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38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import pyshader as ps
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import kp
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import numpy as np
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def test_array_multiplication():
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# 1. Create Kompute Manager (selects device 0 by default)
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mgr = kp.Manager()
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# 2. Create Kompute Tensors to hold data
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tensor_in_a = kp.Tensor([2, 2, 2])
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tensor_in_b = kp.Tensor([1, 2, 3])
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tensor_out = kp.Tensor([0, 0, 0])
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# 3. Initialise the Kompute Tensors in the GPU
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mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
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# 4. Define the multiplication shader code to run on the GPU
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@ps.python2shader
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def compute_shader_multiply(index=("input", "GlobalInvocationId", ps.ivec3),
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data1=("buffer", 0, ps.Array(ps.f32)),
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data2=("buffer", 1, ps.Array(ps.f32)),
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data3=("buffer", 2, ps.Array(ps.f32))):
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i = index.x
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data3[i] = data1[i] * data2[i]
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# 5. Run shader code against our previously defined tensors
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mgr.eval_algo_data_def(
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[tensor_in_a, tensor_in_b, tensor_out],
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compute_shader_multiply.to_spirv())
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# 6. Sync tensor data from GPU back to local
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mgr.eval_tensor_sync_local_def([tensor_out])
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assert tensor_out.data() == [2.0, 4.0, 6.0]
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assert np.all(tensor_out.numpy() == [2.0, 4.0, 6.0])
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