mirror of
https://github.com/zebrajr/pytorch.git
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1c7886701e
Summary: As per discussion in https://www.prod.facebook.com/groups/184236721951559/permalink/354591931582703/, KaimingHe pointed out that scaling LR is not same as scaling Loss, since LR scaling will affect the weight decay (which is implemented by modifying the gradient, which thus is not yet correctly 'averaged'). Actually prigoyal tried to convince me earlier that loss scaling is the way to go, but I was then not convinved :/. So this diff removes the LR scaling parameter passed by data_parallel_model and instead passes a loss_scale parameter to the model creation function. Unfortunately, this will break all existing code that uses the data parallel model. But that is not only a bad thing, since it will bring awareness to this change. I will inform in the FB groups about this. In this diff I modified all my models to work correctly. Reviewed By: Yangqing Differential Revision: D4507002 fbshipit-source-id: 16c7221663282f71a1b754b34de0c8ccd5c2ca90
104 lines
3.6 KiB
Python
104 lines
3.6 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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import numpy as np
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import unittest
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from caffe2.proto import caffe2_pb2
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from caffe2.python import core, workspace, data_parallel_model, cnn
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from caffe2.python.test_util import TestCase
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@unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
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@unittest.skipIf(workspace.NumCudaDevices() < 2, "Need at least 2 GPUs.")
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class GPUDataParallelModelTest(TestCase):
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def run_model(self, gpu_devices):
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'''
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Helper function for test_equiv
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'''
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def input_builder_fun(model):
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return None
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def model_build_fun(model, loss_scale):
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fc = model.FC("data", "fc", 16, 1,
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("ConstantFill", {}), ("ConstantFill", {}))
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fc_fl = model.FlattenToVec(fc, "fc_fl")
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sigm = model.Sigmoid(fc_fl, "sigm")
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sq = model.SquaredL2Distance([sigm, "label"], "sq")
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loss = model.AveragedLoss(sq, "loss")
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loss = model.Scale(loss, scale=loss_scale)
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return [loss]
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def param_update_fun(model):
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ITER = model.Iter("ITER")
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LR = model.net.LearningRate(
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[ITER],
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"LR",
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base_lr=(-0.1),
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policy="fixed",
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)
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ONE = model.param_init_net.ConstantFill(
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[], "ONE", shape=[1], value=1.0,
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)
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for param in model.GetParams():
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grad = model.param_to_grad[param]
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model.WeightedSum([param, ONE, grad, LR], param)
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workspace.ResetWorkspace()
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model = cnn.CNNModelHelper(
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order="NHWC",
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name="test{}".format(gpu_devices),
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)
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data_parallel_model.Parallelize_GPU(
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model,
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input_builder_fun=input_builder_fun,
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forward_pass_builder_fun=model_build_fun,
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param_update_builder_fun=param_update_fun,
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devices=gpu_devices,
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)
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np.random.seed(2603)
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# Each run has same input, independent of number of gpus
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batch_size = 64
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for i in range(0, 10):
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full_data = np.random.rand(batch_size, 16)
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full_labels = np.round(full_data[:, 0])
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batch_per_device = batch_size // len(gpu_devices)
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for (j, g) in enumerate(gpu_devices):
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st = j * batch_per_device
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en = st + batch_per_device
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data = full_data[st:en, :].astype(np.float32)
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labels = full_labels[st:en].astype(np.float32)
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with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, g)):
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workspace.FeedBlob("gpu_{}/data".format(g), data)
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workspace.FeedBlob("gpu_{}/label".format(g), labels)
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if i == 0:
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workspace.RunNetOnce(model.param_init_net)
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workspace.CreateNet(model.net)
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workspace.RunNet(model.net.Proto().name)
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return workspace.FetchBlob("gpu_0/fc_w")
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def test_equiv(self):
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'''
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Test that the model produces exactly same results given
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total batchsize, independent of number of GPUs.
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'''
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result_2gpus = self.run_model([0, 1])
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result_1gpus = self.run_model([0])
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self.assertTrue(np.allclose(result_1gpus, result_2gpus))
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if workspace.NumCudaDevices() >= 4:
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result_4gpus = self.run_model(range(4))
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self.assertTrue(np.allclose(result_1gpus, result_4gpus))
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if workspace.NumCudaDevices() >= 8:
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result_8gpus = self.run_model(range(8))
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self.assertTrue(np.allclose(result_1gpus, result_8gpus))
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