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* start mkdocs * simple docs for tensor * more docs * move those back * more docs * copy markdown extensions * docs legacy * docs building workflow * fix showcase links * only that? * install tinygrad * add docs to setup.py * Delete examples/llm.c/data
26 lines
1.2 KiB
Markdown
26 lines
1.2 KiB
Markdown
tinygrad has four pieces
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* frontend (Tensor -> LazyBuffer)
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* See tensor.py, function.py, multi.py, and lazy.py
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* The user interacts with the Tensor class
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* This outputs LazyBuffers, which form the simple compute graph
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* scheduler (LazyBuffer -> ScheduleItem)
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* See engine/schedule.py
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* When a Tensor is realized, the scheduler is run to get its LazyBuffers to be computed
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* This takes in LazyBuffers and groups them as appropriate into kernels.
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* It returns a list of ScheduleItems + all the Variables used in the graph
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* lowering (TODO: lots of work to clean this up still)
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* See codegen/ (ScheduleItem.ast -> UOps)
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* ScheduleItems have an ast that's compiled into actual GPU code
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* Many optimization choices can be made here, this contains a beam search.
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* renderer/compiler (UOps -> machine code)
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* UOps are tinygrad's IR, similar to LLVM IR
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* Here we either convert them to a high level language or machine code directly
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* engine/realize.py (ScheduleItem -> ExecItem)
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* runtime
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* See runtime/
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* Runtime actually interacts with the GPUs
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* It manages Buffers, Programs, and Queues
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* Sadly, METAL and GPU (OpenCL) don't have a compiler that can be pulled out from the device itself
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