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https://github.com/saymrwulf/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
101 lines
3 KiB
Python
101 lines
3 KiB
Python
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from caffe2.python import core
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from hypothesis import given, settings
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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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import hypothesis.strategies as st
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import numpy as np
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class TestWeightedSumOp(serial.SerializedTestCase):
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@given(
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n=st.integers(1, 8), m=st.integers(1, 10), d=st.integers(1, 4),
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in_place=st.booleans(), engine=st.sampled_from(["", "CUDNN"]),
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seed=st.integers(min_value=0, max_value=65535),
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**hu.gcs)
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@settings(deadline=10000)
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def test_weighted_sum(
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self, n, m, d, in_place, engine, seed, gc, dc):
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input_names = []
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input_vars = []
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np.random.seed(seed)
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for i in range(m):
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X_name = 'X' + str(i)
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w_name = 'w' + str(i)
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input_names.extend([X_name, w_name])
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var = np.random.rand(n, d).astype(np.float32)
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vars()[X_name] = var
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input_vars.append(var)
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var = np.random.rand(1).astype(np.float32)
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vars()[w_name] = var
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input_vars.append(var)
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def weighted_sum_op_ref(*args):
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res = np.zeros((n, d))
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for i in range(m):
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res = res + args[2 * i + 1] * args[2 * i]
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return (res, )
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op = core.CreateOperator(
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"WeightedSum",
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input_names,
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[input_names[0]] if in_place else ['Y'],
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engine=engine,
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=input_vars,
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reference=weighted_sum_op_ref,
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)
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self.assertDeviceChecks(dc, op, input_vars, [0])
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@given(n=st.integers(1, 8), m=st.integers(1, 10), d=st.integers(1, 4),
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grad_on_w=st.booleans(),
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seed=st.integers(min_value=0, max_value=65535), **hu.gcs_cpu_only)
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@settings(deadline=1000)
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def test_weighted_sum_grad(
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self, n, m, d, grad_on_w, seed, gc, dc):
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input_names = []
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input_vars = []
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np.random.seed(seed)
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for i in range(m):
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X_name = 'X' + str(i)
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w_name = 'w' + str(i)
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input_names.extend([X_name, w_name])
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var = np.random.rand(n, d).astype(np.float32)
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vars()[X_name] = var
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input_vars.append(var)
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var = np.random.rand(1).astype(np.float32)
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vars()[w_name] = var
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input_vars.append(var)
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op = core.CreateOperator(
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"WeightedSum",
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input_names,
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['Y'],
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grad_on_w=grad_on_w,
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)
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output_to_check_grad = (
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range(2 * m) if grad_on_w else range(0, 2 * m, 2))
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for i in output_to_check_grad:
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self.assertGradientChecks(
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device_option=gc,
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op=op,
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inputs=input_vars,
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outputs_to_check=i,
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outputs_with_grads=[0],
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)
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if __name__ == "__main__":
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serial.testWithArgs()
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