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* Rename FusedOps to TernaryOps * Support ternary broadcast * Add where llop and mlop * Make where op work in cstyle codegen * Don't skip test_inf_where * Add backward path to where op * Use bool in cstyle codegen * Add LLVM where op * Add numpy where op * Add torch where op * Simplify where mlop * Update documentation * Forgot a rename * Merged relevant changes from PR #1195 onto PR #1196 * Add test to cover changes to linearizer.ast_parse for WHERE op Without this METAL will try to use ternary op on float4 and fail * Make where op work in wgsl backend * Allow ternary ops to be merged * Make mypy happy --------- Co-authored-by: Francis Lam <flam@alum.mit.edu>
36 lines
1.8 KiB
Markdown
36 lines
1.8 KiB
Markdown
# Adding a new accelerator to tinygrad
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It's pretty easy to add a new accelerator to tinygrad. All you need to do is implement a total of 27 (optionally 28) low level ops. Then tinygrad takes care of the rest, handling derivatives and syntactic sugar.
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## llops
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These are the ops that you must implement for your accelerator of choice. Compiled Accelerators do not need to implement movement_ops, as they are handled by the ShapeTracker.
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```
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Buffer # class of memory on this device
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unary_op (NOOP, EXP2, LOG2, CAST, SIN, SQRT) # A -> A
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reduce_op (SUM, MAX) # A -> B (smaller size, B has 1 in shape)
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binary_op (ADD, SUB, MUL, DIV, CMPEQ, MAX) # A + A -> A (all the same size)
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movement_op (EXPAND, RESHAPE, PERMUTE, PAD, SHRINK, STRIDE) # A -> B (different size)
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load_op (EMPTY, RAND, CONST, FROM, CONTIGUOUS, CUSTOM) # -> A (initialize data on device)
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ternary_op (WHERE) # A, A, A -> A
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ternary_op [[optional]] (MULACC) # A * A -> B
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```
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## mlops
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These are the mid level ops that handle the derivatives.
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```
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Relu, Log, Exp, Sin # unary ops
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Sum, Max # reduce ops (with axis argument)
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Maximum, Add, Sub, Mul, Pow, Div, Equal # binary ops (no broadcasting, use expand)
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Expand, Reshape, Permute, Pad, Shrink, Flip # movement ops
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Where # ternary ops
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```
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These are implemented in [mlops.py](/tinygrad/mlops.py).
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## hlops
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These are the syntax sugar. They are built on top of the mlops and support most of the things that you could expect from a tensor library.
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These are implemented in [tensor.py](/tinygrad/tensor.py).
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