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133cacadde
* Switch to dawn, all tests passing locally * Use dawn-python * Skip failing test * Skip midcast and fix timestamp on metal ci * Autogen webgpu * Try fetch dawn lib again * /usr/lib * Without lib prefix * Test autogen diff * Delete webgpu support, move everything to ops_webgpu * mypy fix * Simplify, refactor * Line savings * No ResultContainer * Type annotation for result * Some more simplifications * Why was this explicit sync used at all? * Refactor: delete functions that are only used once * Create shader module inline * Clear unit tests cache, maybe that solves it * That wasn't it * Try deleting cache to pass failing weight compare * weights_only=False for pytorch 2.6 * Simplify ctype array creation * Remove nanosecond precision timestamps * Simplify error handling * Refactor, add back type annotations * Deleted custom submit function, refactor * read_buffer simplify * Fix use after free, refactor * Simplify supported_features * Runtime docs --------- Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
20 lines
722 B
Python
20 lines
722 B
Python
import unittest
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import numpy as np
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from tinygrad import Tensor, Variable, Device
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@unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU can only run kernels with up to 10 buffers")
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class TestSample(unittest.TestCase):
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def test_sample(self):
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X = Tensor.rand(10000, 50).realize()
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BS = 16
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idxs = np.random.randint(0, X.shape[0], size=(BS))
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# this uncovered a bug with arg sort order
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batch = [Variable(f'idx{i}', 0, X.shape[0]-1).bind(s) for i,s in enumerate(idxs.tolist())]
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x = Tensor.cat(*[X.shrink(((batch[i], batch[i]+1), None)) for i in range(BS)])
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print(idxs)
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ret = x.numpy()
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base = X.numpy()[idxs]
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np.testing.assert_equal(ret, base)
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if __name__ == '__main__':
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unittest.main() |