorch, no data copy, just stride permutation for fast and memory-efficient… https://t.co/T5yyDnwlOE
~50 people are using this lmao, its been almost a month
~50 people are using this lmao, its been almost a month
> rewrote maths ops with stride-aware, so operations now use strides instead of assuming contiguous memory. > this makes elementwise ops work correctly on views, slices, and transposed tensors. > framework is now stride-aware for all element wise ops with broadcasting support.
> rewrote maths ops with stride-aware, so operations now use strides instead of assuming contiguous memory. > this makes elementwise ops work correctly on views, slices, and transposed tensors. > framework is now stride-aware for all element wise ops with broadcasting support. https://t.co/bofMTZHkeb
> removed unflatten_index and rewriting math ops with stride-aware iteration now > this makes ops work on views (slice/transpose) without copying and replaces slow div/mod loops with cheap stride math > future ops (broadcasting, slicing, batching) will work without extra steps.
> removed unflatten_index and rewriting math ops with stride-aware iteration now > this makes ops work on views (slice/transpose) without copying and replaces slow div/mod loops with cheap stride math > future ops (broadcasting, slicing, batching) will work without extra steps. https://t.co/FWGzebnioR
shifting to linux, amazing trick
> implemented 2D matmul with correct shape logic (%) > I also have to rewrite math ops to stride-aware so that tensors created by views (slice, transpose) can share same memory without copying. If ops only use flat indexing, those views break on non-contiguous tensors.
> implemented 2D matmul with correct shape logic (%) > I also have to rewrite math ops to stride-aware so that tensors created by views (slice, transpose) can share same memory without copying. If ops only use flat indexing, those views break on non-contiguous tensors. https://t.co/Pavkh5Jho2
> added add, sub, mul, div for elementwise tensor math with full broadcasting support, so tensors of different but compatible shapes interact automatically. > added shared memory for views/reshape, letting tensors share data without copying. > framework can do basic math now.
> added add, sub, mul, div for elementwise tensor math with full broadcasting support, so tensors of different but compatible shapes interact automatically. > added shared memory for views/reshape, letting tensors share data without copying. > framework can do basic math now. https://t.co/oj0VuMlvMp
my dl framework can do this too now btw (in c++)
my dl framework can do this too now btw (in c++) https://t.co/Pngu8pfZAc
> added metadata (dtype, device, owns_data) > added reshape to create views without copying memory. > improved printing to show dtype and device. > added flat and multidimensional indexing for direct + stride based element access.
> added metadata (dtype, device, owns_data) > added reshape to create views without copying memory. > improved printing to show dtype and device. > added flat and multidimensional indexing for direct + stride based element access. https://t.co/hW8zxyUe7i
incase if anyone needs it: github.com/Mog9/micrograd…
incase if anyone needs it: github.com/Mog9/micrograd…
Implemented the first step and built the core skeleton of a tensor: > checks valid data vs shape > computes strides > track of grad storage (0 for now) > doesnt do any math yet > prints tensor in readable format.
Implemented the first step and built the core skeleton of a tensor: > checks valid data vs shape > computes strides > track of grad storage (0 for now) > doesnt do any math yet > prints tensor in readable format. https://t.co/gmwHtqs1Zt
used a different approach and it worked. the output for the equation gives correct forward and grad results. used to 2 equations and both gave correct output. the code can be made way better, but im still learning and its fine, made a working micrograd in c++
used a different approach and it worked. the output for the equation gives correct forward and grad results. used to 2 equations and both gave correct output. the code can be made way better, but im still learning and its fine, made a working micrograd in c++ https://t.co/xDRHmfrgZL
I tried to convert andrej karpathy's micrograd python code to c++ I would say 95% is working but there is still small issue where its giving wrong grad output, been on this for HOURS trying to fix
I tried to convert andrej karpathy's micrograd python code to c++ I would say 95% is working but there is still small issue where its giving wrong grad output, been on this for HOURS trying to fix https://t.co/8BoDRPsl7D
for hours thinking there is smtn wrong with my code and thats why its not giving the gradient output but my dumbass didnt even print grad, i wanna kms 😭
the knowledge u get on how autograd/backpropagation works under the hood from this lecture is insane youtube.com/watch?v=VMj-3S…
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