53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
# https://tvm.apache.org/docs/tutorial/tensor_expr_get_started.html#example-2-manually-optimizing-matrix-multiplication-with-te
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M, N, K = 1024, 1024, 1024
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try:
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import tvm
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from tvm import te
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# print(tvm.target.Target.list_kinds())
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# c, opencl
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target = tvm.target.Target(target="c")
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# TVM Matrix Multiplication using TE
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k = te.reduce_axis((0, K), "k")
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A = te.placeholder((M, K), name="A")
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B = te.placeholder((K, N), name="B")
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C = te.compute((M, N), lambda x, y: te.sum(A[x, k] * B[k, y], axis=k), name="C")
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# Default schedule
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s = te.create_schedule(C.op)
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# print(tvm.lower(s, [A, B, C], simple_mode=True))
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# Output C code
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func = tvm.build(s, [A, B, C], target=target, name="mmult")
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print(func.get_source())
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except ImportError:
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print("** please install TVM for TVM output")
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# tinygrad version
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import os
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from tinygrad.tensor import Tensor
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# define the compute
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A = Tensor.rand(M, K, device="clang")
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B = Tensor.rand(K, N, device="clang")
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C = (A.reshape(M, 1, K) * B.permute(1, 0).reshape(1, N, K)).sum(axis=2)
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sched = C.lazydata.schedule()
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from tinygrad.codegen.linearizer import Linearizer
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from tinygrad.codegen.kernel import LinearizerOptions
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lin = Linearizer(
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sched[-1].ast, LinearizerOptions(has_local=False, supports_float4=False)
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)
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# lin.hand_coded_optimizations()
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lin.linearize()
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from tinygrad.runtime.ops_clang import renderer
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src = renderer("mmult", lin.uops)
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print(src)
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