From a591992b80c8dc52ce25bdf0278ec1f8ae5ff671 Mon Sep 17 00:00:00 2001 From: Xu Song Date: Tue, 7 Nov 2023 03:22:54 +0800 Subject: [PATCH 1/5] Fix config format (#4594) Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/activation_checkpointing/config.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepspeed/runtime/activation_checkpointing/config.py b/deepspeed/runtime/activation_checkpointing/config.py index 13df4b981298..dc07388a95da 100755 --- a/deepspeed/runtime/activation_checkpointing/config.py +++ b/deepspeed/runtime/activation_checkpointing/config.py @@ -17,7 +17,7 @@ "partitioned_activations": [true|false], "number_checkpoints": 100, "contiguous_memory_optimization": [true|false], - "cpu_checkpointing": [true|false] + "cpu_checkpointing": [true|false], "profile": [true|false], "synchronize_checkpoint_boundary": [true|false], } From b1cb0dfc4623fc44e9931d1481c246bd8a59b778 Mon Sep 17 00:00:00 2001 From: Guanhua Wang Date: Mon, 6 Nov 2023 14:15:16 -0800 Subject: [PATCH 2/5] Guanhua/partial offload rebase v2 (#590) (#4636) This PR introduces Twin-Flow feature of ZeRO-Offload++, which improves e2e training iteration time by up to 6x on DGX-H100s. This PR includes: * Twin-Flow implementation inside ZeRO optimizer * json config tutorial * example using deepspeed * unit tests cc @jeffra @awan-10 @tjruwase @mrwyattii Co-authored-by: Olatunji Ruwase Co-authored-by: Jeff Rasley --- deepspeed/runtime/engine.py | 4 + deepspeed/runtime/zero/config.py | 9 +- deepspeed/runtime/zero/offload_config.py | 3 + deepspeed/runtime/zero/stage3.py | 60 ++++++-- docs/_pages/config-json.md | 7 + .../partial_offload_test.py | 128 ++++++++++++++++++ tests/unit/ops/adam/test_hybrid_adam.py | 78 +++++++++++ .../unit/runtime/zero/test_zero_offloadpp.py | 75 ++++++++++ 8 files changed, 355 insertions(+), 9 deletions(-) create mode 100644 tests/small_model_debugging/partial_offload_test.py create mode 100644 tests/unit/ops/adam/test_hybrid_adam.py create mode 100644 tests/unit/runtime/zero/test_zero_offloadpp.py diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 04611671fe06..3d75022ccd86 100644 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -700,6 +700,9 @@ def zero_cpu_offload(self): return self._config.zero_config.offload_optimizer.device == OffloadDeviceEnum.cpu return False + def zero_partial_offload(self): + return getattr(self._config.zero_config.offload_optimizer, "ratio", 1.0) + def zero_sub_group_size(self): return self._config.zero_config.sub_group_size @@ -1565,6 +1568,7 @@ def _configure_zero_optimizer(self, optimizer): offload_optimizer_config=self.zero_offload_optimizer(), offload_param_config=self.zero_offload_param(), sub_group_size=self.zero_sub_group_size(), + offload_ratio=self.zero_partial_offload(), mpu=self.mpu, postscale_gradients=self.postscale_gradients(), gradient_predivide_factor=self.gradient_predivide_factor(), diff --git a/deepspeed/runtime/zero/config.py b/deepspeed/runtime/zero/config.py index 35d60b5b3290..f16dfd7ac4c0 100644 --- a/deepspeed/runtime/zero/config.py +++ b/deepspeed/runtime/zero/config.py @@ -6,7 +6,7 @@ import sys from typing import Optional from enum import Enum -from deepspeed.pydantic_v1 import Field, validator +from deepspeed.pydantic_v1 import Field, validator, root_validator from deepspeed.runtime.config_utils import get_scalar_param, pp_int, DeepSpeedConfigModel from deepspeed.utils import logger from .offload_config import DeepSpeedZeroOffloadParamConfig, DeepSpeedZeroOffloadOptimizerConfig, OffloadDeviceEnum @@ -300,3 +300,10 @@ def overlap_comm_valid(cls, field_value, values): assert ("stage" in values), "DeepSpeedZeroConfig: 'stage' must be defined before 'overlap_comm'" field_value = values["stage"] == ZeroStageEnum.weights return field_value + + @root_validator + def offload_ratio_check(cls, values): + offload_config = getattr(values, "offload_optimizer", {}) + if offload_config and offload_config.ratio < 1.0: + assert values.get("stage") == ZeroStageEnum.weights, "Partial offloading only supported for ZeRO Stage 3." + return values diff --git a/deepspeed/runtime/zero/offload_config.py b/deepspeed/runtime/zero/offload_config.py index 1bd79412d39f..b7adc13a0ea2 100644 --- a/deepspeed/runtime/zero/offload_config.py +++ b/deepspeed/runtime/zero/offload_config.py @@ -92,3 +92,6 @@ class DeepSpeedZeroOffloadOptimizerConfig(DeepSpeedConfigModel): def set_pipeline(cls, field_value, values): values["pipeline"] = field_value or values.get("pipeline", False) return field_value + + ratio: float = Field(1.0, ge=0.0, le=1.0) + """ Percentage of offloaded optimizer states to CPU Adam. Only valid with ZeRO Stage 3.""" diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index 1030ec3e1120..7fdf4e4d9998 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -20,7 +20,7 @@ from deepspeed.runtime.zero.config import ZeroStageEnum from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum from deepspeed.runtime.zero.parameter_offload import DeepSpeedZeRoOffload -from deepspeed.ops.adam import DeepSpeedCPUAdam +from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam from deepspeed.runtime.swap_tensor.partitioned_param_swapper import PartitionedParamStatus from deepspeed.runtime.swap_tensor.partitioned_optimizer_swapper import PartitionedOptimizerSwapper from deepspeed.runtime.swap_tensor.pipelined_optimizer_swapper import PipelinedOptimizerSwapper @@ -104,6 +104,7 @@ def __init__( offload_optimizer_config=None, offload_param_config=None, sub_group_size=1000000000000, + offload_ratio=0.0, mpu=None, clip_grad=0.0, gradient_accumulation_dtype=torch.float32, @@ -159,6 +160,7 @@ def __init__( self.offload_param_pin_memory = False self.params_in_nvme_and_cpu = False self.max_params_in_cpu = 0 + self.partial_offload = offload_ratio #num of ranks in a ZeRO param partitioning group self.zero_hpz_partition_size = zero_hpz_partition_size @@ -191,6 +193,23 @@ def __init__( self.persistent_parameters = self.parameter_offload.persistent_parameters self._configure_offloading(offload_optimizer_config, offload_param_config) + # backup fused_adam optimizer init + if self.offload_optimizer and self.partial_offload != 1.0: + backup_gpu_tensor = torch.randn(1, device='cuda').to(self.dtype) + backup_gpu_param = torch.nn.Parameter(backup_gpu_tensor) + assert type(init_optimizer) == DeepSpeedCPUAdam, 'Hybrid Optimizer Only Supports DeepSpeedCPUAdam' + self.backup_optimizer = FusedAdam([backup_gpu_param], + lr=self.optimizer.param_groups[0]["lr"], + bias_correction=self.optimizer.param_groups[0]["bias_correction"], + betas=self.optimizer.param_groups[0]["betas"], + eps=self.optimizer.param_groups[0]["eps"], + weight_decay=self.optimizer.param_groups[0]["weight_decay"], + amsgrad=self.optimizer.param_groups[0]["amsgrad"]) + # Multiple param_groups configs for back-up optimizer + if len(self.optimizer.param_groups) > 1: + for i in range(1, len(self.optimizer.param_groups)): + self.backup_optimizer.add_param_group(self.optimizer.param_groups[i]) + self.module = module self.elastic_checkpoint = elastic_checkpoint @@ -780,6 +799,17 @@ def _create_fp32_partitions(self): nvme_fp32_dest_tensors = [] fp32_element_size = torch.tensor([], dtype=torch.float32).element_size() + # Assign portion of subgroup to cpu, the other to gpu. + if self.offload_optimizer: + self.subgroup_to_device = {} + sub_group_size = len(self.fp16_partitioned_groups_flat) + # print(f"Partial offload sub_group_size is {sub_group_size}, ratio is {self.partial_offload}\n") + for i in range(sub_group_size): + if i < int(self.partial_offload * sub_group_size): + self.subgroup_to_device[i] = 'cpu' + else: + self.subgroup_to_device[i] = get_accelerator()._name + for i, tensor in enumerate(self.fp16_partitioned_groups_flat): num_elements = self.fp16_partitioned_groups_flat_numel[i] @@ -816,8 +846,12 @@ def _create_fp32_partitions(self): self._swap_in_sub_group_to_flat_buffer(unpinned_fp32_buffer, i) self.fp32_partitioned_groups_flat.append(unpinned_fp32_buffer) else: - self.fp32_partitioned_groups_flat.append(self.fp16_partitioned_groups_flat[i].to( - self.device).clone().float().detach()) + if self.offload_optimizer: + self.fp32_partitioned_groups_flat.append(self.fp16_partitioned_groups_flat[i].to( + self.subgroup_to_device[i]).clone().float().detach()) + else: + self.fp32_partitioned_groups_flat.append(self.fp16_partitioned_groups_flat[i].to( + self.device).clone().float().detach()) self.fp32_partitioned_groups_flat[i].requires_grad = True # keep this in case internal optimizer uses it @@ -885,10 +919,20 @@ def _release_ipg_buffers(self): def _optimizer_step(self, sub_group_id): param_group_id = self.sub_group_to_group_id[sub_group_id] fp32_param = self.fp32_partitioned_groups_flat[sub_group_id] - self.optimizer.param_groups[param_group_id]['params'] = [fp32_param] - - self.optimizer.step() - self.optimizer.param_groups[param_group_id]['params'] = [] + if self.offload_optimizer: + cur_device = self.subgroup_to_device[sub_group_id] + if cur_device == 'cpu': + self.optimizer.param_groups[param_group_id]['params'] = [fp32_param] + cpu_loss = self.optimizer.step() + self.optimizer.param_groups[param_group_id]['params'] = [] + else: + self.backup_optimizer.param_groups[param_group_id]['params'] = [fp32_param] + gpu_loss = self.backup_optimizer.step() + self.backup_optimizer.param_groups[param_group_id]['params'] = [] + else: + self.optimizer.param_groups[param_group_id]['params'] = [fp32_param] + self.optimizer.step() + self.optimizer.param_groups[param_group_id]['params'] = [] def _swappable_optimizer_subgroup(self, sub_group_id): if not self.swap_optimizer: @@ -955,7 +999,7 @@ def initialize_optimizer_states(self): if self.offload_optimizer_pin_memory: subgroup_gradient_buffer = get_accelerator().pin_memory(subgroup_gradient_buffer) - self.fp32_partitioned_groups_flat[i].grad = subgroup_gradient_buffer + self.fp32_partitioned_groups_flat[i].grad = subgroup_gradient_buffer.to(self.subgroup_to_device[i]) else: self.fp32_partitioned_groups_flat[i].grad = gradient_buffer.narrow(0, 0, num_elements) diff --git a/docs/_pages/config-json.md b/docs/_pages/config-json.md index cdd18e62a29e..e9d7166b05b3 100755 --- a/docs/_pages/config-json.md +++ b/docs/_pages/config-json.md @@ -576,6 +576,7 @@ Note that if the value of "device" is not specified or not supported, an asserti "device": "[cpu|nvme]", "nvme_path": "/local_nvme", "pin_memory": [true|false], + "ratio": 0.3, "buffer_count": 4, "fast_init": false } @@ -598,6 +599,12 @@ Note that if the value of "device" is not specified or not supported, an asserti | ---------------------------------------------------------------------------------------------------- | ------- | | Offload to page-locked CPU memory. This could boost throughput at the cost of extra memory overhead. | `false` | +***ratio***: [float] + +| Description | Default | +| ------------------------------------------------------------------- | ------- | +| the ratio of parameters updating (i.e. optimizer step) on CPU side. | 1 | + ***buffer_count***: [integer] | Description | Default | diff --git a/tests/small_model_debugging/partial_offload_test.py b/tests/small_model_debugging/partial_offload_test.py new file mode 100644 index 000000000000..2094448d534d --- /dev/null +++ b/tests/small_model_debugging/partial_offload_test.py @@ -0,0 +1,128 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import os +import json +import argparse +import torch +import deepspeed +from torch.utils.data.distributed import DistributedSampler +import deepspeed.comm as dist + + +class SimpleModel(torch.nn.Module): + + def __init__(self, hidden_dim, empty_grad=False): + super(SimpleModel, self).__init__() + self.linear = torch.nn.Linear(hidden_dim, hidden_dim) + self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim) + self.linear3 = torch.nn.Linear(hidden_dim, hidden_dim) + self.linear4 = torch.nn.Linear(hidden_dim, hidden_dim) + if empty_grad: + self.layers2 = torch.nn.ModuleList([torch.nn.Linear(hidden_dim, hidden_dim)]) + self.cross_entropy_loss = torch.nn.CrossEntropyLoss() + + def forward(self, x, y): + hidden = x + hidden = self.linear(hidden) + hidden = self.linear2(hidden) + hidden = self.linear3(hidden) + hidden = self.linear4(hidden) + return self.cross_entropy_loss(hidden, y) + + +def create_config_from_dict(tmpdir, config_dict): + config_path = os.path.join(tmpdir, 'temp_config.json') + with open(config_path, 'w') as fd: + json.dump(config_dict, fd) + return config_path + + +def get_data_loader(model, total_samples, hidden_dim, device): + batch_size = model.train_micro_batch_size_per_gpu() + train_data = torch.randn(total_samples, hidden_dim, device=device, dtype=torch.half) + train_label = torch.empty(total_samples, dtype=torch.long, device=device).random_(hidden_dim) + train_dataset = torch.utils.data.TensorDataset(train_data, train_label) + sampler = DistributedSampler(train_dataset) + train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=sampler) + return train_loader + + +def get_args(tmpdir, config_dict): + parser = argparse.ArgumentParser() + parser.add_argument("--local_rank", type=int, default=0) + parser.add_argument('--zero', type=int, default=0) + args = parser.parse_args() #args='' + + config_dict["zero_optimization"]["stage"] = args.zero + print('config_dict["zero_optimization"]', config_dict["zero_optimization"]) + config_path = create_config_from_dict(tmpdir, config_dict) + + args.deepspeed_config = config_path + return args + + +def print0(msg): + if dist.get_rank() == 0: + print(msg, flush=True) + + +rank = int(os.environ['RANK']) +print('seed:', 2222 + rank) +torch.random.manual_seed(2222 + rank) + +config_dict = { + "train_batch_size": 256, + "steps_per_print": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.00015, + } + }, + "fp16": { + "enabled": True, + "initial_scale_power": 15 + }, + "zero_optimization": { + "stage": 0, + "sub_group_size": 8, + "reduce_bucket_size": 20, + "offload_optimizer": { + "device": "cpu", + "pin_memory": True, + "ratio": 0.3 + } + } +} +# "initial_scale_power": 15 +args = get_args('/tmp/', config_dict) +hidden_dim = 4 * 1024 + +model = SimpleModel(hidden_dim, empty_grad=False) + +model, _, _, _ = deepspeed.initialize(args=args, + model=model, + model_parameters=model.parameters(), + dist_init_required=True) + + +def print_params(tag, model): + if dist.get_rank() == 0: + for n, p in model.named_parameters(): + print0("{} {}:{}".format(tag, n, p)) + + +data_loader = get_data_loader(model=model, total_samples=1000, hidden_dim=hidden_dim, device=model.device) +#print_params('pre-train', model) +#while True: +for n, batch in enumerate(data_loader): + loss = model(batch[0], batch[1]) + if dist.get_rank() == 0: + print("LOSS:", loss.item()) + model.backward(loss) + model.step() + #print_params('step={}'.format(n), model) + if n == 2: break diff --git a/tests/unit/ops/adam/test_hybrid_adam.py b/tests/unit/ops/adam/test_hybrid_adam.py new file mode 100644 index 000000000000..c7ef4890b322 --- /dev/null +++ b/tests/unit/ops/adam/test_hybrid_adam.py @@ -0,0 +1,78 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import torch +import numpy as np +import pytest + +from cpuinfo import get_cpu_info + +import deepspeed +from deepspeed.accelerator import get_accelerator +from deepspeed.ops.adam import FusedAdam, DeepSpeedCPUAdam +from deepspeed.ops.op_builder import CPUAdamBuilder +from unit.common import DistributedTest + +if not deepspeed.ops.__compatible_ops__[CPUAdamBuilder.NAME]: + pytest.skip("hybrid-adam is not compatible", allow_module_level=True) + +pytest.cpu_vendor = get_cpu_info()["vendor_id_raw"].lower() + + +def check_equal(first, second, atol=1e-2, verbose=False): + x = first.detach().numpy() + y = second.detach().numpy() + print("ATOL", atol) + if verbose: + print("x = {}".format(x.flatten())) + print("y = {}".format(y.flatten())) + print('-' * 80) + np.testing.assert_allclose(x, y, err_msg="param-update mismatch!", atol=atol) + + +@pytest.mark.parametrize('dtype', [torch.half, torch.float], ids=["fp16", "fp32"]) +@pytest.mark.parametrize('model_size', [8, 16]) +class TestHybridAdam(DistributedTest): + world_size = 1 + reuse_dist_env = True + requires_cuda_env = False + if not get_accelerator().is_available(): + init_distributed = False + set_dist_env = False + + @pytest.mark.skipif(not get_accelerator().is_available(), reason="only supported in CUDA environments.") + def test_hybrid_adam_equal(self, dtype, model_size): + if ("amd" in pytest.cpu_vendor) and (dtype == torch.half): + pytest.skip("cpu-adam with half precision not supported on AMD CPUs") + + ref_data = torch.randn(model_size).to(dtype) + total_data = ref_data.clone().detach() + + ref_param = torch.nn.Parameter(ref_data) + ref_optimizer = DeepSpeedCPUAdam([ref_param]) + + cpu_data, cuda_data = total_data.chunk(2) + cpu_param = torch.nn.Parameter(cpu_data) + cuda_param = torch.nn.Parameter(cuda_data.to(get_accelerator().device_name())) + + cpu_optimizer = DeepSpeedCPUAdam([cpu_param]) + cuda_optimizer = FusedAdam([cuda_param]) + + ref_grad = torch.randn(model_size).to(dtype) + cpu_grad, cuda_grad = ref_grad.clone().detach().chunk(2) + + ref_param.grad = ref_grad + cpu_param.grad = cpu_grad + cuda_param.grad = cuda_grad.to(get_accelerator().device_name()) + + ref_optimizer.step() + cpu_optimizer.step() + cuda_optimizer.step() + + cuda_param_copy = cuda_param.cpu() + + total_param = torch.cat((cpu_param, cuda_param_copy)) + + check_equal(ref_param, total_param) diff --git a/tests/unit/runtime/zero/test_zero_offloadpp.py b/tests/unit/runtime/zero/test_zero_offloadpp.py new file mode 100644 index 000000000000..c376686f8052 --- /dev/null +++ b/tests/unit/runtime/zero/test_zero_offloadpp.py @@ -0,0 +1,75 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team +import pytest +import deepspeed.comm as dist +from unit.common import DistributedTest +from unit.simple_model import random_dataloader + +import deepspeed + +from deepspeed.runtime.zero.offload_config import DeepSpeedZeroOffloadOptimizerConfig + +import torch.nn as nn + + +class NNModel(nn.Module): + + def __init__(self, h_dim=1024, n_layers=2): + super(NNModel, self).__init__() + self.layers = nn.ModuleList([nn.Linear(h_dim, h_dim) for i in range(n_layers)]) + self.cross_entropy_loss = nn.CrossEntropyLoss() + + def forward(self, x, y): + for layer in self.layers: + x = layer(x) + return self.cross_entropy_loss(x, y) + + +def test_zero_partial_offload_config(): + config = DeepSpeedZeroOffloadOptimizerConfig(**{"ratio": 0.3}) + assert config.ratio == 0.3 + + +#Large sweep along hidden dim, num_layers of different sizes +@pytest.mark.parametrize("h_dim", [1024]) +@pytest.mark.parametrize("n_layers", [4, 8]) +class TestZeroPartialOffloadConfigSweep(DistributedTest): + world_size = 4 + + def test(self, h_dim: int, n_layers: int) -> None: + config_dict = { + "train_batch_size": 256, + "steps_per_print": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.00015, + } + }, + "fp16": { + "enabled": True, + "initial_scale_power": 15 + }, + "zero_optimization": { + "stage": 3, + "sub_group_size": 8, + "reduce_bucket_size": 20, + "offload_optimizer": { + "device": "cpu", + "pin_memory": True, + "ratio": 0.3 + } + } + } + + model = NNModel(h_dim, n_layers) + model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + data_loader = random_dataloader(model=model, total_samples=20, hidden_dim=h_dim, device=model.device) + dist.barrier() + + for n, batch in enumerate(data_loader): + loss = model(batch[0], batch[1]) + model.backward(loss) + model.step() From 7e480ea02fa1837aad0e9d3eb8056da3055a392c Mon Sep 17 00:00:00 2001 From: Guanhua Wang Date: Mon, 6 Nov 2023 14:17:15 -0800 Subject: [PATCH 3/5] offload++ blog (#623) (#4637) This PR is the blog for ZeRO-Offload++, it describes the details of how our new Twin-Flow feature works and its performance numbers on both DGX-A100 and DGX-H100 machines. Corresponding code PR is https://github.com/microsoft/DeepSpeed/pull/4636 cc @jeffra @awan-10 @tjruwase @mrwyattii --------- Co-authored-by: Jeff Rasley --- blogs/deepspeed-offloadpp/README.md | 52 ++++++++++++++++++ blogs/deepspeed-offloadpp/images/a100-8.png | Bin 0 -> 22879 bytes .../images/cpu-offload.png | Bin 0 -> 42294 bytes blogs/deepspeed-offloadpp/images/h100-8.png | Bin 0 -> 20621 bytes .../images/twin-offload.png | Bin 0 -> 59949 bytes 5 files changed, 52 insertions(+) create mode 100644 blogs/deepspeed-offloadpp/README.md create mode 100644 blogs/deepspeed-offloadpp/images/a100-8.png create mode 100644 blogs/deepspeed-offloadpp/images/cpu-offload.png create mode 100644 blogs/deepspeed-offloadpp/images/h100-8.png create mode 100644 blogs/deepspeed-offloadpp/images/twin-offload.png diff --git a/blogs/deepspeed-offloadpp/README.md b/blogs/deepspeed-offloadpp/README.md new file mode 100644 index 000000000000..22910ffef7b1 --- /dev/null +++ b/blogs/deepspeed-offloadpp/README.md @@ -0,0 +1,52 @@ +# DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow + +Deep learning has been successfully adopted in a wide range of applications such as speech recognition, chatbot, text and image generation, etc. To achieve better model serving accuracy, model size grows significantly. Take language models as example, from BERT with 110 million parameters to Megatron-Turing NLG with 530 billion parameters, the model size grows almost 5000x. Given limited GPU memory size, we need to efficiently utilize GPU memory to achieve good system throughput. + +ZeRO offers memory efficient data parallel training scheme. For training large models like LLMs using ZeRO, GPU memory size is still often insufficient to hold all the model parameters. Thus, ZeRO-Offload is introduced to solve this insufficient GPU memory issue. ZeRO-Offload releases GPU memory pressure by offloading data and compute to the CPU side while minimizing CPU-GPU data copy overhead. Given CPU memory is often orders-of-magnitude larger than GPU memory, ZeRO-Offload was the first piece of work that enables billion-level parameter training even with very limited GPU memory resources (e.g., to an extreme: single GPU). ZeRO-Offload provides excellent performance when model size is multiple times larger than total GPU memory size. + +However, system efficiency is still far from optimal when adopting ZeRO-Offload in some scenarios. Especially in the cases like small batch training, model that could not fit into GPU memory but not orders-of-magnitude bigger than GPU memory capacity, CPU offload not only introduce long end-to-end latency, but also underutilize GPU computation resources. To reduce memory copy latency as well as inefficient utilization of GPU introduced in these offload cases, we propose ZeRO-Offload++, which leverages both CPU and GPU coherently. ZeRO-Offload++ mainly includes 3 new features as _Twin-Flow_, MemCpy reduction, CPUAdam optimization. Now we release our __Twin-Flow__ feature. + +The key benefits are: +* With _Twin-Flow_, ZeRO-Offload++ achieves up to **6x** training speedup compared with ZeRO-Offload. +* High-level API provided in DeepSpeed config JSON makes it easy to use and fine-tune. + +![h100-img](./images/h100-8.png) + +## Twin-Flow + +In DeepSpeed, when training using popular optimizer like Adam, optimizer offloading follows an all-or-nothing policy. For simplifed example shown as Figure below, without offloading, all the parameters will be updated using GPU adam as FusedAdam optimizer. On the other hand, if offloading is enabled, all model weights use CPUAdam to update. + +![cpu-offload-img](./images/cpu-offload.png) + +The major downside of this all-or-nothing offloading is, when offload all optimizer states to CPU side, both GPU memory and compute resources remain under-utilized. Although increasing batch size improves GPU utilization rate, each training iteration time is still super long compared with no-offloading case. To improve GPU compute and memory utilization rate as well as decrease training iteration time, we introduce a new feature in our DeepSpeed training engine called _Twin-Flow_. + +In comparison, _Twin-Flow_ allows a portion of optimizer states to be held in CPU memory and the other portion of optimizer states remaining in GPU memory. When optimization step is triggered, both CPU and GPU can do parameter updates simultaneously. Once offloading is enabled, we provide an offload ratio configuration which allows users to adjust how many percentages of model weights are updated on CPU side and the rest are happened on GPU side. "_Twin_" comes from the idea that both CPU and GPU are using the same optimizer function here. "_Flow_" means parameters are not only hold in both host and device memory, but also computed using both CPU and GPU cores. + +As shown in Figure below, with ZeRO-Offload enabled and we set _Twin-Flow_ ratio of 0.4 (40%). DeepSpeed Training engine will automatically assign first 40% (i.e. 0-40%) of weights step procedure on the CPU side using CPUAdam, and use GPU side FusedAdam to update the rest 60% (i.e., 40-100%) model parameters jointly. Therefore, with _Twin-Flow_, we can achieve decent GPU memory and core utilization rate, at the same time reduce training iteation time in optimizer offloading cases. + +![_Twin-Flow_-img](./images/twin-offload.png) + +Note that this _Twin-Flow_ ratio can be adjusted based on how much GPU idle memory is available. The smaller this ratio is, the more GPU memory and cores are used and the shorter training iteration time it achieves. The ideal case is to be as near as GPU memory upper bound in order to minimize training iteration time. +Note that _Twin-Flow_ is not limited to Adam optimizer only, it can be applied to any optimizer (e.g., AdaGrad) from the user side. + +## Performance Evaluation + +We conduct our performance evaluations over both A100 and H100 DGX machine and test for OPT model with 13B and 30B parameters. We run 13B OPT model training on a 8 A100 DGX machine, and run OPT-30B model training using a 8 H100 DGX machine. With some tuning on offload ratio in ZeRO-Offload++, we achieve 6x and 3x training speedup of Meta OPT models on single DGX-H100-80GB and DGX-A100-40GB, respectively (top-most figure and bottom figure here). + +![a100-img](./images/a100-8.png) + +## On-going Optimizations + +* Reduce uncessary D2H/H2D memcpy + +* On-the-fly fp16 to fp32 casting for CPUAdam + +## Tutorials + +Examples and Tutorials are [here](https://github.com/microsoft/Megatron-DeepSpeed/blob/guanhua/partial-offload/examples_deepspeed/offload_pp/README.md) + +## Contributors: + +This project was made possible by the contributions of the following people from DeepSpeed Team: + +[Guanhua Wang](https://www.microsoft.com/en-us/research/people/guanhuawang/), Masahiro Tanaka, Xiaoxia Wu, Lok Chand Koppaka, Samyam Rajbhandari, [Olatunji Ruwase](https://www.microsoft.com/en-us/research/people/olruwase/), [Yuxiong He](https://www.microsoft.com/en-us/research/people/yuxhe/) (team lead) diff --git 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zgkc#3|BHM}I1wpODic>e4G@TbNSA=+OhW&uhoTv#8DZCu^oZ?~R!XEDB#db9UcWf7d$pRnB7YcFw`zzgvbIsqL`&TC%)u--vMqXY}qn)~-8 z93-zd$29QLnMW1a{k4Wa{>B^zfBO}Z157NnSmY&?7E=;OC1+wj&?vqs1v=lJ+y?SY zYUZDqYSt4)1pM#&pofrY|9qQAQWP}G3YKef+H zjqIHE&Cg24Y^H@?2M2F#m$ZbgmQSW$KkEK(jkV;HYD|1pe}t!rF|n(6>)j-NTjX!L z``bXOje3iM$dh?%1kHC3z zi~A(#Z(JabEX`oENQi%q`@-Jf_*Q;r1X&+-*-}i&@l)y2CL7Uxs!>Hx06Kb?N(Ud! zc?CC#xiQ7JvrFKJv{}7=zj2ui*|Qh=vPMYH@Yw-}4&HV8Twg}wbC$KO>%V11;bDal zJw8y(MZ3=Xj0%nh`yGQojyGFar>m^ermT?gkFC{d6iw!l+#$sT;?xBV|8#dm(O(27 zaHSkmD@w&&qPp^x~wS0HN>P99ck1u~S9 zTVX~^`&rAL${x8}84$CcZVpFsLqz-ie8OWq%_Y_i5Y)17hwpCY+3IclHzSoyF+1*$ z1oWzh2zD7cKdkATKThHf5TqpCCM?LebmUTjQx?T7z2wrOQ%G)Ui>ZhnGIagF*eTp zBtc3V9c%SxhL9rh)brPdy!{Yp@HqFw2aljtAMhw=6O zhj( Date: Mon, 6 Nov 2023 14:52:29 -0800 Subject: [PATCH 4/5] Update README in offloadpp blog (#4641) update tutorial link --- blogs/deepspeed-offloadpp/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blogs/deepspeed-offloadpp/README.md b/blogs/deepspeed-offloadpp/README.md index 22910ffef7b1..1441da5a35c0 100644 --- a/blogs/deepspeed-offloadpp/README.md +++ b/blogs/deepspeed-offloadpp/README.md @@ -43,7 +43,7 @@ We conduct our performance evaluations over both A100 and H100 DGX machine and t ## Tutorials -Examples and Tutorials are [here](https://github.com/microsoft/Megatron-DeepSpeed/blob/guanhua/partial-offload/examples_deepspeed/offload_pp/README.md) +Examples and Tutorials are [here](https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples_deepspeed/offload_pp/README.md) ## Contributors: From cbec96b00ec16bd7c724dcb7ae00ceb1d319cefe Mon Sep 17 00:00:00 2001 From: Jeff Rasley Date: Mon, 6 Nov 2023 15:55:25 -0800 Subject: [PATCH 5/5] [docs] update news items (#4640) Co-authored-by: Guanhua Wang --- README.md | 18 ++++++++++++++---- docs/index.md | 21 ++++++++++++++++----- 2 files changed, 30 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 721ba62cee37..e0463beb1a77 100755 --- a/README.md +++ b/README.md @@ -15,13 +15,23 @@ ## Latest News DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat). +* [2023/11] [DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-offloadpp) * [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)] * [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] -* [2023/08] [DeepSpeed ZeRO-Inference: 20X faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) -* [2023/08] [DeepSpeed-Chat: Llama/Llama-2 system support, efficiency boost, and training stability improvements](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/ds-chat-release-8-31/README.md) -* [2023/08] [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ulysses) [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/japanese/README.md)] -* [2023/06] [ZeRO++: A leap in speed for LLM and chat model training with 4X less communication](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)[[English](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/zeropp/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/zeropp/japanese/README.md)] +* [2023/08] [DeepSpeed ZeRO-Inference: 20x faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) + + +

 --- diff --git a/docs/index.md b/docs/index.md index 60bcf19b84da..a4027ca1e52e 100755 --- a/docs/index.md +++ b/docs/index.md @@ -7,13 +7,24 @@ title: "Latest News" --- DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat). +* [2023/11] [DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-offloadpp) * [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)] -* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] -* [2023/08] [DeepSpeed ZeRO-Inference: 20X faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) -* [2023/08] [DeepSpeed-Chat: Llama/Llama-2 system support, efficiency boost, and training stability improvements](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/ds-chat-release-8-31/README.md) -* [2023/08] [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ulysses) [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/japanese/README.md)] -* [2023/06] [ZeRO++: A leap in speed for LLM and chat model training with 4X less communication](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)[[English](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/zeropp/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/zeropp/japanese/README.md)] +* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] +* [2023/08] [DeepSpeed ZeRO-Inference: 20x faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) + + + +
+ More news + +
# Extreme Speed and Scale for DL Training and Inference
+ More news + +