diff --git a/experiment_plan.md b/experiment_plan.md new file mode 100644 index 0000000000..3232152a71 --- /dev/null +++ b/experiment_plan.md @@ -0,0 +1,34 @@ +# Experiment Plan + - [x] CDF图 **DDL: 9月5日** + XPUTimer要使用CUPTI版本的 + - [x] FSDP: + - [x] Megatron: + - [x] DLRM: + - [ ] 使用FSDP、Megatron、DLRM构造任务100个 **DDL: 9月8日前构造完脚本，9月12日前跑完50个，9月16日前跑完100个** + 20个有问题的，80个没有问题的，80个没问题的可以直接跑，然后看CDF、空泡率有没有不正常的，这种就是假阳性。 + - [ ] FSDP + - [ ] 模型大小, 7B, 13B, 70B + - [ ] GPU规模, 8, 16, 32, 48, 64 + - [ ] Megatron + - [ ] 模型大小, 7B, 13B, 70B + - [ ] GPU规模, 8, 16, 32, 48, 64 + - [ ] DLRM + - [ ] 模型大小, + - [ ] GPU规模, + 10 + +| GPU | 8 | 16 | 32 | 64 | +| :--------------: | :--: | :--: | :--: | :--: | +| Megatron | 8 | 8 | 8 | 4 | +| FSDP | 8 | 8 | 8 | 4 | +| DLRM | 8 | 8 | 8 | 0 | +| Megatron-badsync | 1 | 1 | 1 | 1 | +| FSDP-Mem | 1 | 1 | 1 | 1 | +| FSDP-OPshape | 1 | 1 | 1 | 1 | +| FSDP-Dataloader | 1 | 1 | 1 | 1 | +| DLRM-badsync | 1 | 1 | 1 | 1 | + + - [x] Greyhound: [https://github.com/wutianyuan1/Greyhound](https://github.com/wutianyuan1/Greyhound) **DDL: 9月12日前** + - [x] 首先是overhead，这里的XPU timer使用CUPTI版本 + - [x] 其次是能不能检测慢 + - [x] 以及FSDP、megatron、DLRM的适配性 diff --git a/xpu_timer/experiments/dlrm/dlrm_s_pytorch.py b/xpu_timer/experiments/dlrm/dlrm_s_pytorch.py new file mode 100644 index 0000000000..e1b42b7466 --- /dev/null +++ b/xpu_timer/experiments/dlrm/dlrm_s_pytorch.py @@ -0,0 +1,1908 @@ +# Copyright (c) Meta Platforms, Inc. and 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 argparse + +# miscellaneous +import builtins +import datetime +import json +import sys +import time + +# onnx +# The onnx import causes deprecation warnings every time workers +# are spawned during testing. So, we filter out those warnings. +import warnings + +# data generation +import dlrm_data_pytorch as dp + +# For distributed run +import extend_distributed as ext_dist +import mlperf_logger + +# numpy +import numpy as np +import optim.rwsadagrad as RowWiseSparseAdagrad +import sklearn.metrics + +# pytorch +import torch +import torch.nn as nn + +# dataloader +try: + from internals import fbDataLoader, fbInputBatchFormatter + + has_internal_libs = True +except ImportError: + has_internal_libs = False + +from torch._ops import ops +from torch.autograd.profiler import record_function +from torch.nn.parallel.parallel_apply import parallel_apply +from torch.nn.parallel.replicate import replicate +from torch.nn.parallel.scatter_gather import gather, scatter +from torch.nn.parameter import Parameter +from torch.optim.lr_scheduler import _LRScheduler +from torch.utils.tensorboard import SummaryWriter + +# mixed-dimension trick +from tricks.md_embedding_bag import md_solver, PrEmbeddingBag + +# quotient-remainder trick +from tricks.qr_embedding_bag import QREmbeddingBag + +with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=DeprecationWarning) + try: + import onnx + except ImportError as error: + print("Unable to import onnx. ", error) + +# from torchviz import make_dot +# import torch.nn.functional as Functional +# from torch.nn.parameter import Parameter + +exc = getattr(builtins, "IOError", "FileNotFoundError") + + +def time_wrap(use_gpu): + if use_gpu: + torch.cuda.synchronize() + return time.time() + + +def dlrm_wrap(X, lS_o, lS_i, use_gpu, device, ndevices=1): + with record_function("DLRM forward"): + if use_gpu: # .cuda() + # lS_i can be either a list of tensors or a stacked tensor. + # Handle each case below: + if ndevices == 1: + lS_i = ( + [S_i.to(device) for S_i in lS_i] + if isinstance(lS_i, list) + else lS_i.to(device) + ) + lS_o = ( + [S_o.to(device) for S_o in lS_o] + if isinstance(lS_o, list) + else lS_o.to(device) + ) + return dlrm(X.to(device), lS_o, lS_i) + + +def loss_fn_wrap(Z, T, use_gpu, device): + with record_function("DLRM loss compute"): + if args.loss_function == "mse" or args.loss_function == "bce": + return dlrm.loss_fn(Z, T.to(device)) + elif args.loss_function == "wbce": + loss_ws_ = dlrm.loss_ws[T.data.view(-1).long()].view_as(T).to(device) + loss_fn_ = dlrm.loss_fn(Z, T.to(device)) + loss_sc_ = loss_ws_ * loss_fn_ + return loss_sc_.mean() + + +# The following function is a wrapper to avoid checking this multiple times in th +# loop below. +def unpack_batch(b): + if args.data_generation == "internal": + return fbInputBatchFormatter(b, args.data_size) + else: + # Experiment with unweighted samples + return b[0], b[1], b[2], b[3], torch.ones(b[3].size()), None + + +class LRPolicyScheduler(_LRScheduler): + def __init__(self, optimizer, num_warmup_steps, decay_start_step, num_decay_steps): + self.num_warmup_steps = num_warmup_steps + self.decay_start_step = decay_start_step + self.decay_end_step = decay_start_step + num_decay_steps + self.num_decay_steps = num_decay_steps + + if self.decay_start_step < self.num_warmup_steps: + sys.exit("Learning rate warmup must finish before the decay starts") + + super(LRPolicyScheduler, self).__init__(optimizer) + + def get_lr(self): + step_count = self._step_count + if step_count < self.num_warmup_steps: + # warmup + scale = 1.0 - (self.num_warmup_steps - step_count) / self.num_warmup_steps + lr = [base_lr * scale for base_lr in self.base_lrs] + self.last_lr = lr + elif self.decay_start_step <= step_count and step_count < self.decay_end_step: + # decay + decayed_steps = step_count - self.decay_start_step + scale = ((self.num_decay_steps - decayed_steps) / self.num_decay_steps) ** 2 + min_lr = 0.0000001 + lr = [max(min_lr, base_lr * scale) for base_lr in self.base_lrs] + self.last_lr = lr + else: + if self.num_decay_steps > 0: + # freeze at last, either because we're after decay + # or because we're between warmup and decay + lr = self.last_lr + else: + # do not adjust + lr = self.base_lrs + return lr + + +### define dlrm in PyTorch ### +class DLRM_Net(nn.Module): + def create_mlp(self, ln, sigmoid_layer): + # build MLP layer by layer + layers = nn.ModuleList() + for i in range(0, ln.size - 1): + n = ln[i] + m = ln[i + 1] + + # construct fully connected operator + LL = nn.Linear(int(n), int(m), bias=True) + + # initialize the weights + # with torch.no_grad(): + # 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)) + bt = np.random.normal(mean, std_dev, size=m).astype(np.float32) + # approach 1 + LL.weight.data = torch.tensor(W, requires_grad=True) + LL.bias.data = torch.tensor(bt, requires_grad=True) + # approach 2 + # LL.weight.data.copy_(torch.tensor(W)) + # LL.bias.data.copy_(torch.tensor(bt)) + # approach 3 + # LL.weight = Parameter(torch.tensor(W),requires_grad=True) + # LL.bias = Parameter(torch.tensor(bt),requires_grad=True) + layers.append(LL) + + # construct sigmoid or relu operator + if i == sigmoid_layer: + layers.append(nn.Sigmoid()) + else: + layers.append(nn.ReLU()) + + # approach 1: use ModuleList + # return layers + # approach 2: use Sequential container to wrap all layers + return torch.nn.Sequential(*layers) + + def create_emb(self, m, ln, weighted_pooling=None): + emb_l = nn.ModuleList() + v_W_l = [] + for i in range(0, ln.size): + if ext_dist.my_size > 1: + if i not in self.local_emb_indices: + continue + n = ln[i] + + # construct embedding operator + if self.qr_flag and n > self.qr_threshold: + EE = QREmbeddingBag( + n, + m, + self.qr_collisions, + operation=self.qr_operation, + mode="sum", + sparse=True, + ) + elif self.md_flag and n > self.md_threshold: + base = max(m) + _m = m[i] if n > self.md_threshold else base + EE = PrEmbeddingBag(n, _m, base) + # use np initialization as below for consistency... + W = np.random.uniform( + low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, _m) + ).astype(np.float32) + EE.embs.weight.data = torch.tensor(W, requires_grad=True) + else: + EE = nn.EmbeddingBag(n, m, mode="sum", sparse=True) + # initialize embeddings + # nn.init.uniform_(EE.weight, a=-np.sqrt(1 / n), b=np.sqrt(1 / n)) + W = np.random.uniform( + low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, m) + ).astype(np.float32) + # approach 1 + EE.weight.data = torch.tensor(W, requires_grad=True) + # approach 2 + # EE.weight.data.copy_(torch.tensor(W)) + # approach 3 + # EE.weight = Parameter(torch.tensor(W),requires_grad=True) + if weighted_pooling is None: + v_W_l.append(None) + else: + v_W_l.append(torch.ones(n, dtype=torch.float32)) + emb_l.append(EE) + return emb_l, v_W_l + + def __init__( + self, + m_spa=None, + ln_emb=None, + ln_bot=None, + ln_top=None, + arch_interaction_op=None, + arch_interaction_itself=False, + sigmoid_bot=-1, + sigmoid_top=-1, + sync_dense_params=True, + loss_threshold=0.0, + ndevices=-1, + qr_flag=False, + qr_operation="mult", + qr_collisions=0, + qr_threshold=200, + md_flag=False, + md_threshold=200, + weighted_pooling=None, + loss_function="bce", + ): + super(DLRM_Net, self).__init__() + + if ( + (m_spa is not None) + and (ln_emb is not None) + and (ln_bot is not None) + and (ln_top is not None) + and (arch_interaction_op is not None) + ): + # save arguments + self.ndevices = ndevices + self.output_d = 0 + self.parallel_model_batch_size = -1 + self.parallel_model_is_not_prepared = True + self.arch_interaction_op = arch_interaction_op + self.arch_interaction_itself = arch_interaction_itself + self.sync_dense_params = sync_dense_params + self.loss_threshold = loss_threshold + self.loss_function = loss_function + if weighted_pooling is not None and weighted_pooling != "fixed": + self.weighted_pooling = "learned" + else: + self.weighted_pooling = weighted_pooling + # create variables for QR embedding if applicable + self.qr_flag = qr_flag + if self.qr_flag: + self.qr_collisions = qr_collisions + self.qr_operation = qr_operation + self.qr_threshold = qr_threshold + # create variables for MD embedding if applicable + self.md_flag = md_flag + if self.md_flag: + self.md_threshold = md_threshold + + # If running distributed, get local slice of embedding tables + if ext_dist.my_size > 1: + n_emb = len(ln_emb) + if n_emb < ext_dist.my_size: + sys.exit( + "only (%d) sparse features for (%d) devices, table partitions will fail" + % (n_emb, ext_dist.my_size) + ) + self.n_global_emb = n_emb + self.n_local_emb, self.n_emb_per_rank = ext_dist.get_split_lengths( + n_emb + ) + self.local_emb_slice = ext_dist.get_my_slice(n_emb) + self.local_emb_indices = list(range(n_emb))[self.local_emb_slice] + + # create operators + if ndevices <= 1: + self.emb_l, w_list = self.create_emb(m_spa, ln_emb, weighted_pooling) + if self.weighted_pooling == "learned": + self.v_W_l = nn.ParameterList() + for w in w_list: + self.v_W_l.append(Parameter(w)) + else: + self.v_W_l = w_list + self.bot_l = self.create_mlp(ln_bot, sigmoid_bot) + self.top_l = self.create_mlp(ln_top, sigmoid_top) + + # quantization + self.quantize_emb = False + self.emb_l_q = [] + self.quantize_bits = 32 + + # specify the loss function + if self.loss_function == "mse": + self.loss_fn = torch.nn.MSELoss(reduction="mean") + elif self.loss_function == "bce": + self.loss_fn = torch.nn.BCELoss(reduction="mean") + elif self.loss_function == "wbce": + self.loss_ws = torch.tensor( + np.fromstring(args.loss_weights, dtype=float, sep="-") + ) + self.loss_fn = torch.nn.BCELoss(reduction="none") + else: + sys.exit( + "ERROR: --loss-function=" + self.loss_function + " is not supported" + ) + + def apply_mlp(self, x, layers): + # approach 1: use ModuleList + # for layer in layers: + # x = layer(x) + # return x + # approach 2: use Sequential container to wrap all layers + return layers(x) + + def apply_emb(self, lS_o, lS_i, emb_l, v_W_l): + # WARNING: notice that we are processing the batch at once. We implicitly + # assume that the data is laid out such that: + # 1. each embedding is indexed with a group of sparse indices, + # corresponding to a single lookup + # 2. for each embedding the lookups are further organized into a batch + # 3. for a list of embedding tables there is a list of batched lookups + + ly = [] + for k, sparse_index_group_batch in enumerate(lS_i): + sparse_offset_group_batch = lS_o[k] + + # embedding lookup + # We are using EmbeddingBag, which implicitly uses sum operator. + # The embeddings are represented as tall matrices, with sum + # happening vertically across 0 axis, resulting in a row vector + # E = emb_l[k] + + if v_W_l[k] is not None: + per_sample_weights = v_W_l[k].gather(0, sparse_index_group_batch) + else: + per_sample_weights = None + + if self.quantize_emb: + s1 = self.emb_l_q[k].element_size() * self.emb_l_q[k].nelement() + s2 = self.emb_l_q[k].element_size() * self.emb_l_q[k].nelement() + print("quantized emb sizes:", s1, s2) + + if self.quantize_bits == 4: + QV = ops.quantized.embedding_bag_4bit_rowwise_offsets( + self.emb_l_q[k], + sparse_index_group_batch, + sparse_offset_group_batch, + per_sample_weights=per_sample_weights, + ) + elif self.quantize_bits == 8: + QV = ops.quantized.embedding_bag_byte_rowwise_offsets( + self.emb_l_q[k], + sparse_index_group_batch, + sparse_offset_group_batch, + per_sample_weights=per_sample_weights, + ) + + ly.append(QV) + else: + E = emb_l[k] + V = E( + sparse_index_group_batch, + sparse_offset_group_batch, + per_sample_weights=per_sample_weights, + ) + + ly.append(V) + + # print(ly) + return ly + + # using quantizing functions from caffe2/aten/src/ATen/native/quantized/cpu + def quantize_embedding(self, bits): + n = len(self.emb_l) + self.emb_l_q = [None] * n + for k in range(n): + if bits == 4: + self.emb_l_q[k] = ops.quantized.embedding_bag_4bit_prepack( + self.emb_l[k].weight + ) + elif bits == 8: + self.emb_l_q[k] = ops.quantized.embedding_bag_byte_prepack( + self.emb_l[k].weight + ) + else: + return + self.emb_l = None + self.quantize_emb = True + self.quantize_bits = bits + + def interact_features(self, x, ly): + if self.arch_interaction_op == "dot": + # concatenate dense and sparse features + (batch_size, d) = x.shape + T = torch.cat([x] + ly, dim=1).view((batch_size, -1, d)) + # perform a dot product + Z = torch.bmm(T, torch.transpose(T, 1, 2)) + # append dense feature with the interactions (into a row vector) + # approach 1: all + # Zflat = Z.view((batch_size, -1)) + # approach 2: unique + _, ni, nj = Z.shape + # approach 1: tril_indices + # offset = 0 if self.arch_interaction_itself else -1 + # li, lj = torch.tril_indices(ni, nj, offset=offset) + # approach 2: custom + offset = 1 if self.arch_interaction_itself else 0 + li = torch.tensor([i for i in range(ni) for j in range(i + offset)]) + lj = torch.tensor([j for i in range(nj) for j in range(i + offset)]) + Zflat = Z[:, li, lj] + # concatenate dense features and interactions + R = torch.cat([x] + [Zflat], dim=1) + elif self.arch_interaction_op == "cat": + # concatenation features (into a row vector) + R = torch.cat([x] + ly, dim=1) + else: + sys.exit( + "ERROR: --arch-interaction-op=" + + self.arch_interaction_op + + " is not supported" + ) + + return R + + def forward(self, dense_x, lS_o, lS_i): + if ext_dist.my_size > 1: + # multi-node multi-device run + return self.distributed_forward(dense_x, lS_o, lS_i) + elif self.ndevices <= 1: + # single device run + return self.sequential_forward(dense_x, lS_o, lS_i) + else: + # single-node multi-device run + return self.parallel_forward(dense_x, lS_o, lS_i) + + def distributed_forward(self, dense_x, lS_o, lS_i): + batch_size = dense_x.size()[0] + # WARNING: # of ranks must be <= batch size in distributed_forward call + if batch_size < ext_dist.my_size: + sys.exit( + "ERROR: batch_size (%d) must be larger than number of ranks (%d)" + % (batch_size, ext_dist.my_size) + ) + if batch_size % ext_dist.my_size != 0: + sys.exit( + "ERROR: batch_size %d can not split across %d ranks evenly" + % (batch_size, ext_dist.my_size) + ) + + dense_x = dense_x[ext_dist.get_my_slice(batch_size)] + lS_o = lS_o[self.local_emb_slice] + lS_i = lS_i[self.local_emb_slice] + + if (len(self.emb_l) != len(lS_o)) or (len(self.emb_l) != len(lS_i)): + sys.exit( + "ERROR: corrupted model input detected in distributed_forward call" + ) + + # embeddings + with record_function("DLRM embedding forward"): + ly = self.apply_emb(lS_o, lS_i, self.emb_l, self.v_W_l) + + # WARNING: Note that at this point we have the result of the embedding lookup + # for the entire batch on each rank. We would like to obtain partial results + # corresponding to all embedding lookups, but part of the batch on each rank. + # Therefore, matching the distribution of output of bottom mlp, so that both + # could be used for subsequent interactions on each device. + if len(self.emb_l) != len(ly): + sys.exit("ERROR: corrupted intermediate result in distributed_forward call") + + a2a_req = ext_dist.alltoall(ly, self.n_emb_per_rank) + + with record_function("DLRM bottom nlp forward"): + x = self.apply_mlp(dense_x, self.bot_l) + + ly = a2a_req.wait() + ly = list(ly) + + # interactions + with record_function("DLRM interaction forward"): + z = self.interact_features(x, ly) + + # top mlp + with record_function("DLRM top nlp forward"): + p = self.apply_mlp(z, self.top_l) + + # clamp output if needed + if 0.0 < self.loss_threshold and self.loss_threshold < 1.0: + z = torch.clamp(p, min=self.loss_threshold, max=(1.0 - self.loss_threshold)) + else: + z = p + + return z + + def sequential_forward(self, dense_x, lS_o, lS_i): + # process dense features (using bottom mlp), resulting in a row vector + x = self.apply_mlp(dense_x, self.bot_l) + # debug prints + # print("intermediate") + # print(x.detach().cpu().numpy()) + + # process sparse features(using embeddings), resulting in a list of row vectors + ly = self.apply_emb(lS_o, lS_i, self.emb_l, self.v_W_l) + # for y in ly: + # print(y.detach().cpu().numpy()) + + # interact features (dense and sparse) + z = self.interact_features(x, ly) + # print(z.detach().cpu().numpy()) + + # obtain probability of a click (using top mlp) + p = self.apply_mlp(z, self.top_l) + + # clamp output if needed + if 0.0 < self.loss_threshold and self.loss_threshold < 1.0: + z = torch.clamp(p, min=self.loss_threshold, max=(1.0 - self.loss_threshold)) + else: + z = p + + return z + + def parallel_forward(self, dense_x, lS_o, lS_i): + ### prepare model (overwrite) ### + # WARNING: # of devices must be >= batch size in parallel_forward call + batch_size = dense_x.size()[0] + ndevices = min(self.ndevices, batch_size, len(self.emb_l)) + device_ids = range(ndevices) + # WARNING: must redistribute the model if mini-batch size changes(this is common + # for last mini-batch, when # of elements in the dataset/batch size is not even + if self.parallel_model_batch_size != batch_size: + self.parallel_model_is_not_prepared = True + + if self.parallel_model_is_not_prepared or self.sync_dense_params: + # replicate mlp (data parallelism) + self.bot_l_replicas = replicate(self.bot_l, device_ids) + self.top_l_replicas = replicate(self.top_l, device_ids) + self.parallel_model_batch_size = batch_size + + if self.parallel_model_is_not_prepared: + # distribute embeddings (model parallelism) + t_list = [] + w_list = [] + for k, emb in enumerate(self.emb_l): + d = torch.device("cuda:" + str(k % ndevices)) + t_list.append(emb.to(d)) + if self.weighted_pooling == "learned": + w_list.append(Parameter(self.v_W_l[k].to(d))) + elif self.weighted_pooling == "fixed": + w_list.append(self.v_W_l[k].to(d)) + else: + w_list.append(None) + self.emb_l = nn.ModuleList(t_list) + if self.weighted_pooling == "learned": + self.v_W_l = nn.ParameterList(w_list) + else: + self.v_W_l = w_list + self.parallel_model_is_not_prepared = False + + ### prepare input (overwrite) ### + # scatter dense features (data parallelism) + # print(dense_x.device) + dense_x = scatter(dense_x, device_ids, dim=0) + # distribute sparse features (model parallelism) + if (len(self.emb_l) != len(lS_o)) or (len(self.emb_l) != len(lS_i)): + sys.exit("ERROR: corrupted model input detected in parallel_forward call") + + t_list = [] + i_list = [] + for k, _ in enumerate(self.emb_l): + d = torch.device("cuda:" + str(k % ndevices)) + t_list.append(lS_o[k].to(d)) + i_list.append(lS_i[k].to(d)) + lS_o = t_list + lS_i = i_list + + ### compute results in parallel ### + # bottom mlp + # WARNING: Note that the self.bot_l is a list of bottom mlp modules + # that have been replicated across devices, while dense_x is a tuple of dense + # inputs that has been scattered across devices on the first (batch) dimension. + # The output is a list of tensors scattered across devices according to the + # distribution of dense_x. + x = parallel_apply(self.bot_l_replicas, dense_x, None, device_ids) + # debug prints + # print(x) + + # embeddings + ly = self.apply_emb(lS_o, lS_i, self.emb_l, self.v_W_l) + # debug prints + # print(ly) + + # butterfly shuffle (implemented inefficiently for now) + # WARNING: Note that at this point we have the result of the embedding lookup + # for the entire batch on each device. We would like to obtain partial results + # corresponding to all embedding lookups, but part of the batch on each device. + # Therefore, matching the distribution of output of bottom mlp, so that both + # could be used for subsequent interactions on each device. + if len(self.emb_l) != len(ly): + sys.exit("ERROR: corrupted intermediate result in parallel_forward call") + + t_list = [] + for k, _ in enumerate(self.emb_l): + d = torch.device("cuda:" + str(k % ndevices)) + y = scatter(ly[k], device_ids, dim=0) + t_list.append(y) + # adjust the list to be ordered per device + ly = list(map(lambda y: list(y), zip(*t_list))) + # debug prints + # print(ly) + + # interactions + z = [] + for k in range(ndevices): + zk = self.interact_features(x[k], ly[k]) + z.append(zk) + # debug prints + # print(z) + + # top mlp + # WARNING: Note that the self.top_l is a list of top mlp modules that + # have been replicated across devices, while z is a list of interaction results + # that by construction are scattered across devices on the first (batch) dim. + # The output is a list of tensors scattered across devices according to the + # distribution of z. + p = parallel_apply(self.top_l_replicas, z, None, device_ids) + + ### gather the distributed results ### + p0 = gather(p, self.output_d, dim=0) + + # clamp output if needed + if 0.0 < self.loss_threshold and self.loss_threshold < 1.0: + z0 = torch.clamp( + p0, min=self.loss_threshold, max=(1.0 - self.loss_threshold) + ) + else: + z0 = p0 + + return z0 + + +def dash_separated_ints(value): + vals = value.split("-") + for val in vals: + try: + int(val) + except ValueError: + raise argparse.ArgumentTypeError( + "%s is not a valid dash separated list of ints" % value + ) + + return value + + +def dash_separated_floats(value): + vals = value.split("-") + for val in vals: + try: + float(val) + except ValueError: + raise argparse.ArgumentTypeError( + "%s is not a valid dash separated list of floats" % value + ) + + return value + + +def inference( + args, + dlrm, + best_acc_test, + best_auc_test, + test_ld, + device, + use_gpu, + log_iter=-1, +): + test_accu = 0 + test_samp = 0 + + if args.mlperf_logging: + scores = [] + targets = [] + + for i, testBatch in enumerate(test_ld): + # early exit if nbatches was set by the user and was exceeded + if nbatches > 0 and i >= nbatches: + break + + X_test, lS_o_test, lS_i_test, T_test, W_test, CBPP_test = unpack_batch( + testBatch + ) + + # Skip the batch if batch size not multiple of total ranks + if ext_dist.my_size > 1 and X_test.size(0) % ext_dist.my_size != 0: + print("Warning: Skiping the batch %d with size %d" % (i, X_test.size(0))) + continue + + # forward pass + Z_test = dlrm_wrap( + X_test, + lS_o_test, + lS_i_test, + use_gpu, + device, + ndevices=ndevices, + ) + ### gather the distributed results on each rank ### + # For some reason it requires explicit sync before all_gather call if + # tensor is on GPU memory + if Z_test.is_cuda: + torch.cuda.synchronize() + (_, batch_split_lengths) = ext_dist.get_split_lengths(X_test.size(0)) + if ext_dist.my_size > 1: + Z_test = ext_dist.all_gather(Z_test, batch_split_lengths) + + if args.mlperf_logging: + S_test = Z_test.detach().cpu().numpy() # numpy array + T_test = T_test.detach().cpu().numpy() # numpy array + scores.append(S_test) + targets.append(T_test) + else: + with record_function("DLRM accuracy compute"): + # compute loss and accuracy + S_test = Z_test.detach().cpu().numpy() # numpy array + T_test = T_test.detach().cpu().numpy() # numpy array + + mbs_test = T_test.shape[0] # = mini_batch_size except last + A_test = np.sum((np.round(S_test, 0) == T_test).astype(np.uint8)) + + test_accu += A_test + test_samp += mbs_test + + if args.mlperf_logging: + with record_function("DLRM mlperf sklearn metrics compute"): + scores = np.concatenate(scores, axis=0) + targets = np.concatenate(targets, axis=0) + + metrics = { + "recall": lambda y_true, y_score: sklearn.metrics.recall_score( + y_true=y_true, y_pred=np.round(y_score) + ), + "precision": lambda y_true, y_score: sklearn.metrics.precision_score( + y_true=y_true, y_pred=np.round(y_score) + ), + "f1": lambda y_true, y_score: sklearn.metrics.f1_score( + y_true=y_true, y_pred=np.round(y_score) + ), + "ap": sklearn.metrics.average_precision_score, + "roc_auc": sklearn.metrics.roc_auc_score, + "accuracy": lambda y_true, y_score: sklearn.metrics.accuracy_score( + y_true=y_true, y_pred=np.round(y_score) + ), + } + + validation_results = {} + for metric_name, metric_function in metrics.items(): + validation_results[metric_name] = metric_function(targets, scores) + writer.add_scalar( + "mlperf-metrics-test/" + metric_name, + validation_results[metric_name], + log_iter, + ) + acc_test = validation_results["accuracy"] + else: + acc_test = test_accu / test_samp + writer.add_scalar("Test/Acc", acc_test, log_iter) + + model_metrics_dict = { + "nepochs": args.nepochs, + "nbatches": nbatches, + "nbatches_test": nbatches_test, + "state_dict": dlrm.state_dict(), + "test_acc": acc_test, + } + + if args.mlperf_logging: + is_best = validation_results["roc_auc"] > best_auc_test + if is_best: + best_auc_test = validation_results["roc_auc"] + model_metrics_dict["test_auc"] = best_auc_test + print( + "recall {:.4f}, precision {:.4f},".format( + validation_results["recall"], + validation_results["precision"], + ) + + " f1 {:.4f}, ap {:.4f},".format( + validation_results["f1"], validation_results["ap"] + ) + + " auc {:.4f}, best auc {:.4f},".format( + validation_results["roc_auc"], best_auc_test + ) + + " accuracy {:3.3f} %, best accuracy {:3.3f} %".format( + validation_results["accuracy"] * 100, best_acc_test * 100 + ), + flush=True, + ) + else: + is_best = acc_test > best_acc_test + if is_best: + best_acc_test = acc_test + print( + " accuracy {:3.3f} %, best {:3.3f} %".format( + acc_test * 100, best_acc_test * 100 + ), + flush=True, + ) + return model_metrics_dict, is_best + + +def run(): + ### parse arguments ### + parser = argparse.ArgumentParser( + description="Train Deep Learning Recommendation Model (DLRM)" + ) + # model related parameters + parser.add_argument("--arch-sparse-feature-size", type=int, default=2) + parser.add_argument( + "--arch-embedding-size", type=dash_separated_ints, default="4-3-2" + ) + # j will be replaced with the table number + parser.add_argument("--arch-mlp-bot", type=dash_separated_ints, default="4-3-2") + parser.add_argument("--arch-mlp-top", type=dash_separated_ints, default="4-2-1") + parser.add_argument( + "--arch-interaction-op", type=str, choices=["dot", "cat"], default="dot" + ) + parser.add_argument("--arch-interaction-itself", action="store_true", default=False) + parser.add_argument("--weighted-pooling", type=str, default=None) + # embedding table options + parser.add_argument("--md-flag", action="store_true", default=False) + parser.add_argument("--md-threshold", type=int, default=200) + parser.add_argument("--md-temperature", type=float, default=0.3) + parser.add_argument("--md-round-dims", action="store_true", default=False) + parser.add_argument("--qr-flag", action="store_true", default=False) + parser.add_argument("--qr-threshold", type=int, default=200) + parser.add_argument("--qr-operation", type=str, default="mult") + parser.add_argument("--qr-collisions", type=int, default=4) + # activations and loss + parser.add_argument("--activation-function", type=str, default="relu") + parser.add_argument("--loss-function", type=str, default="mse") # or bce or wbce + parser.add_argument( + "--loss-weights", type=dash_separated_floats, default="1.0-1.0" + ) # for wbce + parser.add_argument("--loss-threshold", type=float, default=0.0) # 1.0e-7 + parser.add_argument("--round-targets", type=bool, default=False) + # data + parser.add_argument("--data-size", type=int, default=1) + parser.add_argument("--num-batches", type=int, default=0) + parser.add_argument( + "--data-generation", + type=str, + choices=["random", "dataset", "internal"], + default="random", + ) # synthetic, dataset or internal + parser.add_argument( + "--rand-data-dist", type=str, default="uniform" + ) # uniform or gaussian + parser.add_argument("--rand-data-min", type=float, default=0) + parser.add_argument("--rand-data-max", type=float, default=1) + parser.add_argument("--rand-data-mu", type=float, default=-1) + parser.add_argument("--rand-data-sigma", type=float, default=1) + parser.add_argument("--data-trace-file", type=str, default="./input/dist_emb_j.log") + parser.add_argument("--data-set", type=str, default="kaggle") # or terabyte + parser.add_argument("--raw-data-file", type=str, default="") + parser.add_argument("--processed-data-file", type=str, default="") + parser.add_argument("--data-randomize", type=str, default="total") # or day or none + parser.add_argument("--data-trace-enable-padding", type=bool, default=False) + parser.add_argument("--max-ind-range", type=int, default=-1) + parser.add_argument("--data-sub-sample-rate", type=float, default=0.0) # in [0, 1] + parser.add_argument("--num-indices-per-lookup", type=int, default=10) + parser.add_argument("--num-indices-per-lookup-fixed", type=bool, default=False) + parser.add_argument("--num-workers", type=int, default=0) + parser.add_argument("--memory-map", action="store_true", default=False) + # training + parser.add_argument("--mini-batch-size", type=int, default=1) + parser.add_argument("--nepochs", type=int, default=1) + parser.add_argument("--learning-rate", type=float, default=0.01) + parser.add_argument("--print-precision", type=int, default=5) + parser.add_argument("--numpy-rand-seed", type=int, default=123) + parser.add_argument("--sync-dense-params", type=bool, default=True) + parser.add_argument("--optimizer", type=str, default="sgd") + parser.add_argument( + "--dataset-multiprocessing", + action="store_true", + default=False, + help="The Kaggle dataset can be multiprocessed in an environment \ + with more than 7 CPU cores and more than 20 GB of memory. \n \ + The Terabyte dataset can be multiprocessed in an environment \ + with more than 24 CPU cores and at least 1 TB of memory.", + ) + # inference + parser.add_argument("--inference-only", action="store_true", default=False) + # quantize + parser.add_argument("--quantize-mlp-with-bit", type=int, default=32) + parser.add_argument("--quantize-emb-with-bit", type=int, default=32) + # onnx + parser.add_argument("--save-onnx", action="store_true", default=False) + # gpu + parser.add_argument("--use-gpu", action="store_true", default=False) + # distributed + parser.add_argument("--local_rank", type=int, default=-1) + parser.add_argument("--dist-backend", type=str, default="") + # debugging and profiling + parser.add_argument("--print-freq", type=int, default=1) + parser.add_argument("--test-freq", type=int, default=-1) + parser.add_argument("--test-mini-batch-size", type=int, default=-1) + parser.add_argument("--test-num-workers", type=int, default=-1) + parser.add_argument("--print-time", action="store_true", default=False) + parser.add_argument("--print-wall-time", action="store_true", default=False) + parser.add_argument("--debug-mode", action="store_true", default=False) + parser.add_argument("--enable-profiling", action="store_true", default=False) + parser.add_argument("--plot-compute-graph", action="store_true", default=False) + parser.add_argument("--tensor-board-filename", type=str, default="run_kaggle_pt") + # store/load model + parser.add_argument("--save-model", type=str, default="") + parser.add_argument("--load-model", type=str, default="") + # mlperf logging (disables other output and stops early) + parser.add_argument("--mlperf-logging", action="store_true", default=False) + # stop at target accuracy Kaggle 0.789, Terabyte (sub-sampled=0.875) 0.8107 + parser.add_argument("--mlperf-acc-threshold", type=float, default=0.0) + # stop at target AUC Terabyte (no subsampling) 0.8025 + parser.add_argument("--mlperf-auc-threshold", type=float, default=0.0) + parser.add_argument("--mlperf-bin-loader", action="store_true", default=False) + parser.add_argument("--mlperf-bin-shuffle", action="store_true", default=False) + # mlperf gradient accumulation iterations + parser.add_argument("--mlperf-grad-accum-iter", type=int, default=1) + # LR policy + parser.add_argument("--lr-num-warmup-steps", type=int, default=0) + parser.add_argument("--lr-decay-start-step", type=int, default=0) + parser.add_argument("--lr-num-decay-steps", type=int, default=0) + + global args + global nbatches + global nbatches_test + global writer + args = parser.parse_args() + + if args.dataset_multiprocessing: + assert sys.version_info[0] >= 3 and sys.version_info[1] > 7, ( + "The dataset_multiprocessing " + + "flag is susceptible to a bug in Python 3.7 and under. " + + "https://github.com/facebookresearch/dlrm/issues/172" + ) + + if args.mlperf_logging: + mlperf_logger.log_event(key=mlperf_logger.constants.CACHE_CLEAR, value=True) + mlperf_logger.log_start( + key=mlperf_logger.constants.INIT_START, log_all_ranks=True + ) + + if args.weighted_pooling is not None: + if args.qr_flag: + sys.exit("ERROR: quotient remainder with weighted pooling is not supported") + if args.md_flag: + sys.exit("ERROR: mixed dimensions with weighted pooling is not supported") + if args.quantize_emb_with_bit in [4, 8]: + if args.qr_flag: + sys.exit( + "ERROR: 4 and 8-bit quantization with quotient remainder is not supported" + ) + if args.md_flag: + sys.exit( + "ERROR: 4 and 8-bit quantization with mixed dimensions is not supported" + ) + if args.use_gpu: + sys.exit("ERROR: 4 and 8-bit quantization on GPU is not supported") + + ### some basic setup ### + np.random.seed(args.numpy_rand_seed) + np.set_printoptions(precision=args.print_precision) + torch.set_printoptions(precision=args.print_precision) + torch.manual_seed(args.numpy_rand_seed) + + if args.test_mini_batch_size < 0: + # if the parameter is not set, use the training batch size + args.test_mini_batch_size = args.mini_batch_size + if args.test_num_workers < 0: + # if the parameter is not set, use the same parameter for training + args.test_num_workers = args.num_workers + + use_gpu = args.use_gpu and torch.cuda.is_available() + + if not args.debug_mode: + ext_dist.init_distributed( + local_rank=args.local_rank, use_gpu=use_gpu, backend=args.dist_backend + ) + + if use_gpu: + torch.cuda.manual_seed_all(args.numpy_rand_seed) + torch.backends.cudnn.deterministic = True + if ext_dist.my_size > 1: + ngpus = 1 + device = torch.device("cuda", ext_dist.my_local_rank) + else: + ngpus = torch.cuda.device_count() + device = torch.device("cuda", 0) + print("Using {} GPU(s)...".format(ngpus)) + else: + device = torch.device("cpu") + print("Using CPU...") + + ### prepare training data ### + ln_bot = np.fromstring(args.arch_mlp_bot, dtype=int, sep="-") + # input data + + if args.mlperf_logging: + mlperf_logger.barrier() + mlperf_logger.log_end(key=mlperf_logger.constants.INIT_STOP) + mlperf_logger.barrier() + mlperf_logger.log_start(key=mlperf_logger.constants.RUN_START) + mlperf_logger.barrier() + + if args.data_generation == "dataset": + train_data, train_ld, test_data, test_ld = dp.make_criteo_data_and_loaders(args) + table_feature_map = {idx: idx for idx in range(len(train_data.counts))} + nbatches = args.num_batches if args.num_batches > 0 else len(train_ld) + nbatches_test = len(test_ld) + + ln_emb = train_data.counts + # enforce maximum limit on number of vectors per embedding + if args.max_ind_range > 0: + ln_emb = np.array( + list( + map( + lambda x: x if x < args.max_ind_range else args.max_ind_range, + ln_emb, + ) + ) + ) + else: + ln_emb = np.array(ln_emb) + m_den = train_data.m_den + ln_bot[0] = m_den + elif args.data_generation == "internal": + if not has_internal_libs: + raise Exception("Internal libraries are not available.") + NUM_BATCHES = 5000 + nbatches = args.num_batches if args.num_batches > 0 else NUM_BATCHES + train_ld, feature_to_num_embeddings = fbDataLoader(args.data_size, nbatches) + ln_emb = np.array(list(feature_to_num_embeddings.values())) + m_den = ln_bot[0] + else: + # input and target at random + ln_emb = np.fromstring(args.arch_embedding_size, dtype=int, sep="-") + m_den = ln_bot[0] + train_data, train_ld, test_data, test_ld = dp.make_random_data_and_loader( + args, ln_emb, m_den + ) + nbatches = args.num_batches if args.num_batches > 0 else len(train_ld) + nbatches_test = len(test_ld) + + args.ln_emb = ln_emb.tolist() + if args.mlperf_logging: + print("command line args: ", json.dumps(vars(args))) + + ### parse command line arguments ### + m_spa = args.arch_sparse_feature_size + ln_emb = np.asarray(ln_emb) + num_fea = ln_emb.size + 1 # num sparse + num dense features + + m_den_out = ln_bot[ln_bot.size - 1] + if args.arch_interaction_op == "dot": + # approach 1: all + # num_int = num_fea * num_fea + m_den_out + # approach 2: unique + if args.arch_interaction_itself: + num_int = (num_fea * (num_fea + 1)) // 2 + m_den_out + else: + num_int = (num_fea * (num_fea - 1)) // 2 + m_den_out + elif args.arch_interaction_op == "cat": + num_int = num_fea * m_den_out + else: + sys.exit( + "ERROR: --arch-interaction-op=" + + args.arch_interaction_op + + " is not supported" + ) + arch_mlp_top_adjusted = str(num_int) + "-" + args.arch_mlp_top + ln_top = np.fromstring(arch_mlp_top_adjusted, dtype=int, sep="-") + + # sanity check: feature sizes and mlp dimensions must match + if m_den != ln_bot[0]: + sys.exit( + "ERROR: arch-dense-feature-size " + + str(m_den) + + " does not match first dim of bottom mlp " + + str(ln_bot[0]) + ) + if args.qr_flag: + if args.qr_operation == "concat" and 2 * m_spa != m_den_out: + sys.exit( + "ERROR: 2 arch-sparse-feature-size " + + str(2 * m_spa) + + " does not match last dim of bottom mlp " + + str(m_den_out) + + " (note that the last dim of bottom mlp must be 2x the embedding dim)" + ) + if args.qr_operation != "concat" and m_spa != m_den_out: + sys.exit( + "ERROR: arch-sparse-feature-size " + + str(m_spa) + + " does not match last dim of bottom mlp " + + str(m_den_out) + ) + else: + if m_spa != m_den_out: + sys.exit( + "ERROR: arch-sparse-feature-size " + + str(m_spa) + + " does not match last dim of bottom mlp " + + str(m_den_out) + ) + if num_int != ln_top[0]: + sys.exit( + "ERROR: # of feature interactions " + + str(num_int) + + " does not match first dimension of top mlp " + + str(ln_top[0]) + ) + + # assign mixed dimensions if applicable + if args.md_flag: + m_spa = md_solver( + torch.tensor(ln_emb), + args.md_temperature, # alpha + d0=m_spa, + round_dim=args.md_round_dims, + ).tolist() + + # test prints (model arch) + if args.debug_mode: + print("model arch:") + print( + "mlp top arch " + + str(ln_top.size - 1) + + " layers, with input to output dimensions:" + ) + print(ln_top) + print("# of interactions") + print(num_int) + print( + "mlp bot arch " + + str(ln_bot.size - 1) + + " layers, with input to output dimensions:" + ) + print(ln_bot) + print("# of features (sparse and dense)") + print(num_fea) + print("dense feature size") + print(m_den) + print("sparse feature size") + print(m_spa) + print( + "# of embeddings (= # of sparse features) " + + str(ln_emb.size) + + ", with dimensions " + + str(m_spa) + + "x:" + ) + print(ln_emb) + + print("data (inputs and targets):") + for j, inputBatch in enumerate(train_ld): + X, lS_o, lS_i, T, W, CBPP = unpack_batch(inputBatch) + + torch.set_printoptions(precision=4) + # early exit if nbatches was set by the user and has been exceeded + if nbatches > 0 and j >= nbatches: + break + print("mini-batch: %d" % j) + print(X.detach().cpu()) + # transform offsets to lengths when printing + print( + torch.IntTensor( + [ + np.diff( + S_o.detach().cpu().tolist() + list(lS_i[i].shape) + ).tolist() + for i, S_o in enumerate(lS_o) + ] + ) + ) + print([S_i.detach().cpu() for S_i in lS_i]) + print(T.detach().cpu()) + + global ndevices + ndevices = min(ngpus, args.mini_batch_size, num_fea - 1) if use_gpu else -1 + + ### construct the neural network specified above ### + # WARNING: to obtain exactly the same initialization for + # the weights we need to start from the same random seed. + # np.random.seed(args.numpy_rand_seed) + global dlrm + dlrm = DLRM_Net( + m_spa, + ln_emb, + ln_bot, + ln_top, + arch_interaction_op=args.arch_interaction_op, + arch_interaction_itself=args.arch_interaction_itself, + sigmoid_bot=-1, + sigmoid_top=ln_top.size - 2, + sync_dense_params=args.sync_dense_params, + loss_threshold=args.loss_threshold, + ndevices=ndevices, + qr_flag=args.qr_flag, + qr_operation=args.qr_operation, + qr_collisions=args.qr_collisions, + qr_threshold=args.qr_threshold, + md_flag=args.md_flag, + md_threshold=args.md_threshold, + weighted_pooling=args.weighted_pooling, + loss_function=args.loss_function, + ) + + # test prints + if args.debug_mode: + print("initial parameters (weights and bias):") + for param in dlrm.parameters(): + print(param.detach().cpu().numpy()) + # print(dlrm) + + if use_gpu: + # Custom Model-Data Parallel + # the mlps are replicated and use data parallelism, while + # the embeddings are distributed and use model parallelism + dlrm = dlrm.to(device) # .cuda() + if dlrm.ndevices > 1: + dlrm.emb_l, dlrm.v_W_l = dlrm.create_emb( + m_spa, ln_emb, args.weighted_pooling + ) + else: + if dlrm.weighted_pooling == "fixed": + for k, w in enumerate(dlrm.v_W_l): + dlrm.v_W_l[k] = w.cuda() + + # distribute data parallel mlps + if ext_dist.my_size > 1: + if use_gpu: + device_ids = [ext_dist.my_local_rank] + dlrm.bot_l = ext_dist.DDP(dlrm.bot_l, device_ids=device_ids) + dlrm.top_l = ext_dist.DDP(dlrm.top_l, device_ids=device_ids) + else: + dlrm.bot_l = ext_dist.DDP(dlrm.bot_l) + dlrm.top_l = ext_dist.DDP(dlrm.top_l) + + if not args.inference_only: + if use_gpu and args.optimizer in ["rwsadagrad", "adagrad"]: + sys.exit("GPU version of Adagrad is not supported by PyTorch.") + # specify the optimizer algorithm + opts = { + "sgd": torch.optim.SGD, + "rwsadagrad": RowWiseSparseAdagrad.RWSAdagrad, + "adagrad": torch.optim.Adagrad, + } + + parameters = ( + dlrm.parameters() + if ext_dist.my_size == 1 + else [ + { + "params": [p for emb in dlrm.emb_l for p in emb.parameters()], + "lr": args.learning_rate, + }, + # TODO check this lr setup + # bottom mlp has no data parallelism + # need to check how do we deal with top mlp + { + "params": dlrm.bot_l.parameters(), + "lr": args.learning_rate, + }, + { + "params": dlrm.top_l.parameters(), + "lr": args.learning_rate, + }, + ] + ) + optimizer = opts[args.optimizer](parameters, lr=args.learning_rate) + lr_scheduler = LRPolicyScheduler( + optimizer, + args.lr_num_warmup_steps, + args.lr_decay_start_step, + args.lr_num_decay_steps, + ) + + ### main loop ### + + # training or inference + best_acc_test = 0 + best_auc_test = 0 + skip_upto_epoch = 0 + skip_upto_batch = 0 + total_time = 0 + total_loss = 0 + total_iter = 0 + total_samp = 0 + + if args.mlperf_logging: + mlperf_logger.mlperf_submission_log("dlrm") + mlperf_logger.log_event( + key=mlperf_logger.constants.SEED, value=args.numpy_rand_seed + ) + mlperf_logger.log_event( + key=mlperf_logger.constants.GLOBAL_BATCH_SIZE, value=args.mini_batch_size + ) + + # Load model is specified + if not (args.load_model == ""): + print("Loading saved model {}".format(args.load_model)) + if use_gpu: + if dlrm.ndevices > 1: + # NOTE: when targeting inference on multiple GPUs, + # load the model as is on CPU or GPU, with the move + # to multiple GPUs to be done in parallel_forward + ld_model = torch.load(args.load_model) + else: + # NOTE: when targeting inference on single GPU, + # note that the call to .to(device) has already happened + ld_model = torch.load( + args.load_model, + map_location=torch.device("cuda"), + # map_location=lambda storage, loc: storage.cuda(0) + ) + else: + # when targeting inference on CPU + ld_model = torch.load(args.load_model, map_location=torch.device("cpu")) + dlrm.load_state_dict(ld_model["state_dict"]) + ld_j = ld_model["iter"] + ld_k = ld_model["epoch"] + ld_nepochs = ld_model["nepochs"] + ld_nbatches = ld_model["nbatches"] + ld_nbatches_test = ld_model["nbatches_test"] + ld_train_loss = ld_model["train_loss"] + ld_total_loss = ld_model["total_loss"] + if args.mlperf_logging: + ld_gAUC_test = ld_model["test_auc"] + ld_acc_test = ld_model["test_acc"] + if not args.inference_only: + optimizer.load_state_dict(ld_model["opt_state_dict"]) + best_acc_test = ld_acc_test + total_loss = ld_total_loss + skip_upto_epoch = ld_k # epochs + skip_upto_batch = ld_j # batches + else: + args.print_freq = ld_nbatches + args.test_freq = 0 + + print( + "Saved at: epoch = {:d}/{:d}, batch = {:d}/{:d}, ntbatch = {:d}".format( + ld_k, ld_nepochs, ld_j, ld_nbatches, ld_nbatches_test + ) + ) + print( + "Training state: loss = {:.6f}".format( + ld_train_loss, + ) + ) + if args.mlperf_logging: + print( + "Testing state: accuracy = {:3.3f} %, auc = {:.3f}".format( + ld_acc_test * 100, ld_gAUC_test + ) + ) + else: + print("Testing state: accuracy = {:3.3f} %".format(ld_acc_test * 100)) + + if args.inference_only: + # Currently only dynamic quantization with INT8 and FP16 weights are + # supported for MLPs and INT4 and INT8 weights for EmbeddingBag + # post-training quantization during the inference. + # By default we don't do the quantization: quantize_{mlp,emb}_with_bit == 32 (FP32) + assert args.quantize_mlp_with_bit in [ + 8, + 16, + 32, + ], "only support 8/16/32-bit but got {}".format(args.quantize_mlp_with_bit) + assert args.quantize_emb_with_bit in [ + 4, + 8, + 32, + ], "only support 4/8/32-bit but got {}".format(args.quantize_emb_with_bit) + if args.quantize_mlp_with_bit != 32: + if args.quantize_mlp_with_bit in [8]: + quantize_dtype = torch.qint8 + else: + quantize_dtype = torch.float16 + dlrm = torch.quantization.quantize_dynamic( + dlrm, {torch.nn.Linear}, quantize_dtype + ) + if args.quantize_emb_with_bit != 32: + dlrm.quantize_embedding(args.quantize_emb_with_bit) + # print(dlrm) + + print("time/loss/accuracy (if enabled):") + + if args.mlperf_logging: + # LR is logged twice for now because of a compliance checker bug + mlperf_logger.log_event( + key=mlperf_logger.constants.OPT_BASE_LR, value=args.learning_rate + ) + mlperf_logger.log_event( + key=mlperf_logger.constants.OPT_LR_WARMUP_STEPS, + value=args.lr_num_warmup_steps, + ) + + # use logging keys from the official HP table and not from the logging library + mlperf_logger.log_event( + key="sgd_opt_base_learning_rate", value=args.learning_rate + ) + mlperf_logger.log_event( + key="lr_decay_start_steps", value=args.lr_decay_start_step + ) + mlperf_logger.log_event( + key="sgd_opt_learning_rate_decay_steps", value=args.lr_num_decay_steps + ) + mlperf_logger.log_event(key="sgd_opt_learning_rate_decay_poly_power", value=2) + + tb_file = "./" + args.tensor_board_filename + writer = SummaryWriter(tb_file) + + ext_dist.barrier() + with torch.autograd.profiler.profile( + args.enable_profiling, use_cuda=use_gpu, record_shapes=True + ) as prof: + if not args.inference_only: + k = 0 + total_time_begin = 0 + while k < args.nepochs: + if args.mlperf_logging: + mlperf_logger.barrier() + mlperf_logger.log_start( + key=mlperf_logger.constants.BLOCK_START, + metadata={ + mlperf_logger.constants.FIRST_EPOCH_NUM: (k + 1), + mlperf_logger.constants.EPOCH_COUNT: 1, + }, + ) + mlperf_logger.barrier() + mlperf_logger.log_start( + key=mlperf_logger.constants.EPOCH_START, + metadata={mlperf_logger.constants.EPOCH_NUM: (k + 1)}, + ) + + if k < skip_upto_epoch: + continue + + if args.mlperf_logging: + previous_iteration_time = None + + for j, inputBatch in enumerate(train_ld): + if j == 0 and args.save_onnx: + X_onnx, lS_o_onnx, lS_i_onnx, _, _, _ = unpack_batch(inputBatch) + + if j < skip_upto_batch: + continue + + X, lS_o, lS_i, T, W, CBPP = unpack_batch(inputBatch) + + if args.mlperf_logging: + current_time = time_wrap(use_gpu) + if previous_iteration_time: + iteration_time = current_time - previous_iteration_time + else: + iteration_time = 0 + previous_iteration_time = current_time + else: + t1 = time_wrap(use_gpu) + + # early exit if nbatches was set by the user and has been exceeded + if nbatches > 0 and j >= nbatches: + break + + # Skip the batch if batch size not multiple of total ranks + if ext_dist.my_size > 1 and X.size(0) % ext_dist.my_size != 0: + print( + "Warning: Skiping the batch %d with size %d" + % (j, X.size(0)) + ) + continue + + mbs = T.shape[0] # = args.mini_batch_size except maybe for last + + # forward pass + Z = dlrm_wrap( + X, + lS_o, + lS_i, + use_gpu, + device, + ndevices=ndevices, + ) + + if ext_dist.my_size > 1: + T = T[ext_dist.get_my_slice(mbs)] + W = W[ext_dist.get_my_slice(mbs)] + + # loss + E = loss_fn_wrap(Z, T, use_gpu, device) + + # compute loss and accuracy + L = E.detach().cpu().numpy() # numpy array + # training accuracy is not disabled + # S = Z.detach().cpu().numpy() # numpy array + # T = T.detach().cpu().numpy() # numpy array + + # # print("res: ", S) + + # # print("j, train: BCE ", j, L) + + # mbs = T.shape[0] # = args.mini_batch_size except maybe for last + # A = np.sum((np.round(S, 0) == T).astype(np.uint8)) + + with record_function("DLRM backward"): + # scaled error gradient propagation + # (where we do not accumulate gradients across mini-batches) + if ( + args.mlperf_logging + and (j + 1) % args.mlperf_grad_accum_iter == 0 + ) or not args.mlperf_logging: + optimizer.zero_grad() + # backward pass + E.backward() + + # optimizer + if ( + args.mlperf_logging + and (j + 1) % args.mlperf_grad_accum_iter == 0 + ) or not args.mlperf_logging: + optimizer.step() + lr_scheduler.step() + + if args.mlperf_logging: + total_time += iteration_time + else: + t2 = time_wrap(use_gpu) + total_time += t2 - t1 + + total_loss += L * mbs + total_iter += 1 + total_samp += mbs + + should_print = ((j + 1) % args.print_freq == 0) or ( + j + 1 == nbatches + ) + should_test = ( + (args.test_freq > 0) + and (args.data_generation in ["dataset", "random"]) + and (((j + 1) % args.test_freq == 0) or (j + 1 == nbatches)) + ) + + # print time, loss and accuracy + if should_print or should_test: + gT = 1000.0 * total_time / total_iter if args.print_time else -1 + total_time = 0 + + train_loss = total_loss / total_samp + total_loss = 0 + + str_run_type = ( + "inference" if args.inference_only else "training" + ) + + wall_time = "" + if args.print_wall_time: + wall_time = " ({})".format(time.strftime("%H:%M")) + + print( + "Finished {} it {}/{} of epoch {}, {:.2f} ms/it,".format( + str_run_type, j + 1, nbatches, k, gT + ) + + " loss {:.6f}".format(train_loss) + + wall_time, + flush=True, + ) + + log_iter = nbatches * k + j + 1 + writer.add_scalar("Train/Loss", train_loss, log_iter) + + total_iter = 0 + total_samp = 0 + + # testing + if should_test: + epoch_num_float = (j + 1) / len(train_ld) + k + 1 + if args.mlperf_logging: + mlperf_logger.barrier() + mlperf_logger.log_start( + key=mlperf_logger.constants.EVAL_START, + metadata={ + mlperf_logger.constants.EPOCH_NUM: epoch_num_float + }, + ) + + # don't measure training iter time in a test iteration + if args.mlperf_logging: + previous_iteration_time = None + print( + "Testing at - {}/{} of epoch {},".format(j + 1, nbatches, k) + ) + model_metrics_dict, is_best = inference( + args, + dlrm, + best_acc_test, + best_auc_test, + test_ld, + device, + use_gpu, + log_iter, + ) + + if ( + is_best + and not (args.save_model == "") + and not args.inference_only + ): + model_metrics_dict["epoch"] = k + model_metrics_dict["iter"] = j + 1 + model_metrics_dict["train_loss"] = train_loss + model_metrics_dict["total_loss"] = total_loss + model_metrics_dict["opt_state_dict"] = ( + optimizer.state_dict() + ) + print("Saving model to {}".format(args.save_model)) + torch.save(model_metrics_dict, args.save_model) + + if args.mlperf_logging: + mlperf_logger.barrier() + mlperf_logger.log_end( + key=mlperf_logger.constants.EVAL_STOP, + metadata={ + mlperf_logger.constants.EPOCH_NUM: epoch_num_float + }, + ) + + # Uncomment the line below to print out the total time with overhead + # print("Total test time for this group: {}" \ + # .format(time_wrap(use_gpu) - accum_test_time_begin)) + + if ( + args.mlperf_logging + and (args.mlperf_acc_threshold > 0) + and (best_acc_test > args.mlperf_acc_threshold) + ): + print( + "MLPerf testing accuracy threshold " + + str(args.mlperf_acc_threshold) + + " reached, stop training" + ) + break + + if ( + args.mlperf_logging + and (args.mlperf_auc_threshold > 0) + and (best_auc_test > args.mlperf_auc_threshold) + ): + print( + "MLPerf testing auc threshold " + + str(args.mlperf_auc_threshold) + + " reached, stop training" + ) + if args.mlperf_logging: + mlperf_logger.barrier() + mlperf_logger.log_end( + key=mlperf_logger.constants.RUN_STOP, + metadata={ + mlperf_logger.constants.STATUS: mlperf_logger.constants.SUCCESS + }, + ) + break + + if args.mlperf_logging: + mlperf_logger.barrier() + mlperf_logger.log_end( + key=mlperf_logger.constants.EPOCH_STOP, + metadata={mlperf_logger.constants.EPOCH_NUM: (k + 1)}, + ) + mlperf_logger.barrier() + mlperf_logger.log_end( + key=mlperf_logger.constants.BLOCK_STOP, + metadata={mlperf_logger.constants.FIRST_EPOCH_NUM: (k + 1)}, + ) + k += 1 # nepochs + if args.mlperf_logging and best_auc_test <= args.mlperf_auc_threshold: + mlperf_logger.barrier() + mlperf_logger.log_end( + key=mlperf_logger.constants.RUN_STOP, + metadata={ + mlperf_logger.constants.STATUS: mlperf_logger.constants.ABORTED + }, + ) + else: + print("Testing for inference only") + inference( + args, + dlrm, + best_acc_test, + best_auc_test, + test_ld, + device, + use_gpu, + ) + + # profiling + if args.enable_profiling: + time_stamp = str(datetime.datetime.now()).replace(" ", "_") + with open("dlrm_s_pytorch" + time_stamp + "_shape.prof", "w") as prof_f: + prof_f.write( + prof.key_averages(group_by_input_shape=True).table( + sort_by="self_cpu_time_total" + ) + ) + with open("dlrm_s_pytorch" + time_stamp + "_total.prof", "w") as prof_f: + prof_f.write(prof.key_averages().table(sort_by="self_cpu_time_total")) + prof.export_chrome_trace("dlrm_s_pytorch" + time_stamp + ".json") + # print(prof.key_averages().table(sort_by="cpu_time_total")) + + # plot compute graph + if args.plot_compute_graph: + sys.exit( + "ERROR: Please install pytorchviz package in order to use the" + + " visualization. Then, uncomment its import above as well as" + + " three lines below and run the code again." + ) + # V = Z.mean() if args.inference_only else E + # dot = make_dot(V, params=dict(dlrm.named_parameters())) + # dot.render('dlrm_s_pytorch_graph') # write .pdf file + + # test prints + if not args.inference_only and args.debug_mode: + print("updated parameters (weights and bias):") + for param in dlrm.parameters(): + print(param.detach().cpu().numpy()) + + # export the model in onnx + if args.save_onnx: + """ + # workaround 1: tensor -> list + if torch.is_tensor(lS_i_onnx): + lS_i_onnx = [lS_i_onnx[j] for j in range(len(lS_i_onnx))] + # workaound 2: list -> tensor + lS_i_onnx = torch.stack(lS_i_onnx) + """ + # debug prints + # print("inputs", X_onnx, lS_o_onnx, lS_i_onnx) + # print("output", dlrm_wrap(X_onnx, lS_o_onnx, lS_i_onnx, use_gpu, device)) + dlrm_pytorch_onnx_file = "dlrm_s_pytorch.onnx" + batch_size = X_onnx.shape[0] + print("X_onnx.shape", X_onnx.shape) + if torch.is_tensor(lS_o_onnx): + print("lS_o_onnx.shape", lS_o_onnx.shape) + else: + for oo in lS_o_onnx: + print("oo.shape", oo.shape) + if torch.is_tensor(lS_i_onnx): + print("lS_i_onnx.shape", lS_i_onnx.shape) + else: + for ii in lS_i_onnx: + print("ii.shape", ii.shape) + + # name inputs and outputs + o_inputs = ( + ["offsets"] + if torch.is_tensor(lS_o_onnx) + else ["offsets_" + str(i) for i in range(len(lS_o_onnx))] + ) + i_inputs = ( + ["indices"] + if torch.is_tensor(lS_i_onnx) + else ["indices_" + str(i) for i in range(len(lS_i_onnx))] + ) + all_inputs = ["dense_x"] + o_inputs + i_inputs + # debug prints + print("inputs", all_inputs) + + # create dynamic_axis dictionaries + do_inputs = ( + [{"offsets": {1: "batch_size"}}] + if torch.is_tensor(lS_o_onnx) + else [ + {"offsets_" + str(i): {0: "batch_size"}} for i in range(len(lS_o_onnx)) + ] + ) + di_inputs = ( + [{"indices": {1: "batch_size"}}] + if torch.is_tensor(lS_i_onnx) + else [ + {"indices_" + str(i): {0: "batch_size"}} for i in range(len(lS_i_onnx)) + ] + ) + dynamic_axes = {"dense_x": {0: "batch_size"}, "pred": {0: "batch_size"}} + for do in do_inputs: + dynamic_axes.update(do) + for di in di_inputs: + dynamic_axes.update(di) + # debug prints + print(dynamic_axes) + # export model + torch.onnx.export( + dlrm, + (X_onnx, lS_o_onnx, lS_i_onnx), + dlrm_pytorch_onnx_file, + verbose=True, + opset_version=11, + input_names=all_inputs, + output_names=["pred"], + dynamic_axes=dynamic_axes, + dynamo=False, + ) + # recover the model back + dlrm_pytorch_onnx = onnx.load("dlrm_s_pytorch.onnx") + # check the onnx model + onnx.checker.check_model(dlrm_pytorch_onnx) + total_time_end = time_wrap(use_gpu) + + +if __name__ == "__main__": + run() diff --git a/xpu_timer/experiments/dlrm/train_dlrm.sh b/xpu_timer/experiments/dlrm/train_dlrm.sh new file mode 100644 index 0000000000..43d7028036 --- /dev/null +++ b/xpu_timer/experiments/dlrm/train_dlrm.sh @@ -0,0 +1,17 @@ +if [[ "install" == $1 ]]; then + pip config set global.index-url https://pypi.antfin-inc.com/artifact/repositories/simple + pip install -e ../logging + pip install tensorboard + pip install --force-reinstall /prs/xpu_timer_whl/py_xpu_timer-1.1+cu124-cp311-cp311-linux_x86_64.whl +fi + +#export CUDA_VISIBLE_DEVICES=4,5,6,7 +export XPU_TIMER_DEBUG_MODE=1 +export XPU_TIMER_BASEPORT=28888 +export NCCL_DEBUG=WARN +export WORLD_SIZE=8 +export LOCAL_WORLD_SIZE=8 + +#xpu_timer_launch python dlrm_s_pytorch.py --mini-batch-size=16 --data-size=1000000 --use-gpu +#python -m torch.distributed.launch --nproc_per_node=8 dlrm_s_pytorch.py --mini-batch-size=16 --data-size=1000000 --use-gpu +xpu_timer_launch python -m torch.distributed.launch --nproc_per_node=8 dlrm_s_pytorch.py --arch-embedding-size="80000-80000-80000-80000-80000-80000-80000-80000" --arch-sparse-feature-size=128 --arch-mlp-bot="128-128-128-128" --arch-mlp-top="512-512-512-256-1" --max-ind-range=40000000 --data-generation=random --loss-function=bce --round-targets=True --learning-rate=1.0 --mini-batch-size=2048 --print-freq=2 --print-time --test-freq=2 --test-mini-batch-size=2048 --memory-map --use-gpu --num-batches=100 --dist-backend=nccl diff --git a/xpu_timer/experiments/figs/__init__.py b/xpu_timer/experiments/figs/__init__.py new file mode 100644 index 0000000000..3537800dce --- /dev/null +++ b/xpu_timer/experiments/figs/__init__.py @@ -0,0 +1,13 @@ +# version is genreated by bazel +import io +import sys + +from .version import * + +if isinstance(sys.stdout, io.TextIOWrapper) and sys.version_info >= (3, 7): + sys.stdout.reconfigure(encoding="utf-8") # type ignore[attr-defined] +print(f"git commit is {__version__}") # type: ignore[name-defined] +print(f"build time is {__build_time__}") # type: ignore[name-defined] +print(f"build type is {__build_type__}") # type: ignore[name-defined] +print(f"build platform is {__build_platform__}") # type: ignore[name-defined] +print(f"build platform version is {__build_platform_version__}") # type: ignore[name-defined] diff --git a/xpu_timer/experiments/figs/cuda_gdb_script.py b/xpu_timer/experiments/figs/cuda_gdb_script.py new file mode 100644 index 0000000000..2665679784 --- /dev/null +++ b/xpu_timer/experiments/figs/cuda_gdb_script.py @@ -0,0 +1,339 @@ +# flake8: noqa: E501,E722,F841,E401 +import argparse +import os +import pickle +import re +import shlex +import sys +import time +import traceback +from pathlib import Path + +import gdb + + +def get_argparser(): + parser = argparse.ArgumentParser(description="Parser for cuda-gdb scripts") + + parser.add_argument("--hang-window-size", type=int, help="Window size of hang detection", default=5) + parser.add_argument("--dump-path", type=str, help="Pipe which dumps bytes stream of pickle", required=True) + parser.add_argument("--fifo", action="store_true", help="Dumps to fifo or file, defaults to file") + parser.add_argument("--rank", type=int, required=True) + parser.add_argument("--world-size", type=int, required=True) + return parser + + +class BaseNcclSassParser: + def __init__(self): + self.pc_addresses = [] + + def detect_cycle(self): + tortoise_idx = 0 + hare_idx = 0 + while hare_idx < len(self.pc_addresses) - 1: + tortoise_idx += 1 + hare_idx += 2 + if ( + hare_idx < len(self.pc_addresses) + and self.pc_addresses[tortoise_idx][0] == self.pc_addresses[hare_idx][0] + ): + loop_start = tortoise_idx + loop_end = hare_idx + return self.pc_addresses[loop_start:loop_end] + return None + + def get_register_value(self): + self.register = {} + norm_register = gdb.execute("i r", to_string=True) + # UR54 0x0 0 + for index, reg in enumerate(norm_register.split("\n")): + # nccl max register count is 96 + if index == 96: + break + r, hex_value, int_value = reg.split() + self.register[r] = int(int_value) + + system_register = gdb.execute("i r system", to_string=True) + for index, reg in enumerate(system_register.split("\n")): + # nvidia has 8 predicate register + if index == 8: + break + r, hex_value, int_value = reg.split() + self.register[r] = int(int_value) + + def start_detection(self, window_size, thread_state): + block_range = thread_state["block_range"] + thread_range = thread_state["thread_range"] + block = block_range.split("-")[0] + thread = thread_range.split("-")[0] + gdb.execute(f"cuda block {block}") + gdb.execute(f"cuda thread {thread}") + time.sleep(1) + self.pc_addresses = [] + self.register_value = {} + loop_count = 0 + step = 0 + try: + while True: + # nccl is __forceinline__, ni almost equals si + # FIXME(zhangji.zhang) running start_detection will hang in next time + gdb.execute("ni") + + frame = gdb.selected_frame() + arch = frame.architecture() + pc = frame.pc() + + # Format PC address + pc_addr = int(pc) + insns = arch.disassemble(pc_addr, pc_addr + 16) + asm = insns[0]["asm"] + self.pc_addresses.append((pc_addr, asm)) + + loop_count += 1 + if loop_count > 1000: + # not found + break + if loop_count % window_size == 0: + loop = self.detect_cycle() + if loop: + self.get_register_value() + self.pc_addresses = loop + return self.get_step_reg(loop) + + except gdb.error as e: + print(f"GDB Error: {e}") + except KeyboardInterrupt: + print("Loop detection interrupted.") + return step + + +class LL128Parser(BaseNcclSassParser): + def get_step_reg(self, loop): + operand_pattern = r"([^,\s][^,]*)" + + for addr, line in loop: + if not line.startswith("ISETP"): + continue + parts = line.strip().split(None, 1) + if len(parts) < 2: + continue + + instruction = parts[0] + operands_str = parts[1] + + operands = [] + positions = [] + + # +1 for the instruction and the following space + offset = len(instruction) + 1 + for match in re.finditer(operand_pattern, operands_str): + operand = match.group().strip() + start_pos = offset + match.start() + operands.append(operand) + positions.append(start_pos) + + # we need 4th arg, check if there are at least four operands + if len(operands) >= 4: + operand = operands[3] + position = positions[3] + # ISETP.NE.U32.AND P3, PT, R6, R89, PT + if not operand.startswith("R") or "U32" not in instruction: + continue + # nvcc maybe compiled as R89.reuse + if "." not in operand: + return gdb.parse_and_eval(f"${operand}") + + +class LLParser(BaseNcclSassParser): + def get_step_reg(self, loop): + operand_pattern = r"([^,\s][^,]*)" + register_pattern = r"R\d+" + + for addr, line in loop: + if not line.startswith("ISETP"): + continue + parts = line.strip().split(None, 1) + if len(parts) < 2: + continue + + instruction = parts[0] + operands_str = parts[1] + + operands = [] + positions = [] + + # +1 for the instruction and the following space + offset = len(instruction) + 1 + for match in re.finditer(operand_pattern, operands_str): + operand = match.group().strip() + start_pos = offset + match.start() + operands.append(operand) + positions.append(start_pos) + + # we need 4th arg, check if there are at least four operands + if len(operands) >= 4: + operand = operands[3] + position = positions[3] + # ISETP.NE.AND P0, PT, R17, R0, PT + if not operand.startswith("R"): + continue + # nvcc maybe compiled as R89.reuse + if "." not in operand and re.search(register_pattern, operand): + return gdb.parse_and_eval(f"${operand}") + + +class SimpleParser(BaseNcclSassParser): + def get_step_reg(self, loop): + # detect + # @!P0 LDG.E.64.STRONG.SYS R16, desc[UR4][R36.64] + # @P0 LDGMC.E.MIN.64.STRONG.SYS R16, [R36.64+URZ] + operand_pattern = re.compile(r".*(LDG.*\sR\d+),.*") + regs = set() + + for addr, line in loop: + if "LDG" not in line: + continue + + matched = operand_pattern.search(line) + if not matched: + continue + # we need 4th arg, check if there are at least four operands + ldg = matched.group(1) + regs.add(ldg.split()[-1]) + for reg in regs: + return gdb.parse_and_eval(f"${reg}") + + +class FindNcclHang(gdb.Command): + def __init__(self): + super(FindNcclHang, self).__init__("find_nccl_hang", gdb.COMMAND_USER) + self.nccl_proto_parser = { + "SIMPLE": SimpleParser(), + "LL": LLParser(), + "LL128": LL128Parser(), + } + self.arg_parser = get_argparser() + + def parse_arg(self, arg_str): + arg_list = shlex.split(arg_str) + args, unknown = self.arg_parser.parse_known_args(arg_list) + return args + + def dump_value_to_disk_of_fifo(self, value): + fifo_path = self.args.dump_path + is_fifo = self.args.fifo + if is_fifo: + while not os.path.exists(fifo_path): + print(f"waiting for {fifo_path}", file=sys.stdout) + time.sleep(1) + with open(fifo_path, "wb") as fifo: + fifo.write(pickle.dumps(value)) + return + d = Path(fifo_path) + d.mkdir(parents=True, exist_ok=True) + dump_name = f"{fifo_path}/{str(self.args.rank).zfill(5)}-{str(self.args.world_size).zfill(5)}.nccl.status" + with open(dump_name, "wb") as f: + pickle.dump(value, f) + + def find_nccl_proto(self): + bt = gdb.execute("bt", to_string=True) + for i in bt.split("\n"): + if "nccl" not in i: + continue + if "SIMPLE" in i: + return "SIMPLE" + elif "LL128" in i: + return "LL128" + elif "LL" in i: + return "LL" + raise ValueError("No proto found") + + def invoke(self, arg, from_tty): + try: + self.args = self.parse_arg(arg) + nccl_parser = self.nccl_proto_parser[self.find_nccl_proto()] + all_threads_state = self.list_cuda_threads() + running_threads_state = [i for i in all_threads_state if i["has_next"]] + for running_state in running_threads_state: + running_state["hang_step"] = int(nccl_parser.start_detection(self.args.hang_window_size, running_state)) + running_state["hang_sass"] = [i[1] for i in nccl_parser.pc_addresses] + running_state["registers"] = nccl_parser.register + self.dump_value_to_disk_of_fifo(all_threads_state) + except Exception as e: + tb_exception = traceback.TracebackException.from_exception(e) + errs = [] + for line in tb_exception.format(): + errs.append(line) + self.result.pstack_stderr = "".join(errs) + print("EOF", file=sys.stdout) + print("EOF", file=sys.stderr) + + def can_run_instruction(self, sass, block, thread): + # refers https://arxiv.org/html/2407.02944v1 + gdb.execute(f"cuda block {block}") + gdb.execute(f"cuda thread {thread}") + if "WARPSYNC" in sass or "BSYNC" in sass: + return False + if "BRA" in sass: + address = sass.split()[-1] + # => 0x7f349f290630 <_Z33ncclDevFunc_AllGather_RING_SIMPLEv+39984>: @P0 BRA 0x9cb0 + # when nccl is hanging, we assuming that all condition of all predicate registers are true + # so we just find the jump destination sass code + sass = gdb.execute("x/i $pc", to_string=True) + match = re.search("_Z[_\w\d]+", sass) + if not match: + # no match, for safe, we do not run next instruction + return False + fn = match.group() + new_sass = gdb.execute(f"x/i {fn}+{address}", to_string=True) + try: + sass_code = new_sass.split(":")[-1].replace("{", "").strip() + except: + sass_code = new_sass + return self.can_run_instruction(sass_code, block, thread) + return True + + def list_cuda_threads(self): + thread_info = gdb.execute("info cuda threads", to_string=True) + lines = thread_info.splitlines()[2:] + bts = [] + for line in lines: + parts = line.split() + if parts[0] == "*": + parts = parts[1:] + start_block_idx = eval(parts[0]) + start_thread_idx = eval(parts[1]) + # nccl kernel are 1 d + start_block_idx_x = start_block_idx[0] + start_thread_idx_x = start_thread_idx[0] + end_block_idx = eval(parts[2]) + end_thread_idx = eval(parts[3]) + end_block_idx_x = end_block_idx[0] + end_thread_idx_x = end_thread_idx[0] + gdb.execute(f"cuda block {start_block_idx_x}") + gdb.execute(f"cuda thread {start_thread_idx_x}") + sass = gdb.execute("x/i $pc", to_string=True) + try: + sass_code = sass.split(":")[-1].replace("{", "").strip() + except: + sass_code = sass + # output of bt + """ + #0 0x00000100007e0c68 in ncclFunction_AllGather_RING_SIMPLE_Sum_int8_t () at /data/nccl/src/collectives/device/./prims_simple.h:54 in _ZN10PrimitivesIa7FuncSumIaE12FanSymmetricILi1EELi1E11ProtoSimpleILi2ELi2ELi4ELi0ELi0EELi0EE7barrierEv inlined from prims_simple.h:277 + #1 0x0000010002ba5e18 in ncclKernel_AllGather_RING_LL_Sum_int8_t<<<(2,1,1),(288,1,1)>>> () at /data/nccl/src/collectives/device/./common.h:84 in _Z10ncclKernelIL10ncclFunc_t2Ea7FuncSumIaELi1ELi0ELi2174EEvP11ncclDevCommmP8ncclWork inlined from all_gather_sum_i8.cu:11 + """ + bt = [i for i in gdb.execute("backtrace", to_string=True).split("\n") if i] + bts.append( + { + "block_range": f"{start_block_idx_x}-{end_block_idx_x}", + "thread_range": f"{start_thread_idx_x}-{end_thread_idx_x}", + "stacks": bt, + "sass": sass_code, + "has_next": self.can_run_instruction(sass_code, start_block_idx_x, start_thread_idx_x), + "hang_step": 0, + } + ) + return bts + + +FindNcclHang() diff --git a/xpu_timer/experiments/figs/dlrover_parse_exception.py b/xpu_timer/experiments/figs/dlrover_parse_exception.py new file mode 100644 index 0000000000..861113ad03 --- /dev/null +++ b/xpu_timer/experiments/figs/dlrover_parse_exception.py @@ -0,0 +1,6 @@ +def xpu_timer_parse_python_exception(exc_type, exc_value, exc_traceback, infos): + print("not impl") + + +def xpu_timer_parse_cpp_exception(stack_infos): + print("not impl") diff --git a/xpu_timer/experiments/figs/dump_driver.py b/xpu_timer/experiments/figs/dump_driver.py new file mode 100644 index 0000000000..6b514228fe --- /dev/null +++ b/xpu_timer/experiments/figs/dump_driver.py @@ -0,0 +1,575 @@ +# flake8: noqa: E501,E722,F841,E401 +import argparse +import copy +import ctypes +import fcntl +import itertools +import json +import os +import pickle +import re +import selectors +import shutil +import subprocess +import sys +import sysconfig +import time +import traceback +from pathlib import Path +from typing import Dict, List, Union + +from py_xpu_timer import hosting_service_pb2 # type: ignore[attr-defined] +from py_xpu_timer.util import parallel_job + + +class HashableNamespace(argparse.Namespace): + def __hash__(self): + return hash(tuple(sorted(vars(self).items()))) + + def __eq__(self, other): + if not isinstance(other, HashableNamespace): + return NotImplemented + return vars(self) == vars(other) + + +class PstackStacktrace: + # Thread 5 (Thread 0x7efc0bfff700 (LWP 4127) "python"): + thread_parser_1 = re.compile(r'Thread (\d+).*\(LWP (\d+)\) "(.*)"\):') + # Thread 183 (Thread 0x7f1b892a8700 (LWP 122226)): + thread_parser_2 = re.compile(r"Thread (\d+).*\(LWP (\d+)\)\):") + # remove args ' (throwflag=0, f=0x1bfdb60) at ' + # 27 0x0000000000586c20 in PyEval_EvalFrameEx (throwflag=0, f=0x1bfdb60) at /usr/local/src/conda/python-3.8.18/Python/ceval.c:741 # noqa: E501 + remove_arg_pattern = re.compile(r" \(.*\) (from|at)") + + def __init__(self, thread_line, result): + self.thread_line = thread_line + self.frames = [] + self.pstack_trace = result.stacktrace.add() + + def __str__(self): + frames_str = "\n".join(self.frames) + return f"{self.thread_line}\n{frames_str}" + + def __repr__(self): + return str(self) + + def parse(self): + self._parse_thread() + self._parse_frame() + + def _parse_frame(self): + for orig_line in self.frames: + if not orig_line: + continue + frame = self.pstack_trace.frames.add() + line = PstackStacktrace.remove_arg_pattern.sub("@@@@@", orig_line) + sp = line.split("@@@@@") + frame.origin = orig_line + if len(sp) == 1: # no replace + # 6 0x000000000041c8ee in main () + sp = sp[0].split() + frame.func_name = sp[3].strip() + frame.file_name = "??" + elif " in " in line: + # 21 0x00000000004e81a6 in PyEval_EvalFrameEx /usr/local/src/conda/python-3.8.18/Python/ceval.c:741 + # 3 0x00007f69dfa71429 in Monitor::wait(bool, long, bool) () from /root/jdk/lib/server/libjvm.so + frame.func_name = sp[0].split(" in ")[-1].strip() + frame.file_name = sp[-1].strip() + else: + # 7 call_function /usr/local/src/conda/python-3.8.18/Python/ceval.c:4963 + frame.func_name = sp[0].split()[1].strip() + frame.file_name = sp[-1].strip() + + def _parse_thread(self): + parse_1 = PstackStacktrace.thread_parser_1.search(self.thread_line) + if parse_1 is not None: + _, lwp, thread_name = parse_1.groups() + lwp = int(lwp) + self.pstack_trace.thread_name = thread_name + self.pstack_trace.pid = lwp + return + parse_2 = PstackStacktrace.thread_parser_2.search(self.thread_line) + if parse_2 is not None: + thread_no, lwp = parse_2.groups() + lwp = int(lwp) + thread_name = "Unknown" + self.pstack_trace.thread_name = thread_name + self.pstack_trace.pid = lwp + return + self.pstack_trace.thread_name = "Unknown" + self.pstack_trace.pid = 0 + + +class CudaStacktrace: + cuda_stack_pattern = re.compile( + # r"#(\d+)\s+(0x[\d\w]+\sin\s)?([\d\w_]+)(<<<.*>>>)?\s(\(.*\))\sat\s([/?\w\d\.]+:\d+)\sin\s(_Z[\w\d]+)\s(inlined\s)?from\s([/?\w\d\.]+:\d+)" + r"#(\d+)\s+(0x[\d\w]+\sin\s)?([\/?\d\w_]+)(<<<.*>>>)?\s(\(.*\))(\sat\s([\/\w\d\._-]+:\d+)\sin\s([\w\d_]+)\s(inlined\s)?from\s([\/?\w\d\.]+:\d+))?" + ) + + def __init__(self, cuda_stacks: List[Dict[str, Union[str, List[str]]]], pb_message): + # structure of cuda_stacks + # {'block': '1-1', + # 'stacks': ['#0 0x00000100007def68 in ' + # 'ncclFunction_AllGather_RING_SIMPLE_Sum_int8_t () at ' + # '/data/nccl/src/collectives/device/./prims_simple.h:228 in ' + # '_ZN10PrimitivesIa7FuncSumIaE12FanSymmetricILi1EELi1E11ProtoSimpleILi2ELi2ELi4ELi0ELi0EELi0EE9genericOpILi1ELi0ELi1ELi0ELin1ELi1EEEvllib ' + # 'inlined from prims_simple.h:595', + # '#1 0x0000010002ba5e18 in ' + # 'ncclKernel_AllGather_RING_LL_Sum_int8_t<<<(2,1,1),(288,1,1)>>> ' + # '() at /data/nccl/src/collectives/device/./common.h:84 in ' + # '_Z10ncclKernelIL10ncclFunc_t2Ea7FuncSumIaELi1ELi0ELi2174EEvP11ncclDevCommmP8ncclWork ' + # 'inlined from all_gather_sum_i8.cu:11'], + # 'thread': '32-255'}, + libc = ctypes.CDLL("libstdc++.so.6") + self.cxa_demangle = getattr(libc, "__cxa_demangle") + self.cxa_demangle.restype = ctypes.c_char_p + self.result = pb_message + for stacks in cuda_stacks: + device_stacktrace = self.result.device_stacktrace.add() + self._parse_each_thread(stacks, device_stacktrace) + + def _demangle(self, symbol): + status = ctypes.c_int(0) + demangled = self.cxa_demangle(symbol.encode("utf-8"), None, None, ctypes.byref(status)) + if status.value == 0: + return demangled.decode("utf-8") + return None + + def _parse_each_thread(self, stack_dict, device_stacktrace): + stacks = stack_dict["stacks"] + + for stack in stacks: + frame = device_stacktrace.devices_frames.add() + cuda_frame = frame.cuda_frame + match = CudaStacktrace.cuda_stack_pattern.search(stack) + if match is None: + frame.stderr = "PARSING_REGEX_ERROR" + frame.origin = stack + continue + ( + frame_id, + addr, + func_name, + kernel_args, + func_args, + curr_location, + mangled_symbol, + inline, + inlined_location, + ) = match.groups() + curr_symbol = self._demangle(mangled_symbol) + cuda_frame.device_func = func_name + cuda_frame.curr_location = curr_location + cuda_frame.curr_symbol = curr_symbol + cuda_frame.inlined_location = inlined_location + cuda_frame.block = stack_dict["block"] + cuda_frame.thread = stack_dict["thread"] + cuda_frame.sass = stack_dict["sass"] + if kernel_args is not None: + cuda_frame.kernel_args = kernel_args + + +class StacktraceDriver: + def __init__(self, args): + self.result = hosting_service_pb2.Stacktrace() + self.result.pid = args.pid + self.result.rank = args.rank + self.result.process_state = args.state + self.fifo_path = f"/tmp/xpu_timer_gdb_pipe_{args.rank}" + self.cuda_gdb_script_path = f"{str(Path(__file__).parent)}/cuda_gdb_script.py" + + self.gdb_bin = args.gdb_bin + self.cuda_gdb_bin = args.cuda_gdb_bin + self.pstack_bin = shutil.which(args.pstack_bin) or shutil.which("pstack") or "pstack NOT_FOUND" + self.pyspy_bin = shutil.which(args.pyspy_bin) or shutil.which("py-spy") or "py-spy NOT_FOUND" + self.pid = str(args.pid) + self.rank = str(args.rank) + self.world_size = str(args.world_size) + self.dump_path = args.dump_path + self.do_gdb = args.gdb + self.do_cuda_gdb = args.cuda_gdb + self.do_pyspy = args.pyspy + if args.state.startswith("D"): + print(f"Process {args.pid} is {args.state}", file=sys.stderr) + with open( + f"{self.dump_path}/{self.rank.zfill(5)}-{self.world_size.zfill(5)}.stacktrace", + "wb", + ) as f: + f.write(self.result.SerializeToString()) + exit(0) + + def dump(self): + d = Path(f"/proc/{self.result.pid}/environ") + if not d.exists(): + print("The process is not found...", file=sys.stderr) + return + ret = [] + if self.do_gdb: + ret.append(self._dump_pstack()) + if self.do_pyspy: + ret.append(self._dump_pyspy()) + if self.do_cuda_gdb and os.path.exists(self.cuda_gdb_bin) and os.path.exists(self.cuda_gdb_script_path): + ret.append(self._dump_cuda_gdb()) + + d = Path(self.dump_path) + d.mkdir(parents=True, exist_ok=True) + with open( + f"{self.dump_path}/{self.rank.zfill(5)}-{self.world_size.zfill(5)}.stacktrace", + "wb", + ) as f: + f.write(self.result.SerializeToString()) + exit(sum(ret)) + + def _non_blocking_fd(self, fd): + flags = fcntl.fcntl(fd, fcntl.F_GETFL) + fcntl.fcntl(fd, fcntl.F_SETFL, flags | os.O_NONBLOCK) + + def _ensure_pipe(self): + if os.path.exists(self.fifo_path): + os.remove(self.fifo_path) + while not os.path.exists(self.fifo_path): + os.mkfifo(self.fifo_path) + time.sleep(1) + + def _dump_pstack(self): + if shutil.which(self.pstack_bin) is None: + print(f"{self.pstack_bin} in path", file=sys.stderr) + return 0 + command = [self.pstack_bin, self.pid] + proc = subprocess.Popen( + command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + ) + stdout, stderr = proc.communicate() + if proc.returncode != 0: + print(stderr, file=sys.stderr) + return proc.returncode + threads = [] + frames = [] + try: + lines = stdout.split("\n") + this_line_iter, next_line_iter = itertools.tee(iter(lines), 2) + next(next_line_iter) + for this_line, next_line in zip(this_line_iter, next_line_iter): + if this_line.startswith("Thread"): + threads.append(PstackStacktrace(this_line.strip(), self.result)) + else: + frames.append(this_line.strip()) + if next_line.startswith("Thread"): + threads[-1].frames = frames[:] + frames = [] + frames.append(next_line) + threads[-1].frames = frames[:] + for t in threads: + t.parse() + except Exception as e: + self.result.pstack_stdout = stdout + tb_exception = traceback.TracebackException.from_exception(e) + errs = [] + for line in tb_exception.format(): + errs.append(line) + self.result.pstack_stderr = "".join(errs) + return 0 + + def _dump_cuda_gdb(self): + if shutil.which(self.cuda_gdb_bin) is None: + print(f"{self.cuda_gdb_bin} not found in path", file=sys.stderr) + return 1 + self._ensure_pipe() + # gdb -batch -ex "attach 1142" -ex "source cuda_gdb_script.py" -ex "find_nccl_hang" -ex quit + command = [ + self.cuda_gdb_bin, + "--batch", + "-ex", + f"attach {self.pid}", + "-ex", + f"source {self.cuda_gdb_script_path}", + "-ex", + f"find_nccl_hang --dump-path {self.fifo_path} --fifo --rank {self.rank} --world-size {self.world_size}", + "-ex", + "detach", + "-ex", + "quit", + ] + + env = os.environ.copy() + env.pop("LD_PRELOAD", "") + env["LD_LIBRARY_PATH"] = f"{sysconfig.get_config_var('LIBDIR')}:{env.get('LD_LIBRARY_PATH', '')}" + proc = subprocess.Popen( + command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + env=env, + ) + + fd = os.open(self.fifo_path, os.O_RDONLY | os.O_NONBLOCK) + fifo = os.fdopen(fd, "rb") + self._non_blocking_fd(proc.stdout.fileno()) + self._non_blocking_fd(proc.stderr.fileno()) + + sel = selectors.DefaultSelector() + sel.register(proc.stdout, selectors.EVENT_READ, data=(proc, sel)) + sel.register(proc.stderr, selectors.EVENT_READ, data=(proc, sel)) + sel.register(fifo, selectors.EVENT_READ, data=(proc, sel)) + + def safe_unregister(selector, fileobj): + try: + selector.unregister(fileobj) + except Exception: + pass + + def parse_to_proto(message, data): + for each in data: + device_status_message = message.device_status.add() + device_status_message.block_range = each["block_range"] + device_status_message.thread_range = each["thread_range"] + device_status_message.sass = each["sass"] + device_status_message.has_next = each["has_next"] + device_status_message.hang_step = each["hang_step"] + if "hang_sass" in each: + device_status_message.hang_sass.extend(each["hang_sass"]) + device_status_message.stack_trace.extend(each["stacks"]) + if "registers" in each: + for key, value in each["registers"].items(): + device_status_message.registers[key] = value + + has_err = False + err = [] + while sel.get_map(): + events = sel.select(timeout=None) + for key, mask in events: + if mask & selectors.EVENT_READ == 0: + # not read event + continue + if key.fileobj is fifo: + # key is named pipe + parse_to_proto(self.result, pickle.loads(key.fileobj.read())) + sel.unregister(key.fileobj) + key.fileobj.close() + continue + # others, stdout and stderr + for line in key.fileobj.readlines(): + line = line.strip() + if key.fileobj is proc.stderr: + err.append(line) + if line == "EOF": + sel.unregister(key.fileobj) + continue + if proc.poll() is not None: + safe_unregister(sel, proc.stderr) + safe_unregister(sel, proc.stdout) + safe_unregister(sel, fifo) + fifo.close() + has_err = proc.returncode != 0 + time.sleep(1) + if has_err: + print("\n".join(err), file=sys.stderr) + return 1 + return 0 + + def _dump_pyspy(self): + if shutil.which(self.pyspy_bin) is None: + print(f"{self.pyspy_bin} in path", file=sys.stderr) + return 1 + command = [self.pyspy_bin, "dump", "-j", "-p", self.pid] + proc = subprocess.Popen( + command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + ) + stdout, stderr = proc.communicate() + if proc.returncode != 0: + print(stderr, file=sys.stderr) + return proc.returncode + try: + stacktrace = json.loads(stdout) + for thread in stacktrace: + thread_trace = self.result.py_stacktrace.add() + thread_trace.pid = thread["pid"] or 0 + thread_trace.owns_gil = thread["owns_gil"] or False + thread_trace.thread_name = thread["thread_name"] or "unknown" + thread_trace.os_thread_id = thread["os_thread_id"] or 0 + thread_trace.thread_id = thread["thread_id"] or 0 + thread_trace.active = thread["active"] or False + for f in thread["frames"]: + frame = thread_trace.frames.add() + frame.func_name = f["name"] or "unknown" + frame.file_name = f"{f['filename']}:{f['line']}" + frame.module = f["module"] or "unknown" + except Exception as e: + self.result.pyspy_stdout = stdout + tb_exception = traceback.TracebackException.from_exception(e) + errs = [] + for line in tb_exception.format(): + errs.append(line) + self.result.pyspy_stderr = "".join(errs) + + # print human readable stack + command = [self.pyspy_bin, "dump", "-p", self.pid] + proc = subprocess.Popen( + command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + ) + stdout, stderr = proc.communicate() + if proc.returncode != 0: + print(stderr, file=sys.stderr) + return proc.returncode + return 0 + + +def parse_env(args): + pid = args.pid + result = {} + with open(f"/proc/{pid}/environ", "r") as f: + environ = f.read() + env_vars = environ.split("\0") + for var in env_vars: + if not var: + continue + try: + k, v = var.split("=") + except: + # maybe wrong env, a="b=c" + continue + result[k] = v + + if args.rank != -1: + result["RANK"] = args.rank + if args.world_size != -1: + result["WORLD_SIZE"] = args.world_size + return result + + +def parse_one_pid_file(pid_path): + sched_file = pid_path / "sched" + if not sched_file.exists(): + return {} + container_pid = pid_path.name + pid_pattern = re.compile(r"\d+") + + with open(sched_file) as f: + first_line = f.readline().strip() + host_pid = pid_pattern.search(first_line).group() + return {host_pid: container_pid} + + +def find_gpu_pid_in_container(): + # refers https://stackoverflow.com/a/74575469 + pid_host_to_container = {} + + proc_path = Path("/proc") + + pid_dir_pattern = re.compile(r"^\d+$") + + pid_dirs = [p for p in proc_path.iterdir() if p.is_dir() and pid_dir_pattern.match(p.name)] + + pid_dict = parallel_job(parse_one_pid_file, tuple(pid_dirs), f"Parsing pids", concurrency=16) + for i in pid_dict: + pid_host_to_container.update(i) + if not pid_host_to_container: + return [] + + nvidia_smi = ["nvidia-smi", "--query-compute-apps=pid", "--format=csv,noheader,nounits"] + process = subprocess.Popen(nvidia_smi, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) + + stdout, stderr = process.communicate() + gpu_pid_on_host = stdout.splitlines() + + return [int(pid_host_to_container[i]) for i in gpu_pid_on_host if i in pid_host_to_container] + + +def get_process_state(pid): + status_file = f"/proc/{pid}/status" + + if not os.path.exists(status_file): + print(f"Process with PID {pid} not found.", file=sys.stderr, flush=True) + return None + + with open(status_file, "r") as file: + for line in file: + if line.startswith("State:"): + return line.split(":", 1)[1].strip() + return None + + +def run_by_pid(args_tuple): + (args,) = args_tuple + + d = Path(f"/proc/{args.pid}/environ") + if not d.exists(): + print("The process is found...", file=sys.stderr) + return + envs = parse_env(args) + if "RANK" not in envs or "WORLD_SIZE" not in envs: + print("The RANK or WORLD_SIZE is not set, exit...") + return + if args.rank == -1: + args.rank = int(envs["RANK"]) + if args.world_size == -1: + args.world_size = int(envs["WORLD_SIZE"]) + + state = get_process_state(args.pid) + args.state = state + StacktraceDriver(args).dump() + + +def run_auto_detect_mode(args): + pids = find_gpu_pid_in_container() + if not pids: + print( + "We do not find any gpus process in containers, maybe kernel is >4.14 or revert https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?id=74dc3384fc7983b78cc46ebb1824968a3db85eb1" + ) + return + items = [] + for pid in pids: + args_copy = copy.deepcopy(args) + args_copy.pid = pid + items.append((args_copy,)) + + parallel_job( + run_by_pid, + tuple(items), + f"Dumping on all gpu process", + len(pids), + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--dump-path", type=str, required=True) + + parser.add_argument("--pid", type=int, required=False, default=-1) + parser.add_argument("--gdb-bin", type=str, default="/opt/conda/bin/gdb") + parser.add_argument("--cuda-gdb-bin", type=str, default="/usr/local/cuda/bin/cuda-gdb") + parser.add_argument("--pyspy-bin", type=str, default="/opt/conda/bin/py-spy") + parser.add_argument("--pstack-bin", type=str, default="/usr/bin/pstack") + parser.add_argument("--rank", type=int, default=-1) + parser.add_argument("--world-size", type=int, default=-1) + parser.add_argument("--gdb", action="store_true") + parser.add_argument("--cuda-gdb", action="store_true") + parser.add_argument("--pyspy", action="store_true") + + args = HashableNamespace() + parser.parse_args(namespace=args) + if not any((args.gdb, args.cuda_gdb, args.pyspy)): + print(f'You should open at least one switch, "--gdb", "--cuda-gdb", "--pyspy", exit...') + return + d = Path(args.dump_path) + if d.exists() and not d.is_dir(): + print(f"dump path {args.dump_path} is file already exists, exit...") + return + if args.pid == -1: + run_auto_detect_mode(args) + return + run_by_pid((args,)) + + +if __name__ == "__main__": + # StacktraceDriver(1142, 0,1, "/root/").dump() + main() diff --git a/xpu_timer/experiments/figs/dump_timeline.py b/xpu_timer/experiments/figs/dump_timeline.py new file mode 100644 index 0000000000..13b3e066a4 --- /dev/null +++ b/xpu_timer/experiments/figs/dump_timeline.py @@ -0,0 +1,123 @@ +import argparse +import asyncio +import time + +import aiohttp + + +async def fetch(session, url, data): + try: + async with session.get(url, json=data) as response: + response.raise_for_status() + return await response.json() + except aiohttp.ClientConnectionError as e: + print(e) + + +async def request(urls, data): + async with aiohttp.ClientSession() as session: + tasks = [fetch(session, url, data) for url in urls] + return await asyncio.gather(*tasks) + + +def parse_host_ranks(host_list, rank_list, port, dry_run=False): + combined_hosts = [] + for host, rank in zip(host_list, rank_list): + if not rank: + combined_hosts.append(f"{host}:{port}") + continue + if "-" not in rank: + combined_hosts.append(f"{host}-{rank}:{port}") + continue + start, end = map(int, rank.split("-")) + for r in range(start, end + 1): + combined_hosts.append(f"{host}-{r}:{port}") + if dry_run: + return combined_hosts + return [f"http://{host}/HostingService/DumpKernelTrace" for host in combined_hosts] + + +def AES128_CBC(text): + from base64 import b64encode + + from Crypto.Cipher import AES + from Crypto.Util.Padding import pad + + iv = "xpu_timer" + password = "xpu_timer" + iv = iv.ljust(16, "\0").encode("utf-8") + password = password.ljust(16, "\0").encode("utf-8") + text = pad(text.encode("utf-8"), AES.block_size) + + cipher = AES.new(password, AES.MODE_CBC, iv) + cipher_text = cipher.encrypt(text) + return b64encode(cipher_text).decode("utf-8") + + +def main(): + # curr=`date +%s` + # curl -H 'Content-Type: application/json' \ + # -d "{\"dump_path\":\"/root/cc/dd/ee\", \"dump_count\": 110, \"dump_time\": $((curr+3))}" \ + # 127.0.0.1:18888/HostingService/DumpKernelTrace + parser = argparse.ArgumentParser() + parser.add_argument("--host", action="append", required=True, help="Specify the host") + parser.add_argument("--rank", action="append", required=True, help="Specify the host rank range") + parser.add_argument("--port", type=int, default=18888, help="Specify the port on host") + parser.add_argument("--dump-path", type=str, default="/root/timeline", help="Specify dump path") + parser.add_argument("--dump-count", type=int, default=1000, help="Specify how many events to dump") + parser.add_argument("--delay", type=int, default=5, help="Specify when dump after request") + parser.add_argument("--reset", action="store_true", help="Specify reset dump flag") + parser.add_argument("--dry-run", action="store_true", help="Dry run") + parser.add_argument("--no-nccl", action="store_true", help="Disable nccl trace") + parser.add_argument("--no-matmul", action="store_true", help="Disable matmul(fa) trace") + parser.add_argument("--no-memory", action="store_true", help="Disable memory trace") + parser.add_argument("--oss-path", type=str, default="", help="Specify oss dump path") + parser.add_argument("--oss-ak", type=str, default="", help="Specify oss ak") + parser.add_argument("--oss-sk", type=str, default="", help="Specify oss sk") + parser.add_argument("--oss-endpoint", type=str, default="", help="Specify oss endpoint") + + args = parser.parse_args() + + if len(args.host) != len(args.rank): + parser.error("--host and --rank must be provided in pairs") + + dump_kernel_type = 7 # [00][11] // first bits is matmul, second bits is nccl + if args.no_nccl: + dump_kernel_type -= 2 + print("Disable nccl traces") + if args.no_matmul: + dump_kernel_type -= 1 + print("Disable matmul traces") + if args.no_memory: + dump_kernel_type -= 4 + print("Disable memory traces") + if dump_kernel_type == 0: + raise ValueError("No Kernel to trace") + combined_hosts = parse_host_ranks(args.host, args.rank, args.port, args.dry_run) + now = int(time.time()) + print(f"dumping to {args.dump_path}, with count {args.dump_count}") + data = { + "dump_path": args.dump_path, + "dump_time": now + args.delay, + "dump_count": args.dump_count, + "reset": args.reset, + "dump_kernel_type": dump_kernel_type, + } + if args.oss_path and args.oss_ak and args.oss_sk and args.oss_endpoint: + data["oss_args"] = { + "oss_ak": AES128_CBC(args.oss_ak), + "oss_sk": AES128_CBC(args.oss_sk), + "oss_endpoint": args.oss_endpoint, + "oss_path": args.oss_path, + } + print(data) + if args.dry_run: + print(f"dump host {combined_hosts}") + print(f"other data {data}") + return + for i in asyncio.run(request(combined_hosts, data)): + print(i) + + +if __name__ == "__main__": + main() diff --git a/xpu_timer/experiments/figs/gdb_script.py b/xpu_timer/experiments/figs/gdb_script.py new file mode 100644 index 0000000000..2baedc03b3 --- /dev/null +++ b/xpu_timer/experiments/figs/gdb_script.py @@ -0,0 +1,61 @@ +# flake8: noqa: E402 +import os +import re +import sys +import time + +import gdb +from py_xpu_timer import hosting_service_pb2 # type: ignore[attr-defined] + + +class AllThreadsBacktrace(gdb.Command): + def __init__(self): + super(AllThreadsBacktrace, self).__init__("sbt", gdb.COMMAND_USER) + self.remove_arg_pattern = re.compile(r" \(.*\) (from|at)") + self.line_buffer = [] + + def invoke(self, arg, from_tty): + fifo_path = arg + result = hosting_service_pb2.Stacktrace() + for thread in gdb.inferiors()[0].threads(): + pstack_trace = result.stacktrace.add() + thread.switch() + pid, tid, _ = thread.ptid + self.line_buffer.append(f"Thread {tid}, name {thread.name}") + pstack_trace.thread_name = thread.name + pstack_trace.pid = tid + self.backtrace_and_parse(pstack_trace) + print("\n".join(self.line_buffer)) + while not os.path.exists(fifo_path): + print(f"waiting for {fifo_path}", file=sys.stdout) + time.sleep(1) + with open(fifo_path, "wb") as fifo: + fifo.write(result.SerializeToString()) + print("EOF", file=sys.stdout) + print("EOF", file=sys.stderr) + + def backtrace_and_parse(self, pstack_trace): + bt_output = gdb.execute("bt", to_string=True) + self.line_buffer.append(bt_output) + for orig_line in bt_output.splitlines(): + frame = pstack_trace.frames.add() + line = self.remove_arg_pattern.sub("@@@@@", orig_line) + sp = line.split("@@@@@") + frame.origin = orig_line + if len(sp) == 1: # no replace + # 6 0x000000000041c8ee in main () + sp = sp[0].split() + frame.func_name = sp[3].strip() + frame.file_name = "??" + elif " in " in line: + # 21 0x00000000004e81a6 in PyEval_EvalFrameEx /usr/local/src/conda/python-3.8.18/Python/ceval.c:741 + # 3 0x00007f69dfa71429 in Monitor::wait(bool, long, bool) () from /root/jdk/lib/server/libjvm.so + frame.func_name = sp[0].split(" in ")[-1].strip() + frame.file_name = sp[-1].strip() + else: + # 7 call_function /usr/local/src/conda/python-3.8.18/Python/ceval.c:4963 + frame.func_name = sp[0].split()[1].strip() + frame.file_name = sp[-1].strip() + + +AllThreadsBacktrace() diff --git a/xpu_timer/experiments/figs/gen_trace_timeline.py b/xpu_timer/experiments/figs/gen_trace_timeline.py new file mode 100644 index 0000000000..d73f14300f --- /dev/null +++ b/xpu_timer/experiments/figs/gen_trace_timeline.py @@ -0,0 +1,508 @@ +import os +import sys +from argparse import ArgumentParser +from collections import OrderedDict, defaultdict +from pathlib import Path +from typing import Dict, List + +from py_xpu_timer import hosting_service_pb2 # type: ignore[attr-defined] +from py_xpu_timer import hook_pb2 +from py_xpu_timer.perfetto_trace_pb2 import Trace, TrackEvent # type: ignore[attr-defined] +from py_xpu_timer.util import GetRankHelper, parallel_job +from tqdm import tqdm + +TRUSTED_PACKET_SEQUENCE_ID = 1 +HOST_TRACE_META = "xpu_timer_host_trace" + + +def format_function_name(s): + if HOST_TRACE_META in s: + return s.replace("@", ".") + return s + + +class TracingData: + tracing_code_to_interned_data: Dict[str, int] = {} + COMPUTE_LINE = 1 + IO_LINE = 2 + HOST_LINE = 0 + global_line_offset: Dict[str, int] = {"compute": COMPUTE_LINE, "io": IO_LINE, "host": HOST_LINE} + local_line_offset: Dict[str, int] = { + "matmul": COMPUTE_LINE, + "nccl": IO_LINE, + "device_memory": IO_LINE, + "host_memory": HOST_LINE, + } + kernel_type: List[str] = ["matmul", "nccl", "device_memory"] + debug_annotation = { + "count": 1, + "delay(us)": 2, + "comm_hash": 3, + "nranks": 4, + "nodes": 5, + "input_size(bytes)": 6, + "blocks": 7, + "grids": 8, + "dtype": 9, + "TFLOPS": 10, + "seq": 11, + "rank": 12, + "cublas_api": 13, + "ldA": 15, + "ldB": 16, + "ldC": 17, + "strideA": 18, + "strideB": 19, + "strideC": 20, + "cublas_algo": 21, + "trans": 22, + "direction": 23, + "Bandwidth(GiB/s)": 24, + "bytes": 25, + "collected": 26, + "uncollectable": 27, + } + debug_annotation_string_values = { + "cublasGemmEx": 1, + "cublasGemmStridedBatchedEx": 2, + "cublasSgemm": 3, + "cublasSgemmStridedBatched": 4, + "cublasLtMatmul": 5, + "aclnnMatmul": 6, + } + timeline_version = 2 + + @staticmethod + def parse_one_host_trace_data(packet, rank, host_traces, header_uuid, base_uuid, group_index): + host = packet.add() + host.track_descriptor.uuid = base_uuid + TracingData.global_line_offset["host"] + host.track_descriptor.parent_uuid = header_uuid + host.track_descriptor.thread.pid = group_index + host.track_descriptor.thread.tid = host.track_descriptor.uuid + host.track_descriptor.thread.thread_name = f"rank {rank} host" + for host_trace in host_traces: + uuid = host.track_descriptor.uuid + start = packet.add() + iid = TracingData.tracing_code_to_interned_data[host_trace.name.replace("@", ".")] + start_ns = host_trace.start_us * 1000 + dur_in_ns = host_trace.dur_us * 1000 + + start.timestamp = start_ns + start.track_event.type = TrackEvent.TYPE_SLICE_BEGIN + start.track_event.track_uuid = uuid + start.track_event.name_iid = iid + start.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID + start.track_event.categories.append("host") + + count = start.track_event.debug_annotations.add() + count.name_iid = 1 # count + count.uint_value = host_trace.count + + if host_trace.name == "GC": + collected = start.track_event.debug_annotations.add() + collected.name_iid = 26 # collected + collected.int_value = host_trace.gc_debug.collected + + uncollectable = start.track_event.debug_annotations.add() + uncollectable.name_iid = 27 # uncollectable + uncollectable.int_value = host_trace.gc_debug.uncollectable + + end = packet.add() + end.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID + end.timestamp = start_ns + dur_in_ns + end.track_event.type = TrackEvent.TYPE_SLICE_END + end.track_event.track_uuid = uuid + + @staticmethod + def parse_one_trace_data(args): + path, header_uuid, group_index = args + name = path.name + # name: 00000-00001.timeline + rank = int(name[:5]) + trace = Trace() + packet = trace.packet + base_uuid = header_uuid + (rank + 1) * 10000 + compute = packet.add() + compute.track_descriptor.uuid = base_uuid + TracingData.global_line_offset["compute"] + compute.track_descriptor.parent_uuid = header_uuid + compute.track_descriptor.thread.pid = group_index + compute.track_descriptor.thread.tid = compute.track_descriptor.uuid + compute.track_descriptor.thread.thread_name = f"rank {rank} compute" + + coll = packet.add() + coll.track_descriptor.uuid = base_uuid + TracingData.global_line_offset["io"] + coll.track_descriptor.parent_uuid = header_uuid + coll.track_descriptor.thread.pid = group_index + coll.track_descriptor.thread.tid = coll.track_descriptor.uuid + coll.track_descriptor.thread.thread_name = f"rank {rank} io" + + with path.open("rb") as f: + timeline_traces = hook_pb2.KernelTraces() + timeline_traces.ParseFromString(f.read()) + matmul_debug_info = defaultdict(list) + + for each_trace in timeline_traces.traces: + debug_data_field_name = each_trace.WhichOneof("debug_data") + debug_data = getattr(each_trace, debug_data_field_name) + tracing = TracingData( + path, + each_trace, + debug_data, + timeline_traces.rank, + ) + tracing.parse_pb(packet, base_uuid, matmul_debug_info) + if hasattr(timeline_traces, "host_traces"): + TracingData.parse_one_host_trace_data( + packet, timeline_traces.rank, timeline_traces.host_traces, header_uuid, base_uuid, group_index + ) + return trace.SerializeToString(), (timeline_traces.rank, matmul_debug_info) + + def __init__(self, path, each_trace, debug_data=None, rank=None): + abs_path = str(path.absolute()) + naming_dict: Dict[int, str] = {} + with open(abs_path + ".meta") as f: + for line in f: + # skip extra traces + if HOST_TRACE_META in line: + continue + k, v = line.strip().split(",") + naming_dict[int(k)] = v + self.name_for_encode = naming_dict[each_trace.trace_code] + self.name = self.name_for_encode.replace("xpu_timer_", "") + self.kernel_type = TracingData.kernel_type[each_trace.kernel_type] + if each_trace.is_host: + self.kernel_type = "host_memory" + self.delay = each_trace.delay_us + + self.kernel_code = each_trace.kernel_type + self.start = each_trace.start_us + self.dur = each_trace.dur_us + self.trace_id = each_trace.trace_id + self.debug_data = debug_data + self.rank = rank + + def add_annotation(self, debug_annotations, matmul_debug_info, packet): + if isinstance(self.debug_data, hook_pb2.MatmulDebugData): + flop = 2 + # bmnk + for field, value in zip("bmnk", self.debug_data.shapes): + annotation = debug_annotations.add() + annotation.name = field + annotation.uint_value = value + flop = flop * value + + # lds + for field, value in zip(range(15, 18), self.debug_data.lds): + annotation = debug_annotations.add() + annotation.name_iid = field + annotation.uint_value = value + + # strides + for field, value in zip(range(18, 21), self.debug_data.strides): + annotation = debug_annotations.add() + annotation.name_iid = field + annotation.uint_value = value + cublas_api = debug_annotations.add() + cublas_api.name_iid = 13 + cublas_api.string_value_iid = TracingData.debug_annotation_string_values[self.debug_data.api] + + cublas_algo = debug_annotations.add() + cublas_algo.name_iid = 21 # cublas algo + cublas_algo.int_value = self.debug_data.algo + + trans = debug_annotations.add() + trans.name_iid = 22 # trans + trans.string_value = self.debug_data.trans + + dtype = debug_annotations.add() + dtype.name_iid = 9 # dtype + dtype.string_value = self.debug_data.dtype + + tflops = debug_annotations.add() + tflops.name_iid = 10 # tflops + tflops.double_value = round(flop / self.dur / 1e6, 2) + matmul_debug_info[self.debug_data.SerializeToString()].append(tflops.double_value) + + elif isinstance(self.debug_data, hook_pb2.FaDebugData): + for field, value in zip("bssh", self.debug_data.shapes): + annotation = debug_annotations.add() + annotation.name = field + annotation.uint_value = value + + elif isinstance(self.debug_data, hook_pb2.GroupedMatmulDebugData): + tflops = debug_annotations.add() + tflops.name_iid = 10 + tflops.double_value = round(self.debug_data.tflops / self.dur / 1e6, 2) + + elif isinstance(self.debug_data, hook_pb2.NcclDebugData): + grids = debug_annotations.add() + grids.name_iid = 8 # grids + grids.string_value = f"[{','.join(map(str,self.debug_data.grids))}]" + + blocks = debug_annotations.add() + blocks.name_iid = 7 # block + blocks.string_value = f"[{','.join(map(str,self.debug_data.blocks))}]" + + comm_hash = debug_annotations.add() + comm_hash.name_iid = 3 # hash + comm_hash.uint_value = self.debug_data.comm_hash + + input_size = debug_annotations.add() + input_size.name_iid = 6 # bytes + input_size.uint_value = self.debug_data.input_size_in_bytes + + dtype = debug_annotations.add() + dtype.name_iid = 9 # dtype + dtype.string_value = self.debug_data.dtype + + nranks = debug_annotations.add() + nranks.name_iid = 4 # nranks + nranks.uint_value = self.debug_data.ranks + + nodes = debug_annotations.add() + nodes.name_iid = 5 # nodes + nodes.uint_value = self.debug_data.nodes + + seq = debug_annotations.add() + seq.name_iid = 11 # seq num + seq.uint_value = self.debug_data.seq + + bandwidth = debug_annotations.add() + bandwidth.name_iid = 24 # GiB/s + bandwidth.double_value = round(self.debug_data.problem_size / (1 << 30) / self.dur * 1e6, 2) + + elif isinstance(self.debug_data, hook_pb2.MemoryDebugData): + direction = debug_annotations.add() + direction.name_iid = 23 # direction + direction.string_value = self.debug_data.direction + bandwidth = debug_annotations.add() + bandwidth.name_iid = 24 # GiB/s + bandwidth.double_value = round(self.debug_data.size / (1 << 30) / self.dur * 1e6, 2) + copy_bytes = debug_annotations.add() + copy_bytes.name_iid = 25 # bytes + copy_bytes.uint_value = self.debug_data.size + else: + raise ValueError("Debug data shoule be FA/Matmul/Nccl/Memory") + + def parse_pb(self, packet, uuid, matmul_debug_info): + uuid = uuid + TracingData.local_line_offset[self.kernel_type] + start = packet.add() + self.iid = TracingData.tracing_code_to_interned_data[self.name] + self.start = self.start * 1000 + dur_in_ns = (self.dur - 10) * 1000 if self.dur > 100 else self.dur * 1000 + + start.timestamp = self.start + start.track_event.type = TrackEvent.TYPE_SLICE_BEGIN + start.track_event.track_uuid = uuid + start.track_event.name_iid = self.iid + start.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID + start.track_event.categories.append(self.kernel_type) + + count_annotation = start.track_event.debug_annotations.add() + count_annotation.name_iid = 1 # count + count_annotation.uint_value = self.trace_id + + delay_annotation = start.track_event.debug_annotations.add() + delay_annotation.name_iid = 2 # delay(us) + delay_annotation.uint_value = self.delay + + if self.rank is None: + raise ValueError("Rank is not set") + rank_annotation = start.track_event.debug_annotations.add() + rank_annotation.name_iid = 12 # rank + rank_annotation.uint_value = self.rank + + if self.debug_data is not None: + self.add_annotation(start.track_event.debug_annotations, matmul_debug_info, packet) + + end = packet.add() + end.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID + + end.timestamp = self.start + dur_in_ns + end.track_event.type = TrackEvent.TYPE_SLICE_END + end.track_event.track_uuid = uuid + + +def add_interned_data(trace): + trace_header = None + for packet in trace.packet: + if packet.HasField("track_event"): + trace_header = packet + break + + if trace_header is None: + raise ValueError("No track events") + interned_data = trace_header.interned_data + for name, iid in TracingData.tracing_code_to_interned_data.items(): + data = interned_data.event_names.add() + data.iid = iid + data.name = name + for name, iid in TracingData.debug_annotation.items(): + data = interned_data.debug_annotation_names.add() + data.iid = iid + data.name = name + for name, iid in TracingData.debug_annotation_string_values.items(): + data = interned_data.debug_annotation_string_values.add() + data.iid = iid + data.str = name.encode() + + trace_header.first_packet_on_sequence = True + trace_header.previous_packet_dropped = True + trace_header.sequence_flags = 3 + + +def serialize_to_file_in_chunks(protobuf_message, file_path, chunk_size=1024 * 1024): + print("Serizlize tarce to bytes, it's slow...") + serialized_data = protobuf_message.SerializeToString() + total_size = len(serialized_data) + with open(file_path, "wb") as f: + with tqdm(total=total_size, unit="B", unit_scale=True, desc=f"Serializing to {file_path}") as progress_bar: + for i in range(0, total_size, chunk_size): + end = min(i + chunk_size, total_size) + f.write(serialized_data[i:end]) + progress_bar.update(end - i) + + +def parse_group(group_name, timelines, group_index, add_iternal, fd, concurrency=32): + header_uuid = group_index * int(1e8) + trace = Trace() + header = trace.packet.add() + header.track_descriptor.uuid = header_uuid + header.track_descriptor.process.pid = group_index + header.track_descriptor.process.process_name = group_name + + args = [(path, header_uuid, group_index) for path in timelines] + TracingData.parse_one_trace_data(args[0]) + sub_traces_matmul_info = parallel_job( + TracingData.parse_one_trace_data, + args, + f"generate perfetto timeline for {group_name} with parallel{concurrency}", + concurrency, + ) + all_matmul_info = hook_pb2.RankMatmulInfo() + + def parse_matmul_info(matmul_info): + rank, info = matmul_info + mm_infos = hook_pb2.MatmulInfos() + for mm_debug_pb, tflops in info.items(): + each_info = mm_infos.infos.add() + each_info.mm_debug.ParseFromString(mm_debug_pb) + each_info.tflops.extend(tflops) + all_matmul_info.mm_infos[rank].CopyFrom(mm_infos) + + fd.write(trace.SerializeToString()) + for sub_trace, matmul_info in tqdm(sub_traces_matmul_info, desc=f"Write {group_name}"): + if matmul_info is not None: + parse_matmul_info(matmul_info) + if add_iternal: + trace = Trace() + trace.ParseFromString(sub_trace) + add_interned_data(trace) + fd.write(trace.SerializeToString()) + continue + fd.write(sub_trace) + + return all_matmul_info + + +def parse_timeline_stack(tracestack_path): + path = Path(tracestack_path) + + def parse_single(timeline_path): + timeline = hosting_service_pb2.PythonStackInTimeline() + if not timeline_path.exists(): + print(f"tracing_kernel_callstack not found in {tracestack_path}") + return timeline + + with open(timeline_path, "rb") as f: + timeline.ParseFromString(f.read()) + return timeline + + timelines = [parse_single(p) for p in path.glob("*tracing_kernel_callstack")] + result = {} + for timeline in timelines: + for kernel_name, frames in timeline.named_frames.items(): + if kernel_name in result: + continue + stack_list = [] + for frame in frames.frames: + stack_list.append(f"{frame.func_name}@{frame.file_name}") + result[kernel_name] = ";".join(stack_list) + + merged_path = f"{tracestack_path}/merged_tracing_kernel_stack" + with open(merged_path, "w") as f: + for k, v in result.items(): + f.write(f"{v};{k} 1\n") + + os.system( + "flamegraph.pl --color tracing_kernel --width 1600 --title " + f"'callstack of tracing kernels' < {merged_path} " + f"> {tracestack_path}/tracing_kernel_stack.svg" + ) + + +def generate_perfetto_trace(args): + timeline_dir = args.path + files = Path(timeline_dir) + timeline_dict = {i: f for i, f in enumerate(sorted(list(files.glob("*timeline"))))} + if not timeline_dict: + print("There are no timeline files, exit...", file=sys.stderr, flush=True) + exit(1) + groups_dict = {} + if args.groups: + # args.groups: tp4-cp2-dp4-pp2 + groups_dict = OrderedDict((pair[:2], int(pair[2:])) for pair in args.groups.split("-")) + else: + groups_dict["dp"] = len(timeline_dict) + rank_helper = GetRankHelper(groups_dict) + timelines = {group: [timeline_dict[i] for i in rank_helper.get_ranks(group, group_0=True)] for group in groups_dict} + # perpare interned data + # https://perfetto.dev/docs/reference/synthetic-track-event#interning + all_kernel_names = set() + for timeline in timeline_dict.values(): + meta_path = str(timeline.absolute()) + ".meta" + with open(meta_path) as f: + for line in f: + line = line.strip() + iid, name = format_function_name(line).split(",") + all_kernel_names.add(name) + for iid, name in enumerate(all_kernel_names): + # perfetto's internal id is start at 1 + name = name.replace("xpu_timer_", "") + TracingData.tracing_code_to_interned_data[name] = iid + 1 + + all_matmul_info = hook_pb2.RankMatmulInfo() + trace_name = args.output if args.output else "_".join([f"{k}{v}" for k, v in groups_dict.items()]) + fd = open(f"{timeline_dir}/trace_{trace_name}.bin", "wb") + add_internel = True + for index, (name, files) in enumerate(timelines.items()): + all_matmul_info.MergeFrom(parse_group(name, files, index, add_internel, fd, args.c)) + add_internel = False + serialize_to_file_in_chunks(all_matmul_info, f"{timeline_dir}/matmul_{trace_name}.bin", chunk_size=1024 * 1024) + + +def main(): + parser = ArgumentParser(usage="""python gen_timeline.py dump_dir""") + parser.add_argument("--path", "-p", type=str, default=".") + parser.add_argument("--no-matmul", action="store_true") + parser.add_argument("--no-nccl", action="store_true") + parser.add_argument("-c", type=int, default=16, required=False) + parser.add_argument("--timeline-version", type=int, default=2, required=False) + parser.add_argument( + "--groups", type=str, default="", required=False, help='Group configurations like "tp4-cp2-dp4-pp2"' + ) + parser.add_argument("--output", type=str, default="", required=False, help="Output name for timeline file") + args = parser.parse_args() + TracingData.timeline_version = args.timeline_version + + timeline_dir = args.path + files = Path(timeline_dir) + if not files.exists(): + print("path {timeline_dir} not exists") + return + generate_perfetto_trace(args) + parse_timeline_stack(timeline_dir) + + +if __name__ == "__main__": + main() diff --git a/xpu_timer/experiments/figs/parse_matmul.py b/xpu_timer/experiments/figs/parse_matmul.py new file mode 100644 index 0000000000..7cff35e1f6 --- /dev/null +++ b/xpu_timer/experiments/figs/parse_matmul.py @@ -0,0 +1,165 @@ +# flake8: noqa: E402 +import json +import subprocess +from argparse import ArgumentParser + +import matplotlib.pyplot as plt +import pandas as pd + +plt.rcParams["figure.dpi"] = 300 +from py_xpu_timer import hook_pb2 + + +class MatmulPlayBack: + def __init__(self, path): + self.matmul_debug = hook_pb2.RankMatmulInfo() + with open(path, "rb") as f: + self.matmul_debug.ParseFromString(f.read()) + self.matmul_database = {} + self.rank_mean_tflops = {} + + def run(self, path="relative_matmul_performance.pkl", plot=False, p=0.9): + if plot: + try: + import seaborn as sns + except ImportError: + print("seaborn is not found, run `pip install seaborn` if you need plot") + return + for rank, all_debug in self.matmul_debug.mm_infos.items(): + mean_tflops = {} + for each_debug in all_debug.infos: + self.parse_gemm(each_debug, mean_tflops) + self.rank_mean_tflops[rank] = mean_tflops + + relative_performance = {} + for rank, ops in self.rank_mean_tflops.items(): + relative_performance[rank] = { + op: ops[op] / self.matmul_database[op] if op in self.matmul_database else 1.0 for op in ops + } + + df = pd.DataFrame.from_dict(relative_performance) + df.to_pickle(path) + percentile_90 = df.apply(lambda row: row.quantile(p), axis=1) + comparison_p90 = df.apply(lambda row: row < percentile_90[row.name], axis=1) + comparison_abs = df.apply(lambda row: row < p, axis=1) + for row in comparison_p90.index: + for col in comparison_p90.columns: + if comparison_p90.loc[row, col]: + print( + f"{row}, rank: {col} is slow than p{int(p * 100)}, {self.rank_mean_tflops[col][row]} vs {self.matmul_database[row]}" + ) + for row in comparison_abs.index: + for col in comparison_abs.columns: + if comparison_abs.loc[row, col]: + print( + f"{row}, rank: {col} is slow than {p}, {self.rank_mean_tflops[col][row]} vs {self.matmul_database[row]}" + ) + + if not plot: + return + + def get_figsize(df, aspect_ratio=1.0): + n_rows, n_cols = df.shape + width = max(n_cols / aspect_ratio, 1) + height = max(n_rows / aspect_ratio, 1) + return (width, height) + + plt.figure(figsize=get_figsize(df, 1.5), dpi=300) + + sns.heatmap(df, annot=True, cmap=sns.color_palette("GnBu", as_cmap=True), linewidths=0.5, cbar=True) + plt.title("Relative Performance Heatmap") + plt.xlabel("Rank") + plt.ylabel("Matrix Multiplication Operation") + plt.xticks(rotation=0) + plt.yticks(rotation=0) + plt.show() + + def parse_gemm(self, debug_data_tflops, mean_tflops): + debug_data = debug_data_tflops.mm_debug + all_tflops = debug_data_tflops.tflops + name = f"{debug_data.api}_[{','.join(map(str, debug_data.shapes))}]_{debug_data.trans}" + mean_tflops[name] = round(sum(all_tflops) / len(all_tflops), 2) + if name in self.matmul_database: + return + if debug_data.api == "cublasLtMatmul": + command, base_tflops = self.parse_cublaslt_gemm(debug_data) + else: + command, base_tflops = self.parse_cublas_gemm(debug_data) + if base_tflops == -1: + print(f"{command} error") + return + self.matmul_database[name] = base_tflops + + def parse_cublaslt_gemm(self, debug): + # ./cublaslt_gemm -m 512 -n 512 -k 8192 -b 10 -w 5 -i 100 -t fp16 + commands = ["cublaslt_gemm", "-w", "50", "-i", "10"] + for arg, value in zip("bmnk", debug.shapes): + commands.append(f"-{arg}") + commands.append(str(value)) + commands.append("-t") + commands.append(debug.dtype) + transa, transb = debug.trans + if transa == "T": + commands.append("--trans_a") + if transb == "T": + commands.append("--trans_b") + p = subprocess.Popen(commands, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + if p.returncode: + return " ".join(commands), -1 + out, err = p.communicate() + out = out.decode() + # A:T B:T m:1024 n:1024 k:2048 batch:1 time_us:790.169617 tflops:5.434173 + tflops = out.split(":")[-1] + return " ".join(commands), float(tflops) + + def parse_cublas_gemm(self, debug): + # cublas_benchmark --num_test 10 --config_json '{"name":"cublasGemmEx","m":4096,"n":4096,"k":4096,"transa":0,"transb":0,"datatype":"float"}' + commands = ["cublas_benchmark", "--num_test", "10", "--warm_up", "50"] + config_json = { + "name": "cublasGemmEx", + "m": 4096, + "n": 4096, + "k": 4096, + "transa": 0, + "transb": 0, + "datatype": "float", + } + config_json["name"] = debug.api + for arg, value in zip(["batchCount", "m", "n", "k"], debug.shapes): + config_json[arg] = value + if "16" in debug.dtype: + config_json["datatype"] = "half" + elif "32" in debug.dtype: + config_json["datatype"] = "float" + transa, transb = debug.trans + if transa == "T": + config_json["transa"] = 1 + if transb == "T": + config_json["transb"] = 1 + commands.append("--config_json") + commands.append(json.dumps(config_json)) + p = subprocess.Popen(commands, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + out, err = p.communicate() + if p.returncode: + return " ".join(commands), -1 + out = out.decode() + # A:T B:T m:1024 n:1024 k:2048 batch:1 time_us:790.169617 tflops:5.434173 + tflops = out.split(":")[-1] + return " ".join(commands), float(tflops) + + +def main(): + parser = ArgumentParser(usage="""python parse_perfetto.py""") + parser.add_argument("--path", default="matmul.bin", help="Trace path for diff") + parser.add_argument("--output-path", default="relative_matmul_performance.pkl") + parser.add_argument("--plot", action="store_true") + parser.add_argument("--p", default=0.9, type=float) + + args = parser.parse_args() + if args.p >= 1.0: + raise ValueError("p should less than 1") + MatmulPlayBack(args.path).run(args.output_path, args.plot, args.p) + + +if __name__ == "__main__": + main() diff --git a/xpu_timer/experiments/figs/parse_nv_timeline.ipynb b/xpu_timer/experiments/figs/parse_nv_timeline.ipynb new file mode 100644 index 0000000000..6aa56ce9cd --- /dev/null +++ b/xpu_timer/experiments/figs/parse_nv_timeline.ipynb @@ -0,0 +1,705 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "8c23b94b", + "metadata": {}, + "outputs": [], + "source": [ + "from parse_perfetto import plot_tflops_grouped_by_operation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8bbcd696", + "metadata": {}, + "outputs": [], + "source": [ + "from parse_perfetto import *" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f0ed0dd4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PPZR3f62yWspX6WfNNdfM6/V++eUXs8cee5hhw4aZcrJgwQLTs2dPmykrpHLdvpWjqOTj/Pnnn2+uu+66yM+fMWOGOfTQQ823335rbrjhhsDPGzNmjDnmmGNMKSk/06NHD/Pzzz+Hfu7MmTPNYYcdZkaMGGGuvPLKwM/78ccf81rf3u1ugw02MI8//rjZZ5998n49oFJQ1AUAKpga8/fdd5/vv6222mpm2223NR07drTh5mWWWcb89ddfttE1evRo8/XXX9sL5/nz5xd9uQEA6XRToS7qV1hhhVIvCoAKoeOF2nx+1O777bffzJ9//un77z/88IPZfvvtzaBBgzK+BpDPze1uUF4DU7o5GQCAQlC/16abbmp/mjdvbvvC1OelMK+KNbz//vv2Ri6v77//3my33Xb231dcccXIg4u77LKLbXu52rRpY/+tXbt2ZuWVVzb16tWzbbOxY8fa91Y7bPbs2aZQVMhj2rRpppBUkMANscoSSyxhv4ttttnGtGjRwn4f8+bNs4VmNJistoJ+3O8kLvrcp556qqlUvXv39i3oUqdOHbvNbrXVVqZ169amYcOGdpvSOn377bftf11aD1r/Bx54oKkEI0eONLvttpvvvrHOOuvYIMIaa6xh93mFKlSE5vXXXzdz5syp+TttVyeeeKJp0qSJ2W+//fJaHm3LG264oenWrZtp1aqVLQKlx6ZPn26+/PJLWxzK+96i7Vz7/gcffGC6dOliKpHCfvfff39BXluhL4Xc/Qq6rLLKKjYoo+94qaWWsoGuV155xQZnvBTw2n333W0BnTBh+XLZvnQDgPbJF198Me3f9Hm6d+9utzmdO3R++ueff+z1vYIxClLqvwAAANWKDAwAVA8yMADCIgODckUGBgBQjByCCgn4FXRRH9jBBx9s1ltvPdO0aVNbRFPjpBqr1thrksZANU6tPrK2bdsGfl+NRWpCDr+JnZS/UbEX3XSvcctZs2bZdpfGcN2Jj1SsYrnllstrUoubb77ZnHnmmb5ZCY3b77DDDnZMWeP2Kr6hfkGNAX/33Xd2/L7QBTmqhcbiN954YztRTcuWLe361Bj21KlT7Rj5W2+9ldbHqraQ+ma17YXZvrx++uknOxauvIVLY+PaDjt06GCXR3kYbW/a3pMZK10PFMpll11W8HH4ct2+dQw46qijCpbhKrQbb7wxY4GRzTbbzOasVl99dVuUUdkPjSUoR/jVV1/5vpbGHs4991xTCTSZkSb88puITrnFvfbay2Zhll9+efP777/bvMlrr72Wti9dddVV9npcBbryoW1Z4zfKtek4oXW59NJL2/yN3vvNN9+0/+9uf8oQvfzyyzbPA9QGFHUBgAqlC2G/MMtGG21krr/+envBpAZRNmr8KPCvxo9+NEMmAKA01PGiUIuO4QAQhG44e+SRR7L+jW4a1mxEutnWvalXnXiqsBxldnQgm8GDB6c9Vik3EgNAIelG7U6dOtmb/J977jk7+FxtFNDQQLMKmeQ720suDRo0MHvvvbcdWN56663Nkkv6D3dohkbNRnHSSSfZQgVeGpDXgHmuNpUfDZxr9hi3oIu+XwVtttxyy6zPX7hwoQ076L31WeKebUezUiZpJiYN8MdJYVW3oIuKXRx33HE27KCQQTYapNVAsfo3c/VhhnX88cfbwhqF/PyF8sUXX5i77ror7XEVydG2kmmW0ZtuusmuT61/NwBz2mmn2UH8xo0bR14ufUft27e327eKx/gFuvKlmYgUKHALbui4cs8999igml/xIm1Lp59+ekohHIXXdGzQbGRhA/z6rCpIdOSRR9rlUbAjEx3HL7/8crsvuIE5FSdRoK3SZujUTRHemYR0bFVAUKH1OGjmUhVKcWk9avYkFcDyuvTSS+1YyOGHH55yvNU1xyWXXBJ49p9y2b7k5JNPTivooiCLAjIKB2mZslFwTcf5TDc7AwAAVCoyMABQXcjAAAiLDAzKFRkYAKi9GZhi5RA0Fqtsi0sFVR5++OG0YpkaU9S4vTIzo0aNSrkOO+SQQ2xhjqA5DBVZdsf/9dyLLrrILpNfnkX5BOViLr74YlsMxDsWrLH2ffbZx4SljItfQYEDDjjA5lNyFRLROLAKBjzwwAOmbt26phJo3apgjvahfIrhBKFxfxVN0Tj3zjvvbMenM1Fxl3POOcc8+eSTKY9PnjzZHH300eajjz6KVHxixx13TMuzaNIVbU+a1CXbNquCI2qTPf7447FPcKXiHt6+CxXDUD+zljku5bx9K6cxZsyYisxYjRs3zmZKXGuvvba9ttRn8XP11Vfbidy0P3g/u/Tp08dO8KNCMPnQZF3at7UdKR8YN+VW9t9//7SCLjo3qziNcpLK8bmUu9E6U5Ehr/POO88WY1HBp7CUnVHGStuzJkjKltvRtbzWsXKTSfr/nj172sJP2Z4PVIvKaKUAANIaX5oJ16WLX12Aq5JgkItwVa/Uc3RhowssNcp0UQQAKA11yOnGDACIizoV1dGmmwm32GKLtH9XR6HfDXVAPtwZitTJSkcrgNomeRO8BrNVoEEDy5rtQTPi3HvvvXYGhEqn2YDUvujdu7ftWxoxYoQN6QwYMMDeaF8oKkzRt29fM2XKFBsg0MyLmQq6JKnIjG7q8hYq8M4W5Df7RjaakUZhTTeUpP46DWznKugiKlygG9L0/rfddpuJi2bTOPHEE1MGa+MusKM2pMI5Xip8oeCGtvdcBV1Es3H06tXLri/N/hMXrU8VN0nSd6FwVaVQ/6y3OIgoZPDhhx9mLOjiDbxr/3OLtygg4IZtclFgRIP/KpjxwQcf2PCXwmAqbKFQUSHcfffddoDea9lll7XblV/BDe+2pM+nQIIbRFAxjqB0HFFgQ7Py6DOriEi2gi6ida2wmoJ0bn+81lehg1eFoOOYt29KAZ511103tuOTe+wQHQN13nALuiSpGIv2AbdAjvYXfV+VsH15w1pqB3i1aNHCtg+uvPLKnAVdpFWrVvbcO2zYMDtTFwAAQDUgAwMA1YkMDIC4kYFBKZCBAYDakYEpZQ7h1FNPNf/++2/KYyoqoIke3IIuSeuss47tN9PkLO5EMo8++mjgYjLqR3NpUowrrrgi4wRFGrfVOLJfDkGTzujG/TA0tq4JbLxZCY0dq1jQs88+m7PghWic9aCDDjLvv/9+1vHfUllqqaVsgQcV0XnwwQdtIR3tQ/oO48wMufQdJjMAr776qh17z1bQJTl+/cQTT9iJWVzKpLgTmOSi71X5C7dNpQIQmoxLE2rl6vtVIRNNhnTnnXeaF154wcRF+S8Vk/AWJ7r//vtjnZyrnLfvgQMH2mJMSepLV8GNSqEciHu8UUZQ14WZCrokdevWzf6divh4zZs3z75uGC1btrSZLe0zb731lp08SAVntK0qG1gIKmrvFjRVERcVz9c5xa+gSzIno5yTO4GS9gHlDd3MWibaZ1VY7Ntvv7XZPz031zWS9qsLLrjAFihy8z+///67nUQOqA0o6gIAFUgXbmrgeSm8q0qCutiLQh0pCiHfcMMNMS0lACCXfffdN60TwK9aLADkq2nTprZDXv91+Q3KAPn4888/0276B4DaRGFSzZChAXgNxGvQSrMYuINRlUztCh3vNWuFZlDQzIea5SLoTD9RaSA9OcuGW7giFy2bgiduQQoNRiqMEIYGNr///vuUxzQgqkHPKDOixLneVHjAO1uMCiUozBMXzY5x7LHHpgQatG2/++67gYrZFPLzq9CPCh14B4O1DxZ6u4zLggUL7MC1S9tt0MCI9kO/ohnaZ4PYeuut7UC5ZjtVgEQzfClgUOgQnoIyfrM2a/aYjTbaKNBr6GZNBQndEMN3332X87mdO3c2I0eOtAWQFPCIcmw6+eSTfcN9lUTbnwrUJOnYEWeRLoXRdH700mxcOn7movOo22em45Fm/C737StJM/e5n3XFFVe0Qamox+lKOb4BAADkQgYGAKoDGRgAxUIGBsVEBgZAbVcbMjClzCFocgv1jXk1b97cFsvJlT/RDfQa43b/7qqrrjKLFy/O+d4q3OJS4QgVoAhCkyGpSImXCi3fd999JgxtU5pUw50sQxPRVMMYqgqeqoCLJpxSsQgVR9LYc6aiC3HZeeedbfZDWSZtU2EpK7D77rvnnYNQ+1yZIi8Vg9CkQlGOI3F+v/qMmsQsSZNTaXKxOJXr9v3333/biY+SRTz0ujq2xVnQptCU2XDddNNNvteJfrRfKDcSNWO15pprmqlTp9rjnp6j7Un7XdD3z4ff8fucc84xu+yyS6DnKw/m/q0KtXkLmGWiQjgqCqbCY1EmBtM+dvXVV6c9rmOCW+AMqEYUdQGACqTKfa4jjzwy58yhAIDyopvt3Oqu6uhS1WEAiJsGcFQF36VZ6IMM4ABBKaBZzoNkAFBomrGkkgY4o2jdunXBwwV+ttlmm7yKS2iZ/QoUhJm1UQVTrr322rRB2nK4SUxFOJ5//vma3zVwqtmR4qTg0E8//ZTymN5DM+KUmorNzJo1q+Z3zYBSSTOyT5o0KS3I0aFDh9AD4IceemjaY+53lomKJanIRLENGjTITJ8+PeWxVq1amWOOOSbwayjs41fQRjPF5bLuuutGKubipRuU3OOiij9VymzU2ne0DyUp+KcAYFxhTBWC0oxWriBFWZLOPvvstJl9FHZyA/Xltn0lKZSiokleCghV0nEKAACgUMjAAEB1IAMDoJjIwKBYyMAAqO1qQwamlDkEFfnwuzE/6Li9Jt/ZddddUx4bM2aMee+997I+b+7cuWl9cjrHXXbZZSYMZXCWXHLJyGOoyuuosI3XEUcckVY0tJJpbLpevXpFf1/leKIUc/Hq27dv2mMq0KJJi4LQBCwXXHBBWjtehX9KkTvz+uKLL2wBkKSWLVum/B6Hct6+dZz55ZdfUvp0ok7mVQoq/vHzzz+nPLbccsuZ3XbbLdTr9OjRI20cQq/rnewsk4YNG+a9j0WhYk3uBERaFhVhy3f/vvPOO3M+T31/UYq5eOla3s3/aKKkzz//PK/XBSpBaqsJAFARxo8f7zubaDnRjQL60Y0uv/32m509aaWVVrIXpF27djVLL7107O+p6oZDhgyx1XDVmFM19LZt29pKwLrAqg0mTJhgbxaYMWOG/dGFrqo86vNvttlmZtllly34MowePdpWaNT3oYtwNbT1/t26dTMrrLBCQd5TnQL67nXzi4LxM2fOtNucLsp0Q4ZmOo3zYqmY27c6zIYNG2YrwOpzqUqvvldddGl96kY6fUbNVFaOF8r6XrT8Wk+6GUQdu9ov1UlU6o6YcqAbQq688ko7q32SBpV1Q1yYmwrjpGq7Os9ohmodU1RdXfuYbuzSNqftTbMYux2whTymqJKr9m8NkGq/1k1tOqYFnfVCn0kV4vWj/VadLCuvvLI9NmyyySYFGWjVe2i5tS51PNb+q2XXsUKd+RtuuGFZDfCW2/eum95UkVzL9M8//9jBCX1nW2yxhf1v3NT5pu1D5y599uT5Q8d0t8Oo0qmqsjsbms6dusFNx+gotH1r29H+qv/Xjag6TyW/N+1nxboxVOdNdbRrebQsGkzUftepUyez/vrrl9V+V0qjRo0yw4cPt8dEff86njZr1sy2ZfR9lWIAqVR++OEHu/9PmzbNtl11zNP2ov0/6AywavOo/atAarLNo21fndaaUaGQFi5caAYPHmy/z+QNonpvHbO1zQelY5+Ou2rjahBa24TazzrurrrqqqbSad18+eWX9nivz6qCELpG0TrK9wbuIHRuVbtY5zXNcKBBoPbt25vNN9881DWSrgm+/fZbW91f52ntt1p+XWsV+hxdius8oFA0I4aO1d5Ar/ZP/Z5rlqPkDDpqc3ipoEvUmcPjovP6ySefXPO7jgsqyBDn8UHXOW6AQW3ICy+80JSaPuubb75Z87vaNGeccYapJPoOXbr+DUvncF3LeYNlbkGLcqOQvd8MYGH7jjSbk/o/vcVxVOxIoYNCt3F13dGlSxfb5nDDFGpnlzsFJtSvnnT66afbNnFcBgwYYNvcXuq3V7s1KB1ndWOv9zikviLNvqTHy3n70v6oolhe+uya+QoAAABkYDIhA0MGhgxMeSEDkxsZmNzIwNTO750MTOGQgSmf/a6UyMD8hwzM/5GBKSwyMKhNCp1D0D7Uv3//lMd0DZ5t7NPP8ccfn3bNpckxdtppp4zP+fTTT+3276Xx7rXWWivUe2vf2W677cw777yTcm7W8VzrK5frrrsu5XedrzQxBsqDzsE6h3rH+nUtq2O3+nByeeKJJ1JyCKL2u/r3SkntbO1n3uzYPffck9dEY5W0fStDoc+bpO/SneCs3Kmd6hbzVHskbBtC1wpqf3iLpOh11fdRioItQfhlYFTMRn0eYWicQcd870Rhem3t31Gvp8Os9+233948++yzaRmrSiouBESSAABUnB133DGhQ7j355133in1YiV++umnxAknnJBo3bp12vJ5f5ZeeunETjvtFNsyv/TSS4ktt9wyUadOHd/30+Obb7554tVXX015nrucPXv2zPle+hvvc/QaUXz00Udpy6nHopg0aVLirLPOSqy99tpZ13u9evXsenrmmWcSixcvDv0+7uv16dOn5t8WLVqUeOihhxKdOnXK+P5LLLFEYrvttkt8/vnniTj8+++/iccee8xuSw0bNsz62fWz1lprJXr37p345ptvKmL71na9++672+8t12fTT8uWLRP7779/4oknnkj8+eefiUL65Zdf0t7/4Ycfrvn3adOmJU4//fRE48aNMy5vs2bNEqecckri119/DfSeo0ePTtvHg+yzuey5554pr1m/fv3E9OnTE4WgfcZdD5988ondHzfYYIO0fxs4cGDg1+7Vq1fa8/U9BTVx4sTErbfemujRo0dihRVWyLm9aZ/bZZdd8trOsx2DtX/fc889WY9ryy67bOLUU09N/P777xnfY/bs2Ym+ffsmVllllYyvs/LKKyduvPHGxMKFCxNxeO+99xL77bdf1u1fP02aNEkcccQRiREjRiRKpdy+9+Sxb9NNN824DDoObLLJJok333wz78+vfe+BBx5IbLTRRlnPXTqmf/zxx4HPiYXi13aIchz8448/fD/r999/H/g15s6da7+r4447LrHmmmvm3Hb0vXXs2NHua3PmzEnEdQz1GjNmTOLII4/M2ibQ/q5jwl9//RX6/aO0G93t7dxzz01bJh0rPvzww0QxTJ482baF2rRpk/X7atSoUWKvvfYK1TbN1T7K9eNtR8Ql236qbfjaa69NtGrVKutx+tJLL038888/Gd9DbYYzzzzT/m2m12nbtq39fGHb37muF/R9nnTSSfb7yvTe2j/VXs5m6NChtg251FJLZXydzTbbLPHpp58mylGuY8OAAQPs9bOO55k+n84D999/f6RrpO7du6e8ln73evbZZxNdunTJ+N5a74cffrg9J2cyb948e75u3759xtfRseSCCy5I/P333xV1nad9I582rAwbNsz3GHTAAQfYdVfO/Ja7mvhdN7r7SCmttNJKacuna9lctF+41xdqY+gaptT22WeflOW68MILU/7d/bxR+rTefvvttNdRn1Sp6Ti6/PLLp1zb//jjj1mPN8Vow4f19ddfpy3nQQcdFOm1tF16X0frJy5u/6jbTolCfWDua+qaI4ptttkm7bWK1eZWu8p9b/XFljv1m3uXWe0Obzs4ju/87LPPTnuN6667LvSyqs3hd94v9+1L/Vzu826//fZIywAAAFCNyMCkIgNDBoYMDBmYqMjAlGcWggxMcZTb9y5kYDIjA0MGJl9kYMjA+CEDQwYm275CBqawqjEDU4wcwuuvv572GmqjhqX2v64pvK/TvHnzrMcCtWXc9z7ttNMSUahN4r6Wzju5jBw5Mu15++67b6I2ctdD1D66Qth4443Tlu+LL74I9Fz3ekj7Ubbr3mI544wzUpbrkEMOif2YVq7bt/oW3M/37rvvxn6tVmjajtzl1PYWRdeuXdNea+bMmQVr3+V7vaA+SPc1+/XrF+m1jjrqqLTXUnupGM4555y099Z1DVDtKr+VDAC1kAae3IbLfffdV7Ll+e233+xgarZOwkw/6vDL1nmXzYwZM9IGo3P9qLM22clX6YEWda4rDK8ARdj1vt566yV++OGHUO+XqdNJgRp1cod5f/fGorBeeOGFrB26uX5uuummst2+x48fn9hqq60ifzb9nHfeeRHWajyBljfeeCOx4oorBl5WDQA9/fTTgd535513TnlugwYN7MBsVPpu3O/14IMPThRKpkCLKCDg/ps6B4oRaOnWrVvGQGCQHwUGoxzHMx2DNUDn1zGS6WfVVVe1g3GuQYMGJVZbbbXAr6P3zKfzRWGAbbfdNvT60zZ4/PHHF33Apdy+91mzZiX22GOPUMug50YNIo0dO9augzDvpwBV8mbdSg60LFiwwPfzBR2svuWWWxLLLbdc5G1H4akXX3wx1kHrO+64I1R7SIGOUaNGFS3QovCEBlbd5dBraiC20LTdXnLJJYECwO7Prrvuatsm1RRoGT58eKAgVvJHYSy/daCwfJg2j45x8+fPj+V6QfuQd8A4yDWQe57RgK0GU4O2cXXOuOqqqxLlJtOxQdv9ySefHGo71HlB54c4Ai0KmO+2226B31vf5wcffJD2+tpes4VJ3B9dI02YMKFirvPyDbRonfmFiBUgjBJQKrZqDLRUUlGXZZZZJm35gtwcoms493kaKC21p556KmWZOnTokHbsd5c7Sp+W37XnkCFDEqWmELp3mS6//PKUf6+Uoi7qg3Kv07bYYovQr6M+Q/ccrwBnORd18esHUJGbuJYvSGAsDn7nf/cmy3IMuijQ5233KRQc93fu9x2r7ybKNa36BL2v06JFi7LfvhQA9j5H61njGwAAAPg/MjD/RwaGDEzYz538IQOTHzIw0ZCBSUUG5v8/ZGDIwARFBoYMTFRkYFL3UzIw/0cGJv2HDEzqvkIGpvCqMQNTjByCrnfd14h6fNp+++3TXksFXcO8d5j+hVzrQn0BuVxxxRVpz1MfSW1UzkVd1H5wl8/vutWlY7j7PBX8LTVdK9StWzelUK/6y+I+ppXr9q1ilt5lOvroo1P+vVKKuujc6LYhVQQ3ChWX9r6O2sVxKURRlwMPPDDtNaNcl2ZaPhXtLQa/9q2K/wHVrq4BAFSc5s2bpz323HPPlWRZRo0aZTbddFPz4IMPmkWLFoV+/sCBA83GG29svvrqq1DP+/333812221nXn311VDPe/75580uu+xiFixYYCrZtGnTzNZbb21uvPFGM2/evNDP//77783mm29u3nzzzbyWY+zYsaZr167m888/D/W8q6++2lx88cWh32/x4sXmnHPOMfvtt58ZM2aMiWr27NlluX2PGzfOdOvWzXz88cemEvXv39/sueee5o8//gi1Lx9yyCHm/vvvz/m3p5xySsrvc+fONY888oiJ6r777kv7Xk888URTCjvuuKM9pnl9+eWX5sUXXyz4e3/66afqZYr8/M8++8x06dLFDB06NO9lmTJlij026bMHNWnSJLv+Jk6cWPPYu+++a7bffnszYcKEwK+j99x5553Nv//+G2nb32yzzcyHH34Y+rnaBu+99157TP/1119NsZTT967jgI59Wo9hPProo+bII4+MdO7aaqut7DoI4/bbbzeHH354XuutHMyaNcv38UaNGgV6/rfffhv4POpn5syZ9jx+7bXXmjhceOGF9vwQpj2UPN9OnjzZFNpvv/1mj+9uW71z587miy++MOuss05B3/+ff/4xe++9t7niiivs/4eltqKOb2o7VoMff/zRbLHFFraNF9SwYcPMTjvtlLLdq/2h9RqmzaNjnNo8+XriiSfsPvTnn3+GugY6+uija37XcezYY481ffr0CdzG1XMuuugic8cdd5hyp2U99NBDzZ133hnqeTovdO/e3Z4n8jFnzhyzzTbbmDfeeCPwc/R97rHHHua7776reUz/v+WWW9rtNihdI6lNEWb7KIfrvCgee+wx23bznteWWGIJc/fdd5vrrrvO1KlTpyjLgcrtU/n7779THmvYsKFZbrnlcj73vffeS3tM/QalNH36dHPqqafW/F63bl3z0EMPmaWWWir293I//9JLL2022GADU0rqT3jnnXdqfl9//fXN+eefbypRkyZN7PJ7DR48OFSbI3lN7J7j3T6HcuP3GYPsk34aN26c9tjXX39tisGvv7JFixamnOn4oeNi0gknnGDbRHHS9uj2H9SrV8/2K4Sl56m/1Wvq1Kn2p1y3L/Vhun0Q7dq1M02bNo20DAAAANWIDAwZGDIwZGDKERmY6MjA/B8ZGDIwYZCBiYYMDBmYMMjAkIHxPocMTDBkYMjA1FbFyiH4jTPqXBWFrj2CvH6xxlDVzsrV1izHDA5S6dz6yy+/RMpBvP/++2X3/aoNedRRR9k+Oe+1kfI6cSvH7VvLpP6jpJYtW5qbbrrJVCKdG9VG8dI1UZi2hqgfQP03XmSsyFgBhbRkQV8dAFAQuuD2NqSTFzy6mPDexFFow4cPt4MBGphwbbLJJrazeK211jIrrLCCDZAoWK0BsLfeesvMnz8/JZyx6667mm+++ca0atUq5/suXLjQdir7dayvssoqZt999zUdOnQwK664or2xRR0Cr7zySs1yKixw1llnmUqlz6SLufHjx6f9W6dOnWwnbMeOHWsa1xogVUekBiTUuZqk/9e60ney4YYbhl4OPV/hoORgkC6KtG1qEHm11VYzyy67rJkxY4YZNGiQefnll9MGmjSQpU5bdZQGpYHEp556yvff9Nl32GGHmkC8thNdrGg7VaAkSMdQqbdvDTR4B+WTdIOUOqbXXHNN+73q5gWtfy3byJEj7b4wZMiQSKGbuKizRsefZBgguT3osyc/tz6b1o+2Ce93of8//vjj7T6rbTITbW/6fn/++eeaxxQEOOOMM0Ivr5bzgQceSHlM+40GDUpFHe8KQHnXjQZqFRJacsniNNt1059CChpgTW7fGuTW+tLAiLY3Dfa4F+o6LmlwTY9H7RDQPrvXXnvVHNu0Den70H6tbUjLpvDK22+/bT744IO091cY6fXXXzcjRoywy6LAkzRo0MAGXhRgUCBU+4m2oRdeeMEOUrqhln79+plzzz038HLrmKRjk7dzT+rXr2+23XZbe4zT8i+//PLmr7/+sgPpWn43TKGB7X322cd89NFHdh8vplJ+73oPfe/eDjSdk3SeX3311e0xT8dyfTcvvfRS2gDdk08+aZ+v5QhCx0114GlbcrVv395+B/qvPo+O3zqua0AyedPv008/bYMIlcyv/aQbf9u2bRvp9bTNaMBKbS91pGnb0fav85RCZTr/KmSW3CdFxzkd39Zdd12z2267Rf4sOgdcc801Nb+vtNJK9lyhY6n+X20PdfipHeLu72of6dyj40ahjB492p4H3U7H3Xff3TzzzDNmmWWWMYWk45LOIX4DI/qe1A5Tm0bHRoU1tLxaV+7yqrNax1Dt6zoP+9Hxw9uxq+/e24bScS1beEdtgELT8UPrPrlcWiYdp/WjgQntBzpGv/baa2nBSh0HL7jgAhuQGDBggA2DJI/7Or5ru9NgarNmzWw7UOeiZ5991r6el4KqCjcdcMABkT6D2nvad5JtBa1zbWO6NtB7az/Te+s93PfW+UrHy/3339+GDhTYTmrdurVdN2pPa3BKAQHdQK7XcQNs5513nv3bNm3amHKlQSatf+/2rn1Bx4aVV17Zfj59p/o+3La3ftc2oTCJ32BFEEcccYRt+yfpvKFtRMdZXSPp/KIQrEJO3vaDBgx79uxpn6swnNazQp/J7VXnr+T2qrahvmPd4OFurwqjaHu96667Ii1/Ka7zwrrssstM3759Ux7Tcul71z4B5KJrAZdbICATbfsub1ETtW/VB6X9Q30R2pd0DaLjq9onOvfqGkf7mbbbOKhNkzxeSO/evQsSAlAb2r1pQOcOhcmSdIxT4EztP7W/tFwq/KLPvuqqq9pjmdr6UUNILi2Pt49Px0cVtCn2NVWcTjrpJHPcccelXCsrhKp+3yDUFtHfe2m9lOoGnqB07e7yXkOE4RfkVhup0LTN//TTT2mfS/1N5UrHK29fq8716p+Km9otbptBfSW6doxC1yXuDYFa95nCHaXevnQt5e0vFrcgltpwOn6qz0htVbVZdZ5QP7fakWoHqp2z3nrrRVpuAACAckcGhgwMGZh0ZGDIwIRBBsYfGRgyMGRgyMCERQYmMzIwZGDIwJQGGRgyMCi+YuYQtP+61H6Lwu+85o5fF3MMVfuf2qmZ+g50LePu8+r/UF+Y93ynAmDqA1K7T20efQ/6O7XVdE7X9ZGOKTr3IX7q93C/X51r1T7NN2OlNoDa1jpHa1vQ9bPaHmpL6fX1t7qGVhs7rqIrOqeonZikwnZR2zLZlOP2rXZQr169Uh5T8bCobYRyyVipn9xL7Uu1KYIURNM2qG3CVcwxiSgKffxW0Uhtw95MYNzUN6JrkagZTqCiJQAAFefXX39NNGjQQL2YaT977LFH4pNPPin4Mvz111+JddZZJ+39d99998Tw4cOzPnfq1KmJww47LO25m2yySWLRokU537tPnz5pz11qqaUS1157beLff//NuLy9e/eu+fs6derY53hfo2fPnjnfW3/jfU7r1q0TUXz00Udpn0GP5aL1s+2226Y9d/PNN0988cUXWZ87c+bMxJlnnmk/u/e5bdu2TcyePTvne7vvufTSS9f8f9euXRNff/11xuf+8ssviY022ijtNXbaaadEUDfccIPvNr/11lsnBg8enPP5kyZNStx8882J1Vdf3W5D5bZ9a791n6Nl/fTTTxNB/PHHH4knn3wysdVWWyXOP//8RCHp+8y2Pay99tpZt8cvv/wy0aFDh7TXWGmllRIzZszI+t433nhj2vM++OCD0J/hhRdeSHudO+64I1FIfscu93h90EEHpf3N3XffnfO1e/XqlfY8fU9BNGzY0B7b3n777cQ///wT6Dk//vhjYocddkh7z5NPPjkRlI6f7nE8+f8bbrhhYsiQIRmfq2VdZpll0t7/448/ts9N/q71OWXKFN/X0P545ZVXpr3G8ssvH2o9aP15n7/kkksmzjnnHHuuzubbb79NdO7cOe39zz777EQxlMv37j12rLnmmln3Z61TnTfc99cxJyj3PJ78zh988MGMz9Fx6eCDD675e782WK7zShz82g5B2i6uE088Me111ltvvcDPP/LIIxObbbZZ4r777ktMnDgx0HPmzJmTuOyyyxL169dPO+4H3f78jqHJ7adevXqJa665JjF37lzf5y5evDhx6623JurWrZv2Gp9//nmkbTfXutf5u0mTJmnvd9JJJ2Vsr8ZN68Sv7XTssccmZs2alfF5Dz30UKJx48a+bZmFCxcGeu+42sz5cJffe57ZbrvtEqNGjcr43EceecQez73PX2KJJexxcJVVVql57LTTTrNtbD/z5s3z3d90rIu6z3s/w6mnnpr1vXVsdp+/1lpr2XZYcl/QuUDtjEzbpNq1us5wX+e4445LlItsxwb9HHXUURnXk9oCalt6/z75o2NdEN27d8/4Hek667333sv4XLV1Vl555bT3fuqpp+x1ffL37bffPuv2qn1W26f3NfQdBz1Gl/I67+GHHw7Vhl2wYEHiiCOOSHtOixYtEt98802i0rjnFv1UE7/rRu0zpaZ2gdo+7rL169cv0PObNWuW9tz58+fbf9P1SLt27XzPv+5P06ZNbT+Hjtn5eOyxx1JeV+//999/+/6tuwxhz89vvvlm2muonSw6l+j6ym3vZfrR+SVIP1iu71LHSO/rZuoP8TveFKMNH4XWpdrb7vJeccUVOftN1Te06667pj338ssvj3UZ/a6r8v0+t9lmm1j6e2TPPfdMey217YK2ZaNS+8x93x49eiTK1W+//ZbWFlEfQSG+8/fffz/t+frO42wDZruuL/X2ddddd6U954ILLrD/pmtiXVf4Xa/6/Wgfr8R2DwAAQC5kYMjA+PUdkIHJjgxMfMjAREMGJhUZGDIw7rGDDEx2ZGDIwIRFBib9eiH5/2RgyMAIGZj/y7b+yMAUVrVkYIqZQ1DWxD2f63gd9dzqd6w9/PDDM/5937590/7+kksuifTe6p/wO09n6xtSf4T792qXJd17772JRo0a+b6u+9OxY8fEiy++mKhk7mcqRXvDj/cY7m0zBKH2lvvcn376yf7bDz/84HvM9ftZdtllExdddFHWNl8QAwcOTOlHXGGFFWz7oBDHtHLcvo8++uiU11UfQyGv1YrlgAMOSFveE044IWcmT9du7jrRjx4rdPtO5498qE3ovma2foBsvGMMQdswcbjpppvyupYHKllltpIBAHawLFvDXR2taqhpsOG7776LPbTu1zl73XXX5d04ffbZZ7M+Z8yYMXbQwu28ePnllwO9p27SybTOKiHQonXsPk+d1epACkodnlG+u0zrTSGPTANIXr///ntah606osaPH5/zubpodztp9XP66acHCkF5qaNrwoQJZbd9axDb+7fazkePHp2IItMNXIUMtHg7DXRTSC76G/1t2ItgBXfcweT99tsv9GfQQJb3NRSO+PPPPxOlDrT8/PPPace45s2b25BVoQItUTu4tO+5HRlaj/qOgvDr7NKPQlka/M5Fgz3uc703WOo8GcQxxxyT9joKhwX5/J06dUr7/B9++GEiTIe8GxDRgH/QAah8lNv3vvHGG9vzRC7qYHPXu9++lKkz1q+jN2igQZ3QmY59lRJo0WC8G+jVz/XXXx/4NfLpFNfArhsSUMd01GOofvR5dINgEFdddVXa83X8jDvQ8txzz6UNkGsgQOHcYtE5wD2f6EdBviC++uqrxHLLLZf2fA3+V2qgJfmjDvwg10ZXX3111vPMnXfemfM11Eb3CwIOGjQo0j6f/FFAKwi/EGDyM+j4lysQLzq+K3zmfQ1tG0HDaIWW6dign6Ah7/79+6cdm/Sj80bYQEvyR+HtTAN+XtoW3BsOvNuZApVBtle/kK6OeUGU8jovTKBF5x+/mzvULgjyXuWoWgItlVbUxa9PRNeY06dPz/lcXZe5z9U5X1Q0I+gN+d6fTTfdNDFt2rRIn2Xy5Mk2YOBtb2TrW3LfO+z5WTeB+A2C6zigAFzYz67lVeg5KrdIgoLumQbkK6moi2ib8LvRav3117efW+163Zymc4TWv8LUF198sW/RIQVRw/Qblqqoi19gMWxfXJI3gOz9ibqvBTFy5EjfokZqZ1RKsCVboDff7/yJJ55Ie76+86geeOCBtNdT+71cty+331c/ujl37NixNvAe9vipaz2N+wAAAFQbMjD/PYcMDBmYMMjA5I8MTDRkYFKRgSED4/0hA5MbGRgyMGGQgSEDI2RgyMAEkWn9kYEpvGrJwBQzh6Bire7zV1tttcjLrj4m9/V23HHHUP0pu+yyS6T3PvTQQ333vWeeeSbjc15//fW0v995553t+tY+m2l/zvajYm8qmFSJyrGoi64H3eXScX7o0KGBnq8Jr/zG1VV8wq8dn+tHY+vJojBhqR9CxYa9r6fCd4U6ppXb9u1O5KXvJlMh4kor6qLvVn0v7jJrYjRds6igm8676kNVcTxdGyiv0aZNG9/2Qr4TtBWjqMull16a9prq/41iiy228N3egrSto9K251c09Pbbby/YewLlpDJbyQAA27HkN2trph91qqvSpapZP//883mF2NWB4IbUVXE9CreDV7NLZKNZdvKtCOudaaCSAi0KKbgXtrvttluk93cHcDVgnpxZOhO/daYLmTADW36zkiqEH+U7O+SQQxKFUKrte5999kn5Wz23XGUKtGi9heko0d+661odNLlmKnLDGxqAyDQTTab3dQcOilFtP0igJdPMzrlm1c4n0JLvuahVq1Yp7xtkgC9TZ5cuzsN8l343uelHg0tBw266EdK9+VIzjeWiAWv3ffVYWDqGusf2M844I1HO4v7eNRtKmIEot2NRP7qBMRf3OBu2KrEGpv1m66iUQIs6JTXg475Gy5YtA4XI4uIGOBVmymfQ+rbbbgv83urMdm/AW3XVVWMNtCgc5J5j1A5X+7uYzjrrrLR1pX0gDA2oua+hbSjITBDlGmhZY401coZEk9Qx771RPugMFi51bEc5ZmUKtIRpA2smT7/XCHv80wCG+/ygQbJCy3Rs0EymYaiogPsa++67b6RAi9qz33//feD33nXXXX0/g2aVChpU17nZnV2sW7dugZ5byuu8oIGWcePG+bb7FFLPdwaOUqqWQEslFXXRtqQZKqMcl0U3nbjPVehPA4p++5L2S+3L+q79ij14z5NBisq41CfkfZ3jjz8+69/7vW8YmvXGfQ1dv/oN7uoaS+1MBfz8Co24hWHCUiEEhTO975ctMFppRV1ERVvUfxelWJB+tN4VwiqEQhR10U2Z7mtuueWWoV9HMwJnWicK+BeCrjN0XeO+X9BzcSno+sSd8S/bjSr5fue6bnOfH7W/NdO1iq6BynX78gsxqq/Pr6CLbkxQn4sCon4hFu+P2ukAAADVhAzMfz9kYMIjA5MZGZjcyMBEQwYmFRmY/yMDQwYmKDIwZGDCIANDBkbIwARHBib1hwxMcVRDBqbYOQTtW+7ztW1Epb4x9/U6d+6c8e9HjRqV9vdRCoSq/yVTLkSTB2Vy//33p/29ipzuv//+aY+rPaKCS8qgaGw7W45CfUthC+aWg3Ir6qLjkV/RiyDXl8m2qtuO1I8Kmvp9f40aNbJtGxVecYvfen/UnhkxYkToz6O+MLfASiGPaeW0fauQiXvd8PTTT2f8+0or6pI8DqkAXpRiQckifyqAXYhjRyGKurz77ru+7Z2wy6+8YqZMowoTFcpee+3lW4Qn7oI6QLmqvFYyACClcb3HHntEanSq4a/ON3Vkhm34uB3kasDqRoMoNJjrLps6q/xoOVdcccW0QZiwFbI1WOrXWC/3QIt7k5Au1tR5FIVm6XEvkgcMGJD1OX7bUdiLCd0c4M42lKsipDo03efopqlCzShTqu3bDb8cdNBBiUoLtGS7eSLMgFuuWRz8bqDIFfjwUljAff4333yTKJdAy6+//mo7prx/p9/1eLkFWvyChgqgBeHX2RV09owkBRr9tsUgsz94acAnbKe4OrrzGTjzuuKKK9I6VcpdnN972DCIBtPdGTNyVYfXud+dgWKjjTYKPWO9QiGVEmjRIKjOOS+++KI9p/jNWKOOuFzn/7i5A1H6XoIM2PodQ9V5H7YD0i8cHeQm6lyBFm2XuiHZfW0F1sIek/LlN7Ct71qh3bB0bHM/k7apSg20PPHEE6FeQ8EV9zXULg07I4qCU97XUIAhyj4f5b39gmwKSoQ5/vnN4hFmdrNSBFrCBEqS+437PQUJTfsFWnTzQBi6Jvf7DEFmTfTSgKnbfgzyPZfqOi9ooGXIkCH2Bgy/c2ClzqxSTYGWSirqoj6lLl26+B4Tg/Yr+QUFdY71DqarzaXZLTXbsnuc0axoXbt29d3vttlmm0Ch0UwzJilwnqvvwn3PsOdnN+CgHzdIoeVQoM2dgVTtEF3rZwqLhik+omOb20bp3bt31udUYlEX7005Ch5kWnfuT/v27e36LOSMgoUo6uLX3tCPzgP5nA+9P19++WWiEPxuTFKofdiwYYlypP4l95r65ZdfLuh3fs0118TSj5j06quvpr1etrZHqbcvvwCze/xUX5RmuHdvOFFBMQXi/YJrOv9oNmAAAIBqQgaGDAwZmPiRgcmNDEw0ZGBSkYH5DxkYMjBBkIEhAxMUGZj/89tXycD8/4cMTDoyMMW/zhMyMJWdgSlFDuHzzz8PVYQlF/UruK+nIhHZuMUN9XPjjTeGel9lRPz2Pf2oSEImOva6f++OiapQZr9+/WzxSi8VT9XEHm6hiqgFk8tBORV10f7Qo0cP37Zg0MLauub3+26837H68tQ+0Vi79xi/cOHCxIcffpjYaaedfF9D17hBC9vJBx98kNJvqHOK+hILeUwrp+37iCOOSHn+nnvumfXvK7GoS5KuEdSnlmnduT86J9966612XKJQClHURdecfuMBL7zwQqjXURYl07pRfqUQbrrpprT30v5ZLkUWgWKoawAAFatx48bm1VdfNY899php3759qOfqum/gwIGmV69eZs011zRPPvlk4Oe+8MILKb8fcMABplGjRiaKzTff3H4OrwEDBvj+7Zdffmn++OOPlMcOO+ww06BBg1Dv2aJFC7PHHnuYSuOu92233da0bds20mu1atXKrLvuuoHWeybLLLOMOeSQQ0I9Z4UVVjBrrLFGymM//fRT1ue88cYbZtGiRSmPnXDCCWa55ZYzhVCq7btJkyZp2/u///5rKsmxxx4b+jnHHXdc2mNvvfVW1udssMEGZosttkh57P7770/bTvzMmzfPPProoymPde3a1Wy44YamXKy00krmnHPOSXlszpw55oorrjDlyN2nv/jii8ivpXNSGH7f29prr233vXxeZ9SoUVn/fty4cebrr79OeeyYY44xUe22225prz9+/HhTzuL83sMeO5ZYYgnTuXPnUOeS9957L+2Yqu+sTp06od57o402Ml26dDHlQsczfQa/H52n27RpY/bdd1/zzDPPmIULF6add3SO7d69e0m3HX0vQ4YMifRaRx99tKlbN1y3xiabbJL2WK7tJ5e//vrL9OjRw9xzzz0pj6t9/vnnn4c+JuXrq6++MrNmzUp5TMu3yiqrhH6tk046yXd/qkRqz6ldl+95ZocddjCrrbZaXq8TdZvbfvvtQ7+32k2uo446KtTxr127dmlt73z3m0LadNNN0651cll66aXN4YcfnnZ8ev/990vSntH2uv/+++f1Omo/Tp482YRVrOu8IPr372/PU9OmTUt5vE+fPuaRRx4x9erVy/s9UDuoD+zII49Ma3MstdRStj8saL/S/Pnz0x5bsGCBWbx4cc2+8PHHH5tbb73VdOrUKe04s/vuu9u2gXu9Jx999JG9rg1i0qRJ5owzzkh57N57743cdxGU3+efO3duynlq+PDh9ji44oorpvyd2iFnn322GTZsmO8x+tRTTzW///57oOW44447UvpWVl99dXP11VebajR16lTz0EMPmaeeesrMnDkz0HPGjBljLrnkErtOfvvtN1Mp1N7YaaedfNst3u0sm9dff9088cQTGf9d+2vcdA1w++23pz1+ww03mHXWWceUoxNPPNHMmDGj5vcDDzzQ7LXXXgV9T/d6NHlcjMrvuJ3t+y319pXr+Kl26NChQ+31yrLLLpt2Xak+wW+++casuuqqKf+m84+ui/3WLwAAQKUiA0MGhgxM/MjAREcGJh5kYIIjA1M6ZGDKAxkYMjB+yMD4IwPzHzIw6cjA/IcMDIIqRQ6h1GOomc6N2iaDbvMTJ070zcDEMYaqY74yJsrHtGzZMuXvmjZtatvdI0aMMNtss03a61x55ZU2v4JoLrjgAvPaa6+lPKZzrfIrK6+8cqDX8Pt+vd+xtvWXX37Z9gOrTes9ly+55JL2e3377bfNbbfdlnae13cbtE9BbVudy/5fN+f/rr/+etuXWEjlsn3r3KN1nKR+vbvvvttUI10z6LNqXCBom0HnZG1LOu5NmDDBVIqGDRvaLKRf/s6bB8pGGRTtC8XMWOma/dxzz017/MwzzzTbbbdd7O8HlCuKugBAhdMFijre1GBXg/vggw8OPdCvxqeCIerYzHTxlKQBNneQzR1YDkODEK1bt0557Ntvv/X9W7/Bsn322SfS+0Z9Xqnoe1HAIa71Lm4YJtN6z9ZRXL9+/dDvqw5xrz///DPr3/sFQNzO5riUcvtWqMLrl19+sRfj//zzj6kEChGstdZaoZ+nQF3Hjh3TBuKSN8Vlcsopp6R1CuoiL5dnn302LRinm1nKjS5MmzdvnnaT3tixYwv+3rqR7pVXXrEdFBrM2GWXXUy3bt3sIIk6kNwft9Na30UUGgRxP3Mu7v4lW265Zej31qC/O4ClTrxMFAh15XOs8Asnhj0mV+r3rnNClEH2sOeS2tyGcKmz96yzzrKDLhocz5fChJ9++qkd1Dr++ONtcEKdyAr++G07aj+4onaERgnjuNtOkO0nmylTppitttrKvPnmmymPK8SiMEvY0HkcBg0alPbYfvvtF+m19txzT3vDe67XrwTa9sIOfhfqPOMGjoLSthZWXJ/BfZ2on6EYot6U7Hd8DxvY1GBJ2OCj33cUZXt1t7Oo31OxrvNy0Xll7733Nn///XfNY1onCrJcdtlleb02ap/zzjvPBnxdKsTgBqWz8Q7w+/XPvfTSS75tHffvNCCqfjiXHg9yo4aC2d597IgjjrDt90LL9vk7dOhg+yXdYgR+N5kpHKs2qZf2dRXDyeXnn382559/fsr61A0uOv5WE61rbZ86tl533XVp7WW1zxRyVX+KrqcUaPFSOEEhDvW5qB+kUlx00UVpj/3www+2GEeuAjUK++QKL4e9oSEXhZjc/ik59NBDfR8vB08//bR58cUXa37XvuhXlKYY8vk+/J6b7RhV6u0r27LpplXdrOLux359rwqtuddn6o95/PHHsz4XAACg0pCBqR3jV2RgUpGBKT9kYOJFBiYYMjDxIQNTvW0IFxkYMjBhkIEhA5PpdcjA+CMD8x8yMNWtnHIIxR5DVVEXFTLy0naqMcxcbXj13an9km2CmqhjqM2aNTPvvvtuzgIiKhqlyTl0Le6+9rXXXpv1ufB311132ZyK68ILLwxVVDrXtqc+AbXNclGRCr+xfhUmCXJe0LWCCo0mbb311radX2jlsH2rv8j9rP369bPZrWqj/jGdu7WdukWplMXQBDrqr1MxH/d6QHkRFQ9SX6D+WylUHMVtX2nyMB2X1Q+bja6Bdtxxx6yFW+LOWA0ePNhOOuVmI7W8HK9R22RPiAEAKoYamprxVz9q5Hz33Xd2cEEDw7qgVsM0140h6hjS4Hm2sL9fB7ZuErjlllsiL7tmkPXKFKJ2Z4TQZ15//fUjvWeYm3bKgT67ZlfxUqVTDT5G5Q4ehZ29161CHdTyyy8fqqNTg0HuDC5R3zuXUm7fukDRRaS3Aqv2SQ2QqYqmOtg33njj0LMxFEs++5Rm/VClWW9Fdc0SowvjTDTrhjoUdOHp7ZzRQGo2bmVZzR6udV9uVB1eFV+9YRtdNKtTSjfgFMIHH3xgt/V33nknrxl+k2GQXDfzuaIM+vrNIBbX6+jYlOkz+B0r4p7Vulgzqpf6ey/WucRtQyhEE7RaeKW3IVzq/FIo0J0dLyxVp1Ynmm6Qnj59el6vFXVgPMr24247+Qy66gZAzTLmBroUHtFNdfnMmpAPVc92RZ1dS4Pa6623nr2mSNI5W2Frt2O73JXTeSbqNlcNn6EYoh6nNbORrjG9s9q5548g4ZRcN+QWuz0TVrHOzZko2K5w980335z2+roJnYr8CEtB7BtuuCHt8UsuuST0bJnZgmY9e/a0A/9BqYiCZh7X9W+SBlRV8GTnnXfO+DwFh9R2T9JNAfn0WcT1+XWtHbTto3a42pHuTK/6bJdffnnW44NuxvPe+KPvUGGiaqLwhfoCFGBxZ2vVzQea7VmzFHnDh1on6ge+8847U2ZLUnjqoIMOsm333r17m3Kn0O3pp5+etk1/8skn9tyosI76ffT/6jfR7DLqN3z44YdTAua6LtV69IYiJc72+YcffugbNtBNAw8++KApR7pu0zr0UiBFfa2lOH54+0DD8nturkBuKbevTMdP7dc69gUNwyhkpJnu1F/tdd9999lZfAEAAKoNGZjqHr8iA/MfMjBkYIQMTPVlIcppXE/IwIRDBqZykIEhAxMGGZjyyo9Uw2coBjIw/yEDg0IoZQ6hHMZQVUhZ/TEqOuQWetM1uyYZUuZAxexU/EXtnB9//NE8//zz5oEHHkhpc6s96rajooyhJidFCjqOreI7KkTiFvjTtaYyFH7HE/h78skn0zIFyUyUCmaGke37Vb5K21ZQl156qV02b6GK2bNn27b7CSeckPF5ymBpLD2pQYMGdruNu1hFuW7f+i69/Uwq4qHjXTVmA/0K/6hokApXqcCrt0iX+qNUYETbxlNPPVUzxqBjqLJV2s7c83Y5Wn311W0mUjkYt+DWOuusYwv6qL9RE7Wp3aHit2oL6jNr+0kWHlJbL/nvXnFe/+m8oWK7bs5G12aaZClsexOodGzxAFCFFAZWJ563I08dDZrl5qOPPrIX0SNHjvR97nPPPWcbrX4XYzJp0iTf6rRxchuDSb/++mvK75qRNmpDUZ10Wk9BZkAuB37rXYMnUWeFCLPeM1EQII4L1FwDqO73ro7mQinl9q1whi4ozzjjjLTPrwt3/TRu3NjOeqAZjVQ9XP8fdvC4UKLMUJTkF1zR584WaNF2pBuZ+vbtW/OYBuR1Ee0344so6OfO9qWwUKkGHHPRDW7qEFC4J0mBQ928oRBQXNSppQ5oHf/jki0Mkolb6Ttqh1dcr5Pt2OR3rBg6dKiJU9hjcqV+73GdS7yDj0HOJXEfs0pF27vaQy6FDbQN6SY0lwYD33//fdsJptmEolBHpo5F2o7iEHXQM8r2E3Z/zxYaUZvZXQeq6q5O0mJ0+gcNxGlgzm92pqDUuesNtGggUzcIh51ZrtTK6TyT65hVCZ8hnxBkoUU9xiukpXbk6NGjM54/Kuk7ivo9Fes6LxMNAuvmHC/NkKCgf6dOnSK9JmovFSvwG7TVdXe24iHZbjzIJFM/Wia6vj/00EPNPffckzZTc6aiLroxSW0NLw3QRzlmRJHp86ufJuzskfrsak96Z2xSYFr9lZna27feeqstvpCkWVT8CvZUOvX/uAVdNEuP2vFqf/pR+EDhC/2ona/Z6L2hM23zKgYRxyylhabvdPjw4XZWIveaQTeducUsXOrvVd+J1oEbBtB+F4fPPvvMhj/cmw/1/ejmw3INfiuw4e1r0CxaBx98cFHe228Wu7gDidmO0aXevjIt26677mqDNmHo5jf1IXtneFfwJspNRgAAAJWEDExuZGDSkYEhA5MPMjDxIwOTGxmYyv/eycDkhwwMGRg/ZGDK/zxDBqawyMD8hwwMCqGUOYRyGUNVQUddi6oQp5f6mDQBhn5yOf/8820BZrXJ8h1Dbdq0aehxbBVIUuECFTPwnp9UwDLbxEr4z0svvWT7FLzj0MkCf5rYJmx7MM6MlY7JyjtoO3MzVpmKuqhd26tXr5THlAfIpx0ZRqm3b2VXVLwjSde23gI31UJ9AG42UMfWJ554Iq1Ylfd6Qtc++lE/grZxbxtJhWKVsXInKytHKkLz/fff2+JcXsoz6fymn1zuuOMOm1Nz+23iylipH3CHHXYwf/zxR1pfhAovFSv3CJST8ixzDwCInRqmGrTQTStqzL/99tu2oelHFyve4H8xB9iydUa4Fez9qswHpYvKSqq6Wsr1HqV6aFw0W43bEVrIRnup17OqZKojMFOlSe0D6shVp9lOO+1k14VmmdWFVLFmNMkkn/3R77lBZqxQoMW7HapaqHvjU7YZinQcyFadt9S0HVx11VUpj+kznnfeebG9hzqstC3FGWqIOoAR1zGlGMemUh8r8lWN33sucbYh8nlu3DQzm8J67o/aeupgVLV8nSM00OQGSDSzpTekEJQCluocjyvMks+gZ7G2n0wzFLnrQOfxG2+8saRhFvHeHB3HNuvX9nI7VytBJZ1nqvkzFEOcx/iws6hVw3dU6u3DDbNoQFE3zxNmQVgqlqKBS7+b4fv16xfpNTPN8qgB9yg3HagAR659wEthA/WTJO2///52RuNiyfT5/T5HLrqxRP0ZQT+/wobuILyu/zVrVDUZP368ueSSS9LCBK+//nrGgi4uhRI0Q5Hbl6A+lEq4sU99IW+88YadLScs9fVqtlAVyvDuK96byfKlayjNHqMCGl6bbLKJXe4gobhSUGDl1VdfTWnzuEWlin38cNdhGH7PDTITb6m2rziPny1btkxrFymw9cUXX4R+LQAAgEpHBiYVGZh0ZGBKv57JwPyHDAwZmKjIwNTO7z0XMjBkYOJGBoYMTClUw2coBjIwlfveQgamvJU6h1AuY6hy6aWX2qIAyieEoXaCisCpqGtcY6iaVCjscoiKBoTJ4OA/yqQcdNBBaYXa1D5XYRAVkg5L/ZR+32PdunUjTUgUNmN15plnphSPVjFhtW+LpZTbt67x3f6h6667zrRu3dpUEx3zlAN0aZvNVNDFpX7I/v37p7UXtP2EbTeVioouafwj7HWbtsObbrrJXg8XKmM1duxYs+2229qJ3rxUXOmDDz6wk5wBtRFFXQCgltJAVjKE7tIAyGuvvRaog7yY3IZiviH5cg3Zl9t6LyW/wbJCzspTDuv57LPPNj/++KOdtTrX7DnqONHFuKrV6iJbM0a4s8QWSz77k99z/S4M/W5icC+41aG4YMEC323JvalJVWs1Y1k5U+VXzUrlpdlF3BmFo1KHg99NH1ovmjFLgYfBgwfbQXENgGvmE4VqvD9BKnBXm3I4VuSjNn7vcbYhKqn9oA61k08+2Z5Xtt5665R/U3hZx5gwMwTpnOMXqtM6OeSQQ8xdd91lK5+PGTPG7id6D1Vtd7efauAXPtU5qBwGgYrRZg5yngZKJc5jPNt66Y+vGnzT4H85z4yF8qNzst8N+yqKcuedd0Z+3ZVWWsk3qLDBBhtEer0NN9ww7TG1wf2oja5rQe/gv4LLxZRpsNbvc8T9+VWgxxv6P+yww2xhhWpz2223pR3vNHi+8cYbh3odzaSlALuXZnX2FvUo93OB9lVdW2y11VaBnqPQj2YP1oxGug5xw0Yrr7yy70xnYXz77be2T93tr1RRJ82cXa5FhnRNf9ppp6U8pmCG+tWKRes/yAzQQXnDV9neo1y2r1IePwEAAGoTMjCVNYZV6eOtUZGBIQOTDRmY/NXGLES1HCvyURu/dzIwZGDiRgaGXADKFxmYykYGpryVOoegDIoKXHhNnTrVtjeKPYYqRx11lBk+fLg54ogjAhXxWHPNNe31XLIInF87bPXVV8/4fMZQy4OyDmpHu8cl9bE+//zzeRXHat68ue82ESVbsd5666VtlyoU4dce1/WfCl0kLbXUUvZ3d38rpFJu35pMavr06TW/KxfhV/yk0uk63i3OuMcee5g999wz1OtoEidls9w2k66HKoUKlH3zzTe2EFMQXbp0MV9++aXtTxH3+K19pU2bNnlPbKaCLpMnT055XK/74YcfFjWzBJQb/xL4AIBaoUGDBuaZZ56xVe7cGU5U9U7VNv2e43rrrbfMzjvvbArNnVUo34H7Ug38R+G33jXjSjnPsBIHvwv2fCoQ51LK7dtrrbXWsjPo6gYDzR770Ucf2QGyn376KeNAoAYN1SmmMJrCDsWupJrP/uT33KCziJ1yyikpM63MmDHDvPDCC3Zg1Usz6brvUyn7jyrjugPR559/vq24m89MGJphw+1sUGBMMzVr/QV97ULOplOu/I4VWg+5QmjloLZ+7zqmeCsmx33MKneadUPnh80228wMGzas5vEJEybYEFPQjkf9revII480N998s2ncuHFVbjuZJNvJOl+7M4C9/PLLkWZcr6Q2cyXN9onaR9ts1Juq3e2dbb341G5XMQ5vgFjtfV3vaKC4EtpbKK3HHnvMHHvssWnXzj179jT33XdfXtdQCiq0atXKjBs3LtLMRi6/52WaEdUdiNfny7e9odf0K0ij2ZH9Bk4zhW1K8fkV0A9TTMdvhkVdA73yyitpg9YPPPCAKRX1Abm07Uah52k2JTeMs88++5hKoRmKBg4caAPzCoMpUKAbMdWPrQCBttPOnTvbUIJ3NjvN2upad91181oWFfRQYQ/3Bpf111/fvPfee4Gvh0pB12Duct9+++32JyhdO7qOOeaYtBsP1dfm19/Wtm1b3wBHVH7L4/ce5bJ9lfL4CQAAUNuQgamcMSwyMP8hA0MGxosMDBmYUiADU3nfOxkYMjBxIwNDLgDliwxMZSMDU95KnUOoX7++HZP0ToahwhparlVXXdWUYgxVfWqPPvqonSREfWkff/yxvSFf16ZatqZNm9qxU/UrbLPNNjXtbV3b6xrfS9t3tsKjjKGWnsbJNWmQCl966fr8pZdesttoPvQdu9tl1O9XBV10HeDd7xYtWmSvi1ZYYYWs+7byXm6/Si5+BVP8jg/at7WPl3PGSjm3MMVk/Poqdf3lfn4dv5Qxq5aMlTu5mjJWyaInlUDfjyb70jFb+/agQYNsoTBtJ9pXVORLGafddtvNbLrppjXP07/rGO9uv/lMnKUiYzpHuLmg1VZbzfYL679AbUZRFwCo5dTRp4EIDYR7uRfVSboQD9IhUQjuQEmYqvoudRyUsuJ02ArLpVzvpaQOZF1Ee9dXIWcHKbf1rI4HdSAkOxH02T/77DPzySef2AtgDUy7Ro0aZS+0VGkz346UMPLZH/2eG3RgdMstt7QXl7q5xhv2cjte1EnqdiKErcJaKrrJRN+pt+NBs0M/9dRTdjarqJ599tm0gJQ6YsPe4FUbjkVBjxWVUDG2tn7vOqZ4Ay1xH7Mq5ZyqQUJVlPbOKv7II4+Y4447LqWDzo9udBsyZEhaReuwM1ZV2raTbYBAnz0ZCEvSgKtu+lNoXIMdpeAOUOS7zXr3naQVV1wxr9cECknbfNRAi7u/lPMN2tVK5yMN3Ghw2DtYpMIEahNrgLCSZgxEcekaSTMHubMW6bpJAd44Zlzp0KFDWlEXzegShV9Aa968eYHbVPm2q9TX4r2WTvKb+Tf52f2U4vOrbZovzUrjnZmm1Md9hWTc/lj162y00UaRXs+vff/999+bStS+fXv7E/TGJL/Pufnmm0d+f836qoIu7j6nQh4KQlRi29hv3w/r559/TntMM2H50Y132ue9+7hCHDreROm/9Hvvtdde25Tr9lVOx08AAIDagAxMaZCBCYYMDBmYXMjAkIEpBTIwlfe9k4EhAxM3MjCV18+P2oMMTGUjA1NZSpFDUBFUb1GX5HJEKeoS5xiqrhEOPPBA+xOEcjQqCOeldpraGJloUiW1PdwCEoyhFoeOTWrnueto2223tYUh4ig6pXHyAQMGxPL95vMdaxuLIyPh9xqZijWX0/atwjp+RZ/CUB+e24fp164uJr/sRq7rwEyUzXL7bCs1Y7XKKqvYIjVBC9zEnbFSURkVdPnll1/SluvDDz80bdq0ifzaQLXIP8UMAKh4umB2ubMWJak6X5yzaobRrFmzlN91YRH1Qnv06NG22mBYaqjnE0yJWvW1lOu91Jo3b57yu1+IIy7lvp41QKVO3GuvvdZePCl4duKJJ6Z1eGkGigcffLCoy6YgTVR+ATp3f8/m5JNPTvldMzp5Z+HQ77oBx51NeMklK6e+ob5z9wbESy65JOPNdkFoJmmvjh07Rpqxe+zYsaa2KfdjRTa19Xt3jymZgrtBjBw50lQqVdrW7G5eCjide+65obcdufTSS6t+28lGswwoRHn22Wen3ZC7//772+BhKay00kopv+tc4TdgF9Tw4cNTftf5iEALylnUdqn2FbdQQ5g2KeKjwLpmetFAjpcGdRR0qdRwKQpLYdIjjjgiraCLZhZUgDuOgi6y8cYbpz0WdZv0G9yOOiNLMeiY6DdTRm35/IXmF/zW+og6O7HbJsz0HtVIM6i5tttuu0ivpf4lhZbcvnJdS2tmMr8bXpBOfXDrrbdeWtvr66+/Dv1a6o//6quv0vqPW7RoYcp1+1Lbxu/GQ46fAAAAhUMGJjgyMMVHBuY/ZGD8kYGJprZmIWrDsSKb2vq9k4H5PzIw8SIDA5QnMjCVjwwMsuncuXPaY59//nmk11LRVFfUSWSKMYaqc7Df8jGGWngDBw40u+++u5k7d25aEdb+/fubBg0alF3GqtK+Y7bvwvPrj/bLSgX9vtz1ScYqvClTptiCLu71mooGq5BUu3btIi8nUE0o6gIAsDOhuDIN9Hbt2jXtMXU0laLTQhX2o1bMjBIYF7fatVtRtlCVfLt06ZI2mF2s9V5qm222WcrvqlSdT3gim1Ju31Gsueaa5q677jKPPfZY2r+9+OKLRV2WqPuU33M1k4Y+W1CaqcetYq0BRr//FwWAjj32WFNJOnXqZG9Q9FL1Un3/UWlGZHfGpyiidh5Xsko7VnjV1u/dbUOoCrBbDb8Yx7tycPHFF6e1/TT73dtvvx1q21HVb7VPqn3bCeKGG24wffv2TWunHn744eb+++8v+vL4DQS4M0yFGeB3q3DrnFTMmRCBYh2nta27Nyz4DZyjODRTjILpq6++etp5RAM/mW7CQe30/PPPm8MOOyztxiUFTJ944omsM/+EtfXWW6c95s4sEZQbostncLlYavvnLyS/WXXcAE0Ymj3T1bBhQ1PtdC5/4YUX0sIB3bp1C/1aI0aMsEEF76x5yXOUCrrU5u01Cr/+B53rw9LM3e72vdVWW5ly3r4025bfjFAcPwEAAAqHDExwZGCKjwxMZmRg/o8MTDS1NQtRG48VXrX1eycD8x8yMPEjAwOUFzIw1YEMDAo9hqq8jNsuUfHGMNei+fAr/nbAAQfkfB4ZlOJTW1kFdt0xd22Lb7zxRqy5Er/vVwVEVYgxLPWfucusfs1ybsexfVd2zqo2ZKz8jt/Kt+y5556hX2fatGl20iwVn3cLnKuQ3RprrJH3sgLVgqIuAADfAR2/GRhEM2q6lZo1iBd21p0o/ILPL7/8cqTXeumllyI9zx00/+uvvyJ1pKm6aRiqBO92pmqWBLdqfDXyu5h9/PHHC/Jepdy+83HIIYeYDTbYIOUxd/Cp0HSjS5SgkZ7jnVEoWZU3zCzqumA+6qij0raRv//+2+6f7s0We+yxh1l11VVNpbn88svTOh+uuuqqyME699gVZcYJzRqm7762UXX8uM4rxVZbv/dyaEOUC1WSPuOMM9Ie79OnT8G3HXnuuedMNdKMTf369Ut5bPHixea4444zN998c1GXZfPNN097zD0XBqWq++6soH6vD5STV155Jbbju9/5A8XTpk0bO5DcoUOHtJu5dfO2KvsD2nd1TewWdNFMnBp0jLOgi+jGffdmJ13TRrku85slSbNK+jn99NNtqCGfH1fr1q19/077XiYKdgT5HHF//u+++y6vz/7www/7tn/dvxswYIApFfU5ujcYaoaemTNnRno9v9lBa0Og49VXX02bLefII48M1c+U7K9S2MDtN19rrbXs7DGZ+s7LkbatfI8fPXv2THtdrQf37y677LKMy7HzzjvHcp3i9xy/1y637Suu46euM/1mSsp0/AQAAKityMAERwam+MjA5EYGhgxMFLU1CxEHMjCV972XQxuiXJCBKQwyMED5IANTPcjAlJ9yyCGoj0A30Hvp5vewOYF33nnH9ul47bTTTqZOnTqm0LTt6v3d86uKGeVSqgxKbaV1tOuuu9r+Ba8tttjCvPnmm2aZZZaJ9f3atm2bdsybNWtWpP6/MN/vXnvtlXdGQpkql9/f+fXzlXr7Vtshn8+uLIhLmRH37/wKzRSTXwYqatEc9ZW6/VG1IWOlYpk//vhjymP77ruvb9H8bH799Vebsfrpp5/SxmR0TlPWCsB/KOoCALCNJFe7du18/1YX9j169EirSHjrrbeaYswK4Q6eaNDa7WQPUgFQnfNR+DUmv/zyy9AzFPmt81z8qh1ee+21ptrtvvvuaTe13HvvvZEH8bMp5fadL7fjSzf+FFuUmRj8nrPLLruEfp2TTz45peNR24du4HvooYfsDAteJ554oqlErVq1MqecckraAPN1110X6fXcjrco4Tx38La2WGedddKqxQ4ePDjSsb3Yauv3rhCSey554IEHQlf71iBO1NleyoluCl5hhRXStuHXX3898LajQSMFNsIGeit9lqdsFBS677770kKZZ555prniiiuKthybbLJJWghbbd8oA79+s+HtuOOOeS0fUGiadcQNTOcyf/78tNC8zhvbb799zEuHsFq2bGlvMnAHIRWuVail1IODKK3XXnvNHHTQQXZ2QHdw/plnnsk4A3c+6tWrZw488MCUx1RQxm/WoVz8ZhzebrvtTDnTDSJuURvN1BM2UKSQvNrW7s0q7kzVtYn6NNq3b5/ymK5X3nrrrUivp8CNq1izcJWKZtw599xzUx7TjUGnnnpq6L5bzYinPmQv9QPoul+zyCA8rVP3Jlpdh7rHgmzUx/fII4+kfcd77723Kfft6+CDD067Vnz66afTzmG56JjrFpbRrI7ZCnIBAADURmRggiMDU3xkYIIhA0MGJqzamoWIAxmYyvveycCkIgNTGGRggPJABqa6kIGBa9lll7X9BF7qF3r00UdDvY76FfzGJ4vhrLPOMgsXLkx5zB1XzXaed69F1D4NW3RR1wF+2Ypyz+AUk/r71D/hFv9RTkfrTttiIRx22GGxFDeuxIwV23dhues2U1YqiNqYsVL/Qe/evVMeU1/kOeecE+p1ZsyYYQu6uNu1Co2rX8kt7ASAoi4AUJHUIR21gqDr559/9q0W71cVMun8889PGxTSgGrYYEdYqkKrmS+91DEfNtRx9tln2w7LKNyZgiTsjTvnnXde6AEg0UC2OzjxxBNPRJ5loZIG8TULj9vwL1QooVTbd76mTp1a8sqgt99+uw1sBaW/1XNy7edBKITnzsqrQTi3k1I3SPnNMFMpLrzwwrRB6FtuuSXt+w+iRYsWKb+///77oY5N+vuwncbV5KKLLkp77Jhjjkm7sabc1NbvXZ/bDSwqWBHms6jzKuzNgOVKFZQVsggzU5G77eiGNs0cEZQCopqxp9ode+yxtn3mtiU0i5HagMWgc6mOR15q+yrIFIZmNnKDeqo+7+5LQDlyBxtyuf76682kSZPSbihwj30ojaZNm9oZINxZ0tSfseWWW0aaLRWVT4Op+++/f1owREVH1M+l4iuFov4ZdzYjHUfc4EM2r776qvnmm2/SippoRpxy1qBBg7R2htqF11xzTajX8Wt3KqjkzkBV22imLJf6pFQ4KAzd5HTbbbcFev1qomCB22eux8IUYdHzFTZww+Dqd9K5SEFLRLPEEkvY6yXXBRdcEOoGG/ULe6m4l9tnXo7bl/q4VXTMa/LkyfamiKBUAEazmLv222+/wK8BAABQrsjA/IcMDBmYuJGBiY4MTOGRgSkfZGAqCxmYVGRgCocMDFAeyMBUFzIwcPn1B2g/Djq5zqBBg9KK2emashjXis8//7yddMlLBYr8Cvlm4tcmzdaO83PVVVel9Y2poIb6YPD/awXlRdyCwlpHb7/9tmnUqFHB3rtXr142a+Slvo0wBfqUr1LOyku5rX333deUO7bvwvHLQCkrFSa/l8xh+OXeqj1jdeONN5rPPvssrQjTBhtsEPg11GekooFuAUK1dT744ANbRBhAOoq6AEAF0myMqvp31FFHmZEjR0Z+HV0IaRZLDTC4A+HZLuJ1ka/39tIsILr4dht1QenmBF3Q+w0Qup0W7o05ukjJVlXfbaQ/+eSTJip9dreio5b7q6++CvT8K6+80rz00kuRB5/cqocaXDviiCPSLlLDUNXOcp+1ReF+93tXkEiDcWFnmNC2NnHixLLbvlWlePjw4ZFeXx0V7qDi+uuvb4pNnQX77LNPoE5E/Y3+1u1gUHhJF3FRuDP4aDaRsWPHpjymwVT35rtKojCLQldeOoZHqSqrzn8vrat77rkn8DanCt5h979qok4Ld3YwhU11E2aUmUBEnaXqiNdgeKHU5u/9tNNO830s6DlcgdhPP/3UVNNgb5MmTdK+41deeSXQtiM6r7k3UvtR+EU3XdeWAUftJwqDuDcla//WrHrF2Id0TnTbThpAu+GGGwI9/9tvv00LxSS3G92MCZQ7DQhcfPHFga+H/GYS8ztvoHR0Pfzuu++mzTChIJLOUd9//33Jlg3F995779nrSXdGWt2cpXNwIQu6yHrrrZcWDNC1gK43gxTf0M0dfkHfk046Ka19Vo7UN+XO0HPTTTeF6ptzb87Sdfoll1xiarsDDzww7TEd39z+jmy0XxxwwAFpM9Kqr6USZukJW8BG1L5WH+Gdd96Z8rhmewlTMET9hSro4vYbrr766jZYucoqq4ReNqS3r9xAmEJi7neXKWh22WWXpTymGwncfrJy3b6S4Sz3ekqz1AWdDVk3Sbh/q4Jg6q8AAACodGRgyMAkkYEhAyNkYP6PDEw4tTkLEQcyMJWHDEwqMjCFQwYGKD0yMNWHDAy8tB1suummKY+p0KX6NnIVLNQN9brmd/9OBTSDnuOijKEmz8Vu0VoV7wjaHvcWkXOLU+i177777kDPVx/Srbfemva4itDBmKFDh5odd9zRTg7k1aVLF3scWm655Qr6/iuvvLJtM3rNmjXLHHroobZdncuvv/5q/9bdTpXbqoSCEWzfhaNxAPcaYfr06fa4pEItQehaRn1rI0aMSHlc1z7upD3lKOrxWwWVlVVx99Wg11fJ/VhjLm6bRdflart26tQp0rIBtQFFXQCgQqmR+cgjj9jwsC7i77jjjsCzVGjwUxfLG264ofnhhx/S/l0NsaWXXjrnrJjrrrtuWgN46623tuHiadOmBb5IVEejZg5R579+z0Z/p04Gd11oNkpVCszUKNVnVmAgWZ1dA9pRZ/91O/fVCaIbh1TlNhN9Nz179qy5QSXX+s1EDWdVMvRSJUldkOhiwh28z2T06NHm6quvtg1lDb6GmWWgFDRorBuEXDfffLPtyAoSfNd3oAtahcEefPDBstu+tUwdO3a0Fzb333+/7YAIQmGuXXbZJa0zToPtxZTcpnVM6datmxk8eHDGv9XgsTqd3eOPwnSaESoqrQcFkjLRPu+GlSqRBlfiqKyrm7z8Bio1u1OmwVYdY3UTyzbbbFNzg1ihO/PKlTq71anm3oykGc10ftV6nDdvXs7X0TlMM//oGL7aaquFOsZEUZu/9+7du9tzsdecOXNsZ3W22Yp+//13e0zVuUHciuGVStuu341fuknOb1tQVW/NUOOlto/Cie4No+4xX5X3k8G7Stx2olAQVudo3WDnpX1N56KoHalB6btSkNqvLakBErfivtdjjz1m21fuII62gWqZqQvVLdku1Y0PGhRzt+UktZ81OKHjmBvO06yZOnahvCyzzDL2Bp899tgj5XFdO+laLds1CKrHwIED7XnWvTlC14MvvviiqV+/flGWQ/0Rbrvm6aeftv0z7qxnXtqGdc3sXvO3bNmyaDMa5qt58+a2T8k9pup4qn6XTAPjf//9t/2MfjPJqa+NwVxjNttss7RjnKgfd/fdd885a70C6prRTcEbv0CH2zYtRwqZKhjRv3//QNfUuvmsa9eudp90r3eeffbZwNdvujFFBV3GjRuX8nibNm1sQZfaPsNRXNT3d/nll6c9rusMtd0yHT9ee+012yfhHvvV17/WWmuV/fblLQrmHgN1bNS2l+0mXPVLqH2a7JdwbzQpxWz1AAAAhUAG5j9kYMjAkIEhAyNkYMKpzVmIOJCBqbzvnQxMKjIwhUUGBigdMjDViwwMvNQP5hZh0Xik+od047wfFSFQPkB9Iu45Tvt9UBoP33jjjc0DDzyQtR2UNH78eNsnoLa4O76rtoH69sIe5/T5/SZG0lhopsIfOtapb0XryG3vqU2svq3aTsV21S+j4j9eG220kZ1QSwWmikGTn+j60GvAgAG2jZatwLfG69Xf4v6NJqLyaxuWI7bvwl4jnHDCCWmPKw8SpECajp0777yzefjhh9P+TdcYlZAT0niK9iMV3852TZQ0bNgws9NOO5kzzjgjbUIlFeRVYZcg1PegdaecmteKK65o+4SUjQGQ2ZJZ/g0AUCE0eKYfdTAraK6gsapOaqYPVblTeEMNNF1Aa0D9ww8/tGFhP7q4dgd7/OhCSGFqhWk00O+9eFAFdnUM6kYENYZXXXVV2zjTTC/qVNBAnWYP0eyaQUM4Xrp40QCBnp+kQLdm8FFgQVU31Rmg2TxmzJhhq6xrBmDvxaga2Wqsa52EpYa/Lqy8M93ofTSIrwskXfjqM2ugQp9PYRFd9CYvuHRhds0116Q1hINQY/m5556zHTDei1NdqCkE8dBDD9mqqep8bdu2rV3v6qjVetcy6sJE6829QaESaPtWB6U7e0eyI0kNf617zZ6rbV/bombCUYeVAi/6yVWtuBy2b13E6EfbmQIuGhjX/qx9uXHjxna70rasz6Xtyq8jQ8vlN7tzIWmATIO9ClipA0jrTvuEQibJC1rtM5p9V/uE27mg45QqzuZzE4JeQ1WpM81Oqxkyos6AVE50DOnbt685+uij83odXcDrWPHxxx/XPKbOVR0fdSxVUE7bngbvdfz48ccfbbVf7ww8unBXWNCt1FpbKBSom240EObtmNagitajzlcKUei83KxZM9v5qfOvjhUTJkywxwmdo4J0osSltn/vCkLqvKH1n6TvQwMYGvjUd6nZCHUe0PH/888/t20OHdu8N4FVyyzYOrfq2K3vOkltRd0QrY5gLw0YqWPdPfZo/aj9qfaXjv06zmp9aR1r5g+du73HfLWhNMNibaAQsm6oVee5d0BdASodCzTjolslPE7aTtVWULvCHTjTsatHjx62DaV9WsehMWPG2Nk03UE+0fFLz1FbFCh3ulE4eY7SYLOun3TzrrZ3nY913FcbWsc67/nAO9jj3riL8qGQuo5Vhx9+uB2MStK1n467Oi+VSxgp1+yVfv+2wQYbZH1NBURV/KMcaPn1GTPRNbJL1+XZPqM+W64ZWHWTkd+AtopdqN2dDx0z1KcShPoCdE5X+9Hb1lFfk869WjfaFnWeVdBdNyApkOXty0pSIRodkyrpelUzIipg4Z2JW30mupFMx1BdV6hPQ20IBcQ1iKvrCr+bd3R8vv3224v8CcqXZljXvuL2KWn7UZ+Kij+o30eBFwX9ku04Xed88cUXGa8D/YIMmagAjPrFMvE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6I444Ir785S/HwoULo6ysbM94JvTk+uuv3+86hx12WDYc5amnnsoGpmTCYCZPnpz4tTNnzozPfvaz8cADD8SECRP2jLe3t8enPvWp6CsXXXRR/P73v8/WmAmeOfnkk6OwsOfTf+zYsfHBD34w/vjHP2brfKVrr702OjryewKf4aG7uzvqG9Ynzp119MQYXVacek0AAAwdF9QmN9uv2NAWzza1pl4PAINPriCw8tKieN1RyeFhAAAAAPSPT/2yIdZv3dUnx84c99O/auiTYzO8eJ7mn8cUhiev/fzzmDIcQ10qRwt1AQAAAOhvQl1S9pnPfCaWLVsW733ve2PkyJH7/fpMuMuNN94Yp556ao/xTCjMvrz+9a+P5cuXxw033JANkTkQc+fOzYayvNLNN98cra37bgQsLy+Po48+OnrjxBNPjM997nM9xm6//fbYuXNn5FNVVVX87ne/i9tuuy3OOOOMA/qeadOmxc9+9rMeoS8rVqyIe+65J6+1MTys2NAaz29sS5yrq0kOPwIAgAM1d86kKChInqtvTG7SB4AeYbSNyWG0582eFCNKilKvCQAAAIBkdy1dH7984qU+XeP/Pf5Sdh04WJ6n+ecxheHJaz//PKYMdc05Ql0qyoW6AAAAAPQ3oS4pu+iii6K0tHcnxjLBLtddd12PsYULF+7ze04++eSYPn16r+v767/+6xg3btye+7t27eqzMJN3vetdPYJT2tra4oUXXsjrGpMnT84G3PRWJnTmTW96U68ec0iysCH5w5lM4+3cOUJdAAA4NJPGjIiTj5iQOFef470oAPzZ02u2xtotySHLwmgBAAAABpZv3PNcOuvcm846DE2ep/nnMYXhyWs//zymDHUtOUNdylKvBQAAAICehLoMEmeffXaP+83NzbF9+/a8r1NSUhJnnHFGj7F8B6382dixY6OqqqrH2MaNG2OgPuZ99TgwtOXa7fqUIyZE1RgnyQEAOHS5mu4ff3FzrN+a3KgPABn1jesSx0uKCuINsyelXg8AAAAAyZau2xoPr2xJZa2Hn2+JZeu2pbIWQ4vnaf55TGF48trPP48pwznUpbK8dxsSAwAAAJB/Ql0GiQkT9t55fcuWLams1VfrZOzc2bPBcPz48TFQpPk4MDSt27Iznnhxc+JcXa3drgEAyI+62uqcc4tyhAwCQEZ9Q/LfiTOOrIyxI0pSrwcAAACAZL98/KV013tiTarrMTR4nuafxxSGJ6/9/POYMhw0t+5KHK8Q6gIAAADQ74r7uwAOzJo1e5/craysTGWtvlpn2bJlPYJSRo8eHccee2wMFGk9Dgxdi5bkbqCdV5O78RYAAHpj5sTyOGbS6HimqXWvufrG9XHlGdP7pS4ABraVG9ti2fptvQ4MAwAAACB9T6zenO56L9r8ioH/PP3RQy/EkrXJ5ziHij++sCnV9bz2YWDw+zT//D5lqOvo7IqtO3cnzlUKdQEAAADod0JdBon777+/x/3p06dHaWn+T7C1tbXFH/7whx5jfRW08i//8i897l9xxRVRXFw8YB/zgRQ4w+BQ37AucfzYyaOzjbcAAJAvdbWTE0Ndfr9iY2zd2RFjR5T0S10ADFyLGvcRRjtncqq1AAAAAJBbd3d3PL1ma6prPrVmS3bdgoKCVNdl8OqP5+mm7R1x19KmVNcc6rz2of/5fTo0+H1K2ja1teecqxgt1AUAAACgvxX2dwEcmO985zs97l944YV9ss4tt9ySDXb5s7Fjx8aZZ56Z1zV27twZ1157bfzwhz/cM1ZVVRWf/exnY6BYsWJF3Hvvvak85gxNW3Z0xO9XNCfO1dXY7RoAgPzK9R6zo7M77lm2IfV6ABj46huTw2hPmDY+qseNSL0eAAAAAJK17tqdvQ4lTZn12to7U12Twa0/nqfkn9c+9D+/T4cGv09JW/O+Ql3KhboAAAAA9Lfi/i6A/bvjjjvivvvu6zE2f/78vK+zffv2vYJVrrjiiigp6f2O7g899FBs27atR5BLU1NTPProo3Hrrbdm//vPqqursz/jpEmTYqC4/vrrswnpf1ZTUxOnnXZav9bE4HLPsqbY3fXyc+iV6mrtdg0AQH4dd/i4qB47ItZt3bnXXH3DurjkhCn9UhcAA9PG1l3x6KpNiXN1Nc5bAAAAAAwkmQD3/tC+uyuirF+WZhDqr+cp+ee1D/3L79Ohw+9T0tSyj1CXCaOEugAAAAD0N6EuA1xLS0t84AMf6DF22WWXxemnn573tf7+7/8+Vq5cued+eXl5fOITnzioY2VqfuKJJ/b5NSNGjMiG02SCZKqqqmKguOWWW+KnP/1pj7F//dd/jYKCgrytkQm12bBhQ6++59lnn83b+vS9+ob1ieOHjRuRbbgFAIB8KiwsiHk1k+MHD67aa+6eZRti1+7OKCsu6pfaABh4Fi9ZH6/IM+7hAmG0AAAAAANKSVH+rlnqjdLiwn5Zl8Gpv56n5J/XPvQvv0+HDr9PSVNzjlCXcSNLoqTIcxEAAACgvwl1GcC6urriyiuvjNWrV+8ZGzduXNxwww15X+vHP/5xfP3rX+8x9rnPfS6mTOmb3dxLS0vjwx/+cLz//e8fUIEuy5Yti//1v/5Xj7G3vvWtcemll+Z1nRtvvDE+85nP5PWYDBw7OzrjnmVNOXe7zmdAEAAA/FldbXKoS+uu3fH7Fc1x3qxJ/VIXAIMnjPbIieVxVNXo1OsBAAAAILfRZcXZZswtOzpSW3NUaVHoQ6Y310otalwXRYUF0dmVI02aQaG8tChGevHDsPu7T/5l/g0zv1MhLS2tuxLHK8tLU68FAAAAgL0JdRnAPvaxj8Wvf/3rHmPf/OY3Y9q0aXld5+GHH473vve9PcYuvPDC+NCHPhR9pb29Pb74xS/Gv/3bv8X8+fPj//yf/xNjxoyJ/tTS0hIXX3xxbN26dc/Y1KlT41vf+la/1sXgk2mYbWvvTJyrq61OvR4AAIaH186sjDEjimPbzt17zdU3rhfqAkBW267dcf+zGxPn5tUKowUAAAAYaDLna15z+Nj43bPNqa25vb0zXve5u+J/nDQ13nnatJhV3b/XdTHwdHd3x9NrtsbNj74Q/+/xlxI/n+prh48fERced1gMZbc/tTZe2rwztfUy17yd++V74vJTpsXlp06NKeNHprY20H9/9/0+zb/jDh/nMzdS1bI9OQiqQqgLAAAAwIAg1GWAuuGGG+IrX/lKj7Hrrrsu3vnOd+Z1nWeffTYbZLJjx449Y7Nnz44f/OAHh3Qy+fHHH+9xf9u2bbFu3bpsgMx//dd/7Qmr6ezsjG9/+9vxyCOPxF133RWVlZXRHzI//yWXXBLPPPPMnrHy8vL42c9+1m81MXjVN65LHB87ojhOn1mRej0AAAwPpcWF8cbZk7IXzr7aosb18S+XviYKC100BDDc3bd8Q7Tv7kqcq6sRRgsAAAAwEJ0wdXyqzd0Zm7Z3xHd+93z2dsK08fHOU6fFxSccFmNGlKRaBwPL5u3t8Ys/rombH10dS9a+vHlaf7jspMPjYxfMjqGspKgwbrxnRaprrt60I/7Pncvjq4uXx9nHVGVf++fXTIqy4qJU64DhLO2/+36f5t8J08althZktLTtShwX6gIAAAAwMAh1GYB+9KMfxbXXXttjbP78+fH5z38+r+u89NJLUVdXF01NTXvGpk2bFvX19VFRkd/giTFjxmRvxxxzTFxxxRWxaNGibEDNpk2bsvNPPvlkXHXVVXHbbbdF2nbv3h3veMc74ne/+92esdLS0mygy2mnndYna15zzTVx+eWX9zqA57LLLuuTesifzq7ubMNskrlzJmc/GAIAgL6SacZPCnXZsG1XPL56c5x8xIR+qQuAgWNhQ3IY7cTRZXHStPGp1wMAAADA/l1y4pTUgx1e6YkXN2dv/3xbY1x43GHxztOmxWkzJhzSpmEMHl1d3fHAiua4+dEXs+cXc4VGp+2SEw6Poa4/X/vd3X8KCc/cMg3hl514ePa1P6t6TL/UA8NJ2q99v0/7YL1h8JgysLS0tSeOV44W6gIAAAAwEAh1GWAyoSaZcJPuzCdi/5+3vvWtcdNNN+X1Q/CWlpZsoMvzzz+/Z6yqqiobtpIJdulr8+bNi9tvvz3OOuus6Or604e8mfuZ9TNzack8zq8OkykqKor/+q//yj4+fWXSpEnZG0PP4y9uio2tySfG62omp14PAADDy7mzqqK0uDDxYtrMRbZCXQCGt47Orli89OWA51eaVzM5Cgs14QAAAAAMRLOrx8bpMyri4ZUt/VrHjo7OuPUPq7O3IyeWx+WnTou3nXJ4TBozol/rom+8tHlH/PTR1fHTx16M1Zt2xEBy+syKYREuMlBe+5lG8e/87vns7YRp4+Odp06Li084LMaMKOnXumCoWr6+NTIf2XS9fCl5n/H7NP8ywXfD4TFlYGnOce16JpgNAAAAgP5X2N8F8LK77747Lr/88ti9e/eesUzAyY9//ONs0Ei+bNu2Ld70pjdFQ0PDnrFx48bFwoULY9asWZGW173udXHllVf2GFuwYEGk6Zprrokf/ehHe+5ngnO+9a1vxdvf/vZU62DoqG9Ynzieaaw959iq1OsBAGB4GV1WHGcdPTHne9VXBogCMPw89FxLbNv58rnHV7qgVhgtAAAAwED2V+cdGQPJcxvb4gu/WRqv+9xd8b7vPRqLGtfH7s69Q+cZXHbt7ozbn1wbf/mdh+PML9wV/+fO5QMu0CXjr889KoaLgfbaf+LFzfGPP38qTv//LY6//+kT8cjKFp9BQp5kNm/59C8b4sM//mMqgS4Zfp/m36btHbFuy85U1oJXBrAlqSgvS70WAAAAAPYm1GWAeOihh+KSSy6JnTtfPon7+te/Pn7+859HaWn+EpJ37NgRb3nLW+KRRx7ZMzZq1Ki4/fbb46STToq0vfWtb+1x/4EHHkht7euuuy6+8Y1v9Bj7yle+EldffXVqNTC0ZD6cXtiwLnHu7KMnRnlZceo1AQAw/NTVJDflP7+xLVZsaE29HgAGjvrGdTlDwV53VGXq9QAAAABw4N44e3JccsKUPl1jeuWo7Lmi3ujs6o47l6yP//X9R+N1n78rPv/rpdnPJBhclq3bFp/9VWM2pOeDP/pD3Ld8Q/Q2p2PcyJK46nXT49w+3vjq0hOnxBtmT4rhIo3X/pRxI6KkqKBX37OjozP++7HVcfk3fh9zv3JvfOPeFdG0TYgBHKxMCMi7/vPBWPDAytTW9Pu0bzzb1BoX3XB/PPDsxj5fC/Yf6lKSei0AAAAA7E3CwADw5JNPxpvf/OZobX25uS4TsHLHHXdEeXl53tZpb2+Pt73tbXHfffftGSsrK4tf/OIXceaZZ0Z/OOqongnv69YlN5bk2z//8z/Hl770pR5jn/nMZ+Laa69NZX2GpswHMSubtyfO1dntGgCAlMydMzkKCp5KvNB2YcP6OHrSmP4oC4ABEEZb37A+ce68WVVRVlyUek0AAAAA9M5nLqmNh55vjvVbd+X92JPHlsUvrjkzykoK4/Yn18Ytj74Yj6zc1KtjbNi2KxvskLmdPqMi3nHatLjwuOoYVeoyxYFo286O+NUTa+PmR1+MJ17cfNDHOfPoynjHqdPigtrqGFFSFJva2uNN/35fnz1PP31xbQw3ff3av/3DZ0dXd3f8/I9rsq/95et7t1HEcxvasoFOX1q4LN44e1K889Rp2fPOxUX2nYQDkQn/+Jsf/zGac4Qy9AW/T/P/+/SVMv+WV377ofj7C2bFX51zVBQW9i44C3qjq6s7Nm3PFepSlno9AAAAAOzNp6X9bNmyZTFv3rzYtOnlD8DnzJkTCxcujHHjxuVtnc7Oznj3u98dv/71r/eMFRcXx09+8pPs+gNFSUnfp0F/9atfjU9+8pM9xj760Y/uNQa9Vd+Y3BhVUPCnxloAAEhD1ZiyOOWICfHoqk2J71k/+Iaj+6UuAPrXU2u2xLqtybuk1tVWp14PAAAAAL03obw0vnf16fHObz4YW3Z05O2440aWZI+bOX7G5adOy95WbGjNBjzc+tia2Njau+bnh1e2ZG+f/mVDXHLilGzIw/FTx0VB5kIa+jX8ORPWc/MjL8YdT62NHR2dB3Wcw8aNiMtPmZp9nkyrGNUvz9PhJK3H9H1nHxnvPWtmPP7i5uxrPxP607pr9wEfr7OrOxY1rs/eJo0pi7edMjUb+DNzYv42N4ShFsTwjftWxJcXLouuhE1b+orfp/n/fZok82/6xd8siz+s2hz/9o4Tso879IXNOzpy/g6pHIavcwAAAICBSAx+P1q1alWcf/750dTUtGds5syZsWjRoqiqqsrrB7FXX3113HrrrXvGCgsL47vf/W5cdtll0d+PwStNnty3wRc33XRTfOQjH+kx9v73vz++/OUv9+m6DA/1DesSx0+dPiEmjpZ0DgBAeupqk/+/VWaXxXVbkhv6ARja6huSw2hLigqyO6YCAAAAMDjMrh4bN3/gjJg8Nj/XomSOkzle5rivdlTV6Lj+zXPi99e/Mb71P0+J8+dMiqLC3oWyZAIhfvTQC3Hp134Xb/73++M7v30+NrW156V2DlzTtp3x9XtWxNx/uzfe8c3fx61/WN3rQJfMucQLj6uOBe85LX77D2+Mj9TN2ivQpT+ep8NFWo9pJnjppCMmxOfeenw8/E9z40tvPz5OmzGh18dv2rYr+5x7w5fv+dNz7rHVsaP94EKEYCjKBIq8/wePZUM/9hXokvmze/WZM/w+HcC/T/eXV3fnkvVx8X/8Nhpe2pKX9eDVWtpyhy9WCHUBAAAAGBCEuvSTtWvXxty5c2P16tV7xg4//PBYvHhx9n/z6UMf+lB8//vf7zF24403xpVXXhn97Ve/+lWP+8cff3yfrfWTn/wkPvCBD2RDbv7siiuuiK9//et9tibDx9otO+KJ1ckfuNTV2O0aAIB0zdvHe9BFS5Kb+gEY2uobk8NoX3fUxBg7ws6AAAAAAIOtGfk3f3tOXHrilEM6Tub7M8fZX2N3SVFh1NVWx01XnRYPfPyNcd2bZsWMyuQwj31Zum5bfPa2xnjtvy6OD/7oD3Hf8g3Rta9Odg7J7s6uuLNxffyv7z8ar/vcXfGF3yyN5za29fo4x04eHZ+4aE48eP3cuPGKU+K8WQcW7pP283Q4SPsxHVVaHJefOi1++levj8UfPTc+cO6RB7W52cPPt8RHf/pEnP7/uzP+8edPZTeieOV1nDDcZMI9MiEfmbCPfZk4ujR++L7XxicvrvX7dCD/Pv3wOXHyEeP3+XUvtGyPt974QPz00RcPaT1I0tyaOzBRqAsAAADAwFDc3wUMRy0tLTFv3rxYsWLFnrGqqqpYtGhRzJw5M69rXX/99dkAl1f68pe/nA036W9LliyJ7373uz3GLr300j5Z67bbbou//Mu/jK6urj1jl112WSxYsCAKC2UbcegWNeb+cG1ezeRUawEAgJkTy7MX2C5f37rXXH3DuvifZ0zvl7oA6B/Pb2xL/JuQUee8BQAAAMCgNKG8NP79L07KNhN/497nsqEJB+r0mRXx1+ceFW+YPanX604eOyKuOe/o7Pdn1rz50RfjjqfWxs6Ol6/L2p/2zq64/cm12dvh40fG20+ZGpefOjWmTuh9UAzJ5wNvefTFuPWx1dG0bddBHWN0WXFcfMJh8Y5Tp8WJ08ZHQcH+Q1wG0vN0KOuvx/SoqtFx/ZvnxN/XzYq7lzZln2N3L9sQnb0IZtq2a3f86KEXsrfZ1WOyz6//cdLh2Z8JhotMqMcnfvF07Nq977+bmZCQTJBW9bgR2ft+n+ZfPh/Tn7z/dfGvdyyJBQ+szPk9mX/zj/33k/GHFzbFpy6ujRElRXn5OaClLTnUpby0yPMMAAAAYIAQ6pKybdu2xZve9KZoaGjYMzZ+/Pior6+POXPm5HWtL3zhC/H5z3++x9inPvWp+OhHP5rXn+cf/uEf4h//8R9j6tSpB/x9Tz/9dFx44YXR3v7yScTp06fH5Zdfvs/vW7ly5V7BN88//3zMmDEj5/fcc8892eN2dHTsGaurq4ubb745iou9BMiP+obkUJdZk8fEjInlqdcDAAB1NdWxfP2ze43/fkVzbNnREeNGlvRLXQCkb1HjupxzwmgBAAAABrc3zp6cvS1bty1++cSaeOLFLfHUmi3ZzwL+LPOZwHGHj4sTpo2LS044PGZVjznkdTMhH689sjJ7+/QltfGrJ16KWx55MZ5YvaVXx1mzeUf8++Jn4oa7nomzjp6YDXmoq50cZcUaUHtjR3tnNlwnE7LTm6b0VzttxoTsv8FFxx8Wo0qLB/3zdCjrr8e0pKgw6mqrs7f1W3fGrX9YnX3tr2ze3qvjLF23LT57W2N8/tdLY17t5HjnqdOyvwMKCw8uQAgGup0dnfGZXzXEjx9+cb9f+54zZ2RDlEqL99600u/T/MvHY5r5t8q8Hzp5+oT4+K1Pxvb2zpzrZZ4DmeN//YpTYlqFQDsOXXOOUJeK0ULTAAAAAAYKiRYpu+SSS+KRRx7pMfaRj3wkNm7cGHfeeWevjnXKKafEhAkTEue+973vxcc//vEeY2eddVb21tt1pkyZEjU1NYlznZ2d8fWvfz1uuummuOCCC7LhKWeeeWYceeSRe+3OkQlwefTRR+MHP/hBfPvb3+4RspL52v/4j/+IkSNHRj5lwmMyj/nOnTv3jFVXV8cHP/jBuO+++3p1rExtmZ8NXm3L9o548LnmxLkLajVGAQDQPzIXPP/fu/cOddnd1R33LGuKS088vF/qAmDghNFmdtjN7KwMAAAAwOCXaS7+WPXs7H93d3dHW3tntO/uyjYZl5cW7XUtVz6NHVESV7x2eva2ZO3WuPmRF+MXj6+Jzdtfvj5sf7q7I+5/ZmP2Nn5USVx24uHxztOmxZzDxvZZ3YNd5t/5ydVbskEuv3r8pdi2a/dBHWfi6LJ42ymHZ8NcjqoaHUP1eTpU9edjmjm/fM15R8dfn3tUNkwo81zMhAvt7Og64GO0d3bF7U+uzd4OHz8y3n7K1Lj81KkxdYKgA4aOF1u2x1//12Px9Jqt+/y6UaVF8YW3HR8XnzBlv8f0+zT/8vGYXnLClJhTPSb+6oePxYoNbTm/LvNceMt//Da++s4T4w2zJ+X152D4ackV6lJelnotAAAAACQT6pKye+65Z6+xT37ykwd1rLvvvjvOO++8nHOv9tvf/jbmzZvX63WuuuqqWLBgwT6/JhPQctttt2VvGWPGjMmGp4wfPz57YnvLli2xcuXKHkEuf5Y5yf2f//mfcfHFF0e+ZUJktm3b1mNs3bp1cemll/b6WNOnT8/+DPBqdy9ryjbGJsnsSAIAAP0hs0tU9dgRsW7ryyGXf1bfuF6oC8AwsWHbrnjshU05A8AAAAAAGHoy12ONLiuO6Ic+zkwIy6cvqY3rL5ydDRu+5dEX47fPbsyGthyoTBjMggdWZm/HTx2XDRu55MQp2fAYIja1tcfP/7gm+9guXdfz2rgDVVRYEG+YVZV9bDPN5CVFhTGcnqdDVX89ppl1X3tkZfaWef3/6omX4pZHXownVm/p1XHWbN4R/774mbjhrmfirKMnZp+fmfPYZcVFfVY79LW7lzbFtTc/Hlt27Dvo7Kiq8vjm/zwljp40ptdr+H2af4fymB4zeUz8vw+dFf9w65PZwKpcMs+J9yx4JD78xqPjb88/Nvu3GfIZ6lJZXpp6LQAAAAAkE+pCn8gEqbw6TCXJMcccE9/85jfjDW94Qyp1QV+ob1yXOD5l3IionWK3IAAA+u8io8xFjt///aq95u5Z2hS7dne6ABJgGFi8ZH3Ohpm6GmG0AAAAAPSNzGcQF58wJXtbvWl7/PTR1fHfj63Ohjb0xpOrt2Rv/3J7Y1z4msPiHadNi9fOrMh+DjKcdHV1Z8Nxbn70xVjUsD7aO7sO6jgzKkdlH8O3nTw1Jo8dkfc6IRO+dMVrp2dvS9dtjZsfeTEbQpQJazpQmXPa9z+zMXsbP6okLjvx8HjnadOyoVEwWHR2dce/37k8brjr2f1+7VuOPyw+/7bj/xQiwpCQ+bf8v+86KU45YkL86x1Lcm4cmZF5jvzxxc3x739xUlQI4eAgNOcIdfF8AgAAABg4nP3lkIwbNy7uvffeuOOOO+Luu++Oxx9/PNrbk08M/llJSUmcc8458Z73vCfe/va3R1mZaHgGr50dnXHPsg2Jc3W11cPuAhIAAAaWTLN+UqhLW3tnPLCiOd4wa1K/1AVAeuob1yeOH1lVHkdPGp16PQAAAAAMP1MnjIq/m3dsfHjuMfG7gwwm2dnRFT/745rsLRNMcvmp0+Ltp6QXTNLd3R2tu3ZHR2d3lBQVZJu107gu6FACcf5sRElhXHjcYfHOU6fF6cMwEIf+M7t6bHzq4tr4+Jtnx6LG9dmAl0w4Ua4g8iSZMJgFD6zM3o6fOi7eceq0uOTEKdnwmKH82mdwa2lrj7/9yR+zwUT7UlxYEP900ZyY//oZnldDUObf9OqzZmZ/d33wR3+I9Vt35fzazHPlLTfcH1+74uQ46YgJqdbJ4NfSlvzcqhTqAgAAADBgCHVJWeYDnjQsWLAge0vjhHMmoCVzy9i1a1c0NjbGihUrYu3atbFt27Y94S/jx4+P2bNnx3HHHRelpQd3knDGjBm9egznz5+fvUFfeWDFxtje3pk4V1czOfV6AADglV57ZEWMHVEcW3fu3muuvmG9UBeAIS5zoflvc1wwnAn+AgAAAIA0FRUWxDnHVmVvm9ra4+d/XBO3PPpiLF33p2vMDtTK5u3xpYXL4iuLlsd5x1bFO06bFm+cPSlKigrzWu/SdVvjl4+/FE+s3hxPr9kaW3Z07JkbN7IkXnP42Dhh6vi49MTDY1b1mLytu2t3Z/ZznMxj09sAjFc6IROAcdq0uPiE9AIwIElZcVG85fgp2duhBBU9uXpL9vYvtzf2aVBRf732GRr++MKm+OB//SFe2rJzn183eWxZ3HjFyXHK9IrUaqN/nDqjIm77m7Pjwz/+Y/z+ueacX5d5zrzjm7+PT76lJq48Y7qgHw5Yc2vyhrwVQl0AAAAABgyhLuRVWVlZnHTSSdkbDAeZCyiSZD68PW2mD9sAAOhfmYuX586ZnL0o+tUyu+H9y2WvyV5ADcDQdO+yDTl3O66rFUYLAAAAQP+ZUF4aV581M95z5oxsSMPNj74Yv3r8pdi2a++g+lw6u7pj8dKm7G3i6LJ428mHZ0NMjqoafUi13bV0fXzjnufi4ZUtOb8mE/Lwu2ebs7cb71kRp8+oiL8+76h4w+yDD9RfsnZr3PzIi/GLx9fE5u0vh0j0xvhRJfE/Tjo83nnatJhdPfaga4G+MnXCqPi7ecfGh+ceE797dmP2tb+oYX3Oc9lJdnZ0xc/+sCZ7mzmxPC4/dWq8/eSpMWnsiEH52mdoyGxY+cMHV8Vnb2uMjs59p3G97sjK+I93n5T928XwUDWmLH7w3tPj3xYtj6/fsyLn12WeO//7/zXEY6s2xb++9bgYVarVg/1raRPqAgAAADDQOdMHcJAyF4bcuSQ51GVuH+z+AwAAB6OuJjnUZWPrrnj8xU12/gIYwuob1yWOTxpTFidOHZ96PQAAAADwagUFBXHCtPHZ2/++qCbueGptNuTh4edzhyokyXzu8c37nsveTp0+IRvu8pbjD+tVM/Smtvb41C8b4pdPvNTrnyMTAvHwgpa49MQp8emLa7OhNQdi686O+OXjL8Utj76YDbc5GAUFEWcdPTEb5DKvZnKUFRcd1HEgTZmNJ845tip7y7z2Mp9nZl4HS9dt69Vxnt/YFl/8zbL4t/rl8YZZVfGOU6dlA1Z6c+1ef7z2GVq2t++Of/zZU/GLx/f/HLrmvKPiI/OOjWLXlw47mX/zf3jT7Dhp2vj46E+fiG07cwfZZZ5LjWu3xjeuPCWOPMSwOoZ+oNSm7cmhLpWj/U0CAAAAGCiEugAcpD++sCk2tiafCLfbNQAAA0XmQsjS4sJo37337nb1DeuFugAMUZnf+3ctbUqcyzR2FBYWpF4TAAAAAOzLyNKieNspU7O3TFBDJuDh1sdWR9O2Xb06zqOrNmVvn/llQ1x8wpRswEumgToTIJPLkrVbY/53H471W3u31qv9v8dfigefa47vXX16zK4em7P59qHnW+KWR16MO55eGzs79v4M50AcPn5kvP2UqXH5qVNj6oRRh1Q39KdMEMrVZ82M95w5IxtulAl2+tXjL8W2XbkDD5I3aGvK3iaOLou3nXx49rV/1H7CENJ87TM0PbehNf7qh4/F8vWt+/y6MSOK498uPyHqaqtTq42BKfMc+NXkMdnnzb6CrDLPqUv+7+/iS28/Pt583GGp1sjgsXXn7ujo7E6cqygvS70eAAAAAJIJdQE4SPWN6xPHy4oLs42zAAAwEJSXFcfZR0+MxQmN/Qsb1sXH3zx7nxcxAzA4PfR8c84d/lwwDAAAAMBAN3NiefzDm2bHR+cdG/cs25ANeciEGGeCGw5UW3tn/OSRF7O3YyePjnecOi3eevLUqCgv3SvU4S++9WBs2dGRl9oz4RDv/OaDcfMHzugR7tC0dWf89x9Wx08fXZ0NrTkYpUWFMa92crzz1Glx5tETo0h4M0NI5jPLE6aNz97+90U1ccdTa7Ov/Yefb+nVcTa27opv3vdc9nbq9AnZcJe3HH9YjCot7pfXPkPXr59aGx/77yejdT8BRLOrx8Q3rjwlZkwsT602BrbMc+Hn15wZ//SLp+Jnf1iT8+syz62//q8/xP86e2Zc96bZUVJUmGqdDHwtbcmbk2ZUvuo9LwAAAAD9R6gLwEHI7JiTaYBNcvYxE/f6ABgAAPpTXe3kxFCXlc3b49mm1jjm/8/efYBJVZ/94352WRaQ3ruClaJSVOwFC4kVNDEa9Y2oSdSYYt6oMSa+amKKJjGJMcYYCxpjLLGXRCxgjYpiQUQUC1JEeu+w/+vM78+GgV3YhdmZ2Zn7vq655Jw95dl15+ycc57z+XZsnpO6AKg7I8dXHUbbvFFZ7Lt926zXAwAAAABboqxBaRzep2PqNXPR8tSDz/eMmRIf1TIU5f3PF8eVj02Iq/79XhzRp2Mq4OXAndrHwmWrYvitr2Ys1GGdZHun3/JqPPKdA+LNT+fHPa9NiVETZ9UqlGbDQICk5uMHdI3WHtClCDQpbxBf2qNb6pWEICXvoftenxozF62o1XZemzwv9frpI+/Gsf06p95H/bu3ivlL6/a9/+/vHeS9WsBWrVkbV//7vfjr8x9vdtkvDewWVw7bNfU7DetLfid+e2K/2HO7NnH5w+Nj5Zq11S6b/K69NWVBXHfKgOjQonFW6yS/zV1S/d/FDYMMAQAAAMgdqQMAW+CDmYtj8pylVX5tSB+jXQMAkF8O690xSkrGRUUVfcIj3/1cqAtAgVm7tiKefLfqUJdDenWI8jKj+AEAAABQ/3Ro3jjOOXiHOPug7VMhDXePmRKPvf1ZLFu1psbbWLWmIh4fNyP16tKycTRrXBafL6xdSERNJds98KpRsWJ19Q9pb0oS0Hxs/y5x0p7dY/duLaOkpCTjNUJ90LNd0/jhF3vFD47YOUZPnBV3vzYlnnlvZq1CkhavWB3/eHVK6rVzx2bRsEFpnb73L39kfPzh5AF1sn1ya+bC5fHtO9+IVz+Zu8nlyhuUxhVD+8bJe3V3/KZaye/GKXtvG7t2bRHn3jE2ps1fVu2yye/c0X98Ia776oDY2wAO/P/mLF5Z5fxGZaWxjTApAAAAgLwh1AVgCzzxzowq55eWJA/Mdsh6PQAAsCntmjWKPbdrHWM+mbfR10aOnxHnDd4xJ3UBUDfGTVsQMxYur/JrQ/p0zHo9AAAAAJDpB6D36tEm9brs2D7x6NufpQJe3pwyv1bbmb5gecSCqFNbEugyqGebVJDLUbt1jiYexoVKZQ1K4/A+HVOvmYuWx/1jp8U9Y6bER7OX1Go773++OOraQ29Oj6H9u8ShvVyTLySvfDQnzrvzjZi9eNOBQF1bNYk/nzYwdu/WKmu1Ub8lvyuPfueAOP/uN+PZ92dVu9ysRSvilJteiYu/2Cu+fmBPgUHE3CVVh7q0bVru9wMAAAAgjwh1AdgCI6sZ7XrP7dpE22aNsl4PAABszpA+naoMdXlr6oL4bMGy6NyySU7qAiDzRr5bdRhtwwYlccgu7bNeDwAAAADUleaNG8ZXB22bek2csSjueW1KPPDGtGofcM1XHZo3ii/t0S2+smf36Nmuaa7LgbzXoXnjOOfgHeLsg7aP1ybPSwU7Pfb2Z7Fs1ZrIFzc8+5FQlwJRUVERf33+o7jq3xNjzdqKTS6b3If5/Un9o9U25Vmrj8LQuml53Dp8r/jjM5Pi90+/HxXV/Kolv4M/f3xCvD55Xvz6xN1Tn4UoXnOq+czbppljEAAAAEA+EeoCUEvT5y9LjXhdlSF93YQFACA/HdGnY6qxpypPvft5/M++PbJeEwB1Y+T4qsNo99uhncZOAAAAAArWLp2ax6XH9IkffrFXPDXh81TIw3MfzKr2oehca1BaEof26hAn7dk9FQJQ1qA01yVBvVNSUhJ79WiTel12bJ949O3PUu/9N6fMz3Vp8erHc1NhU8mxifpr4fJVcdG9b8e/x1cdqL9OSUnE9w/fOb49eMcoLS3JWn0UluR353uH7xT9t20V37vrjZi/dFW1yya/kxM/XxQ3nLaH40wRqy7IsE1TA5QCAAAA5BOhLgC19OS7VT8YlRjSp1NWawEAgJrq0a5p7NKxeaqpZ0MjhboAFIyPZi2OD2YurvJrwmgBAAAAKAblZaVx1G6dU69k8KZ/vj417nltSkydtyzywfbtmsZX9uoeJwzsGh2aN851OVAwklDzrw7aNvVKwlSS9/0Db0yr9oH3bHj4rWlxYadeOds/Wyf5PTrnjtfj49lLNrlc620axh9OHhAH7dw+a7VR2A7euX08+p0D4ry/j423plY9CGUi+d0c9qcX45cn7BbDBnTNao3kh3nV/I1r27Q867UAAAAAUD2hLgC1NPLdqkdc6NWpeWzbdpus1wMAADWVPMxfVajLfz6cEwuWrYqWTRrmpC4AshNGe0RvoS4AAAAAFJcurZrEdw/bKb49eMd46cM5cfdrU+KJ8TNi5eq1Wa2jtCTihIHd4qS9usee27WOkpKSrO4fis0unZrHpcf0iR9+sVc8NeHzuHvMlHjug1lRUZHdOt6aUn0YA/ntwTemxY/uHxfLVq3Z5HL9ureK608dGF1bNclabRSHbq23iXvO2Td+9ui7ccfLn1a7XPI7ev7db8brk+fFT47pHY3KGmS1TnJrTjWhLm2EugAAAADkldJcFwBQnyxYuipe/mhulV8b0rdT1usBAIDaGNKn6s+sq9dWxOiJM7NeDwCZN7KaUJcB27aKDi2M+gsAAABAcSotLYkDdmoXf/zqgHjlR4dG44bZbZ1s1qgsfv3l3WOvHm0EukAWlZeVxlG7dY7bzhwUL1w0OBqVZfe9P27agqjIdpIMW2XF6jVx6YPvpEIyNhfo8j/7bBf3nL2PQBfqTBLQcuWw3eKar/Tb7GeXv708Ob7yl5dj2vxlWauP3Jsr1AUAAACgXhDqAlALz0z8PNasrfom65A+RrsGACC/7dq1RXRuWfUD/SPHVx0CAED9MXPR8hj76bxaBXsBAAAAQLEpa1Aay1etzeo+Fy5fHUtWbjocAKhbLZo0jBWrs/veX7Bslfd+PZKEYSShGEk4xqYk4Rq/O6lf/GzYrqnQDahrJwzsFg+et3/0aLvNJpd7a8r8OOba5+O592dlrTbyM9SlrVAXAAAAgLwi1AWgFqp70DUZaaFvlxZZrwcAAGojGfmxujDC0RNnxvLNjDQGQH576t2ZUd2An0P6CqMFAAAAgMSqNdVcRKtjK7McJgGk895nU5IQjCQMIwnF2JSe7ZqmwjWOH9Ata7VBolenFvHwdw6IL2zmnt+8pavi9FtfjWuf/iDWVjOIJYVjzpIVVc5vI9QFAAAAIK8IdQGooeQB12erSa8/ok/H1AOyAACQ777Qt1OV85MR4l76cHbW6wEgc0a+O6PK+Tu0bxo7tG+W9XoAAAAAIB81bJCbHp/yMu2akEve+1QlCb1Iwi+SEIwkDGNTkjCNh769fypcA3KhReOGccNpe8QlR/WKBqXVH9OSQSCuefL9OPO2MTF/6cqs1kj2LF25Opavqjo4rG0zoS4AAAAA+cSdAoAaenHS7Fi6ck2VXzPaNQAA9cVePdtEyyYNq/zayPGfZ70eADJj0fJV8dKkObUK9AIAAACAYtSsUVm190rqSrK/puUNsrpPIPfv/SYNG/y/dAXyUhJ2kYReJOEXm/rflIRnJCEaSZhGEqoBuZQMQPnNg3aIv39972jXrNEmlx09cVYcfe0LMW7qgqzVR/bMWVx9YE/rbYS6AAAAAOSTslwXAFBfVPeAa3Kjd1CPNlmvBwAAtkTDBqVxWK8Ocf8b0zb62lMTPo81ays2OaITAPnp2fdnxco1VY/ENkSoCwAAAEWuoqIiPvnkkxg3blxMnTo15s+fH40aNYrWrVvHTjvtFHvttVc0btw46qPkexkzZkx8/PHHqX+vXbs2WrZsGd26dUt9X506uS4AVT0MvWvXFvFiNSHJdWG3ri1T+wWK672/bNWa2PsXT8cxu3eJr+zVPQZu28qxIE8kIRfn3PF6TJu/bJPLJaEZ150yIPbZvm3WaoOaSH4nH//uAXHenWNjzCfzql0u+R3/0g0vxU+P6xsn7dXdMaiAzF1SfahL26abDvwBAAAAILuEugDUQPJga/KAa1UO690hyhqUZr0mAADYUkP6dqwy1GX24pXxxqfzYk+hhQAFE0bbsUWj2L1ry6zXAwAAALk2b968ePDBB+Pf//53PPPMMzF79uxql23YsGEcffTRcf7558fBBx9c432MHj06Bg8enKGKI0aNGhWHHHJIjZa9//7747rrrkvVkITWVGfAgAFxzjnnxJlnnhllZVrFYJ1+3VplNdihX3fX6KAY3/uJJSvXxN2vTUm9duzQLE7as3scP7BrKiyE7Es+N901Zkpc9tD4asPy10kG+0sCXTq0qJ/hfxS+5Hfzzm/sE1f/+7346/MfV7vcytVr4+L7x8Xrk+fFz4btGo0bNshqnWQ31KWstCRaNHHuBwAAAJBPpBAA1MDYT+fFnGoufg/pY1QrAADql4N2bh+Nyqq+JDDy3apDAQDIX0kj5qj3Zlb5tSP6dIzSUiPuAQAAUFzOO++86NSpUyrI5J577tlkoEti1apVqQCYJFDl9NNPj4ULF0YulJeXb3aZOXPmpAJovvSlL6VCYDYV6JJ444034uyzz4599tknJk2alMFqoX47rn+X7O6vX9es7g/Ij/f+hibNXBw/f3xC7POLp+Ocv72eurafDDhHdixbuSYu/Ofb8aP7x2020OWbB20ff//G3gJdyHsNG5TGj4/uE9efOjCaNdp0kMe9r0+N469/KSbPWZK1+qg71fW1t25aHiUl7g8DAAAA5BOhLgA1MHL8jCrnJw/CHrRzu6zXAwAAW2Ob8rI4cKeqP8c+MX7GZh8CACC/vPzRnFi0YnWVXxNGCwAAQDF65ZVXYuXKjR9wa9CgQXTr1i322GOP2H333aNly5YbLXP77bfHEUccEYsXL45s6tChQwwaNGiTyyRhM0OGDInHH398o6+1b98+Bg4cmPrekkCbDb3++usxePDg+OSTTzJaN9RXvTq1iEE92mRlX4N6toldOjXPyr6A/Hnvb8rqtRXx7/Ez4owRY2L/Xz0Tv3liYnw6Z2muyypon8xeEif8+aX45+tTN7lcEorx51MHxiVH9U6FZUB9cdRuneOhb+8fO3dstsnlJny2MI754wvxpAF/6r25S1ZUOb9t082HhQIAAACQXZuOYwYg9UDrE+Orvnlx4E7tUw/EAgBAfZM85P/UhJkbzZ88Z2l8MHNx7NxRczFAfTHy3arDaJs3Kot9tm+b9XoAAAAgn7Rq1SpOOeWUOProo+PAAw+M5s3/e+1zzZo18fzzz8f//d//pf67zquvvhrDhw+Pf/7zn5vcdr9+/eLJJ5/corq+8Y1vpAWsnHrqqVFWtun+g0suuSTGjh2bNu+4446Lyy+/PAYMGJA2f8KECfHzn/88/v73v1fOmzp1anzzm9+MkSNHblHNUGjOOWT7eHXE3Drfz7kH71Dn+wDy771fUzMWLo/rRk1Kvfbdvm2ctFf3+OKunaJxwwa5Lq1gJOEV/3vPm7FoedUB+evs0rF5/Pm0gbF9+02HYkC+2qF9s3jwvP3jR/ePi4fenF7tcsl74Ru3vxbfOmSH+N8jdo4yAUb10pwlGweZJtoIdQEAAADIO5IIADZj4ueL4tO5VY+CMaRvx6zXAwAAmXBY7w5RWhKxtmLjr40cP0OoC0A9sXZtRbUj6Q3u1SHKyzRhAgAAUJx69OgRP/nJT1KBLk2aNKlymQYNGsQhhxwSo0aNim9961tx4403Vn7tvvvuS80fPHhwtfto3bp1HH744bWu7f33308LdEkkITKbMnPmzLjhhhvS5p177rlx/fXXV7l8796944477ohddtklFVqzThJC85///Cf23XffWtcNhebQXh3juH5d4uG3qn/oeWsN7d8ldZ0OKK73/g7tm8b8pauqfeC+Ov/5aE7q1eKhshjav2sq4GXXri3rrM5Ct3rN2rjmyffj+tEfbnbZYf27xC9O2M0gf9R7ye/w70/qH3tu1zp++ui7sWpNFU0h/7/kvfHmlPlx7VcHRLtmjbJaJ1tv7mKhLgAAAAD1hY5+gM0YOb7qB6OSB2AP03QBAEA91bZZo9hzuzZVfm1kNeEAAOSft6ctiM8Xrqjya8JoAQAAKFZXXHFFTJw4Mc4666xqA102DHdJwlH23HPPtPk33XRTndQ3YsSItOkBAwbE7rvvvsl1Hn300VizZk3ldPv27eM3v/nNZvf14x//OBXwsr5HHnmk1jVDobriuL7RsUXdPMScbPfyY/vWybaB/H7v//Oc/eI/PzosbjhtYAzepX2q17A2Fi5fHX97eXIc88cX4qg/PB+3vfRJLFi6qk7qLVSzF6+Ir93y6mYDXRo2KImfDe0bvzupv0AXCkZJSUn8z7494u6z943OLRtvctmXPpwTR1/7fLw+eW7W6iMz5lYTHNZWqAsAAABA3hHqArAZI9+dUeX8vXq0ST0ICwAA9VV1D/u/PXVBTJ+/LOv1AFB7I8dXfd2ivEFpHLxz+6zXAwAAAPng6KOPjvLy2j3IlgS7XHTRRWnznnjiiQxXFrF27dr429/+ljZv+PDhm10vCalZ3xe+8IXYZpttNrteaWlpHH/88WnzJk2aVON6odC1bloet505KFo2aZjR7SbbS7abbB8ozvd+eVlpfHHXznHrGYPixYsPjQuG7Bzbttn83+4NvfvZwrjs4fGx1y+eiu/+4414cdLsWLu2IqN1F5oknCIJqUjCKjYlCbu45+x9U+EXSQgGFJqB27aOR79zQBywY7tNLpcMIHHSX16OW1/8OCoqHF/qiznVhLq0aaq3HQAAACDfCHUB2IRp85fFO9MWVvm1IX07Zb0eAADIpCP6VB3qknhqwudZrQWALTPy3aqP1/vt2DaaN85sMzoAAAAUugMPPDBtes6cObF06dKM7uPpp5+OqVOnVk43bNgwTjnllM2uN3fu3LTp7t2713if2267bdr0/Pnza7wuFINenVrE3WfvEx1bZOYB2GQ7yfaS7QL5K5vv/c4tm8S3D90pRl9wSNz5jb1jWP8u0aisdi3cK1evjYffmh6n3vRKHPybUXHt0x8YqGMDSRhFEkqRhFMkIRWbcuBO7VJhFwO2bZ21+iAXksErk7Cp7xy64yaXW722Iq545N34zj/eiCUrVmetPrbc3OpCXZoJFQQAAADIN0JdADbhyWpGu04M2cQDsAAAUB9s17Zp9OrUvMqvjRwv1AUg3304a3FMmrm4yq8N6SOMFgAAAGqrdeuNH+pdsGBBRvcxYsSItOljjjkm2rVrt9n1WrZsmTa9bFnNH+LecNma7A+KTRLC8O/vHRRD+3fZqu0k6yfbEegC9UO23/ulpSWx3w7t4vcnD4hXLzk8fja0b+zatfbHiylzl8U1T74fB1z1TJx+y6vx+LjPUqEvxSwJoUjCKJJQiiScYlO+e+iOMeKMQamwCygGDUpL4gdDdolbhu8ZLRqXbXLZR9/+LIb+6cWYNHNR1uojs6EubZsKdQEAAADIN5u+KgdQ5Kob7bp35xbRvc02Wa8HAAAyLQkrfG/Gxs04L380JxYsXRUtt2mYk7oA2Lwnq7luUVIScXifDlmvBwAAAOq7adOmbTSvbdu2Gdv+woUL44EHHkibN3z48Bqt279//7TpMWPG1Hi/r776atr0oEGDarwuFJPWTcvjDycPSIUz3PDsR/Hqx3NrvO6gnm3i3IN3iMG9XJeD+iZX7/3kPuz/7Nsj9Ro/fUHcM2ZKPPDGtFi4fHWNt5Fklzz7/qzUq03T8jh+QNc4aa/usXPHqgf2KFRJ+MQ5d4ytNgh/nZZNGsbvTuoXh/YyoB/FKfndf+y7B8Y5d7we46cvrHa55L103HUvxtVf3j2O2X3rQq+oGytWr4nFK6r+e5H8PQAAAAAgvwh1AajG/KUr45VqbtAmD74CAEAhGNK3U1z7zKSN5iejl42aODOGDeiak7oA2LyR42dUOX9A91bRoXnjrNcDAAAA9d3zzz+fNr3ddttFeXnmHoi75557YtmyZZXTHTp0iKOOOqpG6x5zzDHRtGnTWLJkSWr6xRdfjP/85z+x7777bnK9SZMmxX333Vc53bhx4zjllFO2+HuAYnngOXlNnLEoHn5rWrw1ZUGMm7YgFixblRYMsFvXltGve8s4rl/X2KVTcQUoQCHK5Xu/b5eWccXQlvGjo3rHE+NnxD2vTYkXJ82p1TbmLlkZN7/wcerVv3urVLjLsf26RLNGhd0q/shb0+OH970dS1eu2eRyu3ZtEX8+dQ+D+VH0kvfAfefuF5c/PD7uGjOl2uWS99S373wjXp88L350ZO8oLyvNap1s/phfnbZCXQAAAADyTmFfqQfYCs+8NzPWJENZVGFIX6EuAAAUhr5dWkTXVk1i2vz/PkiwTtIwKNQFID/NXLg8xn46v8qvfaFvp6zXAwAAAIXglltuSZuuaeBKTY0YMSJt+rTTTouyspq1b7Vq1SouueSS+PGPf1w570tf+lI89NBDsddee1W5zoQJE2Lo0KGxcuV/H/i78sorU2EywOYlYQ0XduqV+ndFRUUsWbkmVq5em3qouWl5gygpKcl1iUCBvfcbN2wQQ/t3Tb2mzF0a9742Je59fWp8tmB5rbbz5pT5qddPH3k3jt69c5y8V/fYY7vWBXXcSv6f/PJfE+LWFz/Z7LJfHdQ9Lju2b+rnC/y/Y82vvrR7DNy2dVz60DuxYvXaapdN3mNvT10QfzplYHRqaVCJfDFncfWhLm2EugAAAADkHaEuANUYOf7zKucnD7z26dwi6/UAAEBdSBr3jujTMUa8tHGz27Pvz4rlq9ZobgPIQ09OqPq6RWKIUBcAAACotccffzyee+65tHnDhw/P2PYnTZoUL7744lZt/+KLL47x48fHnXfemZr+7LPPYt99942jjz46hgwZEtttt13qmu+0adPimWeeifvvvz9WrVqVtv4PfvCDDH1HUFyS91azRmURjXJdCVAs7/3ubbaJ/x2yS3zv8J3j+Q9mxT2vTYkn3/08Vq2peqC6qixbtSb++frU1Gv79k3jpD27xwkDu0X75vX7YDZjwfI4786x8frkeZtcrlFZafxs2K7xlT27Z602qE++slf36NOlRXzr72Pj07lLq10uea8d88fn49qTB8R+O7bLao1Ube6SqkNdkuyuVtsIdQEAAADIN0JdAKqQPLiaPMBalSF9OxbUiBUAAJB8xq0q1GXpyjXx4qTZcVjvjjmpC4Dah9Hu1KFZ9GzXNOv1AAAAQH02d+7cOPvss9PmDRs2LAYNGpSxfdx2221p0wMHDozddtutVtsoLS2NO+64I/bbb7+44oorYtasWbFmzZp4+OGHU6/q7L///qnlDzvssMi0mTNnpuqobcANAFAzDUpL4pBdOqRecxaviAfemBZ3j5kSH8xcXKvtfDRrSfzyX+/Fr5+YGIf26hAn7dU9Dt65fZQ1KI365KVJs+M7/3gj5lQTaLDOtm22iT+fNjD6dmmZtdqgPtq1a8t45NsHxA/ufTOemjCz2uVmL14Zp938SlzwhV3inIN2iNJSfdT5GOrSepvy1N8NAAAAAPKLUBeA/19FRUUsXrE6NZLFix/MTo1SUZUhfYx2DQBAYRnUo020bNIwFiz774it6zzxzowY1LNN6nNywwb/byQ6IYcAub12MW/JynhxUvVhtAAAAEDNrV27Nk477bSYOnVq5byWLVvGtddem9Fz+ttvvz1t3vDhw7doW8n12fPOOy+GDh0a5557bjz66KObXD4JdPnBD34QgwcPjrpw/fXXpwJjAIC617ZZo/j6gdvHWQf0jDemzI97xkyJR96aHktWVt3rWJXVayti5Lufp14dmjeKL+/RLb6yZ/fokaXA+PX7NGtz/3nt2oq44bkP4zdPTIy1FZte9vDeHeK3X+mfugcObF7LbRrGjf+zZ/z52Q/jtyOrf48l86/+98QYO3l+/PYr/Wr1HtvS9z5Vqy7Yqk3T8qzXAgAAAMDmCXUBitp7MxbGw29Oj7emzo93pi2s8iHW9bXapmHs1aN11uoDAIBsSEZfO6x3h7h/7LSNvnbv61PjntfXe5ihScPYtWuL6NetVQzt3zV26dQ8y9UCFJfaXrsQRgsAAAC1c+GFF8a//vWvtHl/+ctfonv37hnbx6hRo+LTTz+tnC4vL49TTjlli7a1ZMmSuPTSS+OGG26IZcuWbXb5F198MfXq1atX3HrrrbHPPvts0X4BgPyRBCEM3LZ16nXpMX3isXGfpQJeXps8r1bbmbloRVw/+sPUa++ebeKkvbrHkbt2jiblDbJ2r6Mm95+T5X9wz1vx1ITPN7mf0pKIHwzZJc49eIcoTSaAGkveM+cN3jH6d28V3/3HG9WGhiSS9+Jx170Q1586MPp2aVln732q/5k+/Nb0Kr8+c+HyuPrf7/mZAgAAAOQZoS5AUXrmvc/jhtEfxaufzK3Ven06t0g98AoAAIUmCQGoKtRlwwGYkiabFyfNSb2S5r5BPdrEuYfsEIN7dcharQDFYEuuXSSj2s1evKJO6wIAAIBCcu2118Y111yTNu+iiy6Kk046KaP7GTFiRNr0McccE23btq31dqZPnx6HHXZYvPfee5Xzdtlll/je974Xhx56aHTr1i1KS0vjs88+i+effz7++Mc/xuuvv55aLlnnwAMPjHvvvTeGDRuWge8KAMgHTRuVxVf27J56TZq5OO59bUrcN3ZqzF5cfSBDVV75eG7qddlD4+O4/l1SAS+7dW2ZCpCpy3sdm7v/PH76gvjW38fG5DlLN7mvtk3L49qvDoj9d2y3xfUCkXoPPfrdA1Lvuzc+nV/tcsl78oTrX4orh+0aJ+7ZPePvfbbs3vHC5asrw7r8TAEAAADyR0lFRcWGz2cBeWj8+PGx6667Vk6/88470bdv35zWVB/NW7IyLnt4fLUJ5TUxtH+XuPzYvtG6aXlGawMAgFx+Tv7Jg+PisXEztngbPicDZIZrFwAAQD5xn5pCduedd8Zpp50W67dODR8+PG655Zatenh5Q4sXL45OnTrFkiVLKuc9/PDDceyxx9ZqO8uXL48999wz9b5c5+tf/3r86U9/ivLyqq8BJN/bpZdeGj//+c8r5zVu3DjGjh0bvXv3jkyYOXNmzJo1q1brTJo0KS1YxrEFADJr1Zq18cx7M+OeMVNi1MSZsXYLO8V7dWqeCncZ1r9rre45ZOpex4DureKX/3ovVqxeu8llB27bKv506sDo3LLJFu8PSLdy9dr4xeMTYsRLn2x22a8O6h6XHds3lq1c4z5nhrl3DAAAAFAYPTBCXaCe0Cy39SZ8tjCG3/pqfL5w60es7tiiUdx25qDo1alFRmoDAIBc8TkZIH84JgMAAPnGfWoK1aOPPhrHH398rF69unLeCSecEPfcc080aNAgo/u69dZb48wzz6yc7tixY0ydOjXKyspqtZ0rrrgiLr/88srpQw89NJ588skoLS3d7Lpf+9rX4m9/+1vl9JFHHhmPP/545IpjCwBkz+cLl8c/X58a9742JT6Zs3SLtlHeoDSG9O2YCnjZf4d2UVpakpV7HTUxfL8ecclRvaO8bPOfiYDae+jNaXHxfeNi2ao1m1xuxw7NYv7SlTF78cqt3qf7nP+Pe8cAAAAAhXOf2hVsoGgubJ9848sZu1GYbOekv7wc781YmJHtAQBALvicDJA/HJMBAAAgO0aNGhUnnnhiWqDLEUccEf/4xz8yHuiSGDFiRNr0qaeeWutAlzVr1sR1112XNu/KK6+sUaBL4uc//3nasv/+979jypQptaoBAKifOrZoHOcN3jFGXXBI3PXNfeKEAV2jccPatY+vXLM2Hn37s/ifm1+NA68eFb9/6v2YNn9Znd/r2JRtyhvEtV8dEJcf11egC9Shof27xkPf3j+2b990k8tNmrk4I4EuCfc53TsGAAAAKDSuYgMFb96Slamk8gXLVmV0u8n2Tr/l1dT2AQCgvvE5GSB/OCYDAABAdrzyyitx3HHHxfLlyyvn7bfffvHAAw9EeXl5xvf38ccfx/PPP582b/jw4bXezttvvx2zZ8+unG7Xrl3ss88+NV6/e/fu0a9fv8rpioqKeOGFF2pdBwBQf5WUlMQ+27eNa07qH6/++PC4ctiusXu3lrXeThLm8vunPogDrnom/ufmV+LRt6fHitVr6uxeR1V2aN80Hjpv/ziuX5c63xcQsXPH5vHwtw+Io3frnLV9FvN9TveOAQAAAAqPUBeg4F3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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "timeline_path = \"/Users/sangbo/local_data/projects/xpu_timer/paper/experiments/figs/trace_dp4.bin\"\n", + "save_image = \"./fsdp.svg\"\n", + "plot_tflops_grouped_by_operation(timeline_path, save_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d0820c64", + "metadata": {}, + "outputs": [], + "source": [ + "timeline_path = \"/Users/sangbo/local_data/projects/xpu_timer/paper/experiments/figs/trace_dp4.bin\"\n", + "trace = PerfettoParser(trace=timeline_path)\n", + "filter_rank =[ i for i in range(8)]\n", + "matmul = trace.parse(tflops_sql)\n", + "# Loop over each name and rank to plot them separately\n", + "matmul = matmul[matmul['rank'].isin(filter_rank)]\n", + "grouped = matmul.groupby(['name', 'rank'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "33ed1294", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pylab import mpl\n", + "import warnings\n", + "\n", + "# Suppress all warnings\n", + "warnings.filterwarnings('ignore')\n", + "plt.rcParams['axes.unicode_minus'] = False\n", + "def plot_sss(data):\n", + " if(len(data) < 10):\n", + " return\n", + " y = np.array(data)\n", + " x = np.arange(1, (len(y)+1))\n", + "\n", + " z1=np.polyfit(x,y,deg=1)#deg=100,100次多项式，返回值为系数\n", + " p1=np.poly1d(z1)#通过多项式系数，返回方程\n", + " print(p1)#输出方程\n", + " print(np.polyval(p1,12))#进行预测\n", + " print(np.polyval(z1,13))#这两种方法都可以，可以p1，或者z1作为参数，12，13为x，输入x得到预测值\n", + "\n", + " y_pred=p1(x)#预测值\n", + " plt.plot(x,y,'*',label='origin value')\n", + " plt.plot(x,y_pred,'r',label='pred value')\n", + "# plt.title('多项式拟合')\n", + " plt.xlabel('xlable')\n", + " plt.ylabel('ylabel')\n", + " plt.legend(loc=3, borderaxespad=0., bbox_to_anchor=(0, 0))#画出图例\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1eef78b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===============mm_bmnk_1_126464_8192_4096_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_126464_8192_4096_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_126464_8192_4096_------------2====================\n", + "===============mm_bmnk_1_126464_8192_4096_------------3====================\n", + "===============mm_bmnk_1_4096_126464_8192_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_4096_126464_8192_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_4096_126464_8192_------------2====================\n", + "===============mm_bmnk_1_4096_126464_8192_------------3====================\n", + "===============mm_bmnk_1_4096_4096_8192_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_4096_4096_8192_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_4096_4096_8192_------------2====================\n", + "===============mm_bmnk_1_4096_4096_8192_------------3====================\n", + "===============mm_bmnk_1_4096_512_8192_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_4096_512_8192_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_4096_512_8192_------------2====================\n", + "===============mm_bmnk_1_4096_512_8192_------------3====================\n", + "===============mm_bmnk_1_4096_8192_126464_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_4096_8192_126464_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_4096_8192_126464_------------2====================\n", + "===============mm_bmnk_1_4096_8192_126464_------------3====================\n", + "===============mm_bmnk_1_4096_8192_4096_------------0====================\n", + "[1801, 1829]\n", + "===============mm_bmnk_1_4096_8192_4096_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_4096_8192_4096_------------2====================\n", + "===============mm_bmnk_1_4096_8192_4096_------------3====================\n", + "===============mm_bmnk_1_4096_8192_512_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_4096_8192_512_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_4096_8192_512_------------2====================\n", + "===============mm_bmnk_1_4096_8192_512_------------3====================\n", + "===============mm_bmnk_1_4096_8192_8192_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_4096_8192_8192_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_4096_8192_8192_------------2====================\n", + "===============mm_bmnk_1_4096_8192_8192_------------3====================\n", + "===============mm_bmnk_1_512_8192_4096_------------0====================\n", + " \n", + "1 x + 1200\n", + "1212.0\n", + "1213.0\n" + ] + }, + { + "data": { + "image/png": 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Wrx3Qt2suGrRp1Z8vHDY2946cuNR7hWYsL6EZAAAAAABQv958MznkkORPfypZeuNWg3L5jsdkXqvWVZ87tmuVS/ffPIO2XL+65qohvTNg4y45774xmVGxoMb9s+ctTGVlZcrKylbCF2FVIjQDAAAAAADqz4MPJkcdlUydWryuU6dU/PL6vNnuc5n/59eTymSbXuX58ZDe6Va+5Bllg3t3Td8e5Tntd6PyjzempZCRHbPdBvnW7hsJzFguQjMAAAAAAGDlmzcvOffc5MorS9d+8YvJ736Xtj175sIke22+Xka8MS3H7rBBWrVssczbunfukDuO65frh7+WrXqWZ5tenev2O7BKE5oBAAAAAAAr1+uvJwcfnPz1r6Vrv/Wt5PvfT1ovHsdYUAi/ljcAK4RqJwzYcEW6pZkSmgEAAAAAACvP73+fHHNMMn168brOnZObb0723ru+OoMalr2HEQAAAAAA4JOqqEi++c1k//1LB2bbb5+MGiUwo0EJzQAAAAAAgLr1yiuLg7Brrilde845yTPPJN2710dnsEzGMwIAAAAAAHXnrruSr30t+eCD4nVduiS33JLsvnt9dQZF2WkGAAAAAACsuLlzkxNPTA46qHRgtuOOi8cxCsxoRIRmAAAAAADAinnppaRfv+TnPy9eV1aWXHBB8uSTyfrr11d3sFyMZwQAAAAAAD65229Pjj8+mTmzeN066yS33Zbsskt9dQa1YqcZAAAAAABQe7NnJ1//enLYYaUDs0JQVhjHKDCjEROaAQAAAAAAtfPvfydf/GJy/fXF61q0SL73veSxx5J1162v7uATMZ4RAAAAAABYfjfdlJx44uKdZsUUziwrjG4cMKC+OoMVYqcZAAAAAABQ2qxZydFHL/5VKjDbY4/F4xgFZjQhdpoBAAAAAEAzMX32vAy9fWSNtWsP7ZNOHdoUv3HMmOQrX0lefLF4XcuWyaWXJt/+9uLRjNCECM0AAAAAAKCZeGD0Wxk+bkrNtX++nSP69Vz6DZWVyQ03JCefnMydW/zh3bold9yRbL99HXYM9UfMCwAAAAAAzcTdz09YrrUqM2Ykhx+efP3rpQOzffZZPI5RYEYTJjQDAAAAAIBm4KVJMzJ6wvtLrI9+c3penjSj5mIhANt66+T224s/tFWr5Mork/vvT9Zcs447hvolNAMAAAAAgGbgnmXtKCvsNhsx4X/jGH/+86Rfv+Sll4o/sGfPZPjw5PTTk7KyOu4W6p/QDAAAAAAAVnELFi7KvSMnLvP6fSMmZsHUacnBBycnnphUVBR/4H77JSNHJl/8Yt03Cw2kVUO9GAAAAAAAqBuVlZWZOmveMq//5dX3MnnGsoOwdV4ak4otv5ZWE94o/qI2bZIrrkiGDrW7jFWO0AwAAAAAAJq4F97+IHtfM7z2N1ZW5ujnH8i5T9+YNosWFC2d17NX2txzd7LVVp+8UWjEhGYAAAAAANDEPTbmnVrf86m5M/PDR67OHi/9pWTtgxvvkNcv/0mGCsxYhQnNAAAAAACgiXt0bO1Cs95v/SfXDvtBun3wbtG6ipatc/EuX89tvffMxq/NytAV7BMaM6EZAAAAAAA0YVNmVuSVybOWr7iyMl/7+3056483pfWihUVLXy1fP0MHn50X1vl01edxk2dWvWut1dvWRdvQ6LRo6AYAAAAAAIBPrhBi3Xn8tuneuX3Ruk5zPsj191yc856+sWRg9vtNBmTfo35SHZgVnn3XCdsKzFil2WkGAAAAAABN3FY9y/PwKf1z4bCxuXfkxCWvT3gh19z/o3SdMbnoc+a2apMLdj0hd24xMCkrq1o7oE/XXDR403Rs13ql9Q+NgdAMAAAAAABWAYVQ66ohvTNg4y45774xmVGxIGWVi3LCX+/JGc/eklaVi4re//Ka3XPS4LPyUpde/31eq1yy32YZ3LtrPX0DaFhCMwAAAAAAWIUUQq6+Pcpz/q/+kKN/cVF2fO35kvfcvdkuOX/gNzKnTbuqz317dMo1h/RJt/IO9dAxNA5CMwAAAAAAWMV0/9ffc+0VX8vqUyYVrZvdum3OH3hi7tl8lxrrAzdZV2BGsyM0AwAAAACAVcXChcn3v59897tZfVHxcYwvrtUzJw0+O6+s1X2JayPGT1uJTULjJDQDAAAAAIBVwTvvJIcfnjz1VMnS326xWy7a9bjMbb14HOPHjRw/LZWVlSkrK1sJjULjJDQDAAAAAICmrhCUHXZYMqn4OMaZbdrn3N1Pyv2b7Fi0bsrMeXlz6pz0WNOIRpqPFg3dAAAAAAAAsALjGC+8MBk4sGRg9sLaG2Tfo35SMjD7kBGNNDdCMwAAAAAAaIreeivZZZfk4ouTysqipTf32Tv7H3FlXuvctepzj84d0r1z+6L3CM1oboxnBAAAAACApuaxxxafXzZlStGyD9p0yNl7npKHP7dD9doBfbvmokGbVv35wmFjc+/IiUu9V2hGcyM0AwAAAACApmLBguT885PLLy9Z+s91P5Ohg87K+PL1qj53bNcql+6/eQZtuX51zVVDemfAxl1y3n1jMqNiQY37Z89bmMrKypSVla2ELwKNj9AMAAAAAACagjffTA45JPnTn0qWzj9paO7f+at58+9vJZXJNr3K8+MhvdOtvMMStYN7d03fHuU57Xej8o83pqWQkR2z3Qb51u4bCcxoVoRmAAAAAADQ2D30UHLkkcnUqcXrOnVKfv3rtN5vv5yXZPe+PTPijWk5docN0qpli2Xe1r1zh9xxXL9cP/y1bNWzPNv06lz33wEaOaEZAAAAAAA0VvPnJ+eck1x5ZenaL34xueOOpFev6qVC+LW8AVghVDthwIYr0i00acuOlQEAAAAAgIbz+utJ//7LF5idcUby7LM1AjOgduw0AwAAAACAxub3v0+OOSaZPr14XefOyU03JfvsU1+dwSrLTjMAAAAAAGgsKiqSb34z2X//0oHZ9tsno0YJzKCOCM0AAAAAAKAxeOWVxUHYNdeUrj377OTpp5Pu3eujM2gWjGcEAAAAAICGdtddyde+lnzwQfG6tdZKbrkl2WOP+uoMmg07zQAAAAAAoKHMnZuceGJy0EGlA7MBA5LRowVmsJIIzQAAAAAAoCG8/HKy7bbJz39evK6sLDn//OTJJ5P116+v7qDZMZ4RAAAAAADq229/mxx3XDJzZvG6ddZJbr012XXX+uoMmi07zQAAAAAAoL7Mnp18/evJoYeWDsx22SUZNUpgBvXETrNmYubMmRk7dmxefPHFvPfee5k7d246deqUtddeO1tvvXV69epVJ+9ZtGhRxo0bl3/96195++2388EHH6R9+/bp3LlzPv/5z6dPnz5p3bp1nbwLAAAAAKBJ+fe/F59dNmZM8boWLZLvfjc599ykZcv66g6aPaFZA5s4cWL+9re/5a9//WvV7//4xz8yY8aM6us9e/bM66+//omeXXjm73//+zz11FN5/vnnqwKtZSm854QTTsjxxx+f8vLyWr3nnXfeyT333JPHH388zzzzTFVQtiyFAO3AAw/MaaedVhWg1Ubh2TvttFM+qRX51xIAAAAAYIXcdFNy4omLd5oVs956i0c3DhhQX50B/yU0awB/+tOfcuWVV1aFWm+99VadP3/UqFH58pe/nFdffXW573njjTdyzjnn5Oqrr86vf/3r7LHHHst13+DBg/Pggw8WDeQ+as6cObnlllty22235fTTT8+ll16aNm3aLHefAAAAAABNyqxZyUknLQ7NStl99+Tmm5O1166PzoCPEZo1gL///e+57777VtrzJ0yYsMzAbI011si6665b9fuUKVPy2muvpbKyssausb333rsq1Dr44IOXKwBcWmBWGMG4/vrrp0uXLlWjIAv9zP7IT1AU7rniiivy0ksvVe1Sa9XKvxUBAAAAgMZr+ux5GXr7yBpr1x7aJ506FNkUUBjDWBjHWBjLWExhBOMllyRnnrl4NCPQICQVjczqq69edf5YXerXr18OP/zwqtGGm2yySY1rkydPzq9+9auqHV8fhlqFQOvII4/MxhtvXKsRiuuss07VfYVdatttt13atWtXfW3+/Pl59NFH853vfKfqvLMP3X///Tn77LOrArTaOuKII6ret7wKoyEBAAAAAD6JB0a/leHjptRc++fbOaJfzyWLCxsVbrwxGTo0mTu3+IO7dVs8jnGHHeq4Y6C2hGYNqGPHjtlqq62yzTbb5Atf+ELV74WdXytybteHWrRokUMPPbQqkNp0002XWVfYCXbuuedmn332qXrv1KlTq0OuU089NX/84x9LvmuzzTbLhRdemP3222+ZO8YKO8/23XffDBw4MF/5yleqRjp+6Jprrslxxx2XjTbaqFbf8dOf/nR23XXXWt0DAAAAAPBJ3P38hKWuLRGazZiRfOMbyW23lX7o3nsvHtu45pp12CnwSQnNGkAhPNptt93yuc99rirc+qhCaLaiCuHTP//5z6Jh2cdtscUWVWeZFc4o+9Czzz6bcePG5TOf+cwy77vhhhuqvs/Hv8eyFHaf3XHHHVXfvTBG8sOA7qabbqra7QYAAAAA0Ni8NGlGRk94f4n10W9Oz8uTZuSz63RcvDBqVDJkSPLSS8UfWNh8cPnlyWmnGccIjYj/NDaADTfcsGpM4vIGTZ8kNKtNYPahQYMGLTG+sTBSsZhCyFbb77HaaqvllFNOqbH22GOP1eoZAAAAAAD15Z6l7DL70N0jJiwex/jznxfOyikdmPXsmTz3XHLGGQIzaGT8J5Ia+vfvX+Pz+PHjm/R7AAAAAABWxIKFi3LvyInLvP74n1/KosLushNPTCoqij9sv/2SkSMXh2tAo2M8IzWUl5fX+Pz+++836fcAAAAAABRTWVmZqbPmLfP6X159L5NnLD0M2+ydcblu2OVpMf2d4u9o3TplV1yRnHxyUla2wj0DK4fQjBomTqz5ExNrrqQDKOvrPQAAAAAAxbzw9gfZ+5rhtbupsjJHjXgw5z59Q9ouXFC09I1O62bh7b/Np/fcccUaBVY6oRk1fqJi+PDhS5yPtjI8V5jZWwfvKfT82muv5d13383ChQvTuXPnrLvuukvsZAMAAAAAWJrHxhTfJfZxn5o7Mz985Ors8dJfStY+tPH2OXvPU3JMq/Vz+gr0CNQPoRnVnnnmmaoA6kNlZWXZY4896vw9hXDr5ptvrrG211571fo5N910U376059m6tSpS1z73Oc+l5133jknnnhiNt100xXqFwAAAABYdT06dvlDs95v/Sc/vf+H6f7+pKJ1FS1b53u7fD239t6zahxjIZg7feDK2aAA1J0WdfgsmrBFixblnHPOqbFWCMwKu7bq2i9+8Yu8+uqr1Z9bt26dQw89tNbPef3115camBW8+OKL+dnPfpbNN988X/nKV5ZZBwAAAAA0X1NmVuSVybNKF1ZW5ti/3Ze7bjuzZGD2avn62f+IK3Nrn72qzy8bN3lm1buAxs1OM6pcccUV+etf/1r9uUWLFrn00kvr/D2vvPJKzj777BprJ510Urp165aVoTC+8e67787f/va3PPLII9lkk03q/B2F0ZCTJ0+u1T3jxo2r8z4AAAAAgNpZa/W2ufP4bXPq70bmzalzllrTac4HueKhH2fXV/5e8nnDPj8g5+5+Uma17VC91r1z+1x9cJ+qdwGNm9CMqvPFvvOd79RYO/XUU9OnT586fc/s2bNz4IEHZsaMGdVrPXv2zMUXX1yr5xTOP9tnn30yYMCAqtGLa6+9dtq3b59p06blpZdeyhNPPFG1m+2dd/63rXr8+PFVIyALweA666xTp9+rsKPtoosuqtNnAgAAAAD1Y6ue5Xn4lP65cNjY3DtyYo1rfSf8u2ocY9cZxX9ofm6rNrlw1+Pzuy12q95dVnBAn665aPCm6diu9UrrH6g7QrNmrjAm8YADDsiCBQuq13r37p3vf//7db7j66ijjsqoUaOq11q1apXbbrstHTt2XK5n9OrVK08//XR23HHHpV7v0qVL1a/tt9++ajdbIfgrhGcfeuONN6rOOLvnnnvq4BsBAAAAAKuKQqh11ZDeGbBxl5x335jMnDsvx//13nzr2ZvTqnJR0XvHde6Wk/Y7O//p0usjz2uVS/bbLIN7d62H7oG6IjRrxqZMmZI999yz6vcPFXZh3XvvvWnbtm63Cp9xxhlVYxI/6uqrr64KuJZXITQr/Foe7dq1y//93/9V7UD7yU9+Ur1e+G7/+Mc/svXWW9eiewAAAACgOSiEXFt3WJBJ+w9J3xf+d5zNstyz2c45f+A3MrtN++q1vj065ZpD+qRb+f9GNAJNg9CsmSqMSCwEZoVxhh9aY4018thjj2WDDTao03ddfvnl+fGPf1xj7cILL6za9bWy/ehHP8qDDz5Y4wyxW2+9tU5Ds8L3+MpXvlKrewr97LfffnXWAwAAAABQB559Nl0POSRd33qraNns1m1zwcBv5O7Nd13i2sBN1hWYQRMlNGuG5s6dm0GDBlXtuPpQhw4d8tBDD2XLLbes03cVxiOec845NdZOOeWUfPe73019KIyALLyv8OtDjz/+eJ2+o3CmWuEXAAAAANBELVyYXHZZ4af9k0XFxzH+Z60eOWnw2Rm3Vo+lXh8xftpKahJY2YRmzcz8+fNz0EEH5Zlnnqlea9OmTdXYwtqMSlwet99++xK7yQrnmn10XGJ92GWXXWp8fvnll6vOWCv7yIGcAAAAAEAzNWlScthhyVNPlSz97Ra75aJdj8vc1u2WWTNy/DR//whNlNCsGVm0aFGOPPLIPPDAA9VrLVu2rAq3dt999zp917Bhw6oCssI7P/TlL385N9xwQ73/w6J79+41Pi9YsCDTpk1L586d67UPAAAAAKCRKQRlhcCsEJwVMbNN+5y7+0m5f5MdSz5yysx5eXPqnPRY04hGaGpaNHQD1I/CTzYcd9xxueOOO6rXCuHV9ddfXxVm1aUnn3wyQ4YMqQqnPlQI5QrhXCGkq2+tW7de6o47AAAAAKAZj2MsjGIcOLBkYPbC2htk36N+slyB2YeMaISmSWjWTJx22mlVu7w+6pprrsnRRx9dp+/505/+lMGDB6eioqJ6rX///rnvvvuqxkA2hHfeeafG50JYuOaaazZILwAAAABAA3vrrWTXXZOLLy7sNihaemvvPbP/EVfmtc5dq9d6dO6Q7p3bF71PaAZNk/GMzcD555+fq6++usba97///QwdOrRO3zNixIjsvffemT17dvXa1ltvnQcffDDt2xf/h8jKNHz48Bqf11tvvbRq5d/6AAAAANDsPPZYcsQRyeTJRctmtGmfs/c4JQ99vn+N9QP6ds1Fgzat+vOFw8bm3pETl3q/0AyaJsnBKu5HP/pRLrnkkhpr55xzTtWvuvTCCy9UjWB8//33q9c222yzPProo/nUpz6VhvTxHXa77LJLg/UCAAAAADSAwlEyF1yQXHZZydKXun42X9v7zIwvX696rWO7Vrl0/80zaMv1q9euGtI7AzbukvPuG5MZFf87qqZg9ryFVUfmFKZeAU2H8YyrsF/84hc588wza6wVdpcVdpnVpddeey0DBw7MlClTqtc+85nP5IknnmjwMYi33nprnnnmmRpr++23X4P1AwAAAADUswkTkp12Wq7ALCefnG7/Hpld9tk2H+Zd2/QqzyPf7F8jMPvQ4N5d8/A3+2frnuVVnwv3fHX7DfLgyTsIzKAJstNsFXX77bfnxBNPrLF2zDHHVJ1jVpfeeuut7LrrrlW/f6hHjx556qmnsu6669bZe+64446qM9H233//5f6HzW9/+9t87Wtfq7HWu3fvqmcAAAAAAM3AQw8lRx2VvPde8bpOnZIbb0z23z8dCqMX9900e22+Xka8MS3H7rBBWrVc9v6T7p075I7j+uX64a9lq57l2aZX57r/HkC9EJo1kD/96U+ZM2fOEuujR4+u8Xnu3Ll58sknl/qM9ddfP5tssskS64X6o446KosWLape+9znPpchQ4ZUhVm1UV5enq222mqp1wpnl+2222559dVXq9datmyZs88+Oy+99FLVr9rYYYcd0q5du6Vee/HFF3PRRRdV7WA76KCDss8++2SLLbbIaqutVqNu3rx5VWeYFc5wu//++2tcKzz75z//uZ/wAAAAAIBV3fz5ybnnJldcUbr2C19Ifve7pFevGsuF8Gt5A7BCqHbCgA0/abdAIyE0ayCHHXZY3njjjZJ1kyZNqhp9uDSFYOw3v/nNEuuF0GhBYUbvx0KnPfbYo9Z9DhgwYInxhh969913M3bs2BprCxcuXGKHW23GPPb62D+YPm7cuHFV4yULv1q0aJFu3bqlU6dOad++fdV5aq+//npV0PhxrVu3zm233ZZ+/fp9ot4AAAAAgCai8PeuBx+c/L//V7r2jDOSwnE2bdrUR2dAIyc0o8kq7KQbP3581a9iNtpoo6pxlcvaMQcAAAAArCKGDUuOPjqZPr14XefOSWFDwr771ldnQBMgNKNJKIxkLIxeLOx6GzVq1FJHW35Uq1atsu222+Yb3/hGDjzwwKqdZgAAAADAKmrevOTMM5Orry5du912yW9/m/ToUR+dAU1IWWVlZWVDNwG1URgB+Z///KfqLLUJEybkgw8+qArUVl999aoz2DbYYINss802VSMbG6vCWMvNNtus+vOYMWOy6aabNmhPAAAAANAkvfpqMmRI8o9/lK4966zke98rnOdSH50BTezvz+00o8lp2bJlNtlkk6pfAAAAAEAzdvfdybHHJh98ULxurbWSW25J9tijvjoDmqAWDd0AAAAAAADUyty5yUknJV/5SunA7EtfSkaNEpgBJQnNAAAAAABoOl5+efG5ZD/7WfG6srLkvPOSp55Kunatr+6AJsx4RgAAAAAAmoY77ki+/vVk5szideusk9x6a7LrrvXVGbAKsNMMAAAAAIDGbc6c5Pjjk0MOKR2Y7bzz4nGMAjOgloRmAAAAAAA0Xi++mHzxi8kvf1m8rkWL5KKLkscfT9Zdt766A1YhxjMCAAAAANA43XJL8o1vJLNmFa9bb73k9tuTHXesr86AVZDQDAAAAACAlWb67HkZevvIGmvXHtonnTq0WfZNhZBs6NDkN78p/YLddlscrq29dh10CzRnQjMAAAAAAFaaB0a/leHjptRc++fbOaJfz6XfMHZsctBByQsvFH9wy5bJ976XnHXW4tGMACvIf5MAAAAAALDS3P38hOVaS2VlcuONyTbblA7MunZNnnkmOeccgRlQZ/y3CQAAAAAAK8VLk2Zk9IT3l1gf/eb0vDxpxv8WZs5MjjgiOfbYZM6c4g/de+9k1Khkhx1WQsdAcyY0AwAAAABgpbhnaTvK/uvuEf+9Nnp0stVWyW23FX9Yq1bJj36U3H9/stZaddwpgDPNAAAAAABYCRYsXJR7R05c5vX7np+QM19/Ji1POy2pqCj+sJ49kzvuSPr1q/tGAf5LaAYAAAAAQK1VVlZm6qx5y7z+l1ffy+QZSw/DVq+YnfOH/SAtX3yu9IsGD05+/eukvHxF2gUoSWgGAAAAAECtvfD2B9n7muG1vm/Td8blumE/SK/pbxetm9eiVaZeeEnWPf/MpKxsBToFWD7ONAMAAAAAoNYeG/NO7W6orMyRzz+Qe2/9VsnAbPwa6+TAw3+Y27fdX2AG1Bs7zQAAAAAAqLVHxy5/aPapuTPzg0euyZ4v/blk7UMbb5+z9zwlM9quloox7+T0gRutYKcAy0doBgAAAABArUyZWZFXJs9artot3n4p1w77QXq8P6loXUXL1vneLl/Prb33rN5dNm7yzKp3rbV62zrpG6AY4xkBAAAAAKiVQoh15/Hbpnvn9ssuqqzMsX//fe6+9cySgdmr5etn/yOuzK199qoOzArPvuuEbQVmQL2x0wwAAAAAgFrbqmd5Hj6lfy4cNjb3jpxY49oac2bkiod/koHj/lryOcM+PyDn7n5SZrXtUL12QJ+uuWjwpunYrvVK6R1gaYRmAAAAAAB8IoVQ66ohvTNg4y45774xmVGxIH0n/js/HfbDdJ0xuei9c1u1yXd3OS53bLl79e6yju1a5ZL9Nsvg3l3r6RsA/I/QDAAAAACAFVIIufp2WyNPf/3MHDrsF2lVuaho/bjO3XLSfmfnP116Va/17dEp1xzSJ93K/7fjDKA+Cc0AAAAAAFgxkyen+1FH5chHHilZes+mO+X83U7M7DY1z0MbuMm6AjOgQQnNAAAAAAD45J57Ljn44OStt4qWzW7dNhcM/Ebu3nzXpV4fMX7aSmoQYPkIzQAAAAAAqL1Fi5LLLksuuGDxn4v4z1o9ctLgszNurR7LrBk5floqKytT9t/zzQDqm9AMAAAAAIDamTQpOeKI5IknSpbescVu+e6ux2Vu63ZF66bMnJc3p85JjzWNaAQahtAMAAAAAIDl94c/JIcdlrzzTtGyWa3b5dzdT8qwTXda7kcXRjQKzYCG0qLB3gwAAAAAQNOxcGHy3e8mu+5aMjB7Ye0Nss/RV9cIzHp07pDundsXvc+5ZkBDstMMAAAAAIDi3n578e6yp58uWXpr7z3zvZ2/lorWbavXDujbNRcN2rTqzxcOG5t7R05c6r1CM6AhCc0AAAAAAFi2xx9PDj88mTy5aNmsth1y5u4n56HP969e69iuVS7df/MM2nL96rWrhvTOgI275Lz7xmRGxYIaz5g9b2EqKytTVla2Er4IQHHGMwIAAAAAsKQFC5LvfCfZY4+SgVn69k2LEc9n7a8dkQ/zrm16leeRb/avEZh9aHDvrnn4m/2zdc/yqs+Fe766/QZ58OQdBGZAg7HTDAAAAACAmiZMSA49NHnuudK1J5+c/OhHad+2bS7cJNlr8/Uy4o1pOXaHDdKq5bL3bXTv3CF3HNcv1w9/LVv1LM82vTrX7XcAqCWhGQAAAAAA//Pww8mRRybvvVe8bo01khtvTA44oMZyIfxa3gCsEKqdMGDDFekWoM4YzwgAAAAAQDJ/fnLmmcnee5cOzLbZJhk5conADKAps9MMAAAAAKC5Gz8+Ofjg5C9/KV17+unJZZclbdrUR2cA9UZoBgAAAADQnN1/f3L00cm0acXrysuTm25K9t23vjoDqFfGMwIAAAAANEfz5iWnnZYMHlw6MNt222TUKIEZsEoTmgEAAAAANDevvZbssEPyk5+Uri2cc/bHPyY9etRHZwANxnhGAAAAAIDm5J57kmOPTd5/v3jdWmslN9+c7LlnfXUG0KDsNAMAAAAAaA7mzk2GDk0OPLB0YNa//+JxjAIzoBkRmgEAAAAArOrGjUu22y657rridWVlyXnnJX/4Q9K1a311B9AoGM8IAAAAALAqu+OO5LjjkhkzitetvXZy663JwIH11RlAo2KnGQAAAADAqmjOnOT445NDDikdmO200+JxjAIzoBkTmgEAAAAArGr+85+kX7/kl78sXteiRXLRRckTTyTrrVdf3QE0SsYzAgAAAAA0QtNnz8vQ20fWWLv20D7p1KFN8RsLIxZPOCGZNat43brrJr/9bbLjjnXQLUDTJzQDAAAAAGiEHhj9VoaPm1Jz7Z9v54h+PZd+QyEkO/nk5Ne/Lv3w3XZLbrll8TlmAFQxnhEAAAAAoBG6+/kJy7VWZezY5AtfKB2YFcYxXnpp8sgjAjOAjxGaAQAAAAA0Mi9NmpHRE95fYn30m9Pz8qQZ/1uorFwclG2zTfLCC8Uf2rVr8swzybnnLg7PAKjBfzMCAAAAADQy9yxrR1lht9mI/16bOTM58sjkq19N5swp/sC99kpGjUr696/jTgFWHUIzAAAAAIBGZMHCRbl35MRlXr9vxMQsGDkq2Xrr5NZbiz+sVavkhz9MHnggWWutum8WYBXSqqEbAAAAAABoTiorKzN11rxlXv/Lq+9l8oyKZd2cXZ4blrKLf5XMW0bNh3r0SO64I9l22xXsGKB5EJoBAAAAANSjF97+IHtfM7zW961eMTvff+zaDPr3syVrZ+y2Vzr+9pakc+dP2CVA8yM0AwAAAACoR4+NeafW92z6zrhce/8PssG0t4vWzWvRKpftdEw6nnFGTheYAdSKM80AAAAAAOrRo2NrEZpVVubI5x/Ivbd+q2RgNn6NdXLg4T/Mr7cenMfGTlrxRgGaGTvNAAAAAADqyZSZFXll8qzlqv3U3Jm5/JFrstdLfy5Z+/BG2+XsPU/JB+1Wr/o8bvLMqnettXrbFe4ZoLmw0wwAAAAAoJ4UQqw7j9823Tu3L1q3xdsv5cHffLNkYFbRslXOH3hCTtzvnOrArPDsu07YVmAGUEtCMwAAAACAerRVz/I8fEr/HNCn65IXKyvz1b8Py923npke7xcfsfha+Xo54Igrc0vffZKysqq1wjMLz+7bo3xltQ+wyjKeEQAAAACgnnVs1zpXDemdARt3yXn3jcmMigVZY86MXPHwTzJw3F9L3n//57+Uc3cfmpltO/z3ea1yyX6bZXDvpQRxACwXoRkAAAAAQAMphFyFXWHXff+WDP3N+en2weSi9XNbtclFuxyX3265e/Xusr49OuWaQ/qkW/niAA2AT0ZoBgAAAADQUBYtSvcbrsulPzk3LRcuKFr6SuduOWnwWXlx7Q1qrA/cZF2BGUAdEJoBAAAAADSEKVOSo45KHn44LUuU3rPpTjl/txMzu037Ja6NGD9tpbUI0JwIzQAAAAAA6ttzzyWHHJJMnFi0bE6rtrlg4Am5a/Ndq8cxftzI8dNSWVmZsmVcB2D5tFjOOgAAAAAAVtSiRcn3v5/stFPJwOylNXtk0JFX5a4tBi4zMCuYMnNe3pw6ZyU0C9C82GkGAAAAAFAfJk1KjjgieeKJkqW/23xgLhx4fOa2brdcjy6MaOyxpnPNAFaEnWYAAAAAACvb008nvXuXDMxmtW6X0/Y+PWft9c3qwKxH5w7p3nnJs8w+yrlmACvOTjMAAAAAgJVl4cLkkkuSiy9ePJqxiH936ZWhg8/KK2t2r147oG/XXDRo06o/XzhsbO4dufSRjkIzgBUnNAMAAAAAWBnefjs57LDFu8xKuK33Hrl456+nonXbqs8d27XKpftvnkFbrl9dc9WQ3hmwcZecd9+YzKhYUOP+2fMWprKyMmVFzj4DoDihGQAAAABAXSuMYTz88OTdd4vXdeyYip/9POPW6J15f349qUy26VWeHw/pnW7lS55RNrh31/TtUZ7Tfjcq/3hjWgoZ2THbbZBv7b6RwAxgBQnNAAAAAADqyoIFyXe/m3z/+0llZfHaPn2SO+9M2898Jhcm2Wvz9TLijWk5docN0qpli2Xe1r1zh9xxXL9cP/y1bNWzPNv06lz33wOgGRKaAQAAAADUhQkTkkMPTZ57rnTt0KHJj36UtGtXvVQIv5Y3ACuEaicM2HBFugXgY4RmAAAAAAAr6pFHkiOOSN57r3jdGmskN9yQfPnL9dUZAMtp2Xt8AQAAAAAobv785Kyzkr32Kh2YbbNNMmKEwAygkbLTDAAAAADgkxg/Pjn44OQvfylde+qpyQ9+kLRpUx+dAfAJCM0AAAAAAGrr/vuTo49Opk0rXldenvzmN8mgQfXVGQCfkPGMAAAAAADLa9685PTTk8GDSwdm/folI0cKzACaCKEZAAAAAMDyeO21pH//5Mc/Ll175pnJs88mPXvWR2cA1AHjGQEAAAAASrn33uSrX03ef7943ZprJjffnOy1V311BkAdsdMMAAAAAGBZKiqSk09Ovvzl0oHZDjsko0YJzACaKKEZAAAAAMDSjBuXbLddcu21xevKypLvfCd5+umkW7f66g6AOmY8IwAAAADAx/3ud8nXv57MmFG8rkuX5LbbkoED66szAFYSO80AAAAAAD40Z05ywgnJwQeXDsx22ikZPVpgBrCKEJoBAAAAABT85z9Jv37JL35RehzjhRcmTzyRrLdefXUHwEpmPCMAAAAAsMqYPnteht4+ssbatYf2SacObYrfeOuti3eYzZpVvG7ddRePY9x55zroFoDGRGgGAAAAAKwyHhj9VoaPm1Jz7Z9v54h+PZd+w+zZycknJzfeWPrhhTGMt9ySrLNOHXULQGNiPCMAAAAAsMq4+/kJy7VW5YUXki98oXRg1qJFcsklyaOPCswAVmFCMwAAAABglfDSpBkZPeH9JdZHvzk9L0+aUXPxN79Jtt46GTu2+EPXXz95+unkO99ZHJ4BsMry3/IAAAAAwCrhnmXtKCvsNhvx32szZyZHHZUcc0wyZ07xB+65ZzJqVPKlL9VxpwA0RkIzAAAAAKDJW7BwUe4dOXGZ1+8bMTELRo1Ottkmufnm4g9r2TL5wQ+SBx9MunSp+2YBaJRaNXQDAAAAAAClVFZWZuqsecu8/pdX38vkGRXLujk7PzcsZd+7PqmYW/xF3bsnd9yRbLfdCnYMQFMjNAMAAAAAGr0X3v4ge18zvNb3rV4xO99/7NoM+vezJWtnDNwzHe+4Nenc+RN2CUBTJjQDAAAAABq9x8a8U+t7Np30Sq4ddnk2mPZ20bp5LVrlBzsendW+9a2cLjADaLacaQYAAAAANHqPjq1FaFZZmcNHPJR7bzmjZGD25hrr5CuH/SA3bLNfHhs7acUbBaDJstMMAAAAAGjUpsysyCuTZy1XbceKWbn8kWuy93/+VLL2kY22y1l7npIP2q1e9Xnc5JlV71pr9bYr3DMATY+dZgAAAABAo1YIse48ftt079y+aN0Wb7+Uh359SsnArKJlq1yw6/H5xn7nVAdmhWffdcK2AjOAZkxoBgAAAAA0elv1LM/Dp/TPAX26LnmxsjLH/GNY7r71zPR4v/iIxdc7rZcDDr8iN2+1b1JWVrVWeGbh2X17lK+s9gFoAoxnBAAAAACahI7tWueqIb0zYOMuOe++MZlRsSBrzJmRHz1ydXZ7+f+VvP+Bz/XPOXucnJltO/z3ea1yyX6bZXDvpQRxADQ7QjMAAAAAoEkphFyFXWHXXXZLhv7m/HT7YHLR+oqWrXPRrsfl9i33qN5d1rdHp1xzSJ90K18coAGA0AwAAAAAaFoWLUr3G3+WS398TlouXFC09JXOXTN08Fn599qfrrE+cJN1BWYA1CA0AwAAAACajilTkqOPTh56KC1LlN676U45b7cTM7tN+yWujRg/baW1CEDTJDQDAAAAAJqG4cOTQw5JJkwoWjanVdtcMPD43LX5wOpxjB83cvy0VFZWpmwZ1wFoflo0dAMAAAAAAEUtWpRcdlmy444lA7OX1+yeQUdelbu22G2ZgVnBlJnz8ubUOSuhWQCaKjvNAAAAAIDG6913kyOOSB5/vGTpnZvvmgt3PSFz2rRbrkcXRjT2WNO5ZgAsZqcZAAAAANA4PfNM0rt3ycBsVut2OW3v03PmXqdWB2Y9OndI985LnmX2Uc41A+Cj7DQDAAAAABqXhQuTSy9NLrpo8WjGIv7dpVeGDj4rr6zZvXrtgL5dc9GgTav+fOGwsbl35MSl3is0A+CjhGYAAAAAQOPxzjvJYYclf/hDydLbt9wjF+3y9VS0blv1uWO7Vrl0/80zaMv1q2uuGtI7AzbukvPuG5MZFQtq3D973sJUVlamrMjZZwA0H0IzAAAAAKBxePLJxYFZ4RyzYlZfPRU//7+8vEbvzPvz60llsk2v8vx4SO90K1/yjLLBvbumb4/ynPa7UfnHG9NSyMiO2W6DfGv3jQRmAFQTmgEAAAAADWvBgsWjGAsjGSsri9cWzji78860/exnc2GSvTZfLyPemJZjd9ggrVq2WOZt3Tt3yB3H9cv1w1/LVj3Ls02vznX/PQBo0oRmAAAAAEDDmTgxOfTQ5NlnS9eedFJyxRVJu3bVS4Xwa3kDsEKodsKADVekWwBWYUIzAAAAAKBhPPpocsQRyZQpxes+9ankhhuSAw+sr84AaIaWvV8ZAAAAAGBlmD8/OfvsZM89SwdmW2+djBwpMANgpbPTDAAAAACoP+PHJ4cckvz5z6VrTz01ufzypG3b+ugMgGZOaAYAAAAA1I8HHkiOPjqZOrV4XadOyW9+kwweXF+dAYDxjAAAAADASjZvXnLGGcmgQaUDs379klGjBGYA1DuhGQAAAACw8rz2WtK/f3LVVaVrv/3t5Nlnk54966MzAKjBeEYAAAAAYOW4777kmGOS998vXrfmmslNNyV7711fnQHAEuw0AwAAAADqVkVFcsopyQEHlA7Mdthh8ThGgRkADUxoBgAAAADUnVdeSbbfPvnpT0vXnnNO8vTTSbdu9dEZABRlPCMAAAAAUDfuvDP52teSGTOK13XpktxyS7L77vXVGQCUZKcZAAAAALBi5sxJvvGNZMiQ0oHZjjsuHscoMAOgkbHTDAAAAADI9NnzMvT2kTXWrj20Tzp1aFP8xv/8JznooOSf/yxeV1aWnH9+csEFScuWddAxANQtoRkAAAAAkAdGv5Xh46bUXPvn2zmiX89l33TbbcnxxyezZhV/+LrrLq7deec66hYA6p7xjAAAAABA7n5+wnKtVZk9e/HZZYcfXjow23XXxeMYBWYANHJCMwAAAABo5l6aNCOjJ7y/xProN6fn5UkfO6PshReSL3whueGG4g9t0SK55JLk0UeTddap444BoO4JzQAAAACgmbtnWTvKCrvNRnzk2m9+k2yzTTJ2bPEHrr9+8vTTyXe+4/wyAJoMoRkAAAAANGMLFi7KvSMnLvP6fSMmZsH7HyRHHZUcc8zi0YzF7LHH4nGMX/pS3TcLACtRq5X5cAAAAACgYVVWVmbqrHnLvP6XV9/L5BkVy7xe/up/MrfPN7L6a+OKv6iwo+zSS5Nvf3vxaEYAaGKEZgAAAACwCnvh7Q+y9zXDa39jZWUOHv1YvvvUL9NuwbJDt4L563dL67t+l2y33SdvFAAamNAMAAAAAFZhj415p9b3rFYxO99/7LoM/vcfS9Y+8Zkv5OXLr8mJ233xE3YIAI2D0AwAAAAAVmGPjq1daLbppFdy7bDLs8G0t4vWzW/RMpcPODo3bLNfNh5fkRNXsE8AaGhCMwAAAABYRU2ZWZFXJs9avuLKyhw+6pGc/9Sv0nbh/KKlb66xToYOOjOj19+46vO4yTOr3rXW6m3rom0AaBBO5AQAAACAVVQhxLrz+G3TvXP7onUdK2blumGX55LHf1YyMHt0o22z99FXVwdmhWffdcK2AjMAmjw7zQAAAABgFbZVz/I8fEr/XDhsbO4dOXGJ65u//XKuvf8H6Tm9+BjHipatculOx+bmvvskZWVVawf06ZqLBm+aju1ar7T+AaC+CM0AAAAAYBVXCLWuGtI7AzbukvPuG5MZFQuqxjEe8/z9OefpX6fNogVF73+903oZOvisjFn3M/99Xqtcst9mGdy7az19AwBY+YRmAAAAANBMFEKuvj3Kc+6Nf8zhv7w4u7/8/0re8+Dn+uecPYZmRtvVqj737dEp1xzSJ93KO9RDxwBQf4RmAAAAANCMdH/pn7nuiq/nU5OWHNX4URUtW+fiXb6e23rvWT2OsWDgJusKzABYJQnNAAAAAKA5WLQoueqq5Jxz8qkFxccxvtK5a9U4xn+v/eklro0YP20lNgkADUdoBgAAAACruvfeS446KnnooZKl922yY87b7cTMarv03WQjx09LZWVlyj6y+wwAVgVCMwAAAABYlQ0fnhxySDJhQtGyOa3a5sJdj8+dWwysMY7x46bMnJc3p85JjzWNaARg1dKioRsAAAAAAFbSOMbLLkt23LFkYPbymt0z+Mgrc+eWuxUNzD5kRCMAqyKhGQAAAACsat59N9lrr+Tcc5OFC4uW3rXZrhl05I/zUpdeVZ97dO6Q7p3bF71HaAbAqsh4RgAAAABYlfzxj4vHMb79dtGy2a3bVp1ddu9mu1SvHdC3ay4atGnVny8cNjb3jpy41HuFZgCsioRmAAAAALAqKOwou/TS5KKLFo9mLOLFtXrmpP3Ozitrdq/63LFdq1y6/+YZtOX61TVXDemdARt3yXn3jcmMigU17p89b2EqKytTthyjHAGgqRCaAQAAAEBT9847yWGHJX/4Q8nSBV89Nvfs+Y28+vw7SWWyTa/y/HhI73Qr77BE7eDeXdO3R3lO+92o/OONaVXHnR2z3Qb51u4bCcwAWOUIzQAAAACgKXvyyeTww5NJk4rXrb568stfptUhh+Q7SXbbempGvDEtx+6wQVq1bLHM27p37pA7juuX64e/lq16lmebXp3r/jsAQCMgNAMAAACApmjBgsWjGAsjGSsri9f27p387nfJRhtVLxXCr+UNwAqh2gkDNlzRjgGgUROaAQAAAEBTM3FicuihybPPlq498cTkyiuTdu3qozMAaLKEZgAAAADQlDz6aHLEEcmUKcXrPvWp5IYbkgMPrK/OAKBJW/awYgAAAACg8Zg/PznnnGTPPUsHZltvnYwcKTADgFqw0wwAAAAAGrs330wOPjj5859L137zm8kPfpC0bVsfnQHAKkNoBgAAAACN2YMPJkcdlUydWryuU6fk179O9tuvvjoDgFWK8YwAAAAA0BjNm5eccUay776lA7MvfjEZNUpgBgArQGgGAAAAAI3N668n/fsnV11VuvZb30qeey7p2bM+OgOAVZbxjAAAAADQmNx3X/LVrybTpxevW3PN5Kabkr33rq/OAGCVZqcZAAAAADQGFRXJKackBxxQOjDbfvvF4xgFZgBQZ+w0ayZmzpyZsWPH5sUXX8x7772XuXPnplOnTll77bWz9dZbp1evXnXynkWLFmXcuHH517/+lbfffjsffPBB2rdvn86dO+fzn/98+vTpk9atW6cuFb7Pn/70p7zyyiuZNWtWVltttWy44YbZfvvts2bhJ64AAAAAGrtXXkmGDEmef7507TnnJBdfnLTyV3sAUJf8k7WBTZw4MX/729/y17/+ter3f/zjH5kxY0b19Z49e+b1wgzrT6DwzN///vd56qmn8vzzz1cFWstSeM8JJ5yQ448/PuXl5bV6zzvvvJN77rknjz/+eJ555pmqoGxZCgHagQcemNNOO60qQFsRo0ePzgUXXJAHH3xwqd+tZcuW2XvvvfO9730vW2yxxQq9CwAAAGClueuu5GtfS4r8nUqVLl2SW25Jdt+9vjoDgGalrLKysrKhm2huCruirrzyyqpQ66233ipa+0lCs1GjRuXLX/5yXn311Vr3tu666+bXv/519thjj+WqHzx48DJDq2JatGiR008/PZdeemnatGlT6z6vvvrqfOtb38qCBQtK1rZq1SpXXXVVTj755DQWhV1/m222WfXnMWPGZNNNN23QngAAAIBPZvrseRl6+8gaa9ce2iedOpT4O4+5c5PTT09+/vPSL9lxx+S225L111/BbgGgcRvbgH9/bqdZA/j73/+e+woHuq4kEyZMWGZgtsYaa1QFY4Xfp0yZktdeey0fzU0Lu8YKu7Nuu+22HHzwwcsVAC4tMCuMYFx//fXTpUuXqlGQhX5mz55dfb1wzxVXXJGXXnqpapdaIdhaXoUA7Iwzzlhifb311qt6ZyGILIyG/FAhWDvllFOqvmfhdwAAAIC69MDotzJ83JSaa/98O0f067nsm156KTnooMIoneIPLytLzj8/ueCCwlidOuoYAFiaFktdpcGsvvrqdf7Mfv365dprr61KZ6dPn151rllhl1vhDLBJkyZV7fbq0KFDjUDryCOPzMiRNX9CqpR11lkn3/72t6vGQRZGNBZ2yBUCwsL5ZoX33n///dl8881r3FNYO/vss5f7HX/+859z5pln1ljbcccdq8ZPFsKywnjLwu+F9w4YMKBGXSFoK4zABAAAAKhLdz8/YbnWqhV2jPXtWzowW2ed5IknkosuEpgBQD0QmjWgjh07VgU+haDprrvuqgqZHnjggTp5dmH84eGHH161bfEvf/lLTjrppGyyySZL1BV2gp177rlVNZ07d65enz9/fk499dTleldhm2Sh/8IOtx/+8IfZeeed065duyV2nu27775VodU+++xT49o111xTteNseRT+tVq4cGH158IzH3vssfQt/A/Nj9h6662rzlgr7Jr76I6zwv0AAAAAdeWlSTMyesL7S6yPfnN6Xp70v3PrqxSm8BTOLjv88GTWrOIP3nXXxaHaLrvUcccAwLIIzRpAIej5cNfX008/XRU0HXjggVXnl9WFjTbaKP/85z9zyy23LPeczy222KLqLLOPevbZZzNu3Lii991www0ZPXp0Vf/LM2KxEKbdcccd6datW42A7qabbip57yOPPFK10+xDa665ZtX7l3UmWmH9xhtvrKr76Hd6ovATWgAAAAB14J4iO8ruHvGRa//+d/LFLxb+MqX4A1u0SL73veTRRxfvNAMA6o3QrAFsuOGGVbu+CrvBVoZCaPZJDsUbNGjQErvRHi38D7QiBg8eXOvvsdpqqy1xtlhht1gp119/fY3Phd1zhZ1yxay99to58cQTiz4HAAAA4JNYsHBR7h05cZnX7xsxsaomhR8W3nrrZMyY4g9cf/3kD39IzjvPOEYAaABCM2ro379/jc/jx49vFO+pqKhYIlj76le/ulzv+nhdYcfavHnzlrtXAAAAoHmqrKzMezMrlvnr0bHvZPKMimXeP+O99/PO/gcnRx+9eDRjMXvskYwalXzsjHYAoP6UnqdHs1JeXl7j8/vvv98o3vPMM89k1kdmfW+88cbLPc6yV69e+exnP5uXX3656vOMGTPyxz/+MQMHDvxEvQMAAADNwwtvf5C9rxn+ie7dePLrue73l6fb1GWPbyyobNkyZZdeWjjIffFoRgCgwfgnMTVMnFhzpMBHzwNryPeMKvyk1Udst912tXrf9ttvX/R5AAAAAB/32Jh3an9TZWWGjH4sw24+PZ8pEZi91XGt/O6KW5KzzhKYAUAjYKcZNUYODB8+fInz0VaG5557rlbv+XfhsNyP+PjZa6V8vP7jzwMAAAD4uML4xdpYrWJ2vv/YdRn87z+WrH1yw23yrb1PyzqVXXPwCvQIANQdoRk1RiC+9tpr1Z/LysqyR2Gedh1buHBhbr755hpre+21V9F7/vOf/9T43L1791q98+P1H38eAAAAwEdNmVmRVyb/76iIUjaZ9GquHXZ5Pj3traJ181u0zOUDjs4N2+xX+MuXzJg8s+pda63etg66BgBWhNCMKosWLco555xTY60QmK277rp1/q5f/OIXefXVV6s/t27dOoceemjRe959990an7t161ard3bt2rXo81ZE4VmTJ0+u1T3jxo2rs/cDAAAAda8QYt15/LY59Xcj8+bUOcsurKzM4aMeyflP/SptF84v+swJn1o7QweflVHrb1z1uXvn9rn64D4CMwBoJIRmVLniiivy17/+tfpzixYtcmnhENo69sorr+Tss8+usXbSSSeVDMFmzpxZ4/Nqq61Wq/d+vP7jz1sRP/vZz3LRRRfV2fMAAACAxmGrnuV5+JT+uXDY2Nw7sub57AUdK2blskd+mn3+U/O4i6V5dKNtc+ae38wH7Vav+nxAn665aPCm6diu9UrpHQCoPaEZVeeLfec736mxduqpp6ZPnz51+p7Zs2fnwAMPzIwZM6rXevbsmYsvvrjkvR8Pudq1a1erd7dv377o8wAAAACWphBqXTWkdwZs3CXn3TcmMyoWVK1v/vbLufb+H6Tn9OLnnlW0bJXv73Rsbuq7T9U4xo7tWuWS/TbL4N41p+IAAA1PaNbMFcYkHnDAAVmwYPH/4Cvo3bt3vv/979fpeyorK3PUUUdl1KhR1WutWrXKbbfdlo4dO5a8f+7cuTU+t2nTplbvb9u25piDOXOKjFUAAAAA+JhCyNW3R3m++dsR2eK+W3Lu0zemzaL//X3K0rzRad2cNPjsjFn3M1Wf+/bolGsO6ZNu5R3qqWsAoDaEZs3YlClTsueee1b9/qF11lkn99577xIh04o644wzcvfdd9dYu/rqq7P99tsv1/2FnWWFnWofmjdvXq3eX1FRscTz6sqJJ56Yr3zlK7U+02y//farsx4AAACAla97WUWuuOPifHr4EyVrH/xc/5yzx9DMaPu/IyMGbrKuwAwAGjGhWTNVGJFYCMxeeuml6rU11lgjjz32WDbYYIM6fdfll1+eH//4xzXWLrzwwqqwaXmtvvrqNUKzj+88K+XjO8sKz6sra6+9dtUvAAAAYBVWOAt+yJB8+o03ipZVtGydi3f5em7rvWfVOMaPGjF+2kpuEgBYES1W6G6apELgNGjQoPzjH/+oXuvQoUMeeuihbLnllnX6rl/84hc555xzaqydcsop+e53v1ur53w85Jo1a1at7v94fV2GZgAAAMAqrLIyufLKZIcdkhKB2avl62f/I67MbX32WiIwKxg5flrVERYAQOMkNGtm5s+fn4MOOijPPPNMjfPBCiMZl3dU4vK6/fbbl9hNVjjX7Cc/+Umtn/XxnVwTJkyo1f0TJ04s+jwAAACAJbz3XjJoUPKtbyUfOQ9+aX6/yYDse9RP8sI6n15mzZSZ8/LmVOesA0BjJTRrRhYtWpQjjzwyDzzwQPVay5Ytq8Kt3XffvU7fNWzYsKqArPDOD335y1/ODTfckLKl/KRVKRtvvHGNz+PHj6/V/R+v/9znPlfrHgAAAIBm5E9/Snr3Th58sGjZ3FZtcuYep+TUfb6VWW1Ln1dmRCMANF5Cs2aisPX/uOOOyx133FG9Vgivrr/++qowqy49+eSTGTJkSBZ85CewCqFcIZwrhHSfxMdDrhdeeKFW9//73/8u+jwAAACAKoUfAL788mTAgMKom6KlL6/ZPYOOvCp3brlb1TjGHp07pHvn9kXvEZoBQOPVqqEboH6cdtppVbu8Puqaa67J0UcfXafv+dOf/pTBgwenoqKieq1///657777qsZAflK9Cz/Z9RF//vOfa91XsecBAAAAZPLk5Mgjk0cfLVl692a75PyB38icNu2qPh/Qt2suGrRp1Z8vHDY2946seVTEh4RmANB4Cc2agfPPPz9XX311jbXvf//7GTp0aJ2+Z8SIEdl7770ze/bs6rWtt946Dz74YNq3L/5TVqXsuOOOWW211TJr1qyqzy+99FLeeOON9OzZs+S9r7/+el5++eXqzx07dqx6HgAAAEC1P/4xOfTQ5K23ipbNbt025w88MfdsvkvV547tWuXS/TfPoC3Xr665akjvDNi4S867b0xmVNQ8C232vIVVE4E+yfEVAMDKZTzjKu5HP/pRLrnkkhpr55xzTtWvulQYl1gYwfj+++9Xr2222WZ59NFH86lPfWqFn9+uXbvstttuNdZuvPHG5br343V77LHHCu16AwAAAFYhCxcm3/tesvPOJQOzRZtuml//5M7cu8XiwGybXuV55Jv9awRmHxrcu2se/mb/bN2zvOpzISP76vYb5MGTdxCYAUAjZafZKuwXv/hFzjzzzBprhd1lhV1mdem1117LwIEDM2XKlOq1z3zmM3niiSey5ppr1tl7jj322Koxjx+67rrrqr5Ply5dlnnPu+++m5/97GdLPAcAAAAg77yTHH548tRTpWu/9rW0uPrqnNShQ77w+tSMeGNajt1hg7RqueyfSe/euUPuOK5frh/+WrbqWZ5tenWu2/4BgDplp9kq6vbbb8+JJ55YY+2YY46pOsesLr311lvZddddq37/UI8ePfLUU09l3XXXrdN3FUY/9uvXr/rze++9VxWAzZ8/f6n18+bNq7peqPvo+WqFHXEAAABAM1cIygpnnpcKzFZfPbnttuRXv0o6dKhaKoRfxw/YsGhg9qFCzQkDNhSYAUATYKdZA/nTn/6UOXPmLLE+evToGp/nzp2bJ598cqnPWH/99bPJJpsssV6oP+qoo7Jo0aLqtc997nMZMmRIVZhVG+Xl5dlqq62Weq1wdllhZOKrr75avdayZcucffbZVWeOFX7Vxg477FA1hrHUuMkBAwZUf7cHHnigqocrr7wyffv2ra57/vnnc8YZZ+SPhXnkH+nthz/8Ya16AgAAAFbBcYwXX7x4JGNlZfHaLbdM7rwz2Wij+uoOAGhAQrMGcthhh+WNN94oWTdp0qSq0YdLUwjGfvOb3yyxPnz48CxYUPOQ2RdffLHqLK/aKgRUzzzzzDJHH44dO7bG2sKFC5fY4VabMY+9evUqGaxddtllOeuss6rXCv0Vgr1CiLjeeutV7Xp7++23l7i3EJh9dKcaAAAA0MwUJuUcemjykR+yXabC329ceWXhoPX66AwAaASEZjQ5hXPaCrvGCsFZIaT7UCEs++iYyA8Vaq+44oqceuqp9dwpAAAA0Gg89tji88s+cib7Un3qU8n11ydf+Up9dQYANBLONKNJKoxe/Mc//lF1zlmLFkv/t3FhfZ999qka1SgwAwAAgGaqMI3nnHOSwgSeUoFZ4YiKESMEZgDQTJVVVpYa3gyN25QpU6pGUhbOVps1a1ZWW221bLjhhtl+++2z1lprpTEqjLXcbLPNqj+PGTMmm266aYP2BAAAAKucN99MDjmkcLh86dpTTimc7ZC0bVsfnQEAjfDvz41npMkrBGP77bdfQ7cBAAAANCYPPZQceWQydWrxuk6dkl//OvF3CwDQ7BnPCAAAAMCqY/785FvfSvbZp3Rg9sUvJiNHCswAgCpCMwAAAABWDa+/nvTvn1x5ZenaM85Inn026dWrPjoDAJoA4xkBAAAAaPp+//vkmGOS6dOL13XunNx00+KdaAAAH2GnGQAAAABNV0VFcuqpyf77lw7Mtt8+GTVKYAYALJXQDAAAAICm6dVXFwdhV19duvbss5Onn066d6+PzgCAJsh4RgAAAACanrvvTo49Nvngg+J1a62V3HJLssce9dUZANBECc0AAAAAaBDTZ8/L0NtH1li79tA+6dShzbJvmjs3OeOM5Gc/K/2CL30puf32pGvXOugWAFjVCc0AAAAAaBAPjH4rw8dNqbn2z7dzRL+eS7/h5ZeTgw5afC5ZMWVlyXnnJRdckLTy118AwPJxphkAAAAADeLu5ycs11qV3/426du3dGC2zjrJ448nF18sMAMAakVoBgAAAEC9e2nSjIye8P4S66PfnJ6XJ83438KcOclxxyWHHprMnFn8oTvvvDhU23XXldAxALCqE5oBAAAAUO/uWdaOssJusxH/vfbii8kXvpD86lfFH9aixeKdZYUdZuuuW8edAgDNhT3qAAAAANSrBQsX5d6RE5d5/b4RE3PmpL+m5UknJbNnF3/Yeuslt9+e7Lhj3TcKADQrQjMAAAAA6lRlZWWmzpq3zOt/efW9TJ5RsdRr7efNzbfv+Elajnmy9It22y255ZZk7bVXpF0AgCpCMwAAAADq1Atvf5C9rxle6/s+O/mNXDfsB9novfFF6xaUtch7Z5+fdS65YPFoRgCAOuB/VQAAAABQpx4b807tbqiszEGjH8/9N59eMjB7q+NaOfjQy3LbTocKzACAOmWnGQAAAAB16tGxyx+arVYxO5c8/rPs/8IzJWuf2nCbfGuvUzOtwxqZMeadnD5woxXsFADgf4RmAAAAANSZKTMr8srkWctV+/l3X821w36QDadOLFo3v0XL/PBLR+X6L+yXyrLFu8vGTZ5Z9a61Vm9bJ30DANjDDgAAAECdKYRYdx6/bbp3br/sosrKHDrqkfz+5jNKBmYTPrV2Djr0B/nVFw+oDswKz77rhG0FZgBAnbLTDAAAAIA6tVXP8jx8Sv9cOGxs7h1ZMxRbvWJ2Ln/0p9nnxedKPuexz/bLt/c6NR+0W7167YA+XXPR4E3TsV3rldI7ANB8Cc0AAAAAqHOFUOuqIb0zYOMuOe++MZlRsSCbvTOuahxjr+lvF713XotW+f5OX81vtto3KSv77/Na5ZL9Nsvg3l3r6RsAAM2N0AwAAACAlaYQcvXt3imPfeO8HHHvtWm7cEHR+jc6rZuhg87Kv9b7bPVa3x6dcs0hfdKtvEM9dAwANFdCMwAAAABWnunT0/24Y/O1e+8tWfrQxtvn7D1PyYy2q9VYH7jJugIzAGClE5oBAAAAsHL87W/JkCHJ668XLato2Trf2+XrubX3ntXjGD9qxPhpK7FJAIDFhGYAAAAA1K3KyuTHP07OOitZUHwc46vl62fo4LPzwjqfXmbNyPHTUllZmbKlBGoAAHVFaAYAAABA3Zk6NTn66OSBB0qWDvv8gJy7+0mZ1bb46MUpM+flzalz0mNNIxoBgJVHaAYAAABA3fjzn5ODD07efLNo2dxWbfLdXY7LHVvuvtRxjEtTGNEoNAMAVqYWK/XpAAAAAKz6Fi1KfvjD5EtfKhmYjevcLYOPvCp39N6jOjDr0blDunduX/Q+55oBACubnWYAAAAAfHKTJydHHZU88kjJ0ns22znnD/xGZrf5X0B2QN+uuWjQplV/vnDY2Nw7cuJS7xWaAQArm9AMAAAAgE/m2WeTQw5J3nqraNmc1u1y/sATcvfmu1avdWzXKpfuv3kGbbl+9dpVQ3pnwMZdct59YzKjYkGNZ8yetzCVlZUpW85xjgAAtWU8IwAAAAC1s3BhcsklyU47lQzMsummyd//mo4nfK36+LJtepXnkW/2rxGYfWhw7655+Jv9s3XP8qrPhXu+uv0GefDkHQRmAMBKZacZAAAAAMtv0qTk8MOTJ58sXXvssck116R9hw65cMtkr83Xy4g3puXYHTZIq5bL/lnu7p075I7j+uX64a9lq57l2aZX57r9DgAASyE0AwAAAGD5/OEPyWGHJe+8U7xutdWSX/xice1HFMKv5Q3ACqHaCQM2XJFuAQBqxXhGAAAAAEqPY7zwwmTXXUsHZltumYwYsURgBgDQ2NlpBgAAAMCyFc4sKwRgzzxTuvaEE5Krrkrat6+PzgAA6pTQDAAAAICle/zxxeeXTZ5cvK5jx+T665ODDqqvzgAA6pzxjAAAAADUtGBB8p3vJHvsUTow69t38ThGgRkA0MTZaQYAAADA/0yYkBxySDJ8eOnak09OfvSjpG3b+ugMAGClEpoBAAAAsNjDDydHHpm8917xujXWSG68MTnggPrqDABgpTOeEQAAAKC5mz8/OfPMZO+9SwdmX/hCMnKkwAwAWOXYaQYAAADQnL3xRnLwwcn/+3+la08/PbnssqRNm/roDACgXgnNAAAAAJqrYcOSY45Jpk0rXldentx0U7LvvvXVGQBAvTOeEQAAAKC5mTcvOfXUZL/9Sgdm222XjBolMAMAVnlCMwAAAIDm5NVXk+23T66+unTtWWclzzyT9OhRH50BADQo4xkBAAAAmpjps+dl6O0ja6xde2ifdOpQ4qyxu+9Ojj02+eCD4nVrrZXcfHOy55510C0AQNMgNAMAAABoYh4Y/VaGj5tSc+2fb+eIfj2XfsPcuckZZyQ/+1nph/fvn/z2t0nXrnXULQBA02A8IwAAAEATc/fzE5ZrrcrLLy8+l6xUYFZWlpx3XvKHPwjMAIBmSWgGAAAA0IS8NGlGRk94f4n10W9Oz8uTZtRcvOOOpG/fZGTNUY5LWHvt5LHHku99L2llMBEA0DwJzQAAAACakHuWtaOssNtsxH+vzZmTHH98csghycyZxR+4887JqFHJwIF13CkAQNMiNAMAAABoIhYsXJR7R05c5vX7RkzMgrEvJF/8YvLLXxZ/WIsWyUUXJY8/nqy3Xt03CwDQxNhvDwAAANBIVFZWZuqsecu8/pdX38vkGRXLvL79Xx5JLv2/ZM7s4i8qhGS3357suOOKtAsAsEoRmgEAAAA0Ei+8/UH2vmZ4re9rP29uLnry/3LQv54sWTtzwM5Z/c7fLj7HDACAakIzAAAAgEbisTHv1Pqez05+I9cN+0E2em980boFZS1yVf/D0+qcc3K6wAwAYAnONAMAAABoJB4dW4vQrLIyX/nn47n/5tNLBmZvr75mDj70svxs24Py2AvvrnijAACrIDvNAAAAABqBKTMr8srkWctV22HenFzy+M9ywNinS9b+4dNb54y9T8u0DmtUfR43eWbVu9Zave0K9wwAsCqx0wwAAACgESiEWHcev226d25ftO7z776aB246tWRgNr9Fy1y641dz7IEXVAdmhWffdcK2AjMAgKUQmgEAAAA0Elv1LM/Dp/TPAX26LnmxsjKHjnokv7/5jGw4dWLR50z4VJcMOfTy/OqLB6SybPFf/xSeWXh23x7lK6t9AIAmzXhGAAAAgEakY7vWuWpI7wzYuEvOu29MZlQsyOoVs3PZoz/Nvi8+V/L+xz/bL9/e85t5v33H/z6vVS7Zb7MM7r2UIA4AgGpCMwAAAIBGqBByFXaFXf3DOzL0N+el1/S3i9bPa9Eql+10TH691aCkrKxqrW+PTrnmkD7pVt6hnroGAGi6hGYAAAAAjVFlZbrffmN+cNXpaTl/ftHS8Wusk6GDz8o/19uoxvrATdYVmAEALCehGQAAAEBjM316cuyxyb33pmWJ0oc32i5n73lKPmi3+hLXRoyfttJaBABY1QjNAAAAABqTv/0tGTIkef31omUVLVvlezt/Pbf22at6HOPHjRw/LZWVlSlbxnUAAP6nxUf+DAAAAEBDqaxMfvzjZIcdSgZmr5WvlwOOuDK39t17mYFZwZSZ8/Lm1DkroVkAgFWPnWYAAAAADW3q1OToo5MHHihZev/nv5Rzdx+amW2X76yywojGHms61wwAoBQ7zQAAAAAa0p//nPTuXTIwm9uqTc7efWhO2ffb1YFZj84d0r1z+6L3OdcMAGD52GkGAAAA0BAWLUquuCI599xk4cKipa907paTBp+VF9feoHrtgL5dc9GgTav+fOGwsbl35MSl3is0AwBYPkIzAAAAgPo2eXJy1FHJI4+ULL1n051y/m4nZnabxTvKOrZrlUv33zyDtly/uuaqIb0zYOMuOe++MZlRsaDG/bPnLUxlZWXKipx9BgCA0AwAAACgfj33XHLwwclbbxWva98+Fdf8NGPW6Zc5f349qUy26VWeHw/pnW7lS55RNrh31/TtUZ7Tfjcq/3hjWgoZ2THbbZBv7b6RwAwAYDkIzQAAAADqaxzjZZclF1yw+M/FbLppcuedabvJJrkwyV6br5cRb0zLsTtskFYtl31EfffOHXLHcf1y/fDXslXP8mzTq3Pdfw8AgFWU0AwAAABgZZs0KTniiOSJJ0rXfvWryU9/mnT4326yQvi1vAFYIVQ7YcCGK9ItAECzJDQDAAAAWJn+8IfksMOSd94pXrfaasn//V9y+OH11RkAAB+x7P38AAAAAHxyCxcm3/1usuuupQOzLbZInn9eYAYA0IDsNAMAAACoa2+/nRx6aPLMM6VrTzghueqqpH37+ugMAIBlEJoBAAAA1KXHH1+8Y2zy5OJ1HTsmv/pVMmRIfXUGAEARxjMCAAAA1IUFC5LvfCfZY4/SgVnfvsmIEQIzAIBGxE4zAAAAgBU1YcLicYzPPVe69uSTkx/9KGnbtj46AwBgOQnNAAAAAFbEww8nRx6ZvPde8bo11khuvDE54ID66gwAgFownhEAAADgk5g/PznzzGTvvUsHZttsk4wcKTADAGjE7DQDAAAAqK033kgOPjj5f/+vdO3ppyeXXZa0aVMfnQEA8AkJzQAAAABqY9iw5JhjkmnTiteVlyc33ZTsu299dQYAwAownhEAAABgecybl5x2WrLffqUDs223TUaNEpgBADQhdpoBAAAAzcb02fMy9PaRNdauPbRPOnUoMTrxtdeSIUOSv/+99EsK55xdcknSuvUKdgsAQH0SmgEAAADNxgOj38rwcVNqrv3z7RzRr+eyb7rnnuTYY5P33y/+8LXWSm6+OdlzzzrqFgCA+mQ8IwAAANBs3P38hOVaqzJ3bjJ0aHLggaUDs/79F49jFJgBADRZQjMAAACgWXhp0oyMnrBk+DX6zel5edKMmosvv5xst11y3XXFH1pWlpx3XvKHPyRdu9ZxxwAA1CehGQAAANAs3LOsHWWF3WYjPnLtjjuSrbZKRtY8+2wJa6+dPPZY8r3vJa2cgAEA0NQJzQAAAIBV3oKFi3LvyInLvH7fiIlZMHNWcvzxySGHJDM+tvPs43baafE4xoED675ZAAAahB+DAgAAAJq8ysrKTJ01b5nX//Lqe5k8o2KZ1zu+Pi6z+56ST7387+IvatEiufDC5DvfSVq2XJGWAQBoZIRmAAAAQJP3wtsfZO9rhn+ie/cf84dc8vjPstr8uUXr5q+9Tlr/7o5kxx0/YZcAADRmQjMAAACgyXtszDu1vqf9vLm56Mn/y0H/erJk7bO9+mTsD67LN3bc9hN2CABAYyc0AwAAAJq8R8fWLjT77OQ3ct2wH2Sj98YXrVtQ1iJX9T88P+93YDaaOD/fWME+AQBovIRmAAAAQJM2ZWZFXpk8a/mKKyvzlX89mYuf+L+0X7DsM84K3l59zZw8+Mz8o9umVZ/HTZ5Z9a61Vm9bF20DANDItGjoBgAAAABWRCHEuvP4bdO9c/uidR3mzclVD12VHz1ydcnA7A+f3jp7HXNNdWBWePZdJ2wrMAMAWIXZaQYAAAA0eVv1LM/Dp/TPhcPG5t6RE5e4/rl3X8t1wy7PhlOXvPZR81u0zA+/dFSu/8J+qSxb/LPGB/TpmosGb5qO7VqvtP4BAGh4QjMAAABglVAIta4a0jsDNu6S8+4bkxkVC6rGMR46+tFc+OQv03bh/KL3T/hUl5wy6MyM6Pr5/z6vVS7Zb7MM7t21nr4BAAANSWgGAAAArFIKIVffHuU5+9fDc/D1l2TfF58rec8Tn/livrXXqXm/fceqz317dMo1h/RJt/IO9dAxAACNgdAMAAAAWOV0f/3FXPfj49Jp4htF6+a1aJXLdzwmN249KCkrq14fuMm6AjMAgGZGaAYAAACsOiork+uuS844I53mzStaOn6NdTJ08Fn553obLXFtxPhpK7FJAAAaI6EZAAAAsGqYPj352teSe+4pWfrwRtvl7D1PyQftVl/q9ZHjp6WysjJlH9l9BgDAqk1oBgAAADR9f/97MmRI8tprRcsqWrbK93b+em7ts1eNcYwfN2XmvLw5dU56rGlEIwBAc9GioRsAAAAAWKFxjD/5SbL99iUDs9fK18sBR1yZW/vuXTQw+5ARjQAAzYvQDAAAAGiapk5N9tsvOe20ZP78oqX3f/5L2feoqzN2nQ2rPvfo3CHdO7cveo/QDACgeTGeEQAAAGh6/vKXxeMY33yzaNncVm3y3V2Oyx1b7l69u+yAvl1z0aBNq/584bCxuXfkxKXeKzQDAGhehGYAAABA07FoUXLFFcm55yYLFxYtfaVzt5w0+Ky8uPYGVZ87tmuVS/ffPIO2XL+65qohvTNg4y45774xmVGxoMb9s+ctTGVlZcqWY5QjAABNn9AMAAAAaBqmTEmOPDJ55JGSpQsOOyy/G/TN/Gfku0llsk2v8vx4SO90K++wRO3g3l3Tt0d5TvvdqPzjjWlVG9KO2W6DfGv3jQRmAADNiNAMAAAAaPyeey455JBk4tJHKVZr3z657rq0OvronFtWloFfmJoRb0zLsTtskFYtl320e/fOHXLHcf1y/fDXslXP8mzTq3PdfwcAABo1oRkAAADQuMcxXnZZcsEFi/9czCabJHfdtfj3/yqEX8sbgBVCtRMGbLiiHQMA0EQJzQAAAIDGadKk5IgjkieeKF371a8mP/1p0mHJ8YsAALA8hGYAAABA4/P008mhhybvvFO8brXVkv/7v+Tww+urMwAAVlHLHuYNAAAAUN8WLkwuuijZddfSgdkWWyT/+IfADACAOmGnGQAAANA4vP12cthhi3eZlXL88cmPf5y0b18fnQEA0AwIzQAAAICGVzi3rLBj7N13i9d17Jj88pfJwQfXV2cAADQTxjMCAAAADWfBguS885Lddy8dmPXpk4wYITADAGClsNMMAAAAaBgTJiSHHpo891zp2qFDkx/9KGnXrj46AwCgGRKaAQAAAPXv4YeTI49M3nuveN0aayQ33JB8+cv11RkAAM2U8YwAAABA/Zk/PznzzGTvvUsHZttss3gco8AMAIB6YKcZAAAAUD/Gj198Htlf/lK69rTTkssvT9q0qY/OAABAaAYAAADUg/vvT44+Opk2rXhdeXnym98kgwbVV2cAAFDFeEYAAABg5Zk3Lzn99GTw4NKB2bbbJiNHCswAAGgQdpoBAAAAJU2fPS9Dbx9ZY+3aQ/ukU4ci4xNfey0ZMiT5+99Lv6BwztkllyStW9dBtwAAUHtCMwAAAKCkB0a/leHjptRc++fbOaJfz6XfcO+9yVe/mrz/fvEHr7lmcvPNyV571WG3AABQe8YzAgAAACXd/fyE5VpLRUVy8snJl79cOjDbYYdk1CiBGQAAjYLQDAAAACjqpUkzMnrCkgHY6Den5+VJM/63MG5cst12ybXXFn9gWVnyne8kTz+ddOu2EjoGAIDaE5oBAAAARd2ztB1l/3X3iP9e+93vkr59kxEjij+sS5fksccWn1/WyqkRAAA0HkIzAAAAYJkWLFyUe0dOXOb1h//6ahYdf3xy8MHJjI/sOluanXZKRo9OBg6s+0YBAGAF+ZEuAAAAaMYqKyszdda8ZV7/y6vvZfKMiqVe+/R7E3LdsMvTYvLrxd9RVpayCy5Izj8/adlyhXsGAICVQWgGAAAAzdgLb3+Qva8ZXuv79hv7dC597LqsNn9u0bp3VyvP7BtvSq+D9l2BLgEAYOUznhEAAACascfGvFOr+nbz5+YHD1+dnzx4ZcnA7NlefbLXMdfk3vKNV7BLAABY+YRmAAAA0Iw9Onb5Q7PPTBmf+286PUP+9UTRuoVlLfKj/kfkqIMuypTVymsdzAEAQEMwnhEAAACaqSkzK/LK5FmlCysr85V/PZmLn/i/tF+w9PPNPvTO6p1zyqAz87fum1WvjZs8s+pda63eti7aBgCAlcJOMwAAAGimCiHWncdvm+6d2y+zpsO8Obnyoavyo0euLhmYPf3prbLXMT+tEZgVnn3XCdsKzAAAaPTsNAMAAIBmbKue5Xn4lP65cNjY3DtyYo1rn3v3tVw77Af5zNQJRZ+xoDCOccCR+eUXDkhl2f9+PveAPl1z0eBN07Fd65XWPwAANPnQ7Nlnn01D+9KXvtTQLQAAAECDK4RaVw3pnQEbd8l5943JjLnzc8jox3LhU79MuwXzit47sWOXnDzozIzo9vmPPK9VLtlvswzu3bUeugcAgCYemu24444pKytrqNdXvXvBggUN9n4AAABobAoh11adWua1Aw9P/+efKln/xGe+kG/tdVreb9+xeq1vj0655pA+6VbeYSV3CwAAq9h4xsrKyoZuAQAAACgYOTLdDjoo3caNK1o2r0Wr/GDHo3PD1oMLP5Va49rATdYVmAEA0CQ1aGgmMAMAAIBGoPD/n//sZ8nppyfzio9jfHONdTJ00JkZvf7GS70+Yvy0ldQkAACsoqHZhRde2FCvBgAAAD40fXry9a8nd99dsvSRjbbLWXv+f/buAzqqam3j+JMChBRIQlEISSgKSJEkFEFA0E+vSheU3pEi0kUU9V5EQUUUpCqIYrkiIIKAIFgQkd4CXEBFpIeWkAQSSvq3zomJDElmEtImyf+31qzM7HnPPnu4Lq/hmf3uEbri4p5hTfCpCPNLsvl5JAMAAABwOwjNAAAAAAAoqnbtkrp0kY4ft1oW4+SsyQ8O0GdBbdK0Y7xVWHSsTodfl18ZWjQCAACgYHHM7wUAAAAAAIB8aMf43ntS06Y2A7MTnhXUsec7+qx+W5uBWQpaNAIAAKAgIjQDAAAAAKAoCQ+XnnhCGj1aiouzWrq6ZnO16TtDh+68K3XMz9tVvt4lrV5HaAYAAICCKN/aMwIAAAAAgDy2fXtyO8ZTp6yWxTgV06sPD9aX9R612F3WMchHE9vVNp9PWHlIy4ND0r2e0AwAAAAFEaEZAAAAAACFXWKi9O670ksvSfHxVktPl/PVwDbj9Hv5KqljHi7OmvxEXbWrVzF1bFqXALWoUU6vrDioqBjLOa/FJigpKUkOmWznCAAAANiDAh2ahYeH67fffjN/Xr58WYmJiXr00Ud1xx135PfSAAAAAACwD2FhUt++0po1tmt79lSZGbPUZEuI/th6wjz6rGFlL03vEqBKXq5pytsH+CjIz0ujl+zT7pMR5qa0fvdX0dhHqxOYAQAAoMApcKHZxYsXNXv2bH399df6/fff07z/ww8/pBuaLVy4UKdPnzafV6xYUU8//XSerBcAAAAAgHyzebPUtasUkn4bxVQlS0qzZ0v9+snVwUET2nqqVd0K2nsyQgOaVZGzU8ZHovt6u2rxoMZasPm46vt7qWFl75z/HAAAAEAeKFCh2dSpU/Wf//xHsbGxZpuHW1n7Flt0dLReffVVs8bJyUlt27ZlRxoAAAAAoPC2Y5wyRfr3v6WEBOu1tWpJS5dKtZPPKkthhF+ZDcCMUG1Ii2rZWTEAAACQ7zL+qpgdSUhIUMeOHfXiiy8qJiYmzfuZafkwYMAAlSpVygzbjPkWLVqUS6sFAAAAACAfXbwoPf548vlltgKzfv2knTvTBGYAAABAUVQgQrNnn31W33zzTeohwsbPwMBAvfDCC5ozZ066u85u5erqau4uS7F27dpcXjUAAAAAAHls40YpIED6/nvrdW5u0mefSR9/nPwcAAAAgP2HZps3b9b8+fPNsMx4lC1bVmvWrNGePXv05ptv6plnnsn0brMOHTqYP42QbcuWLWabRwAAAAAACjxjR9nEidL//Z907pz12rp1pd27pV698mp1AAAAQIFg96GZcYZZStDl4eGhX375RY8bbSZuw3333Zf63Gjz+Mcff+TYOgEAAAAAyBfnz0v/+pf06qvJZ5lZM2iQtGOHVLNmXq0OAAAAKDDsOjSLiIjQr7/+mrrL7JVXXlHNbPyHfaVKleTl5ZX6+vfff8+hlQIAAAAAkA9+/FGqV0/asMF6nbu79OWX0rx5UsmSebU6AAAAoEBxtPfWjAkJCeYuM0dHRz399NPZnrN8+fKpzy8ahyMDAAAAAFDQxMdLr7ySvMPM1u+2gYHS3r1S1655tToAAACgQHKWHTt79qz509hlVrVqVXl6emZ7ztKlS6c+j4qKyvZ8AAAAAADkqZAQqXt3adMm27XPPiu9847k4pIXKwMAAAAKNLsOzcLDw1Ofe3t758icxllmKYoVK5YjcwIAAAAAkCfWrZN69ZLCwqzXlSolffSR9OSTebUyAAAAoMCz6/aMubEr7OaWjGXLls2ROQEAAAAAyFVxcdKLL0qPP247MGvQQAoOJjADAAAACtNOs3Llypk/jTPNTp48qcTERPNss9t1+vRpnTt3LvV1xYoVVVRER0fr0KFD+v3333Xp0iXduHHDbHdpnPHWoEEDVa5cOb+XCAAAAABIz6lTUrdu0tattmtHjZKmTJGKF8+LlQEAAACFil2HZvXq1Ut9fu3aNW3ZskXNmze/7fm++uqr1OdOTk5q3Lix8ltISIh27typHTt2mD93795tsavO399fJ06cuK25jTm/+eYb/fTTT9qzZ48ZOmbEuM+QIUM0ePBgeXl56XYdO3bM4vMEBwfr+vXrqe+3aNFCGzduvK25jT+HKlWqKDuMABYAAAAACozVq6W+fY3zC6zXGWeAf/KJ1L59Xq0MAAAAKHTsOjSrXr26GZKkhEbTpk277dDsypUrmj59uhwcHMzXDRs2lIeHh/KDEf69++67ZrB09uzZHJ9/37596tSpkxlgZZaxk2/8+PGaMWOGFi5cqMceeyzT165atUrz5s0zQ7IwW21CAAAAAKCIibwWq2GLgi3GZncPlKerld1gsbHS+PHGL8K2b2B8IXTxYuPbkDmwWgAAAKDosuszzQy9e/c2dwcZDyOc+fTTT7M8R0JCgjmPsasrZafR0KFDlV927dqlFStW5EpgZjhz5kyGgZlxTlyNGjXUqFEjVa1aNTVETHH+/Hm1bt1ai41fuDJpw4YNWrt2LYEZAAAAAKRj9f6z2nw0zOKx+sA/Rwekcfy4ZHxhNDOB2fPPS5s2EZgBAAAAhX2nmWHs2LF6//33FRoaagZeTz/9tC5evKgxY8aYLRZtMc7wMloObt68OTUgMnawde/eXfbI3d3dPH8sJxltKHv27KkHH3xQtWrVsnjP+HP98MMPNXnyZLMFpsFo42iEjEa4FhgYmK17u7m56erVq8oN//rXv/S88QsiAAAAANixZXvOpDvWq3E6Qdfy5VL//tLly9YnLVNGMr5U2rp1Dq4UAAAAKNrsPjQzQpcFCxboiSeeMMMcY9fYiy++qLlz56pbt26qX7++WWcEakYoZpzdFR4erqNHj5o7oIxHyk41Q8mSJbVo0aI0O6zyg9Ee0li/0SrS2Pll/Dx+/LgZbmWXo6OjGQwaf1a1a9fOsK5cuXJ66aWX1KZNG/O+xp+dIS4uTqNGjdIvv/yS6Xt6e3ubnyHlYXymdevWqV+/fsoNFSpU0MMPP5wrcwMAAABATjhyIUr7z6QNwPafjtSfF6J09x1/HxsQE2N8a1SaPdv2pM2aSV9+KVWqlAsrBgAAAIouuw/NDEagM2fOnNSWikYAZpzBNWXKFIs6Y9wIiW4dSwnIihUrZp7Xld3dU9nVtm1bc5dUzZo1zXDrZkZoll3GTroDBw5YDctude+995p/Nu1vOjR606ZNZvh41113Wb124MCBGj58uKpVq5atdQMAAABAYfN1OrvMUizbe0bjH79HOnpU6tJF2rvX+mTG77bGOWcTJ0rOBeLXeQAAAKBAsfszzVIMGjRI69ev1x133GG+TgnCUkKxlEfKrrKUnWUpY8Z1P/30kzp37qz8ZoRLRpvEWwOznGKEZlkJzFK0a9cuTftGY6eYLca9CMwAAAAAwFJ8QqKWB4dk+P6KvSFKMM6TDgqyHZiVK2f8giZNnkxgBgAAABT10Mzwf//3f/rtt9/0xhtvmK35UoKxW4OyFMZrT09PTZw4UX/88YeaGS0sYFVz47Dpm5w6dSrf1gIAAAAA9sz4nfNSdEyGj3WHzis0Kibda0vExWjEsmly6tZNioqyfqOWLaX9+42DnXPngwAAAAAwFbivp5UuXdpswThu3Djt379fv/76qxmkXbp0SZGRkXJ1dVXZsmVVpUoV84wu41wtZ76Fl2leXl4Wry/bOnwaAAAAAIqow+euqPXMzVm+ruqlM5qz8i3dE3rCal2iHHRpzAsq9/YkyckpGysFAAAAkBkFNk0yWhsaZ5Pl9/lkhU1IiGXrkDJlyuTbWgAAAADAnq0/eD7L17Q/9LPeWD9HbnE3rNZddPPSyLZj1fCxJzWGwAwAAADIEwU2NEPutBbZvHlzmvPR7N3p06d1/vx53bhxQ97e3ipfvrzKGf3+AQAAACAXGe0XM8sl7oZe/XG+uh743mbtr/4BGt32OYW5eSn84HmNecT+fy8DAAAACgNCM6TauHGjjh8/nvrawcFBjz32mOzV999/r4oVK+rcuXNp3qtcubJatmypQYMGqUmTJvmyPgAAAACFV1h0jP4KvZqp2rvCTpntGGuEWT8zOsHBUdOa9dDcJk8pySH5CPKjodHmvcq6l8iRdQMAAADIGKEZTImJiRo/frzFmBGY3XnnnbJX6YVlKU6cOKFPPvnEfDz00ENauHCh/Pz8cmUdFy9eVGhoaJauOXr0aK6sBQAAAEDeMEKspYObaNSSYJ0Ov55h3ZP/+1Gv/fC+XONirM533t1bI9qN007fOqljvt4lNaNrIIEZAAAAkEcKbGh25coVbd++XTt37jR3R0VGRio6Olru7u7y9PRUlSpV1KhRIzVu3FilSpXK7+XavXfeeUc7duywODNu8uTJKgw2bNhgnn23YsUKPfDAAzk+/9y5czVx4sQcnxcAAACAfavv76W1I5prwspDWh5seT60a+x1vf7D++p0cIPNeX6uWl/PtR6jcNfSqWMdA300sX1tebgUy5W1AwAAACgEodmuXbs0Y8YMLV++XDEx1r+pZyhRooQ6duyoESNGmCEa0vr111/18ssvW4yNGjXKDJrsUaVKldS2bVtzB1mdOnVUoUIFubm56fLly2aA+vPPP+uDDz7QsWPHUq8JDw9X+/bttW3bNtWsWTNf1w8AAACg8DBCrWldAtSiRjm9suKgomLiVSP0hOZ885buCj9j9dp4B0dNbdFb8xt1TG3H6OHirEkd6qh9gE8efQIAAAAABS40u3r1qsaNG6d58+YpKSnJfNx89tatUt6/ceOGvvzySy1evNg83+rtt982d6MhmREsGaFifHx86lhAQIDeeOMN2ZvSpUtr1apVat26tbkT7lZlypQxHw0aNNCYMWP0+uuvmw+j9aTB2I3Ys2dPM3hN758ZAAAAALhdRsgV5Oup1SNeU/+l78klPtZqfYhHOQ1vN057K92TOhbk56mZ3QJVycs1D1YMAAAAoECGZhEREeb5Wrt37zbDMCPwMB4pwdjNAdqtUsIRIzgxAjcjMPn+++/l5eWloi4sLEyPP/64+TPFHXfcYe7iM3bo2RvjfzNjh1lmODk56dVXXzWvMXbNpdizZ4/5+Tp16pRj6xo6dKieeuqpLJ9p1qFDhxxbAwAAAIB8FhUl3+GDNfTLL22W/nBXI41tNVqXS3pYjD9S604CMwAAACAf2X1oZoRdrVq1MsMuQ0pYZuw0atmypfkwWvQZO4yMFn3GjrRLly7pf//7n3755RfzYcyRcp0Rmhjzbd26tUjvNoqKijIDsyNHjljs5Fq/fr15HlxhMXLkSPMsM+OfgxSff/55joZm5cuXNx8AAAAAiqjgYKlzZ+PbcVbLYh2dNaVlX33UoL3xy22a9/eeisjFRQIAAAAo8KHZe++9px07dqQGXEbw1bVrV7311lvy8/PL8LonnnjC/Hnq1Cm98MILWrJkSWpwtnPnTk2fPt1s4VcUGS0r27VrZ+7cS+Hq6qo1a9aoXr16Kmyee+45i9Bsw4YNZjtKZ2e7/8cfAAAAgD0zup68/75k/G5p48zt06Xv0LB247S/Yo0Ma4JPRaR2VwEAAACQ99IeDGVHjF8Wpk2bZtGK0QjRFi1aZDUwu5lRZ5xpZlyX8stHyrxFUVxcnDp37qyNGzemjhUvXtxsWdi0aVMVRg899JDFL53GLrtz587l65oAAAAAFHCXLyfvLnv2WZuB2XfV71frvjOsBmaGsOhYnQ6/nsMLBQAAAFAoQjOjheLZs2fN50bo0a9fP40YMeK25jKuM65PCd+M0GTLli0qSow2lb1799bq1astzv4yQshHH31UhZXRtvPWM+xCQ0PzbT0AAAAACjija0dQkLRsmdWyGCdn/efhwXqmw3hdcXHP1NS0aAQAAADyj12HZinnbaUEXS+//HK25nvllVfMnym7jv744w8VFcaf4aBBg7R48eLUMePPYcGCBTl6vpe9KlasWJoddwAAAACQJcbvpjNmSPffLx07ZrX0hGcFder5jj6r3zb1/DI/b1f5epe0eh2hGQAAAJB/7Do0O3/+fOpzf39/ValSJVvzGddXrlw5NYS7ePGiiorRo0fro48+shibOXOm+vbtq8LOOL/s0qVLFmPlypXLt/UAAAAAKIAiIqSOHaVRo4xv4Vkt/bZmc7XpO0MH77wrdaxjkI/WjGimtSOaq2OgT4bXEpoBAAAA+cdZdszFxSV1R1ROhRzGPCdOnDCflyhRQkXBv//9b80wvg15kzfeeEPDhg1TUbB9+3YzOEvh7OysO++8M1/XBAAAAKAA2bFD6tJFOnnSalmsczG9+n+DtKjeY6m7yzxcnDX5ibpqV69iat20LgFqUaOcXllxUFEx//yuYrgWm5B6HjcAAACAvGXXO82qVq2a+vzWnUK36+Z5bp6/sJo6daomTZpkMTZ+/HjzUVTcusOuSZMmcnV1zbf1AAAAACggEhOld9+VmjWzGZipenUlbNumEs8+IwfH5MCrYWUvfTeyuUVglqJ9gI/WjmyuBv7J5y8bGVn/plX07fBmBGYAAABAPrHrnWbNmzeXk5OTEhISzN1hZ86cUaVKlW57PuP648ePm8+NeY35C7N58+Zp3LhxFmPG7jJjl1lRsXHjRn3++ecWYx06dMi39QAAAAAoIIwvXPbpI61ZY7u2Rw/p/fdV0sNDEyS1qltBe09GaECzKnJ2yvi7qr7erlo8qLEWbD6u+v5ealjZO2c/AwAAAIDCs9PM29tbTzzxhPncaE/xrvENv2zuukppc2HMa8xfWC1atEhDhw61GOvXr595jllB9MMPP2jhwoUWbRZt2bBhgzp27GiGrikqVKigIUOG5NIqAQAAABQKmzdLAQG2A7OSJY3WFpLxRT0Pj9RhI/wa3KKa1cAshVEzpEU1AjMAAADADtj1TrOUoGv9+vWKjo7WrFmz1KhRI3Xr1u22QqTZs2ebgVmpUqXMefPTli1bdP369TTj+/fvt3h948YN/fjjj+nOUbFiRdWqVSvNuFHfp08fJRqtRP5Ws2ZNdenSRT/99FOW1unl5aX69evbrMtojYcPH7Z4HRERkWGt0S4zo5aZISEh6t+/v3k+21NPPaV27dopKChIpUuXtqgzArLdu3dr7ty5+u9//2vxZ+Do6Kg5c+bQmhEAAABA+ozfH6ZMMQ6GNn65sF57zz3S0qVSnTp5tToAAAAART008/f31zfffKP27dubwVmvXr20Y8cOvfrqq/L09LR5fWRkpCZMmGCGJcYuMw8PD61YsUJ+fn7KTz169NBJWz3xJV24cEGPPPJIuu8Zwdgnn3ySZnzz5s1pdmT9/vvveuyxx7K8zhYtWpgtDm3JaI23OnDgQIa1xv9Oxv+u1hjh2XvvvWc+DD4+PuaOQTc3N125ckWnTp0y/zm5lRGWGtek7FwEAAAAAAsXL0q9e0vr19uu7ddPmjVLcnPLi5UBAAAAKOyhmRFuZFa1atW0ZMkSs92gETQZO84+/PBDtW7dWi1btlSdOnVUpkwZcwfRtWvXFBYWpoMHD+qXX37RmjVrzN1aRmBWuXJlcweSsZvJuH9+B2fIPiNEMx7WGC0ZP/3000wHewAAAACKmF9+kYyOJufOWa8zulZ88IHUq1derQwAAABAUQjNjADL2P2TVcY1RgBmtDb8+uuvzYc1Rm3KdUbgZgRtKa+zcj4W8tdDDz2kiRMn6ueff9aePXsUFRVltd5oxRgQEKBBgwaZuxNpyQgAAAAgDaMF4+TJ0sSJya0ZralbN7kdY82aebU6AAAAAHnMISklVcpjRqiREoBlVnohm7XrrdUb7xnnX6HgMf43/Ouvv3T06FGdPn3abMFp7CY0WjQaZ7D5+vqaZ98ZZ9fZq0OHDpk7JFMYOyNr166dr2sCAAAAipTz56WePaXMnPs8cKA0Y4ZUsmRerAwAAAAo0g7l49+f2/2ZZjfLar6XT3kgcpkReN51113mAwAAAACyzAjKevQwDpG2XufuLs2fn9y6EQAAAEChl2+hWZ8+ffLr1gAAAACAosho0f/aa9KkSca3LK3XBgQkt2O8++68Wh0AAACAohqaLVy4ML9uDQAAAAAoas6eTd4xtmmT7dqhQ6V335VcXPJiZQAAAADsRIFqzwgAAAAAQJatWyf16iWFhVmvM85F/ugj6ckn82plAAAAAOyIY34vAAAAAACAXBEXJ40fLz3+uO3ArEEDKTiYwAwAAAAowthpBgAAAAAofE6flrp2lbZutV07cqQ0ZYpUokRerAwAAACAnSI0AwAAAADYpchrsRq2KNhibHb3QHm6Frd+4bffSn36SOHh1us8PY0Dt6UOHXJgtQAAAAAKOkIzAAAAAIBdWr3/rDYftWyruPrAOfVq7J/+BbGxye0Yp02zPfl990lLlkj+GcwFAAAAoMjhTDMAAAAAgF1atudMpsZMJ05IDzyQucBs7Fjp118JzAAAAAAU7J1miYmJ+v7777V161YFBwcrLCxMkZGRiomJydI8Dg4O+uuvv3JtnQAAAACA23fkQpT2n7mcZnz/6Uj9eSFKd9/h8c/gN99I/fpJkZHWJ/X2lj77TGrdOhdWDAAAAKCgK1Ch2YwZM/Tuu+8qJCTEYjwpKSnLcxmhGQAAAADAPn2d0Y4yY7fZ3jMa//g9kvHlyXHjpJkzbU/YtKn05ZeSr2/OLhQAAABAoVEgQrNr166pQ4cO+umnn1IDspTQy3id2QAspfZ2QjYAAAAAQN6IT0jU8mDLL0vebMXeED1fzVnO3btJe/bYntA45+y11yTnAvErMAAAAIB8UiB+Y+jdu7d+/PFH83lK6OXo6Kjy5cvr3LlzqXXGa+P9iIgIi3aNKaFa2bJl5ebmlg+fAAAAAACQwvidLvxqbIbvbzt2SaFRGbfgb7DrJyW9NUeKjrJ+o3LlpM8/lx59NDvLBQAAAFBE2H1otnLlSi1fvjw1+PLy8tLbb7+trl27ytXV1QzPUnzxxRd66KGHzOfGeWUbNmzQ+++/r3379pnXOzk56YMPPtCj/MIEAAAAAPnm8Lkraj1zc5avKxEfq1c2LFCv4LU2a682aSa3ZUukihVvc5UAAAAAipp/Eic7ZQRkKd9EdHFxMYOw/v37m4GZNdWqVdPAgQO1d+9ezZ49W8WLF9fFixfVtm1bLVu2LI9WDwAAAAC41fqD57N8TZXwEK34/DmbgVmiHDTj/m6a/595BGYAAAAACk9oFhkZqe3bt5u7xIzH6NGjde+992Z5nqFDh+qrr74y54iPj1efPn109OjRXFkzAAAAAMC6dYeyFpq1O7xRqz8dpVoXj1utC3XzVM8ur2t68x5a91tYNlcJAAAAoKix69Bs27Zt5g4z42EwdpjdrjZt2mjQoEHm8xs3buiVV17JsXUCAAAAADInLDpGf4VezVStS9wNvfndTM1c/Y7cY69brd3sX0+t+s7S1soB5uujodHmvQAAAACgUIRmISEhqc/Lli2rqlWrWq03wjBrxowZY/40QrgVK1boypUrObRSAAAAAEBmlHUvoaWDm8jXu6TVumphp/XNZ8+p24HvrdYlODjq3WY91Lvzawp19zLHjLm/GtLEvBcAAAAAFIrQLDw83PxptFX08fFJt6ZYsWKZDs3uuusuVapUyXxutGncunVrjq4XAAAAAGBbfX8vrR3RXB0D0/89r9P/ftLqz0apZthJq/Ocd/dW966TNatpNyU6OpljxpzG3EF+yQEaAAAAAGSWswoIFxeXdMdLlSqlS5cumcHa+fO2++JXqFBBZ86cMZ9zrhkAAAAA5A8Pl2Ka1iVALWqU0ysrDioqJl4lY2/o9R/e15MHf7J5/S9VgjS6zXMKdy3993zOmtShjtoHpB/EAQAAAECBDs1Kl07+5ccQFRWVbo2Xl5cZmhmOHTtmc86YmH962tOeEQAAAADylxFyGbvC3p32tYZ9+JLuCk/+kmNG4o12jA/00gf3dVKSQ3LzlCA/T83sFqhKXq55tGoAAAAAhZFdh2ZVqlRJPYMsNDQ03ZpatWql7hiz1W7RCMyOHDli7kozuLryCxUAAAAA5KukJPkuX6R3pg6Tc+w/X3JMz1mPshrR7nntrlTbYvyRWncSmAEAAAAo3Gea1a79zy9CRmiWcsbZzYKCglKDtZ07d+rPP//McL7PPvvMPPfMqDWknG8GAAAAAMgHRkeRnj2lgQNtBmY/VmuoVv1mpgnMDHtPReTiIgEAAAAUFXYdmvn4+Mjf3z/1tRGK3erJJ580fxq7xxITE9WnTx9dvnw5Td327dv1/PPPp+4yMzRv3jzX1g4AAAAAsGLfPqlBA2nRIqtlcY5OmvRgfz3d6T+KLFkq3ZrgUxGpX44EAAAAgEIZmhkefvjh1Odr165Ntz1jSvhlBGI7duzQPffco+eee07z58/X7Nmz1bVrVz3wwAPmGWbGL1JGXdu2bXXHHXfk6WcBAAAAgCLPCLfef19q3Fg6csRq6ZlS5dW5+xQtaNTR+IUvw7qw6FidDr+eC4sFAAAAUJTY9ZlmKTvJPvroIzPsWrJkid555x0VL17cosYIxho2bKi4uDjz9fnz5/Xee+9Z1KSEZcZPd3d3vf3223n6OQAAAACgyDO6ggwaJC1darN0/d2N9XyrUbri4p6pqY0WjX5lONcMAAAAQCHfadaxY0e1bt1ajRo1UnBwcJqaunXratmyZWaYlhKOGYznKS06UgKzUqVKmbXVq1fP888CAAAAAEXW7t3GodQ2A7NYR2e9+n+DNPiJl1MDMz9vV/l6l7R6HeeaAQAAACj0O82cnJzMkMuWNm3a6MCBA3rhhRfMNo6xsbGp7xlhmRGoderUSa+//rqqVq2ay6sGAAAAAJiMLzLOmiWNHSv93R0kIyc979Swdi/ofxXuTh3rGOSjie1qm88nrDyk5cEh6V5LaAYAAACg0IdmWXH33Xdr+fLlunr1qvbu3asLFy6Ygdmdd96p+vXry9WVVh0AAAAAkGciIqQBA6QVK2yWflujmcY/PlxRJdzM1x4uzpr8RF21q1cxtWZalwC1qFFOr6w4qKiYeIvrr8UmWHQeAQAAAIAiHZqlcHNzU/PmzfN7GQAAAABQdO3YIXXpIp08ab2uRAnFTn1He/xbKnrbSSlJaljZS9O7BKiSV9ovPrYP8FGQn5dGL9mn3ScjZGRk/e6vorGPVicwAwAAAJAthTI0AwAAAADkYzvGadOkF1+U4i13g6Vx993mGWfFAwI0QVKreytq78kIDWhWRc5OGR/B7evtqsWDGmvB5uOq7++lhpW9c/5zAAAAAChyCM0AAAAAADnj0iWpb1/p229t13bvLn3wgeThkTpkhF+ZDcCMUG1Ii2rZWS0AAAAAWCA0AwAAAABk35YtUrdu0unT1utcXKRZs5LPOqOdIgAAAAA7knG/CwAAAAAAbElMlN56S2rRwnZgVrOmtHOn9PTTBGYAAAAA7E6+7TTbtGmT8tsDDzyQ30sAAAAAgIIrNFTq3Vtat852bZ8+0pw5kptbXqwMAAAAAApOaNayZUs55OM3C417x9s6lBoAAAAAkD7ji5BGO8azZ63XubpKc+cmh2YAAAAAYMfy/UyzpKSk/F4CAAAAACCzEhKkN9+UJkxIbs1oTZ060pIlUq1aebU6AAAAACiYoRmBGQAAAAAUIOfPSz17Sj/9ZLvWOLdsxozknWYAAAAAUADkW2g2wfhWIgAAAACgYDCCsh49pAsXrNe5u0vz5kndu+fVygAAAAAgRxCaAQAAAACst2N87TXp9deNdiHWa+vVk5YulapXz6vVAQAAAEDhOdMMAAAAAGCnzp5N3jH2yy+2a595Rpo2TXJxyYuVAQAAAECOIzQDAAAAAKS1fr3Uq5cUGmq9zsNDWrBA6tw5r1YGAAAAALnCUXbujz/+yO8lAAAAAEDRER8vjR8vPfaY7cCsfn0pOJjADAAAAEChYPeh2T333KOWLVvqiy++UExMTH4vBwAAAAAKr9OnpZYtpbfesl07YoS0ZYtUrVperAwAAAAAcl2BaM/466+/mo8RI0aoZ8+eevrpp1W3bt38XhYAAAAA2KXIa7EatijYYmx290B5uhbP+KI1a6TevaXwcOuTe3pKH38sPfFEDq0WAAAAAOyD3e80S5GUlKSIiAjNnj1bAQEBaty4sT7++GNdu3Ytv5cGAAAAAHZl9f6z2nw0zOKx+sC59Ivj4qTnn5fatLEdmDVqlNyOkcAMAAAAQCFk96GZEZAZgZnBwcHB/Gm83rlzpwYOHKgKFSpoyJAh2r17dz6vFAAAAADsw7I9ZzI1ppMnpebNpXfesT3pc88ZbUCkypVzaJUAAAAAYF/sPjTbu3evGYgNGjRIHh4eFgGa8TwqKkoffvih7rvvPgUGBur999/XlStX8nvZAAAAAJAvjlyI0v4zl9OM7z8dqT8vRP0z8M03xrcUpR07rE/o7S2tWpUcrBW30t4RAAAAAAo4uw/NDEFBQfrggw909uxZffTRR2rSpEm6u8/279+vYcOGqWLFiurfv7+2bt2azysHAAAAgLz1dXo7yv62bO8ZKSZGGjUqucViZKT1ye6/X9q3T2rbNucXCgAAAAB2pkCEZilcXV3Vr18/bdmyRQcPHtTIkSPl7e2dZveZcc7Zp59+qubNm6t27dqaMWOGwm315gcAAACAAi4+IVHLg0MyfH/Hj7uV1LSpNGOG7clefFHauFHy9c3ZRQIAAACAnSpQodnNatWqpenTpyskJERffPGFHnroodT3bt599ttvv2nMmDHy8fFRjx499PPPP+fjqgEAAADg9hm/41yKjsnwse7QeYVGxaR77eO/b9Zns4fIYc8e6/coW1b67jvpzTelYsVy6ZMAAAAAgP1xSErZplUIHDt2zDzfzNhldv78eYvdZynPDVWrVtXAgQPVt29flS9fPl/XjKLp0KFDqlOnTuprY+eksSsSAAAAsObQ2ctqPXNzlq4pER+rlzd8pN7Ba2zW7vCtI68VX6l6/ZrZWCUAAAAAFMy/Py+wO83SY4Rhb775pk6fPq3ly5fr8ccfl6Ojo0V4Zjz++usvjR8/Xr6+vnryySf1ww8/5PfSAQAAAMCm9QeTvxyYWZXDQ7T887E2A7NEOWjG/V3VvetkfRteqH5NBAAAAIBMK5S/DTk5OalDhw5as2aNTpw4oeHDh5thmRGcpTyM13FxcVqxYoUee+wx1ahRQx988IE5BgAAAAD2yGi/mFntDv+ibz8dpdoXj1mtC3XzVK8ur2t6855KcHTKcjAHAAAAAIVFoQzNUhjnlz3//POaP39+amvGm9189tmff/6pZ5991gzP1q1blw+rBQAAAICMhUXH6K/QqzbrSsTF6I11szRz9VS5x163WrvF/1616jtLWyoHpI4dDY027wUAAAAARU2hC80uXryoKVOm6O6779bDDz+sJUuWKCbmn1/4jIDMaNn46KOPys/Pz+K8M+O5sTOtdevWeuedd/LxUwAAAACApbLuJbR0cBP5epfMsKbapdNa+dkYdd+/3upcCQ6OerdZD/Xq/LpC3b1Sx425vxrSxLwXAAAAABQ1hSY0M3aHderUyTyn7KWXXjLPLUsJxAzG8woVKug///mPjh8/ru+++878uX79ej3xxBOpu85SwrMXX3xR27Zty8dPBAAAAACW6vt7ae2I5uoY6JPmvY4Hf9LqT0epZthJq3NccPdWj66TNKtpNyU6Ov1zfaCPOXeQ3z8hGgAAAAAUJc4qwM6cOaOPPvpIH3/8sfnccPPZZSmhmbGrbMiQIWrTpo153tnNHnnkEfNx5MgRDR06VBs2bEi9dubMmWrSpEm+fDYAAAAASI+HSzFN6xKgFjXK6ZUVBxUfFa3XfvhATx380ea1myoHanSb53TJzfOm+Zw1qUMdtQ9IG8QBAAAAQFFS4EKzhIQErV69Wh9++KG+//57JSYmWrRYNBivy5cvr/79+2vQoEGqXLmyzXmrV69uztegQQPt27fPHNuyZUsufxoAAAAAuD1GyNXo2jnFdRoiv/MnrNbGG+0YH+ilD+7rpCSHfxqOBPl5ama3QFXycs2DFQMAAACAfSswodmxY8e0YMECffLJJ7pw4UK6u8qMx4MPPmjuKjNaLjo7Z+3jGWedGSGbsePMkHIfAAAAALArxhcHP/5YFYYPl65ft1p61qOsRrR7Xrsr1U7z3iO17iQwAwAAAICCEpotXrzYDMs2btyYGozduqusTJky6tOnjwYPHqy77747W/erUqVK6vP4+Phsrh4AAAAAclhUlPTMM9IXX9gs/alaQ41tNUoRrqXTfX/vqYhcWCAAAAAAFEx2H5p17949dSfZrbvKmjZtau4qe+qpp1S8ePEcuV9Wd6cBAAAAQJ7Zv1/q3Fk6csRqWZyjk6a06KOPGnawaMd4q+BTEam/awEAAABAUVegEiLjl7nSpUurV69eZlhWq1atHL+Hl5eXWrRokePzAgAAAMBtMzpuzJsnjRolxcRYLT1TqryGtxunYJ+aNqcNi47V6fDr8itDi0YAAAAAcC4oYVmjRo3M9otdu3ZVyZIlc+1eQUFB+vnnn3NtfgAAAADIkitXpIEDpaVLbZauv7uxnm81Sldc3DM9vdGikdAMAAAAAApAaGYEZcYjICAgv5cCAAAAAHlrzx6pSxfpr7+slsU6OuuNB/vrk/ptjQOgU8f9vF2VpCRzN5m10KxDoE+OLhsAAAAACiK7D83ef//9/F4CAAAAAOR9O8bZs6WxY6XYWKulJz3v1LB2L+h/Fe62GO8Y5KOJ7WqbzyesPKTlwSEZhmYAAAAAgAIQmgEAAABAkRIZKQ0YIC1fbrP0l3tbaNhDQxVVwi11zMPFWZOfqKt29Sqmjk3rEqAWNcrplRUHFRUTbzHHtdgEsyW+w0071AAAAACgKHLM7wUAAAAAAP62c6cUGGg7MCtRQpo7Vw13/qAnH6qd2pGxYWUvfTeyuUVglqJ9gI/WjmyuBv5e5mvjmv5Nq+jb4c0IzAAAAACAnWYAAAAAYCftGKdPl154QYq33AmWxt13S0uXSgEBcjVaL7atrVZ1K2jvyQgNaFZFzk4ZfzfS19tViwc11oLNx1Xf30sNK3vn/GcBAAAAgAKK0AwAAAAA8lN4uNS3r7R6te3abt2kefMkDw+LYSP8ymwAZoRqQ1pUu93VAgAAAEChRWgGAAAAAPll61apa1fp9GnrdS4u0qxZyWed0UoRAAAAAHIFZ5oBAAAAQF5LTJTeflt64AHbgVnNmslnnT39NIEZAAAAAOQidpoBAAAAQF4KDZX69JG++852be/e0pw5krt7XqwMAAAAAIo0QjMAAAAAyCubNiWfS3b2rPU6V9fksMw46wwAAAAAkCdozwgAAAAAuS0hQZo0SXrwQduBWe3a0q5dBGYAAAAAkMfYaQYAAAAAuenCBalnT+nHH23XGueWzZiRvNMMAAAAAJCnCM0AAAAAILds2CD16CGdP2+9zjizbN48qXv3vFoZAAAAAOAWtGcEAAAAgNxoxzhhgvTww7YDs3r1pD17CMwAAAAAIJ+x0wwAAAAAcpJxZpmxu2zjRtu1Q4ZI06dLLi55sTIAAAAAgBWEZgAAAACQU77/Pvn8stBQ63UeHtKCBVLnznm1MgAAAACADbRnBAAAAIDsio+XXn5Zeuwx24FZUJAUHExgBgAAAAB2hp1mAAAAACAp8lqshi0Kthib3T1Qnq7FrV945ozUrZu0ebPtmwwfLk2dKpUokc3VAgAAAAByGqEZAAAAAEhavf+sNh8Nsxw7cE69GvtnfNHatVLv3tKlS9Yn9/SUPv5YeuKJHFotAAAAACCn0Z4RAAAAACQt23MmU2OmuDhp3DipdWvbgVmjRsntGAnMAAAAAMCuEZoBAAAAKPKOXIjS/jOX04zvPx2pPy9EWQ6ePCk98EBym0VbnntO+vVXqXLlHFwtAAAAACA3EJoBAAAAKPK+zmhHmbHbbO9N761cKQUGStu3W5/Q21tatUp65x2puI0z0QAAAAAAdoHQDAAAAECRFp+QqOXBIRm+v2JviOKv35BGjZI6dJAiIqxPeP/9ye0Y27bN+cUCAAAAAHKNc+5NDQAAAAD5LykpSeFXYzN8f9uxSwqNisnw/RKnTyq64Vh5Htpv+2Yvvii99ppUrNjtLhcAAAAAkE8IzQAAAAAUaofPXVHrmZtv69rH/tiit9fOUKnYa1br4r3LyPmL/0qPPXabqwQAAAAA5DdCMwAAAACF2vqD57N8TYn4WL3080fqs3eNzdodvnW0f8pcDXqs+W2uEAAAAABgDwjNAAAAABRq6w5lLTSrHB6i2aveVp0Lf1mtS5SDZjfprBnNuuuu80kalM11AgAAAADyF6EZAAAAgEIrLDpGf4VezXR928O/6I31s+URe91qXaibp0a1GastlQPM10dDo817lXUvke01AwAAAADyh2M+3RcAAAAAcp0RYi0d3ES+3iWt1pWIi9Eb62Zr1uqpNgOzLf73qlXfWamBmTH3V0OaEJgBAAAAQAHHTjMAAAAAhVp9fy+tHdFcE1Ye0vLgkDTvV7t0WrNXTtE9oSeszpPg4KgZTbuZLRkTHZ3MsY6BPprYvrY8XIrl2voBAAAAAHmD0AwAAABAoWeEWtO6BKhFjXJ6ZcVBRcXEm+NPHNygSd/PlVvcDavXX3D31si2Y7Xd796/53PWpA511D7AJ0/WDwAAAADIfYRmAAAAAIoMI+QK8vPSuE+36omFU9T5fz/avGZT5UCNbvOcLrl5mq+D/Dw1s1ugKnm55sGKAQAAAAB5hdAMAAAAQJHie+645sx4Rt4nj1qti3dw1LTmPfV+4yeV5PDPcdCP1LqTwAwAAAAACiFCMwAAAABFQ1KStHChNGyYvK9ft1p61qOsRrR7Xrsr1U7z3t5TEbm4SAAAAABAfiE0AwAAAFD4RUdLzzwj/fe/Nkt/qtZQY1uNUoRr6XTfDz4VoaSkJDk4OOTCQgEAAAAA+YXQDAAAAEDhduCA9NRT0pEjVsviHJ309gN9tKBRB4t2jLcKi47V6fDr8itDi0YAAAAAKEwIzQAAAAAU3naM8+dLI0dKMTFWS8+UKq/h7cYp2KdmpqY2WjQSmgEAAABA4ZLx1ycBAAAAoKC6ckXq1k0aMsRmYPb93Y3Vqt/M1MDMz9tVvt4lrV7DuWYAAAAAUPiw0wwAAABA4bJ3r9Sli3T0qNWyWEdnvflgPy2s3076+3yyjkE+mtiutvl8wspDWh4ckv4tCM0AAAAAoNAhNAMAAABQeNoxzpkjPfecFBtrtfRU6Ts0rP0LOlChuvnaw8VZk5+oq3b1KqbWTOsSoBY1yumVFQcVFRNvcf212AQlJSXJ4e+wDQAAAABQ8BGaAQAAACj4IiOlp5+Wvv7aZml8x05a9NRY/W//JSlJaljZS9O7BKiSV9ozytoH+CjIz0ujl+zT7pMR5oa0fvdX0dhHqxOYAQAAAEAhQ2gGAAAAoGDbuTO5HeOJE9brSpSQpk+X85AhetHBQf/XOFx7T0ZoQLMqcnbK+LhnX29XLR7UWAs2H1d9fy81rOyd858BAAAAAJDvCM0AAAAAFNx2jO+9J73wghQXZ7327rulpUulgIDUISP8ymwAZoRqQ1pUy+6KAQAAAAB2jNAMAAAAQMETHi716yetWmW7tls3ad48ycMjL1YGAAAAACigCM0AAAAAFCzbtiW3Yzx92nqdi4s0c2byWWecPwYAAAAAsCHjxv0AAAAAYE8SE6W335aaN7cdmNWsmXzW2cCBBGYAAAAAgExhpxkAAAAA+xcWJvXuLX33ne1ao27OHMndPS9WBgAAAAAoJAjNAAAAANi3X39NPpcsJMR6natrcljWt29erQwAAAAAUIjQnhEAAACA/bZjnDxZatnSdmBWu7a0axeBGQAAAADgtrHTDAAAAID9uXBB6tVL+uEH27UDBkgzZybvNAMAAAAA4DYRmgEAAACwLxs2SD16SOfPW69zc5PmzUuuBQAAAAAgm2jPCAAAAMA+JCRIr74qPfyw7cDs3nulPXsIzAAAAAAAOYadZgAAAADy37lzyQHYzz/brh0yRJo2TSpZMi9WBgAAAAAoIgjNAAAAAOQv49yynj2lixet13l4SB9+KHXpklcrAwAAAAAUIYRmAAAAALIl8lqshi0Kthib3T1Qnq7FrV8YH5/cjvGNN6SkJOu1QUHSkiXSXXflwIoBAAAAAEiL0AwAAABAtqzef1abj4ZZjh04p16N/TO+6MwZqXt36ddfbd9g+HBp6lSpRIkcWC0AAAAAAOlzzGAcAAAAADJl2Z4zmRpLtXatFBBgOzArXVr6+mtp5kwCMwAAAABAriM0AwAAAHDbjlyI0v4zl9OM7z8dqT8vRFkOxsVJ48ZJrVtLly5Zn7hhQyk4WOrYMYdXDAAAAABA+gjNAAAAANy2r63sKFu296b3Tp2SWrRIbrNoy5gx0ubNUpUqObRKAAAAAABsIzQDAAAAcFviExK1PDgkw/dX7A0xa7RqVXI7xm3brE/o5ZVc++67UvHiOb9gAAAAAACscLb2JgAAAICiKykpSeFXYzN8f9uxSwqNisnw/YjIaJ3qO0RV//uh7Zs1aSItXiz5+d3ucgEAAAAAyBZCMwAAAADpOnzuilrP3Hxb11aKPK/Zq6ao6rk/bRcb55xNmiQVK3Zb9wIAAAAAICcQmgEAAABI1/qD52/rukf/2Kqp381QqZirVusulSylX15+Rx1fHnibKwQAAAAAIOcQmgEAAABI17pDWQvNSsTHavzPH6vv3m9t1u6oVFsj2j0vT9cq6piNNQIAAAAAkFMIzQAAAACkERYdo79Cre8Uu5l/xFnNWTlFdS78ZbUuUQ6a3aSzZjTrrgRHJ4WFRpv3KuteIgdWDQAAAADA7XPMxrUAAAAACikjxFo6uIl8vUvarG3z2yZ9+8lIm4FZqKunend+TdMe6GUGZsbcXw1pQmAGAAAAALAL7DQDAAAAkK76/l5aO6K5Jqw8pOXBIWneLxEXo/9s+FA99q2zOddWv3s1su1Yhbp7m687BvpoYvva8nAplitrBwAAAAAgqwjNAAAAAGTICLWmdQlQixrl9MqKg4qKiTfHq146ozkr39I9oSesXp/g4KgZTbuZLRkTHZ3k4eKsSR3qqH2ATx59AgAAAAAAMofQDAAAAIBNRsgV5OelkYuD5ffdCk1eP0ducTesXnPRzUsj2j2v7X73mq+D/Dw1s1ugKnm55tGqAQAAAADIPEIzAAAAAJni6yJNXj1d96z72mbtpsqBGt3mOV1y80wde6TWnQRmAAAAAAC7RWgGAAAAwLZDh6TOnXXP4cM22zG+27yn3m/8pJIcHC3e23sqIpcXCQAAAADA7SM0AwAAAJCxpCTpk0+kZ5+Vrl+3WnrOvYzZjnGXb5103w8+FaGkpCQ5ODjk0mIBAAAAALh9hGYAAAAA0hcdLQ0dKn3+uc3SDVUb6LnWoxXhWjrDmrDoWJ0Ovy6/MrRoBAAAAADYH0IzAAAAAGkdOGC2Y9Qff1gti3N00tQHeuvDRk+kaceYHqNFI6EZAAAAAMAe2f6tFgAAAEDRasc4f77UqJHNwOxMqXLq0v0tzb+vkxmY+Xm7yte7pNVrONcMAAAAAGCv2GkGAAAAINmVK9LgwdLixTZLf7jrPo1tNUqXS3qYrzsG+Whiu9rm8wkrD2l5cEi61xGaAQAAAADsFaEZAAAAACk4OLkd49GjVstiHZ31Vst++rhBO8nBQR4uzpr8RF21q1cxtWZalwC1qFFOr6w4qKiYeIvrr8UmKCkpSQ4ODrn2UQAAAAAAuB2EZgAAAEBRb8c4d640ZowUG2u1NLFyFX06/A0tDPWQkqSGlb00vUuAKnmlPaOsfYCPgvy8NHrJPu0+GWHka+p3fxWNfbQ6gRkAAAAAwC4RmgEAAABFVWSk9PTT0tdf267t1EmOCxZooKenAk6Ea+/JCA1oVkXOThkfk+zr7arFgxprwebjqu/vpYaVvXN2/QAAAAAA5CBCMwAAAKAo2rVL6tJFOn7cel3x4tK0adLQoWY7RoMRfmU2ADNCtSEtquXEigEAAAAAyFUZfy0UAAAAQOFsx/jee1LTprYDs7vukrZvl559NjUwAwAAAACgsGKnGQAAAFBUhIdL/fpJq1bZru3aVZo3TypVKi9WBgAAAABAviM0AwAAAIqCbduSg7BTp6zXubhIM2ZIAweyuwwAAAAAUKTQnhEAAAAozBITpalTpQcesB2Y1agh7dghDRpEYAYAAAAAKHLYaQYAAAAUVmFhUp8+0tq1tmt79ZLmzpXc3fNiZQAAAAAA2B1CMwAAAKAw+vVXqVs3KSTEel3JktKcOVLfvuwuAwAAAAAUabRnBAAAAApbO8bJk6WWLW0HZrVqSbt2Sf36EZgBAAAAAIo8dpoBAAAAhcWFC8ltFn/4wXZt//7SrFmSq2terAwAAAAAALtHaAYAAAAUBj//LHXvLp0/b73OzU16//3kcA0AAAAAAKQiNCsioqOjdejQIf3++++6dOmSbty4IU9PT5UvX14NGjRQ5cqVVVBFRUVpy5YtOnLkiK5cuaKSJUvK399f999/vypWrJjfywMAAMhdCQnSpEnSa68lt2a0pm5daelSqWbNvFodAAAAAAAFBqFZPgsJCdHOnTu1Y8cO8+fu3bvNECiFEf6cOHHituY25vzmm2/0008/ac+ePUq08pcoxn2GDBmiwYMHy8vLS7fr2LFjFp8nODhY169fT32/RYsW2rhxo3LC8ePH9Z///EdLly5VbGxsmvcdHBzM+02cOFEPPPBAjtwTAADArpw7J/XokbzLzJbBg6Xp06WSJfNiZQAAAAAAFDiEZvnA2BX17rvvmsHS2bNnc3z+ffv2qVOnTmaAlVknT57U+PHjNWPGDC1cuFCPPfZYpq9dtWqV5s2bZ4ZkYWFhygtGUNavXz9du3Ytw5qkpCQzoGvZsqXGjRunN9980wzSAAAACgXj3LKePaWLF63XeXhI8+dLXbvm1coAAAAAACiQCM3ywa5du7RixYpcm//MmTMZBmalS5fWnXfeaf40Ai5jt5YRLqU4f/68WrdurS+++EJdM/kXKxs2bNDatWuVV7766it169Ytzc65cuXKydfXVxcvXjR38KV8LuPnlClTFBMTo+nGt6sBAADsROS1WA1bFGwxNrt7oDxdi2d8UXy89Oqr0htvGP+hY/0GgYHSkiXS3Xfn0IoBAAAAACi8HPN7AbDk7u6e43M2btxYs2fPNs80i4yMNM81M3a5/fXXX7pw4YImT54sV1fX1HojjOrdu7fZWjG73IyD5nOQsWZjh9nNgVm9evXM4M4Iy4w2lKdPn9Zvv/2mjh07Wlz73nvvafny5Tm6HgAAgOxYvf+sNh8Ns3isPnAu4wvOnJEeekiaPNl2YDZsmLR1K4EZAAAAAACZRGiWjzw8PMzWgc8//7y5e8o4u2z16tU5Mrejo6N69uypgwcPatu2bXr22WdVq1atNHXG7qyXXnrJrPH29k4dj4uL06hRo7J0T+P6Rx99VK+88opWrlypc+fOmWFdTvr3v/+tq1evpr5u2LChNm3apAcffNCirkaNGlq2bJkGDRpkMW60aYw3vp0NAABgB5btOZOpMZOxsz8gQPr1V+uTli4tLVsmzZolubjk0EoBAAAAACj8aM+YD9q2bat//etfqlmzphlu3cxol5hd1atX14EDB1S7du1MX3PvvfeaZ5m1b98+dcwIo44ePaq77rrL6rUDBw7U8OHDVa1aNeUmY6fcEqO90N+KFy+uTz/9VKVKlUq33ji/zDij7eeff9aff/6ZulPN+JzGmgEAAPLTkQtR2n/mcprx/acj9eeFKN19h0fyQFyc9PLL0tSptidt2FBavFiqWjUXVgwAAAAAQOHGTrN8YIRLxq6vWwOznGKEZlkJzFK0a9cuzW60devW2bzOuFduB2aGjz/+2KIto3Hm2j333GP1GhcXF7344osWYwsWLMi1NQIAAGTW1xntKDN2m+39+71Tp6QWLTIXmBldAjZvJjADAAAAAOA2EZrBQvPmzS1enzL+osZOrFq1yuL1gAEDMnVdly5dLM5W27Vrl86ePZvj6wMAAMis+IRELQ8OyfD9FXtDlPDNN8ntGLdtsz6Zl5e0cqU0fbqxFT/nFwsAAAAAQBFBaAYLXsZfutzk8uW0LYPywx9//GG2ikxhhGD3339/pq69tTYpKUlr1qzJlXUCAACk/PfGpeiYDB/rDp1XaFRMutcWS4jT4G9my+mJJ6SICOs3atxYCg42WgbkzgcBAAAAAKAI4UwzWAgJsfzGc5kyZWQP9u3bZ/G6UaNGcnbO/D++TZs21Q8//JDhfAAAADnp8Lkraj1zc5avqxR5XrNXTVHAueTzWK0JGzpSZd+bKhUrdpurBAAAAAAANyM0g8U3ojcb52Dccj6aPfjtt98sXt969pott9bfOh8AAEBOWn/wfJavefSPrZr63QyVirlqtS68ZCmNaT1a93borjEEZgAAAAAA5BhCM6TauHGjjh8/nvrawcFBjz32mOylPePNfH19s3T9rfW3zgcAAJCTjPaLmVUiPlbjf/5Yffd+a7N2Z6VaGtF2nM6XKqtzB89rzCP28QUnAAAAAAAKA0IzmBITEzV+/HiLMSMwu/POO2UPLl68aPG6UqVKWbrex8fH4nVoaGiOrAsAAOBWYdEx+ivU+m6xFP4RZzVn5RTVufCX1bpEOWhuk6c0vVkPJTg6mWNHQ6PNe5V1L5Ej6wYAAAAAoKgjNIPpnXfe0Y4dO1JfOzo6avLkybIX0dHRFq/d3NyydP2t9XFxcYqJiVGJEiVyJNDLagh39OjRbN8XAADYJyPEWjq4iUYtCdbp8OsZ1rX5bZPeXDdLHrEZ1xjCXEtrdJvn9GuVoNQxX++SmtE1kMAMAAAAAIAcRGgG/frrr3r55ZctxkaNGqXAwEDZa2jm4uKSpetLliyZ7pw5EZrNnTtXEydOzPY8AACg8Kjv76W1I5prwspDWh4cYvFeibgY/WfDh+qxb53Nebb51dWIts8r1N07daxjoI8mtq8tDxfOMwMAAAAAICcRmhVxx44dU8eOHRUfH586FhAQoDfeeEP25MaNGxavixcvnqXr0wvHrl+3/q1uAACA7DBCrWldAtSiRjm9suKgomLiVfXSGc1Z+ZbuCT1hsx3jzKZdNfP+rkr8ux2jh4uzJnWoo/YBlm2nAQAAAABAziA0K8LCwsL0+OOPmz9T3HHHHVq+fHmO7MDKSbfuLIuNjc3S9UYrRltzAgAA5AYj5Ary89LXY97SwEVT5RZn+WWgW11089LItmO1zb9e6liQn6dmdgtUJS/XPFgxAAAAAABFE6FZERUVFWUGZkeOHEkdK126tNavX68qVarI3ri7u1vdeWZLervKbp3zdg0dOlRPPfVUls8069ChQ47cHwAA2Llr1+Q7dphGfbrQZummyoEa02aMwty8LMYfqXUngRkAAAAAALmM0KwIMgKndu3aaffu3aljrq6uWrNmjerV++cbzfbk1oDr6tWrWbr+1npnZ+cc22lWvnx58wEAAJDGoUNS587S4cNWyxIcHDWtWQ/NbfKUkhwc07y/91RELi4SAAAAAAAYCM2KmLi4OHXu3FkbN260OB/MaMnYtGlT2atbQ6kzZ85k6fqQkBCL1+XKlcuRdQEAAKQrKUn65BPp2WeNLe9WS8+7e2tEu3Ha6Vsnw5rgUxFKSkqSg4NDLiwWAAAAAAAYCM2KkMTERPXu3VurV69OHXNyctKiRYv06KOPyp7VqFHD4vWpU6eydP2t9TVr1syRdQEAAKQRHW30b5Y+/9xm6c9V6+u51mMU7lraal1YdKxOh1+XXxlaNAIAAAAAkFsIzYoI45vJgwYN0uLFi1PHjG8qL1iwQJ06dZK9uzXkOmyjxdGtfvvtN6vzAQAA5IgDB5LbMf7xh9WyeAdHTW3RW/MbdUy3HWN6jBaNhGYAAAAAAOSezP2GjgJv9OjR+uijjyzGZs6cqb59+6ogCAgIsHi9a9cuxcfHZ/r6LVu2WJ0PAAAg2+0Y58+X7rvPZmAW4lFOnbtP0bz7nkwNzPy8XeXrXdLqdZxrBgAAAABA7iI0KwL+/e9/a8aMGRZjb7zxhoYNG6aCwtgZVq1atdTXV69e1datWzN1rVG7bds2ix12bdq0yZV1AgCAIujKFal7d2nwYOnGDaulP9zVSK36zdTeSvekjnUM8tGaEc20dkRzdQz0yfBaQjMAAAAAAHIXoVkhN3XqVE2aNMlibPz48eajoGnXrp3F61t3zmVkyZIlijbOFvlbgwYNVLFixRxfHwAAKIKCg6X69aWbWmCnJ87JWa8/9LQGdvy3Lpf0MMc8XJw1s1ugpnUOkIdLMfMxrUuAZnQNkEeJtF3Ur8UmmC23AQAAAABA7iA0K8TmzZuncePGWYwZu8uMXWYFUf/+/c1dYimM89luPavsVjdu3NBbb71lMTZgwIBcWyMAACgijPBqzhypcWPp6FHrtZUrK+GXX5Q4apQcHJP/W6ZhZS99N7K52tVL+0We9gE+WjuyuRr4e5mvjf/86d+0ir4d3sziv4UAAAAAAEDOSvsVVhQKixYt0tChQy3G+vXrZ55jVlDVqVNHnTt3NneOGWJjY9WnTx/9+OOPKlWqVJp645vYo0aN0p9//pk6VrVqVTN8AwAAuG2RkdLAgdKyZbZrO3Y0tsfLxdNTEyS1qltBe09GaECzKnJ2yvj7a77erlo8qLEWbD6u+v5ealjZO2c/AwAAAAAASIPQLJ9s2bJF169fTzO+f//+NDuljFAoPUaLwVq1aqUZN+qNMCkxMdHiTLAuXbrop59+ytI6vby8VN9oOWRDRms8fPiwxeuIiIgMa41Ay3hYY7SaXL16ta5du2a+3rVrlx544AG99957atmyZWrdkSNHzBaUy5cvt7je2HVWrFgxm58HAAAgXbt2SV26SMePW68rXlx6913p2WeTt4r9zQi/MhuAGaHakBb/nOkKAAAAAAByl0MSByPki8qVK+vkyZPZmsMIxj755JM046+++qomTpyonNCiRQtt3LjRZl1OtAqaMGGCuXZbjLaM3bt3T3OmR7ly5eTn56eLFy/qzJkzad4fPny43ey0O3TokLlzLsXBgwdVu3btfF0TAACwwvjvihkzJKP1dVyc9dpq1aSlS6WgoLxaHQAAAAAAhcahfPz7c3aaocDp2rWrGYgZZ5PdvFsvNDTUfKRn7Nixevvtt/NwlQAAoNAIDzcOV5VWrrRda+xCmz9fSqd1NAAAAAAAsG8ZH6QA2LFu3bqZ6bKx48xau0WjdaOxU27q1Kk5shsOAAAUMdu3S4GBtgOzEiWkDz6QvvySwAwAAAAAgAKK9owo8K5cuaLNmzfrzz//VFRUlFxcXMw2jU2bNpWPj4/sEe0ZAQCwc8bZsMaZZC+9JMXHW6+tXj25HWO9enm1OgAAAAAACq1DtGcEbl+pUqXUqlWr/F4GAAAoLMLCpL59pTVrbNf27Cm9/77k7p4XKwMAAAAAALmI0AwAAABIsXmzcYCqFBJiva5kSWn2bKlfP4kW0AAAAAAAFAqcaQYAAAAY7RjffFNq2dJ2YFarlrRrl9S/P4EZAAAAAACFCDvNAAAAULRdvCj16iV9/73tWmNn2axZkptbXqwMAAAAAADkIUIzAAAAFF0bN0rdu0vnzlmvM0Iy4+wyI1wDAAAAAACFEu0ZAQAAUPQkJEivvSb93//ZDszq1pV27yYwAwAAAACgkGOnGQAAAIqW8+elHj2kDRts1w4aJL33nlSyZF6sDAAAAAAA5CNCMwAAABQ4kddiNWxRsMXY7O6B8nQtbv3CH39MDsyMc8yscXeXPvxQ6to1B1YLAAAAAAAKAkIzAAAAFDir95/V5qNhlmMHzqlXY//0L4iPlyZOlCZPlpKSrE8eECAtXSrdfXcOrhgAAAAAANg7zjQDAABAgbNsz5lMjZlCQpLPLps0yXZg9uyz0rZtBGYAAAAAABRBhGYAAAAoUI5ciNL+M5fTjO8/Hak/L0RZDq5bl7xzbNMm65OWKiV99ZU0e7bk4pLDKwYAAAAAAAUBoRkAAAAKlK8z2lFm7Dbb+/d7cXHSiy9Kjz8uhVm2cUyjQQMpOFh68skcXikAAAAAAChICM0AAABQYMQnJGp5cEiG76/YG6L4Eyelli2lKVNsTzhqlLRli1S1as4uFAAAAAAAFDjO+b0AAAAAIEVSUpLCr8Zm+P62Y5cUGhWT4ft1gzcpcWpn6XKE9Rt5ekqffCK1b5+d5QIAAAAAgEKE0AwAAAB24/C5K2o9c3OWryuWEKdxv3yqgbu+sVl7LaihXJd/Jfn73+YqAQAAAABAYURoBgAAALux/uD5LF9T6fIFzV45RQHnjtis/aBRR9149TWNIjADAAAAAAC3IDQDAACA3Vh3KGuh2aNHturttTNUOuaq1brwkqX0XOvR+rlaQ9X4/ZJGPZ7NhQIAAAAAgEKH0AwAAAB2ISw6Rn+FWg+/UhSPj9P4jR+r357VNmt3VqqlEW3H6Xypsubro6HR5r3KupfI9poBAAAAAEDh4ZjfCwAAAAAMRoi1dHAT+Xq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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228]\n", + "===============mm_bmnk_1_512_8192_4096_------------2====================\n", + "===============mm_bmnk_1_512_8192_4096_------------3====================\n", + "===============mm_bmnk_1_8192_4096_8192_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_8192_4096_8192_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_8192_4096_8192_------------2====================\n", + "===============mm_bmnk_1_8192_4096_8192_------------3====================\n", + "===============mm_bmnk_1_8192_8192_4096_------------0====================\n", + "[]\n", + "===============mm_bmnk_1_8192_8192_4096_------------1====================\n", + "[]\n", + "===============mm_bmnk_1_8192_8192_4096_------------2====================\n", + "===============mm_bmnk_1_8192_8192_4096_------------3====================\n" + ] + } + ], + "source": [ + "for (name, rank), df in grouped:\n", + " print(f\"==============={name}------------{rank}====================\")\n", + " filter_df = df[df['TFLOPS'] < 200]\n", + " if rank in [0, 1, 7]:\n", + " plot_sss(filter_df['count'].tolist())\n", + " print(filter_df['count'].tolist())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "52876a13", + "metadata": {}, + "outputs": [], + "source": [ + "xx = [6021, 6022, 6025, 6026, 6029, 6030, 6033, 6034, 6037, 6038, 6041, 6042, 6045, 6046, 6049, 6050, 6053, 6054, 6057, 6058, 6061, 6062]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6cf13ce1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "22" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(xx)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9706fd52", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yy = np.array(xx)\n", + "tt = yy[1:] - yy[0:-1]\n", + "tt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "68604e64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(tt == 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e6cee559", + "metadata": {}, + "outputs": [], + "source": [ + "from parse_perfetto import analysis_host_issue" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "22660827", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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77rlsu1evXrHTTjvV6h7ybwAAAMjPt99+G48//nhO38CBA6udo/YaAAAA9V3Lli1z2gsXLqzx3Ipj27RpU+VY9dcAID+ZPMcDABTUO++8k9Ou6cF7ol27dtG5c+dse8mSJTFu3LhajQ8AAADqmw8++CAt/r5Sly5dYquttqrx/L333rva3B8AAAA21uJyFbVu3brK8RXz5T322CMaNmxY4/3k3wAAANRHSbG2UaNG5fTttttuqx375ZdfxowZM7LtJk2aRO/evWu8l9wbAACAjdHll18e7733Xvp1q1at4vrrr1+n9dw9BwAAgLon/wYAAKC++9vf/hbLly/Ptvv27Vvre8i/AQAAID9Dhw7NeTl527Zt49BDD612jtprAAAA1He9evXKaY8fPz7mz59fo7lvvvlmpbx6ddRfA4D8ZdZiDgBAwSR/UFhVjx498ppfcXzF9QAAAID8yNUBAAAgf6+++mpOe5tttonGjRtXOV7+DQAAAPm74447Yvr06dl29+7dq3xIvWKuvO2221abq68p9544cWIsW7Ys75gBAACgWIwbNy4uu+yybPvKK6/M64Vmq+PsGwAAAFZv8eLFaZ772muvxciRI9Mz5wULFqzVWvJvAAAA6rtRo0bltHfZZZfs12PGjIn//u//Tvs222yz2GSTTaJz587Rr1+/uOaaa2LatGk12kP+DQAAAPm56667ctonnHBCNGzYsNo58m8AAADquw4dOkSfPn1y7p1ff/31a5yXjLvuuuty+k4++eTVjlV/DQDyl1mLOQAABbFw4cKYMmVKTl/Hjh3zWqPi+AkTJtRKbAAAAFBfVcyt1zVX/+yzz2LRokW1EhsAAAAU80vKV3XooYeu1/zbWTkAAAAbu7vvvjsGDx6cbWcymfjjH/8YJSUldZJ7b7HFFtG0adNse8mSJfHpp5/mHTcAAAAUgxUrVqSF3pL8NrHvvvvGqaeeus7runsOAAAAlf30pz+NVq1apcXRkxz83/7t32K77baLli1bpl9ffPHFMXPmzBqvJ/8GAACgvhs1alROu2vXrlFWVpaeg/fu3TtuuOGGGDt2bMyZMyeteZ7kvi+++GKcc845aU5+wQUXxNKlS6vdQ/4NAAAANZe8HPz111/P6Rs0aNAa56m9BgAAABFXXnllWkNtpQsvvDCtsVaV5Cz8mGOOifHjx2f7jjjiiPRjddRfA4D8NVyLOQAABfH1119HeXl5tt2oUaNo27ZtXmu0b98+p/3VV1/VWnwAAABQH1XMrTt06JDX/C233DIaNmwYy5YtyxaUnzVrVqUcHgAAADYWzz77bLzyyis5fQMHDqzT/Ltinp1PkXgAAAAoRh999FFMmTIl206KsH/zzTfx/vvvx5NPPhnjxo3Lfq9x48Zxyy23xEEHHVRnuXeiXbt2MWnSpJw1kyLxAAAAsKG5/vrr4x//+EdOXl1SUrLO67p7DgAAAJWter69qiT/HTlyZPqRFHY/++yzY8iQIdGgQYNq15N/AwAAUN8lLx5fVfKytP322y/GjBmzxrkLFy6Myy+/PEaNGhWPPfZYbLrppqsdJ/8GAACAmqv44vLevXtHz5491zhP7TUAAACI2GeffeKPf/xj/PSnP03f6Z2cMyf1zP/0pz/Fj370o9hhhx2iWbNm6Tu/k7vnDzzwQMyePTs7v1+/fvHggw9Wub76awCQv4ZrMQcAoCDKyspy2ptsskneBeWaN29e7ZoAAABAfirm1hVz7zVJcvvkosC8efOqXBMAAAA2Fsnl+NNOOy2n74c//GHssccedZp/VxyfvDh98eLF0aRJk7zWAQAAgGJx4403xh/+8Ic1nkf/4Ac/SIu077LLLnWae69ujrNvAAAANkSffvpp/OY3v8m2zz///OjevXutrO3uOQAAAKyd5AXll1xySbz66qvx1FNPRWlpaZVj5d8AAADUZytWrMjJaRP//d//HWPGjMnmvYcffngceuih6YvN5s+fn37v3nvvjenTp2fnvPjii+lL1R599NHV7iP/BgAAgJpJXl5+zz335PQlOXdNqL0GAAAA/3TGGWfEDjvskJ5/f/DBB2nfqFGj0o+qdO3aNc4999w49dRTI5PJVDlO/TUAyF/V/8sKAFBkKibpTZs2zXuN5OJ7dWsCAAAA+ZGvAwAAQM2Lyp144okxderUbF/Lli3j+uuvr/P8u2Luvbo1AQAAYGPTv3//+PWvfx277LLLGsc6+wYAAIB/+q//+q/0JWiJ7t27xwUXXFBra8u/AQAA4F8vAu/Tp09cdtll8cILL6R3zBcsWBCLFi2KadOmxVNPPRWnnXZapdx52LBh8ZOf/CSWL19e5drybwAAAOqzuXPnpi8fX9Xbb7+dfm7dunUMHz48/vKXv8Tpp58ehx9+eAwYMCCuuOKKmDBhQhx//PE58x577LFKLzFfSf4NAAAANfPSSy/FlClTsu3GjRtXysGrovYaAAAA/MuBBx4Yo0aNirPPPjsaNGhQ7dhOnTql45IcPJPJVDvW+TcA5K/6/3UFACgiycPrq0oO7fPVpEmTnPbChQvXOdl+YeIAAQAASURBVC4AAACoz+TrAAAAUDPnnHNO/PWvf83pu/nmm6Njx451nn9XzL0T8m8AAAA2dkOHDo199tkn9ttvv5g4cWK1Y519AwAAQMTtt98eL774YvZl5bfccsta5chVkX8DAABAxPe+97348MMP4/XXX48LLrggDj744Gjfvn1aGD3Je9u1a5e+pPz//u//4uOPP4699947Z/4zzzwTN954Y5Xry78BAACoz6p60VjygrQkp953331X+/3S0tK4995707x9Vb/73e+ivLy80nj5NwAAANTM3XffndNOzsNbt25do7lqrwEAAMC/JPfLu3XrFtdcc00sX7682rFTpkyJwYMHR+fOneOOO+6odqzzbwDIX2Yt5gAAFETTpk1z2kuWLMl7jcWLF1e7JgAAAJAf+ToAAACs2fXXXx+///3vc/rOPffcGDBgwHrJvyvm3qtbEwAAADYk1113XVpsfeXHggUL4vPPP4+nn346Tj755PTlaSu9+uqrsfvuu8fo0aOrXM/ZNwAAAPXdF198EWeffXa2fcopp1T5crS1Jf8GAACAiD59+sT2229fo7EdOnSIF198Mfbaa6+c/ksvvTQ9J18d+TcAAAD1WVU5bHIGvueee1Y7N5PJxE033ZR+XmnChAkxfPjwNe4j/wYAAIDKysrK4rHHHsvpGzhwYI3nq70GAAAAEUuXLo1jjjkmzjjjjPR58MTmm28eF154Ybz55pvxzTffpDnz9OnT4y9/+UscffTRUVJSko6bPXt2WpPtnHPOqXJ9598AkL9/3TADAChypaWlOe1FixblvcbChQurXRMAAADIj3wdAAAAqvfAAw/EWWedVekh9SuuuGK95d8Vc+/VrQkAAAAbsmbNmqUvRzvssMPitttui7Fjx0avXr2y358zZ0788Ic/TD+vjrNvAAAA6ruf/vSn2bx5q622iquuuqrW95B/AwAAQP6SAun33HNPNGzYMNv31VdfxfPPP7/a8fJvAAAA6rOqcthTTz21RvO7du0aBx98cE7f8OHD17iP/BsAAAAqe/jhh2P+/PnZ9pZbbhmHHHJIjeervQYAAAARZ5xxRjz66KPZ9h577BEffPBBXHzxxbH77rtHq1atolGjRrH11lvHEUccEY899lg88cQT6T30la655pq48847V7u+828AyF9mLeYAABRExSR9wYIFUV5entcaqx78r25NAAAAID8Vc+uKufeaJLm9g3oAAAA2Vk8//XScdNJJOWfbP/rRj9KXlZeUlKy3/Lvi+KRA/KqX9AEAAGBjs+2228YLL7wQHTt2zPZNmzYtrr766jrJvVc3x9k3AAAAG1Lx9ccffzzb/sMf/pAWhKtt7p4DAADA2p+BH3nkkTl9zz///GrHyr8BAACoz5o1axYNGjTI6dt0001j1113rfEa+++/f0579OjRlcbIvwEAAGDN7rrrrpz2CSeckNY/qym11wAAAKjvhg0bFrfffnu23bZt27Tm+VZbbVXtvOTu+Z/+9KecvnPOOafSOXVC/TUAyF9mLeYAABREmzZtcl6StnTp0vjqq6/yWiMp7L6q5A8UAAAAwNqrmFtPnTo1r/lffvllLFu2LNvOZDLp3wAAAABgQ/fyyy9H//79c/Lefv36xYMPPlipuFxd598Vz8q32GKLvOYDAADAhig5e7744ourLSZXW7l3Yvr06dWuCQAAAMUqKeq20mGHHRbHHntsnezj7jkAAACsvYMOOiinPWHChNWOk38DAABQ31XMjbfddts0v62pHXbYIae9uhro8m8AAACo3qRJk+LVV1/N6Rs4cGBea6i9BgAAQH13/fXX57TPOuusGue3SR6+/fbbZ9uzZs2Kxx57rNI49dcAIH81v40GAFBgzZo1i06dOuX0TZkyJa81Ko7v3r17rcQGAAAA9VXFh9nXNVffZpttomnTprUSGwAAABTKyJEj48gjj4xFixZl+/r06ROPP/54NG7cuOD5t7NyAAAA6oujjz46SkpKch4k/+yzz2o9904Kv6/6d4Ak/+/atetaxQwAAADr25w5c7JfP/PMM2kuvaaPvn375qyR5NsVx7zzzjs5Y9w9BwAAgLXXsWPHnPbMmTNXO07+DQAAQH2344475rRbtGiR1/yK47/55ptKY+TfAAAAUL177rknysvLs+3evXtHz54981pD7TUAAADqsySvfumll3L6jjjiiBrPz2Qycdhhh+X0vfLKK5XGqb8GAPnLrMUcAICCqXhYPm7cuLzmjx8/vtr1AAAAgPzI1QEAACDX2LFj45BDDomysrJs36677hrPPvtsNG/efK3WlH8DAADA2mnVqlVsvvnmOX0zZsyoNK5irvzJJ5/EkiVL1jr37tatWzRs2DDveAEAAGBj5uwbAAAA1l6jRo1y2kuXLl3tOPk3AAAA9V2PHj1y2osXL85r/qo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77rlsu1evXrHTTjvV6h7ybwAAAMjPt99+G48//nhO38CBA6udo/YaAAAA9V3Lli1z2gsXLqzx3Ipj27RpU+VY9dcAID+ZPMcDABTUO++8k9Ou6cF7ol27dtG5c+dse8mSJTFu3LhajQ8AAADqmw8++CAt/r5Sly5dYquttqrx/L333rva3B8AAAA21uJyFbVu3brK8RXz5T322CMaNmxY4/3k3wAAANRHSbG2UaNG5fTttttuqx375ZdfxowZM7LtJk2aRO/evWu8l9wbAACAjdHll18e7733Xvp1q1at4vrrr1+n9dw9BwAAgLon/wYAAKC++9vf/hbLly/Ptvv27Vvre8i/AQAAID9Dhw7NeTl527Zt49BDD612jtprAAAA1He9evXKaY8fPz7mz59fo7lvvvlmpbx6ddRfA4D8ZdZiDgBAwSR/UFhVjx498ppfcXzF9QAAAID8yNUBAAAgf6+++mpOe5tttonGjRtXOV7+DQAAAPm74447Yvr06dl29+7dq3xIvWKuvO2221abq68p9544cWIsW7Ys75gBAACgWIwbNy4uu+yybPvKK6/M64Vmq+PsGwAAAFZv8eLFaZ772muvxciRI9Mz5wULFqzVWvJvAAAA6rtRo0bltHfZZZfs12PGjIn//u//Tvs222yz2GSTTaJz587Rr1+/uOaaa2LatGk12kP+DQAAAPm56667ctonnHBCNGzYsNo58m8AAADquw4dOkSfPn1y7p1ff/31a5yXjLvuuuty+k4++eTVjlV/DQDyl1mLOQAABbFw4cKYMmVKTl/Hjh3zWqPi+AkTJtRKbAAAAFBfVcyt1zVX/+yzz2LRokW1EhsAAAAU80vKV3XooYeu1/zbWTkAAAAbu7vvvjsGDx6cbWcymfjjH/8YJSUldZJ7b7HFFtG0adNse8mSJfHpp5/mHTcAAAAUgxUrVqSF3pL8NrHvvvvGqaeeus7runsOAAAAlf30pz+NVq1apcXRkxz83/7t32K77baLli1bpl9ffPHFMXPmzBqvJ/8GAACgvhs1alROu2vXrlFWVpaeg/fu3TtuuOGGGDt2bMyZMyeteZ7kvi+++GKcc845aU5+wQUXxNKlS6vdQ/4NAAAANZe8HPz111/P6Rs0aNAa56m9BgAAABFXXnllWkNtpQsvvDCtsVaV5Cz8mGOOifHjx2f7jjjiiPRjddRfA4D8NVyLOQAABfH1119HeXl5tt2oUaNo27ZtXmu0b98+p/3VV1/VWnwAAABQH1XMrTt06JDX/C233DIaNmwYy5YtyxaUnzVrVqUcHgAAADYWzz77bLzyyis5fQMHDqzT/Ltinp1PkXgAAAAoRh999FFMmTIl206KsH/zzTfx/vvvx5NPPhnjxo3Lfq9x48Zxyy23xEEHHVRnuXeiXbt2MWnSpJw1kyLxAAAAsKG5/vrr4x//+EdOXl1SUrLO67p7DgAAAJWter69qiT/HTlyZPqRFHY/++yzY8iQIdGgQYNq15N/AwAAUN8lLx5fVfKytP322y/GjBmzxrkLFy6Myy+/PEaNGhWPPfZYbLrppqsdJ/8GAACAmqv44vLevXtHz5491zhP7TUAAACI2GeffeKPf/xj/PSnP03f6Z2cMyf1zP/0pz/Fj370o9hhhx2iWbNm6Tu/k7vnDzzwQMyePTs7v1+/fvHggw9Wub76awCQv4ZrMQcAoCDKyspy2ptsskneBeWaN29e7ZoAAABAfirm1hVz7zVJcvvkosC8efOqXBMAAAA2Fsnl+NNOOy2n74c//GHssccedZp/VxyfvDh98eLF0aRJk7zWAQAAgGJx4403xh/+8Ic1nkf/4Ac/SIu077LLLnWae69ujrNvAAAANkSffvpp/OY3v8m2zz///OjevXutrO3uOQAAAKyd5AXll1xySbz66qvx1FNPRWlpaZVj5d8AAADUZytWrMjJaRP//d//HWPGjMnmvYcffngceuih6YvN5s+fn37v3nvvjenTp2fnvPjii+lL1R599NHV7iP/BgAAgJpJXl5+zz335PQlOXdNqL0GAAAA/3TGGWfEDjvskJ5/f/DBB2nfqFGj0o+qdO3aNc4999w49dRTI5PJVDlO/TUAyF/V/8sKAFBkKibpTZs2zXuN5OJ7dWsCAAAA+ZGvAwAAQM2Lyp144okxderUbF/Lli3j+uuvr/P8u2Luvbo1AQAAYGPTv3//+PWvfx277LLLGsc6+wYAAIB/+q//+q/0JWiJ7t27xwUXXFBra8u/AQAA4F8vAu/Tp09cdtll8cILL6R3zBcsWBCLFi2KadOmxVNPPRWnnXZapdx52LBh8ZOf/CSWL19e5drybwAAAOqzuXPnpi8fX9Xbb7+dfm7dunUMHz48/vKXv8Tpp58ehx9+eAwYMCCuuOKKmDBhQhx//PE58x577LFKLzFfSf4NAAAANfPSSy/FlClTsu3GjRtXysGrovYaAAAA/MuBBx4Yo0aNirPPPjsaNGhQ7dhOnTql45IcPJPJVDvW+TcA5K/6/3UFACgiycPrq0oO7fPVpEmTnPbChQvXOdl+YeIAAQAASURBVC4AAACoz+TrAAAAUDPnnHNO/PWvf83pu/nmm6Njx451nn9XzL0T8m8AAAA2dkOHDo199tkn9ttvv5g4cWK1Y519AwAAQMTtt98eL774YvZl5bfccsta5chVkX8DAABAxPe+97348MMP4/XXX48LLrggDj744Gjfvn1aGD3Je9u1a5e+pPz//u//4uOPP4699947Z/4zzzwTN954Y5Xry78BAACoz6p60VjygrQkp953331X+/3S0tK4995707x9Vb/73e+ivLy80nj5NwAAANTM3XffndNOzsNbt25do7lqrwEAAMC/JPfLu3XrFtdcc00sX7682rFTpkyJwYMHR+fOneOOO+6odqzzbwDIX2Yt5gAAFETTpk1z2kuWLMl7jcWLF1e7JgAAAJAf+ToAAACs2fXXXx+///3vc/rOPffcGDBgwHrJvyvm3qtbEwAAADYk1113XVpsfeXHggUL4vPPP4+nn346Tj755PTlaSu9+uqrsfvuu8fo0aOrXM/ZNwAAAPXdF198EWeffXa2fcopp1T5crS1Jf8GAACAiD59+sT2229fo7EdOnSIF198Mfbaa6+c/ksvvTQ9J18d+TcAAAD1WVU5bHIGvueee1Y7N5PJxE033ZR+XmnChAkxfPjwNe4j/wYAAIDKysrK4rHHHsvpGzhwYI3nq70GAAAAEUuXLo1jjjkmzjjjjPR58MTmm28eF154Ybz55pvxzTffpDnz9OnT4y9/+UscffTRUVJSko6bPXt2WpPtnHPOqXJ9598AkL9/3TADAChypaWlOe1FixblvcbChQurXRMAAADIj3wdAAAAqvfAAw/EWWedVekh9SuuuGK95d8Vc+/VrQkAAAAbsmbNmqUvRzvssMPitttui7Fjx0avXr2y358zZ0788Ic/TD+vjrNvAAAA6ruf/vSn2bx5q622iquuuqrW95B/AwAAQP6SAun33HNPNGzYMNv31VdfxfPPP7/a8fJvAAAA6rOqcthTTz21RvO7du0aBx98cE7f8OHD17iP/BsAAAAqe/jhh2P+/PnZ9pZbbhmHHHJIjeervQYAAAARZ5xxRjz66KPZ9h577BEffPBBXHzxxbH77rtHq1atolGjRrH11lvHEUccEY899lg88cQT6T30la655pq48847V7u+828AyF9mLeYAABRExSR9wYIFUV5entcaqx78r25NAAAAID8Vc+uKufeaJLm9g3oAAAA2Vk8//XScdNJJOWfbP/rRj9KXlZeUlKy3/Lvi+KRA/KqX9AEAAGBjs+2228YLL7wQHTt2zPZNmzYtrr766jrJvVc3x9k3AAAAG1Lx9ccffzzb/sMf/pAWhKtt7p4DAADA2p+BH3nkkTl9zz///GrHyr8BAACoz5o1axYNGjTI6dt0001j1113rfEa+++/f0579OjRlcbIvwEAAGDN7rrrrpz2CSeckNY/qym11wAAAKjvhg0bFrfffnu23bZt27Tm+VZbbVXtvOTu+Z/+9KecvnPOOafSOXVC/TUAyF9mLeYAABREmzZtcl6StnTp0vjqq6/yWiMp7L6q5A8UAAAAwNqrmFtPnTo1r/lffvllLFu2LNvOZDLp3wAAAABgQ/fyyy9H//79c/Lefv36xYMPPlipuFxd598Vz8q32GKLvOYDAADAhig5e7744ourLSZXW7l3Yvr06dWuCQAAAMUqKeq20mGHHRbHHntsnezj7jkAAACsvYMOOiinPWHChNWOk38DAABQ31XMjbfddts0v62pHXbYIae9uhro8m8AAACo3qRJk+LVV1/N6Rs4cGBea6i9BgAAQH13/fXX57TPOuusGue3SR6+/fbbZ9uzZs2Kxx57rNI49dcAIH81v40GAFBgzZo1i06dOuX0TZkyJa81Ko7v3r17rcQGAAAA9VXFh9nXNVffZpttomnTprUSGwAAABTKyJEj48gjj4xFixZl+/r06ROPP/54NG7cuOD5t7NyAAAA6oujjz46SkpKch4k/+yzz2o9904Kv6/6d4Ak/+/atetaxQwAAADr25w5c7JfP/PMM2kuvaaPvn375qyR5NsVx7zzzjs5Y9w9BwAAgLXXsWPHnPbMmTNXO07+DQAAQH2344475rRbtGiR1/yK47/55ptKY+TfAAAAUL177rknysvLs+3evXtHz54981pD7TUAAADqsySvfumll3L6jjjiiBrPz2Qycdhhh+X0vfLKK5XGqb8GAPnLrMUcAICCqXhYPm7cuLzmjx8/vtr1AAAAgPzI1QEAACDX2LFj45BDDomysrJs36677hrPPvtsNG/efK3WlH8DAADA2mnVqlVsvvnmOX0zZsyoNK5irvzJJ5/EkiVL1jr37tatWzRs2DDveAEAAGBj5uwbAAAA1l6jRo1y2kuXLl3tOPk3AAAA9V2PHj1y2osXL85r/qo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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bad_sync_timeline_path = \"/Users/sangbo/local_data/projects/xpu_timer/paper/experiments/figs/trace_dp4.bin\"\n", + "output_dir = \"./bad_sync\"\n", + "group_str = \"tp2-cp1-dp32-pp4\"\n", + "\n", + "bad_sync = analysis_host_issue(bad_sync_timeline_path, output_dir, group_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "752a3fd6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['AllGather', 'ReduceScatter'])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bad_gc.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "429b2860", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['AllGather', 'ReduceScatter'])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "no_sync.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "505f9e3f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['AllGather', 'ReduceScatter'])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bad_sync.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "975ac4e9", + "metadata": {}, + "outputs": [], + "source": [ + "# flake8: noqa: E402\n", + "import os\n", + "import random\n", + "from argparse import ArgumentParser\n", + "from collections import OrderedDict\n", + "from pathlib import Path\n", + "from typing import Dict\n", + "\n", + "import matplotlib.colors as mcolors\n", + "import matplotlib.gridspec as gridspec\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from perfetto.trace_processor import TraceProcessor\n", + "from util import GetRankHelper\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4739001c", + "metadata": {}, + "outputs": [], + "source": [ + "dist_strategy = \"tp2-cp1-dp32-pp4\"\n", + "groups_dict = OrderedDict((pair[:2], int(pair[2:])) for pair in dist_strategy.split(\"-\"))\n", + "rank_helper = GetRankHelper(groups_dict)\n", + "group_ranks = {group: rank_helper.get_ranks(group) for group in groups_dict}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "87f054f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0,\n", + " 1,\n", + " 2,\n", + " 4,\n", + " 6,\n", + " 8,\n", + " 10,\n", + " 12,\n", + " 14,\n", + " 16,\n", + " 18,\n", + " 20,\n", + " 22,\n", + " 24,\n", + " 26,\n", + " 28,\n", + " 30,\n", + " 32,\n", + " 34,\n", + " 36,\n", + " 38,\n", + " 40,\n", + " 42,\n", + " 44,\n", + " 46,\n", + " 48,\n", + " 50,\n", + " 52,\n", + " 54,\n", + " 56,\n", + " 58,\n", + " 60,\n", + " 62,\n", + " 64,\n", + " 128,\n", + " 192}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ranks = []\n", + "ranks.extend(group_ranks['tp'][0])\n", + "ranks.extend(group_ranks['dp'][0])\n", + "ranks.extend(group_ranks['pp'][0])\n", + "ranks.sort()\n", + "ranks = set(ranks)\n", + "ranks" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c0ff862b", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_nccl_host_issue_delay_hist(dfs_list, labels, filter_ranks):\n", + "\n", + " unique_ops = dfs_list[0].keys()\n", + " # Determine the number of subplots needed\n", + " num_plots = len(unique_ops)\n", + " num_cols = 2 # Define the number of columns in the subplot grid\n", + " num_rows = (num_plots + num_cols - 1) // num_cols + 1# Calculate the number of rows needed\n", + " \n", + " fig = plt.figure(figsize=(30, num_rows * 5))\n", + " gs = fig.add_gridspec(num_rows,num_cols)\n", + " axe = fig.add_subplot(gs[0, :])\n", + " \n", + " for i, dfs in enumerate(dfs_list):\n", + " delay = []\n", + " for k,v in dfs.items():\n", + " v = v[v['rank'].isin(filter_ranks)]\n", + " delay.extend(v['delay'].tolist())\n", + " # Step 1: Sort the data\n", + " sorted_delay = np.sort(delay)\n", + "\n", + " # Step 2: Calculate the CDF\n", + " cdf_delay = np.arange(1, len(sorted_delay) + 1) / len(sorted_delay)\n", + "\n", + " # Step 3: Plot the CDF\n", + " axe.plot(sorted_delay, cdf_delay, marker='.', linestyle='-', label=f'{labels[i]}')\n", + "\n", + " axe.set_xlabel('host issue (ms)')\n", + " axe.set_ylabel('Ratio Comulative Distribution (%)')\n", + " axe.set_title(\"CDF of Host Issue\")\n", + " axe.legend(loc=2)\n", + "\n", + " # Create histograms for each unique op\n", + " for i, unique_op in enumerate(unique_ops):\n", + " axe = fig.add_subplot(gs[(i+2)//2, (i+2)%2])\n", + " \n", + " for j, dfs in enumerate(dfs_list):\n", + " delay = []\n", + " for k,v in dfs.items():\n", + " v = v[v['rank'].isin(filter_ranks)]\n", + " v = v[v['op'] == unique_op]\n", + " delay.extend(v['delay'].tolist())\n", + " # Step 1: Sort the data\n", + " sorted_delay = np.sort(delay)\n", + "\n", + " # Step 2: Calculate the CDF\n", + " cdf_delay = np.arange(1, len(sorted_delay) + 1) / len(sorted_delay)\n", + "\n", + " # Step 3: Plot the CDF\n", + " axe.plot(sorted_delay, cdf_delay, marker='.', linestyle='-', label=f'{labels[j]}')\n", + " \n", + " axe.set_xlabel('host issue (ms)')\n", + " axe.set_ylabel('Ratio Comulative Distribution (%)')\n", + " axe.set_title(f\"CDF of {unique_op} Host Issue\")\n", + " axe.legend(loc=2)\n", + "\n", + " \n", + "\n", + " plt.tight_layout()\n", + " plt.grid(True)\n", + " plt.savefig(\"cdf_of_host_issue.svg\", dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "ca3e3b1e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6T95mm20qzNPXlXdPmTIlvv3220rHDAAAAIU2ceLE+O1vf5st33DDDZW6wVlZrHcDAABArmXLlqX57RtvvBFjxoxJ15gXL168XmPJuwEAAKjNxo4dm1Pu27dv9vn48ePjpz/9aVq3xRZbROPGjaNbt25x4IEHxs033xwzZ87Maw65NwAAAOTn/vvvzyn/4Ac/iLp161bYR94NAABAbbXVVlvFgAEDcvaY33bbbevsl7QbOnRoTt3pp59eZltnqwHA+smsZz8AgE3CkiVLYvr06Tl1nTt3rtQYpdtPnjy5SmIDAACA2qh0Xr2hefqnn34aS5curZLYAAAAoCberHxNhx122EbNu62PAwAAsDl74IEH4txzz82WM5lM/PGPf4yioqJqybvbtm0bDRs2zJaXL18en3zySaXjBgAAgEJatWpVehBcktcm9tprrzjzzDM3eFz7zAEAAOB/fvzjH0fLli3TQ9OT3Pu73/1ubLvtttGiRYv0+dVXXx3z5s3Lezx5NwAAALXZ2LFjc8rdu3eP4uLidO27f//+cfvtt8f7778fCxYsSM8zT/Lel19+OS6++OI0H7/88stjxYoVFc4h9wYAAIB1S24UPmrUqJy60047bZ39nK0GAABAbXbDDTek56OtdsUVV6Tnp5UnWfs+/vjjY9KkSdm6I488Mn2UxdlqALB+6q5nPwCATcIXX3wRJSUl2XK9evWiXbt2lRqjU6dOOeW5c+dWWXwAAABQ25TOq7faaqtK9W/fvn3UrVs3vv322+xB8/Pnz18rfwcAAIBN3fPPPx+vvfZaTt2QIUOqNe8unV9X5vB4AAAAqGk++uijmD59eracHM7+1VdfxYQJE+If//hHTJw4Mfta/fr146677or999+/2vLuRMeOHePjjz/OGTM5PB4AAAA2Fbfddlv8+9//zsmni4qKNnhc+8wBAADgf9Zcz15TkveOGTMmfSQHvl900UVx5ZVXRp06dSocT94NAABAbb8J+ZqSG6jtvffeMX78+HX2XbJkSVx33XUxduzYGDZsWDRr1qzMdnJvAAAAWLfSNzHv379/7Ljjjuvs52w1AAAAarM999wz/vjHP8aPf/zj9P7cybpyck75HXfcEccee2xsv/320ahRo/T+3ck+87/+9a/x5ZdfZvsfeOCB8cgjj5Q7vrPVAGD91F3PfgAAm4Ti4uKccuPGjSt92FyTJk0qHBMAAADIX+m8unTevS5JXp9sLli4cGG5YwIAAMCmLtlIf9ZZZ+XUHX300bHbbrtVa95dun1yA/Vly5ZFgwYNKjUOAAAA1AR33nln3Hrrretcgz7kkEPSw9v79u1brXl3WX2sdwMAALAp+eSTT+JXv/pVtnzZZZdFz549q2Rs+8wBAACgcpIblV977bXx+uuvxzPPPBNNmzYtt628GwAAgNpq1apVOfls4qc//WmMHz8+m/MeccQRcdhhh6U3O1u0aFH62kMPPRSzZs3K9nn55ZfTG639/e9/L3MeuTcAAABULLmR+YMPPphTl+Ta+XC2GgAAALXdOeecE9tvv3263v3hhx+mdWPHjk0f5enevXtccsklceaZZ0Ymkym3nbPVAGD9lP9/VwCAzUDp5L5hw4aVHiPZIF/RmAAAAED+5OoAAACw7gPnTjnllPjss8+ydS1atIjbbrut2vPu0jl3WWMCAADA5mTw4MHxy1/+Mvr27bvOtta7AQAAqO1+9KMfpTdFS/Ts2TMuv/zyKhtb3g0AAEBtl9wQfMCAAfHb3/42hg8fnu4nX7x4cSxdujRmzpwZzzzzTJx11llr5cwjRoyIk046KVauXFnu2PJuAAAAaquvv/46vRH5mt55553039atW8fIkSPj6aefjrPPPjuOOOKIOPHEE+P666+PyZMnx/e///2cfsOGDVvrhuaryb0BAACgYq+88kpMnz49W65fv/5auXd5nK0GAAAAEfvtt1+MHTs2LrrooqhTp06Fbbt06ZK2S3LvTCZTYVvr3QCwfir+PywAwCYuucB9Tckif2U1aNAgp7xkyZINjgsAAABqK7k6AAAAVOziiy+OF154Iafu//7v/6Jz587VnneXzrkT8m4AAAA2Z4899ljsueeesffee8eUKVMqbGu9GwAAgNrsnnvuiZdffjl74/K77rprvXLj8si7AQAAqM0OOuig+M9//hOjRo2Kyy+/PA444IDo1KlTemB6ku927NgxvVn5//t//y/++9//xh577JHT/7nnnos777yz3PHl3QAAANRW5d18LLlpWpJP77XXXmW+3rRp03jooYfSnH1Nv/vd76KkpGSt9nJvAAAAqNgDDzyQU07WwFu3bp1XX2erAQAAQKR7yXv06BE333xzrFy5ssK206dPj3PPPTe6desW9957b4VtrXcDwPrJrGc/AIBNQsOGDXPKy5cvr/QYy5Ytq3BMAAAAIH9ydQAAACjfbbfdFn/4wx9y6i655JI48cQTN0reXTrnLmtMAAAA2FQMHTo0PYR99WPx4sUxY8aMePbZZ+P0009Pb6i22uuvvx677rprjBs3rtzxrHcDAABQW33++edx0UUXZctnnHFGuTdLW1/ybgAAAGqzAQMGxHbbbZdX26222ipefvnl+N73vpdT/5vf/CZdFy+LvBsAAIDaqrz8NVn33n333Svsm8lk4k9/+lP672qTJ0+OkSNHrnMeuTcAAAD8T3FxcQwbNiynbsiQIXn3d7YaAAAAtdmKFSvi+OOPj3POOSe95jvRqlWruOKKK+Ktt96Kr776Ks2VZ82aFU8//XQcc8wxUVRUlLb78ssv0/PWLr744nLHt94NAOvnf7vKAAA2Q02bNs0pL126tNJjLFmypMIxAQAAgPzJ1QEAAKBsf/3rX+OCCy5Y60L266+/fqPl3aVz7rLGBAAAgE1Vo0aN0humHX744XH33XfH+++/H/369cu+vmDBgjj66KPTf8tivRsAAIDa6sc//nE2X95yyy3jxhtvrPI55N0AAACQv+Tg9AcffDDq1q2brZs7d2689NJLZbaXdwMAAFBblZe/nnnmmXn17969exxwwAE5dSNHjlznPHJvAAAA+J/HH388Fi1alC23b98+Dj300Lz7O1sNAACA2uycc86Jv//979nybrvtFh9++GFcffXVseuuu0bLli2jXr160aFDhzjyyCNj2LBh8dRTT6V7zle7+eab47777itzfOvdALB+MuvZDwBgk1A6uV+8eHGUlJRUaow1NwqUNSYAAACQv9J5dem8e12SvN7iPgAAAJubZ599Nk499dSc9exjjz02vWl5UVHRRsu7S7dPDo5fc0M/AAAAbE622WabGD58eHTu3DlbN3PmzLjpppuqJe8uq4/1bgAAADaFQ9mffPLJbPnWW29ND4yravaZAwAAQOXXvI866qicupdeeqnMtvJuAAAAaqtGjRpFnTp1cuqaNWsWO++8c95j7LPPPjnlcePGrdVG7g0AAADlu//++3PKP/jBD9LzzfLlbDUAAABqqxEjRsQ999yTLbdr1y49y3zLLbessF+yz/yOO+7Iqbv44ovXWpdOOFsNANZPZj37AQBsEtq0aZNz07QVK1bE3LlzKzVGctj7mpI/bAAAAADrp3Re/dlnn1Wq/5w5c+Lbb7/NljOZTJr/AwAAwKbq1VdfjcGDB+fkuwceeGA88sgjax08V915d+n18bZt21aqPwAAAGxqkvXmq6++usLD5qoq707MmjWrwjEBAACgpkkOfVvt8MMPjxNOOKFa5rHPHAAAACpv//33zylPnjy5zHbybgAAAGqz0nnxNttsk+a2+dp+++1zymWdby73BgAAgLJ9/PHH8frrr+fUDRkypFJjOFsNAACA2uq2227LKV9wwQV557VJ/r3ddttly/Pnz49hw4at1c7ZagCwfvLfgQYAsAlq1KhRdOnSJadu+vTplRqjdPuePXtWSWwAAABQG5W+4H1D8/SuXbtGw4YNqyQ2AAAA2NjGjBkTRx11VCxdujRbN2DAgHjyySejfv36Bc+7rY8DAABQGxxzzDFRVFSUc8H5p59+WuV5d3Ig/Jp/A0hy/+7du69XzAAAALCxLFiwIPv8ueeeS3PodT0GDhyYM0aSZ5du8+677+a0sc8cAAAAKq9z58455Xnz5pXZTt4NAABAbbbDDjvklJs3b16p/qXbf/XVV2u1kXsDAABA2R588MEoKSnJlvv37x877rhjpcZwthoAAAC1UZJPv/LKKzl1Rx55ZN79M5lMHH744Tl1r7322lrtnK0GAOsns579AAA2GaUX1ydOnFip/pMmTapwPAAAACB/8nQAAAD4/3v//ffj0EMPjeLi4mzdzjvvHM8//3w0adJkvcaUdwMAAEDltWzZMlq1apVTN3v27LXalc6Tp06dGsuXL1/vvLtHjx5Rt27dSscLAAAAmyPr3QAAAFB59erVyymvWLGizHbybgAAAGqzXr165ZSXLVtWqf5r3tAs0bhx47XayL0BAACg7BuaP/jggzl1p512WqXHkXcDAABQG3311Vfx9ddf59RtvfXWlRqjdPuZM2eu1cbZagCwfjLr2Q8AYJPRr1+/nPLo0aPz7vv555/HtGnTci6KL72xHwAAAMhf7969cw6dS/LuJP/O16hRoyrM+wEAAGBTMHny5DjwwAPTzfar7bDDDvHiiy9GixYt1nvc0nny2LFj49tvv827v7wbAAAAyr6ZWmLLLbdMH2seEP/222/nPaa8GwAAAMpnnzkAAABU3uzZs3PKbdu2LbOdvBsAAIDarH///jnlOXPmVKr/3Llzc8qtW7deq43cGwAAANY2cuTI+OSTT7Ll+vXrx/e///1Kj+NsNQAAAGqj5Iyz0urWrbtBZ6mtXLlyrTbOVgOA9ZNZz34AAJuMI444Iqf88ssvR0lJSV59X3rppZzywIEDo2nTplUaHwAAANQmzZo1i7333junbvjw4Xn1TfL5JK9f05FHHlml8QEAAEB1+/TTT+OAAw7IORR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{ + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/xpu_timer/experiments/figs/parse_perfetto.py b/xpu_timer/experiments/figs/parse_perfetto.py new file mode 100644 index 0000000000..5b3e3ad502 --- /dev/null +++ b/xpu_timer/experiments/figs/parse_perfetto.py @@ -0,0 +1,687 @@ +# flake8: noqa: E402 +import os +import random +from argparse import ArgumentParser +from collections import OrderedDict +from pathlib import Path +from typing import Dict + +import matplotlib.colors as mcolors +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from perfetto.trace_processor import TraceProcessor, TraceProcessorConfig +from util import GetRankHelper + +plt.rcParams["figure.dpi"] = 300 + +from types import SimpleNamespace + +nccl_sql = SimpleNamespace( + sql=""" +include perfetto module slices.slices; + +WITH comm_hash + AS (SELECT t1.arg_set_id, + t1.int_value AS hash, + t2.int_value AS seq, + t2.RANK AS rank, + t3.delay as delay + FROM (SELECT DISTINCT arg_set_id, + int_value + FROM args + WHERE KEY = 'debug.comm_hash') t1 + JOIN ( (SELECT DISTINCT arg_set_id, + int_value + FROM args + WHERE KEY = 'debug.seq') seq + JOIN (SELECT DISTINCT arg_set_id, + int_value AS RANK + FROM args + WHERE KEY = 'debug.rank') RANK + ON seq.arg_set_id = RANK.arg_set_id ) t2 + ON t1.arg_set_id = t2.arg_set_id + JOIN (SELECT DISTINCT arg_set_id, int_value AS delay FROM args WHERE key = 'debug.delay(us)') t3 + ON t1.arg_set_id = t3.arg_set_id) +SELECT CAST(id as INT) AS id, + CAST(ts as INT) AS ts, + CAST(dur as INT) AS dur, + name AS name, + CAST(comm_hash.hash as INT) as hash, + CAST(comm_hash.seq as INT) as seq, + CAST(comm_hash.rank as INT) as rank, + CAST(comm_hash.delay as INT) as delay +FROM _slice_with_thread_and_process_info + JOIN comm_hash + ON comm_hash.arg_set_id = +_slice_with_thread_and_process_info.arg_set_id +""", + table_dtype={ + "name": str, + "rank": int, + "ts": int, + "dur": int, + "hash": int, + "id": int, + "seq": int, + "delay": int, + }, +) + +tflops_sql = SimpleNamespace( + sql=""" + include perfetto module slices.slices; + +WITH tflops + AS (SELECT t1.arg_set_id, + t2.rank AS rank, + t1.TFLOPS as TFLOPS, + t3.count as count + FROM (SELECT DISTINCT arg_set_id, real_value as TFLOPS FROM args where key ='debug.TFLOPS') t1 + JOIN (SELECT DISTINCT arg_set_id, int_value as rank FROM args where key ='debug.rank') t2 + ON t1.arg_set_id = t2.arg_set_id + JOIN (SELECT DISTINCT arg_set_id, int_value as count FROM args where key = 'debug.count') t3 + ON t1.arg_set_id = t3.arg_set_id) +SELECT CAST(id as INT) AS id, + CAST(ts as INT) AS ts, + CAST(dur as INT) AS dur, + name AS name, + CAST(tflops.rank as INT) as rank, + CAST(tflops.TFLOPS as DOUBLE) as TFLOPS, + CAST(tflops.count as INT) as count +FROM _slice_with_thread_and_process_info + JOIN tflops + ON tflops.arg_set_id = +_slice_with_thread_and_process_info.arg_set_id +""", + table_dtype={ + "name": str, + "rank": int, + "ts": int, + "dur": int, + "TFLOPS": float, + "id": int, + "count": int, + }, +) + +xccl_sql = SimpleNamespace( + sql=""" +include perfetto module slices.slices; +WITH bandwidth AS ( + SELECT + t1.arg_set_id, + t2.rank AS rank, + t1.Bandwidth AS Bandwidth, + t3.seq AS seq, + MAX(t1.Bandwidth) OVER (PARTITION BY t3.seq) AS max_bandwidth + FROM + (SELECT DISTINCT arg_set_id, real_value AS Bandwidth FROM args WHERE key = 'debug.Bandwidth(GiB/s)') t1 + JOIN + (SELECT DISTINCT arg_set_id, int_value AS rank FROM args WHERE key = 'debug.rank') t2 + ON + t1.arg_set_id = t2.arg_set_id + JOIN + (SELECT DISTINCT arg_set_id, int_value AS seq FROM args WHERE key = 'debug.seq') t3 + ON + t1.arg_set_id = t3.arg_set_id +) +SELECT + CAST(id AS INT) AS id, + CAST(ts AS INT) AS ts, + CAST(dur AS INT) AS dur, + name AS name, + CAST(bandwidth.rank AS INT) AS rank, + CAST(bandwidth.max_bandwidth AS DOUBLE) AS 'Bandwidth', + CAST(bandwidth.seq AS INT) AS seq +FROM + _slice_with_thread_and_process_info +JOIN + bandwidth +ON + bandwidth.arg_set_id = _slice_with_thread_and_process_info.arg_set_id; + """, + table_dtype={ + "name": str, + "rank": int, + "ts": int, + "dur": int, + "Bandwidth": float, + "id": int, + "seq": int, + }, +) + + +class PerfettoParser: + def __init__(self, trace): + conf = TraceProcessorConfig( + bin_path="/Users/sangbo/Downloads/software/mac-arm64/trace_processor_shell", + ) + self.trace = TraceProcessor(trace=trace, config=conf) + + def parse(self, sql_spec): + qr = self.trace.query(sql_spec.sql) + df = qr.as_pandas_dataframe() + for key in df.keys(): + df[key] = df[key].astype(sql_spec.table_dtype[key]) + return df + + +def diff_performance(diff_data: Dict[str, pd.DataFrame], key: str, ax, colors, density=True, title=None): + + df = pd.concat([df[[key]].add_suffix(f"({name})") for name, df in diff_data.items()], axis=1) + df.plot( + ax=ax, + kind="hist", + bins=100, + alpha=0.5, + color=colors, + edgecolor="black", + density=density, + ) + ax.set_xlabel(key) + ax.set_ylabel("Density" if density else "Frequency") + ax.set_title(f"{title} Distribution Comparison") + ax.legend() + ax.grid(True) + + +def analysis_launch_time(trace): + new_data = trace[["name", "hash", "seq", "ts"]] + group_data = {k: v.drop(columns=["hash"]) for k, v in new_data.groupby(["hash", "seq"])} + + def parse_one(frame): + first_launch = frame.ts.min() + last_launch = frame.ts.max() + frame["relative_ts_ms"] = (frame.ts - first_launch) / 1e6 + frame = frame.loc[frame["relative_ts_ms"] != 0] + + return frame + + launch_time_diff = {k: parse_one(v) for k, v in group_data.items()} + df = pd.concat(list(launch_time_diff.values()), ignore_index=True) + return df + + +def analysis_dur(trace): + new_data = trace[["name", "hash", "seq", "dur"]] + group_data = {k: v.drop(columns=["hash"]) for k, v in new_data.groupby(["hash", "seq"])} + + def parse_one(frame): + frame["dur_ms"] = frame.dur / 1e6 + return frame + + launch_time_diff = {k: parse_one(v) for k, v in group_data.items()} + df = pd.concat(list(launch_time_diff.values()), ignore_index=True) + return df + + +def plot_diff(named_path, image_path): + colors = list(random.sample(mcolors.TABLEAU_COLORS.keys(), len(named_path))) + + fig = plt.figure(figsize=(24, 16), dpi=300) + gs = gridspec.GridSpec(3, 2, height_ratios=[5, 2, 2]) + + nccl_launch_time_ax = fig.add_subplot(gs[0, 0]) + nccl_dur_time_ax = fig.add_subplot(gs[0, 1]) + matmul_ax = fig.add_subplot(gs[1:, :]) + + nccl_launch_diff_time = {} + nccl_dur_time = {} + matmul_dir_time = {} + for name, path in named_path.items(): + trace = PerfettoParser(trace=path) + nccl_info = trace.parse(nccl_sql) + matmul_dir_time[name] = trace.parse(tflops_sql) + nccl_dur_time[name] = analysis_dur(nccl_info) + nccl_launch_diff_time[name] = analysis_launch_time(nccl_info) + + diff_performance(nccl_dur_time, "dur_ms", nccl_dur_time_ax, colors, title="NCCL dur(ms)") + diff_performance( + nccl_launch_diff_time, + "relative_ts_ms", + nccl_launch_time_ax, + colors, + title="NCCL launch diff(ms)", + ) + diff_performance(matmul_dir_time, "TFLOPS", matmul_ax, colors, title="Matmul TFLOPS ") + + plt.tight_layout() + + plt.show() + fig.savefig(f"{image_path}/performance_diff.svg", dpi=300) + + +def plot_tflops_box(path, image_path): + trace = PerfettoParser(trace=path) + matmul = trace.parse(tflops_sql) + matmul = matmul[["rank", "TFLOPS"]] + + matmul.boxplot( + column="TFLOPS", + by="rank", + color=dict(boxes="r", whiskers="r", medians="r", caps="r"), + boxprops=dict(linestyle="-", linewidth=1.5), + flierprops=dict(linestyle="-", linewidth=1.5), + medianprops=dict(linestyle="-", linewidth=1.5), + whiskerprops=dict(linestyle="-", linewidth=1.5), + capprops=dict(linestyle="-", linewidth=1.5), + showfliers=False, + grid=True, + rot=0, + ) + plt.title("Box Plot of TFLOPS by Rank") + plt.suptitle("") + plt.xlabel("Rank") + plt.ylabel("TFLOPS") + plt.show() + plt.savefig(image_path, dpi=300) + + +def plot_xccl_box(path, image_path): + trace = PerfettoParser(trace=path) + xccl = trace.parse(xccl_sql) + xccl = xccl[["rank", "Bandwidth"]] + + xccl.boxplot( + column="Bandwidth", + by="rank", + color=dict(boxes="r", whiskers="r", medians="r", caps="r"), + boxprops=dict(linestyle="-", linewidth=1.5), + flierprops=dict(linestyle="-", linewidth=1.5), + medianprops=dict(linestyle="-", linewidth=1.5), + whiskerprops=dict(linestyle="-", linewidth=1.5), + capprops=dict(linestyle="-", linewidth=1.5), + showfliers=False, + grid=True, + rot=0, + ) + plt.title("Box Plot of Bandwidth by Rank") + plt.suptitle("") + plt.xlabel("Rank") + plt.ylabel("Bandwidth(GiB/s)") + plt.show() + plt.savefig(image_path, dpi=300) + + +def plot_nccl_host_issue_delay_hist(path, save_file_name, max_delay=1e5, min_duration=0.005): + trace = PerfettoParser(trace=path) + nccl = trace.parse(nccl_sql) + nccl = nccl[["name", "rank", "delay", "seq", "hash", "dur"]] + nccl = nccl[nccl["delay"] < max_delay] + nccl = nccl[nccl["dur"] > min_duration] + nccl["op"] = nccl["name"].apply(lambda x: x.split("_")[0]) + nccl["delay"] = nccl["delay"].apply(lambda x: x / 1000) + unique_ops = nccl["op"].unique() + # Determine the number of subplots needed + num_plots = len(unique_ops) + num_cols = 2 # Define the number of columns in the subplot grid + num_rows = (num_plots + num_cols - 1) // num_cols # Calculate the number of rows needed + +# fig, axes = plt.subplots(num_rows+1, num_cols, figsize=(30, num_rows * 5)) + fig = plt.figure(figsize=(30, num_rows * 5)) + gs = fig.add_gridspec(num_rows+1,num_cols) + axe = fig.add_subplot(gs[0, :]) + + data = nccl["delay"] + # Create the histogram and get the counts and bin edges + counts, bins = np.histogram(data, bins=10, density=False) + + # Calculate the bin widths + bin_widths = np.diff(bins) + + # Convert counts to percentages + percentages = counts / counts.sum() * 100 + + # Plot the bar chart with percentages + axe.bar(bins[:-1], percentages, width=bin_widths, edgecolor="black", alpha=0.75, align="edge") + + axe.set_xlabel("delay(ms)") + axe.set_ylabel("Percentage") + axe.set_title(f"Histogram All Operators with Percentages") + + dfs = {} + # Create histograms for each unique op + for i, unique_op in enumerate(unique_ops): + axe = fig.add_subplot(gs[(i+2)//2, (i+2)%2]) + subset = nccl[nccl["op"] == unique_op] + dfs[unique_op] = subset + data = subset["delay"] + # Create the histogram and get the counts and bin edges + counts, bins = np.histogram(data, bins=10, density=False) + + # Calculate the bin widths + bin_widths = np.diff(bins) + + # Convert counts to percentages + percentages = counts / counts.sum() * 100 + + # Plot the bar chart with percentages + axe.bar(bins[:-1], percentages, width=bin_widths, edgecolor="black", alpha=0.75, align="edge") + + axe.set_xlabel("delay(ms)") + axe.set_ylabel("Percentage") + axe.set_title(f"Histogram {unique_op} with Percentages") + + + dfs_dir = f"./{save_file_name}/host_issue" + if not os.path.exists(dfs_dir): + os.makedirs(dfs_dir) + nccl.to_csv(f"{dfs_dir}/host_issue_all-op.csv") + for op_name, op_df in dfs.items(): + op_df.to_csv(f"{dfs_dir}/host_issue-{op_name}.csv") + + image_path = f"{dfs_dir}/host_issue.svg" + plt.tight_layout() + plt.savefig(image_path, dpi=300) + plt.show() + return dfs + + +def analysis_nccl_kernel_run_duration_ratio(trace): + new_data = trace[ + [ + "name", + "hash", + "seq", + "dur", + "ts", + "rank", + ] + ] + group_data = {k: v for k, v in new_data.groupby(["hash", "seq"])} + + def parse_one(frame): + frame["dur_ms"] = frame.dur / 1e6 + first_launch = frame.ts.min() + last_launch = frame.ts.max() + frame["relative_ts_ms"] = (frame.ts - first_launch) / 1e6 + kernel_time = frame.dur_ms.min() + ratio_time = frame["relative_ts_ms"] / kernel_time + frame["ratio_dur_diff"] = ratio_time + frame["kernel_time"] = kernel_time + return frame + + launch_time_diff = {k: parse_one(v) for k, v in group_data.items()} + df = pd.concat(list(launch_time_diff.values()), ignore_index=True) + return df + +def plot_nccl_host_issue_delay_seq_in_same_communicator( + df_dict, image_dir, delay=10, filter_op=["HcclAllGather"], filter_rank=[0, 1] +): + if not os.path.exists(image_dir): + os.makedirs(image_dir) + + def aggregate_ranks(x): + return np.array(set(x)) + + def is_in_filter_rank(x): + cur_rank = x['rank_set'] + if "Send" in x['name'] or "Recv" in x['name']: + return set(cur_rank).issubset(set(filter_rank)) + else: + return set(cur_rank) == set(filter_rank) + + + for i, (op, df) in enumerate(df_dict.items()): + if op not in filter_op: + continue + + df['rank_set'] = df['rank'] + df['rank_set'] = df.groupby(['name', 'hash', 'seq'])['rank_set'].transform(aggregate_ranks) + + df['is_in_filter_rank'] = df.apply(is_in_filter_rank, axis=1) + grouped = df[df['is_in_filter_rank'] == True] + grouped = grouped[grouped["delay"] <= delay] + grouped = grouped.groupby(["name", "hash", "rank"]) + + + num_plots = len(grouped) + if num_plots == 0: + continue + num_cols = 2 # Define the number of columns in the subplot grid + num_rows = (num_plots + num_cols - 1) // num_cols # Calculate the number of rows needed + + fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, num_rows * 5)) + axes = axes.flatten() # Flatten in case axes is a 2D array + for ax in axes: + ax.axis('off') + axe_num = 0 + for (name, hash_value, rank), group in grouped: + ax = axes[axe_num] + ax.axis('on') + group = group.sort_values(by="seq") + group = group.reset_index(drop=True) + y_label = group["seq"] + x_label = [i for i in range(len(y_label))] + + # Plot the sequences + ax.plot(x_label, y_label, label=f"{name} (rank {rank}) (hash {hash_value})", marker="o", linestyle="-") + + axe_num += 1 + # Add a legend + ax.legend() + + # Add labels and title + ax.set_xlabel("Index") + ax.set_ylabel("Sequence Value") + ax.set_title(f"Sequences by Name and Rank of {op}") + group.to_csv(f"{image_dir}/{name}-rank_{rank}-hash_{hash_value}.csv") + + # Show the plot + # Adjust layout + plt.tight_layout() + + # Save the image + plt.savefig(os.path.join(image_dir, f"{name}-op_{op}.svg")) + + # Show the figure with all subplots + plt.show() + +def plot_nccl_kernel_run_duration_ratio(path, image_path, threshold=1e5): + # threshold is for hpu, because api is not stable + trace = PerfettoParser(trace=path) + nccl = trace.parse(nccl_sql) + nccl = analysis_nccl_kernel_run_duration_ratio(nccl) + nccl["op"] = nccl["name"].apply(lambda x: x.split("_")[0]) + unique_ops = nccl["op"].unique() + unique_ops = [item for item in unique_ops if "Send" not in item and "Recv" not in item] + + # Determine the number of subplots needed + num_plots = len(unique_ops) + num_cols = 2 # Define the number of columns in the subplot grid + num_rows = (num_plots + num_cols - 1) // num_cols # Calculate the number of rows needed + + fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, num_rows * 5)) + axes = axes.flatten() # Flatten in case axes is a 2D array + + dfs = {} + for i, op in enumerate(unique_ops): + axe = axes[i] + df = nccl[nccl["op"] == op] + + # Create 10 bins based on 'dur_ms' + bins = pd.cut(df["dur_ms"], bins=10) + + # Get the midpoint of each bin + bin_midpoints = bins.apply(lambda x: int(x.mid)) + + # Assign these midpoints back to the DataFrame + df.loc[:, "dur_bins_mid"] = bin_midpoints + dfs[op] = df + + df.boxplot( + column="ratio_dur_diff", + by="dur_bins_mid", + ax=axes[i], + color=dict(boxes="r", whiskers="r", medians="r", caps="r"), + boxprops=dict(linestyle="-", linewidth=1.5), + flierprops=dict(linestyle="-", linewidth=1.5), + medianprops=dict(linestyle="-", linewidth=1.5), + whiskerprops=dict(linestyle="-", linewidth=1.5), + capprops=dict(linestyle="-", linewidth=1.5), + ) + axe.set_title(f"Box {op} Plot") + axe.set_xlabel("kernel duration(ms)") + axe.set_ylabel("lanuch_time_diff/kernel_duration") + axe.legend() + axe.grid(True) + plt.tight_layout() + fig.suptitle("") + plt.show() + return dfs + + +def plot_nccl_kernel_run_duration_ratio_longtail( + df_dict, image_dir, filter_kernel_duration=100, filter_ratio=10, filter_op=["HcclAllGather"], filter_rank=[0, 1] +): + if not os.path.exists(image_dir): + os.makedirs(image_dir) + + for i, (op, df) in enumerate(df_dict.items()): + if op not in filter_op: + continue + df = df[(df["ratio_dur_diff"] >= filter_ratio)] + df = df[df["rank"].isin(filter_rank)] + grouped = df.groupby(["name", "rank", "hash"]) + + num_plots = len(grouped) + num_cols = 2 # Define the number of columns in the subplot grid + num_rows = (num_plots + num_cols - 1) // num_cols # Calculate the number of rows needed + + fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, num_rows * 5)) + axes = axes.flatten() # Flatten in case axes is a 2D array + axe_num = 0 + for (name, rank, hash_value), group in grouped: + ax = axes[axe_num] + group = group.reset_index(drop=True) + y_label = group["seq"] + x_label = [i for i in range(len(y_label))] + + # Plot the sequences + ax.plot(x_label, y_label, label=f"{name} (rank {rank}) (hash {hash_value})", marker="o", linestyle="-") + + axe_num += 1 + # Add a legend + ax.legend() + + # Add labels and title + ax.set_xlabel("Index") + ax.set_ylabel("Sequence Value") + ax.set_title(f"Sequences by Name and Rank of {op}") + group.to_csv(f"{name}-rank_{rank}-hash_{hash_value}.csv") + + # Show the plot + # Adjust layout + plt.tight_layout() + + # Save the image + plt.savefig(os.path.join(image_dir, f"{name}-op_{op}.svg")) + + # Show the figure with all subplots + plt.show() + +def analysis_host_issue(timeline_path, output_dir, dist_strategy): + groups_dict = OrderedDict((pair[:2], int(pair[2:])) for pair in dist_strategy.split("-")) + rank_helper = GetRankHelper(groups_dict) + group_ranks = {group: rank_helper.get_ranks(group) for group in groups_dict} + dfs = plot_nccl_host_issue_delay_hist(timeline_path, + output_dir, + ) + return dfs + for parallel_method, parallel_size in group_ranks.items: + if len(parallel_size[0]) == 1: + continue + + print(f"Analysis {parallel_method} Group: {parallel_size[0]}") + plot_nccl_host_issue_delay_seq_in_same_communicator(dfs, + delay=0.05, + image_dir=f"{output_dir}/{parallel_method}", + filter_op=dfs.keys(), + filter_rank=parallel_size[0]) + +def plot_tflops_grouped_by_operation(timeline_path, image_path, filter_rank=[0]): + trace = PerfettoParser(trace=timeline_path) + matmul = trace.parse(tflops_sql) + # Loop over each name and rank to plot them separately + matmul = matmul[matmul['rank'].isin(filter_rank)] + grouped = matmul.groupby(['name', 'rank']) + num_plots = len(grouped) + + + num_cols = 2 # Define the number of columns in the subplot grid + num_rows = (num_plots + num_cols - 1) // num_cols # Calculate the number of rows needed + + fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, num_rows * 5)) + axes = axes.flatten() # Flatten in case axes is a 2D array + for ax in axes: + ax.axis('off') + + axe_num = 0 + for (name, rank), group in grouped: + ax = axes[axe_num] + ax.axis('on') + group = group.sort_values(by="count") + group = group.reset_index(drop=True) + y_label = group["TFLOPS"] + x_label = [i for i in range(len(y_label))] + + # Plot the sequences + ax.plot(x_label, y_label, label=f"{name} (rank {rank})", marker="o", linestyle="-") + + axe_num += 1 + # Add a legend + ax.legend() + + # Add labels and title + ax.set_xlabel("Count") + ax.set_ylabel("TFLOPS") + ax.set_title(f"Sequences by Name and Rank of {name}") + + plt.savefig(image_path, dpi=300) + plt.tight_layout() + plt.show() + + +def main(): + parser = ArgumentParser(usage="""python parse_perfetto.py""") + parser.add_argument("--path", action="append", required=True, help="Trace path for diff") + parser.add_argument("--name", action="append", required=False, help="Trace name for diff") + parser.add_argument( + "--type", + default="tflops-box", + choices=["tflops-box", "xccl-box", "performance-diff"], + ) + parser.add_argument("--output", default="./") + + args = parser.parse_args() + dir_path = Path(args.output) + dir_path.mkdir(parents=True, exist_ok=True) + if args.type == "tflops-box": + for p in args.path: + plot_tflops_box(p, f"{args.output}/{Path(p).name.strip('.bin')}_tflops_boxplot.svg") + elif args.type == "xccl-box": + for p in args.path: + plot_xccl_box(p, f"{args.output}/{Path(p).name.strip('.bin')}_xccl_boxplot.svg") + elif args.type == "performance-diff": + diff_data = {} + if args.name: + if len(args.name) != len(args.path): + raise ValueError( + f"When set --name, length of --name and --path must be same, {len(args.name)} vs {len(args.path)}" + ) + diff_data = dict(zip(args.name, args.path)) + else: + diff_data = {Path(p).name.strip(".bin"): p for p in args.path} + plot_diff(diff_data, args.output) + elif args.type == "host-issue": + timeline_path = args.path[0] + output_dir = args.output + dist_strategy = args.dist + analysis_host_issue(timeline_path, output_dir, dist_strategy) + + +if __name__ == "__main__": + main() diff --git a/xpu_timer/experiments/figs/run_plugin.py b/xpu_timer/experiments/figs/run_plugin.py new file mode 100644 index 0000000000..ad74080566 --- /dev/null +++ b/xpu_timer/experiments/figs/run_plugin.py @@ -0,0 +1,67 @@ +# flake8: noqa: E501,E722,F841,E401 +import os +import runpy +import sys +import time +from pathlib import Path + +PY_EXCEPTION_FN = "xpu_timer_parse_python_exception" +CPP_EXCEPTION_FN = "xpu_timer_parse_cpp_exception" + + +def load_plugins(plugin_paths, pattern): + fns = [] + for plugin in plugin_paths: + try: + plugin_namespace = runpy.run_path(plugin) + if pattern in plugin_namespace: + fns.append(plugin_namespace[pattern]) + except: + print(f"[XPU_TIMER] error when load {plugin}", file=sys.stderr) + # maybe file not found or import error + continue + return fns + + +def xpu_timer_parse_python_exception(exc_type, exc_value, exc_traceback): + + if exc_type is KeyboardInterrupt: + return + job_infos = {} + job_infos["time"] = int(time.time()) + job_infos["pod_name"] = os.environ.get("POD_NAME", "UNKNOWN") + job_infos["job_name"] = os.environ.get("ENV_ARGO_WORKFLOW_NAME", "UNKNOWN") + job_infos["ip"] = os.environ.get("POD_IP", "UNKNOWN") + job_infos["rank"] = int(os.environ.get("RANK", "-1")) + + plugin_paths = [] + path_dir = Path(__file__).parent + plugin_from_env = os.environ.get("XPU_TIMER_EXIT_HOOK_PLUGIN", None) + plugin_paths.append(path_dir / "dlrover_parse_exception.py") + if plugin_from_env is not None: + plugin_paths.extend(plugin_from_env.split(",")) + fns = load_plugins(plugin_paths, PY_EXCEPTION_FN) + for fn, plugin in zip(fns, plugin_paths): + try: + fn(exc_type, exc_value, exc_traceback, job_infos) + except: + # ignore all exceptions + print(f"[XPU_TIMER] error when running {plugin}", file=sys.stderr) + continue + + +def xpu_timer_parse_cpp_exception(stack_infos): + plugin_paths = [] + path_dir = Path(__file__).parent + plugin_from_env = os.environ.get("XPU_TIMER_EXIT_HOOK_PLUGIN", None) + plugin_paths.append(path_dir / "dlrover_parse_exception.py") + if plugin_from_env is not None: + plugin_paths.extend(plugin_from_env.split(",")) + fns = load_plugins(plugin_paths, CPP_EXCEPTION_FN) + for fn, plugin in zip(fns, plugin_paths): + try: + fn(stack_infos) + except: + # ignore all exceptions + print(f"[XPU_TIMER] error when running {plugin}", file=sys.stderr) + continue diff --git a/xpu_timer/experiments/figs/stack_viewer.py b/xpu_timer/experiments/figs/stack_viewer.py new file mode 100644 index 0000000000..5ce596d0fe --- /dev/null +++ b/xpu_timer/experiments/figs/stack_viewer.py @@ -0,0 +1,140 @@ +import argparse +import os +from pathlib import Path + +from py_xpu_timer import hosting_service_pb2 # type: ignore[attr-defined] + + +class TrieNode: + def __init__(self): + self.children = {} + self.is_end_of_stack = False + self.ranks = set() + + def add_rank(self, rank): + self.ranks.add(rank) + + +class StackTrie: + def __init__(self, all_ranks): + self.root = TrieNode() + self.all_ranks = all_ranks + + def insert(self, words, rank): + node = self.root + for word in words: + if word not in node.children: + node.children[word] = TrieNode() + node = node.children[word] + node.ranks.add(rank) + node.is_end_of_stack = True + node.add_rank(rank) + + def _format_rank_str(self, ranks): + + leak_ranks = list(self.all_ranks - set(ranks)) + ranks = list(ranks) + + def _inner_format(ranks): + """fold continuous ranks, [0,1,2,5,6,7]->[0-2,5-7] + return has stack and leak stack, suppose we have 8 ranks(0-7) + [0,1,2,5,6,7]->0-2/5-7|3-4, means rank 0-2,5-7 has this stacktrace, + while rank 3-4 do not have this stacktrace + """ + ranks = sorted(ranks) + str_buf = [] + low = 0 + high = 0 + total = len(ranks) + while high < total - 1: + low_value = ranks[low] + high_value = ranks[high] + while high < total - 1 and high_value + 1 == ranks[high + 1]: + high += 1 + high_value = ranks[high] + low = high + 1 + high += 1 + if low_value != high_value: + str_buf.append(f"{low_value}-{high_value}") + else: + str_buf.append(str(low_value)) + if high == total - 1: + str_buf.append(str(ranks[high])) + return "/".join(str_buf) + + has_stack_ranks = _inner_format(ranks) + leak_stack_ranks = _inner_format(leak_ranks) + return f"@{'|'.join([has_stack_ranks, leak_stack_ranks])}" + + def _traverse_with_all_stack(self, node, path): + for word, child in node.children.items(): + rank_str = self._format_rank_str(child.ranks) + if child.is_end_of_stack: + yield ";".join(path + [word]) + rank_str + word += rank_str + yield from self._traverse_with_all_stack(child, path + [word]) + + def __iter__(self): + yield from self._traverse_with_all_stack(self.root, []) + + +class StackViewer: + def __init__(self, path): + p = Path(path) + self.path = path + self.files = sorted(p.glob("*stacktrace")) + if not self.files: + print(f"no stacktrace files in {path}") + exit(1) + # files format is 00003-00008.stacktrace + self.world_size = int(self.files[0].name[6:11]) + self.all_ranks = set(range(self.world_size)) + + self._parse("cpp") + self._parse("py") + + def _parse(self, mode): + self.stack_trie = StackTrie(self.all_ranks) + for f in self.files: + self._parse_one(f, mode) + with open(f"{self.path}/{mode}_stack", "w") as f: + for stack in self.stack_trie: + f.write(f"{stack} 1\n") + os.system( + "flamegraph.pl --color python --width 1600 --title " + f"'merge stack in {mode}' < {self.path}/{mode}_stack " + f"> {self.path}/{mode}_stack.svg" + ) + + def _frame_hash(self, stracetrace, rank): + for i in stracetrace: + buf = [] + for index, frame in enumerate(i.frames[::-1]): + func_file_name = f"{frame.func_name}@{frame.file_name}" + buf.append(func_file_name) + self.stack_trie.insert(buf, rank) + + def _parse_one(self, path, mode): + st = hosting_service_pb2.Stacktrace() + # 00003-00008.stacktrace + rank = int(path.name[:5]) + with open(path, "rb") as f: + st.ParseFromString(f.read()) + if st.pstack_stderr: + print(st.pstack_stderr) + self.stack_trie.insert([f"State@{st.process_state}"], rank) + if mode == "cpp": + self._frame_hash(st.stacktrace, rank) + else: + self._frame_hash(st.py_stacktrace, rank) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--path", "-p", type=str, default="./") + args = parser.parse_args() + StackViewer(args.path) + + +if __name__ == "__main__": + main() diff --git a/xpu_timer/experiments/figs/util.py b/xpu_timer/experiments/figs/util.py new file mode 100644 index 0000000000..9c68e419ec --- /dev/null +++ b/xpu_timer/experiments/figs/util.py @@ -0,0 +1,64 @@ +import concurrent.futures +import time +import typing +from typing import Dict, Set + +import numpy as np +from tqdm import tqdm + + +def parallel_job(fn, items, desc, concurrency=32): + SLEEP = 0.2 + futures: Set[concurrent.futures.Future] = set() + items: Set[concurrent.futures.Future] = set(items) + objs = [] + with concurrent.futures.ProcessPoolExecutor(max_workers=concurrency) as e: + with tqdm(total=len(items), desc=desc) as bar: + while futures or items: + done = set() + added = set() + for item in items: + futures.add(e.submit(fn, item)) + added.add(item) + if len(futures) > concurrency: + break + for future in futures: + if future.done(): + obj = future.result() + objs.append(obj) + done.add(future) + bar.update(1) + futures -= done + items -= added + time.sleep(SLEEP) + return objs + + +class GetRankHelper: + def __init__(self, groups: typing.OrderedDict[str, int]): + self.world_size = 1 + self.name_to_axis: Dict[str, int] = {} + order = [] + total_dim = len(groups) + for index, (group_name, value) in enumerate(groups.items()): + self.world_size = self.world_size * value + self.name_to_axis[group_name] = total_dim - index - 1 + order.append(value) + order = order[::-1] + self.ranks = np.arange(self.world_size) + self.ranks = self.ranks.reshape(order) + + def get_ranks(self, group, group_0=False): + axis = self.name_to_axis[group] + result = [] + strides = np.array(self.ranks.strides) // self.ranks.itemsize + shape = self.ranks.shape[axis] + skip = strides[axis] + index = [slice(None)] * self.ranks.ndim + index[axis] = 0 + first = self.ranks[tuple(index)].reshape(-1) + if group_0: + return np.array(range(first[0], first[0] + skip * shape, skip)) + for start in first: + result.append(list(range(start, start + skip * shape, skip))) + return np.array(result) diff --git a/xpu_timer/experiments/fsdp/train.sh b/xpu_timer/experiments/fsdp/train.sh new file mode 100644 index 0000000000..5b6baa91fc --- /dev/null +++ b/xpu_timer/experiments/fsdp/train.sh @@ -0,0 +1,35 @@ +#!/bin/sh +#****************************************************************# +# ScriptName: train.sh +# Author: $SHTERM_REAL_USER@alibaba-inc.com +# Create Date: 2024-12-26 10:27 +# Modify Author: $SHTERM_REAL_USER@alibaba-inc.com +# Modify Date: 2025-08-07 14:48 +# Function: +#***************************************************************# +# export XPU_TIMER_SM_COUNT=20 +# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5 +# export NCCL_MAX_NCHANNELS=20 +#nsys profile --stats true -w true -t cuda,nvtx,osrt,cudnn,cublas xpu_timer_launch python -m torch.distributed.launch --nproc_per_node=6 train_llama.py +# xpu_timer_launch +export CUDA_VISIBLE_DEVICES=4,5,6,7 +export XPU_TIMER_DEBUG_MODE=1 +export XPU_TIMER_BASEPORT=28888 +export NCCL_DEBUG=WARN +export WORLD_SIZE=4 +export LOCAL_WORLD_SIZE=4 + +# export GLOG_v=5 + +# CUDA_DEVICE_MAX_CONNECTIONS=1 TORCH_NCCL_ENABLE_TIMING=1 +xpu_timer_launch python -m torch.distributed.launch --nnodes=1 --nproc_per_node=4 train_llama.py + + +# WORLD_SIZE=${WORLD_SIZE:-$WORKER_NUM} +# +# pip show atorch +# +# NUM_GPUS_PER_NODE=$(nvidia-smi -L | wc -l) +# +# python -m torch.distributed.run --nnodes=2 --nproc_per_node=$NUM_GPUS_PER_NODE --master-addr aistudio-zwdx9wmm-edljob-worker-0 --master-port 24444 --node-rank $RANK train_llama.py +# diff --git a/xpu_timer/experiments/fsdp/train_llama.py b/xpu_timer/experiments/fsdp/train_llama.py new file mode 100644 index 0000000000..75a7a929f5 --- /dev/null +++ b/xpu_timer/experiments/fsdp/train_llama.py @@ -0,0 +1,912 @@ +import os +import time +import functools + +import torch +import torch.distributed as dist +from torch.distributed.fsdp import FullyShardedDataParallel as FSDP +from torch.utils.data import DataLoader, Dataset +from transformers import GPTNeoXConfig, GPTNeoXForCausalLM +from transformers import LlamaConfig, LlamaForCausalLM +from transformers.models.llama.modeling_llama import LlamaDecoderLayer +from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy +from torch.distributed.fsdp import MixedPrecision +from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( + apply_activation_checkpointing, + checkpoint_wrapper +) +from contextlib import nullcontext + + +def human_readable_flops(num): + for unit in [ + "", + "KFLOPS", + "MFLOPS", + "GFLOPS", + "TFLOPS", + "PFLOPS", + "EFLOPS", + "ZFLOPS", + ]: + if abs(num) < 1000.0: + return "%3.3f%s" % (num, unit) + num /= 1000.0 + return "%.3f%s" % (num, "Yi") + + +def compute_training_flops( + batch_size, + sequence_length, + hidden_size, + vocab_size, + intermediate_size, + num_layers, + use_gradient_checkpointing=False, + use_peft=False, + use_gqa=False, + kv_head_ratio=1, +): + """Returns: + hardware flops + model flops + + The source of formula: + Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM's + (APPENDIX: FLOATING-POINT OPERATIONS) + + Assuming that backward pass has twice FLOPs as many as forward pass. Only matrix multiplication FLOPs are computed. + For use_peft, backward pass FLOPS is a little more than the forward pass. Assuming equal for simplicity here. + """ + # [b,s,n] -> [b,s,n] + query_proj_flops = batch_size * 2 * sequence_length * hidden_size**2 + if use_gqa: + key_value_proj_flops = ( + 2 + * batch_size + * 2 + * sequence_length + * hidden_size + * hidden_size + / kv_head_ratio + ) + else: + key_value_proj_flops = 2 * query_proj_flops + attention_proj_flops = query_proj_flops + key_value_proj_flops + attention_flops = ( + 2 * batch_size * hidden_size * sequence_length**2 + + 4 * batch_size * sequence_length * hidden_size**2 + ) + attention_forward_flops = attention_proj_flops + attention_flops + # llama2 use gate_proj, has 3 Linears + two_mlps_forward_flops = ( + 3 * 2 * batch_size * sequence_length * hidden_size * intermediate_size + ) + logits_forward_flops = 2 * batch_size * sequence_length * hidden_size * vocab_size + decoder_layer_forward_flops = attention_forward_flops + two_mlps_forward_flops + # forward FLOPs without gradient checkpointing + forward_flops_wo_gc = ( + num_layers * decoder_layer_forward_flops + logits_forward_flops + ) + factor = 2 if use_peft else 3 + if not use_gradient_checkpointing: + return forward_flops_wo_gc * factor, forward_flops_wo_gc * factor + else: + return ( + num_layers * decoder_layer_forward_flops * (factor + 1) + + logits_forward_flops * factor, + forward_flops_wo_gc * factor, + ) + + + + +def apply_fsdp_checkpointing(model, blocks): + wrapper = lambda m: checkpoint_wrapper(m, + checkpoint_fn=torch.utils.checkpoint.checkpoint, + use_reentrant=False, + preserve_rng_state=True) + check_fn = lambda submodule: isinstance(submodule, blocks) + apply_activation_checkpointing(model, checkpoint_wrapper_fn=wrapper, check_fn=check_fn) + + +class DummyDataset(Dataset): + def __init__(self, vocab_size=1000, max_length=128, data_size=100000): + self.vocab_size = vocab_size + self.max_length = max_length + self.data_size = data_size + + def __len__(self): + return self.data_size + + def __getitem__(self, idx): + text = torch.randint(low=0, high=self.vocab_size, size=(self.max_length,)) + return text, text + + +def main(): + # Initialize the process group + dist.init_process_group(backend="nccl") + + # Get local rank and world size + local_rank = int(os.environ["LOCAL_RANK"]) + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + + num_layers = 10 + hidden_size = 4096 + intermediate_size = 8192 + vocab_size = 126464 + num_head = 64 + num_kv_head = 8 + batch_size = 2 + seq_length = 4096 + kv_head_ratio = num_head // num_kv_head + torch.cuda.set_device(local_rank) + + + config = LlamaConfig( + vocab_size=vocab_size, + hidden_size=hidden_size, + num_hidden_layers=num_layers, + num_attention_heads=num_head, + num_key_value_heads=num_kv_head, + intermediate_size=intermediate_size, + max_position_embeddings=seq_length, + initializer_range=0.02, + layer_norm_eps=1e-5, + # attn_implementation="flash_attention_2", + use_cache=False, + use_bfloat16=True + ) + + #init_device = "cpu" if local_rank == 0 else "meta" + init_device = "meta" + + # from liger_kernel.transformers import apply_liger_kernel_to_llama + # apply_liger_kernel_to_llama( + # rope=True, + # swiglu=True, + # cross_entropy=True, + # fused_linear_cross_entropy=False, + # rms_norm=True + # ) + + with torch.device(init_device): + model = LlamaForCausalLM(config) + + + flop, _ = compute_training_flops( + batch_size, + seq_length, + hidden_size, + vocab_size, + intermediate_size, + num_layers, + use_gradient_checkpointing=True, + use_gqa=True, + kv_head_ratio=kv_head_ratio, + ) + + + dataset = DummyDataset(vocab_size=vocab_size, max_length=seq_length) + sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, sampler=sampler) + + #param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) if local_rank != 0 else None + param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) + wrap_policy = functools.partial(transformer_auto_wrap_policy, transformer_layer_cls={LlamaDecoderLayer,},) + #model = model.to(dtype=torch.bfloat16) + + model = FSDP(model, device_id=local_rank, auto_wrap_policy=wrap_policy, + mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), + sync_module_states=False, param_init_fn=param_init_fn, + forward_prefetch=True, limit_all_gathers=True, use_orig_params=True) + + apply_fsdp_checkpointing(model, LlamaDecoderLayer) + optimizer = torch.optim.Adam(model.parameters(), lr=1e-5) + + # Training Loop + def save_profile(prof): + prof.export_chrome_trace(f"fsdp_trace_{rank}.json") + + epoch = 0 + iters = 0 + prof = torch.profiler.profile( + schedule=torch.profiler.schedule( + wait=1, + warmup=1, + active=1000000, + repeat=1), + on_trace_ready=save_profile, + record_shapes=False, + with_stack=False) + + #prof = nullcontext() + # prof.start() + + dur = [] + model.train() + for input_ids, labels in dataloader: + input_ids, labels = input_ids.to(local_rank), labels.to(local_rank) + start = time.time() + optimizer.zero_grad() + loss = model(input_ids=input_ids, labels=labels).loss + loss.backward() + optimizer.step() + torch.cuda.synchronize() + if rank == 0: + dur = time.time() - start + tflops = flop / dur / 1e12 + print(f"Epoch {epoch}, Loss: {loss.item()} time {dur} tflops {tflops}") + iters += 1 + # if iters > 10: + # break + # prof.step() + # prof.stop() + + print("Training Complete") + dist.destroy_process_group() + +def main_ds(): + import deepspeed + dist.init_process_group(backend="nccl") + + # Get local rank and world size + local_rank = int(os.environ["LOCAL_RANK"]) + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + + num_layers = 20 + hidden_size = 8192//4 + intermediate_size = 28672 + vocab_size = 126464 + num_head = 64 + num_kv_head = 8 + batch_size = 2 + seq_length = 4096 + kv_head_ratio = num_head // num_kv_head + torch.cuda.set_device(local_rank) + + flop, _ = compute_training_flops( + batch_size, + seq_length, + hidden_size, + vocab_size, + intermediate_size, + num_layers, + use_gradient_checkpointing=True, + use_gqa=True, + kv_head_ratio=kv_head_ratio, + ) + + + config = LlamaConfig( + vocab_size=vocab_size, + hidden_size=hidden_size, + num_hidden_layers=num_layers, + num_attention_heads=num_head, + num_key_value_heads=num_kv_head, + intermediate_size=intermediate_size, + max_position_embeddings=seq_length, + initializer_range=0.02, + layer_norm_eps=1e-5, + attn_implementation="flash_attention_2", + use_cache=False, + use_bfloat16=True + ) + + #init_device = "cpu" if local_rank == 0 else "meta" + init_device = "meta" + + from liger_kernel.transformers import apply_liger_kernel_to_llama + apply_liger_kernel_to_llama( + rope=True, + swiglu=True, + cross_entropy=True, + fused_linear_cross_entropy=False, + rms_norm=True + ) + + ds_config = { + "train_batch_size": batch_size * world_size, + "train_micro_batch_size_per_gpu": batch_size, + #"steps_per_print": 10, + "zero_optimization": { + "stage": 3, + "overlap_comm": True, + }, + "bf16": { + "enabled": True, + }, + "activation_checkpointing": { + "partition_activations": True, # Partition activations across GPUs + #"contiguous_memory_optimization": True, # Optimize contiguous memory usage + }, + } + + kwargs = {} + kwargs["config"] = ds_config + with deepspeed.zero.Init(config_dict_or_path=ds_config): + model = LlamaForCausalLM(config) + kwargs["model"] = model + + from deepspeed.ops.adam import FusedAdam + #optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4) + optimizer = FusedAdam(model.parameters(), lr=1e-4) + kwargs["optimizer"] = optimizer + model_engine, optimizer, _, _ = deepspeed.initialize(**kwargs) + #from remote_pdb import RemotePdb + #RemotePdb("127.0.0.1", 16666+rank).set_trace() + + dataset = DummyDataset(vocab_size=vocab_size, max_length=seq_length) + sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, sampler=sampler) + model_engine.train() + start = end = 0 + dur = [] + epoch = 0 + # Training Loop + def save_profile(prof): + prof.export_chrome_trace(f"ds_trace_{rank}.json") + + epoch = 0 + iters = 0 + # prof = torch.profiler.profile( + # schedule=torch.profiler.schedule( + # wait=1, + # warmup=1, + # active=3, + # repeat=1), + # on_trace_ready=save_profile, + # record_shapes=True, + # with_stack=True) + + # #prof = nullcontext() + # prof.start() + + for step, (input_ids, labels) in enumerate(dataloader): + start = time.time() + input_ids, labels = input_ids.to(local_rank), labels.to(local_rank) + optimizer.zero_grad() + loss = model_engine(input_ids=input_ids, labels=labels).loss + model_engine.backward(loss) + model_engine.step() + torch.cuda.synchronize() + dur = time.time() - start + tflops = flop / dur / 1e12 + if rank == 0: + print(f"Epoch {epoch}, Step {step}, Loss {loss.item()}, time {dur}, {tflops}") + # if step > 10: + # break + # prof.step() + # prof.stop() + +def main_qwen_vl(): + #dist.init_process_group(backend="nccl") + + #local_rank = int(os.environ["LOCAL_RANK"]) + #rank = int(os.environ["RANK"]) + #world_size = int(os.environ["WORLD_SIZE"]) + world_size = 1 + local_rank = rank = 0 + + torch.cuda.set_device(local_rank) + config ={ + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151645, + "vision_start_token_id": 151652, + "vision_end_token_id": 151653, + "vision_token_id": 151654, + "image_token_id": 151655, + "video_token_id": 151656, + "hidden_act": "silu", + "hidden_size": 8192 // 4, + "initializer_range": 0.02, + "intermediate_size": 29568 // 4, + "max_position_embeddings": 32768, + "max_window_layers": 80, + "model_type": "qwen2_vl", + "num_attention_heads": 64, + "num_hidden_layers": 1, + "num_key_value_heads": 8, + "rms_norm_eps": 1e-06, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "tie_word_embeddings": False, + "torch_dtype": "bfloat16", + "transformers_version": "4.41.2", + "use_cache": False, + "use_sliding_window": False, + "vision_config": { + "depth": 32, + "embed_dim": 1280, + "mlp_ratio": 4, + "num_heads": 16, + "in_chans": 3, + "hidden_size": 8192, + "patch_size": 14, + "spatial_merge_size": 2, + "spatial_patch_size": 14, + "temporal_patch_size": 2 + }, + "rope_scaling": { + "type": "mrope", + "mrope_section": [ + 16 // 4, + 24 // 4, + 24 // 4 + ] + }, + "vocab_size": 152064 + } + + from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig + from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLModel, Qwen2VisionTransformerPretrainedModel, Qwen2VLForConditionalGeneration + from transformers.models.qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessor + + qwen_config = Qwen2VLConfig(**config) + preprocess_config = { + "min_pixels": 3136, + "max_pixels": 12845056, + "patch_size": 14, + "temporal_patch_size": 2, + "merge_size": 2, + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "image_processor_type": "Qwen2VLImageProcessor", + "processor_class": "Qwen2VLProcessor" + } + preprocess = Qwen2VLImageProcessor(**preprocess_config) + with torch.device('cuda'): + #model = Qwen2VLForConditionalGeneration(qwen_config) + text_model = Qwen2VLModel(qwen_config) + vision_model = Qwen2VisionTransformerPretrainedModel(qwen_config.vision_config) + image = [torch.ones(1280, 1280, 3, dtype=torch.uint8) for _ in range(10)] + if rank == 0: + text = torch.randint(low=0, high=qwen_config.vocab_size, size=(1, 4096,)).cuda() + #text[-32:] = 1516545 + #print((text == 151655).sum().item()) + data = preprocess(image, return_tensors="pt") + image_grid_thw = data['image_grid_thw'].cuda() + image_hidden = data['pixel_values'].cuda() + # + t = text_model(text) + v = vision_model(image_hidden, image_grid_thw) + breakpoint() + #m = model(input_ids=text,pixel_values=image_hidden,image_grid_thw=image_grid_thw) + print(1) + + #num_layers = 10 + #hidden_size = 8192 + #intermediate_size = 32768 + #vocab_size = 126464 + #num_head = 128 + #num_kv_head = 16 + #batch_size = 2 + #seq_length = 4096 + #kv_head_ratio = num_head // num_kv_head + #torch.cuda.set_device(local_rank) + + + #config = LlamaConfig( + # vocab_size=vocab_size, + # hidden_size=hidden_size, + # num_hidden_layers=num_layers, + # num_attention_heads=num_head, + # num_key_value_heads=num_kv_head, + # intermediate_size=intermediate_size, + # max_position_embeddings=seq_length, + # initializer_range=0.02, + # layer_norm_eps=1e-5, + # attn_implementation="flash_attention_2", + # use_cache=False, + # use_bfloat16=True + #) + + ##init_device = "cpu" if local_rank == 0 else "meta" + #init_device = "meta" + + #from liger_kernel.transformers import apply_liger_kernel_to_llama + #apply_liger_kernel_to_llama( + # rope=True, + # swiglu=True, + # cross_entropy=True, + # fused_linear_cross_entropy=False, + # rms_norm=True + #) + + #with torch.device(init_device): + # model = LlamaForCausalLM(config) + + # + #flop, _ = compute_training_flops( + # batch_size, + # seq_length, + # hidden_size, + # vocab_size, + # intermediate_size, + # num_layers, + # use_gradient_checkpointing=True, + # use_gqa=True, + # kv_head_ratio=kv_head_ratio, + #) + + + #dataset = DummyDataset(vocab_size=vocab_size, max_length=seq_length) + #sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank) + #dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, sampler=sampler) + + ##param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) if local_rank != 0 else None + #param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) + #wrap_policy = functools.partial(transformer_auto_wrap_policy, transformer_layer_cls={LlamaDecoderLayer,},) + ##model = model.to(dtype=torch.bfloat16) + + #model = FSDP(model, device_id=local_rank, auto_wrap_policy=wrap_policy, + # mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), + # sync_module_states=False, param_init_fn=param_init_fn, + # forward_prefetch=True, limit_all_gathers=True, use_orig_params=True) + + #apply_fsdp_checkpointing(model, LlamaDecoderLayer) + #optimizer = torch.optim.Adam(model.parameters(), lr=1e-5) + + ## Training Loop + #def save_profile(prof): + # prof.export_chrome_trace(f"fsdp_trace_{rank}.json") + + #epoch = 0 + #iters = 0 + #prof = torch.profiler.profile( + # schedule=torch.profiler.schedule( + # wait=1, + # warmup=1, + # active=3, + # repeat=1), + # on_trace_ready=save_profile, + # record_shapes=True, + # with_stack=True) + + ##prof = nullcontext() + ##prof.start() + + #dur = [] + #with prof: + # model.train() + # for input_ids, labels in dataloader: + # start = time.time() + # input_ids, labels = input_ids.to(local_rank), labels.to(local_rank) + # optimizer.zero_grad() + # loss = model(input_ids=input_ids, labels=labels).loss + # loss.backward() + # optimizer.step() + # torch.cuda.synchronize() + # if rank == 0: + # dur = time.time() - start + # tflops = flop / dur / 1e12 + # print(f"Epoch {epoch}, Loss: {loss.item()} time {dur} tflops {tflops}") + # iters += 1 + # if iters > 10: + # break + # prof.step() + # epoch += 1 + + #print("Training Complete") + #dist.destroy_process_group() +def mllama(): + from transformers import MllamaForConditionalGeneration, AutoProcessor + from transformers.models.mllama.configuration_mllama import MllamaConfig, MllamaVisionConfig, MllamaTextConfig + from transformers.models.mllama.modeling_mllama import MllamaVisionEncoderLayer, MllamaSelfAttentionDecoderLayer + from liger_kernel.transformers import apply_liger_kernel_to_mllama + + dist.init_process_group(backend="nccl") + + # Get local rank and world size + local_rank = int(os.environ["LOCAL_RANK"]) + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + torch.cuda.set_device(local_rank) + + apply_liger_kernel_to_mllama( + rope=True, + swiglu=True, + cross_entropy=False, + fused_linear_cross_entropy=True, + rms_norm=True + ) + config = { + "architectures": [ + "MllamaForConditionalGeneration" + ], + "image_token_index": 128256, + "model_type": "mllama", + "text_config": { + "_name_or_path": "", + "add_cross_attention": False, + "architectures": None, + "bad_words_ids": None, + "begin_suppress_tokens": None, + "bos_token_id": 128000, + "chunk_size_feed_forward": 0, + "cross_attention_hidden_size": None, + "cross_attention_layers": [ + 3, + 8, + 13, + 18, + 23, + 28, + 33, + 38, + 43, + 48, + 53, + 58, + 63, + 68, + 73, + 78, + 83, + 88, + 93, + 98 + ], + "decoder_start_token_id": None, + "diversity_penalty": 0.0, + "do_sample": False, + "dropout": 0, + "early_stopping": False, + "encoder_no_repeat_ngram_size": 0, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "exponential_decay_length_penalty": None, + "finetuning_task": None, + "forced_bos_token_id": None, + "forced_eos_token_id": None, + "hidden_act": "silu", + "hidden_size": 4096, + "id2label": { + "0": "LABEL_0", + "1": "LABEL_1" + }, + "initializer_range": 0.02, + "intermediate_size": 28672, + "is_decoder": False, + "is_encoder_decoder": False, + "label2id": { + "LABEL_0": 0, + "LABEL_1": 1 + }, + "length_penalty": 1.0, + "max_length": 20, + "max_position_embeddings": 131072, + "min_length": 0, + "model_type": "mllama_text_model", + "no_repeat_ngram_size": 0, + "num_attention_heads": 64, + "num_beam_groups": 1, + "num_beams": 1, + "num_hidden_layers": 40, + "num_key_value_heads": 8, + "num_return_sequences": 1, + "output_attentions": False, + "output_hidden_states": False, + "output_scores": False, + "pad_token_id": 128004, + "prefix": None, + "problem_type": None, + "pruned_heads": {}, + "remove_invalid_values": False, + "repetition_penalty": 1.0, + "return_dict": True, + "return_dict_in_generate": False, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + }, + "rope_theta": 500000.0, + "sep_token_id": None, + "suppress_tokens": None, + "task_specific_params": None, + "temperature": 1.0, + "tf_legacy_loss": False, + "tie_encoder_decoder": False, + "tie_word_embeddings": False, + "tokenizer_class": None, + "top_k": 50, + "top_p": 1.0, + "torch_dtype": "bfloat16", + "torchscript": False, + "typical_p": 1.0, + "use_bfloat16": False, + "use_cache": False, + "vocab_size": 128256 + }, + "torch_dtype": "bfloat16", + "transformers_version": "4.45.0.dev0", + "vision_config": { + "_name_or_path": "", + "add_cross_attention": False, + "architectures": None, + "attention_heads": 16, + "bad_words_ids": None, + "begin_suppress_tokens": None, + "bos_token_id": None, + "chunk_size_feed_forward": 0, + "cross_attention_hidden_size": None, + "decoder_start_token_id": None, + "diversity_penalty": 0.0, + "do_sample": False, + "early_stopping": False, + "encoder_no_repeat_ngram_size": 0, + "eos_token_id": None, + "exponential_decay_length_penalty": None, + "finetuning_task": None, + "forced_bos_token_id": None, + "forced_eos_token_id": None, + "hidden_act": "gelu", + "hidden_size": 1280, + "id2label": { + "0": "LABEL_0", + "1": "LABEL_1" + }, + "image_size": 560, + "intermediate_layers_indices": [ + 3, + 7, + 15, + 23, + 30 + ], + "intermediate_size": 5120, + "is_decoder": False, + "is_encoder_decoder": False, + "label2id": { + "LABEL_0": 0, + "LABEL_1": 1 + }, + "length_penalty": 1.0, + "max_length": 20, + "max_num_tiles": 4, + "min_length": 0, + "model_type": "mllama_vision_model", + "no_repeat_ngram_size": 0, + "norm_eps": 1e-05, + "num_beam_groups": 1, + "num_beams": 1, + "num_channels": 3, + "num_global_layers": 8, + "num_hidden_layers": 32, + "num_return_sequences": 1, + "output_attentions": False, + "output_hidden_states": False, + "output_scores": False, + "pad_token_id": None, + "patch_size": 14, + "prefix": None, + "problem_type": None, + "pruned_heads": {}, + "remove_invalid_values": False, + "repetition_penalty": 1.0, + "return_dict": True, + "return_dict_in_generate": False, + "sep_token_id": None, + "supported_aspect_ratios": [ + [ + 1, + 1 + ], + [ + 1, + 2 + ], + [ + 1, + 3 + ], + [ + 1, + 4 + ], + [ + 2, + 1 + ], + [ + 2, + 2 + ], + [ + 3, + 1 + ], + [ + 4, + 1 + ] + ], + "suppress_tokens": None, + "task_specific_params": None, + "temperature": 1.0, + "tf_legacy_loss": False, + "tie_encoder_decoder": False, + "tie_word_embeddings": True, + "tokenizer_class": None, + "top_k": 50, + "top_p": 1.0, + "torch_dtype": "bfloat16", + "torchscript": False, + "typical_p": 1.0, + "use_bfloat16": False, + "vision_output_dim": 7680 + } + } + vision_config = MllamaVisionConfig(**config['vision_config']) + text_config = MllamaTextConfig(**config['text_config']) + model_config = MllamaConfig(vision_config, text_config, torch_dtype="bfloat16") + data = torch.load('dummy.pth', map_location='cuda') + label = torch.randint(low=0, high=config['text_config']['vocab_size'], size=data['input_ids'].shape) + with torch.device('meta'): + model = MllamaForConditionalGeneration(model_config) + param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) + wrap_policy = functools.partial(transformer_auto_wrap_policy, transformer_layer_cls={ MllamaVisionEncoderLayer, MllamaSelfAttentionDecoderLayer},) + #model = model.to(dtype=torch.bfloat16) + + model = FSDP(model, device_id=local_rank, auto_wrap_policy=wrap_policy, + mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), + sync_module_states=False, param_init_fn=param_init_fn, + forward_prefetch=True, limit_all_gathers=True, use_orig_params=True) + + apply_fsdp_checkpointing(model, (MllamaVisionEncoderLayer, MllamaSelfAttentionDecoderLayer)) + optimizer = torch.optim.Adam(model.parameters(), lr=1e-5) + def save_profile(prof): + prof.export_chrome_trace(f"mllama_trace_{rank}.json") + + epoch = 0 + iters = 0 + # prof = torch.profiler.profile( + # schedule=torch.profiler.schedule( + # wait=1, + # warmup=1, + # active=2, + # repeat=1), + # on_trace_ready=save_profile, + # record_shapes=True, + # with_stack=True) + + # prof.start() + model.train() + for i in range(100000000): + start = time.time() + optimizer.zero_grad() + loss= model(**data, labels=label).loss + loss.backward() + optimizer.step() + torch.cuda.synchronize() + dur = time.time() - start + if rank == 0: + print(f"Step {i}, Loss {loss.item()}, time {dur}") + # prof.step() + # prof.stop() + + +if __name__ == "__main__": + # main_ds() + main() + # mllama() + diff --git a/xpu_timer/experiments/scripts/cc.py b/xpu_timer/experiments/scripts/cc.py new file mode 100644 index 0000000000..291ac172bd --- /dev/null +++ b/xpu_timer/experiments/scripts/cc.py @@ -0,0 +1,26 @@ + +from enum import Enum, auto, unique + +ANT_PATCH_ENV_NOT_SET = "ANT_PATCH_ENV_NOT_SET" + +@unique +class PatchStatus(Enum): + PATCH_OK_VERSION_CHECK = auto() + PATCH_OK_ENV_CHECK = auto() + PATCH_FAIL_VERSION_CHECK = auto() + PATCH_FAIL_ENV_CHECK = auto() + UNKNOWN = auto() + + def __str__(self): + return PATCH_STATUS_MSG[self] + +PATCH_STATUS_MSG = { + PatchStatus.PATCH_OK_VERSION_CHECK: "Y,Patch OK(VERSION)", + PatchStatus.PATCH_OK_ENV_CHECK: "Y,Patch OK(ENV)", + PatchStatus.PATCH_FAIL_VERSION_CHECK: "N,Patch Fail(VERSION)", + PatchStatus.PATCH_FAIL_ENV_CHECK: "N,Patch Fail(ENV)", + PatchStatus.UNKNOWN: "unknown", +} + +a = PatchStatus.PATCH_OK_VERSION_CHECK +print(a) diff --git a/xpu_timer/experiments/scripts/layernorm_linear.py b/xpu_timer/experiments/scripts/layernorm_linear.py new file mode 100644 index 0000000000..da88b7b0a9 --- /dev/null +++ b/xpu_timer/experiments/scripts/layernorm_linear.py @@ -0,0 +1,1253 @@ +# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# See LICENSE for license information. + +"""LayerNormLinear API""" +import os +import warnings +from typing import Any, Callable, Dict, Optional, Tuple, Union + +import torch +from torch.nn import init + +from .. import cpp_extensions as tex + +from .base import ( + get_workspace, + get_ub, + TransformerEngineBaseModule, + _2X_ACC_FPROP, + _2X_ACC_DGRAD, + _2X_ACC_WGRAD, +) +from ..fp8 import get_fp8_te_dtype, FP8GlobalStateManager +from ..utils import ( + divide, + get_default_init_method, + init_method_constant, + cast_if_needed, + assert_dim_for_fp8_exec, + clear_tensor_data, + requires_grad, +) +from ..distributed import ( + set_tensor_model_parallel_attributes, + get_distributed_world_size, + allreduce, + reduce_scatter_along_first_dim, + gather_along_first_dim, + _fsdp_scatter_tensors, + _fsdp_gather_tensors, +) +from ..constants import GemmParallelModes, dist_group_type, TE_DType +from ..jit import no_torch_dynamo +from ..graph import is_graph_capturing +from ._common import _apply_normalization, _noop_cat +from ..float8_tensor import Float8Tensor +from ..export import is_in_onnx_export_mode +from ..tensor import QuantizedTensor + +__all__ = ["LayerNormLinear"] + + +class _LayerNormLinear(torch.autograd.Function): + """LayerNormLinear semi-top level module + Calls custom cuda extensions. + """ + + @staticmethod + def forward( + ctx, + inp: torch.Tensor, + ln_weight: torch.Tensor, + ln_bias: Union[torch.Tensor, None], + weight: torch.Tensor, + weight_fp8: Optional[torch.Tensor], + bias: torch.Tensor, + use_bias: bool, + eps: float, + is_first_microbatch: Union[bool, None], + fp8: bool, + fp8_calibration: bool, + fp8_meta: Dict[str, Any], + fuse_wgrad_accumulation: bool, + cpu_offloading: bool, + tp_group: Union[dist_group_type, None], + tp_size: int, + sequence_parallel: bool, + tensor_parallel: bool, + activation_dtype: torch.dtype, + parallel_mode: Union[str, None], + return_layernorm_output: bool, + return_layernorm_output_gathered: bool, + is_grad_enabled: bool, + fwd_ln_sm_margin: int, + bwd_ln_sm_margin: int, + zero_centered_gamma: bool, + normalization: str, + ub_bulk_wgrad: bool, + ub_bulk_dgrad: bool, + ub_overlap_rs_dgrad: bool, + ub_overlap_ag: bool, + ub_name: str, + fp8_output: bool, + fsdp_group: Union[dist_group_type, None], + ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]: + # Make sure input dimensions are compatible + in_features = ln_weight.numel() + assert inp.shape[-1] == in_features, "GEMM not possible" + inputmat = inp.view((-1, in_features)) + if fp8: + assert_dim_for_fp8_exec(inputmat) + assert_dim_for_fp8_exec(weight) + + # Cast for native AMP + inputmat = cast_if_needed(inputmat, activation_dtype) + ln_weight = cast_if_needed(ln_weight, activation_dtype) + if ln_bias is not None: + ln_bias = cast_if_needed(ln_bias, activation_dtype) + + if ub_overlap_ag: + tp_world_size = get_distributed_world_size(tp_group) + if tp_world_size == 1 or (not is_grad_enabled): + ub_overlap_ag = False + if ub_overlap_ag: + dim_size = list(inputmat.size()) + dim_size[0] = dim_size[0] * tp_world_size + ub_obj_lnout = get_ub(ub_name + "_fprop") + if return_layernorm_output: + # First prepare LN output in higher precision, + # which will be later copied to a FP8 UB + ln_out = torch.empty_like(inputmat, memory_format=torch.contiguous_format) + else: + ln_out = ub_obj_lnout.get_ubuf_output(0) + else: + ln_out_dtype = torch.uint8 if (fp8 and not return_layernorm_output) else inputmat.dtype + ln_out = torch.empty_like( + inputmat, dtype=ln_out_dtype, memory_format=torch.contiguous_format + ) + + # Objects for FP8 cast + fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True) + ln_out_scale_inv = None + if fp8: + ln_out_scale_inv = torch.empty([1], dtype=torch.float32, device=inputmat.device) + + # Launch normalization kernel + ln_out, mu, rsigma = _apply_normalization( + inputmat, + ln_out, + ln_weight, + ln_bias, + eps, + fp8 and not return_layernorm_output, + fp8_meta, + normalization, + fwd_ln_sm_margin, + zero_centered_gamma, + is_grad_enabled, + fp8_scale_inv=ln_out_scale_inv, + ) + + # Column Parallel Linear + ln_out_gathered = False + if ub_overlap_ag: + ln_out_total = ub_obj_lnout.get_ubuf_output(1) + if not return_layernorm_output: + ln_out = torch.empty_like(ln_out) + if ub_obj_lnout.is_atomic_gemm(): + ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_AG_P2P + else: + ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_AG_P2P + elif parallel_mode == "column" and sequence_parallel: + ln_out_gathered = True + ln_out_total, _ = gather_along_first_dim(ln_out, tp_group) + else: + ln_out_total = ln_out + + # If residual connection is after LN, we need `ln_out_return` + # tensor in higher precision, this comes at the cost + # of an extra fp8 cast. + if return_layernorm_output: + ln_out_return = ln_out_total if return_layernorm_output_gathered else ln_out + if fp8: + if ub_overlap_ag: + ln_out_fp8 = ub_obj_lnout.get_ubuf_output(0) + tex.cast_to_fp8( + ln_out, + fp8_meta["scaling_fwd"], + tex.FP8FwdTensors.GEMM1_INPUT, + fp8_dtype_forward, + out=ln_out_fp8, + scale_inv=ln_out_scale_inv, + ) + ln_out = torch.empty_like(ln_out_fp8) + else: + ln_out_total = tex.cast_to_fp8( + ln_out_total, + fp8_meta["scaling_fwd"], + tex.FP8FwdTensors.GEMM1_INPUT, + fp8_dtype_forward, + scale_inv=ln_out_scale_inv, + ) + if ln_out_gathered: + rank = torch.distributed.get_rank(tp_group) + slice_start = rank * ln_out.size(0) + slice_end = (rank + 1) * ln_out.size(0) + ln_out = ln_out_total[slice_start:slice_end, ...] + else: + ln_out = ln_out_total + + if fp8: + bias_dtype = torch.bfloat16 if activation_dtype == torch.float32 else activation_dtype + bias = cast_if_needed(bias, bias_dtype) if use_bias else bias + + # Use FP8 weights + if weight_fp8 is None: + weight_fp8 = weight + + assert isinstance(weight_fp8, Float8Tensor) + + # Hack for ONNX export + # Note: ONNX models are represented as a graph of tensor + # operations, so the in-place scale-inv update doesn't fit + # very well. We work around this by making it look like + # the scale-inv tensor is initialized with a copy. + # Note: ONNX export expects FP8 scales can be represented + # with constant ops. However, copying into a buffer + # involves an expand op for array broadcasting. We work + # around this by filling the buffer instead. + if is_in_onnx_export_mode(): + ln_out_scale_inv.fill_(ln_out_scale_inv.item()) + + if fp8_output: + out_index, meta_tensor, output_te_dtype, output_dtype = ( + tex.FP8FwdTensors.GEMM1_OUTPUT, + fp8_meta["scaling_fwd"], + fp8_dtype_forward, + torch.uint8, + ) + else: + out_index, meta_tensor, output_te_dtype, output_dtype = ( + None, + None, + None, + activation_dtype, + ) + out, _ = tex.fp8_gemm( + weight_fp8._data, + weight_fp8._scale_inv, + 0, + weight_fp8._fp8_dtype, + ln_out_total, + ln_out_scale_inv, + 0, + fp8_dtype_forward, + output_dtype, + get_workspace(), + bias=bias, + use_bias=use_bias, + use_split_accumulator=_2X_ACC_FPROP, + ub_algo=ub_algo if ub_overlap_ag else None, + ub=ub_obj_lnout if ub_overlap_ag else None, + extra_output_tensor=ln_out if ub_overlap_ag else None, + out_index=out_index, + fp8_meta_tensor=meta_tensor, + D_dtype=output_te_dtype, + ) + if output_dtype == torch.uint8: + out = Float8Tensor( + data=out, + fp8_meta=fp8_meta, + fp8_meta_forward=True, + fp8_meta_index=tex.FP8FwdTensors.GEMM1_OUTPUT, + fp8_dtype=fp8_dtype_forward, + dtype=activation_dtype, + ) + else: + # Cast for native AMP + weight = cast_if_needed(weight, activation_dtype) + bias = cast_if_needed(bias, activation_dtype) if use_bias else bias + + if fp8_calibration: + # amax of input + amin, amax = ln_out_total.aminmax() + fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_INPUT] = torch.max( + -amin, amax + ).float() + # amax of weight + amin, amax = weight.aminmax() + fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_WEIGHT] = torch.max( + -amin, amax + ).float() + + out, _, _ = tex.gemm( + weight, + ln_out_total, + activation_dtype, + get_workspace(), + bias=bias, + use_bias=use_bias, + ub_algo=tex.UbufOverlapAlgo.SPLIT_PIPELINED_AG_P2P if ub_overlap_ag else None, + ub=ub_obj_lnout if ub_overlap_ag else None, + extra_output_tensor=ln_out if ub_overlap_ag else None, + ) + + if is_grad_enabled: + if cpu_offloading: + if fp8 and weight_fp8 is not None: + weight_fp8.weight_offloading = True + ln_weight.weight_offloading = True + weight.weight_offloading = True + + inputmat.activation_offloading = True + if normalization == "LayerNorm": + mu.activation_offloading = True + rsigma.activation_offloading = True + ln_out.activation_offloading = True + + # Scatter intermediate/activation tensors saved for the backward pass + # NOTE: weight_fp8 = weight when ctx.fp8 == False and torch.disttributed.FSDP already + # shards/unshards the base weights so we don't do it ourselves + ctx.fsdp_group = fsdp_group + ctx.fsdp_shapes = _fsdp_scatter_tensors( + fsdp_group, + mu, + rsigma, + weight_fp8 if fp8 and not isinstance(weight, Float8Tensor) else None, + ln_out if weight.requires_grad else None, + ) + + ctx.save_for_backward( + inputmat, + ln_weight, + mu, + rsigma, + weight, + weight_fp8, + weight.main_grad if cpu_offloading and fuse_wgrad_accumulation else None, + ln_out if weight.requires_grad else None, + ln_out_scale_inv, + ) + + ctx.activation_dtype = activation_dtype + ctx.fp8 = fp8 + ctx.fp8_meta = fp8_meta + ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation + ctx.cpu_offloading = cpu_offloading + ctx.is_first_microbatch = is_first_microbatch + ctx.use_bias = use_bias + ctx.sequence_parallel = sequence_parallel + ctx.tensor_parallel = tensor_parallel + ctx.inp_shape = inp.shape + ctx.parallel_mode = parallel_mode + ctx.tp_group = tp_group + ctx.tp_size = tp_size + ctx.return_layernorm_output = return_layernorm_output + ctx.return_layernorm_output_gathered = ( + return_layernorm_output_gathered and ln_out_gathered + ) + ctx.bwd_ln_sm_margin = bwd_ln_sm_margin + ctx.zero_centered_gamma = zero_centered_gamma + ctx.ub_bulk_wgrad = ub_bulk_wgrad + ctx.ub_bulk_dgrad = ub_bulk_dgrad + ctx.ub_overlap_rs_dgrad = ub_overlap_rs_dgrad + ctx.ub_name = ub_name + ctx.requires_dgrad = inp.requires_grad + ctx.normalization = normalization + ctx.reduce_and_update_bwd_fp8_tensors = False + if ctx.fp8 and requires_grad(inp, ln_weight, ln_bias, weight, bias): + ctx.reduce_and_update_bwd_fp8_tensors = ( + ctx.reduce_and_update_bwd_fp8_tensors + or FP8GlobalStateManager.is_first_fp8_module() + ) + + # Row Parallel Linear + if parallel_mode == "row" and sequence_parallel: + out, _ = reduce_scatter_along_first_dim(out, tp_group) + elif parallel_mode == "row" and tensor_parallel: + out, _ = allreduce(out, tp_group) + + # [*, in_features] -> [*, out_features] except first dimension changes for SP + out = out.view(-1, *inp.shape[1:-1], out.shape[-1]) + + if return_layernorm_output: + if return_layernorm_output_gathered: + shape = list(inp.shape) + shape[0] *= tp_size + return out, ln_out_return.view(shape) + return out, ln_out_return.view_as(inp) + return out + + @staticmethod + def backward( + ctx, *grad_outputs: Tuple[torch.Tensor, ...] + ) -> Tuple[Union[torch.Tensor, None], ...]: + if isinstance(grad_outputs[0], Float8Tensor): + ctx.fp8_meta["scaling_bwd"].scale_inv[tex.FP8BwdTensors.GRAD_OUTPUT1] = grad_outputs[ + 0 + ]._scale_inv + + with torch.cuda.nvtx.range("_LayerNormLinear_backward"): + ( + inputmat, + ln_weight, + mu, + rsigma, + weight, + weight_fp8, + main_grad, + ln_out, + ln_out_scale_inv, + ) = ctx.saved_tensors + + # Gather intermediate/activation tensors if needed + # NOTE: weight_fp8 = weight when ctx.fp8 == False and torch.disttributed.FSDP already + # shards/unshards the base weights so we don't do it ourselves + _fsdp_gather_tensors( + ctx.fsdp_group, + ctx.fsdp_shapes, + mu, + rsigma, + weight_fp8 if ctx.fp8 and not isinstance(weight, Float8Tensor) else None, + ln_out, + ) + + if ctx.cpu_offloading and ctx.fuse_wgrad_accumulation: + weight = torch.nn.Parameter(weight, weight.requires_grad) + weight.main_grad = main_grad + + if ctx.ub_overlap_rs_dgrad: + ctx.ub_bulk_dgrad = False + ctx.ub_bulk_wgrad = False + tp_world_size = get_distributed_world_size(ctx.tp_group) + if tp_world_size == 1: + ctx.ub_overlap_rs_dgrad = False + if ctx.ub_bulk_dgrad: + tp_world_size = get_distributed_world_size(ctx.tp_group) + if tp_world_size == 1 or not weight.requires_grad: + ctx.ub_bulk_dgrad = False + if ctx.ub_bulk_dgrad: + dim_size = list(ln_out.size()) + dim_size[0] = dim_size[0] * tp_world_size + ub_obj_lnout = get_ub(ctx.ub_name + "_dgrad") + ub_obj_lnout.copy_input_to_ubuf(ln_out, 1) + ( + grad_output, + grad_output_c, + grad_output_t, + grad_bias, + ) = TransformerEngineBaseModule.grad_output_preprocess( + ctx, grad_outputs[0], ctx.parallel_mode == "row" + ) + + if ctx.ub_bulk_wgrad: + tp_world_size = get_distributed_world_size(ctx.tp_group) + if tp_world_size == 1 or not weight.requires_grad: + ctx.ub_bulk_wgrad = False + + # Column Parallel Linear + # Overlap input AG with dgrad + if ( + weight.requires_grad + and (not ctx.ub_bulk_dgrad) + and ctx.parallel_mode == "column" + and ctx.sequence_parallel + ): + ln_out_total, _ = gather_along_first_dim(ln_out, ctx.tp_group, async_op=False) + handle = None + else: + ln_out_total = ln_out + handle = None + + if ctx.is_first_microbatch is not None: + accumulate_wgrad_into_param_main_grad = ( + ctx.fuse_wgrad_accumulation and not ctx.is_first_microbatch + ) + else: + accumulate_wgrad_into_param_main_grad = ctx.fuse_wgrad_accumulation + + dgrad_size = list(grad_output.size()) + dgrad_size[1] = weight.size(1) + if ctx.ub_bulk_wgrad: # allocate dgrad output + ub_obj_dgrad = get_ub(ctx.ub_name + "_wgrad") + dgrad = ub_obj_dgrad.get_ubuf_output(1) # AllGather output + elif ctx.ub_overlap_rs_dgrad: + ub_obj_dgrad = get_ub(ctx.ub_name + "_dgrad") + dgrad = ub_obj_dgrad.get_ubuf_output(1) # AllGather output + else: + dgrad = torch.empty(dgrad_size, dtype=ctx.activation_dtype, device=weight.device) + + if ctx.ub_bulk_dgrad: + ub_algo = tex.UbufOverlapAlgo.BULK_OVERLAP_AG + ub_obj = ub_obj_lnout + elif ctx.ub_overlap_rs_dgrad: + dim_size = list(grad_output.size()) + dim_size[0] = dim_size[0] // tp_world_size + dim_size[1] = weight.size(1) + rs_out = torch.empty( + dim_size, dtype=ctx.activation_dtype, device=grad_output.device + ) + if ub_obj_dgrad.is_p2p_overlap(): + if ctx.fp8 and ub_obj_dgrad.is_atomic_gemm(): + ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_RS_P2P + else: + ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS_P2P + else: + if ctx.fp8 and ub_obj_dgrad.is_atomic_gemm(): + ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_RS + else: + ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS + ub_obj = ub_obj_dgrad + else: + ub_algo = None + ub_obj = None + + if ctx.fp8: + fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True) + fp8_dtype_backward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=False) + out_index, meta_tensor, out_te_type, out_type = ( + None, + None, + None, + ctx.activation_dtype, + ) + if (ctx.ub_bulk_wgrad or ctx.ub_overlap_rs_dgrad) and ub_obj_dgrad.is_fp8_ubuf(): + out_index = tex.FP8BwdTensors.GRAD_INPUT1 + meta_tensor = ctx.fp8_meta["scaling_bwd"] + out_te_type = fp8_dtype_backward + out_type = torch.uint8 + ub_obj_dgrad.set_ubuf_scale_inv(meta_tensor.scale_inv[out_index]) + + # DGRAD: Evaluated unconditionally to feed into Linear backward + _ = tex.fp8_gemm( + weight_fp8.transpose_2d(), + weight_fp8._scale_inv, + 0, + weight_fp8._fp8_dtype, + ( + grad_output_c._data + if isinstance(grad_output_c, Float8Tensor) + else grad_output_c + ), + ctx.fp8_meta["scaling_bwd"].scale_inv, + tex.FP8BwdTensors.GRAD_OUTPUT1, + fp8_dtype_backward, + out_type, + get_workspace(), + out=dgrad, + use_split_accumulator=_2X_ACC_DGRAD, + ub_algo=ub_algo, + ub=ub_obj, + extra_output_tensor=rs_out if ctx.ub_overlap_rs_dgrad else None, + out_index=out_index, + fp8_meta_tensor=meta_tensor, + D_dtype=out_te_type, + ) + clear_tensor_data(grad_output_c) + else: + # DGRAD: Evaluated unconditionally to feed into Linear backward + _, _, _ = tex.gemm( + weight, + grad_output, + ctx.activation_dtype, + get_workspace(), + out=dgrad, + layout="NN", + grad=True, + ub_algo=ub_algo, + ub=ub_obj, + extra_output_tensor=rs_out if ctx.ub_overlap_rs_dgrad else None, + ) + if ctx.ub_bulk_dgrad: + ln_out_total = ub_obj_lnout.get_ubuf_output(1) + + # Overlap dgrad-RS/AR with wgrad + if ctx.parallel_mode == "column" and ctx.sequence_parallel: + if not ctx.ub_bulk_dgrad and handle is not None: + handle.wait() + if not ctx.ub_bulk_wgrad and not ctx.ub_overlap_rs_dgrad: + if ctx.return_layernorm_output and ctx.return_layernorm_output_gathered: + dgrad = dgrad + grad_outputs[1].view_as(dgrad) + dgrad, _ = reduce_scatter_along_first_dim( + dgrad, ctx.tp_group, async_op=False + ) + handle = None + elif ctx.parallel_mode == "column" and ctx.tensor_parallel: + dgrad, handle = allreduce(dgrad, ctx.tp_group, async_op=True) + + if weight.requires_grad: + if ctx.fp8: + # WGRAD + extra_output_tensor = None + if ctx.ub_bulk_wgrad: + if ub_obj_dgrad.is_fp8_ubuf(): + dim_size = list(ub_obj_dgrad.get_ubuf_output(0).size()) # RS output + extra_output_tensor = torch.empty( + dim_size, dtype=ctx.activation_dtype, device=dgrad.device + ) + dgrad = extra_output_tensor + else: + dgrad = ub_obj_dgrad.get_ubuf_output(0) + if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad: + ln_out_total_t = tex.fp8_transpose(ln_out_total, fp8_dtype_forward) + wgrad, _ = tex.fp8_gemm( + ln_out_total_t, + ln_out_scale_inv, + 0, + fp8_dtype_forward, + ( + grad_output_t._data + if isinstance(grad_output_t, Float8Tensor) + else grad_output_t + ), + ctx.fp8_meta["scaling_bwd"].scale_inv, + tex.FP8BwdTensors.GRAD_OUTPUT1, + fp8_dtype_backward, + ctx.activation_dtype, + get_workspace(), + accumulate=accumulate_wgrad_into_param_main_grad, + out=weight.main_grad if ctx.fuse_wgrad_accumulation else None, + use_split_accumulator=_2X_ACC_WGRAD, + ub_algo=( + tex.UbufOverlapAlgo.BULK_OVERLAP_RS if ctx.ub_bulk_wgrad else None + ), + ub=ub_obj_dgrad if ctx.ub_bulk_wgrad else None, + extra_output_tensor=extra_output_tensor, + ) + clear_tensor_data(ln_out_total_t, grad_output_t) + else: + ln_out_total_c = torch.ops.tex_ts.cast_from_fp8_ts( + ln_out_total, + ln_out_scale_inv, + 0, + fp8_dtype_forward, + TE_DType[ctx.activation_dtype], + ) + wgrad, _, _ = tex.gemm( + ln_out_total_c, + grad_output, + ctx.activation_dtype, + get_workspace(), + layout="NT", + grad=True, + accumulate=accumulate_wgrad_into_param_main_grad, + out=weight.main_grad if ctx.fuse_wgrad_accumulation else None, + ub_algo=( + tex.UbufOverlapAlgo.BULK_OVERLAP_RS if ctx.ub_bulk_wgrad else None + ), + ub=ub_obj_dgrad if ctx.ub_bulk_wgrad else None, + extra_output_tensor=extra_output_tensor, + ) + clear_tensor_data(ln_out_total_c) + else: + # WGRAD + wgrad, grad_bias, _ = tex.gemm( + ln_out_total, + grad_output, + ctx.activation_dtype, + get_workspace(), + layout="NT", + grad=True, + use_bias=ctx.use_bias, + accumulate=accumulate_wgrad_into_param_main_grad, + out=weight.main_grad if ctx.fuse_wgrad_accumulation else None, + ub_algo=tex.UbufOverlapAlgo.BULK_OVERLAP_RS if ctx.ub_bulk_wgrad else None, + ub=ub_obj_dgrad if ctx.ub_bulk_wgrad else None, + ) + clear_tensor_data(ln_out_total) + if ctx.ub_bulk_wgrad: + dgrad = ub_obj_dgrad.get_ubuf_output(0) # Reduce-scatter output + + # Column Parallel Linear + if ( + (not ctx.ub_bulk_wgrad) + and ctx.parallel_mode == "column" + and ctx.tensor_parallel + and handle is not None + ): + handle.wait() + + # LayerNorm gradient + if ctx.ub_overlap_rs_dgrad: + dgrad = rs_out.view(inputmat.shape) + else: + dgrad = dgrad.view(inputmat.shape) + + # Residual gradient + if ctx.return_layernorm_output and not ctx.return_layernorm_output_gathered: + dgrad = dgrad + grad_outputs[1].view_as(dgrad) + + if ctx.normalization == "LayerNorm": + dgrad, dgamma, dbeta = tex.layernorm_bwd( + dgrad, + inputmat, + mu, + rsigma, + ln_weight, + ctx.bwd_ln_sm_margin, + ctx.zero_centered_gamma, + ) + elif ctx.normalization == "RMSNorm": + dgrad, dgamma = tex.rmsnorm_bwd( + dgrad, + inputmat, + rsigma, + ln_weight, + ctx.bwd_ln_sm_margin, + ctx.zero_centered_gamma, + ) + dbeta = None + clear_tensor_data(mu) + clear_tensor_data(rsigma) + + if not ctx.use_bias: + grad_bias = None + + if weight.requires_grad: + # Handle custom DDP from mcore. + if ctx.fuse_wgrad_accumulation and hasattr(weight, "grad_added_to_main_grad"): + weight.grad_added_to_main_grad = True + if getattr(weight, "zero_out_wgrad", False): + wgrad = torch.zeros( + weight.main_grad.shape, + dtype=weight.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + else: + wgrad = torch.empty( + weight.main_grad.shape, + dtype=weight.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + elif ctx.fuse_wgrad_accumulation: + wgrad = None + else: + wgrad = None + + if ctx.reduce_and_update_bwd_fp8_tensors and not is_graph_capturing(): + FP8GlobalStateManager.reduce_and_update_fp8_tensors(forward=False) + + # Scatter fp8 weight buffers + if ctx.fp8 and not isinstance(weight, Float8Tensor): + _fsdp_scatter_tensors(ctx.fsdp_group, weight_fp8) + + return ( + dgrad.view(ctx.inp_shape) if ctx.requires_dgrad else None, + dgamma, + dbeta, + wgrad, + None, # weight_fp8 + grad_bias, + None, # use_bias + None, # eps + None, # is_first_microbatch + None, # fp8 + None, # fp8_calibration + None, # fp8_meta + None, # fuse_wgrad_accumulation + None, # cpu_offloading + None, # tp_group + None, # tp_size + None, # sequence_parallel + None, # tensor_parallel + None, # activation_dtype + None, # parallel_mode + None, # return_layernorm_output + None, # return_layernorm_output_gathered + None, # is_grad_enabled + None, # fwd_ln_sm_margin + None, # bwd_ln_sm_margin + None, # zero_centered_gamma + None, # normalization + None, # ub_bulk_wgrad + None, # ub_bulk_dgrad + None, # ub_overlap_rs_dgrad + None, # ub_overlap_ag + None, # ub_name + None, # fp8_output + None, # fsdp_group + ) + + +class LayerNormLinear(TransformerEngineBaseModule): + r""" + Applies layer normalization followed by linear transformation to the incoming data. + + Parameters + ---------- + in_features : int + size of each input sample. + out_features : int + size of each output sample. + eps : float, default = 1e-5 + a value added to the denominator of layer normalization for numerical stability. + bias : bool, default = `True` + if set to `False`, the layer will not learn an additive bias. + normalization : { 'LayerNorm', 'RMSNorm' }, default = 'LayerNorm' + type of normalization applied. + init_method : Callable, default = `None` + used for initializing weights in the following way: `init_method(weight)`. + When set to `None`, defaults to `torch.nn.init.normal_(mean=0.0, std=0.023)`. + return_layernorm_output : bool, default = `False` + if set to `True`, output of layernorm is returned from the forward + together with the output of the linear transformation. + Example use case: residual connection for transformer module is + taken post layernorm. + return_layernorm_output_gathered : bool, default = `False` + if set to `True`, output of layernorm is returned after the all + gather operation. Ignored if return_layernorm_output is False. + Example use case: with sequence parallel, input to residual connection + for transformer module (e.g. LoRA) will need to be gathered. + Returning layernorm output gathered will prevent a redundant gather. + parameters_split : Optional[Union[Tuple[str, ...], Dict[str, int]]], default = None + Configuration for splitting the weight and bias tensors along dim 0 into + multiple PyTorch parameters. If a list or tuple of strings is provided, + they are used to make the names of equally-sized parameters. If a dict + (preferably an OrderedDict) is provided, the keys are used as names and + values as split sizes along dim 0. The resulting parameters will have + names that end in `_weight` or `_bias`, so trailing underscores are + stripped from any provided names. + zero_centered_gamma : bool, default = 'False' + if set to 'True', gamma parameter in LayerNorm is initialized to 0 and + the LayerNorm formula changes to + + .. math:: + y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} * + (1 + \gamma) + \beta + device : Union[torch.device, str], default = "cuda" + The device on which the parameters of the model will be allocated. It is the user's + responsibility to ensure all parameters are moved to the GPU before running the + forward pass. + + Parallelism parameters + ---------------------- + sequence_parallel : bool, default = `False` + if set to `True`, uses sequence parallelism. + tp_group : ProcessGroup, default = `None` + tensor parallel process group. + tp_size : int, default = 1 + used as TP (tensor parallel) world size when TP groups are not formed during + initialization. In this case, users must call the + `set_tensor_parallel_group(tp_group)` method on the initialized module before the + forward pass to supply the tensor parallel group needed for tensor and sequence + parallel collectives. + parallel_mode : {None, 'column', 'row'}, default = `None` + used to decide whether this Linear layer is Column Parallel Linear or Row + Parallel Linear as described `here `_. + When set to `None`, no communication is performed. + + Optimization parameters + ----------------------- + fuse_wgrad_accumulation : bool, default = 'False' + if set to `True`, enables fusing of creation and accumulation of + the weight gradient. When enabled, it is assumed that the weights + have an additional `main_grad` attribute (used instead of the + regular `grad`) which is a pre-allocated buffer of the correct + size to accumulate gradients in. + return_bias : bool, default = `False` + when set to `True`, this module will not apply the additive bias itself, but + instead return the bias value during the forward pass together with the + output of the linear transformation :math:`y = xA^T`. This is useful when + the bias addition can be fused to subsequent operations. + params_dtype : torch.dtype, default = `torch.get_default_dtype()` + it controls the type used to allocate the initial parameters. Useful when + the model is trained with lower precision and the original FP32 parameters + would not fit in GPU memory. + """ + + def __init__( + self, + in_features: int, + out_features: int, + eps: float = 1e-5, + sequence_parallel: bool = False, + fuse_wgrad_accumulation: bool = False, + tp_group: Optional[dist_group_type] = None, + tp_size: int = 1, + get_rng_state_tracker: Optional[Callable] = None, + init_method: Optional[Callable] = None, + bias: bool = True, + normalization: str = "LayerNorm", + return_bias: bool = False, + params_dtype: Optional[torch.dtype] = None, + parallel_mode: Optional[str] = None, + return_layernorm_output: bool = False, + return_layernorm_output_gathered: bool = False, + parameters_split: Optional[Union[Tuple[str, ...], Dict[str, int]]] = None, + zero_centered_gamma: bool = False, + device: Union[torch.device, str] = "cuda", + ub_bulk_wgrad: bool = False, + ub_bulk_dgrad: bool = False, + ub_overlap_ag: bool = False, + ub_overlap_rs_dgrad: bool = False, + ub_name: Optional[str] = None, + ) -> None: + super().__init__() + + params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype + self.in_features = in_features + self.out_features = out_features + self.fuse_wgrad_accumulation = fuse_wgrad_accumulation + self.normalization = normalization + assert normalization in ["LayerNorm", "RMSNorm"], "Unsupported normalization type!" + self.use_bias = bias + self.return_bias = return_bias + self.apply_bias = self.use_bias and not return_bias + self.return_layernorm_output = return_layernorm_output + self.return_layernorm_output_gathered = return_layernorm_output_gathered + self.zero_centered_gamma = zero_centered_gamma + self.ub_bulk_wgrad = ub_bulk_wgrad + self.ub_bulk_dgrad = ub_bulk_dgrad + self.ub_overlap_ag = ub_overlap_ag + self.ub_overlap_rs_dgrad = ub_overlap_rs_dgrad + if any([ub_bulk_wgrad, ub_bulk_dgrad, ub_overlap_ag, ub_overlap_rs_dgrad]): + assert ub_name is not None, "Userbuffer name [string] is not set." + self.ub_name = ub_name + + if tp_group is None: + self.tp_size = tp_size + if tp_size == 1: + self.set_tensor_parallel_group(tp_group) + else: + self.tp_size = get_distributed_world_size(tp_group) + self.set_tensor_parallel_group(tp_group) + self.set_nccl_overlap_warning_if_tp() + + self.parallel_mode = parallel_mode + assert ( + self.parallel_mode in GemmParallelModes + ), f"parallel_mode {parallel_mode} not supported" + + if self.parallel_mode == "column": + self.out_features = divide(self.out_features, self.tp_size) + elif self.parallel_mode == "row": + self.in_features = divide(self.in_features, self.tp_size) + + if init_method is None: + init_method = get_default_init_method() + + self.sequence_parallel = (self.tp_size > 1) and sequence_parallel + + self.eps = eps + layer_norm_weight = torch.nn.Parameter( + torch.empty(in_features, device=device, dtype=params_dtype) + ) + self.register_parameter( + "layer_norm_weight", + layer_norm_weight, + init_fn=init_method_constant(float(not self.zero_centered_gamma)), + ) + if self.normalization != "RMSNorm": + layer_norm_bias = torch.nn.Parameter( + torch.empty(in_features, device=device, dtype=params_dtype) + ) + self.register_parameter( + "layer_norm_bias", layer_norm_bias, init_fn=init_method_constant(0.0) + ) + else: + self.layer_norm_bias = None + + # Initialize params in FP8 + with_fp8_params = FP8GlobalStateManager.with_fp8_parameters() + + # Contiguous buffers for params + weight_tensor = torch.empty( + self.out_features, + self.in_features, + device=device, + dtype=params_dtype, + ) + bias_tensor = None + if self.use_bias: + bias_tensor = torch.empty( + self.out_features, + device=device, + dtype=params_dtype, + ) + + # Configure parameter splits + self.weight_names = [] + self.bias_names = [] + self.parameter_split_sizes = [] + if parameters_split is None: + # Split into a single parameter by default + self.weight_names = ["weight"] + self.bias_names = ["bias"] + self.parameter_split_sizes = [out_features] + elif not parameters_split: + raise ValueError("Cannot split weight buffer into 0 parameters") + elif isinstance(parameters_split, dict): + # Split parameters with provided sizes + for name, split_size in parameters_split.items(): + self.weight_names.append(f"{name.rstrip('_')}_weight") + self.bias_names.append(f"{name.rstrip('_')}_bias") + self.parameter_split_sizes.append(split_size) + elif all(isinstance(name, str) for name in parameters_split): + # Split parameters evenly + split_size = out_features // len(parameters_split) + for name in parameters_split: + self.weight_names.append(f"{name.rstrip('_')}_weight") + self.bias_names.append(f"{name.rstrip('_')}_bias") + self.parameter_split_sizes.append(split_size) + else: + raise TypeError("Invalid configuration for parameters split") + + # Make sure parameter splits are valid + if sum(self.parameter_split_sizes) != out_features: + raise ValueError( + f"Trying to split weight buffer ({out_features=}) " + f"with split sizes {self.parameter_split_sizes}" + ) + + # Adjust parameter splits for tensor-parallel distribution + if self.parallel_mode == "column": + for i, size in enumerate(self.parameter_split_sizes): + if size % self.tp_size != 0: + raise RuntimeError( + f"Attempting to distribute a parameter with out_features={size} " + f"between {self.tp_size} tensor-parallel processes" + ) + self.parameter_split_sizes[i] = size // self.tp_size + + # Construct weight parameters + # Note: Register weights together so that they are adjacent to + # each other in LayerNormLinear.parameters(). This makes it + # more likely that they will stay contiguous if the weights + # are manipulated externally, e.g. by FSDP. + offset = 0 + for i, split_size in enumerate(self.parameter_split_sizes): + split_start = offset + offset += split_size + split_end = offset + + # Check if parameters are subviews of buffers + is_subview = (split_start, split_end) != (0, self.out_features) + if is_subview and with_fp8_params: + raise RuntimeError("Splitting Float8Tensor into multiple params is not supported") + + # Construct weight parameter + self.register_parameter( + self.weight_names[i], + torch.nn.Parameter(weight_tensor[split_start:split_end]), + init_fn=init_method, + get_rng_state_tracker=get_rng_state_tracker, + fp8_meta_index=tex.FP8FwdTensors.GEMM1_WEIGHT, + ) + + # Construct bias parameters if needed + if self.use_bias: + offset = 0 + for i, split_size in enumerate(self.parameter_split_sizes): + split_start = offset + offset += split_size + split_end = offset + self.register_parameter( + self.bias_names[i], + torch.nn.Parameter(bias_tensor[split_start:split_end]), + init_fn=init_method_constant(0.0), + ) + else: + for name in self.bias_names: + bias = torch.Tensor().to(dtype=params_dtype, device=device) + setattr(self, name, bias) + + if with_fp8_params: + self.init_fp8_metadata() + + self.reset_parameters(defer_init=(device == "meta")) + + # For RPL, bias has to be added after TP collectives + # So it cannot be fused with the GEMM + if self.parallel_mode == "row" and self.apply_bias: + self.gemm_bias_unfused_add = True + else: + self.gemm_bias_unfused_add = False + + # These many SMs are subtracted from the total SM count when calling forward + # and backward LayerNorm C APIs. These envvars can be used to prevent the LN + # kernels from using all SMs in the device. This is useful for cases such as + # communication overlap with LN. + self.fwd_ln_sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0")) + self.bwd_ln_sm_margin = int(os.getenv("NVTE_BWD_LAYERNORM_SM_MARGIN", "0")) + self.inf_ln_sm_margin = int(os.getenv("NVTE_INF_LAYERNORM_SM_MARGIN", "0")) + + def reset_layer_norm_parameters(self) -> None: + """Init LN params""" + warnings.warn( + "This method will be deprecated in an upcoming release. " + "Update your code to use LayerNormLinear.reset_parameters() instead.", + DeprecationWarning, + stacklevel=2, + ) + if not self.zero_centered_gamma: + init.ones_(self.layer_norm_weight) + else: + init.zeros_(self.layer_norm_weight) + if self.layer_norm_bias is not None: + init.zeros_(self.layer_norm_bias) + + def reset_parameters(self, defer_init=False): + super().reset_parameters(defer_init=defer_init) + + if not defer_init: + # Set parallelism attributes for layer norm parameters + setattr(self.layer_norm_weight, "sequence_parallel", self.sequence_parallel) + if self.normalization != "RMSNorm": + setattr(self.layer_norm_bias, "sequence_parallel", self.sequence_parallel) + + # Set parallelism attributes for linear weights + for weight in self.weight_names: + set_tensor_model_parallel_attributes( + tensor=getattr(self, weight), + is_parallel=True, + dim=1 if self.parallel_mode == "row" else 0, + stride=1, + ) + + # Set parallelism attributes for linear biases + if self.use_bias: + for bias in self.bias_names: + if self.parallel_mode == "row": + setattr(getattr(self, bias), "sequence_parallel", self.sequence_parallel) + elif self.parallel_mode == "column": + set_tensor_model_parallel_attributes(getattr(self, bias), True, 0, 1) + + @no_torch_dynamo() + def forward( + self, + inp: torch.Tensor, + is_first_microbatch: Optional[bool] = None, + fp8_output: Optional[bool] = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]: + """ + Apply layer normalization to the input followed by a linear transformation. + + Parameters + ---------- + inp : torch.Tensor + Input tensor. + is_first_microbatch : {True, False, None}, default = None + During training using either gradient accumulation or + pipeline parallelism a minibatch of data is further split + into microbatches. Between the microbatches of the same minibatch + the model weights are not updated. Setting this parameter indicates + whether the current microbatch is the first in a minibatch or not. + When set, this parameter enables additional optimizations: + + * during FP8 training, it allows caching of the FP8 versions of + the weights + * it also allows skipping gradient accumulation during the + first microbatch (since it is the first gradient being + produced) + """ + + skip_fp8_weight_update = FP8GlobalStateManager.get_skip_fp8_weight_update_tensor() + if skip_fp8_weight_update is not None: + is_first_microbatch = False + + with self.prepare_forward(inp, is_first_microbatch) as inp: + + # Get concatenated weight and bias tensors + unfused_weights = [getattr(self, name) for name in self.weight_names] + if any(isinstance(w, QuantizedTensor) for w in unfused_weights): + if self.fp8: + if len(unfused_weights) != 1: + raise RuntimeError( + "Splitting QuantizedTensor into multiple params is not supported" + ) + else: + unfused_weights = [w.dequantize() for w in unfused_weights] + weight_tensor = _noop_cat(unfused_weights) + if self.use_bias: + bias_tensor = _noop_cat( + [getattr(self, name) for name in self.bias_names], + ) + else: + bias_tensor = getattr(self, self.bias_names[0]) # Unused + + # Initialize FP8 weights if needed + weight_fp8 = None + if self.fp8: + if isinstance(weight_tensor, Float8Tensor): + # Make sure transpose cache is valid, if present + # Note: Transpose cache may have been invalidated + # externally, e.g. by optimizer. + if weight_tensor._transpose is not None: + weight_tensor.transpose_2d( + fill_cache=True, + noop_flag=skip_fp8_weight_update, + ) + else: + # FP8 cast to workspace buffer + update_workspace = is_first_microbatch is None or is_first_microbatch + weight_fp8 = self.get_fp8_workspace( + tensor=weight_tensor, + fp8_meta_forward=True, + fp8_meta_index=tex.FP8FwdTensors.GEMM1_WEIGHT, + cache_name=(None if is_first_microbatch is None else "weight"), + update_workspace=update_workspace, + skip_update_flag=skip_fp8_weight_update, + ) + + from ..cpu_offload import CPUOffloadEnabled + + if torch.is_grad_enabled(): + fwd_fn = _LayerNormLinear.apply + args = [] + else: + fwd_fn = _LayerNormLinear.forward + args = [None] + args += ( + inp, + self.layer_norm_weight, + self.layer_norm_bias, + weight_tensor, + weight_fp8, + bias_tensor, + self.apply_bias and not self.gemm_bias_unfused_add, + self.eps, + is_first_microbatch, + self.fp8, + self.fp8_calibration, + self.fp8_meta, + self.fuse_wgrad_accumulation, + CPUOffloadEnabled, + self.tp_group, + self.tp_size, + self.sequence_parallel, + self.tp_size > 1, + self.activation_dtype, + self.parallel_mode, + self.return_layernorm_output, + self.return_layernorm_output_gathered, + torch.is_grad_enabled(), + self.fwd_ln_sm_margin if torch.is_grad_enabled() else self.inf_ln_sm_margin, + self.bwd_ln_sm_margin, + self.zero_centered_gamma, + self.normalization, + self.ub_bulk_wgrad, + self.ub_bulk_dgrad, + self.ub_overlap_rs_dgrad, + self.ub_overlap_ag, + self.ub_name, + fp8_output, + self.fsdp_group, + ) + out = fwd_fn(*args) + + if self.return_layernorm_output: + out, ln_out = out + + if self.gemm_bias_unfused_add: + out = out + cast_if_needed(bias_tensor, self.activation_dtype) + + if self.return_bias: + if self.return_layernorm_output: + return out, cast_if_needed(bias_tensor, self.activation_dtype), ln_out + return out, cast_if_needed(bias_tensor, self.activation_dtype) + if self.return_layernorm_output: + return out, ln_out + return out diff --git a/xpu_timer/experiments/scripts/layernorm_mlp.py b/xpu_timer/experiments/scripts/layernorm_mlp.py new file mode 100644 index 0000000000..c456549b86 --- /dev/null +++ b/xpu_timer/experiments/scripts/layernorm_mlp.py @@ -0,0 +1,1593 @@ +# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# See LICENSE for license information. + +"""LayerNormMLP API""" +import os +import warnings +from typing import Any, Callable, Dict, Optional, Tuple, Union + +import torch +from torch.nn.parameter import Parameter +from torch.nn import init + +from .base import ( + get_workspace, + _ub_communicators, + get_ub, + TransformerEngineBaseModule, + _2X_ACC_FPROP, + _2X_ACC_DGRAD, + _2X_ACC_WGRAD, +) +from ..fp8 import get_fp8_te_dtype, FP8GlobalStateManager +from ..jit import ( + bias_gelu_fused, + bgrad_dgelu_fused, + set_jit_fusion_options, + warmup_jit_bias_gelu_all_dtypes, +) +from ..utils import ( + divide, + get_default_init_method, + init_method_constant, + cast_if_needed, + assert_dim_for_fp8_exec, + clear_tensor_data, + requires_grad, +) +from ..distributed import ( + set_tensor_model_parallel_attributes, + get_distributed_world_size, + allreduce, + reduce_scatter_along_first_dim, + gather_along_first_dim, + use_reentrant_activation_recompute, + _fsdp_scatter_tensors, + _fsdp_gather_tensors, +) + +from .. import cpp_extensions as tex + +from ..constants import dist_group_type, TE_DType +from ..jit import no_torch_dynamo +from ..graph import is_graph_capturing +from ..float8_tensor import Float8Tensor +from ._common import _apply_normalization + +__all__ = ["LayerNormMLP"] + + +def _act_func(activation: str): + funcs = { + "gelu": (tex.gelu, tex.dgelu), + "relu": (tex.relu, tex.drelu), + "geglu": (tex.geglu, tex.dgeglu), + "reglu": (tex.reglu, tex.dreglu), + "swiglu": (tex.swiglu, tex.dswiglu), + "qgelu": (tex.qgelu, tex.dqgelu), + "srelu": (tex.srelu, tex.dsrelu), + } + if activation not in funcs: + raise NotImplementedError("Activation type " + activation + " is not supported!") + return funcs[activation] + + +class _LayerNormMLP(torch.autograd.Function): + """LayerNormMLP semi-top level module + Calls custom cuda extensions. + """ + + @staticmethod + def forward( + ctx, + inp: torch.Tensor, + ln_weight: torch.Tensor, + ln_bias: torch.Tensor, + fc1_weight: torch.Tensor, + fc1_weight_fp8: Optional[torch.Tensor], + fc1_bias: torch.Tensor, + use_fc1_bias: bool, + fc2_weight: torch.Tensor, + fc2_weight_fp8: Optional[torch.Tensor], + fc2_bias: torch.Tensor, + use_fc2_bias: bool, + eps: float, + is_first_microbatch: Union[bool, None], + fp8: bool, + fp8_calibration: bool, + fp8_meta: Dict[str, Any], + fuse_wgrad_accumulation: bool, + cpu_offloading: bool, + tp_group: Union[dist_group_type, None], + tp_size: int, + sequence_parallel: bool, + tensor_parallel: bool, + activation_dtype: torch.dtype, + return_layernorm_output: bool, + return_layernorm_output_gathered: bool, + bias_gelu_nvfusion: bool, + set_parallel_mode: bool, + is_grad_enabled: bool, + fwd_ln_sm_margin: int, + bwd_ln_sm_margin: int, + zero_centered_gamma: bool, + activation: str, + normalization: str, + ub_bulk_wgrad: bool, + ub_bulk_dgrad: bool, + ub_overlap_rs_dgrad: bool, + ub_overlap_rs: bool, + ub_overlap_ag: bool, + gemm_gelu_fusion: bool, + fsdp_group: Union[dist_group_type, None], + ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]: + # Make sure input dimensions are compatible + in_features = ln_weight.numel() + assert inp.shape[-1] == in_features, "GEMM not possible" + inputmat = inp.view((-1, in_features)) + if fp8: + assert_dim_for_fp8_exec(inputmat) + assert_dim_for_fp8_exec(fc1_weight) + assert_dim_for_fp8_exec(fc2_weight) + + activation_func = _act_func(activation)[0] + + # Cast for native AMP + inputmat = cast_if_needed(inputmat, activation_dtype) + ln_weight = cast_if_needed(ln_weight, activation_dtype) + if ln_bias is not None: + ln_bias = cast_if_needed(ln_bias, activation_dtype) + + tp_world_size = get_distributed_world_size(tp_group) + if ub_overlap_ag: + if tp_world_size == 1 or (not is_grad_enabled) or return_layernorm_output: + ub_overlap_ag = False + if ub_overlap_ag: + ub_obj_lnout = get_ub("fc1_fprop") + ln_out = ub_obj_lnout.get_ubuf_output(0) + else: + ln_out_dtype = torch.uint8 if (fp8 and not return_layernorm_output) else inputmat.dtype + ln_out = torch.empty_like( + inputmat, dtype=ln_out_dtype, memory_format=torch.contiguous_format + ) + ub_overlap_rs = False if tp_world_size == 1 else ub_overlap_rs + + fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True) + + ln_out, mu, rsigma = _apply_normalization( + inputmat, + ln_out, + ln_weight, + ln_bias, + eps, + fp8 and not return_layernorm_output, + fp8_meta, + normalization, + fwd_ln_sm_margin, + zero_centered_gamma, + is_grad_enabled, + ) + + # Column Parallel Linear + ln_out_gathered = False + if ub_overlap_ag: + ln_out_total = ub_obj_lnout.get_ubuf_output(1) + ln_out = torch.empty_like(ln_out) + if ub_obj_lnout.is_atomic_gemm(): + ub_algo_ag = tex.UbufOverlapAlgo.ATOMIC_GEMM_AG_P2P + else: + ub_algo_ag = tex.UbufOverlapAlgo.SPLIT_PIPELINED_AG_P2P + elif set_parallel_mode and sequence_parallel: + ln_out_gathered = True + ln_out_total, _ = gather_along_first_dim(ln_out, tp_group) + else: + ln_out_total = ln_out + + # If residual connection is after LN, we need `ln_out` + # tensor in higher precision, this comes at the cost + # of an extra fp8 cast. + if return_layernorm_output: + ln_out_return = ln_out_total if return_layernorm_output_gathered else ln_out + if fp8: + if ub_overlap_ag: + ln_out = tex.cast_to_fp8( + ln_out, + fp8_meta["scaling_fwd"], + tex.FP8FwdTensors.GEMM1_INPUT, + fp8_dtype_forward, + ) + else: + ln_out_total = tex.cast_to_fp8( + ln_out_total, + fp8_meta["scaling_fwd"], + tex.FP8FwdTensors.GEMM1_INPUT, + fp8_dtype_forward, + ) + if ln_out_gathered: + rank = torch.distributed.get_rank(tp_group) + slice_start = rank * ln_out.size(0) + slice_end = (rank + 1) * ln_out.size(0) + ln_out = ln_out_total[slice_start:slice_end, ...] + else: + ln_out = ln_out_total + + if fp8: + bias_dtype = torch.bfloat16 if activation_dtype == torch.float32 else activation_dtype + fc1_bias = cast_if_needed(fc1_bias, bias_dtype) if use_fc1_bias else fc1_bias + fc2_bias = cast_if_needed(fc2_bias, bias_dtype) if use_fc2_bias else fc2_bias + + # Use FP8 weights + if fc1_weight_fp8 is None: + fc1_weight_fp8 = fc1_weight + if fc2_weight_fp8 is None: + fc2_weight_fp8 = fc2_weight + + assert isinstance(fc1_weight_fp8, Float8Tensor) + assert isinstance(fc2_weight_fp8, Float8Tensor) + + # Perform FP8 GEMM + fp8_gemm_args = [ + fc1_weight_fp8._data, + fc1_weight_fp8._scale_inv, + 0, + fc1_weight_fp8._fp8_dtype, + ln_out_total, + fp8_meta["scaling_fwd"].scale_inv, + tex.FP8FwdTensors.GEMM1_INPUT, + fp8_dtype_forward, + activation_dtype, + get_workspace(), + ] + fp8_gemm_kwargs = dict( + bias=fc1_bias, + use_bias=use_fc1_bias, + use_split_accumulator=_2X_ACC_FPROP, + ub_algo=ub_algo_ag if ub_overlap_ag else None, + ub=ub_obj_lnout if ub_overlap_ag else None, + extra_output_tensor=ln_out if ub_overlap_ag else None, + ) + if gemm_gelu_fusion: + fp8_gemm_args[8] = torch.uint8 # out_dtype + fp8_gemm_kwargs.update( + dict( + gelu=True, + out_index=tex.FP8FwdTensors.GEMM2_INPUT, + fp8_meta_tensor=fp8_meta["scaling_fwd"], + D_dtype=fp8_dtype_forward, + ) + ) + fp8_gemm_out = tex.fp8_gemm(*fp8_gemm_args, **fp8_gemm_kwargs) + if not is_grad_enabled: + clear_tensor_data(ln_out_total) + + # Perform activation + if gemm_gelu_fusion: + gelu_out, fc1_out = fp8_gemm_out + else: + fc1_out, _ = fp8_gemm_out + gelu_out = activation_func( + fc1_out, + fp8_meta["scaling_fwd"], + tex.FP8FwdTensors.GEMM2_INPUT, + fp8_dtype_forward, + ) + if not is_grad_enabled: + clear_tensor_data(fc1_out) + + fc2_out_index, fc2_meta_tensor, fc2_te_type, out_type = ( + None, + None, + None, + activation_dtype, + ) + if ub_overlap_rs: + ub_obj_fc2out = get_ub("fc2_fprop") + fc2_out = ub_obj_fc2out.get_ubuf_output(1) + dim_size = list(gelu_out.size()) + dim_size[0] = dim_size[0] // tp_world_size + dim_size[1] = fc2_weight_fp8.size(0) + rs_out = torch.empty(dim_size, dtype=activation_dtype, device=gelu_out.device) + if ub_obj_fc2out.is_p2p_overlap(): + if ub_obj_fc2out.is_atomic_gemm(): + ub_algo_rs = tex.UbufOverlapAlgo.ATOMIC_GEMM_RS_P2P + else: + ub_algo_rs = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS_P2P + else: + if ub_obj_fc2out.is_atomic_gemm(): + ub_algo_rs = tex.UbufOverlapAlgo.ATOMIC_GEMM_RS + else: + ub_algo_rs = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS + + if ub_obj_fc2out.is_fp8_ubuf(): + fc2_out_index = tex.FP8FwdTensors.GEMM2_OUTPUT + fc2_meta_tensor = fp8_meta["scaling_fwd"] + fc2_te_type = fp8_dtype_forward + out_type = torch.uint8 + ub_obj_fc2out.set_ubuf_scale_inv(fc2_meta_tensor.scale_inv[fc2_out_index]) + else: + dim_size = list(gelu_out.size()) + dim_size[1] = fc2_weight_fp8.size(0) + fc2_out = torch.empty(dim_size, dtype=activation_dtype, device=gelu_out.device) + + _ = tex.fp8_gemm( + fc2_weight_fp8._data, + fc2_weight_fp8._scale_inv, + 0, + fc2_weight_fp8._fp8_dtype, + gelu_out, + fp8_meta["scaling_fwd"].scale_inv, + tex.FP8FwdTensors.GEMM2_INPUT, + fp8_dtype_forward, + out_type, + get_workspace(), + bias=fc2_bias, + use_bias=use_fc2_bias, + use_split_accumulator=_2X_ACC_FPROP, + out=fc2_out, + ub_algo=ub_algo_rs if ub_overlap_rs else None, + ub=ub_obj_fc2out if ub_overlap_rs else None, + extra_output_tensor=rs_out if ub_overlap_rs else None, + out_index=fc2_out_index, + fp8_meta_tensor=fc2_meta_tensor, + D_dtype=fc2_te_type, + ) + if not is_grad_enabled: + clear_tensor_data(gelu_out) + else: + # Cast for native AMP + fc1_weight = cast_if_needed(fc1_weight, activation_dtype) + fc2_weight = cast_if_needed(fc2_weight, activation_dtype) + fc1_bias = cast_if_needed(fc1_bias, activation_dtype) if use_fc1_bias else fc1_bias + fc2_bias = cast_if_needed(fc2_bias, activation_dtype) if use_fc2_bias else fc2_bias + + if fp8_calibration: + # amax of fc1 input + amin, amax = ln_out_total.aminmax() + fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_INPUT] = torch.max( + -amin, amax + ).float() + # amax of fc1 weight + amin, amax = fc1_weight.aminmax() + fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM1_WEIGHT] = torch.max( + -amin, amax + ).float() + + fc1_outputs = tex.gemm( + fc1_weight, + ln_out_total, + activation_dtype, + get_workspace(), + bias=fc1_bias, + use_bias=(not bias_gelu_nvfusion) and use_fc1_bias, + gelu=not bias_gelu_nvfusion and (activation == "gelu"), + ub_algo=tex.UbufOverlapAlgo.SPLIT_PIPELINED_AG_P2P if ub_overlap_ag else None, + ub=ub_obj_lnout if ub_overlap_ag else None, + extra_output_tensor=ln_out if ub_overlap_ag else None, + ) + if not is_grad_enabled: + clear_tensor_data(ln_out_total) + + if bias_gelu_nvfusion: + fc1_out, _, _ = fc1_outputs + gelu_out = bias_gelu_fused(fc1_out, fc1_bias) + else: + if activation == "gelu": + gelu_out, _, fc1_out = fc1_outputs + else: + fc1_out, _, _ = fc1_outputs + gelu_out = activation_func( + fc1_out, None, tex.FP8FwdTensors.GEMM2_INPUT, TE_DType[fc1_out.dtype] + ) + if not is_grad_enabled: + clear_tensor_data(fc1_out) + + if fp8_calibration: + # amax of fc2 input + amin, amax = gelu_out.aminmax() + fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM2_INPUT] = torch.max( + -amin, amax + ).float() + # amax of fc2 weight + amin, amax = fc2_weight.aminmax() + fp8_meta["scaling_fwd"].amax_history[0][tex.FP8FwdTensors.GEMM2_WEIGHT] = torch.max( + -amin, amax + ).float() + + if ub_overlap_rs: + ub_obj_fc2out = get_ub("fc2_fprop") + fc2_out = ub_obj_fc2out.get_ubuf_output(1) + dim_size = list(gelu_out.size()) + dim_size[0] = dim_size[0] // tp_world_size + dim_size[1] = fc2_weight.size(0) + rs_out = torch.empty(dim_size, dtype=activation_dtype, device=gelu_out.device) + if ub_obj_fc2out.is_p2p_overlap(): + ub_algo_rs = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS_P2P + else: + ub_algo_rs = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS + else: + dim_size = list(gelu_out.size()) + dim_size[1] = fc2_weight.size(0) + fc2_out = torch.empty(dim_size, dtype=activation_dtype, device=gelu_out.device) + _ = tex.gemm( + fc2_weight, + gelu_out, + activation_dtype, + get_workspace(), + bias=fc2_bias, + use_bias=use_fc2_bias, + out=fc2_out, + ub_algo=ub_algo_rs if ub_overlap_rs else None, + ub=ub_obj_fc2out if ub_overlap_rs else None, + extra_output_tensor=rs_out if ub_overlap_rs else None, + ) + if not is_grad_enabled: + clear_tensor_data(gelu_out) + + if is_grad_enabled: + if cpu_offloading: + if fp8 and fc1_weight_fp8 is not None: + fc1_weight_fp8.weight_offloading = True + if fp8 and fc2_weight_fp8 is not None: + fc2_weight_fp8.weight_offloading = True + ln_weight.weight_offloading = True + fc1_weight.weight_offloading = True + fc2_weight.weight_offloading = True + fc1_bias.weight_offloading = True + + inputmat.activation_offloading = True + if normalization == "LayerNorm": + mu.activation_offloading = True + rsigma.activation_offloading = True + ln_out.activation_offloading = True + fc1_out.activation_offloading = True + gelu_out.activation_offloading = True + + # Scatter intermediate/activation tensors saved for the backward pass + # NOTE: weight_fp8 = weight when ctx.fp8 == False and torch.disttributed.FSDP already + # shards/unshards the base weights so we don't do it ourselves + ctx.fsdp_group = fsdp_group + ctx.fsdp_shapes = _fsdp_scatter_tensors( + fsdp_group, + mu, + rsigma, + ln_out, + fc1_out, + gelu_out, + fc1_weight_fp8 if fp8 and not isinstance(fc1_weight, Float8Tensor) else None, + fc2_weight_fp8 if fp8 and not isinstance(fc2_weight, Float8Tensor) else None, + ) + + ctx.save_for_backward( + inputmat, + ln_weight, + mu, + rsigma, + ln_out if fc1_weight.requires_grad else None, + fc1_out, + gelu_out if fc2_weight.requires_grad else None, + fc1_weight, + fc1_weight_fp8, + fc1_weight.main_grad if (cpu_offloading and fuse_wgrad_accumulation) else None, + fc2_weight, + fc2_weight_fp8, + fc2_weight.main_grad if (cpu_offloading and fuse_wgrad_accumulation) else None, + fc1_bias, + fp8_meta["scaling_fwd"].scale_inv.clone() if fp8 else None, + ) + + ctx.activation_dtype = activation_dtype + ctx.activation = activation + ctx.fp8 = fp8 + ctx.fp8_meta = fp8_meta + ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation + ctx.cpu_offloading = cpu_offloading + ctx.is_first_microbatch = is_first_microbatch + ctx.use_fc1_bias = use_fc1_bias + ctx.use_fc2_bias = use_fc2_bias + ctx.sequence_parallel = sequence_parallel + ctx.tensor_parallel = tensor_parallel + ctx.inp_shape = inp.shape + ctx.tp_group = tp_group + ctx.tp_size = tp_size + ctx.bias_gelu_nvfusion = bias_gelu_nvfusion + ctx.return_layernorm_output = return_layernorm_output + ctx.return_layernorm_output_gathered = ( + return_layernorm_output_gathered and ln_out_gathered + ) + ctx.set_parallel_mode = set_parallel_mode + ctx.bwd_ln_sm_margin = bwd_ln_sm_margin + ctx.zero_centered_gamma = zero_centered_gamma + ctx.ub_bulk_wgrad = ub_bulk_wgrad + ctx.ub_bulk_dgrad = ub_bulk_dgrad + ctx.ub_overlap_rs_dgrad = ub_overlap_rs_dgrad + ctx.ub_overlap_ag = ub_overlap_ag + ctx.requires_dgrad = inp.requires_grad + ctx.normalization = normalization + ctx.reduce_and_update_bwd_fp8_tensors = False + if ctx.fp8 and requires_grad( + inp, ln_weight, ln_bias, fc1_weight, fc2_weight, fc1_bias, fc2_bias + ): + ctx.reduce_and_update_bwd_fp8_tensors = FP8GlobalStateManager.is_first_fp8_module() + + # Row Parallel Linear + if ub_overlap_rs: + fc2_out = rs_out + elif set_parallel_mode and sequence_parallel: + fc2_out, _ = reduce_scatter_along_first_dim(fc2_out, tp_group) + elif set_parallel_mode and tensor_parallel: + fc2_out, _ = allreduce(fc2_out, tp_group) + + # [*, in_features] -> [*, out_features] except first dimension changes for SP + fc2_out = fc2_out.view(-1, *inp.shape[1:-1], fc2_out.shape[-1]) + + if return_layernorm_output: + if return_layernorm_output_gathered: + shape = list(inp.shape) + shape[0] *= tp_size + return fc2_out, ln_out_return.view(shape) + return fc2_out, ln_out_return.view_as(inp) + return fc2_out + + @staticmethod + def backward( + ctx, *grad_outputs: Tuple[torch.Tensor, ...] + ) -> Tuple[Union[torch.Tensor, None], ...]: + with torch.cuda.nvtx.range("_LayerNormMLP_backward"): + ( + inputmat, + ln_weight, + mu, + rsigma, + ln_out, + fc1_out, + gelu_out, + fc1_weight, + fc1_weight_fp8, + fc1_weight_main_grad, + fc2_weight, + fc2_weight_fp8, + fc2_weight_main_grad, + fc1_bias, + fwd_scale_inverses, + ) = ctx.saved_tensors + + # Gather saved autograd context tensors when running with FSDP + # NOTE: weight_fp8 = weight when ctx.fp8 == False and torch.disttributed.FSDP already + # shards/unshards the base weights so we don't do it ourselves + _fsdp_gather_tensors( + ctx.fsdp_group, + ctx.fsdp_shapes, + mu, + rsigma, + ln_out, + fc1_out, + gelu_out, + fc1_weight_fp8 if ctx.fp8 and not isinstance(fc1_weight, Float8Tensor) else None, + fc2_weight_fp8 if ctx.fp8 and not isinstance(fc2_weight, Float8Tensor) else None, + ) + + if ctx.cpu_offloading and ctx.fuse_wgrad_accumulation: + fc1_weight = Parameter(fc1_weight, fc1_weight.requires_grad) + fc2_weight = Parameter(fc2_weight, fc2_weight.requires_grad) + + fc1_weight.main_grad = fc1_weight_main_grad + fc2_weight.main_grad = fc2_weight_main_grad + + activation_func = _act_func(ctx.activation)[1] + + if ctx.ub_overlap_rs_dgrad: + ctx.ub_bulk_dgrad = False + ctx.ub_bulk_wgrad = False + tp_world_size = get_distributed_world_size(ctx.tp_group) + if tp_world_size == 1: + ctx.ub_overlap_rs_dgrad = False + if ctx.ub_bulk_dgrad: + tp_world_size = get_distributed_world_size(ctx.tp_group) + if tp_world_size == 1 or not fc1_weight.requires_grad: + ctx.ub_bulk_dgrad = False + if ctx.ub_bulk_dgrad: + dim_size = list(ln_out.size()) + dim_size[0] = dim_size[0] * tp_world_size + ub_obj_lnout = get_ub("fc1_dgrad") + ub_obj_lnout.copy_input_to_ubuf(ln_out, 1) + if ctx.ub_overlap_ag: + tp_world_size = get_distributed_world_size(ctx.tp_group) + if tp_world_size == 1: + ctx.ub_overlap_ag = False + + if ctx.ub_overlap_ag: + dim_size = list(grad_outputs[0].size()) + dim_size[0] = dim_size[0] * tp_world_size + ctx.ub_obj_gradout = get_ub("fc2_dgrad") + if ctx.ub_obj_gradout.is_atomic_gemm(): + ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_AG_P2P + else: + ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_AG_P2P + + ctx.use_bias = ctx.use_fc2_bias # For grad_output_preprocess + ( + grad_output, + grad_output_c, + grad_output_t, + fc2_bias_grad, + ) = TransformerEngineBaseModule.grad_output_preprocess(ctx, grad_outputs[0], True) + + if ctx.ub_bulk_wgrad: + tp_world_size = get_distributed_world_size(ctx.tp_group) + if tp_world_size == 1 or not fc1_weight.requires_grad: + ctx.ub_bulk_wgrad = False + # Column Parallel Linear + # Overlap input AG with dgrad + if ( + fc1_weight.requires_grad + and (not ctx.ub_bulk_dgrad) + and ctx.set_parallel_mode + and ctx.sequence_parallel + ): + ln_out_total, _ = gather_along_first_dim(ln_out, ctx.tp_group, async_op=False) + handle = None + else: + ln_out_total = ln_out + handle = None + + if ctx.is_first_microbatch is not None: + accumulate_wgrad_into_param_main_grad = ( + ctx.fuse_wgrad_accumulation and not ctx.is_first_microbatch + ) + else: + accumulate_wgrad_into_param_main_grad = ctx.fuse_wgrad_accumulation + + if ctx.fp8: + fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True) + fp8_dtype_backward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=False) + + # FC2 DGRAD; Unconditional + fc2_dgrad, _ = tex.fp8_gemm( + fc2_weight_fp8.transpose_2d(), + fc2_weight_fp8._scale_inv, + 0, + fc2_weight_fp8._fp8_dtype, + grad_output_c, + ctx.fp8_meta["scaling_bwd"].scale_inv, + tex.FP8BwdTensors.GRAD_OUTPUT1, + fp8_dtype_backward, + ctx.activation_dtype, + get_workspace(), + use_split_accumulator=_2X_ACC_DGRAD, + ub_algo=ub_algo if ctx.ub_overlap_ag else None, + ub=ctx.ub_obj_gradout if ctx.ub_overlap_ag else None, + ) + if ctx.ub_overlap_ag: + grad_output_t = tex.fp8_transpose(grad_output_c, fp8_dtype_backward) + clear_tensor_data(grad_output_c) + + # FC2 WGRAD + if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad: + if fc2_weight.requires_grad: + gelu_out_t = tex.fp8_transpose(gelu_out, fp8_dtype_forward) + clear_tensor_data(gelu_out) + fc2_wgrad, _ = tex.fp8_gemm( + gelu_out_t, + fwd_scale_inverses, + tex.FP8FwdTensors.GEMM2_INPUT, + fp8_dtype_forward, + grad_output_t, + ctx.fp8_meta["scaling_bwd"].scale_inv, + tex.FP8BwdTensors.GRAD_OUTPUT1, + fp8_dtype_backward, + ctx.activation_dtype, + get_workspace(), + accumulate=accumulate_wgrad_into_param_main_grad, + out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None, + use_split_accumulator=_2X_ACC_WGRAD, + ) + clear_tensor_data(gelu_out_t, grad_output_t) + + if ctx.activation == "gelu": + fc1_bias_grad, dgelu, dgelu_t = tex.fp8_cast_transpose_bgrad_dgelu_fused( + fc2_dgrad, + fc1_out, + ctx.fp8_meta["scaling_bwd"], + tex.FP8BwdTensors.GRAD_OUTPUT2, + fp8_dtype_backward, + ) + else: + dgelu = activation_func(fc2_dgrad, fc1_out, TE_DType[fc2_dgrad.dtype]) + fc1_bias_grad, dgelu, dgelu_t = tex.fp8_cast_transpose_bgrad_fused( + dgelu, + ctx.fp8_meta["scaling_bwd"], + tex.FP8BwdTensors.GRAD_OUTPUT2, + fp8_dtype_backward, + ) + clear_tensor_data(fc1_out) + else: + if fc2_weight.requires_grad: + gelu_out_c = torch.ops.tex_ts.cast_from_fp8_ts( + gelu_out, + fwd_scale_inverses, + tex.FP8FwdTensors.GEMM2_INPUT, + fp8_dtype_forward, + TE_DType[ctx.activation_dtype], + ) + clear_tensor_data(gelu_out) + fc2_wgrad, _, _ = tex.gemm( + gelu_out_c, + grad_output, + ctx.activation_dtype, + get_workspace(), + layout="NT", + grad=True, + use_bias=False, + accumulate=accumulate_wgrad_into_param_main_grad, + out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None, + ) + clear_tensor_data(gelu_out_c) + + if ctx.activation == "gelu": + fc1_bias_grad, dgelu_no_fp8 = bgrad_dgelu_fused( + fc2_dgrad, fc1_out, fc1_bias + ) + else: + dgelu_no_fp8 = activation_func( + fc2_dgrad, fc1_out, TE_DType[fc2_dgrad.dtype] + ) + fc1_bias_grad = dgelu_no_fp8.sum(dim=0) + clear_tensor_data(fc1_out) + + dgelu = tex.cast_to_fp8( + dgelu_no_fp8, + ctx.fp8_meta["scaling_bwd"], + tex.FP8BwdTensors.GRAD_OUTPUT2, + fp8_dtype_backward, + ) + dgelu_t = None + + out_index, meta_tensor, out_te_type, out_type = ( + None, + None, + None, + ctx.activation_dtype, + ) + fc1_dgrad_size = list(dgelu.size()) + fc1_dgrad_size[1] = fc1_weight.size(1) + # Get/alloc fc1_dgrad + if ctx.ub_bulk_wgrad: # allocate dgrad output + ub_obj_dgrad = get_ub("fc1_wgrad") + fc1_dgrad = ub_obj_dgrad.get_ubuf_output(1) # AllGather output + elif ctx.ub_overlap_rs_dgrad: + ub_obj_dgrad = get_ub("fc1_dgrad") + fc1_dgrad = ub_obj_dgrad.get_ubuf_output(1) # AllGather output + else: + fc1_dgrad = torch.empty( + fc1_dgrad_size, dtype=ctx.activation_dtype, device=fc1_weight.device + ) + + # FP8 RS + if (ctx.ub_bulk_wgrad or ctx.ub_overlap_rs_dgrad) and ub_obj_dgrad.is_fp8_ubuf(): + out_index = tex.FP8BwdTensors.GRAD_INPUT2 + meta_tensor = ctx.fp8_meta["scaling_bwd"] + out_te_type = fp8_dtype_backward + out_type = torch.uint8 + ub_obj_dgrad.set_ubuf_scale_inv(meta_tensor.scale_inv[out_index]) + + # Set UB algo and UB obj for fc1_dgrad bulk/pipelined overlap + if ctx.ub_bulk_dgrad: + ub_algo = tex.UbufOverlapAlgo.BULK_OVERLAP_AG + ub_obj = ub_obj_lnout + elif ctx.ub_overlap_rs_dgrad: + dim_size = list(dgelu.size()) + dim_size[0] = dim_size[0] // tp_world_size + dim_size[1] = fc1_weight_fp8.size(1) + rs_out = torch.empty(dim_size, dtype=ctx.activation_dtype, device=dgelu.device) + if ub_obj_dgrad.is_p2p_overlap(): + if ub_obj_dgrad.is_atomic_gemm(): + ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_RS_P2P + else: + ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS_P2P + else: + if ub_obj_dgrad.is_atomic_gemm(): + ub_algo = tex.UbufOverlapAlgo.ATOMIC_GEMM_RS + else: + ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS + ub_obj = ub_obj_dgrad + else: + ub_algo = None + ub_obj = None + # FC1 DGRAD: Unconditional + _ = tex.fp8_gemm( + fc1_weight_fp8.transpose_2d(), + fc1_weight_fp8._scale_inv, + 0, + fc1_weight_fp8._fp8_dtype, + dgelu, + ctx.fp8_meta["scaling_bwd"].scale_inv, + tex.FP8BwdTensors.GRAD_OUTPUT2, + fp8_dtype_backward, + out_type, + get_workspace(), + out=fc1_dgrad, + use_split_accumulator=_2X_ACC_DGRAD, + ub_algo=ub_algo, + ub=ub_obj, + extra_output_tensor=rs_out if ctx.ub_overlap_rs_dgrad else None, + out_index=out_index, + fp8_meta_tensor=meta_tensor, + D_dtype=out_te_type, + ) + else: + # FC2 DGRAD; Unconditional + fc2_dgrad, _, _ = tex.gemm( + fc2_weight, + grad_output, + ctx.activation_dtype, + get_workspace(), + layout="NN", + gelu=(not ctx.bias_gelu_nvfusion) and (ctx.activation == "gelu"), + grad=True, + gelu_input=fc1_out, + ub_algo=( + tex.UbufOverlapAlgo.SPLIT_PIPELINED_AG_P2P if ctx.ub_overlap_ag else None + ), + ub=ctx.ub_obj_gradout if ctx.ub_overlap_ag else None, + ) + + # FC2 WGRAD + if fc2_weight.requires_grad: + fc2_wgrad, fc2_bias_grad, _ = tex.gemm( + gelu_out, + grad_output, + ctx.activation_dtype, + get_workspace(), + layout="NT", + grad=True, + use_bias=ctx.use_fc2_bias, + accumulate=accumulate_wgrad_into_param_main_grad, + out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None, + ) + clear_tensor_data(gelu_out) + + if ctx.bias_gelu_nvfusion and ctx.activation == "gelu": + fc1_bias_grad, fc2_dgrad = bgrad_dgelu_fused(fc2_dgrad, fc1_out, fc1_bias) + else: + if ctx.activation != "gelu": + fc2_dgrad = activation_func(fc2_dgrad, fc1_out, TE_DType[fc2_dgrad.dtype]) + + # For non-fp8 execution, FC1 bias gradient is fused with FC1 wgrad GEMM + # and will not be calculated in case wgrad is not required. + if not fc1_weight.requires_grad: + fc1_bias_grad = fc2_dgrad.sum(dim=0) + + # Overwrite data. Deleting the tensor does not release underlying memory. + clear_tensor_data(fc1_out) + dgelu = fc2_dgrad + + fc1_dgrad_size = list(dgelu.size()) + fc1_dgrad_size[1] = fc1_weight.size(1) + if ctx.ub_bulk_wgrad: # allocate dgrad output + ub_obj_dgrad = get_ub("fc1_wgrad") + fc1_dgrad = ub_obj_dgrad.get_ubuf_output(1) # AllGather output + elif ctx.ub_overlap_rs_dgrad: + ub_obj_dgrad = get_ub("fc1_dgrad") + fc1_dgrad = ub_obj_dgrad.get_ubuf_output(1) # AllGather output + else: + fc1_dgrad = torch.empty( + fc1_dgrad_size, dtype=ctx.activation_dtype, device=fc1_weight.device + ) + + # Set UB algo and UB obj for fc1_dgrad bulk/pipelined overlap + if ctx.ub_bulk_dgrad: + ub_algo = tex.UbufOverlapAlgo.BULK_OVERLAP_AG + ub_obj = ub_obj_lnout + elif ctx.ub_overlap_rs_dgrad: + dim_size = list(dgelu.size()) + dim_size[0] = dim_size[0] // tp_world_size + dim_size[1] = fc1_weight.size(1) + rs_out = torch.empty(dim_size, dtype=ctx.activation_dtype, device=dgelu.device) + if ub_obj_dgrad.is_p2p_overlap(): + ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS_P2P + else: + ub_algo = tex.UbufOverlapAlgo.SPLIT_PIPELINED_RS + ub_obj = ub_obj_dgrad + else: + ub_algo = None + ub_obj = None + # FC1 DGRAD: Unconditional + _ = tex.gemm( + fc1_weight, + dgelu, + ctx.activation_dtype, + get_workspace(), + out=fc1_dgrad, + layout="NN", + grad=True, + ub_algo=ub_algo, + ub=ub_obj, + extra_output_tensor=rs_out if ctx.ub_overlap_rs_dgrad else None, + ) + + if ctx.ub_bulk_dgrad: + ln_out_total = ub_obj_lnout.get_ubuf_output(1) + # Overlap dgrad-RS/AR with wgrad + if ctx.set_parallel_mode and ctx.sequence_parallel: + if not ctx.ub_bulk_dgrad and handle is not None: + handle.wait() + if not ctx.ub_bulk_wgrad and not ctx.ub_overlap_rs_dgrad: + if ctx.return_layernorm_output and ctx.return_layernorm_output_gathered: + fc1_dgrad = fc1_dgrad + grad_outputs[1].view_as(fc1_dgrad) + fc1_dgrad, _ = reduce_scatter_along_first_dim( + fc1_dgrad, ctx.tp_group, async_op=False + ) + handle = None + elif ctx.set_parallel_mode and ctx.tensor_parallel: + fc1_dgrad, handle = allreduce(fc1_dgrad, ctx.tp_group, async_op=True) + + if fc1_weight.requires_grad: + if ctx.fp8: + # FC1 WGRAD + extra_output_tensor = None + if ctx.ub_bulk_wgrad: + if ub_obj_dgrad.is_fp8_ubuf(): + dim_size = list(ub_obj_dgrad.get_ubuf_output(0).size()) # RS output + extra_output_tensor = torch.empty( + dim_size, dtype=ctx.activation_dtype, device=fc1_dgrad.device + ) + fc1_dgrad = extra_output_tensor + else: + fc1_dgrad = ub_obj_dgrad.get_ubuf_output(0) + if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad: + ln_out_total_t = tex.fp8_transpose(ln_out_total, fp8_dtype_forward) + fc1_wgrad, _ = tex.fp8_gemm( + ln_out_total_t, + fwd_scale_inverses, + tex.FP8FwdTensors.GEMM1_INPUT, + fp8_dtype_forward, + dgelu_t, + ctx.fp8_meta["scaling_bwd"].scale_inv, + tex.FP8BwdTensors.GRAD_OUTPUT2, + fp8_dtype_backward, + ctx.activation_dtype, + get_workspace(), + accumulate=accumulate_wgrad_into_param_main_grad, + out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None, + use_split_accumulator=_2X_ACC_WGRAD, + ub_algo=( + tex.UbufOverlapAlgo.BULK_OVERLAP_RS if ctx.ub_bulk_wgrad else None + ), + ub=ub_obj_dgrad if ctx.ub_bulk_wgrad else None, + extra_output_tensor=extra_output_tensor, + ) + clear_tensor_data(ln_out_total_t, dgelu_t) + else: + ln_out_total_c = torch.ops.tex_ts.cast_from_fp8_ts( + ln_out_total, + fwd_scale_inverses, + tex.FP8FwdTensors.GEMM1_INPUT, + fp8_dtype_forward, + TE_DType[ctx.activation_dtype], + ) + fc1_wgrad, _, _ = tex.gemm( + ln_out_total_c, + dgelu_no_fp8, + ctx.activation_dtype, + get_workspace(), + layout="NT", + grad=True, + accumulate=accumulate_wgrad_into_param_main_grad, + out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None, + ub_algo=( + tex.UbufOverlapAlgo.BULK_OVERLAP_RS if ctx.ub_bulk_wgrad else None + ), + ub=ub_obj_dgrad if ctx.ub_bulk_wgrad else None, + extra_output_tensor=extra_output_tensor, + ) + clear_tensor_data(ln_out_total_c, dgelu_no_fp8) + else: + # FC1 WGRAD + fc1_wgrad_outputs = tex.gemm( + ln_out_total, + dgelu, + ctx.activation_dtype, + get_workspace(), + layout="NT", + grad=True, + use_bias=not ctx.bias_gelu_nvfusion, + accumulate=accumulate_wgrad_into_param_main_grad, + out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None, + ub_algo=tex.UbufOverlapAlgo.BULK_OVERLAP_RS if ctx.ub_bulk_wgrad else None, + ub=ub_obj_dgrad if ctx.ub_bulk_wgrad else None, + ) + clear_tensor_data(ln_out_total, dgelu) + + if ctx.bias_gelu_nvfusion: + fc1_wgrad, _, _ = fc1_wgrad_outputs + else: + fc1_wgrad, fc1_bias_grad, _ = fc1_wgrad_outputs + if ctx.ub_bulk_wgrad: + fc1_dgrad = ub_obj_dgrad.get_ubuf_output(0) # Reduce-scatter output + + # Column Parallel Linear + if ( + (not ctx.ub_bulk_wgrad) + and ctx.set_parallel_mode + and ctx.tensor_parallel + and handle is not None + ): + handle.wait() + + # LayerNorm gradient + if ctx.ub_overlap_rs_dgrad: + dgrad = rs_out.view(inputmat.shape) + else: + dgrad = fc1_dgrad.view(inputmat.shape) + + # Residual gradient + if ctx.return_layernorm_output and not ctx.return_layernorm_output_gathered: + dgrad = dgrad + grad_outputs[1].view_as(dgrad) + + if ctx.normalization == "LayerNorm": + dgrad, dgamma, dbeta = tex.layernorm_bwd( + dgrad, + inputmat, + mu, + rsigma, + ln_weight, + ctx.bwd_ln_sm_margin, + ctx.zero_centered_gamma, + ) + elif ctx.normalization == "RMSNorm": + dgrad, dgamma = tex.rmsnorm_bwd( + dgrad, + inputmat, + rsigma, + ln_weight, + ctx.bwd_ln_sm_margin, + ctx.zero_centered_gamma, + ) + dbeta = None + clear_tensor_data(mu) + clear_tensor_data(rsigma) + + if fc1_weight.requires_grad: + # Handle custom DDP from mcore. + if ctx.fuse_wgrad_accumulation and hasattr(fc1_weight, "grad_added_to_main_grad"): + fc1_weight.grad_added_to_main_grad = True + if getattr(fc1_weight, "zero_out_wgrad", False): + fc1_wgrad = torch.zeros( + fc1_weight.main_grad.shape, + dtype=fc1_weight.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + else: + fc1_wgrad = torch.empty( + fc1_weight.main_grad.shape, + dtype=fc1_weight.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + elif ctx.fuse_wgrad_accumulation: + fc1_wgrad = None + else: + fc1_wgrad = None + + if fc2_weight.requires_grad: + # Handle custom DDP from mcore. + if ctx.fuse_wgrad_accumulation and hasattr(fc2_weight, "grad_added_to_main_grad"): + fc2_weight.grad_added_to_main_grad = True + if getattr(fc2_weight, "zero_out_wgrad", False): + fc2_wgrad = torch.zeros( + fc2_weight.main_grad.shape, + dtype=fc2_weight.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + else: + fc2_wgrad = torch.empty( + fc2_weight.main_grad.shape, + dtype=fc2_weight.dtype, + device=torch.cuda.current_device(), + requires_grad=False, + ) + elif ctx.fuse_wgrad_accumulation: + fc2_wgrad = None + else: + fc2_wgrad = None + + if ctx.reduce_and_update_bwd_fp8_tensors and not is_graph_capturing(): + FP8GlobalStateManager.reduce_and_update_fp8_tensors(forward=False) + + # Scatter Fp8 tranposed-weight buffers + if ctx.fp8: + _fsdp_scatter_tensors( + ctx.fsdp_group, + fc1_weight_fp8 if not isinstance(fc1_weight, Float8Tensor) else None, + fc2_weight_fp8 if not isinstance(fc2_weight, Float8Tensor) else None, + ) + + return ( + dgrad.view(ctx.inp_shape) if ctx.requires_dgrad else None, + dgamma, + dbeta, + fc1_wgrad, + None, # fc1_weight_fp8 + fc1_bias_grad if ctx.use_fc1_bias else None, + None, # use_fc1_bias + fc2_wgrad, + None, # fc2_weight_fp8 + fc2_bias_grad if ctx.use_fc2_bias else None, + None, # use_fc2_bias + None, # eps + None, # is_first_microbatch + None, # fp8 + None, # fp8_calibration + None, # fp8_meta + None, # fuse_wgrad_accumulation + None, # cpu_offloading + None, # tp_group + None, # tp_size + None, # sequence_parallel + None, # tensor_parallel + None, # activation_dtype + None, # return_layernorm_output + None, # return_layernorm_output_gathered + None, # bias_gelu_nvfusion + None, # set_parallel_mode + None, # is_grad_enabled + None, # fwd_ln_sm_margin + None, # bwd_ln_sm_margin + None, # zero_centered_gamma + None, # activation + None, # normalization + None, # ub_bulk_wgrad + None, # ub_bulk_dgrad + None, # ub_overlap_rs_dgrad + None, # ub_overlap_rs + None, # ub_overlap_ag + None, # gemm_gelu_fusion + None, # fsdp_group + ) + + +class LayerNormMLP(TransformerEngineBaseModule): + r""" + Applies layer normalization on the input followed by the MLP module, consisting of + 2 successive linear transformations, separated by the GeLU activation. + + Parameters + ---------- + hidden_size : int + size of each input sample. + ffn_hidden_size : int + intermediate size to which input samples are projected. + eps : float, default = 1e-5 + a value added to the denominator of layer normalization for numerical stability. + bias : bool, default = `True` + if set to `False`, the FC1 and FC2 layers will not learn an additive bias. + normalization : { 'LayerNorm', 'RMSNorm' }, default = 'LayerNorm' + type of normalization applied. + activation : str, default = 'gelu' + activation function used. + Options: 'gelu', 'geglu', 'relu', 'reglu', 'squared_relu', 'swiglu', 'qgelu', 'srelu'. + init_method : Callable, default = `None` + used for initializing FC1 weights in the following way: `init_method(weight)`. + When set to `None`, defaults to `torch.nn.init.normal_(mean=0.0, std=0.023)`. + output_layer_init_method : Callable, default = `None` + used for initializing FC2 weights in the following way: + `output_layer_init_method(weight)`. When set to `None`, defaults to + `torch.nn.init.normal_(mean=0.0, std=0.023)`. + return_layernorm_output : bool, default = `False` + if set to `True`, output of layernorm is returned from the forward + together with the output of the linear transformation. + Example use case: residual connection for transformer module + is taken post layernorm. + return_layernorm_output_gathered : bool, default = `False` + if set to `True`, output of layernorm is returned after the all + gather operation. Ignored if return_layernorm_output is False. + Example use case: with sequence parallel, input to residual connection + for transformer module (e.g. LoRA) will need to be gathered. + Returning layernorm output gathered will prevent a redundant gather. + zero_centered_gamma : bool, default = 'False' + if set to 'True', gamma parameter in LayerNorm is initialized to 0 and + the LayerNorm formula changes to + + .. math:: + y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} * + (1 + \gamma) + \beta + device : Union[torch.device, str], default = "cuda" + The device on which the parameters of the model will be allocated. It is the user's + responsibility to ensure all parameters are moved to the GPU before running the + forward pass. + + Parallelism parameters + ---------------------- + set_parallel_mode : bool, default = `False` + if set to `True`, FC1 is used as Column Parallel and FC2 is used as Row + Parallel as described `here `_. + sequence_parallel : bool, default = `False` + if set to `True`, uses sequence parallelism. + tp_group : ProcessGroup, default = `None` + tensor parallel process group. + tp_size : int, default = 1 + used as TP (tensor parallel) world size when TP groups are not formed during + initialization. In this case, users must call the + `set_tensor_parallel_group(tp_group)` method on the initialized module before the + forward pass to supply the tensor parallel group needed for tensor and sequence + parallel collectives. + + Optimization parameters + ----------------------- + fuse_wgrad_accumulation : bool, default = 'False' + if set to `True`, enables fusing of creation and accumulation of + the weight gradient. When enabled, it is assumed that the weights + have an additional `main_grad` attribute (used instead of the + regular `grad`) which is a pre-allocated buffer of the correct + size to accumulate gradients in. + return_bias : bool, default = `False` + when set to `True`, this module will not apply the additive bias for FC2, but + instead return the bias value during the forward pass together with the + output of the linear transformation :math:`y = xA^T`. This is useful when + the bias addition can be fused to subsequent operations. + params_dtype : torch.dtype, default = `torch.get_default_dtype()` + it controls the type used to allocate the initial parameters. Useful when + the model is trained with lower precision and the original FP32 parameters + would not fit in GPU memory. + seq_length: int + sequence length of input samples. Needed for JIT Warmup, a technique where jit fused + functions are warmed up before training to ensure same kernels are used for forward + propogation and activation recompute phase. + micro_batch_size: int + batch size per training step. Needed for JIT Warmup, a technique where jit + fused functions are warmed up before training to ensure same kernels are + used for forward propogation and activation recompute phase. + """ + + def __init__( + self, + hidden_size: int, + ffn_hidden_size: int, + eps: float = 1e-5, + sequence_parallel: bool = False, + return_bias: bool = False, + get_rng_state_tracker: Optional[Callable] = None, + tp_group: Optional[dist_group_type] = None, + tp_size: int = 1, + init_method: Optional[Callable] = None, + bias: bool = True, + normalization: str = "LayerNorm", + activation: str = "gelu", + output_layer_init_method: Optional[Callable] = None, + fuse_wgrad_accumulation: bool = False, + params_dtype: Optional[torch.dtype] = None, + return_layernorm_output: bool = False, + return_layernorm_output_gathered: bool = False, + seq_length: Optional[int] = None, + micro_batch_size: Optional[int] = None, + set_parallel_mode: bool = False, + zero_centered_gamma: bool = False, + device: Union[torch.device, str] = "cuda", + ub_bulk_wgrad: bool = False, + ub_bulk_dgrad: bool = False, + ub_overlap_rs_dgrad: bool = False, + ub_overlap_rs: bool = False, + ub_overlap_ag: bool = False, + ) -> None: + super().__init__() + + params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype + self.fuse_wgrad_accumulation = fuse_wgrad_accumulation + self.normalization = normalization + assert normalization in ["LayerNorm", "RMSNorm"], "Unsupported normalization type!" + self.use_bias = bias + self.activation = activation + self.return_bias = return_bias + self.apply_bias = bias and not return_bias + self.return_layernorm_output = return_layernorm_output + self.return_layernorm_output_gathered = return_layernorm_output_gathered + self.bias_gelu_nvfusion = ( + bool(int(os.getenv("NVTE_BIAS_GELU_NVFUSION", "1"))) and self.activation == "gelu" + ) + self.set_parallel_mode = set_parallel_mode + self.zero_centered_gamma = zero_centered_gamma + self.ub_bulk_wgrad = ub_bulk_wgrad + self.ub_bulk_dgrad = ub_bulk_dgrad + self.ub_overlap_rs_dgrad = ub_overlap_rs_dgrad + self.ub_overlap_rs = ub_overlap_rs + self.ub_overlap_ag = ub_overlap_ag + # GEMM-GELU fusion is currently only supported with split GEMM-AG overlap + self.gemm_gelu_fusion = ( + bool(int(os.getenv("NVTE_GEMM_GELU_FUSION", "0"))) + and self.activation == "gelu" + and ((_ub_communicators is None) or (not get_ub("fc1_fprop").is_atomic_gemm())) + ) + + if tp_group is None: + self.tp_size = tp_size + if tp_size == 1: + self.set_tensor_parallel_group(tp_group) + else: + self.tp_size = get_distributed_world_size(tp_group) + self.set_tensor_parallel_group(tp_group) + self.set_nccl_overlap_warning_if_tp() + + if init_method is None: + init_method = get_default_init_method() + if output_layer_init_method is None: + output_layer_init_method = get_default_init_method() + + self.sequence_parallel = (self.tp_size > 1) and sequence_parallel + self.size_per_partition = divide(ffn_hidden_size, self.tp_size) + + # Initialize params in FP8 + with_fp8_params = FP8GlobalStateManager.with_fp8_parameters() + + # LN init + self.eps = eps + layer_norm_weight = Parameter(torch.empty(hidden_size, device=device, dtype=params_dtype)) + self.register_parameter( + "layer_norm_weight", + layer_norm_weight, + init_fn=init_method_constant(float(not self.zero_centered_gamma)), + ) + if self.normalization != "RMSNorm": + layer_norm_bias = Parameter(torch.empty(hidden_size, device=device, dtype=params_dtype)) + self.register_parameter( + "layer_norm_bias", layer_norm_bias, init_fn=init_method_constant(0.0) + ) + else: + self.layer_norm_bias = None + + # FC1 init + if self.activation in ["reglu", "geglu", "swiglu"]: + fc1_output_features = 2 * self.size_per_partition + else: + fc1_output_features = self.size_per_partition + + fc1_weight = Parameter( + torch.empty(fc1_output_features, hidden_size, device=device, dtype=params_dtype) + ) + self.register_parameter( + "fc1_weight", + fc1_weight, + init_fn=init_method, + get_rng_state_tracker=get_rng_state_tracker, + fp8_meta_index=tex.FP8FwdTensors.GEMM1_WEIGHT, + ) + + if self.use_bias: + fc1_bias = Parameter( + torch.empty(fc1_output_features, device=device, dtype=params_dtype) + ) + self.register_parameter("fc1_bias", fc1_bias, init_fn=init_method_constant(0.0)) + else: + self.fc1_bias = torch.Tensor().to(dtype=params_dtype, device=device) + + # FC2 init + fc2_weight = Parameter( + torch.empty(hidden_size, self.size_per_partition, device=device, dtype=params_dtype) + ) + self.register_parameter( + "fc2_weight", + fc2_weight, + init_fn=output_layer_init_method, + get_rng_state_tracker=get_rng_state_tracker, + fp8_meta_index=tex.FP8FwdTensors.GEMM2_WEIGHT, + ) + + if self.use_bias: + fc2_bias = Parameter(torch.empty(hidden_size, device=device, dtype=params_dtype)) + self.register_parameter("fc2_bias", fc2_bias, init_fn=init_method_constant(0.0)) + else: + self.fc2_bias = torch.Tensor().to(dtype=params_dtype, device=device) + + if with_fp8_params: + self.init_fp8_metadata(num_gemms=2) + + self.reset_parameters(defer_init=(device == "meta")) + + # For RPL, bias has to be added after TP collectives + # So it cannot be fused with the GEMM + if self.set_parallel_mode and self.apply_bias: + self.gemm_bias_unfused_add = True + else: + self.gemm_bias_unfused_add = False + + if self.bias_gelu_nvfusion: + set_jit_fusion_options() + if seq_length and micro_batch_size: + warmup_jit_bias_gelu_all_dtypes( + self.size_per_partition, seq_length, micro_batch_size + ) + + # These many SMs are subtracted from the total SM count when calling forward + # and backward LayerNorm C APIs. These envvars can be used to prevent the LN + # kernels from using all SMs in the device. This is useful for cases such as + # communication overlap with LN. + self.fwd_ln_sm_margin = int(os.getenv("NVTE_FWD_LAYERNORM_SM_MARGIN", "0")) + self.bwd_ln_sm_margin = int(os.getenv("NVTE_BWD_LAYERNORM_SM_MARGIN", "0")) + self.inf_ln_sm_margin = int(os.getenv("NVTE_INF_LAYERNORM_SM_MARGIN", "0")) + + def reset_layer_norm_parameters(self) -> None: + """Init LN params""" + warnings.warn( + "This method will be deprecated in an upcoming release. " + "Update your code to use LayerNormMLP.reset_parameters() instead.", + DeprecationWarning, + stacklevel=2, + ) + if not self.zero_centered_gamma: + init.ones_(self.layer_norm_weight) + else: + init.zeros_(self.layer_norm_weight) + if self.layer_norm_bias is not None: + init.zeros_(self.layer_norm_bias) + + def reset_parameters(self, defer_init=False): + super().reset_parameters(defer_init=defer_init) + + if not defer_init: + # Set parallel attributes for layer norm parameters + setattr(self.layer_norm_weight, "sequence_parallel", self.sequence_parallel) + if self.normalization != "RMSNorm": + setattr(self.layer_norm_bias, "sequence_parallel", self.sequence_parallel) + + # Set parallel attributes for linear parameters + set_tensor_model_parallel_attributes(self.fc1_weight, True, 0, 1) + set_tensor_model_parallel_attributes(self.fc2_weight, True, 1, 1) + if self.use_bias: + set_tensor_model_parallel_attributes(self.fc1_bias, True, 0, 1) + if self.set_parallel_mode: + setattr(self.fc2_bias, "sequence_parallel", self.sequence_parallel) + + @no_torch_dynamo() + def forward( + self, inp: torch.Tensor, is_first_microbatch: Optional[bool] = None + ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]: + """ + Apply layer normalization to the input followed by a feedforward network (MLP Block). + + Parameters + ---------- + inp : torch.Tensor + Input tensor. + is_first_microbatch : {True, False, None}, default = None + During training using either gradient accumulation or + pipeline parallelism a minibatch of data is further split + into microbatches. Between the microbatches of the same minibatch + the model weights are not updated. Setting this parameter indicates + whether the current microbatch is the first in a minibatch or not. + When set, this parameter enables additional optimizations: + + * during FP8 training, it allows caching of the FP8 versions of + the weights + * it also allows skipping gradient accumulation during the + first microbatch (since it is the first gradient being + produced) + """ + + skip_fp8_weight_update = FP8GlobalStateManager.get_skip_fp8_weight_update_tensor() + if skip_fp8_weight_update is not None: + is_first_microbatch = False + + with self.prepare_forward(inp, is_first_microbatch, num_gemms=2) as inp: + + # Get weight tensors + fc1_weight = self.fc1_weight + fc2_weight = self.fc2_weight + if not self.fp8: + if isinstance(fc1_weight, Float8Tensor): + fc1_weight = fc1_weight.from_float8() + if isinstance(fc2_weight, Float8Tensor): + fc2_weight = fc2_weight.from_float8() + + # Cast weights to FP8 if needed + fc1_weight_fp8 = None + fc2_weight_fp8 = None + if self.fp8: + update_workspace = is_first_microbatch is None or is_first_microbatch + if isinstance(fc1_weight, Float8Tensor): + if fc1_weight._transpose is not None: + fc1_weight.transpose_2d( + fill_cache=True, + noop_flag=skip_fp8_weight_update, + ) + else: + cache_name = None + if is_first_microbatch is not None: + cache_name = "fc1_weight" + fc1_weight_fp8 = self.get_fp8_workspace( + tensor=fc1_weight, + fp8_meta_forward=True, + fp8_meta_index=tex.FP8FwdTensors.GEMM1_WEIGHT, + cache_name=cache_name, + update_workspace=update_workspace, + skip_update_flag=skip_fp8_weight_update, + ) + if isinstance(fc2_weight, Float8Tensor): + if fc2_weight._transpose is not None: + fc2_weight.transpose_2d( + fill_cache=True, + noop_flag=skip_fp8_weight_update, + ) + else: + cache_name = None + if is_first_microbatch is not None: + cache_name = "fc2_weight" + fc2_weight_fp8 = self.get_fp8_workspace( + tensor=fc2_weight, + fp8_meta_forward=True, + fp8_meta_index=tex.FP8FwdTensors.GEMM2_WEIGHT, + cache_name=cache_name, + update_workspace=update_workspace, + skip_update_flag=skip_fp8_weight_update, + ) + + # Disable bias_gelu_nvfusion for determinism checkpointing in non-reentrant mode + if self.bias_gelu_nvfusion and not use_reentrant_activation_recompute(): + self.bias_gelu_nvfusion = False + + from ..cpu_offload import CPUOffloadEnabled + + if torch.is_grad_enabled(): + fwd_fn = _LayerNormMLP.apply + args = [] + else: + fwd_fn = _LayerNormMLP.forward + args = [None] + args += ( + inp, + self.layer_norm_weight, + self.layer_norm_bias, + fc1_weight, + fc1_weight_fp8, + self.fc1_bias, + self.use_bias, + fc2_weight, + fc2_weight_fp8, + self.fc2_bias, + self.apply_bias and not self.gemm_bias_unfused_add, + self.eps, + is_first_microbatch, + self.fp8, + self.fp8_calibration, + self.fp8_meta, + self.fuse_wgrad_accumulation, + CPUOffloadEnabled, + self.tp_group, + self.tp_size, + self.sequence_parallel, + self.tp_size > 1, + self.activation_dtype, + self.return_layernorm_output, + self.return_layernorm_output_gathered, + self.bias_gelu_nvfusion, + self.set_parallel_mode, + torch.is_grad_enabled(), + self.fwd_ln_sm_margin if torch.is_grad_enabled() else self.inf_ln_sm_margin, + self.bwd_ln_sm_margin, + self.zero_centered_gamma, + self.activation, + self.normalization, + self.ub_bulk_wgrad, + self.ub_bulk_dgrad, + self.ub_overlap_rs_dgrad, + self.ub_overlap_rs, + self.ub_overlap_ag, + self.gemm_gelu_fusion, + self.fsdp_group, + ) + out = fwd_fn(*args) + + if self.return_layernorm_output: + out, ln_out = out + + if self.gemm_bias_unfused_add: + out = out + cast_if_needed(self.fc2_bias, self.activation_dtype) + + if self.return_bias: + if self.return_layernorm_output: + return out, cast_if_needed(self.fc2_bias, self.activation_dtype), ln_out + return out, cast_if_needed(self.fc2_bias, self.activation_dtype) + if self.return_layernorm_output: + return out, ln_out + return out diff --git a/xpu_timer/experiments/scripts/llama3.1/train_llama3.1_8b.sh b/xpu_timer/experiments/scripts/llama3.1/train_llama3.1_8b.sh new file mode 100755 index 0000000000..4530b86995 --- /dev/null +++ b/xpu_timer/experiments/scripts/llama3.1/train_llama3.1_8b.sh @@ -0,0 +1,213 @@ +#!/bin/bash +set -ex + +export PYTHONPATH=$PWD/ant_patches:$PYTHONPATH + +export OMP_NUM_THREADS=1 +#export CUDA_DEVICE_MAX_CONNECTIONS=1 +export NCCL_DEBUG_SUBSYS=INIT # disable aistudio default nccl env + +GPUS_PER_NODE=$(nvidia-smi -L | wc -l) +#GPUS_PER_NODE=4 +#export CUDA_VISIBLE_DEVICES=4,5,6,7 + +lines=`echo $POD_NAME | grep edljob | wc -l` +if [ $lines -eq 0 ]; then + WORLD_SIZE=${WORLD_SIZE:-1} + NODE_RANK=${RANK:-0} + MASTER_ADDR=${MASTER_ADDR:-127.0.0.1} + RANDOM_PORT=$[$RANDOM + 20000] + MASTER_PORT=${MASTER_PORT:-$RANDOM_PORT} + GPU_NUM=$((${GPUS_PER_NODE}*${WORLD_SIZE})) + echo "---> from pytorch runtime, WORLD_SIZE: ${WORLD_SIZE}, NODE_RANK: ${NODE_RANK}, MASTER_ADDR: ${MASTER_ADDR}, MASTER_PORT: ${MASTER_PORT}" + LAUNCHER=" \ + torchrun \ + --nproc_per_node ${GPUS_PER_NODE} \ + --nnodes ${WORLD_SIZE} \ + --node_rank ${NODE_RANK} \ + --master_addr ${MASTER_ADDR} \ + --master_port ${MASTER_PORT} \ + " +else + WORLD_SIZE=${WORKER_NUM:-1} + NODE_RANK=${RANK:-0} + MASTER_ADDR=${MASTER_ADDR:-127.0.0.1} + RANDOM_PORT=$[$RANDOM + 20000] + MASTER_PORT=${MASTER_PORT:-$RANDOM_PORT} + GPU_NUM=$((${GPUS_PER_NODE}*${WORLD_SIZE})) + echo "---> from edl runtime, WORLD_SIZE: ${WORLD_SIZE}, NODE_RANK: ${NODE_RANK}" + LAUNCHER=" \ + python -m atorch.distributed.run --fault_tolerant --network-check \ + --max_restarts=1 \ + --nnode=$WORLD_SIZE \ + --nproc_per_node=$GPUS_PER_NODE \ + --rdzv_conf join_timeout=300 \ + " +fi + +MODEL_SIZE=${MODEL_SIZE:-8} +if [[ ${MODEL_SIZE} == 8 ]]; then HIDDEN_SIZE=4096; NUM_HEADS=32; NUM_QUERY_GROUPS=8; NUM_LAYERS=32; FFN_HIDDEN_SIZE=14336; MAX_POSITION_EMBEDDINGS=131072; VOCAB_SIZE=128256; +elif [[ ${MODEL_SIZE} == 70 ]]; then HIDDEN_SIZE=8192; NUM_HEADS=64; NUM_QUERY_GROUPS=8; NUM_LAYERS=80; FFN_HIDDEN_SIZE=28672; MAX_POSITION_EMBEDDINGS=131072; VOCAB_SIZE=128256; +elif [[ ${MODEL_SIZE} == 405 ]]; then HIDDEN_SIZE=16384; NUM_HEADS=128; NUM_QUERY_GROUPS=8; NUM_LAYERS=126; FFN_HIDDEN_SIZE=53248; MAX_POSITION_EMBEDDINGS=131072; VOCAB_SIZE=128256; +elif [[ ${MODEL_SIZE} == 1 ]]; then HIDDEN_SIZE=2048; NUM_HEADS=32; NUM_QUERY_GROUPS=8; NUM_LAYERS=16; FFN_HIDDEN_SIZE=8192; MAX_POSITION_EMBEDDINGS=131072; VOCAB_SIZE=128256; +elif [[ ${MODEL_SIZE} == 3 ]]; then HIDDEN_SIZE=3072; NUM_HEADS=24; NUM_QUERY_GROUPS=8; NUM_LAYERS=28; FFN_HIDDEN_SIZE=8192; MAX_POSITION_EMBEDDINGS=131072; VOCAB_SIZE=128256; +else echo "invalid MODEL_SIZE: ${MODEL_SIZE}"; exit 1 +fi + +DEVICE_MODEL=$(nvidia-smi -i 0 -q | grep "Product Name" | awk -F: '{ print $2 }') +DEVICE_MODEL=$(echo "$DEVICE_MODEL" | xargs) # drop white space +DEVICE_MODEL=NVIDIA + +JOB_DIR="/tmp/llama3_${MODEL_SIZE}B_${DEVICE_MODEL}_${GPU_NUM}p" +echo $JOB_DIR +mkdir -p ${JOB_DIR} +CHECKPOINT_PATH=${JOB_DIR} # +TENSORBOARD_LOGS_PATH=${JOB_DIR} + + +cp -r ${0} ${JOB_DIR} +#pip list > ${JOB_DIR}/pip_list.txt + +DATASET_DIR="/dnn_training_sys/dataset/nlp/fineweb-edu/CC-MAIN-2024-10/" +DATASET0="${DATASET_DIR}/CC-MAIN-2024-10_0000_text_document" +DATASET1="${DATASET_DIR}/CC-MAIN-2024-10_0001_text_document" +DATASET2="${DATASET_DIR}/CC-MAIN-2024-10_0002_text_document" +DATASET4="${DATASET_DIR}/CC-MAIN-2024-10_0004_text_document" + +DATA_PATH="0.25 ${DATASET0} 0.25 ${DATASET1} 0.25 ${DATASET2} 0.25 ${DATASET4}" +LOG_PATH="${JOB_DIR}/debug_llama_${RANK}.txt" + + + +GPT_MODEL_ARGS=( + --num-layers $NUM_LAYERS + --hidden-size $HIDDEN_SIZE + --ffn-hidden-size $FFN_HIDDEN_SIZE + --num-attention-heads $NUM_HEADS + --group-query-attention + --num-query-groups $NUM_QUERY_GROUPS + --max-position-embeddings $MAX_POSITION_EMBEDDINGS + --vocab-size $VOCAB_SIZE + --position-embedding-type "rope" + --rotary-base 500000 + --rotary-percent 1.0 + --swiglu + --untie-embeddings-and-output-weights + --normalization "RMSNorm" + --norm-epsilon "1e-05" + --disable-bias-linear + --transformer-impl "transformer_engine" +) + +TRAINING_ARGS=( + --micro-batch-size 2 + --global-batch-size 128 + --seq-length "4096" + --train-iters 800 + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.95 + --init-method-std 0.006 + --clip-grad 1.0 + --bf16 + --lr "3.0e-4" + --lr-decay-style cosine + --min-lr "3.0e-5" + --lr-warmup-fraction 0.001 + --lr-decay-iters 20000 + --seed 42 +) + + +if [ "$DEVICE_MODEL" = "A800-SXM4-80GB" ] || [ "$DEVICE_MODEL" = "A100-SXM4-80GB" ]; then + # Ampere GPUs do not support multicast. If `--tp-comm-overlap` is set on Ampere-arch GPUs, this env must be set. + export UB_SKIPMC=1 +fi +export NVTE_FLASH_ATTN=1 + +# deterministic computation +#export PYTORCH_JIT=0 +#export NVTE_TORCH_COMPILE=0 +#export NVTE_ALLOW_NONDETERMINISTIC_ALGO=0 +#export NCCL_ALGO="Ring" +#export CUBLAS_WORKSPACE_CONFIG=":4096:8" + +MODEL_PARALLEL_ARGS=( + --tensor-model-parallel-size 4 + --use-distributed-optimizer + --no-async-tensor-model-parallel-allreduce + --sequence-parallel + --manual-gc + --manual-gc-interval 50 +) + + #--pipeline-model-parallel-size 4 + #--num-layers-per-virtual-pipeline-stage 2 + + #--overlap-param-gather + #--overlap-grad-reduce +# some optional args + +# --use-distributed-optimizer +# --overlap-param-gather +# --overlap-grad-reduce +# --context-parallel-size 2 +# --tp-comm-overlap +# --decoder-first-pipeline-num-layers +# --decoder-last-pipeline-num-layers + + +DATA_ARGS=( + --mock-data + --tokenizer-type "NullTokenizer" +) + + +EVAL_AND_LOGGING_ARGS=( + --save-interval 1000 + --eval-interval 1000 + --save $CHECKPOINT_PATH + --load $CHECKPOINT_PATH + --ckpt-format "torch_dist" + --async-save + --eval-iters 250 + --log-interval 1 + --log-throughput + --tensorboard-dir $TENSORBOARD_LOGS_PATH + --log-timers-to-tensorboard + --log-memory-to-tensorboard + --log-world-size-to-tensorboard + --log-validation-ppl-to-tensorboard +) + +KERNEL_ARGS=( + --use-flash-attn + --no-masked-softmax-fusion + --attention-softmax-in-fp32 +) +# +# --deterministic-mode +# --use-flash-attn +# --cross-entropy-loss-fusion + +PROFILING_ARGS=( + --profile + --use-pytorch-profiler + --profile-ranks 0 + --profile-step-start 10 + --profile-step-end 20 +) + +CMD="${LAUNCHER} pretrain_gpt.py \ + ${GPT_MODEL_ARGS[@]} \ + ${TRAINING_ARGS[@]} \ + ${MODEL_PARALLEL_ARGS[@]} \ + ${DATA_ARGS[@]} \ + ${EVAL_AND_LOGGING_ARGS[@]} \ + ${KERNEL_ARGS[@]} \ + " + + #${PROFILING_ARGS[@]} \ +echo ${CMD} +nohup ${CMD} > ${LOG_PATH} 2>&1 & +#${CMD} 2>&1 | tee ${LOG_PATH} diff --git a/xpu_timer/experiments/scripts/pretrain_bert.py b/xpu_timer/experiments/scripts/pretrain_bert.py new file mode 100644 index 0000000000..35884ecdc4 --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_bert.py @@ -0,0 +1,193 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Pretrain BERT""" + +from functools import partial + +import torch +import torch.nn.functional as F + +from megatron.training import get_args +from megatron.training import get_tokenizer +from megatron.training import print_rank_0 +from megatron.training import get_timers +from megatron.core import tensor_parallel +from megatron.core.enums import ModelType +import megatron.legacy.model +from megatron.core.models.bert.bert_model import BertModel +from megatron.training import pretrain +from megatron.training.utils import average_losses_across_data_parallel_group +from megatron.training.arguments import core_transformer_config_from_args +from megatron.core.transformer.spec_utils import import_module +from megatron.core.models.bert.bert_layer_specs import bert_layer_with_transformer_engine_spec, bert_layer_local_spec +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.bert_dataset import BERTMaskedWordPieceDataset, BERTMaskedWordPieceDatasetConfig +from megatron.core.datasets.utils import get_blend_from_list +from megatron.core import mpu, tensor_parallel + + +def model_provider(pre_process=True, post_process=True): + """Build the model.""" + + print_rank_0('building BERT model ...') + + args = get_args() + config = core_transformer_config_from_args(args) + num_tokentypes = 2 if args.bert_binary_head else 0 + + if args.use_legacy_models: + model = megatron.legacy.model.BertModel( + config=config, + num_tokentypes=num_tokentypes, + add_binary_head=args.bert_binary_head, + parallel_output=True, + pre_process=pre_process, + post_process=post_process) + else: + if args.spec is None: + transformer_layer_spec = bert_layer_with_transformer_engine_spec #default spec + elif args.spec[0] == 'local': + print_rank_0('Using Local spec for transformer layers') + transformer_layer_spec = bert_layer_local_spec + else : + transformer_layer_spec = import_module(args.spec) + + model = BertModel( + config=config, + transformer_layer_spec=transformer_layer_spec, + vocab_size=args.padded_vocab_size, + max_sequence_length=args.max_position_embeddings, + num_tokentypes=num_tokentypes, + add_binary_head=args.bert_binary_head, + share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, + parallel_output=True, + pre_process=pre_process, + post_process=post_process) + + return model + + +def get_batch(data_iterator): + """Build the batch.""" + + # Items and their type. + keys = ['text', 'types', 'labels', + 'is_random', 'loss_mask', 'padding_mask'] + datatype = torch.int64 + + # Broadcast data. + if data_iterator is not None: + data = next(data_iterator) + else: + data = None + data_b = tensor_parallel.broadcast_data(keys, data, datatype) + + # Unpack. + tokens = data_b['text'].long() + types = data_b['types'].long() + sentence_order = data_b['is_random'].long() + loss_mask = data_b['loss_mask'].float() + lm_labels = data_b['labels'].long() + padding_mask = data_b['padding_mask'].long() + + return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask + + +def loss_func(loss_mask, sentence_order, output_tensor): + lm_loss_, sop_logits = output_tensor + + lm_loss_ = lm_loss_.float() + loss_mask = loss_mask.float() + lm_loss = torch.sum( + lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum() + + if sop_logits is not None: + sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(), + sentence_order.view(-1), + ignore_index=-1) + sop_loss = sop_loss.float() + loss = lm_loss + sop_loss + averaged_losses = average_losses_across_data_parallel_group( + [lm_loss, sop_loss]) + return loss, {'lm loss': averaged_losses[0], + 'sop loss': averaged_losses[1]} + else: + loss = lm_loss + averaged_losses = average_losses_across_data_parallel_group( + [lm_loss]) + return loss, {'lm loss': averaged_losses[0]} + + +def forward_step(data_iterator, model): + """Forward step.""" + args = get_args() + timers = get_timers() + + # Get the batch. + timers('batch-generator', log_level=2).start() + tokens, types, sentence_order, loss_mask, lm_labels, padding_mask = get_batch( + data_iterator) + timers('batch-generator').stop() + + if not args.bert_binary_head: + types = None + + # Forward pass through the model. + output_tensor = model(tokens, padding_mask, + tokentype_ids=types, lm_labels=lm_labels) + + return output_tensor, partial(loss_func, loss_mask, sentence_order) + + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build train, valid, and test datasets.""" + args = get_args() + + tokenizer = get_tokenizer() + + config = BERTMaskedWordPieceDatasetConfig( + random_seed=args.seed, + sequence_length=args.seq_length, + blend=get_blend_from_list(args.data_path), + blend_per_split=[ + get_blend_from_list(args.train_data_path), + get_blend_from_list(args.valid_data_path), + get_blend_from_list(args.test_data_path) + ], + renormalize_blend_weights=args.renormalize_blend_weights, + split=args.split, + path_to_cache=args.data_cache_path, + tokenizer=tokenizer, + masking_probability=args.mask_prob, + short_sequence_probability=args.short_seq_prob, + masking_max_ngram=3, + masking_do_full_word=True, + masking_do_permutation=False, + masking_use_longer_ngrams=False, + masking_use_geometric_distribution=False, + classification_head=args.bert_binary_head, + ) + + print_rank_0('> building train, validation, and test datasets ' + 'for BERT ...') + + train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder( + BERTMaskedWordPieceDataset, + train_val_test_num_samples, + lambda: mpu.get_tensor_model_parallel_rank() == 0, + config, + ).build() + + print_rank_0("> finished creating BERT datasets ...") + + return train_ds, valid_ds, test_ds + + +if __name__ == "__main__": + + # Temporary for transition to core datasets + train_valid_test_datasets_provider.is_distributed = True + + pretrain(train_valid_test_datasets_provider, model_provider, + ModelType.encoder_or_decoder, + forward_step, args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}) diff --git a/xpu_timer/experiments/scripts/pretrain_gpt.py b/xpu_timer/experiments/scripts/pretrain_gpt.py new file mode 100644 index 0000000000..2d1d7c3211 --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_gpt.py @@ -0,0 +1,272 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +"""Pretrain GPT.""" + +import os +import torch +from functools import partial +from contextlib import nullcontext +import inspect + +from typing import Union +import ant_patches + +from megatron.training import get_args +from megatron.training import print_rank_0 +from megatron.training import get_timers +from megatron.training import get_tokenizer +from megatron.core import mpu +from megatron.core.enums import ModelType +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.utils import get_blend_from_list +from megatron.core.datasets.gpt_dataset import GPTDatasetConfig +from megatron.core.datasets.gpt_dataset import MockGPTDataset, GPTDataset +import megatron.legacy.model +from megatron.core.models.gpt import GPTModel +from megatron.training import pretrain +from megatron.core.utils import StragglerDetector +from megatron.core.transformer.spec_utils import import_module +from megatron.training.utils import ( + get_batch_on_this_cp_rank, + get_batch_on_this_tp_rank, +) +from megatron.training.arguments import core_transformer_config_from_args +from megatron.training.yaml_arguments import core_transformer_config_from_yaml +from megatron.core.models.gpt.gpt_layer_specs import ( + get_gpt_layer_local_spec, + get_gpt_layer_with_transformer_engine_spec, +) + + +stimer = StragglerDetector() + +def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megatron.legacy.model.GPTModel]: + """Builds the model. + + If you set the use_legacy_models to True, it will return the legacy GPT model and if not the mcore GPT model. + + Args: + pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True. + post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True. + + + Returns: + Union[GPTModel, megatron.legacy.model.GPTModel]: The returned model + """ + args = get_args() + use_te = args.transformer_impl == "transformer_engine" + + print_rank_0('building GPT model ...') + # Experimental loading arguments from yaml + if args.yaml_cfg is not None: + config = core_transformer_config_from_yaml(args, "language_model") + else: + config = core_transformer_config_from_args(args) + + if args.use_legacy_models: + model = megatron.legacy.model.GPTModel( + config, + num_tokentypes=0, + parallel_output=True, + pre_process=pre_process, + post_process=post_process, + ) + else: # using core models + if args.spec is not None: + transformer_layer_spec = import_module(args.spec) + else: + if use_te: + transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(args.num_experts, args.moe_grouped_gemm, args.qk_layernorm, args.fp8) + else: + transformer_layer_spec = get_gpt_layer_local_spec(args.num_experts, args.moe_grouped_gemm, args.qk_layernorm) + + build_model_context = nullcontext + build_model_context_args = {} + if args.fp8_param_gather: + try: + from transformer_engine.pytorch import fp8_model_init + + build_model_context = fp8_model_init + build_model_context_args["enabled"] = True + + # Check if fp8_model_init supports preserve_high_precision_init_val + if "preserve_high_precision_init_val" in inspect.signature(fp8_model_init).parameters: + build_model_context_args["preserve_high_precision_init_val"] = True + except: + raise RuntimeError("--fp8-param-gather requires `fp8_model_init` from TransformerEngine, but not found.") + + with build_model_context(**build_model_context_args): + model = GPTModel( + config=config, + transformer_layer_spec=transformer_layer_spec, + vocab_size=args.padded_vocab_size, + max_sequence_length=args.max_position_embeddings, + pre_process=pre_process, + post_process=post_process, + fp16_lm_cross_entropy=args.fp16_lm_cross_entropy, + parallel_output=True, + share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base + ) + + return model + + +def get_batch(data_iterator): + """Generate a batch.""" + + # TODO: this is pretty hacky, find a better way + if (not mpu.is_pipeline_first_stage()) and (not mpu.is_pipeline_last_stage()): + return None, None, None, None, None + + # get batches based on the TP rank you are on + batch = get_batch_on_this_tp_rank(data_iterator) + + # slice batch along sequence dimension for context parallelism + batch = get_batch_on_this_cp_rank(batch) + + return batch.values() + + +def loss_func(loss_mask: torch.Tensor, output_tensor: torch.Tensor): + """Loss function. + + Args: + loss_mask (torch.Tensor): Used to mask out some portions of the loss + output_tensor (torch.Tensor): The tensor with the losses + + Returns: + the loss scalar for this micro-batch + the number of non-padded tokens in this microbatch + a dict containing reporting metrics on the loss and number of tokens across + the data parallel ranks + """ + args = get_args() + + losses = output_tensor.float() + loss_mask = loss_mask.view(-1).float() + total_tokens = loss_mask.sum() + loss = torch.cat([torch.sum(losses.view(-1) * loss_mask).view(1), total_tokens.view(1)]) + + if args.context_parallel_size > 1: + torch.distributed.all_reduce(loss, group=mpu.get_context_parallel_group()) + + # Check individual rank losses are not NaN prior to DP all-reduce. + if args.check_for_nan_in_loss_and_grad: + global_rank = torch.distributed.get_rank() + assert not loss[0].isnan(), ( + f'Rank {global_rank}: found NaN in local forward loss calculation. ' + f'Device: {torch.cuda.current_device()}, node: {os.uname()[1]}' + ) + + # Reduce loss for logging. + reporting_loss = loss.clone().detach() + torch.distributed.all_reduce(reporting_loss, group=mpu.get_data_parallel_group()) + + local_num_tokens = loss[1].clone().detach().to(torch.int) + return ( + loss[0] * args.context_parallel_size, + local_num_tokens, + {'lm loss': (reporting_loss[0], reporting_loss[1])}, + ) + + +def forward_step(data_iterator, model: GPTModel): + """Forward training step. + + Args: + data_iterator : Input data iterator + model (GPTModel): The GPT Model + """ + args = get_args() + timers = get_timers() + + # Get the batch. + timers('batch-generator', log_level=2).start() + global stimer + with stimer(bdata=True): + tokens, labels, loss_mask, attention_mask, position_ids = get_batch( + data_iterator) + timers('batch-generator').stop() + + with stimer: + output_tensor = model(tokens, position_ids, attention_mask, + labels=labels) + + return output_tensor, partial(loss_func, loss_mask) + + +def is_dataset_built_on_rank(): + return ( + mpu.is_pipeline_first_stage() or mpu.is_pipeline_last_stage() + ) and mpu.get_tensor_model_parallel_rank() == 0 + + +def core_gpt_dataset_config_from_args(args): + tokenizer = get_tokenizer() + + return GPTDatasetConfig( + random_seed=args.seed, + sequence_length=args.seq_length, + blend=get_blend_from_list(args.data_path), + blend_per_split=[ + get_blend_from_list(args.train_data_path), + get_blend_from_list(args.valid_data_path), + get_blend_from_list(args.test_data_path) + ], + renormalize_blend_weights=args.renormalize_blend_weights, + split=args.split, + num_dataset_builder_threads=args.num_dataset_builder_threads, + path_to_cache=args.data_cache_path, + mmap_bin_files=args.mmap_bin_files, + tokenizer=tokenizer, + reset_position_ids=args.reset_position_ids, + reset_attention_mask=args.reset_attention_mask, + eod_mask_loss=args.eod_mask_loss, + create_attention_mask=args.create_attention_mask_in_dataloader, + s3_cache_path = args.s3_cache_path + ) + + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build the train test and validation datasets. + + Args: + train_val_test_num_samples : A list containing the number of samples in train test and validation. + """ + args = get_args() + + config = core_gpt_dataset_config_from_args(args) + + if args.mock_data: + dataset_type = MockGPTDataset + else: + dataset_type = GPTDataset + + print_rank_0("> building train, validation, and test datasets for GPT ...") + + train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder( + dataset_type, + train_val_test_num_samples, + is_dataset_built_on_rank, + config + ).build() + + print_rank_0("> finished creating GPT datasets ...") + + return train_ds, valid_ds, test_ds + + +if __name__ == "__main__": + + # Temporary for transition to core datasets + train_valid_test_datasets_provider.is_distributed = True + + pretrain( + train_valid_test_datasets_provider, + model_provider, + ModelType.encoder_or_decoder, + forward_step, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, + ) diff --git a/xpu_timer/experiments/scripts/pretrain_ict.py b/xpu_timer/experiments/scripts/pretrain_ict.py new file mode 100644 index 0000000000..205588b5e9 --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_ict.py @@ -0,0 +1,166 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Pretrain BERT for Inverse Cloze Task""" + +from functools import partial +import math + +import torch +import torch.distributed as dist +import torch.nn.functional as F + +from megatron.training import get_args +from megatron.training import print_rank_0 +from megatron.training import get_timers +from megatron.core import mpu +from megatron.core.enums import ModelType +from megatron.legacy.data.biencoder_dataset_utils import get_ict_batch +from megatron.legacy.data.dataset_utils import build_train_valid_test_datasets +from megatron.legacy.model.biencoder_model import biencoder_model_provider +from megatron.training import pretrain +from megatron.training.utils import average_losses_across_data_parallel_group + + +def pretrain_ict_model_provider(pre_process=True, post_process=True): + args = get_args() + + model = biencoder_model_provider( + only_context_model=False, + only_query_model=False, + biencoder_shared_query_context_model=\ + args.biencoder_shared_query_context_model, + pre_process=pre_process, post_process=post_process) + + return model + +def get_group_world_size_rank(): + + group = mpu.get_data_parallel_group() + rank = torch.distributed.get_rank(group=group) + world_size = torch.distributed.get_world_size(group=group) + + return group, rank, world_size + + +class AllgatherFromDataParallelRegion(torch.autograd.Function): + + @staticmethod + def forward(ctx, input_): + assert input_.dim() == 2 + group, rank, world_size = get_group_world_size_rank() + + tensor_list = [torch.empty_like(input_) for _ in range(world_size)] + tensor_list[rank] = input_ + torch.distributed.all_gather(tensor_list, input_, group=group) + + output = torch.cat(tensor_list, dim=0).contiguous() + + return output + + + @staticmethod + def backward(ctx, grad_output): + group, rank, world_size = get_group_world_size_rank() + + assert grad_output.shape[0] % world_size == 0 + dim_size = grad_output.shape[0] // world_size + output_list = torch.split(grad_output, dim_size, dim=0) + + # get chunk from this rank + output = output_list[rank].contiguous() + return output + +def loss_func(output_tensor): + args = get_args() + query_logits, context_logits = output_tensor + + micro_batch_size = query_logits.shape[0] + # recall we assert that tensor_model_parallel_size == 1 + assert mpu.get_tensor_model_parallel_world_size() == 1, \ + "Model parallel size > 1 not supported for ICT" + + global_batch_size = dist.get_world_size() * micro_batch_size + all_query_logits = AllgatherFromDataParallelRegion.apply(query_logits) + all_context_logits = AllgatherFromDataParallelRegion.apply(context_logits) + + # scores are inner products between query and context embeddings + retrieval_scores = torch.matmul(all_query_logits, + torch.transpose(all_context_logits, 0, 1)) + # scaling the retriever scores + if args.retriever_score_scaling: + retrieval_scores = retrieval_scores / math.sqrt(args.hidden_size) + + softmax_scores = F.log_softmax(retrieval_scores, dim=1) + sorted_vals, sorted_indices = torch.topk(softmax_scores, + k=softmax_scores.shape[1], sorted=True) + + def topk_accuracy(k): + return torch.cuda.FloatTensor([sum([int(i in sorted_indices[i, :k]) \ + for i in range(global_batch_size)]) / global_batch_size]) + + topk_accs = [topk_accuracy(int(k)) for k in args.retriever_report_topk_accuracies] + + labels = torch.arange(global_batch_size).long().cuda() + loss = F.nll_loss(softmax_scores, labels, reduction='mean') + reduced_losses = average_losses_across_data_parallel_group([loss, *topk_accs]) + + # Scale the retrieval loss + loss = loss * mpu.get_data_parallel_world_size() + + # create stats_dict with retrieval loss and all specified top-k accuracies + topk_acc_dict = {'top{}_acc'.format(k): v * 100 for k, v in \ + zip(args.retriever_report_topk_accuracies, reduced_losses[1:])} + stats_dict = dict(loss=reduced_losses[0], **topk_acc_dict) + return loss, stats_dict + + + +def forward_step(data_iterator, model): + """Forward step.""" + args = get_args() + timers = get_timers() + + # Get the batch. + timers('batch-generator', log_level=2).start() + query_tokens, query_mask, \ + context_tokens, context_mask, context_indices = get_ict_batch(data_iterator) + timers('batch-generator').stop() + + # Query and Context Types + query_types = torch.cuda.LongTensor(*query_tokens.shape).fill_(0) + context_types = torch.cuda.LongTensor(*context_tokens.shape).fill_(0) + + # Forward model. + output_tensor = model(query_tokens, query_mask, query_types, context_tokens, + context_mask, context_types) + + return output_tensor, partial(loss_func) + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build train, valid and test datasets.""" + args = get_args() + print_rank_0('> building train, validation, and test datasets ' + 'for BERT ICT...') + + train_ds, valid_ds, test_ds = build_train_valid_test_datasets( + data_prefix=args.data_path, + splits_string=args.split, + train_valid_test_num_samples=train_val_test_num_samples, + max_seq_length=args.seq_length, + masked_lm_prob=args.mask_prob, + short_seq_prob=args.short_seq_prob, + seed=args.seed, + binary_head=False, + dataset_type='ict') + print_rank_0("> finished creating BERT ICT datasets ...") + + return train_ds, valid_ds, test_ds + + +if __name__ == "__main__": + print_rank_0("WARNING : This script is DEPRECATED. Will be removed in mcore release 0.9") + pretrain(train_valid_test_datasets_provider, + pretrain_ict_model_provider, + ModelType.encoder_or_decoder, + forward_step, + args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}) diff --git a/xpu_timer/experiments/scripts/pretrain_mamba.py b/xpu_timer/experiments/scripts/pretrain_mamba.py new file mode 100644 index 0000000000..f8202b6eac --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_mamba.py @@ -0,0 +1,242 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +"""Pretrain Mamba.""" + +import os +import torch +from functools import partial + +from megatron.training import get_args +from megatron.training import print_rank_0 +from megatron.training import get_timers +from megatron.training import get_tokenizer +from megatron.core import mpu +# from megatron.core import parallel_state +from megatron.core.enums import ModelType +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.utils import get_blend_from_list +from megatron.core.datasets.gpt_dataset import GPTDatasetConfig +from megatron.core.datasets.gpt_dataset import MockGPTDataset, GPTDataset +from megatron.core.models.mamba import MambaModel +from megatron.training import pretrain +from megatron.core.utils import StragglerDetector +from megatron.core.transformer.spec_utils import import_module +from megatron.training.utils import ( + get_batch_on_this_cp_rank, + get_batch_on_this_tp_rank, +) +from megatron.training.arguments import core_transformer_config_from_args +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec + + +stimer = StragglerDetector() + +def count_parameters_in_layer(model, layer_name): + num_params = 0 + for name, param in model.named_parameters(): + if layer_name in name: + num_params += param.numel() + print_rank_0(f" - {name}: {param.numel()}") + return num_params + + +def model_provider(pre_process=True, post_process=True) -> MambaModel: + """Builds the model. + + Args: + pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True. + post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True. + + + Returns: + MambaModel: The returned model + """ + args = get_args() + + print_rank_0('building Mamba model ...') + config = core_transformer_config_from_args(get_args()) + + assert args.use_legacy_models == False, "Mamba only supported in Mcore!" + + if args.spec is not None: + mamba_stack_spec = import_module(args.spec) + else: + raise("You must provide a valid Mamba layer spec!") + + model = MambaModel( + config=config, + mamba_stack_spec=mamba_stack_spec, + vocab_size=args.padded_vocab_size, + max_sequence_length=args.max_position_embeddings, + pre_process=pre_process, + hybrid_attention_ratio=args.hybrid_attention_ratio, + hybrid_mlp_ratio=args.hybrid_mlp_ratio, + hybrid_override_pattern=args.hybrid_override_pattern, + post_process=post_process, + fp16_lm_cross_entropy=args.fp16_lm_cross_entropy, + parallel_output=True, + share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base + ) + + for l in range(model.decoder.num_layers_per_pipeline_rank): + layer_params = count_parameters_in_layer(model, f'decoder.layers.{l}.') + print_rank_0(f" == params layer {l}: {layer_params}") + + return model + + +def get_batch(data_iterator): + """Generate a batch.""" + + # TODO: this is pretty hacky, find a better way + if (not mpu.is_pipeline_first_stage()) and (not mpu.is_pipeline_last_stage()): + return None, None, None, None, None + + # get batches based on the TP rank you are on + batch = get_batch_on_this_tp_rank(data_iterator) + + # slice batch along sequence dimension for context parallelism + batch = get_batch_on_this_cp_rank(batch) + + return batch.values() + +def loss_func(loss_mask: torch.Tensor, output_tensor: torch.Tensor): + """Loss function. + + Args: + loss_mask (torch.Tensor): Used to mask out some portions of the loss + output_tensor (torch.Tensor): The tensor with the losses + + Returns: + the loss scalar for this micro-batch + the number of non-padded tokens in this microbatch + a dict containing reporting metrics on the loss and number of tokens across + the data parallel ranks + """ + args = get_args() + + losses = output_tensor.float() + loss_mask = loss_mask.view(-1).float() + total_tokens = loss_mask.sum() + loss = torch.cat([torch.sum(losses.view(-1) * loss_mask).view(1), total_tokens.view(1)]) + + if args.context_parallel_size > 1: + torch.distributed.all_reduce(loss, group=mpu.get_context_parallel_group()) + + # Check individual rank losses are not NaN prior to DP all-reduce. + if args.check_for_nan_in_loss_and_grad: + global_rank = torch.distributed.get_rank() + assert not loss[0].isnan(), ( + f'Rank {global_rank}: found NaN in local forward loss calculation. ' + f'Device: {torch.cuda.current_device()}, node: {os.uname()[1]}' + ) + + # Reduce loss for logging. + reporting_loss = loss.clone().detach() + torch.distributed.all_reduce(reporting_loss, group=mpu.get_data_parallel_group()) + + local_num_tokens = loss[1].clone().detach().to(torch.int) + return ( + loss[0] * args.context_parallel_size, + local_num_tokens, + {'lm loss': (reporting_loss[0], reporting_loss[1])}, + ) + + +def forward_step(data_iterator, model: MambaModel): + """Forward training step. + + Args: + data_iterator : Input data iterator + model (MambaModel): The GPT Model + """ + args = get_args() + timers = get_timers() + + # Get the batch. + timers('batch-generator', log_level=2).start() + global stimer + with stimer(bdata=True): + tokens, labels, loss_mask, attention_mask, position_ids = get_batch( + data_iterator) + timers('batch-generator').stop() + + with stimer: + output_tensor = model(tokens, position_ids, attention_mask, + labels=labels) + + return output_tensor, partial(loss_func, loss_mask) + + +def is_dataset_built_on_rank(): + return ( + mpu.is_pipeline_first_stage() or mpu.is_pipeline_last_stage() + ) and mpu.get_tensor_model_parallel_rank() == 0 + + +def core_gpt_dataset_config_from_args(args): + tokenizer = get_tokenizer() + + return GPTDatasetConfig( + random_seed=args.seed, + sequence_length=args.seq_length, + blend=get_blend_from_list(args.data_path), + blend_per_split=[ + get_blend_from_list(args.train_data_path), + get_blend_from_list(args.valid_data_path), + get_blend_from_list(args.test_data_path) + ], + renormalize_blend_weights=args.renormalize_blend_weights, + split=args.split, + num_dataset_builder_threads=args.num_dataset_builder_threads, + path_to_cache=args.data_cache_path, + mmap_bin_files=args.mmap_bin_files, + tokenizer=tokenizer, + reset_position_ids=args.reset_position_ids, + reset_attention_mask=args.reset_attention_mask, + eod_mask_loss=args.eod_mask_loss, + create_attention_mask=args.create_attention_mask_in_dataloader, + ) + + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build the train test and validation datasets. + + Args: + train_val_test_num_samples : A list containing the number of samples in train test and validation. + """ + args = get_args() + + config = core_gpt_dataset_config_from_args(args) + + if args.mock_data: + dataset_type = MockGPTDataset + else: + dataset_type = GPTDataset + + print_rank_0("> building train, validation, and test datasets for GPT ...") + + train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder( + dataset_type, + train_val_test_num_samples, + is_dataset_built_on_rank, + config + ).build() + + print_rank_0("> finished creating GPT datasets ...") + + return train_ds, valid_ds, test_ds + + +if __name__ == "__main__": + + # Temporary for transition to core datasets + train_valid_test_datasets_provider.is_distributed = True + + pretrain(train_valid_test_datasets_provider, + model_provider, + ModelType.encoder_or_decoder, + forward_step, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) diff --git a/xpu_timer/experiments/scripts/pretrain_retro.py b/xpu_timer/experiments/scripts/pretrain_retro.py new file mode 100644 index 0000000000..0aecbf14ce --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_retro.py @@ -0,0 +1,245 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. + +"""Pretrain Retro.""" + +from functools import partial +import torch + +from megatron.training import get_args +from megatron.training import get_timers +from megatron.training import get_tokenizer +from megatron.training import print_rank_0 +from megatron.training.arguments import core_transformer_config_from_args +from megatron.core import tensor_parallel +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.utils import get_blend_from_list +from megatron.core.datasets.retro.query.retro_dataset import get_retro_datasets +from megatron.core.datasets.retro.query.multi_split_gpt_dataset import MultiSplitGPTDataset, MultiSplitGPTDatasetConfig +from megatron.core.enums import ModelType +from megatron.core.models.retro import get_retro_decoder_block_spec, RetroConfig, RetroModel +from megatron.core.models.retro.utils import get_all_true_mask +from megatron.training import pretrain +from megatron.training.utils import get_ltor_masks_and_position_ids +from pretrain_gpt import ( + is_dataset_built_on_rank, + loss_func, + model_provider as default_model_provider, + train_valid_test_datasets_provider as gpt_train_valid_test_datasets_provider, +) + + +def get_retro_config(): + return core_transformer_config_from_args(get_args(), RetroConfig) + + +def core_model_provider(pre_process=True, post_process=True): + """Build the model using Megatron-Core.""" + + args = get_args() + config = get_retro_config() + + # NOTE: Experimental customization feature + if args.spec is not None: + block_spec = import_module(args.spec)() + else: + block_spec = get_retro_decoder_block_spec(config, use_transformer_engine=True) + + print_rank_0('building GPT model ...') + model = RetroModel( + config=config, + transformer_layer_spec=block_spec, + vocab_size=args.padded_vocab_size, + max_sequence_length=args.max_position_embeddings, + pre_process=pre_process, + post_process=post_process, + fp16_lm_cross_entropy=args.fp16_lm_cross_entropy, + parallel_output=True, + share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent + ) + return model + + +def model_provider(pre_process=True, post_process=True): + """Build the model. + + Select between two different model classes: + 1. Default model (uses megatron.legacy.models/gpt_model.py). + 2. Core model (uses megatron/core/models/retro/model.py). + """ + + args = get_args() + if not args.use_legacy_models and args.retro_add_retriever: + provider = core_model_provider + else: + provider = default_model_provider + model = provider(pre_process=pre_process, post_process=post_process) + return model + + +def get_batch(data_iterator): + """Generate a batch""" + + args = get_args() + tokenizer = get_tokenizer() + config = get_retro_config() + + # Items and their type. + keys = ['text'] + if args.retro_add_retriever: + keys.append('neighbor_tokens') + datatype = torch.int64 + + # Broadcast data. + if data_iterator is not None: + data = next(data_iterator) + else: + data = None + + data_b = tensor_parallel.broadcast_data(keys, data, datatype) + + # Unpack. + tokens_ = data_b['text'].long() + labels = tokens_[:, 1:].contiguous() + tokens = tokens_[:, :-1].contiguous() + + # Get the masks and postition ids. + attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( + tokens, + tokenizer.eod, + args.reset_position_ids, + args.reset_attention_mask, + args.eod_mask_loss) + + if args.retro_add_retriever: + # note: [bs * l * k, r] + # note: 2x == neighbor, continuation + neighbor_tokens = data_b['neighbor_tokens'] \ + .view(-1, config.retro_retrieved_length).long() + _, _, neighbor_position_ids = get_ltor_masks_and_position_ids( + neighbor_tokens, + tokenizer.eod, + args.reset_position_ids, + args.reset_attention_mask, + args.eod_mask_loss) + neighbor_attention_mask = get_all_true_mask( + (1, 1, config.retro_retrieved_length, config.retro_retrieved_length), + neighbor_tokens.device) + return tokens, labels, loss_mask, attention_mask, position_ids, \ + neighbor_tokens, neighbor_attention_mask, neighbor_position_ids + + else: + return tokens, labels, loss_mask, attention_mask, position_ids + + +def forward_step(data_iterator, model): + """Forward step.""" + args = get_args() + timers = get_timers() + + # Get the batch. + timers('batch-generator').start() + if args.retro_add_retriever: + tokens, labels, loss_mask, attention_mask, position_ids, \ + neighbor_tokens, neighbor_attention_mask, neighbor_position_ids = \ + get_batch(data_iterator) + else: + tokens, labels, loss_mask, attention_mask, position_ids = get_batch( + data_iterator) + neighbor_tokens, neighbor_attention_mask, neighbor_position_ids = \ + None, None, None + timers('batch-generator').stop() + + # Model call. + if args.use_legacy_models: + forward_kwargs = { + "retriever_input_ids" : neighbor_tokens, + "retriever_position_ids" : neighbor_position_ids, + "retriever_attn_mask" : neighbor_attention_mask, + } + else: + if args.retro_add_retriever: + forward_kwargs = { + "context_input_ids" : neighbor_tokens, + "context_position_ids" : neighbor_position_ids, + "context_mask" : neighbor_attention_mask, + } + else: + forward_kwargs = {} + + output_tensor = model(tokens, position_ids, attention_mask, + labels=labels, **forward_kwargs) + + return output_tensor, partial(loss_func, loss_mask) + + +def train_valid_test_datasets_provider(train_valid_test_num_samples): + """Build train, valid, and test datasets.""" + args = get_args() + + # Dataset config. + retro_config = get_retro_config() + data_config = MultiSplitGPTDatasetConfig( + random_seed=args.seed, + sequence_length=args.seq_length, + blend=get_blend_from_list(args.data_path), + blend_per_split=[ + get_blend_from_list(args.train_data_path), + get_blend_from_list(args.valid_data_path), + get_blend_from_list(args.test_data_path) + ], + renormalize_blend_weights=args.renormalize_blend_weights, + split=args.split, + split_preprocessing=retro_config.retro_split_preprocessing, + path_to_cache=args.data_cache_path, + return_document_ids=False, + tokenizer=get_tokenizer(), + reset_position_ids=args.reset_position_ids, + reset_attention_mask=args.reset_attention_mask, + eod_mask_loss=args.eod_mask_loss, + ) + + # GPT datasets. + print_rank_0(" > multi-split gpt datasets.") + train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder( + MultiSplitGPTDataset, + train_valid_test_num_samples, + is_dataset_built_on_rank, + data_config, + ).build() + + gpt_datasets = { + "train" : (train_ds, train_valid_test_num_samples[0]), + "valid" : (valid_ds, train_valid_test_num_samples[1]), + "test" : (test_ds, train_valid_test_num_samples[2]), + } + + # Retro datasets. + if args.retro_add_retriever: + return get_retro_datasets( + config=retro_config, + gpt_datasets=gpt_datasets, + sample_length=args.seq_length, + eod_token_id=get_tokenizer().eod, + ) + + # Multi-split GPT datasets. + else: + return ( + gpt_datasets["train"][0], + gpt_datasets["valid"][0], + gpt_datasets["test"][0], + ) + + +if __name__ == "__main__": + + # Temporary for transition to core datasets. + train_valid_test_datasets_provider.is_distributed = True + + pretrain(train_valid_test_datasets_provider, + model_provider, + ModelType.retro_decoder, + forward_step, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) diff --git a/xpu_timer/experiments/scripts/pretrain_t5.py b/xpu_timer/experiments/scripts/pretrain_t5.py new file mode 100644 index 0000000000..253d4b19c6 --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_t5.py @@ -0,0 +1,299 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Pretrain T5""" + +from copy import deepcopy +from functools import partial +from typing import Union + +import torch + +from megatron.training import ( + get_args, + get_timers, + get_tokenizer, + print_rank_0 +) +from megatron.core import mpu, tensor_parallel +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.t5_dataset import ( + T5MaskedWordPieceDataset, + T5MaskedWordPieceDatasetConfig, +) +from megatron.core.enums import ModelType +from megatron.core.models.T5 import T5Model +from megatron.training import pretrain +from megatron.training.arguments import core_transformer_config_from_args +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.t5_dataset import T5MaskedWordPieceDataset, T5MaskedWordPieceDatasetConfig +from megatron.core.datasets.utils import get_blend_from_list +from megatron.core.models.T5.t5_spec import (get_t5_encoder_with_transformer_engine_block_spec, + get_t5_decoder_with_transformer_engine_block_spec, + get_t5_encoder_with_local_block_spec, + get_t5_decoder_with_local_block_spec) +from megatron.legacy.model import T5Model as LegacyT5Model +from pretrain_gpt import loss_func + +""" +Pipeline parallelism for T5 + +T5 is a model architecture with both encoder and decoder blocks. +Consequently, pipeline parallelism is implemented slightly differently +compared to architectures like GPT and BERT. + +In particular, when pipeline_model_parallel_world_size > 1, each stage +either executes an encoder block or a decoder block. The +--pipeline-model-parallel-split-rank argument controls the rank at which +the split happens: all ranks lower than this argument execute the +encoder block, and all ranks equal to or higher than this argument value +execute the decoder block. + +In the encoder section of the model, only one tensor is sent downstream: +the intermediate encoder_hidden_state. In the decoder section of the +model, two tensors are sent downstream in the forward pass: the fully +computed encoder_hidden_state, and the intermediate decoder_hidden_state. + +In particular, these are the shapes of the tensors sent between +different workers: + If rank is in decoder section: + intermediate decoder_hidden_state (pre-transpose), + complete encoder_hidden_state (post-transpose). + If rank is at boundary between encoder and decoder sections: + complete encoder_hidden_state (post-transpose). + If rank is in encoder section: + intermediate encoder_hidden_state (pre-transpose). + +Additionally, we have code in the backward_step function in schedules.py +to accumulate the encoder_hidden_state gradient across skip connections +(encoder_hidden_state fed in as input to each layer in the decoder). +""" + + +def model_provider( + pre_process=True, post_process=True, add_encoder=True, add_decoder=True +) -> Union[LegacyT5Model, T5Model]: + """Builds the model. + + Args: + pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True. + post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True. + add_encoder (bool, optional): Defaults to True + add_decoder (bool, optional): Defaults to True + Returns: + T5Model: The returned T5 model + """ + + args = get_args() + + assert ( + args.encoder_tensor_model_parallel_size == 0 or + args.encoder_tensor_model_parallel_size == args.tensor_model_parallel_size + ), f"Because word embeddings are shared between the encoder & decoder, these have to have the same tensor parallel size." + + config = core_transformer_config_from_args(args) + if args.use_legacy_models: + model = LegacyT5Model( + config=config, + num_tokentypes=0, + parallel_output=True, + pre_process=pre_process, + post_process=post_process, + add_encoder=add_encoder, + add_decoder=add_decoder, + ) + else: + encoder_config = deepcopy(config) + encoder_config.num_layers = args.encoder_num_layers + + if args.pipeline_model_parallel_size > 1: + assert args.encoder_pipeline_model_parallel_size > 0, "Need to know how to shard the encoder & decoder." + + if args.encoder_pipeline_model_parallel_size > 0: + encoder_config.pipeline_model_parallel_size = args.encoder_pipeline_model_parallel_size + + encoder_layers_per_pipeline = encoder_config.num_layers // encoder_config.pipeline_model_parallel_size + decoder_layers_per_pipeline = config.num_layers // config.pipeline_model_parallel_size + + if args.transformer_impl == "local": + en_block_spec = get_t5_encoder_with_local_block_spec(encoder_layers_per_pipeline) + de_block_spec = get_t5_decoder_with_local_block_spec(decoder_layers_per_pipeline) + elif args.transformer_impl == "transformer_engine": + en_block_spec = get_t5_encoder_with_transformer_engine_block_spec( + encoder_layers_per_pipeline + ) + de_block_spec = get_t5_decoder_with_transformer_engine_block_spec( + decoder_layers_per_pipeline + ) + + print_rank_0('building T5 model ...') + model = T5Model( + config=config, + encoder_config=encoder_config, + transformer_encoder_layer_spec=en_block_spec, + transformer_decoder_layer_spec=de_block_spec, + vocab_size=args.padded_vocab_size, + max_sequence_length=args.max_position_embeddings, + pre_process=pre_process, + post_process=post_process, + fp16_lm_cross_entropy=args.fp16_lm_cross_entropy, + parallel_output=True, + share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + add_encoder=add_encoder, + add_decoder=add_decoder + ) + + return model + + +def get_batch(data_iterator): + """Build the batch.""" + + keys = ['text_enc', 'text_dec', 'labels', 'loss_mask', 'enc_mask', 'dec_mask', 'enc_dec_mask'] + datatype = torch.int64 + + # Broadcast data. + if data_iterator is not None: + data = next(data_iterator) + else: + data = None + data_b = tensor_parallel.broadcast_data(keys, data, datatype) + + # Unpack. + tokens_enc = data_b['text_enc'].long() + tokens_dec = data_b['text_dec'].long() + labels = data_b['labels'].long() + loss_mask = data_b['loss_mask'].float() + + enc_mask = data_b['enc_mask'] < 0.5 + dec_mask = data_b['dec_mask'] < 0.5 + enc_dec_mask = data_b['enc_dec_mask'] < 0.5 + + return tokens_enc, tokens_dec, loss_mask, labels, enc_mask, dec_mask, enc_dec_mask + + +def forward_step(data_iterator, model: T5Model): + """Forward training step. + + Args: + data_iterator : Input data iterator + model (T5Model): The T5 Model + """ + + args = get_args() + timers = get_timers() + + # Get the batch. + timers('batch generator', log_level=2).start() + tokens_enc, tokens_dec, loss_mask, lm_labels, enc_mask, dec_mask, enc_dec_mask = get_batch( + data_iterator + ) + timers('batch generator').stop() + + # Forward model lm_labels + output_tensor = model( + tokens_enc, tokens_dec, enc_mask, dec_mask, enc_dec_mask, lm_labels=lm_labels + ) + + return output_tensor, partial(loss_func, loss_mask) + + +def train_valid_test_datasets_provider(train_val_test_num_samples: int): + """Build the train test and validation datasets. + + Args: + train_val_test_num_samples : A list containing the number of samples in train test and validation. + """ + args = get_args() + + tokenizer = get_tokenizer() + + config = T5MaskedWordPieceDatasetConfig( + random_seed=args.seed, + sequence_length=args.encoder_seq_length, + sequence_length_decoder=args.decoder_seq_length, + blend=get_blend_from_list(args.data_path), + blend_per_split=[ + get_blend_from_list(args.train_data_path), + get_blend_from_list(args.valid_data_path), + get_blend_from_list(args.test_data_path) + ], + renormalize_blend_weights=args.renormalize_blend_weights, + split=args.split, + path_to_cache=args.data_cache_path, + tokenizer=tokenizer, + masking_probability=args.mask_prob, + short_sequence_probability=args.short_seq_prob, + masking_max_ngram=10, + masking_do_full_word=True, + masking_do_permutation=False, + masking_use_longer_ngrams=False, + masking_use_geometric_distribution=True, + ) + + print_rank_0('> building train, validation, and test datasets for T5 ...') + + train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder( + T5MaskedWordPieceDataset, + train_val_test_num_samples, + lambda: mpu.get_tensor_model_parallel_rank() == 0, + config, + ).build() + + print_rank_0("> finished creating T5 datasets ...") + + return train_ds, valid_ds, test_ds + + +def t5_embedding_ranks(pp_ranks): + """T5's embedding ranks consist of the encoder's first rank, and the decoder's first & last ranks. + Args: + pp_ranks: A list of global ranks that constitute a pipeline group. + """ + args = get_args() + + first_rank = pp_ranks[0] + last_rank = pp_ranks[-1] + + # encoder size is also the index to the first rank of the decoder. + epp = args.encoder_pipeline_model_parallel_size + + if len(pp_ranks) == 1: + return [first_rank] + elif pp_ranks[epp] not in (first_rank, last_rank): + return [first_rank, pp_ranks[epp], last_rank] + else: + return [first_rank, last_rank] + + +def t5_position_embedding_ranks(pp_ranks): + """T5's positional embeddings are the encoder & decoder first rank stages + Args: + pp_ranks: A list of global ranks that constitute a pipeline group. + """ + args = get_args() + + # encoder size is also the index to the first rank of the decoder. + epp = args.encoder_pipeline_model_parallel_size + + if len(pp_ranks) == 1 or pp_ranks[0] == pp_ranks[epp]: + return [pp_ranks[0]] + else: + return [pp_ranks[0], pp_ranks[epp]] + + +if __name__ == "__main__": + + # Temporary for transition to core datasets + train_valid_test_datasets_provider.is_distributed = True + + pretrain( + train_valid_test_datasets_provider, + model_provider, + ModelType.encoder_and_decoder, + forward_step, + args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}, + get_embedding_ranks=t5_embedding_ranks, + get_position_embedding_ranks=t5_position_embedding_ranks, + ) diff --git a/xpu_timer/experiments/scripts/pretrain_vision_classify.py b/xpu_timer/experiments/scripts/pretrain_vision_classify.py new file mode 100644 index 0000000000..8d9b28baeb --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_vision_classify.py @@ -0,0 +1,105 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Pretrain VIT""" + +import torch +import torch.nn.functional as F +from functools import partial +from megatron.training import get_args, get_timers, print_rank_0 +from megatron.core.enums import ModelType +from megatron.legacy.data.vit_dataset import build_train_valid_datasets +from megatron.legacy.model.vision.classification import VitClassificationModel +from megatron.legacy.model.vision.classification import MitClassificationModel +from megatron.training import pretrain +from megatron.training.utils import average_losses_across_data_parallel_group +from megatron.training.arguments import core_transformer_config_from_args + + +def model_provider(pre_process=True, post_process=True): + """Build the model.""" + + args = get_args() + config = core_transformer_config_from_args(args) + if args.vision_backbone_type == 'vit': + print_rank_0("building VIT model ...") + model = VitClassificationModel(config=config, + num_classes=args.num_classes, + pre_process=pre_process, + post_process=post_process) + elif args.vision_backbone_type == 'mit': + print_rank_0("building MIT model ...") + model = MitClassificationModel(num_classes=args.num_classes, + pre_process=pre_process, + post_process=post_process) + else: + raise Exception('{} vision backbone is not supported.'.format( + args.vision_backbone_type)) + return model + + +def get_batch(data_iterator): + """Build the batch.""" + data = next(data_iterator) + + # only data parallelism; no need for broadcast + images = data[0].cuda() + labels = data[1].cuda() + + return images, labels + + +def loss_func(labels, output_tensor): + logits = output_tensor.contiguous().float() + loss = F.cross_entropy(logits, labels) + + outputs = torch.argmax(logits, -1) + correct = (outputs == labels).float() + accuracy = torch.mean(correct) + + averaged_loss = average_losses_across_data_parallel_group([loss, accuracy]) + + return loss, {"loss": averaged_loss[0], "accuracy": averaged_loss[1]} + + +def forward_step(data_iterator, model): + """Forward step.""" + timers = get_timers() + + # Get the batch. + timers("batch-generator", log_level=2).start() + ( + images, + labels, + ) = get_batch(data_iterator) + timers("batch-generator").stop() + + # Forward model. lm_labels + output_tensor = model(images) + + return output_tensor, partial(loss_func, labels) + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build train, valid, and test datasets.""" + args = get_args() + + print_rank_0( + "> building train, validation, and test datasets " "for VIT ..." + ) + train_ds, valid_ds = build_train_valid_datasets( + data_path=args.data_path, + image_size=(args.img_h, args.img_w) + ) + print_rank_0("> finished creating VIT datasets ...") + + return train_ds, valid_ds, None + + +if __name__ == "__main__": + + pretrain( + train_valid_test_datasets_provider, + model_provider, + ModelType.encoder_or_decoder, + forward_step, + args_defaults={'dataloader_type': 'cyclic', 'vision_pretraining': True} + ) diff --git a/xpu_timer/experiments/scripts/pretrain_vision_dino.py b/xpu_timer/experiments/scripts/pretrain_vision_dino.py new file mode 100644 index 0000000000..f75280c42d --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_vision_dino.py @@ -0,0 +1,105 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +import torch +import torch.nn.functional as F +import torch.nn as nn +import numpy as np +import torch.distributed as dist +from functools import partial +from megatron.training import get_args, get_timers, print_rank_0 +from megatron.core.enums import ModelType +from megatron.legacy.data.vit_dataset import build_train_valid_datasets +from megatron.legacy.model.vision.dino import DINOPretrainModel +from megatron.legacy.model.vision.knn_monitor import knn_predict, get_feature_bank +from megatron.training import pretrain +from megatron.training.utils import average_losses_across_data_parallel_group, unwrap_model +from megatron.training.arguments import core_transformer_config_from_args + +def model_provider(pre_process=True, post_process=True): + """Build the model.""" + config = core_transformer_config_from_args(get_args()) + return DINOPretrainModel(config, pre_process=pre_process, post_process=post_process) + +def get_batch(data_iterator): + """Build the batch.""" + data = next(data_iterator) + + # only data parallelism; no need for broadcast + if isinstance(data[0], list): + images = [aug.cuda() for aug in data[0]] + else: + images = data[0].cuda() + labels = data[1].cuda() + + return images, labels + + +def loss_func(model, labels, output_tensor, collect_data=False): + args = get_args() + + model = unwrap_model(model) + if model.training: + student_output, teacher_output = output_tensor + loss = model.dino_loss(student_output, teacher_output, args.curr_iteration) + averaged_loss = average_losses_across_data_parallel_group([loss]) + return loss, {"loss": averaged_loss[0]} + else: + _, teacher_feature = output_tensor + feature_bank, feature_labels, classes = get_feature_bank() + feature = F.normalize(teacher_feature.float(), dim=1) + + knn_accs = [] + for k in [10, 20, 100, 200]: + pred_labels = knn_predict(feature, feature_bank, + feature_labels, classes, k, 0.07) + knn_acc = (pred_labels[:, 0] == labels).float().mean() + knn_accs.append(knn_acc) + + averaged_loss = average_losses_across_data_parallel_group(knn_accs) + return 0, {"knn_acc_10": averaged_loss[0], + "knn_acc_20": averaged_loss[1], + "knn_acc_100": averaged_loss[2], + "knn_acc_200": averaged_loss[3]} + + +def forward_step(data_iterator, model): + """Forward step.""" + timers = get_timers() + + # Get the batch. + timers("batch-generator", log_level=2).start() + ( + images, + labels, + ) = get_batch(data_iterator) + timers("batch-generator").stop() + + return model(images), partial(loss_func, model, labels) + + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build train, valid, and test datasets.""" + args = get_args() + + print_rank_0( + "> building train, validation, and test datasets " "for VIT ..." + ) + train_ds, valid_ds = build_train_valid_datasets( + data_path=args.data_path, + image_size=(args.img_h, args.img_w) + ) + print_rank_0("> finished creating VIT datasets ...") + + return train_ds, valid_ds, None + + +if __name__ == "__main__": + + pretrain( + train_valid_test_datasets_provider, + model_provider, + ModelType.encoder_or_decoder, + forward_step, + args_defaults={'dataloader_type': 'cyclic', 'vision_pretraining': True} + ) + diff --git a/xpu_timer/experiments/scripts/pretrain_vision_inpaint.py b/xpu_timer/experiments/scripts/pretrain_vision_inpaint.py new file mode 100644 index 0000000000..8570baab5b --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_vision_inpaint.py @@ -0,0 +1,141 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""Pretrain VIT""" + +import torch +import torch.nn.functional as F +from functools import partial +from megatron.training import get_args, get_timers, print_rank_0, print_rank_last +from megatron.core.enums import ModelType +from megatron.legacy.data.vit_dataset import build_train_valid_datasets +from megatron.legacy.model.vision.inpainting import VitInpaintingModel +from megatron.legacy.model.vision.inpainting import MitInpaintingModel +from megatron.training import pretrain +from megatron.training.utils import average_losses_across_data_parallel_group +from tasks.vision.segmentation.metrics import SSIM, PSNR +from megatron.training.arguments import core_transformer_config_from_args + +def model_provider(pre_process=True, post_process=True): + """Build the model.""" + args = get_args() + config = core_transformer_config_from_args(args) + if args.vision_backbone_type == 'vit': + model = VitInpaintingModel(config=config, + pre_process=pre_process, + post_process=post_process) + elif args.vision_backbone_type == 'mit': + model = MitInpaintingModel(config=config, + pre_process=pre_process, + post_process=post_process) + else: + raise Exception('{} vision backbone is not supported.'.format( + args.vision_backbone_type)) + return model + + +def get_batch(data_iterator): + """Build the batch.""" + data = next(data_iterator) + + # only data parallelism; no need for broadcast + images = data[0][0].cuda() + masks = data[0][1].cuda() + return images, masks + + +def loss_func(images, masks, masked_images, outputs, non_loss_data=False): + outputs = outputs.contiguous().float() + masks_flip = 1-masks + flip_masked_outputs = outputs.masked_fill(masks_flip.bool(), 0) + flip_masked_images = images.masked_fill(masks_flip.bool(), 0) + + ssim_fun = SSIM() + psnr_fun = PSNR() + + if not non_loss_data: + mask_count = torch.count_nonzero(masks) + loss = F.mse_loss( + flip_masked_outputs, + flip_masked_images.float(), + reduction="sum" + ) + loss = loss/mask_count + ssim = ssim_fun(flip_masked_outputs, flip_masked_images.float()) + psnr = psnr_fun(flip_masked_outputs, flip_masked_images.float()) + + averaged_loss = average_losses_across_data_parallel_group( + [loss, psnr, ssim] + ) + + return loss, {"loss": averaged_loss[0], + "psnr": averaged_loss[1], + 'ssim': averaged_loss[2]} + else: + synth_images = masked_images.float() + flip_masked_outputs + ssim = ssim_fun(synth_images, images.float()) + psnr = psnr_fun(synth_images, images.float()) + return torch.cat((images, masked_images, synth_images), dim=2), ssim, psnr + + +def forward_step(data_iterator, model): + """Forward step.""" + timers = get_timers() + + # Get the batch. + timers("batch-generator", log_level=2).start() + ( + images, + masks, + ) = get_batch(data_iterator) + timers("batch-generator").stop() + + masked_images = images.masked_fill(masks.bool(), 0) + outputs = model(masked_images) + + # Forward mode + return outputs, partial(loss_func, images, masks, masked_images) + + +def process_non_loss_data(data, iteration, writer): + psnr_sum = 0 + ssim_sum = 0 + for (output_tb, ssim, psnr) in data: + output_tb[output_tb < 0] = 0 + output_tb[output_tb > 1] = 1 + writer.add_images("gt-input-output-vald", output_tb, + global_step=iteration, walltime=None, + dataformats='NCHW') + psnr_sum = psnr_sum + psnr.item() + ssim_sum = ssim_sum + ssim.item() + psnr = psnr_sum/len(data) + ssim = ssim_sum/len(data) + writer.add_scalar('PSNR generate value-validation', psnr, iteration) + writer.add_scalar('SSIM generate value-validation', ssim, iteration) + + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build train, valid, and test datasets.""" + args = get_args() + + print_rank_0( + "> building train, validation, and test datasets " "for VIT ..." + ) + train_ds, valid_ds = build_train_valid_datasets( + data_path=args.data_path, + image_size=(args.img_h, args.img_w) + ) + print_rank_0("> finished creating VIT datasets ...") + + return train_ds, valid_ds, None + + +if __name__ == "__main__": + + pretrain( + train_valid_test_datasets_provider, + model_provider, + ModelType.encoder_or_decoder, + forward_step, + process_non_loss_data, + args_defaults={'dataloader_type': 'cyclic', 'vision_pretraining': True} + ) diff --git a/xpu_timer/experiments/scripts/pretrain_vlm.py b/xpu_timer/experiments/scripts/pretrain_vlm.py new file mode 100644 index 0000000000..b7e9aed8c7 --- /dev/null +++ b/xpu_timer/experiments/scripts/pretrain_vlm.py @@ -0,0 +1,318 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +"""Pretrain vision language model.""" +from copy import deepcopy +from functools import partial + +import torch + +from megatron.core import parallel_state, tensor_parallel +from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder +from megatron.core.datasets.multimodal_dataset import MockMultimodalDataset, MultimodalDatasetConfig +from megatron.core.enums import ModelType +from megatron.core.models.multimodal.llava_model import LLaVAModel, IMAGE_TOKEN_INDEX +from megatron.core.models.multimodal.llava_spec import ( + decoder_model_with_transformer_engine_default_spec, + decoder_model_with_local_default_spec, +) +from megatron.core.models.vision.vit_layer_specs import ( + get_vit_layer_with_transformer_engine_spec, + get_vit_layer_with_local_spec, +) +from megatron.core.transformer.spec_utils import import_module +from megatron.training import get_args, get_timers, get_tokenizer, pretrain, print_rank_0 +from megatron.training.arguments import core_transformer_config_from_args +from pretrain_gpt import loss_func + + +def get_num_image_tokens(): + args = get_args() + add_class_token = not args.disable_vision_class_token + + num_patches_per_dim_h = args.img_h // args.patch_dim + num_patches_per_dim_w = args.img_w // args.patch_dim + num_patches = num_patches_per_dim_h * num_patches_per_dim_w + num_image_tokens = num_patches + (1 if add_class_token else 0) + return num_image_tokens + + +def model_provider( + pre_process=True, post_process=True, add_encoder=True, add_decoder=True, parallel_output=True +) -> LLaVAModel: + """Builds the model. + + Note: currently, only LLaVA model is supported. Follow-up changes will make this configurable. + + Args: + pre_process (bool): Include the embedding layer in the gpt decoder (used with pipeline parallelism). Defaults to True. + post_process (bool): Include an output layer and a layernorm in the gpt decoder (used with pipeline parallelism). Defaults to True. + add_encoder (bool): Construct the encoder module (used with pipeline parallelism). Defaults to True. When we use pipelining, the encoder + will live on only a subset of the pipeline stages (specifically, only the first stage). + add_decoder (bool): Construct the decoder module (used with pipeline parallelism). Defaults to True. When we use pipelining, the decoder + will live on only a subset of the pipeline stages (specifically, every stage after the first one). + parallel_output (bool): Enable model parallel output. + + Returns: + model (megatron.core.models.multimodal.llava_model.LLaVAModel): A multimodal model + """ + args = get_args() + + num_image_tokens = get_num_image_tokens() + args.decoder_seq_length = args.seq_length + num_image_tokens + args.seq_length = num_image_tokens + args.max_position_embeddings = max(args.max_position_embeddings, args.decoder_seq_length) + + print_rank_0('building a multimodal model ...') + language_transformer_config = core_transformer_config_from_args(get_args()) + + if args.spec is not None: + language_transformer_layer_spec = import_module(args.spec) + elif args.transformer_impl == "transformer_engine": + language_transformer_layer_spec = decoder_model_with_transformer_engine_default_spec( + args.num_experts, args.moe_grouped_gemm + ) + else: # transformer_impl == "local" + language_transformer_layer_spec = decoder_model_with_local_default_spec( + args.num_experts, args.moe_grouped_gemm + ) + + if args.transformer_impl == "transformer_engine": + vision_transformer_layer_spec = get_vit_layer_with_transformer_engine_spec() + else: # transformer_impl == "local" + vision_transformer_layer_spec = get_vit_layer_with_local_spec() + + # TODO: Make these configurable via input .yaml config. + vision_transformer_config = deepcopy(language_transformer_config) + vision_transformer_config.num_layers = args.encoder_num_layers + vision_transformer_config.first_pipeline_num_layers = None + vision_transformer_config.last_pipeline_num_layers = None + + vision_projection_type = "mlp" + vision_projection_config = deepcopy(language_transformer_config) + + if args.encoder_pipeline_model_parallel_size > 0: + assert ( + args.encoder_pipeline_model_parallel_size == 1 + ), "ViT can only live on 1 pipeline stage." + vision_transformer_config.pipeline_model_parallel_size = ( + args.encoder_pipeline_model_parallel_size + ) + vision_projection_config.pipeline_model_parallel_size = ( + args.encoder_pipeline_model_parallel_size + ) + if args.encoder_tensor_model_parallel_size > 0: + vision_transformer_config.tensor_model_parallel_size = ( + args.encoder_tensor_model_parallel_size + ) + vision_projection_config.tensor_model_parallel_size = ( + args.encoder_tensor_model_parallel_size + ) + + vision_projection_modules = deepcopy(language_transformer_layer_spec.submodules.mlp.submodules) + + model = LLaVAModel( + language_transformer_config=language_transformer_config, + language_transformer_layer_spec=language_transformer_layer_spec, + language_vocab_size=args.padded_vocab_size, + language_max_sequence_length=args.max_position_embeddings, + vision_transformer_config=vision_transformer_config, + vision_transformer_layer_spec=vision_transformer_layer_spec, + drop_vision_class_token=args.disable_vision_class_token, + vision_projection_config=vision_projection_config, + vision_projection_layer_spec=vision_projection_modules, + vision_projection_type=vision_projection_type, + parallel_output=parallel_output, + language_position_embedding_type=args.position_embedding_type, + language_rotary_percent=args.rotary_percent, + pre_process=pre_process, + post_process=post_process, + add_encoder=add_encoder, + add_decoder=add_decoder, + img_h=args.img_h, + img_w=args.img_w, + patch_dim=args.patch_dim, + ) + + return model + + +def train_valid_test_datasets_provider(train_val_test_num_samples): + """Build the train test and validation datasets. + + Args: + train_val_test_num_samples : A list containing the number of samples in train, validation, and test sets. + + Returns: + train_ds, val_ds, test_ds (megatron.core.datasets.multimodal_dataset.MockMultimodalDataset): Train, validation, and test datasets, respectively. + """ + args = get_args() + + config = MultimodalDatasetConfig( + random_seed=args.seed, + split=args.split, + sequence_length=args.decoder_seq_length - args.seq_length, + tokenizer=get_tokenizer(), + reset_position_ids=args.reset_position_ids, + reset_attention_mask=args.reset_attention_mask, + eod_mask_loss=args.eod_mask_loss, + image_h=args.img_h, + image_w=args.img_w, + preprocess_func=_preprocess_data_for_llava, + ) + + print_rank_0("> building train, validation, and test datasets for multimodal ...") + + train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder( + MockMultimodalDataset, + train_val_test_num_samples, + lambda: parallel_state.get_tensor_model_parallel_rank() == 0, + config, + ).build() + + print_rank_0("> finished creating multimodal datasets ...") + + return train_ds, valid_ds, test_ds + + +def _preprocess_data_for_llava(data): + """Preprocess data sample to the format expected by a LLaVA model. + + Note: This doesn't support all the different modes in the official LLaVA repo yet. + + Args: + data (dict): Data sample with keys like 'image', 'tokens', etc. + + Returns: + data (dict): Processed data sample suitable for the model. + """ + # Prepend image token index to tokens. + data["tokens"] = torch.cat( + [ + IMAGE_TOKEN_INDEX + * torch.ones(1, dtype=data["tokens"].dtype, device=data["tokens"].device), + data["tokens"], + ] + ) + # Prepend labels accordingly. + data["labels"] = torch.cat([data["tokens"][1].unsqueeze(0), data["labels"]]) + # Zero loss mask for the image token index. + data["loss_mask"] = torch.cat( + [ + torch.zeros(1, dtype=data["loss_mask"].dtype, device=data["loss_mask"].device), + data["loss_mask"], + ] + ) + # Add one more position id. + data["position_ids"] = torch.cat( + [data["position_ids"], data["position_ids"][-1].unsqueeze(0) + 1] + ) + + return data + + +def get_batch(data_iterator): + """Generate a batch. + + Args: + data_iterator: Iterable dataset. + + Returns: + sample: A data sample with images, tokens, etc. + """ + # Broadcast data. + if data_iterator is not None: + data = next(data_iterator) + else: + data = None + + data_i = tensor_parallel.broadcast_data(["tokens", "position_ids", "labels"], data, torch.int64) + data_f = tensor_parallel.broadcast_data(["image", "loss_mask"], data, torch.float32) + + tokens = data_i["tokens"].long() + position_ids = data_i["position_ids"].long() + labels = data_i["labels"].long() + images = data_f["image"].float() + loss_mask = data_f["loss_mask"].float() + attention_mask = None # Use the attention mask type defined in layer spec. Typically no mask for the vision model and causal mask for the vision model. + + return tokens, position_ids, labels, images, loss_mask, attention_mask + + +def forward_step(data_iterator, model: LLaVAModel): + """Forward training step. + + Args: + data_iterator: Iterable dataset. + model (megatron.core.models.multimodal.llava_model.LLaVAModel): Multimodal model + + Returns: + output_tensor (torch.Tensor): Loss of shape [b, s] if labels are provided, otherwise logits of shape [b, s, vocab_size]. + loss_func (callable): Loss function with a loss mask specified. + """ + timers = get_timers() + + # Get the batch. + timers('batch-generator', log_level=2).start() + tokens, position_ids, labels, images, loss_mask, attention_mask = get_batch(data_iterator) + timers('batch-generator').stop() + + output_tensor, loss_mask = model( + images, tokens, position_ids, attention_mask, labels, loss_mask + ) + + return output_tensor, partial(loss_func, loss_mask) + + +def add_vlm_extra_args(parser): + """Extra arguments.""" + group = parser.add_argument_group(title='vision language model specific arguments') + group.add_argument("--disable-vision-class-token", action="store_true", default=False) + return parser + + +def llava_embedding_ranks(pp_ranks): + """LLava's embedding ranks consist of the decoder's first and last ranks (ie, the ViT has no embeddings). + Args: + pp_ranks: A list of global ranks that constitute a pipeline group. + """ + args = get_args() + + # encoder size is also the index to the first rank of the decoder. + epp = args.encoder_pipeline_model_parallel_size + + last_rank = pp_ranks[-1] + if len(pp_ranks) == 1 or pp_ranks[epp] == last_rank: + return [last_rank] + else: + return [pp_ranks[epp], last_rank] + + +def llava_position_embedding_ranks(pp_ranks): + """LLava's embedding ranks consist of the singular rank of the model or the decoder's first rank. + Args: + pp_ranks: A list of global ranks that constitute a pipeline group. + """ + args = get_args() + + # encoder size is also the index to the first rank of the decoder. + epp = args.encoder_pipeline_model_parallel_size + + last_rank = pp_ranks[-1] + if len(pp_ranks) == 1: + return [last_rank] + else: + return [pp_ranks[epp]] + + +if __name__ == "__main__": + train_valid_test_datasets_provider.is_distributed = True + + pretrain( + train_valid_test_datasets_provider, + model_provider, + ModelType.encoder_and_decoder, + forward_step, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, + extra_args_provider=add_vlm_extra_args, + get_embedding_ranks=llava_embedding_ranks, + get_position_embedding_ranks=llava_position_embedding_ranks, + ) diff --git a/xpu_timer/experiments/scripts/setup.py b/xpu_timer/experiments/scripts/setup.py new file mode 100644 index 0000000000..adb00629ac --- /dev/null +++ b/xpu_timer/experiments/scripts/setup.py @@ -0,0 +1,109 @@ +"""Setup for pip package.""" + +import importlib.util +import subprocess + +import setuptools +from setuptools import Extension + +spec = importlib.util.spec_from_file_location('package_info', 'megatron/core/package_info.py') +package_info = importlib.util.module_from_spec(spec) +spec.loader.exec_module(package_info) + + +__contact_emails__ = package_info.__contact_emails__ +__contact_names__ = package_info.__contact_names__ +__description__ = package_info.__description__ +__download_url__ = package_info.__download_url__ +__homepage__ = package_info.__homepage__ +__keywords__ = package_info.__keywords__ +__license__ = package_info.__license__ +__package_name__ = package_info.__package_name__ +__repository_url__ = package_info.__repository_url__ +__version__ = package_info.__version__ + + +with open("megatron/core/README.md", "r", encoding='utf-8') as fh: + long_description = fh.read() +long_description_content_type = "text/markdown" + +############################################################################### +# Extension Making # +# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # + +extra_compile_args = ( + subprocess.check_output(["python3", "-m", "pybind11", "--includes"]) + .decode("utf-8") + .strip() + .split() +) + +############################################################################### + +setuptools.setup( + name=__package_name__, + # Versions should comply with PEP440. For a discussion on single-sourcing + # the version across setup.py and the project code, see + # https://packaging.python.org/en/latest/single_source_version.html + version=__version__, + description=__description__, + long_description=long_description, + long_description_content_type=long_description_content_type, + # The project's main homepage. + url=__repository_url__, + download_url=__download_url__, + # Author details + author=__contact_names__, + author_email=__contact_emails__, + # maintainer Details + maintainer=__contact_names__, + maintainer_email=__contact_emails__, + # The licence under which the project is released + license=__license__, + classifiers=[ + # How mature is this project? Common values are + # 1 - Planning + # 2 - Pre-Alpha + # 3 - Alpha + # 4 - Beta + # 5 - Production/Stable + # 6 - Mature + # 7 - Inactive + 'Development Status :: 5 - Production/Stable', + # Indicate who your project is intended for + 'Intended Audience :: Developers', + 'Intended Audience :: Science/Research', + 'Intended Audience :: Information Technology', + # Indicate what your project relates to + 'Topic :: Scientific/Engineering', + 'Topic :: Scientific/Engineering :: Mathematics', + 'Topic :: Scientific/Engineering :: Image Recognition', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Software Development :: Libraries', + 'Topic :: Software Development :: Libraries :: Python Modules', + 'Topic :: Utilities', + # Pick your license as you wish (should match "license" above) + 'License :: OSI Approved :: BSD License', + # Supported python versions + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: 3.9', + # Additional Setting + 'Environment :: Console', + 'Natural Language :: English', + 'Operating System :: OS Independent', + ], + packages=setuptools.find_namespace_packages(include=["megatron.core", "megatron.core.*"]), + ext_modules=[ + Extension( + "megatron.core.datasets.helpers", + sources=["megatron/core/datasets/helpers.cpp"], + language="c++", + extra_compile_args=extra_compile_args, + ) + ], + # Add in any packaged data. + include_package_data=True, + # PyPI package information. + keywords=__keywords__, +) diff --git a/xpu_timer/experiments/scripts/train_llama.py b/xpu_timer/experiments/scripts/train_llama.py new file mode 100644 index 0000000000..75a7a929f5 --- /dev/null +++ b/xpu_timer/experiments/scripts/train_llama.py @@ -0,0 +1,912 @@ +import os +import time +import functools + +import torch +import torch.distributed as dist +from torch.distributed.fsdp import FullyShardedDataParallel as FSDP +from torch.utils.data import DataLoader, Dataset +from transformers import GPTNeoXConfig, GPTNeoXForCausalLM +from transformers import LlamaConfig, LlamaForCausalLM +from transformers.models.llama.modeling_llama import LlamaDecoderLayer +from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy +from torch.distributed.fsdp import MixedPrecision +from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( + apply_activation_checkpointing, + checkpoint_wrapper +) +from contextlib import nullcontext + + +def human_readable_flops(num): + for unit in [ + "", + "KFLOPS", + "MFLOPS", + "GFLOPS", + "TFLOPS", + "PFLOPS", + "EFLOPS", + "ZFLOPS", + ]: + if abs(num) < 1000.0: + return "%3.3f%s" % (num, unit) + num /= 1000.0 + return "%.3f%s" % (num, "Yi") + + +def compute_training_flops( + batch_size, + sequence_length, + hidden_size, + vocab_size, + intermediate_size, + num_layers, + use_gradient_checkpointing=False, + use_peft=False, + use_gqa=False, + kv_head_ratio=1, +): + """Returns: + hardware flops + model flops + + The source of formula: + Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM's + (APPENDIX: FLOATING-POINT OPERATIONS) + + Assuming that backward pass has twice FLOPs as many as forward pass. Only matrix multiplication FLOPs are computed. + For use_peft, backward pass FLOPS is a little more than the forward pass. Assuming equal for simplicity here. + """ + # [b,s,n] -> [b,s,n] + query_proj_flops = batch_size * 2 * sequence_length * hidden_size**2 + if use_gqa: + key_value_proj_flops = ( + 2 + * batch_size + * 2 + * sequence_length + * hidden_size + * hidden_size + / kv_head_ratio + ) + else: + key_value_proj_flops = 2 * query_proj_flops + attention_proj_flops = query_proj_flops + key_value_proj_flops + attention_flops = ( + 2 * batch_size * hidden_size * sequence_length**2 + + 4 * batch_size * sequence_length * hidden_size**2 + ) + attention_forward_flops = attention_proj_flops + attention_flops + # llama2 use gate_proj, has 3 Linears + two_mlps_forward_flops = ( + 3 * 2 * batch_size * sequence_length * hidden_size * intermediate_size + ) + logits_forward_flops = 2 * batch_size * sequence_length * hidden_size * vocab_size + decoder_layer_forward_flops = attention_forward_flops + two_mlps_forward_flops + # forward FLOPs without gradient checkpointing + forward_flops_wo_gc = ( + num_layers * decoder_layer_forward_flops + logits_forward_flops + ) + factor = 2 if use_peft else 3 + if not use_gradient_checkpointing: + return forward_flops_wo_gc * factor, forward_flops_wo_gc * factor + else: + return ( + num_layers * decoder_layer_forward_flops * (factor + 1) + + logits_forward_flops * factor, + forward_flops_wo_gc * factor, + ) + + + + +def apply_fsdp_checkpointing(model, blocks): + wrapper = lambda m: checkpoint_wrapper(m, + checkpoint_fn=torch.utils.checkpoint.checkpoint, + use_reentrant=False, + preserve_rng_state=True) + check_fn = lambda submodule: isinstance(submodule, blocks) + apply_activation_checkpointing(model, checkpoint_wrapper_fn=wrapper, check_fn=check_fn) + + +class DummyDataset(Dataset): + def __init__(self, vocab_size=1000, max_length=128, data_size=100000): + self.vocab_size = vocab_size + self.max_length = max_length + self.data_size = data_size + + def __len__(self): + return self.data_size + + def __getitem__(self, idx): + text = torch.randint(low=0, high=self.vocab_size, size=(self.max_length,)) + return text, text + + +def main(): + # Initialize the process group + dist.init_process_group(backend="nccl") + + # Get local rank and world size + local_rank = int(os.environ["LOCAL_RANK"]) + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + + num_layers = 10 + hidden_size = 4096 + intermediate_size = 8192 + vocab_size = 126464 + num_head = 64 + num_kv_head = 8 + batch_size = 2 + seq_length = 4096 + kv_head_ratio = num_head // num_kv_head + torch.cuda.set_device(local_rank) + + + config = LlamaConfig( + vocab_size=vocab_size, + hidden_size=hidden_size, + num_hidden_layers=num_layers, + num_attention_heads=num_head, + num_key_value_heads=num_kv_head, + intermediate_size=intermediate_size, + max_position_embeddings=seq_length, + initializer_range=0.02, + layer_norm_eps=1e-5, + # attn_implementation="flash_attention_2", + use_cache=False, + use_bfloat16=True + ) + + #init_device = "cpu" if local_rank == 0 else "meta" + init_device = "meta" + + # from liger_kernel.transformers import apply_liger_kernel_to_llama + # apply_liger_kernel_to_llama( + # rope=True, + # swiglu=True, + # cross_entropy=True, + # fused_linear_cross_entropy=False, + # rms_norm=True + # ) + + with torch.device(init_device): + model = LlamaForCausalLM(config) + + + flop, _ = compute_training_flops( + batch_size, + seq_length, + hidden_size, + vocab_size, + intermediate_size, + num_layers, + use_gradient_checkpointing=True, + use_gqa=True, + kv_head_ratio=kv_head_ratio, + ) + + + dataset = DummyDataset(vocab_size=vocab_size, max_length=seq_length) + sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, sampler=sampler) + + #param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) if local_rank != 0 else None + param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) + wrap_policy = functools.partial(transformer_auto_wrap_policy, transformer_layer_cls={LlamaDecoderLayer,},) + #model = model.to(dtype=torch.bfloat16) + + model = FSDP(model, device_id=local_rank, auto_wrap_policy=wrap_policy, + mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), + sync_module_states=False, param_init_fn=param_init_fn, + forward_prefetch=True, limit_all_gathers=True, use_orig_params=True) + + apply_fsdp_checkpointing(model, LlamaDecoderLayer) + optimizer = torch.optim.Adam(model.parameters(), lr=1e-5) + + # Training Loop + def save_profile(prof): + prof.export_chrome_trace(f"fsdp_trace_{rank}.json") + + epoch = 0 + iters = 0 + prof = torch.profiler.profile( + schedule=torch.profiler.schedule( + wait=1, + warmup=1, + active=1000000, + repeat=1), + on_trace_ready=save_profile, + record_shapes=False, + with_stack=False) + + #prof = nullcontext() + # prof.start() + + dur = [] + model.train() + for input_ids, labels in dataloader: + input_ids, labels = input_ids.to(local_rank), labels.to(local_rank) + start = time.time() + optimizer.zero_grad() + loss = model(input_ids=input_ids, labels=labels).loss + loss.backward() + optimizer.step() + torch.cuda.synchronize() + if rank == 0: + dur = time.time() - start + tflops = flop / dur / 1e12 + print(f"Epoch {epoch}, Loss: {loss.item()} time {dur} tflops {tflops}") + iters += 1 + # if iters > 10: + # break + # prof.step() + # prof.stop() + + print("Training Complete") + dist.destroy_process_group() + +def main_ds(): + import deepspeed + dist.init_process_group(backend="nccl") + + # Get local rank and world size + local_rank = int(os.environ["LOCAL_RANK"]) + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + + num_layers = 20 + hidden_size = 8192//4 + intermediate_size = 28672 + vocab_size = 126464 + num_head = 64 + num_kv_head = 8 + batch_size = 2 + seq_length = 4096 + kv_head_ratio = num_head // num_kv_head + torch.cuda.set_device(local_rank) + + flop, _ = compute_training_flops( + batch_size, + seq_length, + hidden_size, + vocab_size, + intermediate_size, + num_layers, + use_gradient_checkpointing=True, + use_gqa=True, + kv_head_ratio=kv_head_ratio, + ) + + + config = LlamaConfig( + vocab_size=vocab_size, + hidden_size=hidden_size, + num_hidden_layers=num_layers, + num_attention_heads=num_head, + num_key_value_heads=num_kv_head, + intermediate_size=intermediate_size, + max_position_embeddings=seq_length, + initializer_range=0.02, + layer_norm_eps=1e-5, + attn_implementation="flash_attention_2", + use_cache=False, + use_bfloat16=True + ) + + #init_device = "cpu" if local_rank == 0 else "meta" + init_device = "meta" + + from liger_kernel.transformers import apply_liger_kernel_to_llama + apply_liger_kernel_to_llama( + rope=True, + swiglu=True, + cross_entropy=True, + fused_linear_cross_entropy=False, + rms_norm=True + ) + + ds_config = { + "train_batch_size": batch_size * world_size, + "train_micro_batch_size_per_gpu": batch_size, + #"steps_per_print": 10, + "zero_optimization": { + "stage": 3, + "overlap_comm": True, + }, + "bf16": { + "enabled": True, + }, + "activation_checkpointing": { + "partition_activations": True, # Partition activations across GPUs + #"contiguous_memory_optimization": True, # Optimize contiguous memory usage + }, + } + + kwargs = {} + kwargs["config"] = ds_config + with deepspeed.zero.Init(config_dict_or_path=ds_config): + model = LlamaForCausalLM(config) + kwargs["model"] = model + + from deepspeed.ops.adam import FusedAdam + #optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4) + optimizer = FusedAdam(model.parameters(), lr=1e-4) + kwargs["optimizer"] = optimizer + model_engine, optimizer, _, _ = deepspeed.initialize(**kwargs) + #from remote_pdb import RemotePdb + #RemotePdb("127.0.0.1", 16666+rank).set_trace() + + dataset = DummyDataset(vocab_size=vocab_size, max_length=seq_length) + sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, sampler=sampler) + model_engine.train() + start = end = 0 + dur = [] + epoch = 0 + # Training Loop + def save_profile(prof): + prof.export_chrome_trace(f"ds_trace_{rank}.json") + + epoch = 0 + iters = 0 + # prof = torch.profiler.profile( + # schedule=torch.profiler.schedule( + # wait=1, + # warmup=1, + # active=3, + # repeat=1), + # on_trace_ready=save_profile, + # record_shapes=True, + # with_stack=True) + + # #prof = nullcontext() + # prof.start() + + for step, (input_ids, labels) in enumerate(dataloader): + start = time.time() + input_ids, labels = input_ids.to(local_rank), labels.to(local_rank) + optimizer.zero_grad() + loss = model_engine(input_ids=input_ids, labels=labels).loss + model_engine.backward(loss) + model_engine.step() + torch.cuda.synchronize() + dur = time.time() - start + tflops = flop / dur / 1e12 + if rank == 0: + print(f"Epoch {epoch}, Step {step}, Loss {loss.item()}, time {dur}, {tflops}") + # if step > 10: + # break + # prof.step() + # prof.stop() + +def main_qwen_vl(): + #dist.init_process_group(backend="nccl") + + #local_rank = int(os.environ["LOCAL_RANK"]) + #rank = int(os.environ["RANK"]) + #world_size = int(os.environ["WORLD_SIZE"]) + world_size = 1 + local_rank = rank = 0 + + torch.cuda.set_device(local_rank) + config ={ + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151645, + "vision_start_token_id": 151652, + "vision_end_token_id": 151653, + "vision_token_id": 151654, + "image_token_id": 151655, + "video_token_id": 151656, + "hidden_act": "silu", + "hidden_size": 8192 // 4, + "initializer_range": 0.02, + "intermediate_size": 29568 // 4, + "max_position_embeddings": 32768, + "max_window_layers": 80, + "model_type": "qwen2_vl", + "num_attention_heads": 64, + "num_hidden_layers": 1, + "num_key_value_heads": 8, + "rms_norm_eps": 1e-06, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "tie_word_embeddings": False, + "torch_dtype": "bfloat16", + "transformers_version": "4.41.2", + "use_cache": False, + "use_sliding_window": False, + "vision_config": { + "depth": 32, + "embed_dim": 1280, + "mlp_ratio": 4, + "num_heads": 16, + "in_chans": 3, + "hidden_size": 8192, + "patch_size": 14, + "spatial_merge_size": 2, + "spatial_patch_size": 14, + "temporal_patch_size": 2 + }, + "rope_scaling": { + "type": "mrope", + "mrope_section": [ + 16 // 4, + 24 // 4, + 24 // 4 + ] + }, + "vocab_size": 152064 + } + + from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig + from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLModel, Qwen2VisionTransformerPretrainedModel, Qwen2VLForConditionalGeneration + from transformers.models.qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessor + + qwen_config = Qwen2VLConfig(**config) + preprocess_config = { + "min_pixels": 3136, + "max_pixels": 12845056, + "patch_size": 14, + "temporal_patch_size": 2, + "merge_size": 2, + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "image_processor_type": "Qwen2VLImageProcessor", + "processor_class": "Qwen2VLProcessor" + } + preprocess = Qwen2VLImageProcessor(**preprocess_config) + with torch.device('cuda'): + #model = Qwen2VLForConditionalGeneration(qwen_config) + text_model = Qwen2VLModel(qwen_config) + vision_model = Qwen2VisionTransformerPretrainedModel(qwen_config.vision_config) + image = [torch.ones(1280, 1280, 3, dtype=torch.uint8) for _ in range(10)] + if rank == 0: + text = torch.randint(low=0, high=qwen_config.vocab_size, size=(1, 4096,)).cuda() + #text[-32:] = 1516545 + #print((text == 151655).sum().item()) + data = preprocess(image, return_tensors="pt") + image_grid_thw = data['image_grid_thw'].cuda() + image_hidden = data['pixel_values'].cuda() + # + t = text_model(text) + v = vision_model(image_hidden, image_grid_thw) + breakpoint() + #m = model(input_ids=text,pixel_values=image_hidden,image_grid_thw=image_grid_thw) + print(1) + + #num_layers = 10 + #hidden_size = 8192 + #intermediate_size = 32768 + #vocab_size = 126464 + #num_head = 128 + #num_kv_head = 16 + #batch_size = 2 + #seq_length = 4096 + #kv_head_ratio = num_head // num_kv_head + #torch.cuda.set_device(local_rank) + + + #config = LlamaConfig( + # vocab_size=vocab_size, + # hidden_size=hidden_size, + # num_hidden_layers=num_layers, + # num_attention_heads=num_head, + # num_key_value_heads=num_kv_head, + # intermediate_size=intermediate_size, + # max_position_embeddings=seq_length, + # initializer_range=0.02, + # layer_norm_eps=1e-5, + # attn_implementation="flash_attention_2", + # use_cache=False, + # use_bfloat16=True + #) + + ##init_device = "cpu" if local_rank == 0 else "meta" + #init_device = "meta" + + #from liger_kernel.transformers import apply_liger_kernel_to_llama + #apply_liger_kernel_to_llama( + # rope=True, + # swiglu=True, + # cross_entropy=True, + # fused_linear_cross_entropy=False, + # rms_norm=True + #) + + #with torch.device(init_device): + # model = LlamaForCausalLM(config) + + # + #flop, _ = compute_training_flops( + # batch_size, + # seq_length, + # hidden_size, + # vocab_size, + # intermediate_size, + # num_layers, + # use_gradient_checkpointing=True, + # use_gqa=True, + # kv_head_ratio=kv_head_ratio, + #) + + + #dataset = DummyDataset(vocab_size=vocab_size, max_length=seq_length) + #sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank) + #dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, sampler=sampler) + + ##param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) if local_rank != 0 else None + #param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) + #wrap_policy = functools.partial(transformer_auto_wrap_policy, transformer_layer_cls={LlamaDecoderLayer,},) + ##model = model.to(dtype=torch.bfloat16) + + #model = FSDP(model, device_id=local_rank, auto_wrap_policy=wrap_policy, + # mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), + # sync_module_states=False, param_init_fn=param_init_fn, + # forward_prefetch=True, limit_all_gathers=True, use_orig_params=True) + + #apply_fsdp_checkpointing(model, LlamaDecoderLayer) + #optimizer = torch.optim.Adam(model.parameters(), lr=1e-5) + + ## Training Loop + #def save_profile(prof): + # prof.export_chrome_trace(f"fsdp_trace_{rank}.json") + + #epoch = 0 + #iters = 0 + #prof = torch.profiler.profile( + # schedule=torch.profiler.schedule( + # wait=1, + # warmup=1, + # active=3, + # repeat=1), + # on_trace_ready=save_profile, + # record_shapes=True, + # with_stack=True) + + ##prof = nullcontext() + ##prof.start() + + #dur = [] + #with prof: + # model.train() + # for input_ids, labels in dataloader: + # start = time.time() + # input_ids, labels = input_ids.to(local_rank), labels.to(local_rank) + # optimizer.zero_grad() + # loss = model(input_ids=input_ids, labels=labels).loss + # loss.backward() + # optimizer.step() + # torch.cuda.synchronize() + # if rank == 0: + # dur = time.time() - start + # tflops = flop / dur / 1e12 + # print(f"Epoch {epoch}, Loss: {loss.item()} time {dur} tflops {tflops}") + # iters += 1 + # if iters > 10: + # break + # prof.step() + # epoch += 1 + + #print("Training Complete") + #dist.destroy_process_group() +def mllama(): + from transformers import MllamaForConditionalGeneration, AutoProcessor + from transformers.models.mllama.configuration_mllama import MllamaConfig, MllamaVisionConfig, MllamaTextConfig + from transformers.models.mllama.modeling_mllama import MllamaVisionEncoderLayer, MllamaSelfAttentionDecoderLayer + from liger_kernel.transformers import apply_liger_kernel_to_mllama + + dist.init_process_group(backend="nccl") + + # Get local rank and world size + local_rank = int(os.environ["LOCAL_RANK"]) + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + torch.cuda.set_device(local_rank) + + apply_liger_kernel_to_mllama( + rope=True, + swiglu=True, + cross_entropy=False, + fused_linear_cross_entropy=True, + rms_norm=True + ) + config = { + "architectures": [ + "MllamaForConditionalGeneration" + ], + "image_token_index": 128256, + "model_type": "mllama", + "text_config": { + "_name_or_path": "", + "add_cross_attention": False, + "architectures": None, + "bad_words_ids": None, + "begin_suppress_tokens": None, + "bos_token_id": 128000, + "chunk_size_feed_forward": 0, + "cross_attention_hidden_size": None, + "cross_attention_layers": [ + 3, + 8, + 13, + 18, + 23, + 28, + 33, + 38, + 43, + 48, + 53, + 58, + 63, + 68, + 73, + 78, + 83, + 88, + 93, + 98 + ], + "decoder_start_token_id": None, + "diversity_penalty": 0.0, + "do_sample": False, + "dropout": 0, + "early_stopping": False, + "encoder_no_repeat_ngram_size": 0, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "exponential_decay_length_penalty": None, + "finetuning_task": None, + "forced_bos_token_id": None, + "forced_eos_token_id": None, + "hidden_act": "silu", + "hidden_size": 4096, + "id2label": { + "0": "LABEL_0", + "1": "LABEL_1" + }, + "initializer_range": 0.02, + "intermediate_size": 28672, + "is_decoder": False, + "is_encoder_decoder": False, + "label2id": { + "LABEL_0": 0, + "LABEL_1": 1 + }, + "length_penalty": 1.0, + "max_length": 20, + "max_position_embeddings": 131072, + "min_length": 0, + "model_type": "mllama_text_model", + "no_repeat_ngram_size": 0, + "num_attention_heads": 64, + "num_beam_groups": 1, + "num_beams": 1, + "num_hidden_layers": 40, + "num_key_value_heads": 8, + "num_return_sequences": 1, + "output_attentions": False, + "output_hidden_states": False, + "output_scores": False, + "pad_token_id": 128004, + "prefix": None, + "problem_type": None, + "pruned_heads": {}, + "remove_invalid_values": False, + "repetition_penalty": 1.0, + "return_dict": True, + "return_dict_in_generate": False, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + }, + "rope_theta": 500000.0, + "sep_token_id": None, + "suppress_tokens": None, + "task_specific_params": None, + "temperature": 1.0, + "tf_legacy_loss": False, + "tie_encoder_decoder": False, + "tie_word_embeddings": False, + "tokenizer_class": None, + "top_k": 50, + "top_p": 1.0, + "torch_dtype": "bfloat16", + "torchscript": False, + "typical_p": 1.0, + "use_bfloat16": False, + "use_cache": False, + "vocab_size": 128256 + }, + "torch_dtype": "bfloat16", + "transformers_version": "4.45.0.dev0", + "vision_config": { + "_name_or_path": "", + "add_cross_attention": False, + "architectures": None, + "attention_heads": 16, + "bad_words_ids": None, + "begin_suppress_tokens": None, + "bos_token_id": None, + "chunk_size_feed_forward": 0, + "cross_attention_hidden_size": None, + "decoder_start_token_id": None, + "diversity_penalty": 0.0, + "do_sample": False, + "early_stopping": False, + "encoder_no_repeat_ngram_size": 0, + "eos_token_id": None, + "exponential_decay_length_penalty": None, + "finetuning_task": None, + "forced_bos_token_id": None, + "forced_eos_token_id": None, + "hidden_act": "gelu", + "hidden_size": 1280, + "id2label": { + "0": "LABEL_0", + "1": "LABEL_1" + }, + "image_size": 560, + "intermediate_layers_indices": [ + 3, + 7, + 15, + 23, + 30 + ], + "intermediate_size": 5120, + "is_decoder": False, + "is_encoder_decoder": False, + "label2id": { + "LABEL_0": 0, + "LABEL_1": 1 + }, + "length_penalty": 1.0, + "max_length": 20, + "max_num_tiles": 4, + "min_length": 0, + "model_type": "mllama_vision_model", + "no_repeat_ngram_size": 0, + "norm_eps": 1e-05, + "num_beam_groups": 1, + "num_beams": 1, + "num_channels": 3, + "num_global_layers": 8, + "num_hidden_layers": 32, + "num_return_sequences": 1, + "output_attentions": False, + "output_hidden_states": False, + "output_scores": False, + "pad_token_id": None, + "patch_size": 14, + "prefix": None, + "problem_type": None, + "pruned_heads": {}, + "remove_invalid_values": False, + "repetition_penalty": 1.0, + "return_dict": True, + "return_dict_in_generate": False, + "sep_token_id": None, + "supported_aspect_ratios": [ + [ + 1, + 1 + ], + [ + 1, + 2 + ], + [ + 1, + 3 + ], + [ + 1, + 4 + ], + [ + 2, + 1 + ], + [ + 2, + 2 + ], + [ + 3, + 1 + ], + [ + 4, + 1 + ] + ], + "suppress_tokens": None, + "task_specific_params": None, + "temperature": 1.0, + "tf_legacy_loss": False, + "tie_encoder_decoder": False, + "tie_word_embeddings": True, + "tokenizer_class": None, + "top_k": 50, + "top_p": 1.0, + "torch_dtype": "bfloat16", + "torchscript": False, + "typical_p": 1.0, + "use_bfloat16": False, + "vision_output_dim": 7680 + } + } + vision_config = MllamaVisionConfig(**config['vision_config']) + text_config = MllamaTextConfig(**config['text_config']) + model_config = MllamaConfig(vision_config, text_config, torch_dtype="bfloat16") + data = torch.load('dummy.pth', map_location='cuda') + label = torch.randint(low=0, high=config['text_config']['vocab_size'], size=data['input_ids'].shape) + with torch.device('meta'): + model = MllamaForConditionalGeneration(model_config) + param_init_fn = lambda m: m.to_empty(device=torch.device("cuda")) + wrap_policy = functools.partial(transformer_auto_wrap_policy, transformer_layer_cls={ MllamaVisionEncoderLayer, MllamaSelfAttentionDecoderLayer},) + #model = model.to(dtype=torch.bfloat16) + + model = FSDP(model, device_id=local_rank, auto_wrap_policy=wrap_policy, + mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), + sync_module_states=False, param_init_fn=param_init_fn, + forward_prefetch=True, limit_all_gathers=True, use_orig_params=True) + + apply_fsdp_checkpointing(model, (MllamaVisionEncoderLayer, MllamaSelfAttentionDecoderLayer)) + optimizer = torch.optim.Adam(model.parameters(), lr=1e-5) + def save_profile(prof): + prof.export_chrome_trace(f"mllama_trace_{rank}.json") + + epoch = 0 + iters = 0 + # prof = torch.profiler.profile( + # schedule=torch.profiler.schedule( + # wait=1, + # warmup=1, + # active=2, + # repeat=1), + # on_trace_ready=save_profile, + # record_shapes=True, + # with_stack=True) + + # prof.start() + model.train() + for i in range(100000000): + start = time.time() + optimizer.zero_grad() + loss= model(**data, labels=label).loss + loss.backward() + optimizer.step() + torch.cuda.synchronize() + dur = time.time() - start + if rank == 0: + print(f"Step {i}, Loss {loss.item()}, time {dur}") + # prof.step() + # prof.stop() + + +if __name__ == "__main__": + # main_ds() + main() + # mllama() +