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https://github.com/pytorch/pytorch.git
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3b1a5a1b8a
Summary: Followup to the [first refactor](https://github.com/pytorch/pytorch/pull/11350). Increase coverage of tests Pull Request resolved: https://github.com/pytorch/pytorch/pull/11811 Reviewed By: houseroad Differential Revision: D9923074 Pulled By: ajyu fbshipit-source-id: 0f899bb9e9a75bf7ed939e06cc9b028daa7f6bd9
84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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from caffe2.python.test_util import rand_array
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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from hypothesis import given
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import hypothesis.strategies as st
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import numpy as np
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class TestScatterOps(serial.SerializedTestCase):
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# TODO(dzhulgakov): add test cases for failure scenarios
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@serial.given(num_args=st.integers(1, 5),
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first_dim=st.integers(1, 20),
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index_dim=st.integers(1, 10),
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extra_dims=st.lists(st.integers(1, 4), min_size=0, max_size=3),
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ind_type=st.sampled_from([np.int32, np.int64]),
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**hu.gcs)
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def testScatterWeightedSum(
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self, num_args, first_dim, index_dim, extra_dims, ind_type, gc, dc):
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ins = ['data', 'w0', 'indices']
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for i in range(1, num_args + 1):
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ins.extend(['x' + str(i), 'w' + str(i)])
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op = core.CreateOperator(
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'ScatterWeightedSum',
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ins,
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['data'],
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device_option=gc)
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def ref(d, w0, ind, *args):
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r = d.copy()
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for i in ind:
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r[i] *= w0
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for i in range(0, len(args), 2):
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x = args[i]
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w = args[i+1]
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for i, j in enumerate(ind):
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r[j] += w * x[i]
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return [r]
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d = rand_array(first_dim, *extra_dims)
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ind = np.random.randint(0, first_dim, index_dim).astype(ind_type)
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# ScatterWeightedSumOp only supports w0=1.0 in CUDAContext
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if(gc == hu.gpu_do or gc == hu.hip_do):
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w0 = np.array(1.0).astype(np.float32)
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else:
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w0 = rand_array()
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inputs = [d, w0, ind]
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for _ in range(1, num_args + 1):
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x = rand_array(index_dim, *extra_dims)
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w = rand_array()
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inputs.extend([x,w])
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self.assertReferenceChecks(gc, op, inputs, ref, threshold=1e-3)
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@serial.given(first_dim=st.integers(1, 20),
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index_dim=st.integers(1, 10),
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extra_dims=st.lists(st.integers(1, 4), min_size=0, max_size=3),
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data_type=st.sampled_from([np.float16, np.float32, np.int32, np.int64]),
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ind_type=st.sampled_from([np.int32, np.int64]),
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**hu.gcs)
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def testScatterAssign(
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self, first_dim, index_dim, extra_dims, data_type, ind_type, gc, dc):
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op = core.CreateOperator('ScatterAssign',
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['data', 'indices', 'slices'], ['data'])
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def ref(d, ind, x):
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r = d.copy()
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r[ind] = x
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return [r]
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# let's have indices unique
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if first_dim < index_dim:
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first_dim, index_dim = index_dim, first_dim
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d = (rand_array(first_dim, *extra_dims) * 10).astype(data_type)
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ind = np.random.choice(first_dim, index_dim,
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replace=False).astype(ind_type)
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x = (rand_array(index_dim, *extra_dims) * 10).astype(data_type)
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self.assertReferenceChecks(gc, op, [d, ind, x], ref, threshold=1e-3)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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