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https://github.com/zebrajr/pytorch.git
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dca97b4394
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/60382 Instead of setting weight_decay w uniformly for all ids, for each row i in the sparse embedding table, the actual weight_decay `w_i` becomes `w*freq_i` where `freq_i = halflife/counter_i \in [\log(2), halflife]`. Counter is from `rowwise_counter` with definition `counter_i = 1 + \exp(-iter_{\delta}*\rho)*counter_i`. Test Plan: buck test //caffe2/caffe2/python/operator_test:adagrad_test -- test_row_wise_sparse_adagrad buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test_weight_decay Reviewed By: 0x10cxR1 Differential Revision: D25581030 fbshipit-source-id: 54b3831b20516c76c559b13d8deb809e2ee3b446
242 lines
7.4 KiB
Python
242 lines
7.4 KiB
Python
import functools
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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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from caffe2.python import core
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from caffe2.python.operator_test.adagrad_test_helper import (
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adagrad_sparse_test_helper,
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ref_adagrad,
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)
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from hypothesis import HealthCheck, given, settings
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class TestAdagrad(serial.SerializedTestCase):
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@given(
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inputs=hu.tensors(n=3),
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lr=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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epsilon=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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weight_decay=st.sampled_from([0.0, 0.1]),
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**hu.gcs
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)
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@settings(deadline=10000)
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def test_adagrad(self, inputs, lr, epsilon, weight_decay, gc, dc):
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param, momentum, grad = inputs
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momentum = np.abs(momentum)
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Adagrad",
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["param", "momentum", "grad", "lr"],
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["param", "momentum"],
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epsilon=epsilon,
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weight_decay=weight_decay,
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device_option=gc,
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)
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self.assertReferenceChecks(
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gc,
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op,
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[param, momentum, grad, lr],
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functools.partial(ref_adagrad, epsilon=epsilon, weight_decay=weight_decay),
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)
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@given(
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inputs=hu.tensors(n=3),
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lr=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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epsilon=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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weight_decay=st.sampled_from([0.0, 0.1]),
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**hu.gcs_cpu_only
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)
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@settings(deadline=10000)
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def test_adagrad_output_effective_lr(
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self, inputs, lr, epsilon, weight_decay, gc, dc
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):
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param, momentum, grad = inputs
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momentum = np.abs(momentum)
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Adagrad",
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["param", "momentum", "grad", "lr"],
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["param", "momentum", "effective_lr"],
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epsilon=epsilon,
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weight_decay=weight_decay,
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device_option=gc,
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)
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self.assertReferenceChecks(
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gc,
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op,
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[param, momentum, grad, lr],
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functools.partial(
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ref_adagrad,
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epsilon=epsilon,
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output_effective_lr=True,
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weight_decay=weight_decay,
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),
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)
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@given(
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inputs=hu.tensors(n=3),
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lr=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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epsilon=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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**hu.gcs_cpu_only
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)
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@settings(deadline=10000)
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def test_adagrad_output_effective_lr_and_update(self, inputs, lr, epsilon, gc, dc):
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param, momentum, grad = inputs
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momentum = np.abs(momentum)
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Adagrad",
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["param", "momentum", "grad", "lr"],
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["param", "momentum", "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,
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op,
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[param, momentum, grad, lr],
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functools.partial(
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ref_adagrad, epsilon=epsilon, output_effective_lr_and_update=True
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),
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)
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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(
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inputs=hu.tensors(n=3),
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lr=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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epsilon=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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weight_decay=st.sampled_from([0.0, 0.1]),
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**hu.gcs
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)
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def test_sparse_adagrad(self, inputs, lr, epsilon, weight_decay, gc, dc):
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adagrad_sparse_test_helper(
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self,
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inputs,
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lr,
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epsilon,
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None,
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ref_adagrad,
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gc,
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dc,
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weight_decay=weight_decay,
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)
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@given(
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inputs=hu.tensors(n=2),
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lr=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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epsilon=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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**hu.gcs
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)
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@settings(deadline=10000)
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def test_sparse_adagrad_empty(self, inputs, lr, epsilon, gc, dc):
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param, momentum = inputs
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grad = np.empty(shape=(0,) + param.shape[1:], dtype=np.float32)
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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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if ref_using_fp16:
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print("test_sparse_adagrad_empty with half precision embedding")
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momentum_i = momentum.astype(np.float16)
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param_i = param.astype(np.float16)
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else:
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print("test_sparse_adagrad_empty with full precision embedding")
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momentum_i = momentum.astype(np.float32)
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param_i = param.astype(np.float32)
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adagrad_sparse_test_helper(
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self,
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[param_i, momentum_i, grad],
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lr,
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epsilon,
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None,
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ref_adagrad,
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gc,
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dc,
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)
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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(
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inputs=hu.tensors(n=3),
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lr=st.sampled_from([0.01, 0.99]),
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epsilon=st.sampled_from([0.01, 0.99]),
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weight_decay=st.sampled_from([0.0, 0.1]),
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counter_halflife=st.sampled_from([-1, 5]),
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**hu.gcs
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)
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def test_row_wise_sparse_adagrad(
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self, inputs, lr, epsilon, weight_decay, counter_halflife, gc, dc
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):
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adagrad_sparse_test_helper(
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self,
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inputs,
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lr,
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epsilon,
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None,
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functools.partial(ref_adagrad, row_wise=True),
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gc,
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dc,
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row_wise=True,
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weight_decay=weight_decay,
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counter_halflife=counter_halflife,
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)
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@given(
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inputs=hu.tensors(n=2),
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lr=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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epsilon=st.floats(
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min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False
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),
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**hu.gcs
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)
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@settings(deadline=None)
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def test_row_wise_sparse_adagrad_empty(self, inputs, lr, epsilon, gc, dc):
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param, momentum = inputs
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grad = np.empty(shape=(0,) + param.shape[1:], dtype=np.float32)
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adagrad_sparse_test_helper(
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self,
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[param, momentum, grad],
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lr,
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epsilon,
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None,
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ref_adagrad,
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gc,
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dc,
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row_wise=True,
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)
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