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https://github.com/saymrwulf/pytorch.git
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8bd0522c20
Summary: These GPU paths are probably even buggier than the CPU paths for sparse gradients with duplicate indices. Both paths cause multiple momentum updates in a single iteration, but only the GPU path is non-deterministic. Depending on how we decide to address the issues on the CPU path, pooyadavoodi has a good idea for how to match dense behavior with the sparse GPU ops. Closes https://github.com/caffe2/caffe2/pull/254 Reviewed By: bwasti Differential Revision: D4871680 Pulled By: dzhulgakov fbshipit-source-id: 220be57a0f699a22ea85ed4f7022d92d362d06b3
110 lines
4.3 KiB
Python
110 lines
4.3 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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import functools
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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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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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class TestAdam(hu.HypothesisTestCase):
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@staticmethod
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def ref_adam(param, mom1, mom2, grad, LR, ITER,
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beta1, beta2, epsilon):
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t = ITER + 1
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corrected_local_rate = LR * np.sqrt(1 - np.power(beta2, t)) / \
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(1 - np.power(beta1, t))
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mom1_out = (beta1 * mom1) + (1 - beta1) * grad
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mom2_out = (beta2 * mom2) + (1 - beta2) * np.square(grad)
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param_out = param + corrected_local_rate * mom1_out / \
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(np.sqrt(mom2_out) + epsilon)
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return param_out, mom1_out, mom2_out
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@given(inputs=hu.tensors(n=4),
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ITER=st.integers(min_value=0, max_value=10000),
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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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beta1=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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beta2=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)
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def test_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc):
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param, mom1, mom2, grad = inputs
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ITER = np.array([ITER], dtype=np.int64)
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LR = np.array([LR], dtype=np.float32)
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op = core.CreateOperator(
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"Adam",
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["param", "mom1", "mom2", "grad", "lr", "iter"],
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["output_param", "output_mom1", "output_mom2"],
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beta1=beta1, beta2=beta2, epsilon=epsilon)
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# Iter lives on the CPU
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input_device_options = {'iter': hu.cpu_do}
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self.assertReferenceChecks(
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gc, op,
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[param, mom1, mom2, grad, LR, ITER],
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functools.partial(
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self.ref_adam,
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beta1=beta1, beta2=beta2, epsilon=epsilon),
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input_device_options=input_device_options)
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@given(inputs=hu.tensors(n=4),
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ITER=st.integers(min_value=0, max_value=10000),
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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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beta1=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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beta2=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)
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def test_sparse_adam(self, inputs, ITER, LR, beta1, beta2, epsilon,
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gc, dc):
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param, mom1, mom2, grad = inputs
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mom1 = np.absolute(mom1)
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mom2 = np.absolute(mom2)
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ITER = np.array([ITER], dtype=np.int64)
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LR = np.array([LR], dtype=np.float32)
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indices = np.arange(grad.shape[0])
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indices = indices[indices % 2 == 0]
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grad = grad[indices]
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op = core.CreateOperator(
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"SparseAdam",
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["param", "mom1", "mom2", "indices", "grad", "lr", "iter"],
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["param", "mom1", "mom2"],
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beta1=beta1, beta2=beta2, epsilon=epsilon)
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def ref_sparse(param, mom1, mom2, indices, grad, LR, ITER):
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param_out = np.copy(param)
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mom1_out = np.copy(mom1)
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mom2_out = np.copy(mom2)
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for i, index in enumerate(indices):
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param_out[index], mom1_out[index], mom2_out[index] = \
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self.ref_adam(param[index], mom1[index], mom2[index],
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grad[i], LR, ITER,
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beta1, beta2, epsilon)
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return (param_out, mom1_out, mom2_out)
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# Iter lives on the CPU
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input_device_options = {'iter': hu.cpu_do}
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self.assertReferenceChecks(
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gc, op,
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[param, mom1, mom2, indices, grad, LR, ITER],
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ref_sparse,
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input_device_options=input_device_options)
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