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db5e5781ad
Summary: Per title Test Plan: Fixes existing tests Reviewed By: robieta Differential Revision: D28690296 fbshipit-source-id: d7b5b5065517373b75d501872814c89b24ec8cfc
200 lines
7.7 KiB
Python
200 lines
7.7 KiB
Python
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import functools
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import hypothesis
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from hypothesis import given, settings, HealthCheck
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python import core
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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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class TestAdadelta(serial.SerializedTestCase):
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@staticmethod
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def ref_adadelta(param_in,
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mom_in,
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mom_delta_in,
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grad, lr,
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epsilon,
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decay,
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using_fp16=False):
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param_in_f32 = param_in
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mom_in_f32 = mom_in
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mom_delta_in_f32 = mom_delta_in
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if(using_fp16):
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param_in_f32 = param_in.astype(np.float32)
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mom_in_f32 = mom_in.astype(np.float32)
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mom_delta_in_f32 = mom_delta_in.astype(np.float32)
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mom_out = decay * mom_in_f32 + (1.0 - decay) * grad * grad
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new_grad = (np.sqrt(mom_delta_in_f32 + epsilon) /
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np.sqrt(mom_out + epsilon)) * grad
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param_out = param_in_f32 + lr * new_grad
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mom_delta_out = decay * mom_delta_in_f32 + (1.0 - decay
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) * new_grad * new_grad
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if(using_fp16):
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return (param_out.astype(np.float16), mom_out.astype(np.float16),
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mom_delta_out.astype(np.float16))
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else:
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return (param_out.astype(np.float32), mom_out.astype(np.float32),
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mom_delta_out.astype(np.float32))
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@given(inputs=hu.tensors(n=4),
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lr=hu.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=hu.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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decay=hu.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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**hu.gcs)
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@settings(deadline=10000)
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def test_adadelta(self, inputs, lr, epsilon, decay, gc, dc):
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param, moment, moment_delta, grad = inputs
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moment = np.abs(moment)
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moment_delta = np.abs(moment_delta)
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Adadelta",
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["param", "moment", "moment_delta", "grad", "lr"],
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["param", "moment", "moment_delta"],
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epsilon=epsilon,
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decay=decay,
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device_option=gc,
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)
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self.assertReferenceChecks(
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gc, op,
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[param, moment, moment_delta, grad, lr],
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functools.partial(self.ref_adadelta, epsilon=epsilon, decay=decay))
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# Suppress filter_too_much health check.
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# Likely caused by `assume` call falling through too often.
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@settings(suppress_health_check=[HealthCheck.filter_too_much], deadline=10000)
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@given(inputs=hu.tensors(n=4),
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lr=hu.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=hu.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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decay=hu.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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**hu.gcs)
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def test_sparse_adadelta(self, inputs, lr, epsilon, decay, gc, dc):
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param, moment, moment_delta, grad = inputs
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moment = np.abs(moment)
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moment_delta = np.abs(moment_delta)
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lr = np.array([lr], dtype=np.float32)
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# Create an indexing array containing values that are lists of indices,
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# which index into grad
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indices = np.random.choice(np.arange(grad.shape[0]),
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size=np.random.randint(grad.shape[0]), replace=False)
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# Sparsify grad
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grad = grad[indices]
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op = core.CreateOperator(
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"SparseAdadelta",
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["param", "moment", "moment_delta", "indices", "grad", "lr"],
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["param", "moment", "moment_delta"],
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epsilon=epsilon,
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decay=decay,
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device_option=gc)
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def ref_sparse(param, moment, moment_delta, indices, grad, lr, decay,
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ref_using_fp16):
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param_out = np.copy(param)
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moment_out = np.copy(moment)
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moment_delta_out = np.copy(moment_delta)
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for i, index in enumerate(indices):
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param_out[index], moment_out[index], moment_delta_out[
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index] = self.ref_adadelta(param[index], moment[index],
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moment_delta[index], grad[i], lr,
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epsilon, decay, ref_using_fp16)
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return (param_out, moment_out, moment_delta_out)
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ref_using_fp16_values = [False]
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if gc == hu.gpu_do:
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ref_using_fp16_values.append(True)
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for ref_using_fp16 in ref_using_fp16_values:
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moment_i = None
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moment_delta_i = None
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param_i = None
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if(ref_using_fp16):
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moment_i = moment.astype(np.float16)
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moment_delta_i = moment_delta.astype(np.float16)
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param_i = param.astype(np.float16)
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else:
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moment_i = moment.astype(np.float32)
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moment_delta_i = moment_delta.astype(np.float32)
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param_i = param.astype(np.float32)
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self.assertReferenceChecks(gc, op, [
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param_i, moment_i, moment_delta_i, indices, grad, lr, decay,
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ref_using_fp16
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], ref_sparse)
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@given(inputs=hu.tensors(n=3),
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lr=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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decay=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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**hu.gcs)
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@settings(deadline=None)
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def test_sparse_adadelta_empty(self, inputs, lr, epsilon, decay, gc, dc):
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param, moment, moment_delta = inputs
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moment = np.abs(moment)
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lr = np.array([lr], dtype=np.float32)
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grad = np.empty(shape=(0,) + param.shape[1:], dtype=np.float32)
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indices = np.empty(shape=(0,), dtype=np.int64)
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hypothesis.note('indices.shape: %s' % str(indices.shape))
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op = core.CreateOperator(
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"SparseAdadelta",
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["param", "moment", "moment_delta", "indices", "grad", "lr"],
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["param", "moment", "moment_delta"],
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epsilon=epsilon,
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decay=decay,
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device_option=gc)
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def ref_sparse_empty(param, moment, moment_delta, indices, grad, lr, decay):
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param_out = np.copy(param)
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moment_out = np.copy(moment)
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moment_delta_out = np.copy(moment_delta)
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return (param_out, moment_out, moment_delta_out)
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ref_using_fp16_values = [False]
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if gc == hu.gpu_do:
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ref_using_fp16_values.append(True)
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for ref_using_fp16 in ref_using_fp16_values:
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moment_i = None
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moment_delta_i = None
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param_i = None
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if(ref_using_fp16):
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moment_i = moment.astype(np.float16)
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moment_delta_i = moment_delta.astype(np.float16)
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param_i = param.astype(np.float16)
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else:
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moment_i = moment.astype(np.float32)
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moment_delta_i = moment_delta.astype(np.float32)
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param_i = param.astype(np.float32)
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self.assertReferenceChecks(
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gc,
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op,
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[param_i, moment_i, moment_delta_i, indices, grad, lr, decay],
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ref_sparse_empty
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)
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