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db5e5781ad
Summary: Per title Test Plan: Fixes existing tests Reviewed By: robieta Differential Revision: D28690296 fbshipit-source-id: d7b5b5065517373b75d501872814c89b24ec8cfc
219 lines
8.1 KiB
Python
219 lines
8.1 KiB
Python
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import functools
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import logging
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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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logger = logging.getLogger(__name__)
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def ref_wngrad(param_in, seq_b_in, grad, lr, epsilon,
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output_effective_lr=False,
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output_effective_lr_and_update=False):
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# helper functions for wngrad operator test
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seq_b_out = seq_b_in + 1.0 / (seq_b_in + epsilon) * np.sum(grad * grad)
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effective_lr = lr / (seq_b_in + epsilon)
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grad_adj = effective_lr * grad
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param_out = param_in + grad_adj
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if output_effective_lr_and_update:
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return (param_out.astype(np.float32), seq_b_out.astype(np.float32),
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effective_lr.astype(np.float32),
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grad_adj.astype(np.float32))
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elif output_effective_lr:
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return (param_out.astype(np.float32), seq_b_out.astype(np.float32),
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effective_lr.astype(np.float32))
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return (param_out.astype(np.float32), seq_b_out.astype(np.float32))
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def wngrad_sparse_test_helper(parent_test, inputs, seq_b, lr, epsilon,
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engine, gc, dc):
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# helper functions for wngrad operator test
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param, grad = inputs
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seq_b = np.array([seq_b, ], dtype=np.float32)
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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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"SparseWngrad",
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["param", "seq_b", "indices", "grad", "lr"],
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["param", "seq_b"],
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epsilon=epsilon,
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engine=engine,
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device_option=gc)
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def ref_sparse(param, seq_b, indices, grad, lr):
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param_out = np.copy(param)
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seq_b_out = np.copy(seq_b)
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seq_b_out = seq_b + 1.0 / seq_b * np.sum(grad * grad)
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for i, index in enumerate(indices):
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param_out[index] = param[index] + lr / (seq_b + epsilon) * grad[i]
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return (param_out, seq_b_out)
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logger.info('test_sparse_adagrad with full precision embedding')
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seq_b_i = seq_b.astype(np.float32)
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param_i = param.astype(np.float32)
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parent_test.assertReferenceChecks(
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gc, op, [param_i, seq_b_i, indices, grad, lr],
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ref_sparse
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)
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class TestWngrad(serial.SerializedTestCase):
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@given(inputs=hu.tensors(n=2),
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seq_b=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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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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**hu.gcs_cpu_only)
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@settings(deadline=10000)
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def test_wngrad_dense_base(self, inputs, seq_b, lr, epsilon, gc, dc):
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param, grad = inputs
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seq_b = np.array([seq_b, ], dtype=np.float32)
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Wngrad",
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["param", "seq_b", "grad", "lr"],
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["param", "seq_b"],
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epsilon=epsilon,
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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, seq_b, grad, lr],
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functools.partial(ref_wngrad, epsilon=epsilon))
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@given(inputs=hu.tensors(n=2),
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seq_b=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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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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**hu.gcs_cpu_only)
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@settings(deadline=10000)
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def test_wngrad_dense_output_effective_lr(self, inputs, seq_b,
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lr, epsilon, gc, dc):
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param, grad = inputs
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seq_b = np.array([seq_b, ], dtype=np.float32)
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Wngrad",
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["param", "seq_b", "grad", "lr"],
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["param", "seq_b", "effective_lr"],
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epsilon=epsilon,
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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, seq_b, grad, lr],
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functools.partial(ref_wngrad, epsilon=epsilon,
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output_effective_lr=True))
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@given(inputs=hu.tensors(n=2),
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seq_b=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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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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**hu.gcs_cpu_only)
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@settings(deadline=10000)
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def test_wngrad_dense_output_effective_lr_and_update(
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self, inputs, seq_b, lr, epsilon, gc, dc):
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param, grad = inputs
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seq_b = np.abs(np.array([seq_b, ], dtype=np.float32))
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Wngrad",
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["param", "seq_b", "grad", "lr"],
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["param", "seq_b", "effective_lr", "update"],
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epsilon=epsilon,
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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, seq_b, grad, lr],
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functools.partial(ref_wngrad, epsilon=epsilon,
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output_effective_lr_and_update=True))
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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=2),
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seq_b=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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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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**hu.gcs_cpu_only)
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def test_sparse_wngrad(self, inputs, seq_b, lr, epsilon, gc, dc):
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return wngrad_sparse_test_helper(self, inputs, seq_b, lr, epsilon,
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None, gc, dc)
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@given(inputs=hu.tensors(n=1),
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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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seq_b=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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**hu.gcs_cpu_only)
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@settings(deadline=10000)
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def test_sparse_wngrad_empty(self, inputs, seq_b, lr, epsilon, gc, dc):
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param = inputs[0]
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seq_b = np.array([seq_b, ], dtype=np.float32)
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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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"SparseWngrad",
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["param", "seq_b", "indices", "grad", "lr"],
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["param", "seq_b"],
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epsilon=epsilon,
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device_option=gc)
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def ref_sparse(param, seq_b, indices, grad, lr):
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param_out = np.copy(param)
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seq_b_out = np.copy(seq_b)
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return (param_out, seq_b_out)
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print('test_sparse_adagrad_empty with full precision embedding')
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seq_b_i = seq_b.astype(np.float32)
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param_i = param.astype(np.float32)
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self.assertReferenceChecks(
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gc, op, [param_i, seq_b_i, indices, grad, lr], ref_sparse
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)
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