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* init * removed mulacc * is uoptimize the problem? * lol hax make work temporarily fix l8er * revert extra/ changes * clean up * flaky metal tests? * add back mulacc for metal * revert last commit * try skipping linearizer_failure tests * skip flammit tests... cuz tests all work locally * try narrow down exact linearizer failure test * try 2 * try 4 * generated code is the exact same wtf why CI fails * code for 15 and 17 are exact same with or without mulacc, this should pass * try only 1 failure * try garbage collecting lol... * try del variables lol * try gcing after del lol... * is diskcache the problem??? * try disabling opts cache idk * try remove hack * try disable github metal cache... * try CACHELEVEL=0 :D idk anymore * try increase newCommandQueueWithMaxCommandBufferCount_, im almost out of ideas... * revert * actually not a HACK * oops
34 lines
1.5 KiB
Markdown
34 lines
1.5 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 20 (optionally 21) 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.
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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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load_op (EMPTY, CONST, FROM, CONTIGUOUS, CUSTOM) # -> A (initialize data on device)
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ternary_op (WHERE) # A, A, A -> A
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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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