From 05cb79db8e90d2c35187a1d8bf6ec44818ff4d2c Mon Sep 17 00:00:00 2001 From: Nadav Elyahu <88962733+nelyahu@users.noreply.github.com> Date: Mon, 17 Jun 2024 23:23:47 +0300 Subject: [PATCH 01/36] _exec_forward_pass: place zeros(1) on the same device as the param (#5576) avoid mismatch between the param storage device and the .data device Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/pipe/engine.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepspeed/runtime/pipe/engine.py b/deepspeed/runtime/pipe/engine.py index ab4919a0f0ab..9e84121d50fa 100644 --- a/deepspeed/runtime/pipe/engine.py +++ b/deepspeed/runtime/pipe/engine.py @@ -742,7 +742,7 @@ def _exec_forward_pass(self, buffer_id): raise ValueError("expecting a tensor or a tuple of tensors") part = PartitionedTensor(tensor=first_output, group=self.grid.get_slice_parallel_group()) # Clear the large output data, but save the computation graph - first_output.data = torch.zeros(1) + first_output.data = torch.zeros(1, device=first_output.data.device) self.pipe_buffers['output_tensors'][buffer_id] = first_output # Inject the partitioned tensor into the output before sending outputs = (part.to_meta(), part.data(), *outputs_tail) From 73316307b1040953430fa611e613cd46e9ae7b83 Mon Sep 17 00:00:00 2001 From: Yizhou Wang Date: Tue, 18 Jun 2024 04:24:22 +0800 Subject: [PATCH 02/36] [XPU] adapt lazy_call func to different versions (#5670) Previously, lay_call function was wrapped by torch.xpu.lay_init._lazy_call, which is now changed to torch.xpu._lazy_call. Thus we change this function to adapt different versions. --- accelerator/xpu_accelerator.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/accelerator/xpu_accelerator.py b/accelerator/xpu_accelerator.py index afb129dcf334..790239ad6c23 100644 --- a/accelerator/xpu_accelerator.py +++ b/accelerator/xpu_accelerator.py @@ -159,7 +159,10 @@ def range_pop(self): return def lazy_call(self, callback): - return torch.xpu.lazy_init._lazy_call(callback) + if hasattr(torch.xpu, "_lazy_call"): + return torch.xpu._lazy_call(callback) + else: + return torch.xpu.lazy_init._lazy_call(callback) def communication_backend_name(self): return self._communication_backend_name From 8831b57bb28aad204c1f2a4b6cd426a6bffba6dd Mon Sep 17 00:00:00 2001 From: Liangliang-Ma Date: Tue, 18 Jun 2024 04:39:10 +0800 Subject: [PATCH 03/36] fix IDEX dependence in xpu accelerator (#5666) We don't use IDEX in xpu accelerator from Deepspeed. Fix this hardcode. --- accelerator/xpu_accelerator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/accelerator/xpu_accelerator.py b/accelerator/xpu_accelerator.py index 790239ad6c23..ddc553f3a558 100644 --- a/accelerator/xpu_accelerator.py +++ b/accelerator/xpu_accelerator.py @@ -225,7 +225,7 @@ def pin_memory(self, tensor, align_bytes=1): if align_bytes == 1: return tensor.pin_memory(device=self.current_device_name()) elif align_bytes == 0: - from intel_extension_for_deepspeed.op_builder.async_io import AsyncIOBuilder + from deepspeed.ops.op_builder.xpu import AsyncIOBuilder self.aio_handle = AsyncIOBuilder().load().aio_handle(128 * 1024, 8, False, False, False) aligned_t = self.aio_handle.new_cpu_locked_tensor(tensor.numel(), tensor) aligned_t = aligned_t[:tensor.numel()].copy_(tensor) From 2a0c0e3c27099531f364ff290f22153ab05cb359 Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Mon, 17 Jun 2024 14:42:54 -0700 Subject: [PATCH 04/36] Remove compile wrapper to simplify access to model attributes (#5581) Having a wrapper of a compiled module brings various restrictions about accessing attributes of the compiled model. This PR removes the wrapper of compiled module to simplify the access to the compiled model. --------- Co-authored-by: Olatunji Ruwase --- deepspeed/runtime/compiler.py | 153 +----------------- deepspeed/runtime/config.py | 3 - deepspeed/runtime/engine.py | 22 ++- .../runtime/zero/partition_parameters.py | 10 +- tests/unit/common.py | 3 + .../runtime/compile/test_compile_wrapper.py | 85 ---------- .../unit/runtime/compile/test_compile_zero.py | 4 - .../unit/runtime/compile/test_load_config.py | 131 --------------- tests/unit/runtime/compile/util.py | 2 +- 9 files changed, 25 insertions(+), 388 deletions(-) delete mode 100644 tests/unit/runtime/compile/test_compile_wrapper.py delete mode 100644 tests/unit/runtime/compile/test_load_config.py diff --git a/deepspeed/runtime/compiler.py b/deepspeed/runtime/compiler.py index 4f828d978613..879c0a1a2cc9 100644 --- a/deepspeed/runtime/compiler.py +++ b/deepspeed/runtime/compiler.py @@ -3,165 +3,14 @@ # DeepSpeed Team -from typing import Union, Callable, Dict, Any -import importlib import torch -from ..pydantic_v1 import validator -from .config_utils import DeepSpeedConfigModel - -COMPILE_CONFIG = "compile" def is_compile_supported(): - return hasattr(torch, "compiler") + return hasattr(torch, "compiler") and hasattr(torch.nn.Module, "compile") def disable(func): if is_compile_supported(): return torch.compiler.disable(func) return func - - -def get_compile_config(param_dict): - if COMPILE_CONFIG in param_dict: - compile_config_dict = param_dict[COMPILE_CONFIG] - else: - compile_config_dict = {} - return CompileConfig(**compile_config_dict) - - -def get_backend_fn(backend: Union[str, Callable]) -> Union[str, Callable]: - if isinstance(backend, Callable): - return backend - - elif isinstance(backend, str): - if backend in torch._dynamo.list_backends(exclude_tags=()): - return backend - - # Get module name from backend name - module_name = '.'.join(backend.split('.')[:-1]) - fn_name = backend.split('.')[-1] - - try: - module = importlib.import_module(module_name) - backend_fn = getattr(module, fn_name) - except ImportError: - raise ValueError( - f"The backend {backend} is not in the list of available backends and could not be imported.") - return backend_fn - - raise ValueError(f"backend for torch.compile must be a string or Callable: {backend}") - - -class CompileConfig(DeepSpeedConfigModel): - """ - [EXPERIMENTAL] This configuration enables users to activate `torch.compile` within DeepSpeed and customize its settings. - Please be aware that these features and API designs are experimental and subject to change. - """ - - enabled: bool = False - """ - Enable torch.compile when True. - """ - - backend: str = "inductor" - """ - Passed to `backend` argument of torch.compile. - If the given value is not in torch._dynamo.list_backends(), - DeepSpeed attempts to import and instantiate the module with the given name. - """ - - kwargs: Dict[str, Any] = {} - """ - Passed to `kwargs` argument of torch.compile. - """ - - @validator("enabled") - def validate_enabled(cls, field_value, values): - if field_value and not is_compile_supported(): - raise ValueError("torch.compile is not supported on this version of PyTorch.") - return field_value - - -def CompiledModuleWrapper(mod, compile_config: Union[CompileConfig, None] = None): - - class wrapper(mod.__class__): - - def __init__(self, module, compile_config: Union[CompileConfig, None] = None): - self.__dict__ = {k: module.__dict__[k] for k in module.__dict__ if not k in self.__class__.__dict__} - - assert is_compile_supported(), "torch.compile is not supported on this version of PyTorch." - - self.__dict__['wrapped'] = module - self._is_compiled = False - self._backend = get_backend_fn(compile_config.backend) - self._compile_kwargs = compile_config.kwargs - self._compiler_fn = None - - def set_backend(self, backend: Union[str, Callable]): - """Set the backend for torch.compile. - - Args: - backend (Union[str, Callable]): backend name or a function that takes a torch.nn.Module and returns a compiled module. - You can directly pass a function that works as a backend. - See also `backend` field in `CompileConfig` for more details. - """ - self._backend = get_backend_fn(backend) - - def set_torch_compile_kwargs(self, kwargs: Dict[str, Union[str, Any]]) -> None: - """Set kwargs for torch.compile. Kwargs that are set in DeepSpeed config will be overwritten. - You can also pass a backend name with "backend" key to change the backend. - - Args: - kwargs (Dict[str, Union[str, Any]]): kwargs passed to torch.compile. - """ - - if "backend" in kwargs: - raise ValueError("backend cannot be set as compile kwargs. Use set_backend instead.") - self._compile_kwargs.update(kwargs) - - def set_compiler_fn(self, compiler_fn: Callable) -> None: - """Set a function to be used for compiling the module. - This function should take a torch.nn.Module as input and return a compiled module. - Note that other compile options are ignored when a compiler_fn is set. - - Example: - ```python - def my_compiler_fn(module: torch.nn.Module): - ... - return torch.compile(module, ...) - - engine.set_compiler_fn(my_compiler_fn) - ``` - """ - self._compiler_fn = compiler_fn - - def forward(self, *args, **kwargs) -> Any: - if not self.is_compiled: - if self._compiler_fn is None: - self.__dict__['wrapped'] = torch.compile(self.wrapped, - backend=self._backend, - **self._compile_kwargs) - else: - self.__dict__['wrapped'] = self._compiler_fn(self.wrapped) - self._is_compiled = True - - return self.__dict__['wrapped'](*args, **kwargs) - - @property - def is_compiled(self) -> bool: - return self._is_compiled - - @property - def backend(self) -> Union[str, Callable]: - return self._backend - - @property - def torch_compile_kwargs(self) -> Dict[str, Any]: - return self._compile_kwargs - - @property - def compiler_fn(self) -> Union[Callable, None]: - return self._compiler_fn - - return wrapper(mod, compile_config) diff --git a/deepspeed/runtime/config.py b/deepspeed/runtime/config.py index 04b122963a38..b49b4a8b6086 100755 --- a/deepspeed/runtime/config.py +++ b/deepspeed/runtime/config.py @@ -31,7 +31,6 @@ from ..comm.config import DeepSpeedCommsConfig from ..monitor.config import get_monitor_config from ..inference.config import WeightQuantConfig -from .compiler import get_compile_config from deepspeed import comm as dist from deepspeed.runtime.config_utils import DeepSpeedConfigModel @@ -911,8 +910,6 @@ def _initialize_params(self, param_dict): self.weight_quantization_config = WeightQuantConfig( **param_dict['weight_quantization']) if 'weight_quantization' in param_dict else None - self.compile_config = get_compile_config(param_dict) - self.timers_config = get_timers_config(param_dict) def _batch_assertion(self): diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 08ab05d79b6a..4c418fbc532e 100644 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -90,7 +90,7 @@ from .pipe.module import PipelineModule from .utils import get_ma_status -from .compiler import CompiledModuleWrapper +from .compiler import is_compile_supported from ..ops.adam import FusedAdam from ..moe.sharded_moe import TopKGate, MOELayer from ..moe.layer import MoE @@ -361,8 +361,7 @@ def __init__(self, self.flatten = _flatten_dense_tensors self.unflatten = _unflatten_dense_tensors - if self._config.compile_config.enabled: - self._set_client_model(CompiledModuleWrapper(self.module, self._config.compile_config)) + self._is_compiled = False def destroy(self): if self.optimizer is not None and hasattr(self.optimizer, 'destroy'): @@ -3604,3 +3603,20 @@ def empty_partition_cache(self): self.optimizer.empty_partition_cache() gc.collect() get_accelerator().empty_cache() + + def compile(self, backend=get_accelerator().get_compile_backend(), compile_kwargs={}) -> None: + """Compile the module using the specified backend and kwargs. + If a compiler_fn is set, it will be used instead of torch.compile(). + """ + if not is_compile_supported(): + raise RuntimeError("compile is not supported in your version of PyTorch.") + + if self.is_compiled: + return + + self.module.compile(backend=backend, **compile_kwargs) + self._is_compiled = True + + @property + def is_compiled(self) -> bool: + return self._is_compiled diff --git a/deepspeed/runtime/zero/partition_parameters.py b/deepspeed/runtime/zero/partition_parameters.py index a2d2465c9666..a88c0a2d146c 100755 --- a/deepspeed/runtime/zero/partition_parameters.py +++ b/deepspeed/runtime/zero/partition_parameters.py @@ -933,15 +933,7 @@ def __init__( _ds_config = deepspeed.runtime.config.DeepSpeedConfig(config_dict_or_path, mpu) if config_dict_or_path is not None else None if _ds_config is not None: - if _ds_config.zero_config.memory_efficient_linear and _ds_config.compile_config.enabled: - # memory_efficient_linear displays numerous errors when torch.compile is enabled. - # Refer to https://github.com/pytorch/pytorch/issues/119059 for details. - # Further investigation into performance is necessary, even after resolving this issue because - # the `memory_efficient_linear` module may lead to more graph breaks compared to the original implementation. - logger.warning(f'memory_efficient_linear is disabled when torch.compile is enabled.') - mem_efficient_linear = False - else: - mem_efficient_linear = _ds_config.zero_config.memory_efficient_linear + mem_efficient_linear = _ds_config.zero_config.memory_efficient_linear super().__init__(enabled=enabled, mem_efficient_linear=mem_efficient_linear, ds_config=_ds_config, dtype=dtype) if not dist.is_initialized(): diff --git a/tests/unit/common.py b/tests/unit/common.py index 58bb26ca18b4..1774bcfae9ff 100644 --- a/tests/unit/common.py +++ b/tests/unit/common.py @@ -203,10 +203,13 @@ def _launch_non_daemonic_procs(self, num_procs): master_port = get_master_port() skip_msg = mp.Queue() # Allows forked processes to share pytest.skip reason processes = [] + prev_start_method = mp.get_start_method() + mp.set_start_method('spawn', force=True) for local_rank in range(num_procs): p = mp.Process(target=self._dist_run, args=(local_rank, num_procs, master_port, skip_msg)) p.start() processes.append(p) + mp.set_start_method(prev_start_method, force=True) # Now loop and wait for a test to complete. The spin-wait here isn't a big # deal because the number of processes will be O(#GPUs) << O(#CPUs). diff --git a/tests/unit/runtime/compile/test_compile_wrapper.py b/tests/unit/runtime/compile/test_compile_wrapper.py deleted file mode 100644 index 62af25ac3ba4..000000000000 --- a/tests/unit/runtime/compile/test_compile_wrapper.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# SPDX-License-Identifier: Apache-2.0 - -# DeepSpeed Team - -import pytest -import torch - -import deepspeed -from deepspeed.accelerator import get_accelerator -from deepspeed.utils.torch import required_torch_version - -from unit.common import DistributedTest - -pytestmark = pytest.mark.skipif(not required_torch_version(min_version=2.1), - reason="Compile tests requires Pytorch version 2.1 or above") - - -@pytest.fixture -def base_config(): - config_dict = { - "train_micro_batch_size_per_gpu": 1, - "optimizer": { - "type": "Adam", - "params": { - "lr": 0.00015 - } - }, - "fp16": { - "enabled": True - }, - "compile": { - "enabled": True, - "backend": get_accelerator().get_compile_backend() - } - } - return config_dict - - -class SmallModelWithCustomMethod(torch.nn.Module): - - def __init__(self, hidden_dim, test_value): - super(SmallModelWithCustomMethod, self).__init__() - self.fc = torch.nn.Linear(hidden_dim, hidden_dim, bias=False) - self.v = test_value - - def forward(self, x): - return self.fc(x) - - # Custom function that is not part of DeepSpeed engine. - def get_v(self): - return self.v - - -class TestCustomMethod(DistributedTest): - world_size = 1 - non_daemonic_procs = True - - def _init_engine(self, config, test_value): - hidden_dim = 10 - model = SmallModelWithCustomMethod(hidden_dim, test_value) - engine, _, _, _ = deepspeed.initialize(config=config, model=model, model_parameters=model.parameters()) - return engine - - def _run_model(self, engine): - train_batch_size = 1 - device = torch.device(get_accelerator().current_device_name()) - dtype = engine.module.fc.weight.dtype - hidden_dim = engine.module.fc.weight.shape[1] - x = torch.rand(train_batch_size, hidden_dim, device=device, dtype=dtype) - engine(x) - - @pytest.mark.skipif(not deepspeed.is_compile_supported(), reason="torch.compile is not supported") - def test_custom_function(self, base_config): - if get_accelerator().device_name() == "cpu": - pytest.skip("CPU accelerator does not support this test yet.") - test_value = 10 - - engine = self._init_engine(base_config, test_value) - assert engine.module.get_v() == test_value - self._run_model(engine) - - # The model is compiled after the first run. - # Thus we make sure the custom method is still available after compilation. - assert engine.module.get_v() == test_value diff --git a/tests/unit/runtime/compile/test_compile_zero.py b/tests/unit/runtime/compile/test_compile_zero.py index a0736b0f5425..ca80eef8b31e 100644 --- a/tests/unit/runtime/compile/test_compile_zero.py +++ b/tests/unit/runtime/compile/test_compile_zero.py @@ -50,10 +50,6 @@ def test_compile_zero(self, tmpdir, zero_stage, dtype, offload_device): }, "zero_optimization": { "stage": zero_stage, - }, - "compile": { - "enabled": True, - "backend": get_accelerator().get_compile_backend() } } diff --git a/tests/unit/runtime/compile/test_load_config.py b/tests/unit/runtime/compile/test_load_config.py deleted file mode 100644 index cee8d3b23f6b..000000000000 --- a/tests/unit/runtime/compile/test_load_config.py +++ /dev/null @@ -1,131 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# SPDX-License-Identifier: Apache-2.0 - -# DeepSpeed Team - -import pytest -import torch - -from unit.simple_model import SimpleModel -import deepspeed -from deepspeed.accelerator import get_accelerator -from deepspeed.utils.torch import required_torch_version - -from unit.common import DistributedTest - -pytestmark = pytest.mark.skipif(not required_torch_version(min_version=2.1), - reason="Compile tests requires Pytorch version 2.1 or above") - -custom_backend_called = False -custom_compler_fn_called = False - -if deepspeed.is_compile_supported(): - # PyTorch v1 does not have torch.fx - def custom_backend(gm: torch.fx.GraphModule, example_inputs): - global custom_backend_called - custom_backend_called = True - return gm.forward - - def custom_compiler_fn(module: torch.nn.Module): - global custom_compler_fn_called - custom_compler_fn_called = True - return torch.compile(module) - - -@pytest.fixture -def base_config(): - config_dict = { - "train_micro_batch_size_per_gpu": 1, - "optimizer": { - "type": "Adam", - "params": { - "lr": 0.00015 - } - }, - "fp16": { - "enabled": True - }, - "compile": { - "enabled": True, - "backend": get_accelerator().get_compile_backend() - } - } - - return config_dict - - -class TestConfigLoad(DistributedTest): - world_size = 1 - non_daemonic_procs = True - - def _init_engine(self, config): - hidden_dim = 10 - model = SimpleModel(hidden_dim) - engine, _, _, _ = deepspeed.initialize(config=config, model=model, model_parameters=model.parameters()) - return engine - - def _run_model(self, engine): - train_batch_size = 1 - device = torch.device(get_accelerator().current_device_name()) - dtype = engine.module.linears[0].weight.dtype - hidden_dim = engine.module.linears[0].weight.shape[1] - x = torch.rand(train_batch_size, hidden_dim, device=device, dtype=dtype) - y = torch.randn_like(x) - engine(x, y) - - @pytest.mark.skipif(not deepspeed.is_compile_supported(), reason="torch.compile is not supported") - def test_compile(self, base_config): - if get_accelerator().device_name() == "cpu": - pytest.skip("CPU accelerator does not support this test yet.") - engine = self._init_engine(base_config) - self._run_model(engine) - assert engine.is_compiled - - @pytest.mark.skipif(not deepspeed.is_compile_supported(), reason="torch.compile is not supported") - def test_custom_backend(self, base_config): - if get_accelerator().device_name() == "cpu": - pytest.skip("CPU accelerator does not support this test yet.") - global custom_backend_called - custom_backend_called = False - - engine = self._init_engine(base_config) - engine.set_backend(f"{__name__}.custom_backend") - self._run_model(engine) - assert custom_backend_called - - def test_compile_disabled(self, base_config): - if get_accelerator().device_name() == "cpu": - pytest.skip("CPU accelerator does not support this test yet.") - base_config["compile"]["enabled"] = False - engine = self._init_engine(base_config) - self._run_model(engine) - - @pytest.mark.skipif(not deepspeed.is_compile_supported(), reason="torch.compile is not supported") - def test_compile_kwargs(self, base_config): - if get_accelerator().device_name() == "cpu": - pytest.skip("CPU accelerator does not support this test yet.") - base_config["compile"]["kwargs"] = {"mode": "default"} - engine = self._init_engine(base_config) - self._run_model(engine) - assert "mode" in engine.torch_compile_kwargs - - @pytest.mark.skipif(not deepspeed.is_compile_supported(), reason="torch.compile is not supported") - def test_set_compile_kwargs(self, base_config): - if get_accelerator().device_name() == "cpu": - pytest.skip("CPU accelerator does not support this test yet.") - engine = self._init_engine(base_config) - engine.set_torch_compile_kwargs({"mode": "default"}) - self._run_model(engine) - assert "mode" in engine.torch_compile_kwargs - - @pytest.mark.skipif(not deepspeed.is_compile_supported(), reason="torch.compile is not supported") - def test_set_compiler_fn(self, base_config): - if get_accelerator().device_name() == "cpu": - pytest.skip("CPU accelerator does not support this test yet.") - global custom_compler_fn_called - custom_compler_fn_called = False - - engine = self._init_engine(base_config) - engine.set_compiler_fn(custom_compiler_fn) - self._run_model(engine) - assert custom_compler_fn_called diff --git a/tests/unit/runtime/compile/util.py b/tests/unit/runtime/compile/util.py index 86eadf3f6976..d53886a81429 100644 --- a/tests/unit/runtime/compile/util.py +++ b/tests/unit/runtime/compile/util.py @@ -84,7 +84,6 @@ def compare_loss(self, config, dtype): baseline_config = deepcopy(config) baseline_config["zero_optimization"]["stage"] = 0 baseline_config["zero_optimization"]["offload_optimizer"] = {} - baseline_config["compile"]["enabled"] = False baseline_engine, baseline_optimizer, _, _ = deepspeed.initialize(config=baseline_config, model=baseline_model, model_parameters=baseline_model.parameters()) @@ -101,6 +100,7 @@ def compare_loss(self, config, dtype): target_engine, target_optimizer, _, _ = deepspeed.initialize(config=config, model=target_model, model_parameters=target_model.parameters()) + target_engine.compile() train_batch_size = config["train_micro_batch_size_per_gpu"] From b33873d234cf6679a3046be9a137682c3469d1fb Mon Sep 17 00:00:00 2001 From: Sam Ade Jacobs Date: Mon, 17 Jun 2024 20:21:43 -0700 Subject: [PATCH 05/36] Fix hpZ with zero element (#5652) Fix corner cases where hpz secondary partition has zero element. This ensure that `sec_numel` is at least zero. For this scenario, copying is really not necessary except that all ranks need to synchronize at the end of secondary partition. This is a good solution until [2nd tensor all-gather vs 2nd tensor partition issue ](https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/runtime/zero/partition_parameters.py#L1706) is properly fixed. Fixes: #5642 --------- Co-authored-by: Logan Adams Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/zero/partition_parameters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepspeed/runtime/zero/partition_parameters.py b/deepspeed/runtime/zero/partition_parameters.py index a88c0a2d146c..f76bcf0eb781 100755 --- a/deepspeed/runtime/zero/partition_parameters.py +++ b/deepspeed/runtime/zero/partition_parameters.py @@ -1689,7 +1689,7 @@ def _partition_param_sec(self, param, buffer=None, has_been_updated=False): one_dim_param = param.contiguous().view(-1) # ds_numel is unpadded, so the last chunk of the secondary tensor might not be secondary_partition_size - sec_numel = param.ds_numel - secondary_start if secondary_end > param.ds_numel else secondary_partition_size + sec_numel = max(0, min(param.ds_numel - secondary_start, secondary_partition_size)) # copy from full tensor to secondary tensor param.ds_secondary_tensor.narrow(0, 0, From 3bdd187e7186b60929d8ac5af483509b8cb9d00c Mon Sep 17 00:00:00 2001 From: Jinghan Yao Date: Tue, 18 Jun 2024 08:45:12 -0700 Subject: [PATCH 06/36] Fixing the reshape bug in sequence parallel alltoall, which corrupted all QKV data (#5664) Currently in the implementation of DeepSpeed sequence parallel, two `All_to_all` are used in the distributed attention to scatter and gather sequence. However, the `reshape` operation is wrong in the second [`All_to_all`](https://github.com/microsoft/DeepSpeed/blob/eda5075b88c448d13009301dc73653a224bb24b8/deepspeed/sequence/layer.py#L36). **The model will never converge as the data is corrupted by it.** To easily check the problem with current implementation, we can do the following to [this line](https://github.com/microsoft/DeepSpeed/blob/eda5075b88c448d13009301dc73653a224bb24b8/deepspeed/sequence/layer.py#L104): ``` def forward(self, query: Tensor, key: Tensor, value: Tensor, *args: Any, **kwargs) -> Tensor: """ forward Arguments: query (Tensor): query input to the layer key (Tensor): key input to the layer value (Tensor): value input to the layer args: other args Returns: * output (Tensor): context output """ # TODO Merge three alltoall calls into one # TODO (Reza): change the api on the megatron-deepspeed side so that we only receive all data (q,k, and v) together! #in shape : e.g., [s/p:h:] query_layer = _SeqAllToAll.apply(self.spg, query, self.scatter_idx, self.gather_idx) key_layer = _SeqAllToAll.apply(self.spg, key, self.scatter_idx, self.gather_idx) value_layer = _SeqAllToAll.apply(self.spg, value, self.scatter_idx, self.gather_idx) #out shape : e.g., [s:h/p:] context_layer = self.local_attn(query_layer, key_layer, value_layer, *args, **kwargs) output = _SeqAllToAll.apply(self.spg, context_layer, self.gather_idx, self.scatter_idx) #out e.g., [s/p::h] return output ``` Remove the attention computation, leaving only the `all_to_all`. And we just check the `query` before and after `all_to_all`, which should be the same. ``` query_layer = _SeqAllToAll.apply(self.spg, query, self.scatter_idx, self.gather_idx) key_layer = _SeqAllToAll.apply(self.spg, key, self.scatter_idx, self.gather_idx) value_layer = _SeqAllToAll.apply(self.spg, value, self.scatter_idx, self.gather_idx) #out shape : e.g., [s:h/p:] #context_layer = self.local_attn(query_layer, key_layer, value_layer, *args, **kwargs) # do not perform attn, context_layer = query_layer # just use the input query output = _SeqAllToAll.apply(self.spg, context_layer, self.gather_idx, self.scatter_idx) if torch.distributed.get_rank() == 3: print(query[0][15730][5]) print(output[0][15730][5]) ``` **_In current implementation, `all_to_all` totally messes up the data. The printed values in query are misaligned with output_** The problem is because of this incorrect [reshape](https://github.com/microsoft/DeepSpeed/blob/eda5075b88c448d13009301dc73653a224bb24b8/deepspeed/sequence/layer.py#L36C18-L36C24): ``` def single_all_to_all(input, scatter_idx, gather_idx, group): seq_world_size = dist.get_world_size(group) inp_shape = list(input.shape) inp_shape[scatter_idx] = inp_shape[scatter_idx] // seq_world_size if scatter_idx < 2: input_t = input.reshape( [seq_world_size, inp_shape[scatter_idx]] + \ inp_shape[scatter_idx + 1:] ).contiguous() else: # transpose groups of heads with the seq-len parallel dimension, so that we can scatter them! input_t = input.reshape( [-1, seq_world_size, inp_shape[scatter_idx]] + \ inp_shape[scatter_idx + 1:] ).transpose(0, 1).contiguous() output = torch.empty_like(input_t) dist.all_to_all_single(output, input_t, group=group) # if scattering the seq-dim, transpose the heads back to the original dimension if scatter_idx < 2: output = output.transpose(0, 1).contiguous() return output.reshape( inp_shape[: gather_idx] + \ [inp_shape[gather_idx] * seq_world_size,] + \ inp_shape[gather_idx + 1:]).contiguous() ``` When performing the second `all_to_all`, the [output](https://github.com/microsoft/DeepSpeed/blob/eda5075b88c448d13009301dc73653a224bb24b8/deepspeed/sequence/layer.py#L32) we gathered from other ranks is of shape: ``` dist.all_to_all_single(output, input_t, group=group) # output: [seq_world_size, batch, local_seq_length, num_local_heads, head_dim] if scatter_idx < 2: output = output.transpose(0, 1).contiguous() # output: [batch, seq_world_size, local_seq_length, num_local_heads, head_dim] ``` At this step, we actually want to gather all the heads of the local sequence, therefore, the above line needs to be: ``` if scatter_idx < 2: output = output.transpose(0, 2) # output: [batch, local_seq_length, seq_world_size, num_local_heads, head_dim] ``` Only by doing this, can we then: ``` return output.reshape( inp_shape[: gather_idx] + \ [inp_shape[gather_idx] * seq_world_size,] + \ inp_shape[gather_idx + 1:]).contiguous() ``` which then arranges the data correctly. A more straight-forward example is: ``` # second all_to_all # batch: 1 # sequence parallel size: 4 # local sequence length: 8192 # total number of heads: 16 # head dim: 128 dist.all_to_all_single(output, input_t, group=group) # output: [4, 1, 8192, 4, 128] if scatter_idx < 2: output = output.transpose(0, 1).contiguous() # output: [1, 4, 8192, 4, 128] # At this step, you cannot directly reshape it into [1, 8192, 16, 128] as it corrupts the data. # You need to permute output into [1, 8192, 4, 4, 128], then reshape it into [1, 8192, 16, 128]. ``` For the first `all_to_all`, things work fine. This issue only exists in the second `all_to_all`. Co-authored-by: Jinghan Yao Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/sequence/layer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepspeed/sequence/layer.py b/deepspeed/sequence/layer.py index a876596fb7bb..a715843d8eff 100644 --- a/deepspeed/sequence/layer.py +++ b/deepspeed/sequence/layer.py @@ -33,7 +33,7 @@ def single_all_to_all(input, scatter_idx, gather_idx, group): # if scattering the seq-dim, transpose the heads back to the original dimension if scatter_idx < 2: - output = output.transpose(0, 1).contiguous() + output = output.transpose(0, 2).contiguous() return output.reshape( inp_shape[: gather_idx] + \ From 8ea995ee1f326881098d57fbfbc0db17fda52cc2 Mon Sep 17 00:00:00 2001 From: Yejing-Lai Date: Wed, 19 Jun 2024 04:30:46 +0800 Subject: [PATCH 07/36] enable yuan autotp & add conv tp (#5428) This PR aims to enable yuan model autotp and add conv tp. Yuan model used shared qk. For example: q_linear_out = [q1, q2, q3, q4, q5, ... , q16] k_linear_out = [k1, k2, k3, k4, k5, ... , k16] after share qk: TP=1: q' = [q1,q2,q3,q4, q9,q10,q11,q12, k1,k2 k3,k4, k9,k10,k11,k12] k' = [q5,q6,q7,q8, q13,q14,q15,q16, k5,k6,k7,k8, k13,k14,k15,k16] v' = [v1,v2,v3,v4, v5,v6,v7,v8, v9,v10,v11,v12, v13,v14,v15,v16] TP=2: rank0: q'_0 = [q1,q2,q3,q4, k1,k2 k3,k4] k'_0 = [q5,q6,q7,q8, k5,k6,k7,k8] v'_0 = [v1,v2,v3,v4, v5,v6,v7,v8] -> v'_0 is error! Expect value is: [v1,v2,v3,v4, v9,v10,v11,v12] rank1: q'_1 = [q9,q10,q11,q12, k9,k10,k11,k12] k'_1 = [q13,q14,q15,q16, k13,k14,k15,k16] v'_1 = [v9,v10,v11,v12, v13,v14,v15,v16] -> v'_1 is error! Expect value is: [v5,v6,v7,v8, v13,v14,v15,v16] To avoid modifying the modeling code. We adjust the value and oproj weight to fit this qk type. We also added the conv tp to support some models that including the heavy conv calculation. It is similar to the linear tp policy. if not last_conv_layer: - 1. Divide the conv weight to each rank along the output channel dimension. - 2. To apply conv2d. else: - 1. Divide the conv weight to each rank along the input channel dimension. - 2. Apply conv2d. - 3. Use allreduce to add outputs. --------- Co-authored-by: Olatunji Ruwase Co-authored-by: Lev Kurilenko <113481193+lekurile@users.noreply.github.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/module_inject/auto_tp.py | 26 +++++++--- deepspeed/module_inject/fusedqkv_utils.py | 55 ++++++++++++++++++++ deepspeed/module_inject/layers.py | 62 +++++++++++++++++++++++ deepspeed/module_inject/replace_module.py | 27 ++++++++++ 4 files changed, 163 insertions(+), 7 deletions(-) diff --git a/deepspeed/module_inject/auto_tp.py b/deepspeed/module_inject/auto_tp.py index 3429ceb0a4ee..1c76cbc0a6ef 100644 --- a/deepspeed/module_inject/auto_tp.py +++ b/deepspeed/module_inject/auto_tp.py @@ -13,7 +13,7 @@ from deepspeed import comm as dist from .layers import LinearAllreduce, LinearLayer, LmHeadLinearAllreduce from deepspeed.accelerator import get_accelerator -from .fusedqkv_utils import require_tp_fused_qkvw, prepare_tp_fused_qkvw, shard_chunk_mlp +from .fusedqkv_utils import require_tp_fused_qkvw, prepare_tp_fused_qkvw, shard_value_with_share_qk, shard_chunk_mlp from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list @@ -134,7 +134,7 @@ def is_load_module(module): load_layer_names = [ "LPLayerNorm", "SharedEmbedding", "OPTLearnedPositionalEmbedding", "LlamaRMSNorm", "FalconLinear", "MistralRMSNorm", "T5LayerNorm", "MixtralRMSNorm", "Phi3RotaryEmbedding", "Phi3SuScaledRotaryEmbedding", - "Phi3RMSNorm" + "Phi3RMSNorm", "YuanRMSNorm", "YuanRotaryEmbedding" ] return module.__class__ in load_layers or module._get_name() in load_layer_names @@ -331,6 +331,16 @@ def _replace(self, child, name, conv_linear_layer): # For mixtral-7x8b, need to skip MoE gate linear replace. if name == "block_sparse_moe.gate": return child + # For Yuan model + if 'Yuan' in str(self.module): + if 'v_proj' in name: + weight, bias = shard_value_with_share_qk(child.weight.data, child.bias, dist.get_rank(), + dist.get_world_size(), True) + return LinearLayer(weight=weight, bias=bias) + elif 'o_proj' in name: + weight, bias = shard_value_with_share_qk(child.weight.data, child.bias, dist.get_rank(), + dist.get_world_size(), False) + return LinearAllreduce(weight, bias, self.mp_group) # for phi3. if 'gate_up_proj' in name: weight, bias = shard_chunk_mlp(child.weight.data, child.bias, dist.get_rank(), dist.get_world_size()) @@ -412,11 +422,13 @@ def _slice_embedding(self, child, name, conv_linear_layer): def update_mp_params(self, child): if getattr(child, "replaced", False) == True: return - for param in [ - "n_heads", "inner_dim", "num_heads", "num_kv", "num_attention_heads", "num_attn_heads", - "all_head_size", "embed_dim", "hidden_size", "num_key_value_heads", "num_kv_heads", "kv_n_heads", - "d_model" - ]: + param_list = [ + "n_heads", "inner_dim", "num_heads", "num_kv", "num_attention_heads", "num_attn_heads", "all_head_size", + "embed_dim", "hidden_size", "num_key_value_heads", "num_kv_heads", "kv_n_heads", "d_model" + ] + for param in param_list: + if "Yuan" in str(child) and 'embed_dim' in param_list: + param_list.remove('embed_dim') if hasattr(child, param): param_val = getattr(child, param) setattr(child, param, get_shard_size(param_val, self.mp_size)) diff --git a/deepspeed/module_inject/fusedqkv_utils.py b/deepspeed/module_inject/fusedqkv_utils.py index 33d36fbfae54..f63cbee44d5c 100644 --- a/deepspeed/module_inject/fusedqkv_utils.py +++ b/deepspeed/module_inject/fusedqkv_utils.py @@ -142,6 +142,61 @@ def _transpose_fused_qkvw(src, mp_size, fused_qkv_type=None, module=None): return _bloom_type_transpose(src, mp_size) +# For share qk type: +# q = [q1,...,q_{n/4}, q_{n/2+1},...,q_{3n/4}, k1,...,k_{n/4}, k_{n/2+1},...,k_{3n/4}] +# k = [q_{n/4+1},...,q_{n/2}, q_{3n/4+1},...,qn, k_{n/4+1},...,k_{n/2}, k{3n/4+1},...,kn] +# Avoid modifying the modeling code. We adjust the value and oproj weight to fit this qk type. +def shard_value_with_share_qk( + weight, + bias, + rank, + world_size, + shard_value=True # True -> shard_value; False -> shard_oproj +): + if shard_value: + total_size = weight.shape[0] + weight_cat_dim = 0 + else: + total_size = weight.shape[1] + weight_cat_dim = 1 + num_heads = get_num_kv_heads() + head_dim = total_size // num_heads + assert (num_heads % world_size == 0) + if world_size > num_heads // 2: + RuntimeError(f"world_size {world_size} is larger than half of num_heads {num_heads}") + head_per_rank = num_heads // world_size + q_head_start = rank * head_per_rank + # mapping q_head to v_head + v_head_ids = [] + i = 0 + # mapping neighbor q_head to v_head + while i < head_per_rank: + v_head_ids.append(q_head_start // 2) + q_head_start += 2 + i = i + 2 + + # mapping neighbor k_head to v_head + v_head_ids.extend([i + num_heads // 2 for i in v_head_ids]) + sharded_weight = [] + sharded_bias = [] + for head_id in v_head_ids: + if shard_value: + sharded_weight.append(weight[head_id * head_dim:(head_id + 1) * head_dim]) + if bias is not None: + sharded_bias.append(bias.data[head_id * head_dim:(head_id + 1) * head_dim]) + else: + sharded_weight.append(weight[:, head_id * head_dim:(head_id + 1) * head_dim]) + sharded_weight = torch.cat(sharded_weight, dim=weight_cat_dim) + if bias is not None: + if shard_value: + sharded_bias = torch.cat(sharded_bias, dim=0) + else: + bias = bias / float(world_size) + return torch.nn.Parameter(sharded_weight), torch.nn.Parameter(sharded_bias) + else: + return torch.nn.Parameter(sharded_weight), None + + # For phi3 with chunk mlp, adjust the weight order. def shard_chunk_mlp( weight, diff --git a/deepspeed/module_inject/layers.py b/deepspeed/module_inject/layers.py index 5fb957faa80c..22d24820d404 100644 --- a/deepspeed/module_inject/layers.py +++ b/deepspeed/module_inject/layers.py @@ -13,6 +13,68 @@ from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list +class TensorParallelConv2d(nn.Module): + + def __init__(self, conv, rank, world_size, shard_by_oc): + super().__init__() + self.rank = rank + self.world_size = world_size + self.shard_by_oc = shard_by_oc + self.shard_weights(conv) + + # Split along the input/output channel depending on whether it is the last conv layer. + def shard_weights(self, conv): + if self.shard_by_oc: + total_size = conv.weight.shape[0] + else: + total_size = conv.weight.shape[1] + bias_data = None + cols_per_rank = [0] + for i in range(self.world_size - 1, -1, -1): + cols = total_size // self.world_size + if i < total_size % self.world_size: + cols += 1 + cols_per_rank.append(cols_per_rank[-1] + cols) + weight_data = conv.weight.data + if self.shard_by_oc: + # not last conv layer, split output channel + weight_data = weight_data[cols_per_rank[self.rank]:cols_per_rank[self.rank + 1]] + if conv.bias is not None: + bias_data = conv.bias.data[cols_per_rank[self.rank]:cols_per_rank[self.rank + 1]] + else: + # last conv layer, split input channel + weight_data = weight_data[:, cols_per_rank[self.rank]:cols_per_rank[self.rank + 1]] + if conv.bias is not None: + bias_data = conv.bias.data / float(self.world_size) + self.conv = nn.Conv2d(weight_data.shape[1], weight_data.shape[0], conv.kernel_size, conv.stride, conv.padding, + conv.dilation, conv.groups, conv.bias is not None, conv.padding_mode) + self.conv.weight = torch.nn.Parameter(weight_data) + if conv.bias is not None: + self.conv.bias = torch.nn.Parameter(bias_data) + del conv + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return self.conv(input) + + +class TensorParallelOcShardConv2d(TensorParallelConv2d): + + def __init__(self, conv, rank, world_size): + super().__init__(conv, rank, world_size, True) + + +class TensorParallelIcShardConv2d(TensorParallelConv2d): + + def __init__(self, conv, rank, world_size): + super().__init__(conv, rank, world_size, False) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + out = self.conv(input) + if self.world_size > 1: + dist.inference_all_reduce(out) + return out + + class LinearAllreduce(nn.Module): def __init__(self, weight, bias=None, mp_group=None): diff --git a/deepspeed/module_inject/replace_module.py b/deepspeed/module_inject/replace_module.py index 3029a79698dc..85abd0217039 100644 --- a/deepspeed/module_inject/replace_module.py +++ b/deepspeed/module_inject/replace_module.py @@ -14,6 +14,7 @@ from deepspeed.accelerator import get_accelerator from .replace_policy import replace_policies, generic_policies from .auto_tp import AutoTP, ReplaceWithTensorSlicing, Loading +from .layers import TensorParallelOcShardConv2d, TensorParallelIcShardConv2d from deepspeed import comm as dist from deepspeed.module_inject.tp_shard import set_num_kv_heads, set_n_embd, set_num_attention_heads @@ -340,6 +341,28 @@ def set_lm_head(module): module = replace_wo_policy(module, ("embed_out", ), 0, "embed_out") return module + def conv2d_parallel_shard_weights(model, rank, world_size): + # add conv policy + shard_oc_name = ["conv1"] + shard_ic_name = ["conv2"] + for name, sub_m in model.named_children(): + for l_name, l_sub_m in sub_m.named_children(): + if l_name in shard_oc_name: + TPConv2d = TensorParallelOcShardConv2d( + l_sub_m, + rank, + world_size, + ) + setattr(sub_m, l_name, TPConv2d) + if l_name in shard_ic_name: + TPConv2d = TensorParallelIcShardConv2d( + l_sub_m, + rank, + world_size, + ) + setattr(sub_m, l_name, TPConv2d) + conv2d_parallel_shard_weights(sub_m, rank, world_size) + if checkpoint_dict is not None and not config.replace_with_kernel_inject: # AutoTP shard loading checkpoint = checkpoint_dict["checkpoints"] @@ -354,6 +377,10 @@ def set_lm_head(module): pbar.update(1) gc.collect() replaced_module = set_lm_head(replaced_module) + # conv2d tp module replace + # Now is for yuan model. Add model list and conv policy to decide whether to replace conv. + if 'Yuan' in str(replaced_module): + conv2d_parallel_shard_weights(replaced_module, dist.get_rank(), dist.get_world_size()) else: replaced_module = replace_module(model=model, orig_class=orig_layer_impl, From a4cd5504ca94bd0ba8b5742b5081529704d1bc17 Mon Sep 17 00:00:00 2001 From: Yejing-Lai Date: Wed, 19 Jun 2024 04:31:09 +0800 Subject: [PATCH 08/36] Fix latest pytorch '_get_socket_with_port' import error (#5654) The latest PyTorch deleted the '_get_socket_with_port' API, replacing it with 'get_free_port'. Fixes: #5603 --- deepspeed/elasticity/elastic_agent.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deepspeed/elasticity/elastic_agent.py b/deepspeed/elasticity/elastic_agent.py index 6051529bfa24..039b999dfeca 100644 --- a/deepspeed/elasticity/elastic_agent.py +++ b/deepspeed/elasticity/elastic_agent.py @@ -6,7 +6,7 @@ from torch.distributed.elastic.agent.server.local_elastic_agent import LocalElasticAgent from typing import Any, Dict, Optional, Tuple from datetime import datetime -from torch.distributed.elastic.agent.server.api import _get_socket_with_port +from torch.distributed.elastic.utils.distributed import get_free_port from torch.distributed.elastic.metrics import put_metric from torch.distributed.elastic.agent.server.api import ( RunResult, @@ -48,7 +48,7 @@ def _set_master_addr_port(store: Store, master_port: Optional[int], local_addr: Optional[str] = None): if master_port is None: - sock = _get_socket_with_port() + sock = get_free_port() with closing(sock): master_port = sock.getsockname()[1] From 4000cee8409d7fff266474ca3e533032ce204bf3 Mon Sep 17 00:00:00 2001 From: Yejing-Lai Date: Wed, 19 Jun 2024 05:41:10 +0800 Subject: [PATCH 09/36] Fix numpy upgrade to 2.0.0 BUFSIZE import error (#5680) Numpy 2.0.0 removed the BUFSIZE API, replacing it with 'numpy._core.umath.BUFSIZE'. The minimum numpy version supported by the new code is 1.26.1. If you need to support the lower numpy version, please tell me and we can add another path. Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/autotuning/scheduler.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/deepspeed/autotuning/scheduler.py b/deepspeed/autotuning/scheduler.py index 7d2a1c081da9..751d03d717bb 100755 --- a/deepspeed/autotuning/scheduler.py +++ b/deepspeed/autotuning/scheduler.py @@ -5,7 +5,11 @@ import copy -from numpy import BUFSIZE +import numpy +if numpy.__version__ < '2.0.0': + from numpy import BUFSIZE +else: + from numpy._core.umath import BUFSIZE import json import subprocess import sys From 0c979d6779e3251fa0a65bd27e61f31a0883f022 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 19 Jun 2024 14:19:50 -0700 Subject: [PATCH 10/36] Update BUFSIZE to come from autotuner's constants.py, not numpy (#5686) --- deepspeed/autotuning/scheduler.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/deepspeed/autotuning/scheduler.py b/deepspeed/autotuning/scheduler.py index 751d03d717bb..35f709ead48a 100755 --- a/deepspeed/autotuning/scheduler.py +++ b/deepspeed/autotuning/scheduler.py @@ -5,11 +5,6 @@ import copy -import numpy -if numpy.__version__ < '2.0.0': - from numpy import BUFSIZE -else: - from numpy._core.umath import BUFSIZE import json import subprocess import sys @@ -22,7 +17,7 @@ from tqdm import tqdm from ..utils import logger -from .constants import AUTOTUNING, AUTOTUNING_METRIC_PATH +from .constants import AUTOTUNING, AUTOTUNING_METRIC_PATH, BUFSIZE from .utils import get_val_by_key, search_error, was_interruptted """ thread-0: loop over experiment queue dispatching experiments if they become available From d254d75ef028e2e6bd3305ba0feeb6a61c986443 Mon Sep 17 00:00:00 2001 From: Yizhou Wang Date: Fri, 21 Jun 2024 07:33:25 +0800 Subject: [PATCH 11/36] [XPU] support op builder from intel_extension_for_pytorch kernel path (#5425) #Motivation From our next release, xpu DeepSpeed related kernels would be put into intel_extension_for_pytorch. This PR is to add new op builders and use kernel path from intel_extension_for_pytorch. More ops like MOE and WOQ will be added. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase --- accelerator/xpu_accelerator.py | 46 ++++++++++++++--------------- deepspeed/comm/ccl.py | 3 +- op_builder/xpu/__init__.py | 3 ++ op_builder/xpu/flash_attn.py | 53 ++++++++++++++++++++++++++++++++++ op_builder/xpu/inference.py | 36 +++++++++++++++++++++++ op_builder/xpu/no_impl.py | 33 +++++++++++++++++++++ 6 files changed, 149 insertions(+), 25 deletions(-) create mode 100644 op_builder/xpu/flash_attn.py create mode 100644 op_builder/xpu/inference.py create mode 100644 op_builder/xpu/no_impl.py diff --git a/accelerator/xpu_accelerator.py b/accelerator/xpu_accelerator.py index ddc553f3a558..4fb107b0fa63 100644 --- a/accelerator/xpu_accelerator.py +++ b/accelerator/xpu_accelerator.py @@ -9,6 +9,9 @@ import oneccl_bindings_for_pytorch # noqa: F401 # type: ignore import functools +import importlib +import inspect + class XPU_Accelerator(DeepSpeedAccelerator): @@ -17,6 +20,7 @@ def __init__(self): self._communication_backend_name = 'ccl' self._compile_backend = "inductor" self.aligned_tensors = [] + self.class_dict = None def is_synchronized_device(self): return False @@ -257,35 +261,29 @@ def on_accelerator(self, tensor): else: return False + def _lazy_init_class_dict(self): + if self.class_dict: + return + + op_builder_module = importlib.import_module(self.op_builder_dir()) + + # get op builder class from op_builder/xpu/__init__.py + self.class_dict = {} + for class_name, class_obj in inspect.getmembers(op_builder_module, inspect.isclass): + self.class_dict[class_name] = class_obj + # create an instance of op builder and return, name specified by class_name - def create_op_builder(self, op_name): - builder_class = self.get_op_builder(op_name) - if builder_class != None: - return builder_class() - return None + def create_op_builder(self, class_name): + builder_class = self.get_op_builder(class_name) + return builder_class() # return an op builder class, name specified by class_name def get_op_builder(self, class_name): - try: - # is op_builder from deepspeed or a 3p version? this should only succeed if it's deepspeed - # if successful this also means we're doing a local install and not JIT compile path - from op_builder import __deepspeed__ # noqa: F401 # type: ignore - from op_builder.xpu import CPUAdagradBuilder, CPUAdamBuilder, FusedAdamBuilder, AsyncIOBuilder, PackbitsBuilder - except ImportError: - from deepspeed.ops.op_builder.xpu import CPUAdagradBuilder, CPUAdamBuilder, FusedAdamBuilder, AsyncIOBuilder, PackbitsBuilder - - if class_name == "AsyncIOBuilder": - return AsyncIOBuilder - elif class_name == "CPUAdagradBuilder": - return CPUAdagradBuilder - elif class_name == "CPUAdamBuilder": - return CPUAdamBuilder - elif class_name == "FusedAdamBuilder": - return FusedAdamBuilder - elif class_name == "PackbitsBuilder": - return PackbitsBuilder + self._lazy_init_class_dict() + if class_name in self.class_dict: + return self.class_dict[class_name] else: - return None + return self.class_dict['NotImplementedBuilder'] def build_extension(self): try: diff --git a/deepspeed/comm/ccl.py b/deepspeed/comm/ccl.py index f2a6cb6a36dc..cdf4c030f5d6 100644 --- a/deepspeed/comm/ccl.py +++ b/deepspeed/comm/ccl.py @@ -8,13 +8,14 @@ import torch from deepspeed.accelerator import get_accelerator +from deepspeed.ops.op_builder import NotImplementedBuilder from .reduce_op import ReduceOp from .torch import TorchBackend def build_ccl_op(): builder = get_accelerator().create_op_builder("CCLCommBuilder") - if builder is None: + if builder is None or NotImplementedBuilder: return None ccl_cpp_module = builder.load() print(f'DeepSpeed {builder.absolute_name()} built successfully') diff --git a/op_builder/xpu/__init__.py b/op_builder/xpu/__init__.py index bf82e4248338..093f90b30234 100755 --- a/op_builder/xpu/__init__.py +++ b/op_builder/xpu/__init__.py @@ -7,4 +7,7 @@ from .cpu_adagrad import CPUAdagradBuilder from .fused_adam import FusedAdamBuilder from .async_io import AsyncIOBuilder +from .inference import InferenceBuilder +from .flash_attn import FlashAttentionBuilder +from .no_impl import NotImplementedBuilder from .packbits import PackbitsBuilder diff --git a/op_builder/xpu/flash_attn.py b/op_builder/xpu/flash_attn.py new file mode 100644 index 000000000000..c8c2674d5d27 --- /dev/null +++ b/op_builder/xpu/flash_attn.py @@ -0,0 +1,53 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team +from .builder import SYCLOpBuilder + + +class FlashAttentionBuilderObject(): + + def __init__(self): + pass + + # general functions + def flash_attn_func_v2(self, q, k, v, dropout_p, softmax_scale, is_causal): + try: + import torch + import intel_extension_for_pytorch # noqa + return torch.nn.functional.scaled_dot_product_attention(q, + k, + v, + dropout_p=dropout_p, + is_causal=is_causal, + scale=softmax_scale) + except ImportError: + raise ImportError( + "Please install pytorch and intel_extension_for_pytorch to include scaled dot product attention.") + + +class FlashAttentionBuilder(SYCLOpBuilder): + BUILD_VAR = "DS_BUILD_FlashAttention" + NAME = "flash_attn" + + def __init__(self, name=None): + name = self.NAME if name is None else name + super().__init__(name=name) + + def absolute_name(self): + return f'deepspeed.ops.{self.NAME}_op' + + def sources(self): + return + + def include_paths(self): + return [] + + def extra_ldflags(self): + return [] + + def cxx_args(self): + return [] + + def load(self): + return FlashAttentionBuilderObject() diff --git a/op_builder/xpu/inference.py b/op_builder/xpu/inference.py new file mode 100644 index 000000000000..9114dcc2c315 --- /dev/null +++ b/op_builder/xpu/inference.py @@ -0,0 +1,36 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team +from .builder import SYCLOpBuilder + + +class InferenceBuilder(SYCLOpBuilder): + BUILD_VAR = "DS_BUILD_TRANSFORMER_INFERENCE" + NAME = "transformer_inference" + + def __init__(self, name=None): + name = self.NAME if name is None else name + super().__init__(name=name) + + def absolute_name(self): + return f'deepspeed.ops.transformer.inference.{self.NAME}_op' + + def sources(self): + return + + def include_paths(self): + return [] + + def extra_ldflags(self): + return [] + + def cxx_args(self): + return [] + + def load(self): + try: + import intel_extension_for_pytorch.deepspeed + return intel_extension_for_pytorch.deepspeed.transformer_inference.transformer_inference + except ImportError: + raise ImportError("Please install intel-extension-for-pytorch >= 2.1.30 to include DeepSpeed kernels.") diff --git a/op_builder/xpu/no_impl.py b/op_builder/xpu/no_impl.py new file mode 100644 index 000000000000..8b294f70c279 --- /dev/null +++ b/op_builder/xpu/no_impl.py @@ -0,0 +1,33 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +from .builder import SYCLOpBuilder + + +class NotImplementedBuilder(SYCLOpBuilder): + BUILD_VAR = "DS_BUILD_NOT_IMPLEMENTED" + NAME = "deepspeed_not_implemented" + + def __init__(self, name=None): + name = self.NAME if name is None else name + super().__init__(name=name) + + def absolute_name(self): + return f'deepspeed.ops.{self.NAME}_op' + + def load(self, verbose=True): + raise ValueError("This op had not been implemented on XPU backend.") + + def sources(self): + return [] + + def cxx_args(self): + return [] + + def extra_ldflags(self): + return [] + + def include_paths(self): + return [] From d89e8cdfe55410e666a184d7ab7e664e7887228c Mon Sep 17 00:00:00 2001 From: Michael Wyatt Date: Fri, 21 Jun 2024 10:36:56 -0700 Subject: [PATCH 12/36] Update version.txt after 0.14.4 release (#5694) **Auto-generated PR to update version.txt after a DeepSpeed release** Released version - 0.14.4 Author - @loadams Co-authored-by: loadams --- version.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/version.txt b/version.txt index 3393b5fd47d4..436d0ce0df76 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.14.4 +0.14.5 From b3767d01d4ef0f1585a183350616f745075a84d5 Mon Sep 17 00:00:00 2001 From: Costin Eseanu <19394655+costin-eseanu@users.noreply.github.com> Date: Mon, 24 Jun 2024 13:39:18 -0700 Subject: [PATCH 13/36] Fixed Windows inference build. (#5609) Fix #2427 --------- Co-authored-by: Costin Eseanu Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase --- accelerator/cuda_accelerator.py | 3 +- build_win.bat | 2 - .../transformer/inference/csrc/pt_binding.cpp | 37 ++++++++++--------- .../cuda_layer_norm/layer_norm_cuda.cu | 1 - .../cuda_linear/include/kernel_matmul.cuh | 8 ++-- .../cuda_linear/include/ptx_cp.async.cuh | 12 ++++-- .../core_ops/cuda_linear/include/ptx_mma.cuh | 22 ++++++----- .../cuda_linear/include/utils_core.cuh | 4 +- .../cuda_linear/include/utils_gmem.cuh | 2 +- .../include/utils_paralleldequant.cuh | 24 ++++++------ deepspeed/utils/timer.py | 3 +- op_builder/builder.py | 1 + setup.py | 13 +++++-- 13 files changed, 74 insertions(+), 58 deletions(-) diff --git a/accelerator/cuda_accelerator.py b/accelerator/cuda_accelerator.py index 74b004205f2a..06fd443f9829 100644 --- a/accelerator/cuda_accelerator.py +++ b/accelerator/cuda_accelerator.py @@ -7,6 +7,7 @@ import os import pkgutil import importlib +import sys from .abstract_accelerator import DeepSpeedAccelerator # During setup stage torch may not be installed, pass on no torch will @@ -24,7 +25,7 @@ class CUDA_Accelerator(DeepSpeedAccelerator): def __init__(self): self._name = 'cuda' - self._communication_backend_name = 'nccl' + self._communication_backend_name = 'nccl' if sys.platform != 'win32' else 'gloo' self._compile_backend = "inductor" if pynvml is None: self._init_pynvml() diff --git a/build_win.bat b/build_win.bat index 18ff17347fc9..6e67cb8c2aa2 100644 --- a/build_win.bat +++ b/build_win.bat @@ -6,10 +6,8 @@ set DS_BUILD_AIO=0 set DS_BUILD_CUTLASS_OPS=0 set DS_BUILD_EVOFORMER_ATTN=0 set DS_BUILD_FP_QUANTIZER=0 -set DS_BUILD_INFERENCE_CORE_OPS=0 set DS_BUILD_RAGGED_DEVICE_OPS=0 set DS_BUILD_SPARSE_ATTN=0 -set DS_BUILD_TRANSFORMER_INFERENCE=0 python setup.py bdist_wheel diff --git a/csrc/transformer/inference/csrc/pt_binding.cpp b/csrc/transformer/inference/csrc/pt_binding.cpp index 1b9f91cd9c88..2d5332578edc 100644 --- a/csrc/transformer/inference/csrc/pt_binding.cpp +++ b/csrc/transformer/inference/csrc/pt_binding.cpp @@ -542,22 +542,23 @@ std::vector ds_softmax_context(at::Tensor& query_key_value, 1); if (layer_id == num_layers - 1) InferenceContext::Instance().advance_tokens(); - auto prev_key = torch::from_blob(workspace + offset, - {bsz, heads, all_tokens, k}, - {hidden_dim * InferenceContext::Instance().GetMaxTokenLength(), - k * InferenceContext::Instance().GetMaxTokenLength(), - k, - 1}, - options); - - auto prev_value = - torch::from_blob(workspace + offset + value_offset, - {bsz, heads, all_tokens, k}, - {hidden_dim * InferenceContext::Instance().GetMaxTokenLength(), - k * InferenceContext::Instance().GetMaxTokenLength(), - k, - 1}, - options); + auto prev_key = torch::from_blob( + workspace + offset, + {bsz, heads, all_tokens, k}, + {hidden_dim * static_cast(InferenceContext::Instance().GetMaxTokenLength()), + k * static_cast(InferenceContext::Instance().GetMaxTokenLength()), + k, + 1}, + options); + + auto prev_value = torch::from_blob( + workspace + offset + value_offset, + {bsz, heads, all_tokens, k}, + {hidden_dim * static_cast(InferenceContext::Instance().GetMaxTokenLength()), + k * static_cast(InferenceContext::Instance().GetMaxTokenLength()), + k, + 1}, + options); return {output, prev_key, prev_value}; } @@ -1592,7 +1593,9 @@ std::vector ds_rms_mlp_gemm(at::Tensor& input, auto output = at::from_blob(output_ptr, input.sizes(), options); auto inp_norm = at::from_blob(inp_norm_ptr, input.sizes(), options); auto intermediate_gemm = - at::from_blob(intermediate_ptr, {input.size(0), input.size(1), mlp_1_out_neurons}, options); + at::from_blob(intermediate_ptr, + {input.size(0), input.size(1), static_cast(mlp_1_out_neurons)}, + options); auto act_func_type = static_cast(activation_type); diff --git a/deepspeed/inference/v2/kernels/core_ops/cuda_layer_norm/layer_norm_cuda.cu b/deepspeed/inference/v2/kernels/core_ops/cuda_layer_norm/layer_norm_cuda.cu index 15f52c46622b..fb6dd0578f1d 100644 --- a/deepspeed/inference/v2/kernels/core_ops/cuda_layer_norm/layer_norm_cuda.cu +++ b/deepspeed/inference/v2/kernels/core_ops/cuda_layer_norm/layer_norm_cuda.cu @@ -252,7 +252,6 @@ __global__ void fused_residual_ln(T* output, for (int i = 0; i < unRoll; i++) { T* iteration_buffer = local_buffer + i * T_per_load; T residual_buffer[T_per_load]; - T bias_buffer[T_per_load]; mem_access::load_global( iteration_buffer, input_base + i * stride, thread_offset + i * stride < elems_per_row); diff --git a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/kernel_matmul.cuh b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/kernel_matmul.cuh index 0262baef4614..860f70b226cb 100644 --- a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/kernel_matmul.cuh +++ b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/kernel_matmul.cuh @@ -179,13 +179,13 @@ __global__ void QUANT_GEMM_Kernel(const uint4* Weight1, SMEM_SIZE_IN_BYTES_PER_WARP_A2 / 4 * 4; // 2048 (1)*4: 4 WARPs; (2)/4: int*+1 = char*+16 // Trible-Buffer for B Tile - half __restrict__(*read_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8] = + half(*__restrict__ read_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8] = smem_array + ((tile_id_k + 0) % PIPELINE_LEVEL_GMEM) * TilingConfig::TILE_N; #ifdef PIPELINE_LEVEL_SMEM - half __restrict__(*read2_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8] = + half(*__restrict__ read2_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8] = smem_array + ((tile_id_k + 1) % PIPELINE_LEVEL_GMEM) * TilingConfig::TILE_N; #endif - half __restrict__(*write_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8] = + half(*__restrict__ write_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8] = smem_array + ((tile_id_k + (PIPELINE_LEVEL_GMEM - 1)) % PIPELINE_LEVEL_GMEM) * TilingConfig::TILE_N; // @@ -265,7 +265,7 @@ __global__ void QUANT_GEMM_Kernel(const uint4* Weight1, } #else -#warning "The FP6 functions are only available on Ampere GPUs." + assert(("The FP6 functions are only available on Ampere GPUs.", false)); #endif } diff --git a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/ptx_cp.async.cuh b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/ptx_cp.async.cuh index 39874e023539..982d5a80010c 100644 --- a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/ptx_cp.async.cuh +++ b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/ptx_cp.async.cuh @@ -30,7 +30,8 @@ __device__ __forceinline__ void cp_async(half* smem_ptr, "l"(global_ptr), "n"(SizeInBytes)); #else -#warning "The async copy functions are only supported on Ampere and newer architectures" + assert( + ("The async copy functions are only supported on Ampere and newer architectures", false)); #endif } @@ -40,7 +41,8 @@ __device__ __forceinline__ void cp_async_group_commit() #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800 asm volatile("cp.async.commit_group;\n" ::); #else -#warning "The async copy functions are only supported on Ampere and newer architectures" + assert( + ("The async copy functions are only supported on Ampere and newer architectures", false)); #endif } @@ -51,7 +53,8 @@ __device__ __forceinline__ void cp_async_wait_group() #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800 asm volatile("cp.async.wait_group %0;\n" ::"n"(N)); #else -#warning "The async copy functions are only supported on Ampere and newer architectures" + assert( + ("The async copy functions are only supported on Ampere and newer architectures", false)); #endif } @@ -64,7 +67,8 @@ __device__ __forceinline__ void cp_async_wait_all() #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800 asm volatile("cp.async.wait_all;\n" ::); #else -#warning "The async copy functions are only supported on Ampere and newer architectures" + assert( + ("The async copy functions are only supported on Ampere and newer architectures", false)); #endif } diff --git a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/ptx_mma.cuh b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/ptx_mma.cuh index 8023629caac9..56f86a46f6b5 100644 --- a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/ptx_mma.cuh +++ b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/ptx_mma.cuh @@ -18,8 +18,8 @@ #ifdef PIPELINE_LEVEL_SMEM template __device__ __forceinline__ void B_FromSharedToReg( - uint32_t __restrict__ Reg[][4], - half __restrict__ (*read_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], + uint32_t (*__restrict__ Reg)[4], + half (*__restrict__ read_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], int slice_id) { #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800 @@ -56,7 +56,8 @@ __device__ __forceinline__ void B_FromSharedToReg( } } #else -#warning "The matrix load functions are only supported on Ampere and newer architectures" + assert( + ("The matrix load functions are only supported on Ampere and newer architectures", false)); #endif } #else @@ -64,8 +65,8 @@ __device__ __forceinline__ void B_FromSharedToReg( // B is in column-major template __device__ __forceinline__ void B_FromSharedToReg( - uint32_t __restrict__ Reg[][4], - half __restrict__ (*read_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], + uint32_t (*__restrict__ Reg)[4], + half (*__restrict__ read_SPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], int k_offset) { #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800 @@ -102,14 +103,15 @@ __device__ __forceinline__ void B_FromSharedToReg( } } #else -#warning "The matrix load functions are only supported on Ampere and newer architectures" + assert( + ("The matrix load functions are only supported on Ampere and newer architectures", false)); #endif } #endif -__device__ __forceinline__ void MMA_FP16_M16N8K16(uint32_t __restrict__ c[], - uint32_t __restrict__* a, - uint32_t __restrict__* b) +__device__ __forceinline__ void MMA_FP16_M16N8K16(uint32_t* __restrict__ c, + uint32_t* __restrict__ a, + uint32_t* __restrict__ b) { #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800 asm volatile( @@ -130,7 +132,7 @@ __device__ __forceinline__ void MMA_FP16_M16N8K16(uint32_t __restrict__ c[], "r"(c[2]), "r"(c[3])); #else -#warning "The mma functions are only implemented for Ampere and newer architectures" + assert(("The mma functions are only implemented for Ampere and newer architectures", false)); #endif } diff --git a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_core.cuh b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_core.cuh index a65575a1ba5a..bd8a009a02c6 100644 --- a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_core.cuh +++ b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_core.cuh @@ -32,7 +32,7 @@ __device__ __forceinline__ void initialize_mma_slice( uint32_t (*b)[4], uint32_t* __restrict__ A1_SPTR_read, uint32_t* __restrict__ A2_SPTR_read, - half __restrict__ (*B_SPTR_read)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], + half (*__restrict__ B_SPTR_read)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], uint32_t* RPTR_Scales) { // Writing registers @@ -54,7 +54,7 @@ __device__ __forceinline__ void core_mma_slice( uint32_t (*b)[4], uint32_t* __restrict__ A1_SPTR_read, uint32_t* __restrict__ A2_SPTR_read, - half __restrict__ (*B_SPTR_read)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], + half (*__restrict__ B_SPTR_read)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], uint32_t* RPTR_Scales, int slice_id) // writing slice[slice_id] to registers, k=0 -> slice_id=1 for prefetching { diff --git a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_gmem.cuh b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_gmem.cuh index d0c58352cd56..3dd7e9e0104e 100644 --- a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_gmem.cuh +++ b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_gmem.cuh @@ -57,7 +57,7 @@ __device__ __forceinline__ void CopyFromGlobalToShared_Scales(half* SPTR_QuantSc */ template __device__ __forceinline__ void CopyFromGlobalToShared( - half __restrict__ (*SharedPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], + half (*__restrict__ SharedPTR)[WARP_K + PADDING_SHARED_MEM_FOR_B_8], const half* GlobalPTR, const int GlobalStride, const int NumOfLinesLeft, // To support arbitrary N dimensions. diff --git a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_paralleldequant.cuh b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_paralleldequant.cuh index 9f035f12cfcd..11603fcc576c 100644 --- a/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_paralleldequant.cuh +++ b/deepspeed/inference/v2/kernels/core_ops/cuda_linear/include/utils_paralleldequant.cuh @@ -17,7 +17,7 @@ * Outputs: R1, R2 * Note: Simplified Exponent calculation is applied. */ -__device__ __forceinline__ void FP6_FP16_Cast_4Way(u_int32_t* R1, u_int32_t* R2) +__device__ __forceinline__ void FP6_FP16_Cast_4Way(uint32_t* R1, uint32_t* R2) { *R2 = *R1 & 0x80808080; *R1 = *R1 >> 2; @@ -33,7 +33,7 @@ __device__ __forceinline__ void FP6_FP16_Cast_4Way(u_int32_t* R1, u_int32_t* R2) * Outputs: R1, R2 * Note: Simplified Exponent calculation is NOT applied. */ -__device__ __forceinline__ void FP6_FP16_Cast_4Way_Naive(u_int32_t* R1, u_int32_t* R2) +__device__ __forceinline__ void FP6_FP16_Cast_4Way_Naive(uint32_t* R1, uint32_t* R2) { //*R2 = *R1 & 0x80808080; *R2 = *R1 & 0xc0c0c0c0; @@ -56,7 +56,7 @@ __device__ __forceinline__ void FP6_FP16_Cast_4Way_Naive(u_int32_t* R1, u_int32_ //*R2 = 0x3c003c00; } -__device__ __forceinline__ u_int32_t MultScale(u_int32_t PackedFP16Pair, half Scale) +__device__ __forceinline__ uint32_t MultScale(uint32_t PackedFP16Pair, half Scale) { half* FP16_1 = reinterpret_cast(&PackedFP16Pair); half* FP16_2 = FP16_1 + 1; @@ -67,17 +67,17 @@ __device__ __forceinline__ u_int32_t MultScale(u_int32_t PackedFP16Pair, half Sc return output; } -__device__ __forceinline__ void Dequant_32FP6_4Way(u_int32_t __restrict__ Reg[][4], - u_int32_t __restrict__* read_RPTR_Frag1, - u_int32_t __restrict__* read_RPTR_Frag2, - u_int32_t* Scales) +__device__ __forceinline__ void Dequant_32FP6_4Way(uint32_t (*__restrict__ Reg)[4], + uint32_t* __restrict__ read_RPTR_Frag1, + uint32_t* __restrict__ read_RPTR_Frag2, + uint32_t* Scales) { - u_int32_t* OutputRegs = reinterpret_cast(Reg); - u_int32_t* Frag1_PTR = read_RPTR_Frag1; - u_int32_t* Frag2_PTR = read_RPTR_Frag2; + uint32_t* OutputRegs = reinterpret_cast(Reg); + uint32_t* Frag1_PTR = read_RPTR_Frag1; + uint32_t* Frag2_PTR = read_RPTR_Frag2; half* Scale_RPTR = reinterpret_cast(Scales); - u_int32_t Packed_FP6 = 0; - u_int32_t tmp = 0; + uint32_t Packed_FP6 = 0; + uint32_t tmp = 0; // Dequantizing 32 FP6, each Loop dequantizing 4 FP6 #pragma unroll(8) for (int i = 0; i < 8; i++) { diff --git a/deepspeed/utils/timer.py b/deepspeed/utils/timer.py index dd78b207cc37..00f17dea709c 100755 --- a/deepspeed/utils/timer.py +++ b/deepspeed/utils/timer.py @@ -18,6 +18,7 @@ BACKWARD_REDUCE_GLOBAL_TIMER = 'bwd_allreduce' STEP_MICRO_TIMER = 'step_microstep' STEP_GLOBAL_TIMER = 'step' +TIME_EPSILON = 1e-6 try: import psutil @@ -262,7 +263,7 @@ def stop(self, global_step=False, report_speed=True): self.micro_step_count, self.global_step_count, self.avg_samples_per_sec(), - self.batch_size / self.step_elapsed_time, + self.batch_size / (self.step_elapsed_time + TIME_EPSILON), round(get_accelerator().memory_allocated() / 1024**3, 2), round(get_accelerator().max_memory_allocated() / 1024**3, 2), )) diff --git a/op_builder/builder.py b/op_builder/builder.py index df54415c3b84..03611bf56284 100644 --- a/op_builder/builder.py +++ b/op_builder/builder.py @@ -678,6 +678,7 @@ def builder(self): if not self.build_for_cpu and self.enable_bf16: compile_args['cxx'].append("-DBF16_AVAILABLE") + compile_args['nvcc'].append("-DBF16_AVAILABLE") if self.is_rocm_pytorch(): compile_args['cxx'].append("-D__HIP_PLATFORM_AMD__=1") diff --git a/setup.py b/setup.py index 408b300a78b0..f39eab41938a 100755 --- a/setup.py +++ b/setup.py @@ -18,6 +18,7 @@ The wheel will be located at: dist/*.whl """ +import pathlib import os import shutil import sys @@ -209,9 +210,15 @@ def op_enabled(op_name): git_branch = "unknown" if sys.platform == "win32": - shutil.copytree('.\\csrc', '.\\deepspeed\\ops') - shutil.copytree('.\\op_builder', '.\\deepspeed\\ops') - shutil.copytree('.\\accelerator', '.\\deepspeed\\accelerator') + shutil.rmtree('.\\deepspeed\\ops\\csrc', ignore_errors=True) + pathlib.Path('.\\deepspeed\\ops\\csrc').unlink(missing_ok=True) + shutil.copytree('.\\csrc', '.\\deepspeed\\ops\\csrc', dirs_exist_ok=True) + shutil.rmtree('.\\deepspeed\\ops\\op_builder', ignore_errors=True) + pathlib.Path('.\\deepspeed\\ops\\op_builder').unlink(missing_ok=True) + shutil.copytree('.\\op_builder', '.\\deepspeed\\ops\\op_builder', dirs_exist_ok=True) + shutil.rmtree('.\\deepspeed\\accelerator', ignore_errors=True) + pathlib.Path('.\\deepspeed\\accelerator').unlink(missing_ok=True) + shutil.copytree('.\\accelerator', '.\\deepspeed\\accelerator', dirs_exist_ok=True) egg_info.manifest_maker.template = 'MANIFEST_win.in' # Parse the DeepSpeed version string from version.txt. From 224a05c35040b724069a277b0019e2d1984aa986 Mon Sep 17 00:00:00 2001 From: Wei Wu <45323446+U-rara@users.noreply.github.com> Date: Wed, 26 Jun 2024 23:10:43 +0800 Subject: [PATCH 14/36] Bug fix for the "Link bit16 and fp32 parameters in partition" (#5681) In the function `_link_all_hp_params` [link](https://github.com/microsoft/DeepSpeed/blob/b33873d234cf6679a3046be9a137682c3469d1fb/deepspeed/runtime/zero/stage_1_and_2.py#L575): ```python def _link_all_hp_params(self): dp_world_size = dist.get_world_size(group=self.dp_process_group) if self.cpu_offload: self._get_offload_gradient_dict() for i, _ in enumerate(self.optimizer.param_groups): # Link bit16 and fp32 params in partition partition_id = dist.get_rank(group=self.real_dp_process_group[i]) partition_size = self.bit16_groups_flat[i].numel() // dp_world_size flat_hp_partition = self.single_partition_of_fp32_groups[i] link_hp_params(lp_param_list=self.bit16_groups[i], flat_hp_partition=flat_hp_partition, gradient_dict=self.averaged_gradients, offload_gradient_dict=self.offload_gradient_dict, use_offload=self.cpu_offload, param_group_index=i, partition_start=partition_id * partition_size, partition_size=partition_size, dp_group=self.real_dp_process_group[i]) ``` `dp_world_size = dist.get_world_size(group=self.dp_process_group)` ensures that `dp_world_size` is always the global data parallel word size. However, for the MoEs parameter group, the line `partition_size = self.bit16_groups_flat[i].numel() // dp_world_size` results in an incorrect `partition_size` when `ep_size > 1` (when expert parallelism is enabled). This causes only some of the MoEs parameters to be correctly executed in `link_hp_params` [link](https://github.com/microsoft/DeepSpeed/blob/b33873d234cf6679a3046be9a137682c3469d1fb/deepspeed/runtime/zero/stage_1_and_2.py#L568), while the remaining parameters have `_hp_mapping` set to None. Consequently, this leads to some parameters not being mapped in `self._param_slice_mappings = self._create_param_mapping()`, which directly causes errors in storing the optimizer state file for MoEs parameters. To fix this bug, we need to use the correct `dp_world_size` for each parameter group: ```python def _link_all_hp_params(self): if self.cpu_offload: self._get_offload_gradient_dict() for i, _ in enumerate(self.optimizer.param_groups): # Link bit16 and fp32 params in partition partition_id = dist.get_rank(group=self.real_dp_process_group[i]) partition_size = self.bit16_groups_flat[i].numel() // dist.get_world_size(group=self.real_dp_process_group[i]) # <-- flat_hp_partition = self.single_partition_of_fp32_groups[i] link_hp_params(lp_param_list=self.bit16_groups[i], flat_hp_partition=flat_hp_partition, gradient_dict=self.averaged_gradients, offload_gradient_dict=self.offload_gradient_dict, use_offload=self.cpu_offload, param_group_index=i, partition_start=partition_id * partition_size, partition_size=partition_size, dp_group=self.real_dp_process_group[i]) ``` Co-authored-by: Olatunji Ruwase --- deepspeed/runtime/zero/stage_1_and_2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index 7c8ae8e67b78..bd08ee5d55ef 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -573,14 +573,14 @@ def _create_param_mapping(self): return param_mapping def _link_all_hp_params(self): - dp_world_size = dist.get_world_size(group=self.dp_process_group) if self.cpu_offload: self._get_offload_gradient_dict() for i, _ in enumerate(self.optimizer.param_groups): # Link bit16 and fp32 params in partition partition_id = dist.get_rank(group=self.real_dp_process_group[i]) - partition_size = self.bit16_groups_flat[i].numel() // dp_world_size + partition_size = self.bit16_groups_flat[i].numel() // dist.get_world_size( + group=self.real_dp_process_group[i]) flat_hp_partition = self.single_partition_of_fp32_groups[i] link_hp_params(lp_param_list=self.bit16_groups[i], flat_hp_partition=flat_hp_partition, From 88b2ef71b3f2dfc42932cd2c097397f637ad77f4 Mon Sep 17 00:00:00 2001 From: Chirag Jain Date: Wed, 26 Jun 2024 22:25:00 +0530 Subject: [PATCH 15/36] Fix memory leak from _hp_mapping (#5643) See #5496 I don't really know if this is a good solution --- deepspeed/runtime/bf16_optimizer.py | 19 +++++++++++++++++-- deepspeed/runtime/zero/stage_1_and_2.py | 4 ++++ 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/deepspeed/runtime/bf16_optimizer.py b/deepspeed/runtime/bf16_optimizer.py index 1f3365b20f4e..5e4b738a6951 100644 --- a/deepspeed/runtime/bf16_optimizer.py +++ b/deepspeed/runtime/bf16_optimizer.py @@ -26,6 +26,11 @@ setattr(sys.modules[__name__], 'fragment_address', fragment_address) +def print_rank_0(message, debug=False, force=False): + if dist.get_rank() == 0 and (debug or force): + print(message) + + class BF16_Optimizer(ZeROOptimizer): def __init__(self, @@ -92,7 +97,16 @@ def __init__(self, if self.using_real_optimizer: self._setup_for_real_optimizer() - see_memory_usage('end bf16_optimizer', force=True) + see_memory_usage('end bf16_ optimizer', force=True) + + def destroy(self): + for i, _ in enumerate(self.optimizer.param_groups): + for p in self.bf16_groups[i]: + if getattr(p, '_hp_mapping', None): + p._hp_mapping = None + for hook in self._grad_acc_hooks: + hook.remove() + print_rank_0("Removed grad acc hooks") def _configure_moe_settings(self): assert any( @@ -187,6 +201,7 @@ def _setup_for_real_optimizer(self): self.initialize_optimizer_states() see_memory_usage('end initialize_optimizer', force=True) + self._grad_acc_hooks = [] if self.immediate_grad_update: self.create_grad_acc_hooks() @@ -541,7 +556,7 @@ def wrapper(param, i, j): def accumulate_hp_grads_and_remove_lp(*notneeded): self.accumulate_hp_grads_and_remove_lp(param, i, j) - grad_acc.register_hook(accumulate_hp_grads_and_remove_lp) + self._grad_acc_hooks.append(grad_acc.register_hook(accumulate_hp_grads_and_remove_lp)) self.grad_accs.append(grad_acc) wrapper(param, i, j) diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index bd08ee5d55ef..d3a0559b1b69 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -552,6 +552,10 @@ def __init__(self, self._param_slice_mappings = self._create_param_mapping() def destroy(self): + for i, _ in enumerate(self.optimizer.param_groups): + for p in self.bit16_groups[i]: + if getattr(p, '_hp_mapping', None): + p._hp_mapping = None for hook in self._grad_acc_hooks: hook.remove() self.print_rank_0("Removed grad acc hooks") From 19da95f783072da25c6ae13ac1c1f8832476542b Mon Sep 17 00:00:00 2001 From: "Ma, Guokai" Date: Thu, 27 Jun 2024 02:25:15 +0800 Subject: [PATCH 16/36] [CPU] add fp16 support to shm inference_all_reduce (#5669) This PR adds FP16 support to DeepSpeed SHM inference_all_reduce. Previously only FP32 and BF16 is supported. This is to align with PyTorch CPU support on FP16 datatype. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase --- csrc/cpu/comm/shm.cpp | 128 +++++++++++++++----------------- csrc/cpu/comm/shm_interface.cpp | 1 + tests/unit/comm/test_dist.py | 13 +++- 3 files changed, 72 insertions(+), 70 deletions(-) diff --git a/csrc/cpu/comm/shm.cpp b/csrc/cpu/comm/shm.cpp index 78170b538543..be44681ca062 100644 --- a/csrc/cpu/comm/shm.cpp +++ b/csrc/cpu/comm/shm.cpp @@ -143,13 +143,19 @@ inline __m256i cvt_fp32_to_bf16(const __m512 src) return _mm512_cvtusepi32_epi16(t_value); } -void reduce_2_bf16_buffers_iio(int num_elements, void* in0, void* in1, void* out) - __attribute__((target("avx512bw"))); +__m512 cvt_fp16_to_fp32(const __m256i src) __attribute__((target("avx512bw"))); +inline __m512 cvt_fp16_to_fp32(const __m256i src) { return _mm512_cvtph_ps(src); } + +inline __m256i cvt_fp32_to_fp16(const __m512 src) __attribute__((target("avx512bw"))); +inline __m256i cvt_fp32_to_fp16(const __m512 src) +{ + return _mm512_cvtps_ph(src, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC)); +} void reduce_bf16_buffers(int start_elements, int num_elements, char* to_buffer, char** buffers) __attribute__((target("avx512bw"))); -void reduce_2_fp32_buffers_iio(int num_elements, void* in0, void* in1, void* out) +void reduce_fp16_buffers(int start_elements, int num_elements, char* to_buffer, char** buffers) __attribute__((target("avx512bw"))); void reduce_fp32_buffers(int start_elements, int num_elements, char* to_buffer, char** buffers) @@ -164,26 +170,13 @@ void reduce_all_buffers(int start_elements, { switch (scalar_type) { case c10::ScalarType::BFloat16: - if (world_size == 2) { - // add the other buffer to to_buffer - reduce_2_bf16_buffers_iio(num_elements, - buffers[1 - to_buffer_idx] + start_elements * 2, - to_buffer + start_elements * 2, - to_buffer + start_elements * 2); - } else { - reduce_bf16_buffers(start_elements, num_elements, to_buffer, buffers); - } + reduce_bf16_buffers(start_elements, num_elements, to_buffer, buffers); + break; + case c10::ScalarType::Half: + reduce_fp16_buffers(start_elements, num_elements, to_buffer, buffers); break; case c10::ScalarType::Float: - if (world_size == 2) { - reduce_2_fp32_buffers_iio(num_elements, - buffers[1 - to_buffer_idx] + start_elements * 4, - to_buffer + start_elements * 4, - to_buffer + start_elements * 4); - } else { - assert(world_size > 2); - reduce_fp32_buffers(start_elements, num_elements, to_buffer, buffers); - } + reduce_fp32_buffers(start_elements, num_elements, to_buffer, buffers); break; default: assert(!"Should not get here"); } @@ -197,8 +190,8 @@ void reduce_all_buffers(int start_elements, // Reduce functions down below use vectorized algorithm, the number of bytes processed each // iteration depends on vector length. 256bit vector ==> 32 bytes, 512bit vector ==> 64 bytes -// If you change implementation of reduce_2_bf16_buffers_iio or reduce_2_fp32_buffers_iio, check -// whether this number needs to be changed +// If you change implementation of reduce_bf16_buffers, etc. , check whether this number needs +// to be changed #define VECTOR_LENGTH_IN_BYTES 32 void reduce_bf16_buffers(int start_elements, int num_elements, char* to_buffer, char** buffers) @@ -227,10 +220,9 @@ void reduce_bf16_buffers(int start_elements, int num_elements, char* to_buffer, case 6: CVT_ADD_BF16(5); case 5: CVT_ADD_BF16(4); case 4: CVT_ADD_BF16(3); - case 3: - CVT_ADD_BF16(2); - CVT_ADD_BF16(1); - break; + case 3: CVT_ADD_BF16(2); + case 2: CVT_ADD_BF16(1); + case 1: break; default: for (int j = 1; j < world_size; j++) { auto in_val = cvt_bf16_to_fp32(_mm256_loadu_si256((__m256i*)(buffers[j] + i))); @@ -251,7 +243,13 @@ void reduce_bf16_buffers(int start_elements, int num_elements, char* to_buffer, } } -void reduce_2_bf16_buffers_iio(int num_elements, void* in0, void* in1, void* out) +#define CVT_ADD_FP16(x) \ + do { \ + auto in##x##_val = cvt_fp16_to_fp32(_mm256_loadu_si256((__m256i*)(buffers[x] + i))); \ + inout_val = _mm512_add_ps(inout_val, in##x##_val); \ + } while (0) + +void reduce_fp16_buffers(int start_elements, int num_elements, char* to_buffer, char** buffers) { const int element_size = 2; const int vector_length = VECTOR_LENGTH_IN_BYTES / element_size; @@ -260,19 +258,41 @@ void reduce_2_bf16_buffers_iio(int num_elements, void* in0, void* in1, void* out // process aligned part #pragma omp parallel for - for (int i = 0; i < main_elements * element_size; i += VECTOR_LENGTH_IN_BYTES) { - auto in0_val = cvt_bf16_to_fp32(_mm256_loadu_si256((__m256i*)((char*)in0 + i))); - auto in1_val = cvt_bf16_to_fp32(_mm256_loadu_si256((__m256i*)((char*)in1 + i))); - auto out_val = _mm512_add_ps(in0_val, in1_val); - _mm256_storeu_si256((__m256i*)((char*)out + i), cvt_fp32_to_bf16(out_val)); + for (int i = start_elements * element_size; i < (start_elements + main_elements) * element_size; + i += VECTOR_LENGTH_IN_BYTES) { + auto inout_val = cvt_fp16_to_fp32(_mm256_loadu_si256((__m256i*)(buffers[0] + i))); + switch (world_size) { + case 16: CVT_ADD_FP16(15); + case 15: CVT_ADD_FP16(14); + case 14: CVT_ADD_FP16(13); + case 13: CVT_ADD_FP16(12); + case 12: CVT_ADD_FP16(11); + case 11: CVT_ADD_FP16(10); + case 10: CVT_ADD_FP16(9); + case 9: CVT_ADD_FP16(8); + case 8: CVT_ADD_FP16(7); + case 7: CVT_ADD_FP16(6); + case 6: CVT_ADD_FP16(5); + case 5: CVT_ADD_FP16(4); + case 4: CVT_ADD_FP16(3); + case 3: CVT_ADD_FP16(2); + case 2: CVT_ADD_FP16(1); + case 1: break; + default: + for (int j = 1; j < world_size; j++) { + auto in_val = cvt_fp16_to_fp32(_mm256_loadu_si256((__m256i*)(buffers[j] + i))); + inout_val = _mm512_add_ps(inout_val, in_val); + } + } + _mm256_storeu_si256((__m256i*)(to_buffer + i), cvt_fp32_to_fp16(inout_val)); } // process remaining part - int i = main_elements * element_size; + int i = (start_elements + main_elements) * element_size; while (remain_elements > 0) { - float in0_val = *((at::BFloat16*)((char*)in0 + i)); - float in1_val = *((at::BFloat16*)((char*)in1 + i)); - *((at::BFloat16*)((char*)out + i)) = in0_val + in1_val; + float val = 0.0f; + for (int j = 0; j < world_size; j++) { val += *(at::Half*)(buffers[j] + i); } + *(at::Half*)(to_buffer + i) = val; remain_elements--; i += element_size; } @@ -310,10 +330,9 @@ void reduce_fp32_buffers(int start_elements, int num_elements, char* to_buffer, case 6: CVT_ADD_F32(5); case 5: CVT_ADD_F32(4); case 4: CVT_ADD_F32(3); - case 3: - CVT_ADD_F32(2); - CVT_ADD_F32(1); - break; + case 3: CVT_ADD_F32(2); + case 2: CVT_ADD_F32(1); + case 1: break; default: for (int j = 1; j < world_size; j++) { auto in_val = _mm256_loadu_ps((float*)(buffers[j] + i)); @@ -334,33 +353,6 @@ void reduce_fp32_buffers(int start_elements, int num_elements, char* to_buffer, } } -void reduce_2_fp32_buffers_iio(int num_elements, void* in0, void* in1, void* out) -{ - const int element_size = 4; - const int vector_length = VECTOR_LENGTH_IN_BYTES / element_size; - int main_elements = num_elements - (num_elements % vector_length); - int remain_elements = num_elements % vector_length; - - // process aligned part -#pragma omp parallel for - for (int i = 0; i < main_elements * element_size; i += VECTOR_LENGTH_IN_BYTES) { - auto in0_val = _mm256_loadu_ps((float*)((char*)in0 + i)); - auto in1_val = _mm256_loadu_ps((float*)((char*)in1 + i)); - auto out_val = _mm256_add_ps(in0_val, in1_val); - _mm256_storeu_ps((float*)((char*)out + i), out_val); - } - - // process remaining part - int i = main_elements * element_size; - while (remain_elements > 0) { - float in0_val = *((float*)((char*)in0 + i)); - float in1_val = *((float*)((char*)in1 + i)); - *((float*)((char*)out + i)) = in0_val + in1_val; - remain_elements--; - i += element_size; - } -} - static bool is_initialized = 0; static int world_rank; diff --git a/csrc/cpu/comm/shm_interface.cpp b/csrc/cpu/comm/shm_interface.cpp index 981ea36515fc..d11c8cfa7375 100644 --- a/csrc/cpu/comm/shm_interface.cpp +++ b/csrc/cpu/comm/shm_interface.cpp @@ -79,6 +79,7 @@ int inference_all_reduce(torch::Tensor& data, py::object op) switch (data.scalar_type()) { case c10::ScalarType::BFloat16: data_size = numel * 2; break; + case c10::ScalarType::Half: data_size = numel * 2; break; case c10::ScalarType::Float: data_size = numel * 4; break; default: data_type_fallback = true; } diff --git a/tests/unit/comm/test_dist.py b/tests/unit/comm/test_dist.py index a40805bf0a75..861ba5c7be1a 100644 --- a/tests/unit/comm/test_dist.py +++ b/tests/unit/comm/test_dist.py @@ -127,13 +127,22 @@ def test(self): assert torch.all(x == result) +@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16, torch.float16]) class TestDistInferenceAllReduce(DistributedTest): - world_size = 4 + device_count = get_accelerator().device_count() + if device_count >= 4: + world_size = [1, 2, 4] + elif device_count >= 2: + world_size = [1, 2] + else: + world_size = [1] - def test(self): + def test(self, dtype): x = torch.ones(1, 3).to(get_accelerator().device_name()) * (dist.get_rank() + 1) sum_of_ranks = (dist.get_world_size() * (dist.get_world_size() + 1)) // 2 result = torch.ones(1, 3).to(get_accelerator().device_name()) * sum_of_ranks + result = result.to(dtype) + x = x.to(dtype) dist.inference_all_reduce(x) assert torch.all(x == result) From d2b1d7fc087144a0b280c7de37d50b74e9db6f95 Mon Sep 17 00:00:00 2001 From: Xinyu Lian Date: Thu, 27 Jun 2024 03:47:51 +0900 Subject: [PATCH 17/36] Universal checkpoint for zero stage 3 (#5475) This PR enables the universal checkpoint for zero stage 3. Notes: - The current implementation supports Data parallelism. - Development is ongoing for universal checkpoint Stage 3 with tensor-slicing model parallelism. - Pipeline parallelism is not supported by ZeRO Stage 3, and hence is not included in this universal checkpoint implementation. In this PR: - I've updated `deepspeed/checkpoint/ds_to_universal.py ` to support converting Zero checkpoints into Universal checkpoints. - I've updated `deepspeed/runtime/zero/stage3.py` to enable loading Universal checkpoints using the Stage 3 optimizer. --------- Co-authored-by: Olatunji Ruwase Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> --- deepspeed/checkpoint/ds_to_universal.py | 183 +++++++++++++++--- deepspeed/runtime/bf16_optimizer.py | 3 +- deepspeed/runtime/engine.py | 11 +- deepspeed/runtime/zero/stage3.py | 126 ++++++++++-- deepspeed/runtime/zero/stage_1_and_2.py | 3 +- .../checkpoint/test_universal_checkpoint.py | 18 +- 6 files changed, 291 insertions(+), 53 deletions(-) diff --git a/deepspeed/checkpoint/ds_to_universal.py b/deepspeed/checkpoint/ds_to_universal.py index b1a8276589b6..702ca71e779d 100755 --- a/deepspeed/checkpoint/ds_to_universal.py +++ b/deepspeed/checkpoint/ds_to_universal.py @@ -10,6 +10,7 @@ import argparse import glob import itertools +import math from concurrent.futures import ProcessPoolExecutor import os import re @@ -21,6 +22,7 @@ from deepspeed.checkpoint import DeepSpeedCheckpoint from deepspeed.checkpoint import ( OPTIMIZER_STATE_DICT, + ZERO_STAGE, BASE_OPTIMIZER_STATE, SINGLE_PARTITION_OF_FP32_GROUPS, PARAM_GROUPS, @@ -70,6 +72,19 @@ def parse_arguments(): return args +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + def _create_checkpoint_paths(base_folder, iteration, tp_degree, pp_degree): path_list = [] iter_folder = f'iter_{iteration:07d}' @@ -129,6 +144,26 @@ def extract_zero_shards(dir, ds_checkpoint, indices_3D): fragment_mapping.start, fragment_mapping.numel) +def extract_zero_shards_stage3(optim_files, param_shapes, dp_degree, temp_dir, dp_index): + state_dict = torch.load(optim_files[dp_index], map_location='cpu') + + flat_state = dict( + exp_avg=state_dict[OPTIMIZER_STATE_DICT]['optimizer_state_dict']['state'][0]["exp_avg"], + exp_avg_sq=state_dict[OPTIMIZER_STATE_DICT]['optimizer_state_dict']['state'][0]["exp_avg_sq"], + fp32=state_dict[OPTIMIZER_STATE_DICT]['fp32_flat_groups'][0], + ) + + offset = 0 + for name, shape in param_shapes.items(): + unpartitioned_numel = shape.numel() + partitioned_numel, _ = _zero_partitioned_param_info(unpartitioned_numel, dp_degree) + padding_free_numel = min(partitioned_numel, abs(unpartitioned_numel - dp_index * partitioned_numel)) + for state_key in flat_state.keys(): + dump_param_fragment(temp_dir, 0, dp_index, state_key, flat_state[state_key], name, offset, + padding_free_numel) + offset += partitioned_numel + + cnt = 0 @@ -155,7 +190,7 @@ def dump_param_fragment(dir, tp_index, dp_index, state_name, state_flat_tensor, _save_checkpoint(path, state_flat_tensor) -def _merge_zero_shards(param_base_path, state, tp_degree, slice_shape): +def _merge_zero_shards(param_base_path, state, tp_degree, slice_shape=None): slices = [] for tp_index in range(tp_degree): prefix_path = os.path.join(param_base_path, str(tp_index), f"{state}") @@ -180,7 +215,10 @@ def _merge_zero_shards(param_base_path, state, tp_degree, slice_shape): assert all(v == shards[0] for v in shards), "All shards must have the same step value" slice = shards[0] else: - slice = torch.cat(shards, dim=0).reshape(slice_shape) + if slice_shape is None: + slice = torch.cat(shards, dim=0) + else: + slice = torch.cat(shards, dim=0).reshape(slice_shape) slices.append(slice) return slices @@ -292,6 +330,16 @@ def get_matched_sub_params_pattern(name_): return unmatched_patterns +def merge_zero3_slices(dp_degree, dir, slice_dir, name): + slice_base_path = os.path.join(slice_dir, name) + param_base_path = os.path.join(dir, name) + + for state in ("fp32", "exp_avg", "exp_avg_sq"): + slices = _merge_zero_shards(slice_base_path, state, 1) + final_path = os.path.join(param_base_path, f"{state}.pt") + _save_checkpoint(final_path, slices[0]) + + def _do_parallel_work(do_work, work_chunks, num_workers): results = [] if num_workers > 1: @@ -317,6 +365,11 @@ def _extract_zero_shard_files(args, ds_checkpoint, temp_dir): _do_parallel_work(do_work, _3d_range_list, args.num_extract_workers) +def _extract_zero_shard_files_stage3(args, optim_files, param_shapes, dp_degree, temp_dir): + do_work = partial(extract_zero_shards_stage3, optim_files, param_shapes, dp_degree, temp_dir) + _do_parallel_work(do_work, list(range(dp_degree)), args.num_extract_workers) + + def _merge_tp_slice_files(args, ds_checkpoint, slice_shapes, temp_dir): zero_output_folder = os.path.join(args.output_folder, "zero") do_work = partial(merge_tp_slices, ds_checkpoint, zero_output_folder, temp_dir, ds_checkpoint.tp_degree) @@ -332,6 +385,23 @@ def _merge_tp_slice_files(args, ds_checkpoint, slice_shapes, temp_dir): print(f'Warning: Unused patterns={unmatched_patterns} while merging tp slices') +def _merge_zero3_slice_files(args, param_shapes, dp_degree, temp_dir): + zero_output_folder = os.path.join(args.output_folder, "zero") + do_work = partial(merge_zero3_slices, dp_degree, zero_output_folder, temp_dir) + _do_parallel_work(do_work, param_shapes.keys(), args.num_merge_workers) + + +def _zero_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _parse_model_states_stage3(files): + return torch.load(files[0], map_location=torch.device('cpu'))[PARAM_SHAPES] + + def _save_optimizer_state(args, ds_checkpoint): sharded_states = [BASE_OPTIMIZER_STATE, PARAM_SLICE_MAPPINGS, SINGLE_PARTITION_OF_FP32_GROUPS] sd = ds_checkpoint.get_zero_checkpoint_state(pp_index=0, tp_index=0, dp_index=0) @@ -344,6 +414,39 @@ def _save_optimizer_state(args, ds_checkpoint): _save_checkpoint(output_file_path, output_sd) +def _save_optimizer_state_stage3(args, optim_files): + sd = torch.load(optim_files[0], map_location=torch.device('cpu')) + output_sd = sd[OPTIMIZER_STATE_DICT] + output_sd[PARAM_GROUPS] = output_sd[OPTIMIZER_STATE_DICT][PARAM_GROUPS] + zero_output_folder = os.path.join(args.output_folder, "zero") + output_file_path = os.path.join(zero_output_folder, f"optimizer_state.pt") + _save_checkpoint(output_file_path, output_sd) + + +def _get_optim_files(checkpoint_dir): + return _get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def _get_model_state_files(checkpoint_dir): + return _get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def _get_checkpoint_files(checkpoint_dir, glob_pattern): + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def _get_zero_stage(optim_files): + state_dict = torch.load(optim_files[0], map_location=torch.device('cpu')) + optimizer_state = state_dict[OPTIMIZER_STATE_DICT] + zero_stage = optimizer_state.get(ZERO_STAGE, 1) + return zero_stage + + def _check_for_required_state(ds_checkpoint): universal_checkpoint_info = ds_checkpoint.get_checkpoint_info(UNIVERSAL_CHECKPOINT_INFO) assert universal_checkpoint_info is not None, f'Required {UNIVERSAL_CHECKPOINT_INFO} state is missing in checkpoint. Verify that client creates this state.' @@ -354,38 +457,66 @@ def main(args): print(f'Converting DeepSpeed checkpoint in {args.input_folder} to Universal checkpoint in {args.output_folder}') - ds_checkpoint = DeepSpeedCheckpoint(args.input_folder) - _check_for_required_state(ds_checkpoint) + optim_files = _get_optim_files(args.input_folder) + zero_stage = _get_zero_stage(optim_files) + + if zero_stage <= 2: + ds_checkpoint = DeepSpeedCheckpoint(args.input_folder) + _check_for_required_state(ds_checkpoint) + + iteration = ds_checkpoint.get_iteration() + #_create_latest_file(args.output_folder, iteration) + checkpoint_paths = _create_checkpoint_paths(args.output_folder, iteration, ds_checkpoint.tp_degree, + ds_checkpoint.pp_degree) + + slice_shapes = [] + for mp_rank_file in ds_checkpoint.mp_rank_files: + mp_sd = torch.load(mp_rank_file, map_location=torch.device('cpu')) + slice_shapes += mp_sd[PARAM_SHAPES] - iteration = ds_checkpoint.get_iteration() - #_create_latest_file(args.output_folder, iteration) - checkpoint_paths = _create_checkpoint_paths(args.output_folder, iteration, ds_checkpoint.tp_degree, - ds_checkpoint.pp_degree) + # fix back to normal flat dict, merge duplicates for tp>1 + slice_shapes = dict((k, v) for d in slice_shapes for k, v in d.items()) + temp_dir = os.path.join(args.output_folder, 'tmp') - slice_shapes = [] - for mp_rank_file in ds_checkpoint.mp_rank_files: - mp_sd = torch.load(mp_rank_file, map_location=torch.device('cpu')) - slice_shapes += mp_sd[PARAM_SHAPES] + print('*** 1. Extracting ZeRO fragments') + _extract_zero_shard_files(args, ds_checkpoint, temp_dir) + + print('*** 2. Merging slices .....') + _merge_tp_slice_files(args, ds_checkpoint, slice_shapes, temp_dir) + + print('*** 3. Saving common optimizer states') + _save_optimizer_state(args, ds_checkpoint) + + if not args.keep_temp_folder: + shutil.rmtree(temp_dir, ignore_errors=True) + + # Copy mp* files into output folder + for f in glob.glob(os.path.join(args.input_folder, 'mp*')): + shutil.copy2(f, args.output_folder) + + else: + model_files = _get_model_state_files(args.input_folder) + param_shapes = _parse_model_states_stage3(model_files) + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + dp_degree = len(model_files) - # fix back to normal flat dict, merge duplicates for tp>1 - slice_shapes = dict((k, v) for d in slice_shapes for k, v in d.items()) - temp_dir = os.path.join(args.output_folder, 'tmp') + temp_dir = os.path.join(args.output_folder, 'tmp') - print('*** 1. Extracting ZeRO fragments') - _extract_zero_shard_files(args, ds_checkpoint, temp_dir) + print('*** 1. Extracting ZeRO fragments') + _extract_zero_shard_files_stage3(args, optim_files, param_shapes, dp_degree, temp_dir) - print('*** 2. Merging slices .....') - _merge_tp_slice_files(args, ds_checkpoint, slice_shapes, temp_dir) + print('*** 2. Merging slices .....') + _merge_zero3_slice_files(args, param_shapes, dp_degree, temp_dir) - print('*** 3. Saving common optimizer states') - _save_optimizer_state(args, ds_checkpoint) + print('*** 3. Saving common optimizer states') + _save_optimizer_state_stage3(args, optim_files) - if not args.keep_temp_folder: - shutil.rmtree(temp_dir, ignore_errors=True) + if not args.keep_temp_folder: + shutil.rmtree(temp_dir, ignore_errors=True) - # Copy mp* files into output folder - for f in glob.glob(os.path.join(args.input_folder, 'mp*')): - shutil.copy2(f, args.output_folder) + # Copy *model_states files into output folder + for f in glob.glob(os.path.join(args.input_folder, '*model_states.pt')): + shutil.copy2(f, args.output_folder) # Update latest to output folder checkpoint_root_folder, step_folder = os.path.split(args.output_folder) diff --git a/deepspeed/runtime/bf16_optimizer.py b/deepspeed/runtime/bf16_optimizer.py index 5e4b738a6951..965b446163ec 100644 --- a/deepspeed/runtime/bf16_optimizer.py +++ b/deepspeed/runtime/bf16_optimizer.py @@ -498,7 +498,8 @@ def load_state_dict(self, checkpoint_folder, load_optimizer_states=True, load_from_fp32_weights=False, - load_serial=None): + load_serial=None, + param_shapes=None): if checkpoint_folder: self._load_universal_checkpoint(checkpoint_folder, load_optimizer_states, load_from_fp32_weights) else: diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 4c418fbc532e..366db3a972a3 100644 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -2662,7 +2662,10 @@ def _get_ckpt_name(self, checkpoints_path, tag, mp_placeholder=None): mp_rank_str = f"{mp_rank:02d}" if self.zero_optimization_partition_weights(): - filename = "zero_pp_rank_{}".format(dist.get_rank(group=self.optimizer.dp_process_group)) + if self.load_universal_checkpoint(): + filename = "zero_pp_rank_0" + else: + filename = "zero_pp_rank_{}".format(dist.get_rank(group=self.optimizer.dp_process_group)) ckpt_name = os.path.join( checkpoints_path, str(tag), @@ -2789,7 +2792,7 @@ def load_checkpoint(self, if self._optimizer_has_ckpt_event_epilogue(): self.optimizer.checkpoint_event_epilogue() - if self.load_universal_checkpoint(): + if self.load_universal_checkpoint() and not self.zero_optimization_partition_weights(): self.optimizer.update_lp_params() return load_path, client_states @@ -2956,11 +2959,13 @@ def _load_zero_checkpoint(self, load_dir, tag, load_optimizer_states=True): if zero_sd_list is None: return False + param_shapes = self._get_zero_param_shapes() self.optimizer.load_state_dict(state_dict_list=zero_sd_list, load_optimizer_states=load_optimizer_states, load_from_fp32_weights=self.zero_load_from_fp32_weights(), checkpoint_folder=checkpoint_folder, - load_serial=load_serial) + load_serial=load_serial, + param_shapes=param_shapes) if self.load_universal_checkpoint(): logger.info(f'loaded universal zero checkpoints from {checkpoint_folder} for rank {self.global_rank}') diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index 3f43e865fa72..edeedb691961 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -2593,7 +2593,8 @@ def load_state_dict(self, load_optimizer_states=True, load_from_fp32_weights=False, checkpoint_folder=None, - load_serial=None): + load_serial=None, + param_shapes=None): r"""Loading a ZeRO checkpoint Arguments: state_dict_list: List of all saved ZeRO checkpoints, one for each saved partition. @@ -2622,24 +2623,113 @@ def load_state_dict(self, if self.elastic_checkpoint: raise NotImplementedError("ZeRO-3 does not yet support elastic checkpointing, please disable for now.") - self._rigid_load_state_dict(state_dict_list[dist.get_rank(group=self.dp_process_group)], - load_optimizer_states=load_optimizer_states) - - # when use loading checkpoint serial, after finish loading, we need to - # delete the temp state_dict_list variable to save memory, then trigger - # the next rank's loading - if load_serial is not None: - load_serial += 1 - rank = dist.get_rank(group=self.dp_process_group) - local_rank = dist.get_local_rank() - del state_dict_list[rank] - rank_end = dist.get_world_size() - 1 - if local_rank != rank_end: - dist.send(tensor=load_serial, dst=rank + 1) + if checkpoint_folder: + self._load_universal_checkpoint(checkpoint_folder, load_optimizer_states, load_from_fp32_weights, + param_shapes) + else: + self._rigid_load_state_dict(state_dict_list[dist.get_rank(group=self.dp_process_group)], + load_optimizer_states=load_optimizer_states) + + # when use loading checkpoint serial, after finish loading, we need to + # delete the temp state_dict_list variable to save memory, then trigger + # the next rank's loading + if load_serial is not None: + load_serial += 1 + rank = dist.get_rank(group=self.dp_process_group) + local_rank = dist.get_local_rank() + del state_dict_list[rank] + rank_end = dist.get_world_size() - 1 + if local_rank != rank_end: + dist.send(tensor=load_serial, dst=rank + 1) + + if len(self.persistent_parameters) > 0: + self.persistent_parameters[0].partition(self.persistent_parameters) + # self.persistent_parameters[0].all_gather(self.persistent_parameters) # this will be done in checkpoint_event_epilogue() so remove it to prevent double all_gather + + def _load_universal_checkpoint(self, checkpoint_folder, load_optimizer_states, load_from_fp32_weights, + param_shapes): + self.load_hp_checkpoint_state_from_checkpoint_dir_stage3(checkpoint_folder, param_shapes) + + def load_hp_checkpoint_state_from_checkpoint_dir_stage3(self, checkpoint_dir, param_shapes): + """ Load optimizer and model states from the checkpoint directory. """ + checkpoint_dir = os.path.join(checkpoint_dir, "zero") + optim_state_path = os.path.join(checkpoint_dir, "optimizer_state.pt") + assert os.path.isfile( + optim_state_path), f'{optim_state_path} containing optimizer global state is missing! Cannot proceed.' + + optim_sd = torch.load(optim_state_path) + self._load_global_state_stage3(optim_sd) + + key_list = ["fp32", "exp_avg", "exp_avg_sq"] + + for key in key_list: + key_tensor = torch.empty(0) + for layer in param_shapes[0].keys(): + key_layer_state_partition = self.load_hp_checkpoint_state(os.path.join(checkpoint_dir, layer), key) + key_tensor = torch.cat((key_tensor, key_layer_state_partition)) + if key == "fp32": + self.fp32_partitioned_groups_flat[0].data.copy_(key_tensor) + self.optimizer.param_groups[0]['params'].append(self.fp32_partitioned_groups_flat[0]) + else: + optim_sd[OPTIMIZER_STATE_DICT]['state'][0][key] = key_tensor - if len(self.persistent_parameters) > 0: - self.persistent_parameters[0].partition(self.persistent_parameters) - # self.persistent_parameters[0].all_gather(self.persistent_parameters) # this will be done in checkpoint_event_epilogue() so remove it to prevent double all_gather + if self.swap_optimizer or self.params_in_nvme_and_cpu: + # Purge the swapped optimizer state, it was initialized to the freshly created model and not the checkpoint + for swap_info in self.optimizer_swapper.swap_params_info.values(): + swap_info.tensors = [swap_info.tensors[0]] + swap_info.has_state_tensors = False + + if self.swap_optimizer: + # Touch all parameters to synchronize all buffers + timer_names = set() + self._partition_all_parameters() + for sub_group_id, group in enumerate(self.fp16_groups): + self._prepare_sub_group(sub_group_id, timer_names) + self._reassign_or_swap_out_partitioned_parameters(sub_group_id) + self._release_sub_group(sub_group_id, timer_names) + self._post_step(timer_names) + + self.optimizer.load_state_dict(optim_sd[OPTIMIZER_STATE_DICT]) + for param_group in self.optimizer.param_groups: + param_group['params'] = [] + + for sub_group_id in range(len(self.fp32_partitioned_groups_flat)): + fp32_param = self.fp32_partitioned_groups_flat[sub_group_id] + if sum(fp32_param.size()) > 0: + fp16_param = self.fp16_partitioned_groups_flat[sub_group_id] + fp16_param.data.copy_(fp32_param.data) + + for sub_group_id in range(len(self.fp16_partitioned_groups_flat)): + updated_params = self.unflatten(self.fp16_partitioned_groups_flat[sub_group_id], + self.fp16_partitioned_groups[sub_group_id]) + + for partitioned_param, q in zip(self.fp16_partitioned_groups[sub_group_id], updated_params): + partitioned_param.data = q.data + + def _load_global_state_stage3(self, sd): + self.loss_scaler = sd.get(LOSS_SCALER, self.loss_scaler) + self.dynamic_loss_scale = sd.get('dynamic_loss_scale', self.dynamic_loss_scale) + self.overflow = sd.get('overflow', self.overflow) + + def load_hp_checkpoint_state(self, folder, key): + local_rank = dist.get_local_rank() + + # Load tensors from files and reshape them to flat vectors + loaded_checkpoint_state = torch.load(os.path.join(folder, f"{key}.pt")).view(-1) + + # Partition the loaded data according to the local rank + world_size = dist.get_world_size(group=self.dp_process_group) + unpartitioned_numel = loaded_checkpoint_state.numel() + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + + if world_size * partitioned_numel != unpartitioned_numel: + padding_size = world_size * partitioned_numel - unpartitioned_numel + padding_tensor = torch.zeros(padding_size, dtype=loaded_checkpoint_state.dtype) + loaded_checkpoint_state = torch.cat([loaded_checkpoint_state, padding_tensor]) + checkpoint_state_partition = loaded_checkpoint_state.narrow(0, local_rank * partitioned_numel, + partitioned_numel) + + return checkpoint_state_partition def reset_swap_buffers(self): timer_names = set() diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index d3a0559b1b69..461ad719536a 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -2290,7 +2290,8 @@ def load_state_dict(self, load_optimizer_states=True, load_from_fp32_weights=False, checkpoint_folder=None, - load_serial=None): + load_serial=None, + param_shapes=None): if checkpoint_folder: self._load_universal_checkpoint(checkpoint_folder, load_optimizer_states, load_from_fp32_weights) else: diff --git a/tests/unit/checkpoint/test_universal_checkpoint.py b/tests/unit/checkpoint/test_universal_checkpoint.py index e0c4f4745043..c75419f8683b 100644 --- a/tests/unit/checkpoint/test_universal_checkpoint.py +++ b/tests/unit/checkpoint/test_universal_checkpoint.py @@ -90,7 +90,12 @@ def train_save_convert(ds_config, hidden_dim, load_optim, use_torch_adam, dtype, model.backward(loss) model.step() - sd = model.optimizer.optimizer.state_dict() if load_optim else None + if ds_config["zero_optimization"]["stage"] == 3: + model.optimizer._set_fp32_optimizer_param_groups() + sd = model.optimizer.optimizer.state_dict() if load_optim else None + model.optimizer._clear_fp32_optimizer_param_groups() + else: + sd = model.optimizer.optimizer.state_dict() if load_optim else None client_state = {} client_state[UNIVERSAL_CHECKPOINT_INFO] = {} @@ -114,7 +119,12 @@ def train_save_convert(ds_config, hidden_dim, load_optim, use_torch_adam, dtype, model_state = model.state_dict() optimizer_state = None if load_optim: - optimizer_state = gather_opt_state(model.optimizer.optimizer.state_dict()) + if ds_config["zero_optimization"]["stage"] == 3: + model.optimizer._set_fp32_optimizer_param_groups() + optimizer_state = gather_opt_state(model.optimizer.optimizer.state_dict()) + model.optimizer._clear_fp32_optimizer_param_groups() + else: + optimizer_state = gather_opt_state(model.optimizer.optimizer.state_dict()) if dist.get_rank() == 0: torch.save((model_state, optimizer_state), os.path.join(tmpdir, "baseline_state.pt")) @@ -159,7 +169,7 @@ class baseline_ws4(_baseline): @pytest.mark.parametrize('dtype', [torch.bfloat16, torch.float16, torch.float32]) -@pytest.mark.parametrize("zero_stage", [1]) +@pytest.mark.parametrize("zero_stage", [1, 3]) @pytest.mark.parametrize("use_torch_adam", [False, True]) @pytest.mark.parametrize("load_optim", [False, True]) class TestZeROUniversalCheckpointDP(DistributedTest): @@ -181,7 +191,7 @@ def _run_test(self, tmpdir, dtype, ds_config, load_optim, use_torch_adam): model_state = univ_model.state_dict() compare_state_dicts(model_state, loaded_model_state) - if load_optim: + if load_optim and ds_config["zero_optimization"]["stage"] != 3: optimizer_state = gather_opt_state(univ_model.optimizer.optimizer.state_dict()) # padding sizes may differ when dp sizes are different param_count = sum(p.numel() for p in univ_model.parameters()) From 4f8531bc67c056db9c88479c3458f99b4f20c310 Mon Sep 17 00:00:00 2001 From: Omar Elayan <142979319+oelayan7@users.noreply.github.com> Date: Thu, 27 Jun 2024 00:08:15 +0300 Subject: [PATCH 18/36] inference unit test injectionPolicy split world_size to multiple tests (#5687) Having world size as an array will cause an iteration on launch_proc which takes a lot of time per case. Splitting into different test cases. Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- tests/unit/common.py | 2 +- tests/unit/inference/test_inference.py | 17 ++++++----------- 2 files changed, 7 insertions(+), 12 deletions(-) diff --git a/tests/unit/common.py b/tests/unit/common.py index 1774bcfae9ff..14beeb317198 100644 --- a/tests/unit/common.py +++ b/tests/unit/common.py @@ -450,7 +450,7 @@ def __call__(self, request): world_size = mark.args[0] break else: - world_size = self.world_size + world_size = self._fixture_kwargs.get("world_size", self.world_size) if isinstance(world_size, int): world_size = [world_size] diff --git a/tests/unit/inference/test_inference.py b/tests/unit/inference/test_inference.py index 36003319856c..eee069c95287 100644 --- a/tests/unit/inference/test_inference.py +++ b/tests/unit/inference/test_inference.py @@ -162,6 +162,11 @@ def enable_triton(request): return request.param +@pytest.fixture(params=[1, 2], ids=["ws1", "ws2"]) +def world_size(request): + return request.param + + """ Fixtures for running query """ @@ -490,24 +495,14 @@ def test( ) @pytest.mark.parametrize("dtype", [torch.float], ids=["fp32"]) class TestInjectionPolicy(DistributedTest): - world_size = [1, 2] - def test( - self, - model_w_task, - injection_policy, - query, - inf_kwargs, - assert_fn, - dtype, - ): + def test(self, model_w_task, injection_policy, query, inf_kwargs, assert_fn, dtype, world_size): invalid_test_msg = validate_test(model_w_task, dtype, enable_cuda_graph=False, enable_triton=False) if invalid_test_msg: pytest.skip(invalid_test_msg) model, task = model_w_task local_rank = int(os.getenv("LOCAL_RANK", "0")) - world_size = int(os.getenv("WORLD_SIZE", "2")) pipe = pipeline(task, model=model, From e9ffe0281aa04c739237217760f9942fa54ef5f8 Mon Sep 17 00:00:00 2001 From: Raza Sikander <54884406+raza-sikander@users.noreply.github.com> Date: Thu, 27 Jun 2024 03:48:03 +0530 Subject: [PATCH 19/36] ENV var added for recaching in INF Unit tests (#5688) FORCE_UPDATE_HF_CACHE set to True to recache. HF_CACHE_EXPIRY_DAYS to set the number of cache expiry days --------- Co-authored-by: Shaik Raza Sikander Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- tests/unit/inference/test_inference.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/unit/inference/test_inference.py b/tests/unit/inference/test_inference.py index eee069c95287..c7e53188bbd9 100644 --- a/tests/unit/inference/test_inference.py +++ b/tests/unit/inference/test_inference.py @@ -86,7 +86,8 @@ def _hf_model_list() -> List[ModelInfo]: cache_dir = os.getenv("HF_HOME", "~/.cache/huggingface") cache_file_path = os.path.join(cache_dir, "DS_model_cache.pkl") - cache_expiration_seconds = 60 * 60 * 24 # 1 day + num_days = os.getenv("HF_CACHE_EXPIRY_DAYS", 1) + cache_expiration_seconds = num_days * 60 * 60 * 24 # Load or initialize the cache model_data = {"cache_time": 0, "model_list": []} @@ -97,7 +98,8 @@ def _hf_model_list() -> List[ModelInfo]: current_time = time.time() # Update the cache if it has expired - if (model_data["cache_time"] + cache_expiration_seconds) < current_time: + if ((model_data["cache_time"] + cache_expiration_seconds) < current_time) or os.getenv("FORCE_UPDATE_HF_CACHE", + default=False): api = HfApi() model_data["model_list"] = [ ModelInfo(modelId=m.modelId, pipeline_tag=m.pipeline_tag, tags=m.tags) for m in api.list_models() From b421e8c8f31af254b63ad6e9839f617ab6d9c060 Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Wed, 26 Jun 2024 15:30:56 -0700 Subject: [PATCH 20/36] Disable nvtx decorator to avoid graph break (#5697) `instrument_w_nvtx` breaks a graph as `range_push` and `range_pop` return a non-tensor int. This PR disables the decorator to avoid the break graph. This actually impacts the performance. In my environment, the training iteration time using Llama-3-8B/4GPUs/ZeRO1 is improved from 3.02s -> 2.54s. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/engine.py | 3 +++ deepspeed/utils/nvtx.py | 8 ++++++-- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 366db3a972a3..d40141132aaf 100644 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -3613,6 +3613,9 @@ def compile(self, backend=get_accelerator().get_compile_backend(), compile_kwarg """Compile the module using the specified backend and kwargs. If a compiler_fn is set, it will be used instead of torch.compile(). """ + # Avoid graph breaks + deepspeed.utils.nvtx.enable_nvtx = False + if not is_compile_supported(): raise RuntimeError("compile is not supported in your version of PyTorch.") diff --git a/deepspeed/utils/nvtx.py b/deepspeed/utils/nvtx.py index 3823599e7bf2..7c566480a86a 100644 --- a/deepspeed/utils/nvtx.py +++ b/deepspeed/utils/nvtx.py @@ -5,15 +5,19 @@ from deepspeed.accelerator import get_accelerator +enable_nvtx = True + def instrument_w_nvtx(func): """decorator that causes an NVTX range to be recorded for the duration of the function call.""" def wrapped_fn(*args, **kwargs): - get_accelerator().range_push(func.__qualname__) + if enable_nvtx: + get_accelerator().range_push(func.__qualname__) ret_val = func(*args, **kwargs) - get_accelerator().range_pop() + if enable_nvtx: + get_accelerator().range_pop() return ret_val return wrapped_fn From f0e3f01d7c7a3d8748212e61eaf487fab41168a7 Mon Sep 17 00:00:00 2001 From: Xinyu Lian Date: Thu, 27 Jun 2024 16:34:26 +0900 Subject: [PATCH 21/36] Add an argument to enable the injection of missing state during the conversion of universal checkpoints (#5608) This PR solves the [Issue-5430](https://github.com/microsoft/DeepSpeed/issues/5430). The PR enables the universal checkpoint feature for other platforms like HuggingFace Trainer without requiring changes to the HuggingFace code. It does this by adding an argument that allows the injection of minimal necessary information into the state before this [assertion](https://github.com/microsoft/DeepSpeed/blob/ebf82e8f3ad6d51d49d115e54a11ae4597ff36fb/deepspeed/checkpoint/ds_to_universal.py#L358). --------- Co-authored-by: Olatunji Ruwase Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Abhishek Kulkarni --- deepspeed/checkpoint/ds_to_universal.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/deepspeed/checkpoint/ds_to_universal.py b/deepspeed/checkpoint/ds_to_universal.py index 702ca71e779d..c28f4f74b275 100755 --- a/deepspeed/checkpoint/ds_to_universal.py +++ b/deepspeed/checkpoint/ds_to_universal.py @@ -34,6 +34,8 @@ SUB_PARAM_SHAPE, VOCAB_TENSOR, UNIVERSAL_CHECKPOINT_INFO, + UNIVERSAL_CHECKPOINT_VERSION_KEY, + UNIVERSAL_CHECKPOINT_VERSION_VALUE, VOCABULARY_PARAMETER_PATTERNS, PIPELINE_REPLICATED_PARAMETER_PATTERNS, TP_REPLICATED_PARAMETER_PATTERNS, @@ -67,6 +69,9 @@ def parse_arguments(): dest='strict', action='store_false', help='Do not perform validity checks on converted checkpoint.') + parser.add_argument('--inject-missing-state', + action='store_true', + help='Inject missing checkpoint state into the checkpoint if it is absent.') args = parser.parse_args() print(f'args = {args}') return args @@ -447,6 +452,15 @@ def _get_zero_stage(optim_files): return zero_stage +def _inject_missing_state(ds_checkpoint): + if UNIVERSAL_CHECKPOINT_INFO not in ds_checkpoint.global_state: + sd = torch.load(ds_checkpoint.mp_rank_files[0], map_location=torch.device('cpu')) + if UNIVERSAL_CHECKPOINT_INFO not in sd: + ds_checkpoint.global_state[UNIVERSAL_CHECKPOINT_INFO] = {} + ds_checkpoint.global_state[UNIVERSAL_CHECKPOINT_INFO][ + UNIVERSAL_CHECKPOINT_VERSION_KEY] = UNIVERSAL_CHECKPOINT_VERSION_VALUE + + def _check_for_required_state(ds_checkpoint): universal_checkpoint_info = ds_checkpoint.get_checkpoint_info(UNIVERSAL_CHECKPOINT_INFO) assert universal_checkpoint_info is not None, f'Required {UNIVERSAL_CHECKPOINT_INFO} state is missing in checkpoint. Verify that client creates this state.' @@ -462,7 +476,10 @@ def main(args): if zero_stage <= 2: ds_checkpoint = DeepSpeedCheckpoint(args.input_folder) - _check_for_required_state(ds_checkpoint) + if args.inject_missing_state: + _inject_missing_state(ds_checkpoint) + else: + _check_for_required_state(ds_checkpoint) iteration = ds_checkpoint.get_iteration() #_create_latest_file(args.output_folder, iteration) From 4b8a4a07297bcb01bf3834f3aa397eb3f290a70a Mon Sep 17 00:00:00 2001 From: Liangliang-Ma Date: Sat, 29 Jun 2024 03:50:36 +0800 Subject: [PATCH 22/36] Change source of CPUAdam for xpu accelerator (#5703) Noted that cpu adam for cuda/cpu accelerator has removed the dependency of CUDA, we can now use the same source. --- csrc/xpu/adam/cpu_adam.cpp | 16 --- csrc/xpu/adam/cpu_adam_impl.cpp | 247 -------------------------------- op_builder/xpu/cpu_adam.py | 12 +- 3 files changed, 5 insertions(+), 270 deletions(-) delete mode 100755 csrc/xpu/adam/cpu_adam.cpp delete mode 100644 csrc/xpu/adam/cpu_adam_impl.cpp diff --git a/csrc/xpu/adam/cpu_adam.cpp b/csrc/xpu/adam/cpu_adam.cpp deleted file mode 100755 index 96809827f3e1..000000000000 --- a/csrc/xpu/adam/cpu_adam.cpp +++ /dev/null @@ -1,16 +0,0 @@ -// Copyright (c) Microsoft Corporation. -// SPDX-License-Identifier: Apache-2.0 - -// DeepSpeed Team - -#include "cpu_adam.h" - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) -{ - m.def("adam_update", &ds_adam_step, "DeepSpeed CPU Adam update (C++)"); - m.def("adam_update_copy", - &ds_adam_step_plus_copy, - "DeepSpeed CPU Adam update and param copy (C++)"); - m.def("create_adam", &create_adam_optimizer, "DeepSpeed CPU Adam (C++)"); - m.def("destroy_adam", &destroy_adam_optimizer, "DeepSpeed CPU Adam destroy (C++)"); -} diff --git a/csrc/xpu/adam/cpu_adam_impl.cpp b/csrc/xpu/adam/cpu_adam_impl.cpp deleted file mode 100644 index c24a08fc64eb..000000000000 --- a/csrc/xpu/adam/cpu_adam_impl.cpp +++ /dev/null @@ -1,247 +0,0 @@ -// Copyright (c) Microsoft Corporation. -// SPDX-License-Identifier: Apache-2.0 - -// DeepSpeed Team - -#include -#include -#include -#include -#include -#include -#include -#include "cpu_adam.h" - -static std::unordered_map> s_optimizers; - -// C++ interface - -void Adam_Optimizer::Step_1(float* _params, - float* grads, - float* _exp_avg, - float* _exp_avg_sq, - size_t _param_size, - ds_half_precision_t* dev_params, - bool half_precision) -{ - size_t rounded_size = 0; -#if defined(__AVX512__) or defined(__AVX256__) - Step_AVX<1>(&rounded_size, - _params, - grads, - _exp_avg, - _exp_avg_sq, - _param_size, - dev_params, - half_precision); -#endif - if (_param_size > rounded_size) { - float betta1_minus1 = 1 - _betta1; - float betta2_minus1 = 1 - _betta2; - - float step_size = -1 * _alpha / _bias_correction1; - float w_decay = -1 * _alpha * _weight_decay; - ds_half_precision_t* grads_cast_h; - ds_half_precision_t* params_cast_h; - if (half_precision) { - grads_cast_h = reinterpret_cast(grads); - params_cast_h = reinterpret_cast(_params); - } - - for (size_t t = rounded_size; t < _param_size; t += TILE) { - size_t copy_size = TILE; - if ((t + TILE) > _param_size) copy_size = _param_size - t; - size_t offset = copy_size + t; -#pragma omp parallel for - for (size_t k = t; k < offset; k++) { - float grad = half_precision ? (float)grads_cast_h[k] : grads[k]; - float param = half_precision ? (float)params_cast_h[k] : _params[k]; - float momentum = _exp_avg[k]; - float variance = _exp_avg_sq[k]; - if (_weight_decay > 0 && !_adamw_mode) { grad = param * _weight_decay + grad; } - momentum = momentum * _betta1; - momentum = grad * betta1_minus1 + momentum; - - variance = variance * _betta2; - grad = grad * grad; - variance = grad * betta2_minus1 + variance; - - grad = sqrt(variance); - grad = grad * _bias_correction2 + _eps; - grad = momentum / grad; - if (_weight_decay > 0 && _adamw_mode) { param += w_decay * param; } - param = grad * step_size + param; - if (half_precision) - params_cast_h[k] = (ds_half_precision_t)param; - else - _params[k] = param; - _exp_avg[k] = momentum; - _exp_avg_sq[k] = variance; - } - } - } -} - -void Adam_Optimizer::Step_4(float* _params, - float* grads, - float* _exp_avg, - float* _exp_avg_sq, - size_t _param_size, - ds_half_precision_t* dev_params, - bool half_precision) -{ - size_t rounded_size = 0; -#if defined(__AVX512__) or defined(__AVX256__) - Step_AVX<4>(&rounded_size, - _params, - grads, - _exp_avg, - _exp_avg_sq, - _param_size, - dev_params, - half_precision); -#endif - if (_param_size > rounded_size) - Step_1((_params + rounded_size), - (grads + rounded_size), - (_exp_avg + rounded_size), - (_exp_avg_sq + rounded_size), - (_param_size - rounded_size), - (dev_params != nullptr ? (dev_params + rounded_size) : dev_params), - half_precision); -} - -int create_adam_optimizer(int optimizer_id, - float alpha, - float betta1, - float betta2, - float eps, - float weight_decay, - bool adamw_mode, - bool should_log) -{ - auto opt = - std::make_shared(alpha, betta1, betta2, eps, weight_decay, adamw_mode); - - s_optimizers[optimizer_id] = opt; - - if (should_log) { - std::string avx_type = ""; -#if defined(__AVX512__) - avx_type = "AVX512"; -#else -#if defined(__AVX256__) - avx_type = "AVX2"; -#else - avx_type = "scalar"; -#endif -#endif - - printf("Adam Optimizer #%d is created with %s arithmetic capability.\n", - optimizer_id, - avx_type.c_str()); - printf("Config: alpha=%f, betas=(%f, %f), weight_decay=%f, adam_w=%d\n", - alpha, - betta1, - betta2, - weight_decay, - (int)adamw_mode); - } - - return 0; -} - -void Adam_Optimizer::Step_8(float* _params, - float* grads, - float* _exp_avg, - float* _exp_avg_sq, - size_t _param_size, - ds_half_precision_t* dev_params, - bool half_precision) -{ - size_t rounded_size = 0; -#if defined(__AVX512__) or defined(__AVX256__) - Step_AVX<8>(&rounded_size, - _params, - grads, - _exp_avg, - _exp_avg_sq, - _param_size, - dev_params, - half_precision); -#endif - if (_param_size > rounded_size) - Step_4((_params + rounded_size), - (grads + rounded_size), - (_exp_avg + rounded_size), - (_exp_avg_sq + rounded_size), - (_param_size - rounded_size), - (dev_params != nullptr ? (dev_params + rounded_size) : dev_params), - half_precision); -} - -int ds_adam_step(int optimizer_id, - size_t step, - float lr, - float beta1, - float beta2, - float epsilon, - float weight_decay, - bool bias_correction, - torch::Tensor& params, - torch::Tensor& grads, - torch::Tensor& exp_avg, - torch::Tensor& exp_avg_sq) -{ - auto params_c = params.contiguous(); - auto grads_c = grads.contiguous(); - auto exp_avg_c = exp_avg.contiguous(); - auto exp_avg_sq_c = exp_avg_sq.contiguous(); - - // assert(params.options().dtype() == grads.options().dtype()); - - float* params_ptr = (float*)params_c.data_ptr(); - float* grads_ptr = (float*)grads_c.data_ptr(); - float* exp_avg_ptr = (float*)exp_avg_c.data_ptr(); - float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr(); - - std::shared_ptr opt = - std::static_pointer_cast(s_optimizers[optimizer_id]); - opt->IncrementStep(step, beta1, beta2); - opt->update_state(lr, epsilon, weight_decay, bias_correction); - - opt->Step_8(params_ptr, - grads_ptr, - exp_avg_ptr, - exp_avg_sq_ptr, - params_c.numel(), - nullptr, - (params.options().dtype() == at::kHalf)); - - return 0; -} - -int ds_adam_step_plus_copy(int optimizer_id, - size_t step, - float lr, - float beta1, - float beta2, - float epsilon, - float weight_decay, - bool bias_correction, - torch::Tensor& params, - torch::Tensor& grads, - torch::Tensor& exp_avg, - torch::Tensor& exp_avg_sq, - torch::Tensor& gpu_params) -{ - assert(false); - return 0; -} - -int destroy_adam_optimizer(int optimizer_id) -{ - s_optimizers.erase(optimizer_id); - - return 0; -} diff --git a/op_builder/xpu/cpu_adam.py b/op_builder/xpu/cpu_adam.py index 4c7d4d119839..9d5fdcd3e8ab 100644 --- a/op_builder/xpu/cpu_adam.py +++ b/op_builder/xpu/cpu_adam.py @@ -17,13 +17,11 @@ def absolute_name(self): return f'deepspeed.ops.adam.{self.NAME}_op' def sources(self): - if self.build_for_cpu: - return ['csrc/xpu/adam/cpu_adam.cpp', 'csrc/xpu/adam/cpu_adam_impl.cpp'] + return ['csrc/adam/cpu_adam.cpp', 'csrc/adam/cpu_adam_impl.cpp'] - return [ - 'csrc/xpu/adam/cpu_adam.cpp', 'csrc/xpu/adam/cpu_adam_impl.cpp', - 'csrc/xpu/common/custom_cuda_kernel.dp.cpp' - ] + def libraries_args(self): + args = super().libraries_args() + return args def include_paths(self): - return ['csrc/xpu/includes'] + return ['csrc/includes'] From aecfec7f510eb2a40f7afa50efe370f885e4d258 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Fri, 28 Jun 2024 13:19:21 -0700 Subject: [PATCH 23/36] Add additional paths to trigger xpu tests (#5707) --- .github/workflows/xpu-max1100.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/xpu-max1100.yml b/.github/workflows/xpu-max1100.yml index 34325b4522d7..7a09a610dca4 100644 --- a/.github/workflows/xpu-max1100.yml +++ b/.github/workflows/xpu-max1100.yml @@ -11,6 +11,7 @@ on: - "accelerator/abstract_accelerator.py" - "accelerator/cpu_accelerator.py" - "accelerator/real_accelerator.py" + - "csrc/xpu/**" - "deepspeed/runtime/engine.py" - "deepspeed/runtime/bf16_optimizer.py" - "deepspeed/runtime/zero/stage_1_and_2.py" @@ -20,6 +21,7 @@ on: - "deepspeed/runtime/zero/parameter_offload.py" - "deepspeed/runtime/pipe/engine.py" - "deepspeed/runtime/utils.py" + - "opbuilder/xpu/**" concurrency: group: ${{ github.workflow }}-${{ github.ref }} From df58a784c873e6da2fffe8f80c26131c08c85c5c Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 1 Jul 2024 11:33:12 -0700 Subject: [PATCH 24/36] Update XPU docker version (#5712) --- .github/workflows/xpu-max1100.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/xpu-max1100.yml b/.github/workflows/xpu-max1100.yml index 7a09a610dca4..c5a23fe3f53f 100644 --- a/.github/workflows/xpu-max1100.yml +++ b/.github/workflows/xpu-max1100.yml @@ -36,7 +36,7 @@ jobs: unit-tests: runs-on: [self-hosted, intel, xpu] container: - image: intel/intel-extension-for-pytorch:2.1.20-xpu + image: intel/intel-extension-for-pytorch:2.1.30-xpu ports: - 80 options: --privileged -it --rm --device /dev/dri:/dev/dri -v /dev/dri/by-path:/dev/dri/by-path --ipc=host --cap-add=ALL From e39229676c2af73051bbd3dd71eb983a5ba74ef4 Mon Sep 17 00:00:00 2001 From: baodi Date: Tue, 2 Jul 2024 03:34:11 +0800 Subject: [PATCH 25/36] update xpu fusedadam opbuilder for pytorch 2.3 (#5702) update the way to get queue for FusedAdam OpBuilder. --------- Signed-off-by: baodii Co-authored-by: Logan Adams Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- csrc/xpu/adam/multi_tensor_apply.dp.hpp | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/csrc/xpu/adam/multi_tensor_apply.dp.hpp b/csrc/xpu/adam/multi_tensor_apply.dp.hpp index 04674020a6c5..01964e1a4d38 100644 --- a/csrc/xpu/adam/multi_tensor_apply.dp.hpp +++ b/csrc/xpu/adam/multi_tensor_apply.dp.hpp @@ -10,6 +10,7 @@ This file is adapted from fused adam in NVIDIA/apex, commit a109f85 #include #include +#include #include #include #include "compat.h" @@ -22,10 +23,8 @@ namespace at { namespace cuda { sycl::queue* getCurrentCUDAStream() { - auto device_type = c10::DeviceType::XPU; - c10::impl::VirtualGuardImpl impl(device_type); - c10::Stream c10_stream = impl.getStream(c10::Device(device_type)); - auto& queue = xpu::get_queue_from_stream(c10_stream); + c10::xpu::XPUStream stream = c10::xpu::getCurrentXPUStream(); + auto& queue = stream.queue(); return &queue; } From 121efdbd5c906eb534c8487028810c902aa09510 Mon Sep 17 00:00:00 2001 From: Sam Ade Jacobs Date: Mon, 1 Jul 2024 14:37:24 -0700 Subject: [PATCH 26/36] DeepSpeed Universal Checkpointing: Blog and Tutorial (#5711) Train {GPT,LLaMA, Phi}-like models (or any model) at ultra low-cost with DeepSpeed Universal Checkpointing (UCP). UCP abstracts away the complexities of saving and loading model states. See arxiv paper, blog and tutorial in this PR for details. --------- Co-authored-by: Masahiro Tanaka Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- README.md | 4 + blogs/deepspeed-ucp/README.md | 273 +++++++++++++++++++++ blogs/deepspeed-ucp/japanese/README.md | 115 +++++++++ blogs/deepspeed-ucp/media/flowchart.png | Bin 0 -> 58071 bytes blogs/deepspeed-ucp/media/image1.png | Bin 0 -> 142850 bytes blogs/deepspeed-ucp/media/image2.png | Bin 0 -> 179248 bytes blogs/deepspeed-ucp/media/image3.png | Bin 0 -> 42403 bytes blogs/deepspeed-ucp/media/image4.png | Bin 0 -> 133064 bytes blogs/deepspeed-ucp/media/image5.png | Bin 0 -> 132667 bytes blogs/deepspeed-ucp/media/image6.png | Bin 0 -> 82311 bytes blogs/deepspeed-ucp/media/image7.png | Bin 0 -> 147097 bytes blogs/deepspeed-ucp/media/image8.png | Bin 0 -> 93380 bytes docs/_tutorials/universal-checkpointing.md | 58 +++++ docs/index.md | 3 + 14 files changed, 453 insertions(+) create mode 100644 blogs/deepspeed-ucp/README.md create mode 100644 blogs/deepspeed-ucp/japanese/README.md create mode 100644 blogs/deepspeed-ucp/media/flowchart.png create mode 100755 blogs/deepspeed-ucp/media/image1.png create mode 100644 blogs/deepspeed-ucp/media/image2.png create mode 100755 blogs/deepspeed-ucp/media/image3.png create mode 100755 blogs/deepspeed-ucp/media/image4.png create mode 100755 blogs/deepspeed-ucp/media/image5.png create mode 100644 blogs/deepspeed-ucp/media/image6.png create mode 100644 blogs/deepspeed-ucp/media/image7.png create mode 100644 blogs/deepspeed-ucp/media/image8.png create mode 100644 docs/_tutorials/universal-checkpointing.md diff --git a/README.md b/README.md index f9d81eddfdae..5f990fd70d7d 100755 --- a/README.md +++ b/README.md @@ -15,6 +15,7 @@ ## 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). +* [2024/07] [DeepSpeed Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/README.md) [[中文](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/japanese/README.md)] * [2024/03] [DeepSpeed-FP6:The power of FP6-Centric Serving for Large Language Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README-Chinese.md)] * [2024/01] [DeepSpeed-FastGen: Introducing Mixtral, Phi-2, and Falcon support with major performance and feature enhancements.](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19) * [2023/11] [Llama 2 Inference on 4th Gen Intel® Xeon® Scalable Processor with DeepSpeed](https://github.com/microsoft/DeepSpeed/tree/master/blogs/intel-inference) [[Intel version]](https://www.intel.com/content/www/us/en/developer/articles/technical/xllama-2-on-xeon-scalable-processor-with-deepspeed.html) @@ -270,6 +271,9 @@ Conduct](https://opensource.microsoft.com/codeofconduct/). For more information 30. Xiaoxia Wu, Haojun Xia, Stephen Youn, Zhen Zheng, Shiyang Chen, Arash Bakhtiari, Michael Wyatt, Reza Yazdani Aminabadi, Yuxiong He, Olatunji Ruwase, Leon Song, Zhewei Yao (2023) ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks [arXiv:2312.08583](https://arxiv.org/abs/2312.08583) 31. Haojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song. (2024) FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design [arXiv:2401.14112](https://arxiv.org/abs/2401.14112) +32. Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Reza Yazdani Aminadabi, Shuaiwen Leon Song, Samyam Rajbhandari, Yuxiong He. (2024) [System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://dl.acm.org/doi/10.1145/3662158.3662806) +33. Xinyu Lian, Sam Ade Jacobs, Lev Kurilenko, Masahiro Tanaka, Stas Bekman, Olatunji Ruwase, Minjia Zhang. (2024) Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training [arXiv:2406.18820](https://arxiv.org/abs/2406.18820) + diff --git a/blogs/deepspeed-ucp/README.md b/blogs/deepspeed-ucp/README.md new file mode 100644 index 000000000000..abd5b4ca017d --- /dev/null +++ b/blogs/deepspeed-ucp/README.md @@ -0,0 +1,273 @@ +
+ +# DeepSpeed Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training + +
+ + + +To cite DeepSpeed Universal Checkpoint, please cite our [arxiv report](https://arxiv.org/abs/2406.18820): + +``` +@article{lian2024-ucp, +title={Universal Checkpointing: Efficient and Flexible Checkpointing for +Large Scale Distributed Training}, +author={Xinyu Lian and Sam Ade Jacobs and Lev Kurilenko and Masahiro Tanaka +and Stas Bekman and Olatunji Ruwase and Minjia Zhang}, +journal={arxiv preprint arxiv:406.18820}, +year={2024}, + +} +``` + +# Introduction + +Checkpointing is a crucial technique for reducing the cost of training +machine learning models, as it enables saving the model state during the process. +This way, if the system fails, the training can resume from the most recent checkpoint +instead of from the beginning. Additionally, checkpointing allows for +evaluating the model performance at various stages of training, which +facilitates hyperparameter tuning and finetuning for different and +varied downstream tasks. + +However, there are challenges in the design, implementation and usage of +checkpointing especially in distributed training and finetuning +scenarios. Parallel training methods such as ZeRO data parallelism (ZeRO-DP), +pipeline parallelism (PP), tensor parallelism (TP) and sequence +parallelism (SP) are popular technologies for accelerating LLMs training. +However, elastic and flexible composition of these different parallelism +topologies with checkpointing is not currently available, in part, because +these techniques shard model and/or optimizer states making it difficult to +resume training with a checkpoint that was created on a different number of GPUs or +accelerators. + +In this release, we are excited to introduce DeepSpeed Universal +Checkpointing (*UCP*), a most comprehensive solution to the problem of +distributed checkpointing. *UCP* enables efficient checkpoint creation +while providing the flexibility of resuming on arbitrary parallelism +strategies and hardware configurations. *UCP* also unlocks unprecedented +capabilities for large-scale training such as improved resilience to +hardware failures through continued training on remaining healthy +hardware, and reduced training time through opportunistic exploitation +of elastic capacity. + +In summary, this release of *UCP* unlocks the following capabilities: + +- Flexible checkpoints reshape along any of the training parallelism + techniques (i.e., PP, TP, DP, ZeRO-DP, SP, MoE) + +- Elastic resource management, scale up or scale down of training and + finetuning accelerator resources + +- Real world examples with support for multiple commercial-scale models + (i.e., BLOOM, Megatron GPT, LLAMA, Microsoft Phi) + +# Core Design + +The key insight of DeepSpeed *UCP* is the selection of the optimal +representation in each phase of the checkpointing life cycle: +distributed representation for saving, and consolidated representation +for loading. This is achieved using two key mechanisms. First, the +universal checkpoint format, which consists of a consolidated +representation of each model parameter, and metadata for mapping +parameter fragments to the ranks of an arbitrary parallel training +configuration. Second, the universal checkpoint language, a simple but +powerful and robust specification language for converting distributed +checkpoints into the universal checkpoint format. + +## Universal Checkpoint Format + + + +Figure 1: UCP overview: top row and bottom row are Source and Target +parallelism configurations respectively. The middle row shows UCP as +an intermediate format of translating from Source to Target. + +Figure 1 shows high level schematic description of *UCP* conversion +process and format. Conversion starts with top block of checkpointing in +any parallel format e.g, DP, TP, PP, SP. Saving in the native format of parallel training avoids any overhead of +consolidating into a single global checkpoint. To ensure that +a checkpoint saved in one parallel configuration (herein called *Source*) can be +easily converted and loaded for continuous training in another parallel configuration (herein called *Target*), +we introduce the idea of atomic checkpoint as an intermediate format. + +The concept of atomic checkpoint is central to *UCP*. These are +fine-grained files containing the consolidated representation of each +model parameter, along with optimizer states. The atomic checkpoint +format is useful for three reasons. First, the atomic representation of +checkpoints decouples the dependencies of distributed checkpoints and +specific parallelism techniques and hardware configurations. As such, +one does not need to implement individual converters for each *Source* +and *Target* pair. Instead, *UCP* can act as a common interchange format +between different distributed training techniques, which then can be +easily transformed into other distributed training strategies, as shown +in Fig 2. By keeping the consolidated representation of each model +parameter, *UCP* enables easy splitting and flexible mapping of model states +or fragmented states to different GPUs on a parameter-by-parameter +basis, effectively reducing the working memory needed to load large +model checkpoints. Second, the *UCP* conversion happens lazily and +on-demand, e.g., when a training process detects a change of parallelism +technique and hardware configuration. In other words, the existing +distributed checkpoint saving logic does not need any change. Third, the +structure of the *UCP* also makes it easy to handle advanced techniques +in distributed training, such as mixed-precision training. In practice, +researchers and practitioners may switch between fp16 and bfloat16 mixed +precision training. By keeping the fp32 weight/optimizer values, the +training can resume either with fp16 or bfloat16. + +## Universal Checkpoint Language + + + +Figure 2: UCP language helps transform distributed checkpoints into the +UCP format and load UCP checkpoints based on the Target parallel +technique and new hardware configuration. + + +While *UCP* provides a common interface for different parallelism +strategies, the development of transformation from arbitrary distributed +checkpoints to *UCP* can still incur a high engineering and +implementation cost. This is because the number of distributed checkpoint files +and their contents can vary across the different parallel training techniques. + +To tackle this challenge, *UCP* provides *UCP* language, which is a +simple but powerful specification language for converting a distributed checkpoint +into the atomic checkpoint format, described in previous +section. *UCP* does this in two ways. First, it provides a declarative +system with pre-defined *parameter patterns*, which cover a wide range +of parallelism strategies for model states. Parameter patterns contain +runtime information about how a parameter is partitioned across GPUs. +For instance, *nopattern* means that a parameter is uniquely associated +with a GPU rank, which is the most common pattern seen in techniques +such as ZeRO-1/2 and PP (see our technical report for a completed list +of currently supported parameter patterns). Second, *UCP* language +provides a set of common operators that facilitate the transformation of +distributed checkpoints into consolidated atomic checkpoints. At a +high-level, as illustrated in Figure 3, *UCP* language is invoked when +support for a new *Target* is needed or the hardware +configuration changes. It first transforms distributed checkpoints into +the *UCP* format. It then loads the *UCP* checkpoints based on the +*Target* parallel technique and new hardware configuration. + +# Key Results + +We evaluate *UCP* through a series of experiments on training LLMs. We +focus on the decoder-only Transformers: an architecture chosen due to +its state-of-the-art performance. Some of the largest models are also +decoder-based, making flexible and efficient checkpointing especially +important. In this blog, we present results of correctness verification +across different models and parallel strategies. For more results on +parallel efficiency analysis, detailed system and model architectures +and training hyperparameters, please see our technical report referenced +above. + +*UCP* provides flexible checkpointing from a *Source* parallelism +strategy to a different *Target* with different hardware configurations. +To verify this capability, we conduct correctness tests of *UCP* with +two groups of experiments. + +## Single Source to Multiple Target + + + +Figure 3: Training curves of loading UCP checkpoints into different +Target at iteration 101 with various GPU counts and parallelism +strategies + +To test if UCP allows resuming training with different parallelism +strategies and hardware configuration, we first train the GPT-3 model +using a configuration of TP=2, PP=2, DP=2 (ZeRO-1), and SP=1. Due to +constraints in time and resources, we limited the experiment to the +first 200 iterations. We convert the checkpoints saved at the 100th +iteration to *UCP* checkpoints and resume training with these *UCP* +checkpoints using different GPU counts and parallelism strategies. We +record the LM loss (average losses across the data parallel group) for +each iteration. Figure 3 illustrates that the training can be seamlessly +resumed with *UCP* checkpoints using different *Target* parallelism +strategies, achieving consistent convergence if the training were to +continue with the *Source* strategy. + +## Multiple Source to Single Target + + + +Figure 4: Training curves of transforming different Source parallelism +strategies at iteration 100 to UCP and loading UCP with a different +Target. + +Figure 4 shows the training curves from multiple *Source* configurations +to a single *Target*. Given a fixed random seed, we first train the +GPT-3 model using different *Source* configurations. We then convert +their distributed checkpoints saved at the 100th iteration to *UCP* +checkpoints and resume training with a configuration of TP=2, PP=2, +DP=1, and SP=1. The results show that regardless of the different +*Source* configurations, their checkpoints can all be converted into +*UCP* and resume training with a different configuration. Most +importantly, the resumed training curves match the curves from the +*Source* at iterations 101--200. These results validate the +effectiveness of *UCP* of converting an arbitrary configuration to a +different configuration for resumed training. + +## Varying Model Architectures + +*UCP* is model architecture agnostic. As such, it is not only compatible +with GPT models but also flexible enough to support various other model +architectures and sizes. Figures 5, 6 and 7 show the training +convergence for LLaMA 7B, BLOOM 176B, and a variant of Mixtral-7x8B MoE, +when resuming from *UCP* at the middle of training with new parallelism +strategies. These figures show that training is seamlessly resumed with +*UCP*, achieving consistent convergence that aligns with the initial +training phase across these diverse models. These results suggest that +*UCP* is quite flexible for various model architectures and sizes. + + + +Figure 5: Training curve with LLaMA model architecture. Source is +TP=PP=DP=2. Training is resumed at iteration 101 with new Targets +TP=DP=2, PP=1 and TP=PP=2, DP=1 + + + +Figure 6: Training curve of BLOOM model architecture. Source is TP=2, +PP=24, DP=8. Training is resumed at iteration 94767 with a new Targets +TP=2, DP=4, PP=24. + + + +Figure 7: Training curve with a variant of the Mixtral-MoE model +architecture. Source is TP=1, PP=2, DP=4. Training is resumed at +iteration 501 with a new Target TP=PP=DP=2. + +# General Availability of DeepSpeed Universal Checkpoint + +We are excited to release DeepSpeed Universal Checkpoint. DeepSpeed +Universal Checkpoint is available in DeepSpeed versions >= +[0.14.4](https://github.com/microsoft/DeepSpeed/releases/tag/v0.14.4), +has been fully integrated with [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed) ([commit c3a13be](https://github.com/microsoft/Megatron-DeepSpeed/commit/c3a13be721da0d0de16c338d0d665b0f7d13d14f)). +Detailed tutorial on usage is available on +[DeepSpeed tutorial page](https://www.deepspeed.ai/tutorials/universal-checkpointing/). + +We welcome contributions and collaboration from the broader open-source +community. DeepSpeed Universal Checkpoint is part of the bigger +DeepSpeed ecosystem of large-scale AI training and inference. For more +details on all DeepSpeed technologies and innovations, please visit our +[website]((https://www.deepspeed.ai/)) and follow us +on X, formerly Twitter, ([English](https://twitter.com/MSFTDeepSpeed), +[Japanese](https://twitter.com/MSFTDeepSpeedJP)) +and [Chinese Zhihu](https://www.zhihu.com/people/deepspeed). + +# Acknowledgements and Contributions +We thank the collaboration of University of Illinois at Urbana-Champaign, +Statosphere, and Intel Habana. + +Contributions: +Xinyu Lian $^1$, Sam Ade Jacobs $^2$, Lev Kurilenko $^2$, Masahiro Tanaka $^2$, +Stas Bekman $^3$, Olatunji Ruwase $^2$, Minjia Zhang $^1$, Moshe Island $^4$ + +1: University of Illinois at Urbana-Champaign +2: Microsoft +3: StasoSphere +4: Intel Habana diff --git a/blogs/deepspeed-ucp/japanese/README.md b/blogs/deepspeed-ucp/japanese/README.md new file mode 100644 index 000000000000..7e3c3161a1b5 --- /dev/null +++ b/blogs/deepspeed-ucp/japanese/README.md @@ -0,0 +1,115 @@ +
+ +# DeepSpeed Universal Checkpointing: 大規模分散学習のための効率的かつ柔軟なチェックポイント + +
+ + + +DeepSpeed Universal Checkpointを引用する際は、こちらの[arxiv report](https://arxiv.org/abs/2406.18820)を参照してください。 + +``` +@article{lian2024-ucp, +title={Universal Checkpointing: Efficient and Flexible Checkpointing for +Large Scale Distributed Training}, +author={Xinyu Lian and Sam Ade Jacobs and Masahiro Tanaka and Lev +Kurilenko and Stas Bekman and Olatunji Ruwase and Minjia Zhang}, +journal={arxiv preprint arxiv:406.18820}, +year={2024}, + +} +``` + +# はじめに + +モデルの状態を保存するをチェックポイントは、システム障害が発生した場合に途中から学習を再開するために、LLMのトレーニングコストを削減するための重要な技術です。さらに、学習のさまざまな段階でモデルのパフォーマンスを評価することができるため、ハイパーパラメータの調整や異なる下流タスクのためのファインチューニングが容易になります。 + +しかし、特に分散学習やファインチューニングのシナリオにおいて、チェックポイントの設計、実装、および使用には多くの課題があります。DeepSpeedが備えるZeROを用いたデータ並列化（ZeRO-DP）、パイプライン並列化（PP）、テンソル並列化（TP）、およびシーケンス並列化（SP）などのいくつかの方法は、LLM学習を加速するための優れた技術ですが、一般的なチェックポイント保存と読み込みのメカニズムと互換性がありません。さらに、これらの異なる並列化を用いたエラスティックで柔軟な組み合わせは、現在サポートされていません。主な理由の一つは、こうした並列化技術がモデルおよび/またはオプティマイザの状態を分割するため、異なるGPUまたはアクセラレータの数に基づいて作成されたチェックポイントから学習を再開することが困難であるためです。 + +このリリースでは、分散チェックポイントの問題に対する包括的なソリューションであるDeepSpeed Universal Checkpointing (*UCP*) を紹介します。*UCP*は、任意の並列化戦略とハードウェア構成で再開する柔軟性を提供しながら、効率的なチェックポイント作成を可能にします。また、*UCP*は、ハードウェア障害の際にも、残りの正常なハードウェアでのトレーニングの継続を可能にするため、キャパシティがエラスティックに変化するハードウェアを活用でき、トレーニング時間を短縮するなど、大規模学習を最大限に効率化できます。 + +現在のリリースには、*UCP*の次の機能が含まれます。 + +- 任意のトレーニング並列技術（例：PP、TP、DP、ZeRO-DP、SP、MoE）に沿った柔軟なチェックポイントの再構成 +- ファインチューニングを含む学習およびアクセラレータリソースのエラスティックなリソース管理、スケールアップまたはスケールダウン +- BLOOM、Megatron GPT、LLAMA、Microsoft Phiなどの複数の商用規模モデルのサポートを伴う実利用例 + +# UCPの設計 + +DeepSpeed *UCP*における中心的な考え方は、チェックポイントライフサイクルの各段階で最適な表現を選択することです。保存のための分散表現と、読み込みのための統合表現です。これは、2つの重要なメカニズムを使用して実現されます。一つ目は、各モデルパラメータの統合表現と、パラメータのフラグメントを任意のモデル並列化構成におけるランク（プロセスのインデックス）にマッピングするためのメタデータからなるユニバーサルチェックポイントフォーマットです。二つ目は、分散チェックポイントをユニバーサルチェックポイント形式に変換するためのシンプルで強力かつ堅牢な仕様言語であるユニバーサルチェックポイント言語です。 + +## ユニバーサルチェックポイントフォーマット + + + +図1：*UCP*の概要：上段と下段はそれぞれソースとターゲットの並列化構成です。中央の段は、ソースからターゲットへの翻訳の仲介ブロックとしての*UCP*を示しています。 + +図1は、*UCP*の変換プロセスとフォーマットの抽象レベルの概略図を示しています。変換は、DP、TP、PP、SPなどの任意の並列戦略形式のチェックポイントから始まります。訓練結果のモデルやオプティマイザ状態をネイティブ形式で保存することで、同期されたグローバルチェックポイントの保存に伴うオーバーヘッドを回避します。保存されたチェックポイント（以下、*ソース*と呼びます）を任意の並列戦略に簡単に変換してロードできるようにするために、中間ブロックとして原子チェックポイント (atomic checkpoint) 形式のアイデアを導入します。 + +原子チェックポイントの概念は、*UCP*の中心となるものです。これらは、各モデルパラメータの統合表現とオプティマイザ状態を含む細粒度のファイルです。原子チェックポイント形式は、次の3つの理由で有用です。まず、チェックポイントの原子表現は、分散チェックポイントと特定の並列技術およびハードウェア構成の依存関係を切り離します。そのため、*ソース*から*ターゲット*への個別のコンバータを実装する必要はありません。代わりに、*UCP*は異なる分散トレーニング技術間の共通交換形式として機能し、他の分散トレーニング戦略に簡単に変換できます（図2参照）。各モデルパラメータの統合表現を保持することで、*UCP*はモデル状態またはフラグメント状態をパラメータごとに異なるGPUに柔軟にマッピングし、大規模モデルチェックポイントを読み込むために必要な作業メモリを効果的に削減します。第二に、*UCP*の変換は遅延してオンデマンドで行われます。たとえば、トレーニングプロセスが並列技術とハードウェア構成の変更を検出したときです。つまり、既存の分散チェックポイント保存ロジックには変更が必要ありません。第三に、*UCP*の構造により、混合精度トレーニングなどの高度な技術を分散トレーニングで簡単に処理できます。実際には、研究者や実務者はfp16とbfloat16の混合精度トレーニングを切り替えることがあります。fp32の重み/オプティマイザの値を保持することで、トレーニングはfp16またはbfloat16のいずれかで再開できます。 + +## ユニバーサルチェックポイント言語 + + + +図2：*UCP*言語は、分散チェックポイントを*UCP*形式に変換し、新しいハードウェア構成とターゲットの並列技術に基づいて*UCP*チェックポイントを読み込みます。 + +*UCP*は異なる並列戦略に対する共通インターフェースを提供しますが、任意の分散チェックポイントから*UCP*への変換の開発には依然として高いエンジニアリングおよび実装コストがかかる場合があります。これは、分散トレーニングの各GPUが保存のためのメソッド（例：PyTorchのtorch.save()）を呼び出して、所有するGPUモデル状態のチェックポイントファイルをディスクに保存し、各チェックポイントの正確な内容が異なる技術によって異なるためです。 + +この課題に取り組むために、*UCP*は*UCP*言語を提供します。これは、前述の共通形式にいくつかの種類の分散チェックポイントを変換するためのシンプルで強力な仕様言語です。*UCP*はこれを2つの方法で行います。まず、モデル状態の並列戦略の広範な範囲をカバーする事前定義された*パラメータパターン*を持つ宣言型システムを提供します。パラメータパターンには、パラメータがGPU間でどのように分割されているかについてのランタイム情報が含まれています。たとえば、*nopattern*は、パラメータがGPUランクに一意に関連付けられていることを意味し、これはZeRO-1/2やPPなどの技術で最も一般的に見られるパターンです（現在サポートされているパラメータパターンの完全なリストについては、技術レポートを参照してください）。第二に、*UCP*言語は、分散チェックポイントを統合された原子チェックポイントに変換するための一般的な演算子のセットを提供します。抽象的なレベルで見ると、図2に示すように、ターゲットへの移行後に新しい並列技術が必要な場合やハードウェア構成が変更された場合に、*UCP*言語が使用されます。最初に、分散チェックポイントを*UCP*形式に変換し、次にターゲットの並列技術と新しいハードウェア構成に基づいて*UCP*チェックポイントを読み込みます。 + +# 主要な結果 + +我々は、LLMの訓練に関する一連の実験を通じて*UCP*を評価します。デコーダーのみのトランスフォーマーに焦点を当てました。これは最先端のパフォーマンスを持つアーキテクチャです。いくつかの最大のモデルもデコーダーベースであるため、柔軟で効率的なチェックポイントは特に重要です。このブログでは、さまざまなモデルと並列戦略にわたる正確性の検証結果を紹介します。並列効率分析、詳細なシステムおよびモデルアーキテクチャ、および訓練のハイパーパラメータに関する詳細な結果については、上記の技術レポートを参照してください。 + +*UCP*は、異なるハードウェア構成を持つ異なる*ターゲットの*並列戦略に対する*ソース*並列戦略からの柔軟なチェックポイントを提供します。この能力を検証するために、2つの実験グループで*UCP*の正確さを確認しました。 + +## シングルソースから複数のターゲットへ + + + +図3：さまざまなGPU数と並列戦略で*ターゲット*に*UCP*チェックポイントをロードする訓練lossの曲線（イテレーション100で保存・ロード） + +*UCP*が異なる並列戦略とハードウェア構成での訓練再開を可能にするかどうかをテストするために、まずTP=2、PP=2、DP=2（ZeRO-1）、SP=1の構成でGPT-3モデルを訓練します。時間とリソースの制約のため、この実験は最初の200イテレーションに限定しました。100イテレーション目で保存されたチェックポイントを*UCP*チェックポイントに変換し、異なるGPU数と並列戦略を使用してこれらの*UCP*チェックポイントで訓練を再開します。各イテレーションのLM損失（データ並列グループ全体の平均損失）を記録しました。図3は、異なる*ターゲット*並列戦略を使用して*UCP*チェックポイントで訓練をシームレスに再開し、*ソース*戦略を継続して訓練する場合と一致する収束を達成することを示しています。 + +## 複数ソースからシングルターゲットへ + + + +図4：100イテレーション目で異なるソース並列戦略を*UCP*に変換し、異なるターゲットで*UCP*をロードする訓練lossの曲線 + +図4は、複数の*ソース*構成から単一の*ターゲット*へのlossの曲線を示しています。固定されたランダムシードを使用して、まずGPT-3モデルを異なる*ソース*構成で訓練します。次に、100イテレーション目で保存された分散チェックポイントを*UCP*チェックポイントに変換し、TP=2、PP=2、DP=1、SP=1の構成でトレーニングを再開します。結果は、異なる*ソース*構成にもかかわらず、そのチェックポイントはすべて*UCP*に変換され、異なる構成で訓練を再開できることを示しています。最も重要なのは、再開されたlossの曲線が、イテレーション101～200での*ソース*の曲線と一致することです。これらの結果は、訓練再開時に任意の構成を異なる構成に変換する*UCP*の効果を検証しています。 + +## 異なるモデルアーキテクチャへの対応 + +*UCP*はモデルアーキテクチャに依存しません。したがって、GPTモデルとの互換性だけでなく、さまざまなモデルアーキテクチャとサイズをサポートする柔軟性も備えています。図5、6、7は、新しい並列戦略で*UCP*から訓練を再開したときのLLaMA 7B、BLOOM 176B、およびMixtral-7x8B MoEを元にしたモデルのトレーニング収束を示しています。これらの図は、トレーニングが*UCP*でシームレスに再開され、これらの多様なモデル全体で訓練の初期フェーズと一致する収束を達成することを示しています。これらの結果は、さまざまなモデルアーキテクチャとサイズに対する*UCP*の柔軟性を示しています。 + +A graph of training step Description automatically generated + +図5：LLaMAモデルアーキテクチャの訓練lossの曲線。ソースはTP=PP=DP=2。訓練はイテレーション101で新しいターゲットTP=DP=2、PP=1およびTP=PP=2、DP=1で再開しました。 + +A graph with numbers and lines Description automatically generated + +図6：BLOOMモデルアーキテクチャの訓練lossの曲線。ソースはTP=2、PP=24、DP=8。訓練はイテレーション94767で新しいターゲットTP=2、DP=4、PP=24で再開しました。 + +A graph of training step Description automatically generated + +図7：Mixtral-MoEモデルアーキテクチャに基づくモデルの訓練lossの曲線。ソースはTP=1、PP=2、DP=4。訓練はイテレーション501で新しいターゲットTP=PP=DP=2で再開しました。 + +# DeepSpeed Universal Checkpointの一般公開 + +DeepSpeed Universal Checkpointは、リベースされたMegatron-DeepSpeedバージョンに完全に統合されており、DeepSpeedおよびMegatron-DeepSpeedのGitHubリポジトリを通じてアクセスできます。使用に関する詳細なチュートリアルは、[DeepSpeedチュートリアルページ](https://www.deepspeed.ai/tutorials/universal-checkpointing/)にあります。 + +DeepSpeedでは、広範なオープンソースコミュニティからの貢献とコラボレーションを受け入れています。DeepSpeed Universal Checkpointは、大規模AIトレーニングおよび推論のためのDeepSpeedエコシステムの一部です。すべてのDeepSpeed技術とイノベーションについての詳細は、[ウェブサイト](https://www.deepspeed.ai/)をご覧いただき、X（旧Twitter）での[英語](https://twitter.com/MSFTDeepSpeed)、[日本語](https://twitter.com/MSFTDeepSpeedJP)、および[中国のZhihu](https://www.zhihu.com/people/deepspeed)をフォローしてください。 + +# 謝辞と貢献 + +University of Illinois at Urbana-Champaign、Statosphere、およびIntel Habanaとの協力に感謝します。 + +コントリビュータ： +Xinyu Lian $^1$, Sam Ade Jacobs $^2$, Lev Kurilenko $^2$, Masahiro Tanaka $^2$, Stas Bekman $^3$, Olatunji Ruwase $^2$, Minjia Zhang $^1$, Moshe Island $^4$ + +1: University of Illinois at Urbana-Champaign +2: Microsoft +3: StasoSphere +4: Intel Habana diff --git a/blogs/deepspeed-ucp/media/flowchart.png b/blogs/deepspeed-ucp/media/flowchart.png new file mode 100644 index 0000000000000000000000000000000000000000..d5198ca00e03bd5447c6af3d6493e78b2dc50c2d GIT binary patch literal 58071 zcmZ_0bzBtf_XbQXu=LUr!qOq#Ai^%)ErKH5h_rNfNOyNPsC0KXij)e{jWqZUK0d$i z^M2mX`v=VK%zS3Ue`^MHm!_Lsy6mDW`WU4MB&Lt!yh=9NpYiwYkF3ZX^XozoMFgW&v z8Np=i`QKQ9T4B62!+~6I;aE8R|U(13t+4 zs5$foIc6*wWX9vS_gx{R1_kz}qzww)N{P!(C_^}D3(hSp3}OiLhJxkQpUA@+9KGSD zYG&@zVg9t71?og1r*XfW2qUo~6(2o9F9u8jVZ5bjUz!2ml;hL_9 zd#}+92&G88lUK+eBL%K_#L`T|;drc}q0=Bd6iXvG9M;7yPoRp30+;d$KVx{FMhJHg znIsT*pAcIO6gp>j8G7v?$DVv)P~TFo(b4ub-BzFUbsEIT5cCL9+858@Kbst1>NU;I z>yc&C|LpvG3XG=HBv=d_i9p*4=0v&*hgn&%1O*4 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DeepSpeed: A Practical Guide" +tags: checkpointing, training, deepspeed +--- +# Universal Checkpointing with DeepSpeed: A Practical Guide + +DeepSpeed Universal Checkpointing feature is a powerful tool for saving and loading model checkpoints in a way that is both efficient and flexible, enabling seamless model training continuation and finetuning across different model architectures, different parallelism techniques and training configurations. This tutorial, tailored for both begininers and experienced users, provides a step-by-step guide on how to leverage Universal Checkpointing in your DeepSpeed-powered applications. This tutorial will guide you through the process of creating ZeRO checkpoints, converting them into a Universal format, and resuming training with these universal checkpoints. This approach is crucial for leveraging pre-trained models and facilitating seamless model training across different setups. + + +## Introduction to Universal Checkpointing + +Universal Checkpointing in DeepSpeed abstracts away the complexities of saving and loading model states, optimizer states, and training scheduler states. This feature is designed to work out of the box with minimal configuration, supporting a wide range of model sizes and types, from small-scale models to large, distributed models with different parallelism topologies trained across multiple GPUs and other accelerators. + +## Prerequisites + +Before you begin, ensure you have the following: +- DeepSpeed installed, installation can be done via `pip install deepspeed`. +- A model training script that utilizes DeepSpeed for distributed training. + +## How to use DeepSpeed Universal Checkpointing + +Follow the three simple steps below: + +### Step 1: Create ZeRO Checkpoint + +The first step in leveraging DeepSpeed Universal Checkpointing is to create a ZeRO checkpoint. [ZeRO]((/tutorials/zero/)) (Zero Redundancy Optimizer) is a memory optimization technology in DeepSpeed that allows for efficient training of large models. To create a ZeRO checkpoint, you'll need to: + + - Initialize your model with DeepSpeed using the ZeRO optimizer. + - Train your model to the desired state (iterations). + - Save a checkpoint using DeepSpeed's checkpointing feature. + + +### Step 2: Convert ZeRO Checkpoint to Universal Format + +Once you have a ZeRO checkpoint, the next step is to convert it into the Universal format. This format is designed to be flexible and compatible across different model architectures and DeepSpeed configurations. To convert a checkpoint: + + - Use the [ds_to_universal.py](https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/checkpoint/ds_to_universal.py) script provided by DeepSpeed. + - Specify the path to your ZeRO checkpoint and the desired output path for the Universal checkpoint. + +```bash +python ds_to_universal.py --input_folder /path/to/zero/checkpoint --output_folder /path/to/universal/checkpoint +``` + +This script will process the ZeRO checkpoint and generate a new checkpoint in the Universal format. Pass `--help` flag to see other options. + +### Step 3: Resume Training with Universal Checkpoint +With the Universal checkpoint ready, you can now resume training on potentially with different parallelism topologies or training configurations. To do this add `--universal-checkpoint` to your DeepSpeed config (json) file + + +## Conclusion +DeepSpeed Universal Checkpointing simplifies the management of model states, making it easier to save, load, and transfer model states across different training sessions and parallelism techniques. By following the steps outlined in this tutorial, you can integrate Universal Checkpointing into your DeepSpeed applications, enhancing your model training and development workflow. + +For more detailed examples and advanced configurations, please refer to the [Megatron-DeepSpeed examples]((https://github.com/microsoft/Megatron-DeepSpeed/tree/main/examples_deepspeed/universal_checkpointing)). + +For technical in-depth of DeepSpeed Universal Checkpointing, please see [arxiv manuscript](https://arxiv.org/abs/2406.18820) and [blog](/blogs/deepspeed-ucp/). + +Happy training! +``` diff --git a/docs/index.md b/docs/index.md index 1ca92019bff2..992c8ef6adb5 100755 --- a/docs/index.md +++ b/docs/index.md @@ -7,6 +7,7 @@ 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). +* [2024/07] [DeepSpeed Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/README.md)[[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/japanese/README.md)] * [2024/03] [DeepSpeed-FP6: The Power of FP6-Centric Serving for Large Language Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README-Chinese.md)] * [2024/01] [DeepSpeed-FastGen: Introducting Mixtral, Phi-2, and Falcon support with major performance and feature enhancements.](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19) * [2023/11] [Llama 2 Inference on 4th Gen Intel® Xeon® Scalable Processor with DeepSpeed](https://github.com/microsoft/DeepSpeed/tree/master/blogs/intel-inference) [[Intel version]](https://www.intel.com/content/www/us/en/developer/articles/technical/xllama-2-on-xeon-scalable-processor-with-deepspeed.html) @@ -154,6 +155,8 @@ comments. 27. Zhewei Yao, Xiaoxia Wu, Conglong Li, Minjia Zhang, Heyang Qin, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He. (2023) DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention [arXiv:2309.14327](https://arxiv.org/pdf/2309.14327.pdf) 28. Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, et al. (2023) DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies [arXiv:2310.04610](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/) 29. Zhewei Yao, Reza Yazdani Aminabadi, Stephen Youn, Xiaoxia Wu, Elton Zheng, Yuxiong He. (2023) ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers [arXiv:2310.17723](https://arxiv.org/abs/2310.17723) +30. Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Reza Yazdani Aminadabi, Shuaiwen Leon Song, Samyam Rajbhandari, Yuxiong He. (2024) [System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://dl.acm.org/doi/10.1145/3662158.3662806) +31. Xinyu Lian, Sam Ade Jacobs, Lev Kurilenko, Masahiro Tanaka, Stas Bekman, Olatunji Ruwase, Minjia Zhang. (2024) Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training [arXiv:2406.18820](https://arxiv.org/abs/2406.18820) # Videos 1. DeepSpeed KDD 2020 Tutorial From dd7a5be53d3e7630f22cf8a903ed8f1b73bf59a0 Mon Sep 17 00:00:00 2001 From: Heyang Qin Date: Mon, 1 Jul 2024 15:57:52 -0700 Subject: [PATCH 27/36] UCP Chinese Blog (#5713) Co-authored-by: Sam Ade Jacobs Co-authored-by: Masahiro Tanaka --- blogs/deepspeed-ucp/chinese/README.md | 124 ++++++++++++++++++++++++++ 1 file changed, 124 insertions(+) create mode 100644 blogs/deepspeed-ucp/chinese/README.md diff --git a/blogs/deepspeed-ucp/chinese/README.md b/blogs/deepspeed-ucp/chinese/README.md new file mode 100644 index 000000000000..062063de92df --- /dev/null +++ b/blogs/deepspeed-ucp/chinese/README.md @@ -0,0 +1,124 @@ + +

+ + + +要引用DeepSpeed通用检查点，请引用我们的[arxiv报告](https://arxiv.org/abs/2406.18820)： + +``` +@article{lian2024-ucp, +title={Universal Checkpointing: Efficient and Flexible Checkpointing for +Large Scale Distributed Training}, +author={Xinyu Lian and Sam Ade Jacobs and Lev Kurilenko and Masahiro Tanaka +and Stas Bekman and Olatunji Ruwase and Minjia Zhang}, +journal={arxiv preprint arxiv:406.18820}, +year={2024}, + +} +``` + +# 引言 + +检查点是降低训练大型语言模型成本的关键技术，它使我们在训练过程中可以保存模型状态。这样，如果训练失败，训练可以从最后保存的点继续，而不是从头开始。此外，检查点还允许在训练的不同阶段评估模型性能，从而便于进行超参数调整以及针对不同和多样化下游任务的微调。 + +然而，在分布式训练和微调场景中设计、实施和使用检查点存在困难。ZeRO数据并行（ZeRO-DP）、流水线并行（PP）、张量并行（TP）和序列并行（SP）等方法是加速大型语言模型训练的出色技术，但与传统的默认（Torch）保存和加载检查点机制不兼容。此外，目前尚无技术支持将这些不同的并行拓扑与检查点灵活组合，部分原因是这些技术将模型和/或优化器状态分片，使得在不同GPU或加速器数量上创建的检查点难以用于恢复训练。 + +在此，我们很高兴地发布DeepSpeed通用检查点（*UCP*），这是解决分布式检查点问题的最全面的解决方案。*UCP*在高效创建检查点的同时，提供了在任意并行策略和硬件配置上恢复的灵活性。*UCP*还解锁了大规模训练的前所未有的能力，例如通过在剩余健康硬件上继续训练来提高对硬件故障的抵抗力，以及通过机会性利用弹性容量来减少训练时间。 + +简单来说，当前版本的*UCP*解锁了以下功能： + +- 灵活的检查点可沿任何训练并行技术（即PP、TP、DP、ZeRO-DP、SP、MoE）重塑训练 + +- 弹性资源管理，在训练和微调中随意增加或减少硬件资源 + +- 支持多种商业规模模型的真实世界用例（例如BLOOM、Megatron GPT、LLAMA、Microsoft Phi） + +# 核心设计 + +DeepSpeed *UCP*的关键洞察是在检查点生命周期的每个阶段选择最佳表示：分布式表示用于保存，合并表示用于加载。这通过两个关键机制实现。首先，通用检查点格式，它包括每个模型参数的合并表示和用于将参数片段映射到任意模型并行配置的训练级别的元数据。其次，通用检查点语言，这是一个简单但强大且健壮的规范语言，用于将分布式检查点转换为通用检查点格式。 + +## 通用检查点格式 + + + +图1：UCP概述：顶部行和底部行分别为源并行配置和目标并行配置。中间行显示UCP作为从源到目标的转换中介块。 + +图1显示了*UCP*转换过程和格式的整体概念性描述。转换从任何并行策略格式的检查点顶部块开始。允许以训练的本地格式保存消除了可能因同步全局检查点保存而产生的任何开销。为确保保存的检查点（称为*源*）可以轻松转换并加载到任何并行策略以进行连续训练（称为*目标*），我们引入了作为中介块的原子检查点格式的概念。 + +原子检查点是*UCP*的核心概念。这些是包含每个模型参数的合并表示及其优化器状态的细粒度文件。原子检查点格式有三个用途。首先，原子检查点的表示解除了分布式检查点与特定并行技术和硬件配置的依赖。因此，无需为每个*源*到*目标*实现单独的转换器。相反，*UCP*可以充当不同分布式训练技术之间的通用交换格式，然后可以轻松地转换为其他分布式训练策略，如图2所示。通过保持每个模型参数的合并表示，*UCP*可以轻松地将模型状态或片段状态拆分并灵活地映射到不同GPU上，有效减少加载大型模型检查点所需的工作内存。其次，*UCP*转换是懒惰和按需进行的，例如，当训练过程检测到并行技术和硬件配置的变化时。换句话说，现有的分布式检查点保存逻辑不需要任何改变。第三，*UCP*的结构还易于处理分布式训练中的高级技术，例如混合精度训练。在实践中，研究人员和从业者可能在fp16和bfloat16混合精度训练之间切换。通过保持fp32的权重/优化器值，训练可以继续使用fp16或bfloat16恢复。 + +## 通用检查点语言 + + + +图2：UCP语言帮助将分布式检查点转换为UCP格式，并根据目标并行技术和新硬件配置加载UCP检查点。 + + +虽然*UCP*为不同的并行策略提供了一个公共接口，但从任意分布式检查点到*UCP*的转换仍然可能具有不菲的工程和实施成本。这是因为分布式训练中的每个GPU都调用一个持久方法（例如，在PyTorch中使用torch.save()）将其拥有的GPU模型状态保存到磁盘上的检查点文件中，而每个检查点的具体内容在不同技术之间会有所不同。 + +为了应对这一挑战，*UCP*提供了*UCP*语言，这是一个简单但强大的规范语言，用于将几种类型的分布式检查点转换为前一节中描述的通用格式。*UCP*以两种方式实现这一点。首先，它提供了一个具有预定义*参数模式*的声明式系统，这些模式涵盖了模型状态的广泛并行 + +策略。参数模式包含有关参数如何在GPU之间分区的运行时信息。例如，*nopattern*表示一个参数与某个GPU唯一相关，这是ZeRO-1/2和PP等技术中最常见的模式（参见我们的技术报告，以获得当前支持的参数模式完整列表）。其次，*UCP*语言提供了一组常见操作符，以便将分布式检查点转换为合并的原子检查点。从高层次来看，如图3所示，当需要新的*目标*并行技术或硬件配置发生变化时，将调用*UCP*语言。它首先将分布式检查点转换为*UCP*格式。然后根据*目标*并行技术和新硬件配置加载*UCP*检查点。 + +# 关键结果 + +我们通过一系列实验评估*UCP*，专注于仅解码器的Transformers架构，这是由于其最先进的性能。一些最大的模型也是基于解码器的，这使得灵活高效的检查点尤为重要。在本博客中，我们展示了在不同模型和并行策略下正确性验证的结果。有关并行效率分析、详细的系统和模型架构以及训练超参数的更多结果，请参阅上面引用的技术报告。 + +*UCP*提供了从一个*源*并行策略到不同的*目标*和不同硬件配置的灵活检查点。为验证这一能力，我们进行了正确性测试的两组实验。 + +## 单源到多目标 + + + +图3：在第101次迭代时使用不同目标加载UCP检查点的训练曲线，具有不同GPU数量和并行策略 + +为测试UCP是否允许使用不同并行策略和硬件配置恢复训练，我们首先使用TP=2、PP=2、DP=2（ZeRO-1）和SP=1的配置训练GPT-3模型。由于时间和资源的限制，我们将实验限制在前200次迭代。我们将在第100次迭代保存的检查点转换为*UCP*检查点，并使用不同GPU数量和并行策略恢复训练。我们记录了每次迭代的LM损失（数据并行组的平均损失）。图3显示，训练可以使用不同的*目标*并行策略无缝地使用*UCP*检查点恢复，如果训练继续使用*源*策略，将实现一致的收敛。 + +## 多源到单目标 + + + +图4：在第100次迭代将不同源并行策略转换为UCP并加载UCP的训练曲线，具有不同的目标。 + +图4显示了从多个*源*配置到单一*目标*的训练曲线。在固定随机种子的情况下，我们首先使用不同的*源*配置训练GPT-3模型。然后我们将它们在第100次迭代保存的分布式检查点转换为*UCP*检查点，并使用TP=2、PP=2、DP=1和SP=1的配置恢复训练。结果显示，无论不同的*源*配置如何，它们的检查点都可以转换为*UCP*并使用不同的配置恢复训练。最重要的是，恢复的训练曲线与第101--200次迭代的*源*曲线匹配。这些结果验证了*UCP*将任意配置转换为不同配置以恢复训练的有效性。 + +## 不同模型架构的变化 + +*UCP*与模型架构无关。因此，它不仅与GPT模型兼容，而且足够灵活，可以支持各种其他模型架构和大小。图5、6和7显示了使用新并行策略从*UCP*中恢复训练时的训练收敛情况。这些图表显示，训练可以使用*UCP*无缝恢复，实现与初始训练阶段一致的收敛，这与这些不同模型相符。这些结果表明，*UCP*对于各种模型架构和大小都非常灵活。 + + + +图5：使用LLaMA模型架构的训练曲线。源是TP=PP=DP=2。训练在第101次迭代时使用新目标TP=DP=2, PP=1和TP=PP=2, DP=1恢复 + + + +图6：使用BLOOM模型架构的训练曲线。源是TP=2, PP=24, DP=8。训练在第94767次迭代时使用新目标TP=2, DP=4, PP=24恢复。 + + + +图7：使用Mixtral-MoE模型架构变种的训练曲线。源是TP=1, PP=2, DP=4。训练在第501次迭代时使用新目标TP=PP=DP=2恢复。 + +# DeepSpeed通用检查点的普遍可用性 + +我们很高兴发布DeepSpeed通用检查点。DeepSpeed通用检查点已与Megatron-DeepSpeed的重构版本完全集成，并可通过DeepSpeed和Megatron-DeepSpeed的GitHub仓库访问。详细的使用教程可在[DeepSpeed教程页面](https://www.deepspeed.ai/tutorials/universal-checkpointing/)上找到。 + +我们欢迎来自更广泛开源社区的贡献和合作。DeepSpeed通用检查点是大规模AI训练和推理DeepSpeed生态系统的一部分。有关所有DeepSpeed技术和创新的更多详细信息，请访问我们的[网站](https://www.deepspeed.ai/)并在X（前Twitter）（[英文](https://twitter.com/MSFTDeepSpeed)，[日文](https://twitter.com/MSFTDeepSpeedJP)）和[中文知乎](https://www.zhihu.com/people/deepspeed)上关注我们。 + +# 致谢和贡献 +我们感谢伊利诺伊大学厄巴纳-香槟分校、Statosphere和英特尔Habana的合作。 + +贡献者： +Xinyu Lian $^1$, Sam Ade Jacobs $^2$, Lev Kurilenko $^2$, Masahiro Tanaka $^2$, +Stas Bekman $^3$, Olatunji Ruwase $^2$, Minjia Zhang $^1$, Moshe Island $^4$ + +1: 伊利诺伊大学厄巴纳-香槟分校 +2: 微软 +3: Statosphere +4: 英特尔Habana From 3d347276ce80e1a29e777c839d1d7fabe8e5f034 Mon Sep 17 00:00:00 2001 From: Sam Ade Jacobs Date: Mon, 1 Jul 2024 15:58:21 -0700 Subject: [PATCH 28/36] Fix tutorial links (#5714) --- docs/_tutorials/universal-checkpointing.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/docs/_tutorials/universal-checkpointing.md b/docs/_tutorials/universal-checkpointing.md index 78090491223f..dfc5f920cb8e 100644 --- a/docs/_tutorials/universal-checkpointing.md +++ b/docs/_tutorials/universal-checkpointing.md @@ -1,8 +1,7 @@ --- -#title: "Universal Checkpointing with DeepSpeed: A Practical Guide" +title: "Universal Checkpointing with DeepSpeed: A Practical Guide" tags: checkpointing, training, deepspeed --- -# Universal Checkpointing with DeepSpeed: A Practical Guide DeepSpeed Universal Checkpointing feature is a powerful tool for saving and loading model checkpoints in a way that is both efficient and flexible, enabling seamless model training continuation and finetuning across different model architectures, different parallelism techniques and training configurations. This tutorial, tailored for both begininers and experienced users, provides a step-by-step guide on how to leverage Universal Checkpointing in your DeepSpeed-powered applications. This tutorial will guide you through the process of creating ZeRO checkpoints, converting them into a Universal format, and resuming training with these universal checkpoints. This approach is crucial for leveraging pre-trained models and facilitating seamless model training across different setups. @@ -23,7 +22,7 @@ Follow the three simple steps below: ### Step 1: Create ZeRO Checkpoint -The first step in leveraging DeepSpeed Universal Checkpointing is to create a ZeRO checkpoint. [ZeRO]((/tutorials/zero/)) (Zero Redundancy Optimizer) is a memory optimization technology in DeepSpeed that allows for efficient training of large models. To create a ZeRO checkpoint, you'll need to: +The first step in leveraging DeepSpeed Universal Checkpointing is to create a ZeRO checkpoint. [ZeRO](/tutorials/zero/) (Zero Redundancy Optimizer) is a memory optimization technology in DeepSpeed that allows for efficient training of large models. To create a ZeRO checkpoint, you'll need to: - Initialize your model with DeepSpeed using the ZeRO optimizer. - Train your model to the desired state (iterations). @@ -50,9 +49,8 @@ With the Universal checkpoint ready, you can now resume training on potentially ## Conclusion DeepSpeed Universal Checkpointing simplifies the management of model states, making it easier to save, load, and transfer model states across different training sessions and parallelism techniques. By following the steps outlined in this tutorial, you can integrate Universal Checkpointing into your DeepSpeed applications, enhancing your model training and development workflow. -For more detailed examples and advanced configurations, please refer to the [Megatron-DeepSpeed examples]((https://github.com/microsoft/Megatron-DeepSpeed/tree/main/examples_deepspeed/universal_checkpointing)). +For more detailed examples and advanced configurations, please refer to the [Megatron-DeepSpeed examples](https://github.com/microsoft/Megatron-DeepSpeed/tree/main/examples_deepspeed/universal_checkpointing). -For technical in-depth of DeepSpeed Universal Checkpointing, please see [arxiv manuscript](https://arxiv.org/abs/2406.18820) and [blog](/blogs/deepspeed-ucp/). +For technical in-depth of DeepSpeed Universal Checkpointing, please see [arxiv manuscript](https://arxiv.org/abs/2406.18820) and [blog](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ucp/). Happy training! -``` From 8411816583ee5fe56d95f2241e6e54199b879849 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 8 Jul 2024 12:33:52 -0700 Subject: [PATCH 29/36] Update node16 check on self-hosted runners and remove python 3.6 (#5756) With changes from GitHub [finally deprecating](https://github.com/actions/checkout/issues/1474) [node16 based runners](https://github.blog/changelog/2024-03-07-github-actions-all-actions-will-run-on-node20-instead-of-node16-by-default/) (which the checkout@v3 action uses) we need to make changes to support this. To do this, there are two changes. First we remove the python 3.6 check as with the changes in pydantic v2 that will be merged soon, we will be removing this check there, so we can more easily remove it now so that future PRs are cleaner and it is clear why some changes have been made. Second, node16 is the default on some of our self-hosted runners. To work around tests failing on these, we [set the GitHub env var to override this check](https://github.blog/changelog/2024-03-07-github-actions-all-actions-will-run-on-node20-instead-of-node16-by-default/). Other relevant links: https://github.com/actions/checkout/issues/1474 https://github.com/easybuilders/easybuild-framework/pull/4574/files https://github.com/actions/checkout/issues/1809 https://github.com/actions/runner/issues/3373 https://github.com/actions/checkout/issues/1809 --- .github/workflows/cpu-inference.yml | 2 ++ .github/workflows/nv-lightning-v100.yml | 2 ++ .github/workflows/nv-torch110-p40.yml | 2 ++ .github/workflows/nv-torch110-v100.yml | 2 ++ .github/workflows/python.yml | 4 ++-- 5 files changed, 10 insertions(+), 2 deletions(-) diff --git a/.github/workflows/cpu-inference.yml b/.github/workflows/cpu-inference.yml index d91034270eec..fc0dac5de9a2 100644 --- a/.github/workflows/cpu-inference.yml +++ b/.github/workflows/cpu-inference.yml @@ -24,6 +24,8 @@ jobs: unit-tests: runs-on: [self-hosted, cpu] + env: {ACTIONS_ALLOW_USE_UNSECURE_NODE_VERSION: true} # Allow using Node16 actions + steps: - uses: actions/checkout@v3 diff --git a/.github/workflows/nv-lightning-v100.yml b/.github/workflows/nv-lightning-v100.yml index 8a385fa532b9..6af228f7fb2f 100644 --- a/.github/workflows/nv-lightning-v100.yml +++ b/.github/workflows/nv-lightning-v100.yml @@ -21,6 +21,8 @@ jobs: unit-tests: runs-on: [self-hosted, nvidia, cu111, v100] + env: {ACTIONS_ALLOW_USE_UNSECURE_NODE_VERSION: true} # Allow using Node16 actions + steps: - uses: actions/checkout@v3 diff --git a/.github/workflows/nv-torch110-p40.yml b/.github/workflows/nv-torch110-p40.yml index 5e6df67d8761..ed639aeb3b62 100644 --- a/.github/workflows/nv-torch110-p40.yml +++ b/.github/workflows/nv-torch110-p40.yml @@ -17,6 +17,8 @@ jobs: unit-tests: runs-on: [self-hosted, nvidia, cu111, p40] + env: {ACTIONS_ALLOW_USE_UNSECURE_NODE_VERSION: true} # Allow using Node16 actions + steps: - uses: actions/checkout@v3 diff --git a/.github/workflows/nv-torch110-v100.yml b/.github/workflows/nv-torch110-v100.yml index 406b43c154d8..4b9f278448ab 100644 --- a/.github/workflows/nv-torch110-v100.yml +++ b/.github/workflows/nv-torch110-v100.yml @@ -17,6 +17,8 @@ jobs: unit-tests: runs-on: [self-hosted, nvidia, cu111, v100] + env: {ACTIONS_ALLOW_USE_UNSECURE_NODE_VERSION: true} # Allow using Node16 actions + steps: - uses: actions/checkout@v3 diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml index 6883de4885c6..ba8a79f63469 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/python.yml @@ -21,7 +21,7 @@ jobs: unit-tests: strategy: matrix: - pyVersion: ["3.6", "3.7", "3.8", "3.9", "3.10"] + pyVersion: ["3.7", "3.8", "3.9", "3.10"] fail-fast: false runs-on: ubuntu-20.04 @@ -29,7 +29,7 @@ jobs: image: deepspeed/gh-builder:py${{ matrix.pyVersion }} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: environment run: | From 774b897736277618caae9b4b045374fa7615b2cb Mon Sep 17 00:00:00 2001 From: Xinyu Lian Date: Tue, 9 Jul 2024 05:44:33 +0800 Subject: [PATCH 30/36] fix the missing argument in test and typo (#5730) This PR fixes the issue mentioned in [PR5722](https://github.com/microsoft/DeepSpeed/pull/5722) that causes the hangs in the nv-torch-latest-v100 tests. Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/checkpoint/ds_to_universal.py | 2 +- tests/unit/checkpoint/test_universal_checkpoint.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/deepspeed/checkpoint/ds_to_universal.py b/deepspeed/checkpoint/ds_to_universal.py index c28f4f74b275..e5974a30df22 100755 --- a/deepspeed/checkpoint/ds_to_universal.py +++ b/deepspeed/checkpoint/ds_to_universal.py @@ -69,7 +69,7 @@ def parse_arguments(): dest='strict', action='store_false', help='Do not perform validity checks on converted checkpoint.') - parser.add_argument('--inject-missing-state', + parser.add_argument('--inject_missing_state', action='store_true', help='Inject missing checkpoint state into the checkpoint if it is absent.') args = parser.parse_args() diff --git a/tests/unit/checkpoint/test_universal_checkpoint.py b/tests/unit/checkpoint/test_universal_checkpoint.py index c75419f8683b..f2692ecba3a6 100644 --- a/tests/unit/checkpoint/test_universal_checkpoint.py +++ b/tests/unit/checkpoint/test_universal_checkpoint.py @@ -110,7 +110,8 @@ def train_save_convert(ds_config, hidden_dim, load_optim, use_torch_adam, dtype, num_extract_workers=1, num_merge_workers=1, keep_temp_folder=False, - strict=True) + strict=True, + inject_missing_state=False) dist.barrier() if dist.get_rank() == 0: From 2105976eaf081e92d5d7ddd10a9070390c842fdb Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 8 Jul 2024 16:55:30 -0700 Subject: [PATCH 31/36] Update checkout action for nv-human-eval workflow (#5757) Update workflows that can be updated to use node20/checkout@v4. --- .github/workflows/nv-human-eval.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/nv-human-eval.yml b/.github/workflows/nv-human-eval.yml index 82f7e9beacca..3de878547d6e 100644 --- a/.github/workflows/nv-human-eval.yml +++ b/.github/workflows/nv-human-eval.yml @@ -17,7 +17,7 @@ jobs: options: --gpus all --shm-size "8G" steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Check container state run: | From 7b1ea2256e3c729c8822d4c8bb945ddd95458fca Mon Sep 17 00:00:00 2001 From: Omar Elayan <142979319+oelayan7@users.noreply.github.com> Date: Tue, 9 Jul 2024 03:06:25 +0300 Subject: [PATCH 32/36] [INF] Enable torch compile for inference (#5612) Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> --- deepspeed/inference/engine.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/deepspeed/inference/engine.py b/deepspeed/inference/engine.py index 5cdd99ff0b90..74fc7f74f6bd 100755 --- a/deepspeed/inference/engine.py +++ b/deepspeed/inference/engine.py @@ -6,6 +6,7 @@ import torch import time import os +import deepspeed from deepspeed import comm as dist from deepspeed.utils.logging import log_dist @@ -13,6 +14,7 @@ from packaging import version as pkg_version from deepspeed.runtime.checkpoint_engine.torch_checkpoint_engine import TorchCheckpointEngine from deepspeed.utils.timer import SynchronizedWallClockTimer +from deepspeed.runtime.compiler import is_compile_supported from ..runtime.state_dict_factory import SDLoaderFactory from ..runtime.weight_quantizer import WeightQuantization @@ -185,6 +187,7 @@ def __init__(self, model, config): # Check if local CUDA graphs can be created in replacement modules self.local_cuda_graph = self._local_cuda_graph_used(self.module) + self._is_compiled = False def destroy(self): # Have to import here because inference_module is a global, but python @@ -634,3 +637,22 @@ def _generate(self, *inputs, **kwargs): ) return self.module.generate(*inputs, **kwargs) + + def compile(self, backend=get_accelerator().get_compile_backend(), compile_kwargs={}) -> None: + """ + Compile the module using the specified backend and kwargs. + """ + if not is_compile_supported(): + raise RuntimeError("compile is not supported in your version of PyTorch.") + + if self._is_compiled: + return + + # Avoid graph breaks + deepspeed.utils.nvtx.enable_nvtx = False + self.module.compile(backend=backend, **compile_kwargs) + self._is_compiled = True + + @property + def is_compiled(self) -> bool: + return self._is_compiled From 74f3dcab62c0b4e9879f526002cef6728b5def0e Mon Sep 17 00:00:00 2001 From: Costin Eseanu <19394655+costin-eseanu@users.noreply.github.com> Date: Mon, 8 Jul 2024 18:05:09 -0700 Subject: [PATCH 33/36] Add Windows scripts (deepspeed, ds_report). (#5699) Co-authored-by: Costin Eseanu Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- bin/deepspeed.bat | 2 ++ bin/ds_report.bat | 2 ++ build_win.bat | 1 + deepspeed/env_report.py | 2 +- setup.py | 13 +++++++++---- 5 files changed, 15 insertions(+), 5 deletions(-) create mode 100644 bin/deepspeed.bat create mode 100644 bin/ds_report.bat diff --git a/bin/deepspeed.bat b/bin/deepspeed.bat new file mode 100644 index 000000000000..8e488bde380c --- /dev/null +++ b/bin/deepspeed.bat @@ -0,0 +1,2 @@ +@echo off +python "%~dp0\ds" %* diff --git a/bin/ds_report.bat b/bin/ds_report.bat new file mode 100644 index 000000000000..78c7225f147c --- /dev/null +++ b/bin/ds_report.bat @@ -0,0 +1,2 @@ +@echo off +python "%~dp0\ds_report" %* diff --git a/build_win.bat b/build_win.bat index 6e67cb8c2aa2..9c326aae552d 100644 --- a/build_win.bat +++ b/build_win.bat @@ -1,5 +1,6 @@ @echo off +set CUDA_HOME=%CUDA_PATH% set DISTUTILS_USE_SDK=1 set DS_BUILD_AIO=0 diff --git a/deepspeed/env_report.py b/deepspeed/env_report.py index 85a2f9b2b6c0..37e33b1e8739 100644 --- a/deepspeed/env_report.py +++ b/deepspeed/env_report.py @@ -110,7 +110,7 @@ def installed_cann_version(): def get_shm_size(): try: shm_stats = os.statvfs('/dev/shm') - except (OSError, FileNotFoundError, ValueError): + except (OSError, FileNotFoundError, ValueError, AttributeError): return "UNKNOWN", None shm_size = shm_stats.f_frsize * shm_stats.f_blocks diff --git a/setup.py b/setup.py index f39eab41938a..183d42907e82 100755 --- a/setup.py +++ b/setup.py @@ -285,6 +285,14 @@ def op_enabled(op_name): with open(os.path.join(thisdir, 'README.md'), encoding='utf-8') as fin: readme_text = fin.read() +if sys.platform == "win32": + scripts = ['bin/deepspeed.bat', 'bin/ds', 'bin/ds_report.bat', 'bin/ds_report'] +else: + scripts = [ + 'bin/deepspeed', 'bin/deepspeed.pt', 'bin/ds', 'bin/ds_ssh', 'bin/ds_report', 'bin/ds_bench', 'bin/dsr', + 'bin/ds_elastic' + ] + start_time = time.time() setup(name='deepspeed', @@ -303,10 +311,7 @@ def op_enabled(op_name): extras_require=extras_require, packages=find_packages(include=['deepspeed', 'deepspeed.*']), include_package_data=True, - scripts=[ - 'bin/deepspeed', 'bin/deepspeed.pt', 'bin/ds', 'bin/ds_ssh', 'bin/ds_report', 'bin/ds_bench', 'bin/dsr', - 'bin/ds_elastic' - ], + scripts=scripts, classifiers=[ 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', From 83aa184351778f7eaf96ada070cb85dd3787b9c8 Mon Sep 17 00:00:00 2001 From: Heyang Qin Date: Thu, 11 Jul 2024 08:42:43 -0700 Subject: [PATCH 34/36] Unit Test: Add error handling for rate limit exceeded in model list (#5715) This PR fixes the random failure in our unit test due to HTTP 429 --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- tests/unit/inference/test_inference.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/tests/unit/inference/test_inference.py b/tests/unit/inference/test_inference.py index c7e53188bbd9..d512266c8a6f 100644 --- a/tests/unit/inference/test_inference.py +++ b/tests/unit/inference/test_inference.py @@ -9,6 +9,7 @@ import pickle import os import time +import requests from dataclasses import dataclass from typing import List @@ -101,9 +102,21 @@ def _hf_model_list() -> List[ModelInfo]: if ((model_data["cache_time"] + cache_expiration_seconds) < current_time) or os.getenv("FORCE_UPDATE_HF_CACHE", default=False): api = HfApi() - model_data["model_list"] = [ - ModelInfo(modelId=m.modelId, pipeline_tag=m.pipeline_tag, tags=m.tags) for m in api.list_models() - ] + while True: + try: + model_list = [] + for model in _test_models: + model_list.extend(api.list_models(model_name=model)) + model_data["model_list"] = [ + ModelInfo(modelId=m.modelId, pipeline_tag=m.pipeline_tag, tags=m.tags) for m in model_list + ] + break # Exit the loop if the operation is successful + except requests.exceptions.HTTPError as e: + if e.response.status_code == 429: + print("Rate limit exceeded. Retrying in 60 seconds...") + time.sleep(60) + else: + raise # Re-raise the exception if it's not a 429 error model_data["cache_time"] = current_time # Save the updated cache From db5a875b8de35e15293d572b9ea3527f1b73ede0 Mon Sep 17 00:00:00 2001 From: Avinash Maurya Date: Mon, 15 Jul 2024 12:56:27 -0400 Subject: [PATCH 35/36] Fix memory leak for pipelined optimizer swapper (#5700) We identified a memory leak when training with NVMe offloaded optimizer states. The issue occurs when `pipeline_write=true`, where the tensors that have swapped out and written to NVMe are not deallocated, leading to a memory leak. This PR resolves the issue by deallocating the unused tensors which have swapped out to NVMe. Co-authored-by: amaurya --- deepspeed/runtime/swap_tensor/pipelined_optimizer_swapper.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/deepspeed/runtime/swap_tensor/pipelined_optimizer_swapper.py b/deepspeed/runtime/swap_tensor/pipelined_optimizer_swapper.py index 03dfe663fcb6..66a372877d38 100644 --- a/deepspeed/runtime/swap_tensor/pipelined_optimizer_swapper.py +++ b/deepspeed/runtime/swap_tensor/pipelined_optimizer_swapper.py @@ -8,6 +8,7 @@ from deepspeed.ops.op_builder import AsyncIOBuilder from deepspeed import comm as dist +import torch from deepspeed.runtime.swap_tensor.constants import * from deepspeed.runtime.swap_tensor.utils import swap_in_tensors, swap_out_tensors, print_object @@ -154,6 +155,8 @@ def swap_out_gradients(self, parameter, gradient_offsets, gradient_tensors): def _complete_swap_out(self, swap_out_type): self.swap_ops[swap_out_type].wait() + for buffer in self.swap_ops[swap_out_type].state_buffers: + buffer = torch.Tensor() self.swap_buffer_manager.free(self.swap_ops[swap_out_type].allocated_buffers) self.swap_ops[swap_out_type] = None From 0af9ac314fb3847b33e5b5f63a570ce6e1bdbca4 Mon Sep 17 00:00:00 2001 From: Xu Song Date: Tue, 16 Jul 2024 00:57:14 +0800 Subject: [PATCH 36/36] Remove duplicated variable (#5727) Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/zero/stage3.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index edeedb691961..37b81d42c0d6 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -338,7 +338,6 @@ def __init__( self.params_in_ipg_bucket = [] self.params_already_reduced = {} - self.is_gradient_accumulation_boundary = True self._release_ipg_buffers() self.previous_reduced_grads = None

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