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dlrm_s_caffe2.py
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dlrm_s_caffe2.py
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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Description: an implementation of a deep learning recommendation model (DLRM)
# The model input consists of dense and sparse features. The former is a vector
# of floating point values. The latter is a list of sparse indices into
# embedding tables, which consist of vectors of floating point values.
# The selected vectors are passed to mlp networks denoted by triangles,
# in some cases the vectors are interacted through operators (Ops).
#
# output:
# vector of values
# model: |
# /\
# /__\
# |
# _____________________> Op <___________________
# / | \
# /\ /\ /\
# /__\ /__\ ... /__\
# | | |
# | Op Op
# | ____/__\_____ ____/__\____
# | |_Emb_|____|__| ... |_Emb_|__|___|
# input:
# [ dense features ] [sparse indices] , ..., [sparse indices]
#
# More precise definition of model layers:
# 1) fully connected layers of an mlp
# z = f(y)
# y = Wx + b
#
# 2) embedding lookup (for a list of sparse indices p=[p1,...,pk])
# z = Op(e1,...,ek)
# obtain vectors e1=E[:,p1], ..., ek=E[:,pk]
#
# 3) Operator Op can be one of the following
# Sum(e1,...,ek) = e1 + ... + ek
# Dot(e1,...,ek) = [e1'e1, ..., e1'ek, ..., ek'e1, ..., ek'ek]
# Cat(e1,...,ek) = [e1', ..., ek']'
# where ' denotes transpose operation
#
# References:
# [1] Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang,
# Narayanan Sundaram, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu,
# Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii,
# Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko,
# Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong,
# Misha Smelyanskiy, "Deep Learning Recommendation Model for Personalization and
# Recommendation Systems", CoRR, arXiv:1906.00091, 2019
from __future__ import absolute_import, division, print_function, unicode_literals
import functools
# others
import operator
import time
import copy
# data generation
import dlrm_data_pytorch as dp
# numpy
import numpy as np
import sklearn.metrics
# onnx
# The onnx import causes deprecation warnings every time workers
# are spawned during testing. So, we filter out those warnings.
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
try:
import onnx
import caffe2.python.onnx.frontend
except ImportError as error:
print('Unable to import onnx or caffe2.python.onnx.frontend ', error)
# from caffe2.python import data_parallel_model
# caffe2
from caffe2.proto import caffe2_pb2
from caffe2.python import brew, core, dyndep, model_helper, net_drawer, workspace
"""
# auxiliary routine used to split input on the mini-bacth dimension
def where_to_split(mini_batch_size, ndevices, _add_leftover=False):
n = (mini_batch_size + ndevices - 1) // ndevices # ceiling
l = mini_batch_size - n * (ndevices - 1) # leftover
s = [n] * (ndevices - 1)
if _add_leftover:
ls += [l if l > 0 else n]
return ls
"""
### define dlrm in Caffe2 ###
class DLRM_Net(object):
def FeedBlobWrapper(self, tag, val, add_prefix=True, split=False, device_id=-1):
if self.ndevices > 1 and add_prefix:
if split:
# split across devices
mini_batch_size = val.shape[0]
# approach 1: np and caffe2 operators assume the mini-batch size is
# divisible exactly by the number of available devices
if mini_batch_size % self.ndevices != 0:
sys.exit("ERROR: caffe2 net assumes that the mini_batch_size "
+ str(mini_batch_size)
+ " is evenly divisible by the number of available devices"
+ str(self.ndevices))
vals = np.split(val, self.ndevices, axis=0)
"""
# approach 2: np and caffe2 operators do not assume exact divisibility
if args.mini_batch_size != mini_batch_size:
sys.exit("ERROR: caffe2 net was prepared for mini-batch size "
+ str(args.mini_batch_size)
+ " which is different from current mini-batch size "
+ str(mini_batch_size) + " being passed to it. "
+ "This is common for the last mini-batch, when "
+ "mini-batch size does not evenly divided the number of "
+ "elements in the data set.")
ls = where_to_split(mini_batch_size, self.ndevices)
vals = np.split(val, ls, axis=0)
"""
# feed to multiple devices
for d in range(self.ndevices):
tag_on_device = "gpu_" + str(d) + "/" + tag
_d = core.DeviceOption(workspace.GpuDeviceType, d)
workspace.FeedBlob(tag_on_device, vals[d], device_option=_d)
else:
# feed to multiple devices
for d in range(self.ndevices):
tag_on_device = "gpu_" + str(d) + "/" + tag
_d = core.DeviceOption(workspace.GpuDeviceType, d)
workspace.FeedBlob(tag_on_device, val, device_option=_d)
else:
# feed to a single device (named or not)
if device_id >= 0:
_d = core.DeviceOption(workspace.GpuDeviceType, device_id)
workspace.FeedBlob(tag, val, device_option=_d)
else:
workspace.FeedBlob(tag, val)
def FetchBlobWrapper(self, tag, add_prefix=True, reduce_across=None, device_id=-1):
if self.ndevices > 1 and add_prefix:
# fetch from multiple devices
vals = []
for d in range(self.ndevices):
if tag.__class__ == list:
tag_on_device = tag[d]
else:
tag_on_device = "gpu_" + str(0) + "/" + tag
val = workspace.FetchBlob(tag_on_device)
vals.append(val)
# reduce across devices
if reduce_across == "add":
return functools.reduce(operator.add, vals)
elif reduce_across == "concat":
return np.concatenate(vals)
else:
return vals
else:
# fetch from a single device (named or not)
if device_id >= 0:
tag_on_device = "gpu_" + str(device_id) + "/" + tag
return workspace.FetchBlob(tag_on_device)
else:
return workspace.FetchBlob(tag)
def AddLayerWrapper(self, layer, inp_blobs, out_blobs,
add_prefix=True, reset_grad=False, **kwargs):
# auxiliary routine to adjust tags
def adjust_tag(blobs, on_device):
if blobs.__class__ == str:
_blobs = on_device + blobs
elif blobs.__class__ == list:
_blobs = list(map(lambda tag: on_device + tag, blobs))
else: # blobs.__class__ == model_helper.ModelHelper or something else
_blobs = blobs
return _blobs
if self.ndevices > 1 and add_prefix:
# add layer on multiple devices
ll = []
for d in range(self.ndevices):
# add prefix on_device
on_device = "gpu_" + str(d) + "/"
_inp_blobs = adjust_tag(inp_blobs, on_device)
_out_blobs = adjust_tag(out_blobs, on_device)
# WARNING: reset_grad option was exlusively designed for WeightedSum
# with inp_blobs=[w, tag_one, "", lr], where "" will be replaced
if reset_grad:
w_grad = self.gradientMap[_inp_blobs[0]]
_inp_blobs[2] = w_grad
# add layer to the model
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, d)):
if kwargs:
new_layer = layer(_inp_blobs, _out_blobs, **kwargs)
else:
new_layer = layer(_inp_blobs, _out_blobs)
ll.append(new_layer)
return ll
else:
# add layer on a single device
# WARNING: reset_grad option was exlusively designed for WeightedSum
# with inp_blobs=[w, tag_one, "", lr], where "" will be replaced
if reset_grad:
w_grad = self.gradientMap[inp_blobs[0]]
inp_blobs[2] = w_grad
# add layer to the model
if kwargs:
new_layer = layer(inp_blobs, out_blobs, **kwargs)
else:
new_layer = layer(inp_blobs, out_blobs)
return new_layer
def create_mlp(self, ln, sigmoid_layer, model, tag):
(tag_layer, tag_in, tag_out) = tag
# build MLP layer by layer
layers = []
weights = []
for i in range(1, ln.size):
n = ln[i - 1]
m = ln[i]
# create tags
tag_fc_w = tag_layer + ":::" + "fc" + str(i) + "_w"
tag_fc_b = tag_layer + ":::" + "fc" + str(i) + "_b"
tag_fc_y = tag_layer + ":::" + "fc" + str(i) + "_y"
tag_fc_z = tag_layer + ":::" + "fc" + str(i) + "_z"
if i == ln.size - 1:
tag_fc_z = tag_out
weights.append(tag_fc_w)
weights.append(tag_fc_b)
# initialize the weights
# approach 1: custom Xavier input, output or two-sided fill
mean = 0.0 # std_dev = np.sqrt(variance)
std_dev = np.sqrt(2 / (m + n)) # np.sqrt(1 / m) # np.sqrt(1 / n)
W = np.random.normal(mean, std_dev, size=(m, n)).astype(np.float32)
std_dev = np.sqrt(1 / m) # np.sqrt(2 / (m + 1))
b = np.random.normal(mean, std_dev, size=m).astype(np.float32)
self.FeedBlobWrapper(tag_fc_w, W)
self.FeedBlobWrapper(tag_fc_b, b)
# approach 2: caffe2 xavier
# W = self.AddLayerWrapper(
# model.param_init_net.XavierFill,
# [],
# tag_fc_w,
# shape=[m, n]
# )
# b = self.AddLayerWrapper(
# model.param_init_net.ConstantFill,
# [],
# tag_fc_b,
# shape=[m]
# )
# initialize the MLP's momentum for the Adagrad optimizer
if self.emb_optimizer in ["adagrad", "rwsadagrad"]:
# momentum of the weights
self.FeedBlobWrapper(
"momentum_mlp_{}_{}".format(tag_layer, 2 * i - 1),
np.full((m, n), 0, dtype=np.float32)
)
# momentum of the biases
self.FeedBlobWrapper(
"momentum_mlp_{}_{}".format(tag_layer, 2 * i),
np.full((m), 0, dtype=np.float32)
)
# save the blob shapes for latter (only needed if onnx is requested)
if self.save_onnx:
self.onnx_tsd[tag_fc_w] = (onnx.TensorProto.FLOAT, W.shape)
self.onnx_tsd[tag_fc_b] = (onnx.TensorProto.FLOAT, b.shape)
# approach 1: construct fully connected operator using model.net
fc = self.AddLayerWrapper(
model.net.FC, [tag_in, tag_fc_w, tag_fc_b], tag_fc_y
)
# approach 2: construct fully connected operator using brew
# https://github.com/caffe2/tutorials/blob/master/MNIST.ipynb
# fc = brew.fc(model, layer, tag_fc_w, dim_in=m, dim_out=n)
layers.append(fc)
if i == sigmoid_layer:
# approach 1: construct sigmoid operator using model.net
layer = self.AddLayerWrapper(model.net.Sigmoid, tag_fc_y, tag_fc_z)
# approach 2: using brew (which currently does not support sigmoid)
# tag_sigm = tag_layer + ":::" + "sigmoid" + str(i)
# layer = brew.sigmoid(model,fc,tag_sigmoid)
else:
# approach 1: construct relu operator using model.net
layer = self.AddLayerWrapper(model.net.Relu, tag_fc_y, tag_fc_z)
# approach 2: using brew
# tag_relu = tag_layer + ":::" + "relu" + str(i)
# layer = brew.relu(model,fc,tag_relu)
tag_in = tag_fc_z
layers.append(layer)
# WARNING: the dependency between layers is implicit in the tags,
# so only the last layer is added to the layers list. It will
# later be used for interactions.
return layers, weights
def create_emb(self, m, ln, model, tag):
(tag_layer, tag_in, tag_out) = tag
emb_l = []
weights_l = []
vw_l = []
for i in range(0, ln.size):
n = ln[i]
# select device
if self.ndevices > 1:
d = i % self.ndevices
else:
d = -1
# create tags
on_device = "" if self.ndevices <= 1 else "gpu_" + str(d) + "/"
len_s = on_device + tag_layer + ":::" + "sls" + str(i) + "_l"
ind_s = on_device + tag_layer + ":::" + "sls" + str(i) + "_i"
tbl_s = on_device + tag_layer + ":::" + "sls" + str(i) + "_w"
sum_s = on_device + tag_layer + ":::" + "sls" + str(i) + "_z"
weights_l.append(tbl_s)
# initialize the weights
# approach 1a: custom
W = np.random.uniform(low=-np.sqrt(1 / n),
high=np.sqrt(1 / n),
size=(n, m)).astype(np.float32)
# approach 1b: numpy rand
# W = ra.rand(n, m).astype(np.float32)
self.FeedBlobWrapper(tbl_s, W, False, device_id=d)
# approach 2: caffe2 xavier
# with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, d)):
# W = model.param_init_net.XavierFill([], tbl_s, shape=[n, m])
# save the blob shapes for latter (only needed if onnx is requested)
# initialize the embedding's momentum for the Adagrad optimizer
if self.emb_optimizer == "adagrad":
self.FeedBlobWrapper("momentum_emb_{}".format(i),
np.full((n, m), 0), add_prefix=False, device_id=d)
elif self.emb_optimizer == "rwsadagrad":
self.FeedBlobWrapper("momentum_emb_{}".format(i),
np.full((n), 0), add_prefix=False, device_id=d)
if self.save_onnx:
self.onnx_tsd[tbl_s] = (onnx.TensorProto.FLOAT, W.shape)
# create operator
if self.weighted_pooling is not None:
vw_s = on_device + tag_layer + ":::" + "sls" + str(i) + "_v"
psw_s = on_device + tag_layer + ":::" + "sls" + str(i) + "_s"
VW = np.ones(n).astype(np.float32)
self.FeedBlobWrapper(vw_s, VW, False, device_id=d)
if self.weighted_pooling == "learned":
vw_l.append(vw_s)
grad_on_weights = True
else:
grad_on_weights = False
if self.save_onnx:
self.onnx_tsd[vw_s] = (onnx.TensorProto.FLOAT, VW.shape)
if self.ndevices <= 1:
PSW = model.net.Gather([vw_s, ind_s], [psw_s])
EE = model.net.SparseLengthsWeightedSum(
[tbl_s, PSW, ind_s, len_s], [sum_s],
grad_on_weights=grad_on_weights
)
else:
with core.DeviceScope(
core.DeviceOption(workspace.GpuDeviceType, d)
):
PSW = model.net.Gather([vw_s, ind_s], [psw_s])
EE = model.net.SparseLengthsWeightedSum(
[tbl_s, PSW, ind_s, len_s], [sum_s],
grad_on_weights=grad_on_weights
)
else:
if self.ndevices <= 1:
EE = model.net.SparseLengthsSum(
[tbl_s, ind_s, len_s], [sum_s]
)
else:
with core.DeviceScope(
core.DeviceOption(workspace.GpuDeviceType, d)
):
EE = model.net.SparseLengthsSum(
[tbl_s, ind_s, len_s], [sum_s]
)
emb_l.append(EE)
return emb_l, weights_l, vw_l
def create_interactions(self, x, ly, model, tag):
(tag_dense_in, tag_sparse_in, tag_int_out) = tag
if self.arch_interaction_op == "dot":
# concatenate dense and sparse features
tag_int_out_info = tag_int_out + "_info"
T, T_info = model.net.Concat(
x + ly,
[tag_int_out + "_cat_axis0", tag_int_out_info + "_cat_axis0"],
axis=1,
add_axis=1,
)
# perform a dot product
Z = model.net.BatchMatMul([T, T], tag_int_out + "_matmul", trans_b=1)
# append dense feature with the interactions (into a row vector)
# approach 1: all
# Zflat = model.net.Flatten(Z, tag_int_out + "_flatten", axis=1)
# approach 2: unique
Zflat_all = model.net.Flatten(Z, tag_int_out + "_flatten_all", axis=1)
Zflat = model.net.BatchGather(
[Zflat_all, tag_int_out + "_tril_indices"],
tag_int_out + "_flatten"
)
R, R_info = model.net.Concat(
x + [Zflat], [tag_int_out, tag_int_out_info], axis=1
)
elif self.arch_interaction_op == "cat":
# concatenation features (into a row vector)
tag_int_out_info = tag_int_out + "_info"
R, R_info = model.net.Concat(
x + ly, [tag_int_out, tag_int_out_info], axis=1
)
else:
sys.exit("ERROR: --arch-interaction-op="
+ self.arch_interaction_op + " is not supported")
return R
def create_sequential_forward_ops(self):
# embeddings
tag = (self.temb, self.tsin, self.tsout)
self.emb_l, self.emb_w, self.emb_vw = self.create_emb(
self.m_spa, self.ln_emb, self.model, tag
)
# bottom mlp
tag = (self.tbot, self.tdin, self.tdout)
self.bot_l, self.bot_w = self.create_mlp(self.ln_bot, self.sigmoid_bot,
self.model, tag)
# interactions
tag = (self.tdout, self.tsout, self.tint)
Z = self.create_interactions([self.bot_l[-1]], self.emb_l, self.model, tag)
# top mlp
tag = (self.ttop, Z, self.tout)
self.top_l, self.top_w = self.create_mlp(self.ln_top, self.sigmoid_top,
self.model, tag)
# debug prints
# print(self.emb_l)
# print(self.bot_l)
# print(self.top_l)
# setup the last output variable
self.last_output = self.top_l[-1]
def create_parallel_forward_ops(self):
# distribute embeddings (model parallelism)
tag = (self.temb, self.tsin, self.tsout)
self.emb_l, self.emb_w, self.emb_vw = self.create_emb(
self.m_spa, self.ln_emb, self.model, tag
)
# replicate mlp (data parallelism)
tag = (self.tbot, self.tdin, self.tdout)
self.bot_l, self.bot_w = self.create_mlp(self.ln_bot, self.sigmoid_bot,
self.model, tag)
# add communication (butterfly shuffle)
t_list = []
for i, emb_output in enumerate(self.emb_l):
# split input
src_d = i % self.ndevices
lo = [emb_output + "_split_" + str(d) for d in range(self.ndevices)]
# approach 1: np and caffe2 operators assume the mini-batch size is
# divisible exactly by the number of available devices
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, src_d)):
self.model.net.Split(emb_output, lo, axis=0)
"""
# approach 2: np and caffe2 operators do not assume exact divisibility
ls = where_to_split(args.mini_batch_size, self.ndevices, _add_leftover=True)
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, src_d)):
emb_output_split = self.model.net.Split(
emb_output, lo, split=lp, axis=0
)
"""
# scatter
y = []
for dst_d in range(len(lo)):
src_blob = lo[dst_d]
dst_blob = str(src_blob).replace(
"gpu_" + str(src_d), "gpu_" + str(dst_d), 1
)
if src_blob != dst_blob:
with core.DeviceScope(
core.DeviceOption(workspace.GpuDeviceType, dst_d)
):
blob = self.model.Copy(src_blob, dst_blob)
else:
blob = dst_blob
y.append(blob)
t_list.append(y)
# adjust lists to be ordered per device
x = list(map(lambda x: list(x), zip(*self.bot_l)))
ly = list(map(lambda y: list(y), zip(*t_list)))
# interactions
for d in range(self.ndevices):
on_device = "gpu_" + str(d) + "/"
tag = (on_device + self.tdout, on_device + self.tsout, on_device + self.tint)
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, d)):
self.create_interactions([x[d][-1]], ly[d], self.model, tag)
# replicate mlp (data parallelism)
tag = (self.ttop, self.tint, self.tout)
self.top_l, self.top_w = self.create_mlp(self.ln_top, self.sigmoid_top,
self.model, tag)
# debug prints
# print(self.model.net.Proto(),end='\n')
# sys.exit("ERROR: debugging")
# setup the last output variable
self.last_output = self.top_l[-1]
def __init__(
self,
m_spa,
ln_emb,
ln_bot,
ln_top,
arch_interaction_op,
arch_interaction_itself=False,
sigmoid_bot=-1,
sigmoid_top=-1,
save_onnx=False,
model=None,
test_net=None,
tag=None,
ndevices=-1,
forward_ops=True,
enable_prof=False,
weighted_pooling=None,
emb_optimizer="sgd"
):
super(DLRM_Net, self).__init__()
# init model
if model is None:
global_init_opt = ["caffe2", "--caffe2_log_level=0"]
if enable_prof:
global_init_opt += [
"--logtostderr=0",
"--log_dir=$HOME",
"--caffe2_logging_print_net_summary=1",
]
workspace.GlobalInit(global_init_opt)
self.set_tags()
self.model = model_helper.ModelHelper(name="DLRM", init_params=True)
self.test_net = None
else:
# WARNING: assume that workspace and tags have been initialized elsewhere
self.set_tags(tag[0], tag[1], tag[2], tag[3], tag[4], tag[5], tag[6],
tag[7], tag[8], tag[9])
self.model = model
self.test_net = test_net
# save arguments
self.m_spa = m_spa
self.ln_emb = ln_emb
self.ln_bot = ln_bot
self.ln_top = ln_top
self.arch_interaction_op = arch_interaction_op
self.arch_interaction_itself = arch_interaction_itself
self.sigmoid_bot = sigmoid_bot
self.sigmoid_top = sigmoid_top
self.save_onnx = save_onnx
self.ndevices = ndevices
self.emb_optimizer = emb_optimizer
if weighted_pooling is not None and weighted_pooling != "fixed":
self.weighted_pooling = "learned"
else:
self.weighted_pooling = weighted_pooling
# onnx types and shapes dictionary
if self.save_onnx:
self.onnx_tsd = {}
# create forward operators
if forward_ops:
if self.ndevices <= 1:
return self.create_sequential_forward_ops()
else:
return self.create_parallel_forward_ops()
def set_tags(
self,
_tag_layer_top_mlp="top",
_tag_layer_bot_mlp="bot",
_tag_layer_embedding="emb",
_tag_feature_dense_in="dense_in",
_tag_feature_dense_out="dense_out",
_tag_feature_sparse_in="sparse_in",
_tag_feature_sparse_out="sparse_out",
_tag_interaction="interaction",
_tag_dense_output="prob_click",
_tag_dense_target="target",
):
# layer tags
self.ttop = _tag_layer_top_mlp
self.tbot = _tag_layer_bot_mlp
self.temb = _tag_layer_embedding
# dense feature tags
self.tdin = _tag_feature_dense_in
self.tdout = _tag_feature_dense_out
# sparse feature tags
self.tsin = _tag_feature_sparse_in
self.tsout = _tag_feature_sparse_out
# output and target tags
self.tint = _tag_interaction
self.ttar = _tag_dense_target
self.tout = _tag_dense_output
def parameters(self):
return self.model
def get_loss(self):
return self.FetchBlobWrapper(self.loss, reduce_across="add")
def get_output(self):
return self.FetchBlobWrapper(self.last_output, reduce_across="concat")
def create(self, X, S_lengths, S_indices, T):
self.create_input(X, S_lengths, S_indices, T)
self.create_model(X, S_lengths, S_indices, T)
def create_input(self, X, S_lengths, S_indices, T):
# feed input data to blobs
self.FeedBlobWrapper(self.tdin, X, split=True)
# save the blob shapes for latter (only needed if onnx is requested)
if self.save_onnx:
self.onnx_tsd[self.tdin] = (onnx.TensorProto.FLOAT, X.shape)
for i in range(len(self.emb_l)):
# select device
if self.ndevices > 1:
d = i % self.ndevices
else:
d = -1
# create tags
on_device = "" if self.ndevices <= 1 else "gpu_" + str(d) + "/"
len_s = on_device + self.temb + ":::" + "sls" + str(i) + "_l"
ind_s = on_device + self.temb + ":::" + "sls" + str(i) + "_i"
self.FeedBlobWrapper(len_s, np.array(S_lengths[i]), False, device_id=d)
self.FeedBlobWrapper(ind_s, np.array(S_indices[i]), False, device_id=d)
# save the blob shapes for latter (only needed if onnx is requested)
if self.save_onnx:
lshape = (len(S_lengths[i]),) # =args.mini_batch_size
ishape = (len(S_indices[i]),)
self.onnx_tsd[len_s] = (onnx.TensorProto.INT32, lshape)
self.onnx_tsd[ind_s] = (onnx.TensorProto.INT32, ishape)
# feed target data to blobs
if T is not None:
zeros_fp32 = np.zeros(T.shape).astype(np.float32)
self.FeedBlobWrapper(self.ttar, zeros_fp32, split=True)
# save the blob shapes for latter (only needed if onnx is requested)
if self.save_onnx:
self.onnx_tsd[self.ttar] = (onnx.TensorProto.FLOAT, T.shape)
def create_model(self, X, S_lengths, S_indices, T):
#setup tril indices for the interactions
offset = 1 if self.arch_interaction_itself else 0
num_fea = len(self.emb_l) + 1
tril_indices = np.array([j + i * num_fea
for i in range(num_fea) for j in range(i + offset)])
self.FeedBlobWrapper(self.tint + "_tril_indices", tril_indices)
# create compute graph
if T is not None:
# WARNING: RunNetOnce call is needed only if we use brew and ConstantFill.
# We could use direct calls to self.model functions above to avoid it
workspace.RunNetOnce(self.model.param_init_net)
workspace.CreateNet(self.model.net)
if self.test_net is not None:
workspace.CreateNet(self.test_net)
def run(self, X, S_lengths, S_indices, T, test_net=False, enable_prof=False):
# feed input data to blobs
# dense features
self.FeedBlobWrapper(self.tdin, X, split=True)
# sparse features
for i in range(len(self.emb_l)):
# select device
if self.ndevices > 1:
d = i % self.ndevices
else:
d = -1
# create tags
on_device = "" if self.ndevices <= 1 else "gpu_" + str(d) + "/"
len_s = on_device + self.temb + ":::" + "sls" + str(i) + "_l"
ind_s = on_device + self.temb + ":::" + "sls" + str(i) + "_i"
self.FeedBlobWrapper(len_s, np.array(S_lengths[i]), False, device_id=d)
self.FeedBlobWrapper(ind_s, np.array(S_indices[i]), False, device_id=d)
# feed target data to blobs if needed
if T is not None:
self.FeedBlobWrapper(self.ttar, T, split=True)
# execute compute graph
if test_net:
workspace.RunNet(self.test_net)
else:
if enable_prof:
workspace.C.benchmark_net(self.model.net.Name(), 0, 1, True)
else:
workspace.RunNet(self.model.net)
# debug prints
# print("intermediate")
# print(self.FetchBlobWrapper(self.bot_l[-1]))
# for tag_emb in self.emb_l:
# print(self.FetchBlobWrapper(tag_emb))
# print(self.FetchBlobWrapper(self.tint))
def MSEloss(self, scale=1.0):
# add MSEloss to the model
self.AddLayerWrapper(self.model.SquaredL2Distance, [self.tout, self.ttar], "sd")
self.AddLayerWrapper(self.model.Scale, "sd", "sd2", scale=2.0 * scale)
# WARNING: "loss" is a special tag and should not be changed
self.loss = self.AddLayerWrapper(self.model.AveragedLoss, "sd2", "loss")
def BCEloss(self, scale=1.0, threshold=0.0):
# add BCEloss to the mode
if 0.0 < threshold and threshold < 1.0:
self.AddLayerWrapper(self.model.Clip, self.tout, "tout_c",
min=threshold, max=(1.0 - threshold))
self.AddLayerWrapper(self.model.MakeTwoClass, "tout_c", "tout_2c")
else:
self.AddLayerWrapper(self.model.MakeTwoClass, self.tout, "tout_2c")
self.AddLayerWrapper(self.model.LabelCrossEntropy, ["tout_2c", self.ttar], "sd")
# WARNING: "loss" is a special tag and should not be changed
if scale == 1.0:
self.loss = self.AddLayerWrapper(self.model.AveragedLoss, "sd", "loss")
else:
self.AddLayerWrapper(self.model.Scale, "sd", "sd2", scale=scale)
self.loss = self.AddLayerWrapper(self.model.AveragedLoss, "sd2", "loss")
def sgd_optimizer(self, learning_rate,
T=None, _gradientMap=None, sync_dense_params=True):
# create one, it and lr tags (or use them if already present)
if T is not None:
(tag_one, tag_it, tag_lr) = T
else:
(tag_one, tag_it, tag_lr) = ("const_one", "optim_it", "optim_lr")
# approach 1: feed values directly
# self.FeedBlobWrapper(tag_one, np.ones(1).astype(np.float32))
# self.FeedBlobWrapper(tag_it, np.zeros(1).astype(np.int64))
# it = self.AddLayerWrapper(self.model.Iter, tag_it, tag_it)
# lr = self.AddLayerWrapper(self.model.LearningRate, tag_it, tag_lr,
# base_lr=-1 * learning_rate, policy="fixed")
# approach 2: use brew
self.AddLayerWrapper(self.model.param_init_net.ConstantFill,
[], tag_one, shape=[1], value=1.0)
self.AddLayerWrapper(brew.iter, self.model, tag_it)
self.AddLayerWrapper(self.model.LearningRate, tag_it, tag_lr,
base_lr=-1 * learning_rate, policy="fixed")
# save the blob shapes for latter (only needed if onnx is requested)
if self.save_onnx:
self.onnx_tsd[tag_one] = (onnx.TensorProto.FLOAT, (1,))
self.onnx_tsd[tag_it] = (onnx.TensorProto.INT64, (1,))
# create gradient maps (or use them if already present)
if _gradientMap is not None:
self.gradientMap = _gradientMap
else:
if self.loss.__class__ == list:
self.gradientMap = self.model.AddGradientOperators(self.loss)
else:
self.gradientMap = self.model.AddGradientOperators([self.loss])
# update weights
# approach 1: builtin function
# optimizer.build_sgd(self.model, base_learning_rate=learning_rate)
# approach 2: custom code
# top MLP weight and bias
for w in self.top_w:
# allreduce across devices if needed
if sync_dense_params and self.ndevices > 1:
grad_blobs = [
self.gradientMap["gpu_{}/".format(d) + w]
for d in range(self.ndevices)
]
self.model.NCCLAllreduce(grad_blobs, grad_blobs)
# update weights
self.AddLayerWrapper(self.model.WeightedSum,
[w, tag_one, "", tag_lr], w, reset_grad=True)
# bottom MLP weight and bias
for w in self.bot_w:
# allreduce across devices if needed
if sync_dense_params and self.ndevices > 1:
grad_blobs = [
self.gradientMap["gpu_{}/".format(d) + w]
for d in range(self.ndevices)
]
self.model.NCCLAllreduce(grad_blobs, grad_blobs)
# update weights
self.AddLayerWrapper(self.model.WeightedSum,
[w, tag_one, "", tag_lr], w, reset_grad=True)
# update embeddings
for i, w in enumerate(self.emb_w):
# select device
if self.ndevices > 1:
d = i % self.ndevices
# create tags
on_device = "" if self.ndevices <= 1 else "gpu_" + str(d) + "/"
_tag_one = on_device + tag_one
_tag_lr = on_device + tag_lr
# pickup gradient
w_grad = self.gradientMap[w]
# update weights
if self.ndevices > 1:
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, d)):
self.model.ScatterWeightedSum([w, _tag_one, w_grad.indices,
w_grad.values, _tag_lr], w)
else:
self.model.ScatterWeightedSum([w, _tag_one, w_grad.indices,
w_grad.values, _tag_lr], w)
# update per sample weights
if self.weighted_pooling == "learned":
for i, w in enumerate(self.emb_vw):
# select device
if self.ndevices > 1:
d = i % self.ndevices
# create tags
on_device = "" if self.ndevices <= 1 else "gpu_" + str(d) + "/"
_tag_one = on_device + tag_one
_tag_lr = on_device + tag_lr
# pickup gradient
w_grad = self.gradientMap[w]
# update weights
if self.ndevices > 1:
with core.DeviceScope(
core.DeviceOption(workspace.GpuDeviceType, d)
):
self.model.ScatterWeightedSum(
[w, _tag_one, w_grad.indices,
w_grad.values, _tag_lr], w
)
else:
self.model.ScatterWeightedSum(
[w, _tag_one, w_grad.indices, w_grad.values, _tag_lr], w
)
def adagrad_optimizer(self, learning_rate,
T=None, _gradientMap=None, sync_dense_params=True,
epsilon=1e-10, decay_=0.0, weight_decay_=0.0):
# create one, it and lr tags (or use them if already present)
if T is not None:
(tag_one, tag_it, tag_lr) = T
else:
(tag_one, tag_it, tag_lr) = ("const_one", "optim_it", "optim_lr")
# approach 1: feed values directly
# self.FeedBlobWrapper(tag_one, np.ones(1).astype(np.float32))
# self.FeedBlobWrapper(tag_it, np.zeros(1).astype(np.int64))
# it = self.AddLayerWrapper(self.model.Iter, tag_it, tag_it)
# lr = self.AddLayerWrapper(self.model.LearningRate, tag_it, tag_lr,
# base_lr=-1 * learning_rate, policy="fixed")
# approach 2: use brew
self.AddLayerWrapper(self.model.param_init_net.ConstantFill,
[], tag_one, shape=[1], value=1.0)
self.AddLayerWrapper(brew.iter, self.model, tag_it)
self.AddLayerWrapper(self.model.LearningRate, tag_it, tag_lr,
base_lr=-1 * learning_rate, policy="fixed")
# save the blob shapes for latter (only needed if onnx is requested)
if self.save_onnx:
self.onnx_tsd[tag_one] = (onnx.TensorProto.FLOAT, (1,))
self.onnx_tsd[tag_it] = (onnx.TensorProto.INT64, (1,))
# create gradient maps (or use them if already present)
if _gradientMap is not None:
self.gradientMap = _gradientMap
else:
if self.loss.__class__ == list:
self.gradientMap = self.model.AddGradientOperators(self.loss)
else:
self.gradientMap = self.model.AddGradientOperators([self.loss])
# update weights
# approach 1: builtin function
# optimizer.build_sgd(self.model, base_learning_rate=learning_rate)
# approach 2: custom code
# top MLP weight and bias
for i, w in enumerate(self.top_w):
# allreduce across devices if needed
if sync_dense_params and self.ndevices > 1:
grad_blobs = [
self.gradientMap["gpu_{}/".format(d) + w]
for d in range(self.ndevices)
]
self.model.NCCLAllreduce(grad_blobs, grad_blobs)
# update weights
self.model.Adagrad(
[
w,
"momentum_mlp_top_{}".format(i + 1),
self.gradientMap[w],
tag_lr
],
[w, "momentum_mlp_top_{}".format(i + 1)],
epsilon=epsilon,
decay_=decay_,
weight_decay_=weight_decay_
)
# bottom MLP weight and bias
for i, w in enumerate(self.bot_w):
# allreduce across devices if needed
if sync_dense_params and self.ndevices > 1:
grad_blobs = [
self.gradientMap["gpu_{}/".format(d) + w]
for d in range(self.ndevices)
]
self.model.NCCLAllreduce(grad_blobs, grad_blobs)
# update weights
self.model.Adagrad(
[
w,
"momentum_mlp_bot_{}".format(i + 1),
self.gradientMap[w],
tag_lr
],
[w, "momentum_mlp_bot_{}".format(i + 1)],
epsilon=epsilon,
decay_=decay_,
weight_decay_=weight_decay_
)
# update embeddings
for i, w in enumerate(self.emb_w):
# select device
if self.ndevices > 1:
d = i % self.ndevices
# create tags
on_device = "" if self.ndevices <= 1 else "gpu_" + str(d) + "/"
_tag_one = on_device + tag_one
_tag_lr = on_device + tag_lr
# pickup gradient
w_grad = self.gradientMap[w]
# update weights
def add_optimizer():
self.model.Unique(
w_grad.indices,
["unique_w_grad_indices", "remapping_w_grad_indices"]
)
self.model.UnsortedSegmentSum(
[w_grad.values, "remapping_w_grad_indices"],
"unique_w_grad_values"
)
if self.emb_optimizer == "adagrad":
self.model.SparseAdagrad(
[
w,
"momentum_emb_{}".format(i),
"unique_w_grad_indices",
"unique_w_grad_values",
_tag_lr
],
[w, "momentum_emb_{}".format(i)],
epsilon=epsilon,
decay_=decay_,
weight_decay_=weight_decay_
)
elif self.emb_optimizer == "rwsadagrad":
self.model.RowWiseSparseAdagrad(
[
w,
"momentum_emb_{}".format(i),
"unique_w_grad_indices",
"unique_w_grad_values",
_tag_lr
],
[w, "momentum_emb_{}".format(i)],
epsilon=epsilon,
decay_=decay_,
weight_decay_=weight_decay_
)
if self.ndevices > 1:
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, d)):
add_optimizer()
else:
add_optimizer()