From ff1c54351f087da06f76243ae562504bb166d979 Mon Sep 17 00:00:00 2001 From: Xinyu Lian Date: Thu, 31 Oct 2024 12:56:09 -0500 Subject: [PATCH 001/112] fix memcpy issue on backward for zero-infinity (#6670) This PR is similar to [PR#5301](https://github.com/microsoft/DeepSpeed/pull/5301), that optimizes the D2H time use pinned memory. Previously, the D2H memcpy will be the bottleneck during the final backward pass of each iteration for ZeRO-Infinity(offload), as shown in Trace-1. The new version can eliminate the bottleneck, as shown in Trace-2. _Trace-1_ image _Trace-2_ image cc @tjruwase --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase --- deepspeed/runtime/zero/stage3.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index e2c273fd913f..65460eb72a2f 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -538,10 +538,15 @@ def _setup_for_real_optimizer(self): self.grad_partitions_flat_buffer = get_accelerator().pin_memory(self.grad_partitions_flat_buffer) offset = 0 + max_partition_numel = 0 for param in all_params: self.__param_id_to_grad_partition[param.ds_id] = self.grad_partitions_flat_buffer.narrow( 0, offset, param.partition_numel()) offset += param.partition_numel() + max_partition_numel = max(max_partition_numel, param.partition_numel()) + if self.offload_optimizer: + self.pinned_grad_buffer: Tensor = get_accelerator().pin_memory( + torch.empty(max_partition_numel, device=self.device)) def _link_all_hp_params(self): for p in self.module.parameters(): @@ -1498,9 +1503,13 @@ def partition_grads(self, params_to_release: List[Parameter], grad_partitions: L offload_fp32_gradients[i].append(grad_buffer.float()) offload_fp32_offsets[i].append(dest_offset) else: + buffer_numel = grad_buffer.numel() fp32_grad_tensor = self.fp32_partitioned_groups_flat[i].grad.narrow( - 0, dest_offset, grad_buffer.numel()) - fp32_grad_tensor.copy_(grad_buffer) + 0, dest_offset, buffer_numel) + self.pinned_grad_buffer[:buffer_numel].copy_( + grad_buffer.to(dtype=torch.float32, non_blocking=True)) + get_accelerator().synchronize() + fp32_grad_tensor.copy_(self.pinned_grad_buffer[:buffer_numel], non_blocking=True) # free the gradient if not get_accelerator().is_synchronized_device(): From 95ea95fcd642488519bb599e4618507f10f88494 Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Thu, 31 Oct 2024 11:51:11 -0700 Subject: [PATCH 002/112] Free memory in universal checkpointing tests (#6693) Tests in universal checkpointing were not freeing the engine after use when `reuse_dist_env` was set to `True`, leading to memory leaks. This PR ensure freeing the engine in the tests and enables `reuse_dist_env`. Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- tests/unit/checkpoint/test_universal_checkpoint.py | 5 +++-- tests/unit/common.py | 9 --------- 2 files changed, 3 insertions(+), 11 deletions(-) diff --git a/tests/unit/checkpoint/test_universal_checkpoint.py b/tests/unit/checkpoint/test_universal_checkpoint.py index f2692ecba3a6..27ddf0cdef39 100644 --- a/tests/unit/checkpoint/test_universal_checkpoint.py +++ b/tests/unit/checkpoint/test_universal_checkpoint.py @@ -131,8 +131,7 @@ def train_save_convert(ds_config, hidden_dim, load_optim, use_torch_adam, dtype, torch.save((model_state, optimizer_state), os.path.join(tmpdir, "baseline_state.pt")) dist.barrier() - - return model, sd + model.destroy() @pytest.fixture @@ -213,6 +212,8 @@ def _run_test(self, tmpdir, dtype, ds_config, load_optim, use_torch_adam): univ_model.backward(loss) univ_model.step() + univ_model.destroy() + @pytest.mark.world_size(2) def test_dp_world_size_2to2(self, baseline_ws2, tmpdir, dtype, ds_config, load_optim, use_torch_adam): self._run_test(tmpdir, dtype, ds_config, load_optim, use_torch_adam) diff --git a/tests/unit/common.py b/tests/unit/common.py index 685f943df2fe..f46ac666f27b 100644 --- a/tests/unit/common.py +++ b/tests/unit/common.py @@ -25,8 +25,6 @@ # Worker timeout for tests that hang DEEPSPEED_TEST_TIMEOUT = int(os.environ.get('DS_UNITTEST_TIMEOUT', '600')) -warn_reuse_dist_env = False - def is_rocm_pytorch(): return hasattr(torch.version, 'hip') and torch.version.hip is not None @@ -178,13 +176,6 @@ def _launch_daemonic_procs(self, num_procs, init_method): print("Ignoring reuse_dist_env for hpu") self.reuse_dist_env = False - global warn_reuse_dist_env - if self.reuse_dist_env and not warn_reuse_dist_env: - # Currently we see memory leak for tests that reuse distributed environment - print("Ignoring reuse_dist_env and forcibly setting it to False") - warn_reuse_dist_env = True - self.reuse_dist_env = False - if self.reuse_dist_env: if num_procs not in self._pool_cache: self._pool_cache[num_procs] = mp.Pool(processes=num_procs) From b24dfa9d0822d36b35d9fa214ce432cef5daf1b6 Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Fri, 1 Nov 2024 05:57:47 -0700 Subject: [PATCH 003/112] Explictly set device when reusing dist env (#6696) A rank of a process can change when reusing the environment. This PR explicitly sets the device when reusing the environment. --- tests/unit/common.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tests/unit/common.py b/tests/unit/common.py index f46ac666f27b..1498b0400ee1 100644 --- a/tests/unit/common.py +++ b/tests/unit/common.py @@ -277,7 +277,11 @@ def _launch_procs(self, num_procs, init_method): self._launch_daemonic_procs(num_procs, init_method) def _dist_run(self, local_rank, num_procs, master_port, init_method, skip_msg=""): - if not dist.is_initialized(): + if dist.is_initialized(): + if get_accelerator().is_available(): + # local_rank might not match the rank in the previous run if you are reusing the environment + get_accelerator().set_device(dist.get_rank()) + else: """ Initialize deepspeed.comm and execute the user function. """ if self.set_dist_env: os.environ['MASTER_ADDR'] = '127.0.0.1' From 9068acb6fbbdbaae5429bb89e507977128496bc5 Mon Sep 17 00:00:00 2001 From: jiahao su Date: Tue, 5 Nov 2024 01:49:21 +0800 Subject: [PATCH 004/112] Update URL in README Pipeline Status for Huawei Ascend NPU (#6706) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b302e32dfd9c..31051297d10c 100755 --- a/README.md +++ b/README.md @@ -142,7 +142,7 @@ DeepSpeed has been integrated with several different popular open-source DL fram | PyTorch Nightly | [![nv-torch-nightly-v100](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml) | | Integrations | [![nv-transformers-v100](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-transformers-v100.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-transformers-v100.yml) [![nv-lightning-v100](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-lightning-v100.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-lightning-v100.yml) [![nv-accelerate-v100](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-accelerate-v100.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-accelerate-v100.yml) [![nv-mii](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-mii.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-mii.yml) [![nv-ds-chat](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-ds-chat.yml) [![nv-sd](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-sd.yml/badge.svg)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-sd.yml) | | Misc | [![Formatting](https://github.com/microsoft/DeepSpeed/actions/workflows/formatting.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/formatting.yml) [![pages-build-deployment](https://github.com/microsoft/DeepSpeed/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/microsoft/DeepSpeed/actions/workflows/pages/pages-build-deployment) [![Documentation Status](https://readthedocs.org/projects/deepspeed/badge/?version=latest)](https://deepspeed.readthedocs.io/en/latest/?badge=latest)[![python](https://github.com/microsoft/DeepSpeed/actions/workflows/python.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/python.yml) | -| Huawei Ascend NPU | [![Huawei Ascend NPU](https://github.com/cosdt/DeepSpeed/actions/workflows/huawei-ascend-npu.yml/badge.svg?branch=master)](https://github.com/cosdt/DeepSpeed/actions/workflows/huawei-ascend-npu.yml) | +| Huawei Ascend NPU | [![Huawei Ascend NPU](https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml/badge.svg?branch=main)](https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml) | # Installation From 6c08b7f932bc1c7acaec142c720f6f9a82e9e5c8 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 4 Nov 2024 12:51:01 -0800 Subject: [PATCH 005/112] Pin transformers to 4.45.2 in nv-ds-chat workflow (#6710) This commit causes breaking changes we need to fix, for now we will pin the version but we will fix shortly https://github.com/huggingface/transformers/pull/33325 --- .github/workflows/nv-ds-chat.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/nv-ds-chat.yml b/.github/workflows/nv-ds-chat.yml index 2ad336cac4ed..329a1060f5eb 100644 --- a/.github/workflows/nv-ds-chat.yml +++ b/.github/workflows/nv-ds-chat.yml @@ -12,6 +12,7 @@ on: type: string pull_request: paths: + - ".github/workflows/nv-ds-chat.yml" - "deepspeed/runtime/zero/stage_1_and_2.py" - "deepspeed/runtime/zero/stage3.py" - "deepspeed/runtime/hybrid_engine.py" @@ -42,6 +43,7 @@ jobs: - name: Install deepspeed run: | + pip install transformers==4.45.2 pip install .[dev] ds_report From 2b41d6212c160a3645691b77b210ba7dd957c23f Mon Sep 17 00:00:00 2001 From: Jagadish Krishnamoorthy Date: Mon, 4 Nov 2024 13:51:27 -0800 Subject: [PATCH 006/112] [Bug Fix] Support threads_per_head < 64 for wavefront size of 64 (#6622) When launching apply_rotary_pos_half kernel, only threads_per_head of 64 is supported for wavefront size of 64. This change adds support for threads_per_head < 64 such as 4, 8, 16. Fixes the issue introduced in https://github.com/microsoft/DeepSpeed/pull/5402 --------- Signed-off-by: Jagadish Krishnamoorthy Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Logan Adams --- .../inference/csrc/apply_rotary_pos_emb.cu | 10 ++++- .../ops/transformer/inference/test_rope.py | 38 +++++++++++++++++++ 2 files changed, 47 insertions(+), 1 deletion(-) create mode 100644 tests/unit/ops/transformer/inference/test_rope.py diff --git a/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu b/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu index 25a494111c54..bbb8a7f00b1f 100644 --- a/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu +++ b/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu @@ -101,7 +101,15 @@ __global__ void apply_rotary_pos_half(T* mixed_query, #if defined(__HIP_PLATFORM_AMD__) and ROCM_WAVEFRONT_SIZE == 64 #define LAUNCH_FOR_ALIGNMENT(ALIGNMENT) \ - if (threads_per_head == 64) { \ + if (threads_per_head == 4) { \ + LAUNCH_ROT_POS_EMB_HALF(4, ALIGNMENT); \ + } else if (threads_per_head == 8) { \ + LAUNCH_ROT_POS_EMB_HALF(8, ALIGNMENT); \ + } else if (threads_per_head == 16) { \ + LAUNCH_ROT_POS_EMB_HALF(16, ALIGNMENT); \ + } else if (threads_per_head == 32) { \ + LAUNCH_ROT_POS_EMB_HALF(32, ALIGNMENT); \ + } else if (threads_per_head == 64) { \ LAUNCH_ROT_POS_EMB_HALF(64, ALIGNMENT); \ } else { \ assert(false); \ diff --git a/tests/unit/ops/transformer/inference/test_rope.py b/tests/unit/ops/transformer/inference/test_rope.py new file mode 100644 index 000000000000..1f0ca0578e04 --- /dev/null +++ b/tests/unit/ops/transformer/inference/test_rope.py @@ -0,0 +1,38 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import pytest +import torch +import deepspeed +from deepspeed.ops.op_builder import InferenceBuilder +from deepspeed.accelerator import get_accelerator + +if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: + pytest.skip("Inference ops are not available on this system", allow_module_level=True) + + +@pytest.mark.inference_ops +@pytest.mark.parametrize("num_heads", [64, 32, 16, 8]) +def test_rope_warp_size_alignment(num_heads): + if get_accelerator().device_name() != "cuda": + pytest.skip("This test runs only on GPU") + + batch = 1 + head = 8 + seq_len = 1024 + head_dim = 32 + rotary_dim = 32 + offset = 8 + rotate_half = False + rope_theta = 2 + + cuda0 = torch.device('cuda:0') + query = torch.randn(batch, head, seq_len, head_dim, device=cuda0) + key = torch.randn(batch, head, seq_len, head_dim, device=cuda0) + + inference = InferenceBuilder().load() + # For num_heads values of 64, 32, 16, 8 + # corresponding threads_per_head (defined in apply_rotary_pos_emb.cu) values are 4, 8, 16, 32 + inference.apply_rotary_pos_emb(query, key, rotary_dim, offset, num_heads, rotate_half, rope_theta) From 351569dd4a00dea0e00040a816cbc44b1e38a214 Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Tue, 5 Nov 2024 14:53:01 -0800 Subject: [PATCH 007/112] Use one param coordinator for both train/inference scenarios (#6662) The parameter coordinator in ZeRO3 throws a "backward pass is invalid for module in evaluation mode" error when the training mode is unexpected, as it expects all modules to be in training mode during the backward pass. This is an unnecessarily strict restriction. This PR relaxes the restriction by using a single parameter coordinator (instead of separate ones for training and evaluation modes) and resetting the prefetch state before starting a forward pass. Use of `is_compiling` needs to be fixed after #6663 is merged. --------- Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/zero/parameter_offload.py | 57 +++++++++---------- .../zero/partitioned_param_coordinator.py | 12 +++- deepspeed/runtime/zero/stage3.py | 8 +-- tests/unit/runtime/zero/test_zero.py | 45 +++++++++++++++ 4 files changed, 85 insertions(+), 37 deletions(-) diff --git a/deepspeed/runtime/zero/parameter_offload.py b/deepspeed/runtime/zero/parameter_offload.py index 90afaf03550a..4b0ddb7679a9 100644 --- a/deepspeed/runtime/zero/parameter_offload.py +++ b/deepspeed/runtime/zero/parameter_offload.py @@ -133,7 +133,6 @@ def __init__( self.persistent_parameters = self.mark_persistent_parameters(self.param_numel_persistence_threshold, self.model_persistence_threshold) - self.param_coordinators = {} self._prefetch_bucket_sz = int(prefetch_bucket_size) self._max_reuse_distance_in_numel = int(max_reuse_distance) self._max_available_parameters_in_numel = int(max_live_parameters) @@ -141,12 +140,21 @@ def __init__( ) if overlap_comm else get_accelerator().default_stream() if not hasattr(module, "ds_inflight_param_registry"): - module.ds_inflight_param_registry = dict() - # we need two registries, one for training and one for eval. They will be used when creating PartitionedParameterCoordinator - module.ds_inflight_param_registry[True] = InflightParamRegistry() - module.ds_inflight_param_registry[False] = InflightParamRegistry() + module.ds_inflight_param_registry = InflightParamRegistry() self.__inflight_param_registry = module.ds_inflight_param_registry + self.param_coordinator = PartitionedParameterCoordinator( + prefetch_bucket_sz=self._prefetch_bucket_sz, + max_reuse_distance_in_numel=self._max_reuse_distance_in_numel, + max_available_parameters_in_numel=self._max_available_parameters_in_numel, + allgather_stream=self.__allgather_stream, + inflight_param_registry=self.__inflight_param_registry, + prefetch_nvme=self.offload_device == OffloadDeviceEnum.nvme, + timers=self.timers, + zero_quantized_weights=self.zero_quantized_weights, + zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights, + ) + self.forward_hooks = [] self.backward_hooks = [] self.setup_zero_stage3_hooks() @@ -161,26 +169,13 @@ def partition_all_parameters(self): """Partitioning Parameters that were not partitioned usually if parameters of modules whose input parameters do not require grad computation do not trigger post call and will therefore will remain unpartitioned""" - self.get_param_coordinator(training=self.module.training).release_and_reset_all(self.module) + self.get_param_coordinator().release_and_reset_all(self.module) for param in iter_params(self.module, recurse=True): if param.ds_status != ZeroParamStatus.NOT_AVAILABLE: raise RuntimeError(f"{param.ds_summary()} expected to be released") - def get_param_coordinator(self, training): - if not training in self.param_coordinators: - self.param_coordinators[training] = PartitionedParameterCoordinator( - prefetch_bucket_sz=self._prefetch_bucket_sz, - max_reuse_distance_in_numel=self._max_reuse_distance_in_numel, - max_available_parameters_in_numel=self._max_available_parameters_in_numel, - allgather_stream=self.__allgather_stream, - inflight_param_registry=self.__inflight_param_registry[training], - prefetch_nvme=self.offload_device == OffloadDeviceEnum.nvme, - timers=self.timers, - zero_quantized_weights=self.zero_quantized_weights, - zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights, - ) - - return self.param_coordinators[training] + def get_param_coordinator(self): + return self.param_coordinator def empty_partition_cache(self): self.partition_all_parameters() @@ -228,14 +223,14 @@ def setup_zero_stage3_hooks(self): #reset step if in inference mode @instrument_w_nvtx - def _end_of_forward_hook(module, *args): + def _start_of_forward_hook(module, *args): + + self.get_param_coordinator().reset_step() - if not torch._C.is_grad_enabled(): - self.get_param_coordinator(training=False).reset_step() + self.module.register_forward_pre_hook(_start_of_forward_hook) #likely one of them should be enough but just to be safe self._register_hooks_recursively(self.module) - self.module.register_forward_hook(_end_of_forward_hook) # Add top module to stack trace global FWD_MODULE_STACK @@ -447,7 +442,7 @@ def pre_sub_module_forward_function(self, sub_module): global FWD_MODULE_STACK FWD_MODULE_STACK.append(sub_module) - param_coordinator = self.get_param_coordinator(training=sub_module.training) + param_coordinator = self.get_param_coordinator() param_coordinator.trace_prologue(sub_module) if param_coordinator.is_record_trace(): param_coordinator.record_module(sub_module) @@ -460,7 +455,7 @@ def post_sub_module_forward_function(self, sub_module): see_memory_usage(f"After sub module function {sub_module.__class__.__name__} {sub_module.id} before release", force=False) - param_coordinator = self.get_param_coordinator(training=sub_module.training) + param_coordinator = self.get_param_coordinator() param_coordinator.release_sub_module(sub_module) see_memory_usage(f"After sub module function {sub_module.__class__.__name__} {sub_module.id} after release", @@ -468,8 +463,8 @@ def post_sub_module_forward_function(self, sub_module): @torch.no_grad() def pre_sub_module_backward_function(self, sub_module): - assert sub_module.training, "backward pass is invalid for module in evaluation mode" - param_coordinator = self.get_param_coordinator(training=True) + # assert sub_module.training, "backward pass is invalid for module in evaluation mode" + param_coordinator = self.get_param_coordinator() param_coordinator.trace_prologue(sub_module) if param_coordinator.is_record_trace(): param_coordinator.record_module(sub_module) @@ -477,12 +472,12 @@ def pre_sub_module_backward_function(self, sub_module): @torch.no_grad() def post_sub_module_backward_function(self, sub_module): - assert sub_module.training, "backward pass is invalid for module in evaluation mode" + # assert sub_module.training, "backward pass is invalid for module in evaluation mode" see_memory_usage( f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} before release", force=False) - self.get_param_coordinator(training=True).release_sub_module(sub_module) + self.get_param_coordinator().release_sub_module(sub_module) see_memory_usage( f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} after release", diff --git a/deepspeed/runtime/zero/partitioned_param_coordinator.py b/deepspeed/runtime/zero/partitioned_param_coordinator.py index 5780b2afd6de..49f477cc4a1b 100644 --- a/deepspeed/runtime/zero/partitioned_param_coordinator.py +++ b/deepspeed/runtime/zero/partitioned_param_coordinator.py @@ -18,6 +18,7 @@ from deepspeed.utils.debug import debug_module2name_id, debug_param2name_id from deepspeed.accelerator import get_accelerator import deepspeed.runtime.compiler as compiler +from deepspeed.runtime.compiler import is_compiling import logging @@ -92,7 +93,7 @@ def __init__( # keeps track of the number of submodules invoked so far. self.__step_id: int = 0 # network tracing mode - self.__trace_mode: ZeRoTraceMode = ZeRoTraceMode.RECORD + self.__trace_mode: ZeRoTraceMode = ZeRoTraceMode.INVALID # sequence of submodules/parameters in forward pass + backward pass self.__submodule_order: Iterable[Module] = [] self.__param_order: Iterable[__class__.__ParamInTrace] = [] @@ -188,6 +189,9 @@ def trace_prologue(self, sub_module: Module) -> None: @compiler.disable def record_module(self, sub_module: Module) -> None: """adds sub module to trace""" + if is_compiling(): + return + if not self.is_record_trace(): raise RuntimeError(f"attempted to record trace when status = {self.__trace_mode}") @@ -195,6 +199,8 @@ def record_module(self, sub_module: Module) -> None: self.__step_id_module_fetched_for[sub_module.id].append(self.__step_id) def record_parameters(self, sub_module: Module) -> None: + if is_compiling(): + return """adds sub module to trace""" if not self.is_record_trace(): raise RuntimeError(f"attempted to record trace when status = {self.__trace_mode}") @@ -209,8 +215,12 @@ def construct_parameter_trace_from_module_trace(self): for sub_module in self.__submodule_order: self.record_parameters(sub_module) + @compiler.disable def reset_step(self) -> None: """indicate that we have completed one fwd+bwd for the model""" + if is_compiling(): + return + self._clean_inflight_param_registry() if not self.is_complete_trace(): # not self.trace_complete: diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index 65460eb72a2f..2c0c9d498d13 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -593,8 +593,8 @@ def defragment(tensors: List[Tensor]) -> Tensor: return device_buffer - def _get_param_coordinator(self, training): - return self.parameter_offload.get_param_coordinator(training) + def _get_param_coordinator(self): + return self.parameter_offload.get_param_coordinator() def _configure_offloading(self, offload_optimizer_config, offload_param_config): ###################### offload optimizer setup ################################## @@ -1874,7 +1874,7 @@ def _pre_step(self): see_memory_usage(f"In step before checking overflow", force=False) print_rank_0("Finished Tracing at Beginning of Step") - self._get_param_coordinator(training=True).hierarchy = 0 + self._get_param_coordinator().hierarchy = 0 print_rank_0("Finished Tracing at Beginning of Step") @@ -2258,8 +2258,6 @@ def backward(self, loss, retain_graph=False): else: self.loss_scaler.backward(loss.float(), retain_graph=retain_graph) - self._get_param_coordinator(training=True).reset_step() - if self.swap_optimizer: self.optimizer_swapper.post_backward() diff --git a/tests/unit/runtime/zero/test_zero.py b/tests/unit/runtime/zero/test_zero.py index 7262a1b2c998..5dffd70aab68 100644 --- a/tests/unit/runtime/zero/test_zero.py +++ b/tests/unit/runtime/zero/test_zero.py @@ -1628,3 +1628,48 @@ def test_empty_param_groups(self, dtype, use_client_optimizer, empty_weight_grou optimizer=optimizer, config=config_dict, ) + + +class TestZero3SwitchModes(DistributedTest): + world_size = 2 + + @pytest.mark.parametrize("prefetch_ratio", [0.0, 0.5, 1.0]) + def test(self, prefetch_ratio, zero_stage=3): + + hidden_dim = 10 + model = SimpleModel(hidden_dim) + + prefetch_bucket_size = int(sum([p.numel() for p in model.parameters(recurse=True)]) * prefetch_ratio) + config_dict = { + "train_micro_batch_size_per_gpu": 2, + "gradient_accumulation_steps": 2, + "zero_optimization": { + "stage": zero_stage, + "stage3_prefetch_bucket_size": prefetch_bucket_size + }, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-3 + } + }, + "fp16": { + "enabled": True, + "initial_scale_power": 8 + } + } + + model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters()) + data_loader = random_dataloader(model=model, total_samples=16, hidden_dim=hidden_dim, device=model.device) + + for _ in range(3): + model.train() + for batch in data_loader: + loss = model(batch[0], batch[1]) + model.backward(loss) + model.step() + + model.eval() + with torch.no_grad(): + for batch in data_loader: + loss = model(batch[0], batch[1]) From d2a4718946b544ab5d4f334f05a4ace9670e3ddd Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 6 Nov 2024 10:57:12 -0800 Subject: [PATCH 008/112] Update yapf version (#6721) This update is needed to support eventually running on ubuntu-24.04 from GitHub, specifically because the python version is updated to 3.12 and results in the following error: `ModuleNotFoundError: No module named 'lib2to3'` since that package is deprecated. --- .pre-commit-config.yaml | 2 +- deepspeed/__init__.py | 4 ++-- deepspeed/autotuning/autotuner.py | 12 ++++++------ deepspeed/elasticity/elastic_agent.py | 4 ++-- deepspeed/module_inject/replace_module.py | 7 ++++--- deepspeed/runtime/config.py | 4 ++-- deepspeed/runtime/eigenvalue.py | 4 ++-- deepspeed/runtime/pipe/engine.py | 7 ++++--- deepspeed/runtime/utils.py | 4 ++-- tests/unit/runtime/zero/test_zero_context.py | 6 +++--- 10 files changed, 28 insertions(+), 26 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b89c872eefe5..e249411e4868 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -23,7 +23,7 @@ repos: - id: trailing-whitespace - repo: https://github.com/google/yapf - rev: v0.32.0 + rev: v0.40.0 hooks: - id: yapf diff --git a/deepspeed/__init__.py b/deepspeed/__init__.py index d8655299282f..de405dc40edb 100755 --- a/deepspeed/__init__.py +++ b/deepspeed/__init__.py @@ -165,8 +165,8 @@ def initialize(args=None, if hasattr(args, "deepscale_config") and args.deepscale_config is not None: logger.warning("************ --deepscale_config is deprecated, please use --deepspeed_config ************") if hasattr(args, "deepspeed_config"): - assert (args.deepspeed_config is - None), "Not sure how to proceed, we were given both a deepscale_config and deepspeed_config" + assert (args.deepspeed_config + is None), "Not sure how to proceed, we were given both a deepscale_config and deepspeed_config" args.deepspeed_config = args.deepscale_config args.deepscale_config = None diff --git a/deepspeed/autotuning/autotuner.py b/deepspeed/autotuning/autotuner.py index dfd195bc37eb..a72b3c951e97 100755 --- a/deepspeed/autotuning/autotuner.py +++ b/deepspeed/autotuning/autotuner.py @@ -248,8 +248,8 @@ def mp_size(self): return self.autotuning_config.mp_size def max_train_micro_batch_size_per_gpu(self): - if self.max_train_batch_size( - ) and self.max_train_batch_size() > 0: # if the user specifies a max_train_batch_size + if self.max_train_batch_size() and self.max_train_batch_size( + ) > 0: # if the user specifies a max_train_batch_size max_train_micro_batch_size = self.max_train_batch_size() * self.mp_size() // ( self.exp_num_gpus * self.exp_num_nodes) # gradient accumulation steps >=1 return min(self.autotuning_config.max_train_micro_batch_size_per_gpu, max_train_micro_batch_size) @@ -964,8 +964,8 @@ def get_min_max_micro_batch_size(self, stage, min_micro_batch_size, calculated_m low = mid + 1 self.update_records(tuning_space_name, exp, metric_val, 1) used_micro_batch_sizes.append(mid) - if prev_metric_val and ( - (metric_val - prev_metric_val) / prev_metric_val) < METRIC_PERCENT_DIFF_CONST: + if prev_metric_val and ((metric_val - prev_metric_val) / + prev_metric_val) < METRIC_PERCENT_DIFF_CONST: logger.info(f"performance plateaus at mbs = {low}") break prev_metric_val = metric_val @@ -1026,8 +1026,8 @@ def get_tuning_micro_batch_size_list(self, min_micro_batch_size, max_micro_batch # NUM_GPUS=$(( ${NUM_WORKERS} * ${NUM_GPUS_PER_WORKER} )) # DP_SIZE=$(( ${NUM_GPUS} / (${PP_SIZE} * ${MP_SIZE}) )) # GRAD_ACC_STEPS=$(( ${TARGET_GLOBAL_BATCH_SIZE} / (${BATCH_SIZE} * ${DP_SIZE}) )) - if self.max_train_batch_size( - ) and self.max_train_batch_size() > 0: # if the user specifies a max_train_batch_size + if self.max_train_batch_size() and self.max_train_batch_size( + ) > 0: # if the user specifies a max_train_batch_size max_train_batch_size_per_gpu = self.max_train_batch_size() * self.mp_size() // (self.exp_num_gpus * self.exp_num_nodes) else: diff --git a/deepspeed/elasticity/elastic_agent.py b/deepspeed/elasticity/elastic_agent.py index c6a69dd2a49f..8fd4293d312c 100644 --- a/deepspeed/elasticity/elastic_agent.py +++ b/deepspeed/elasticity/elastic_agent.py @@ -160,8 +160,8 @@ def _invoke_run(self, role: str = "default") -> RunResult: f" Waiting {self._exit_barrier_timeout} seconds for other agents to finish.") self._exit_barrier() return run_result - elif state in {WorkerState.UNHEALTHY, WorkerState.FAILED - } or len(participants) > len(rdzv_handler._state_holder.state.participants): + elif state in {WorkerState.UNHEALTHY, WorkerState.FAILED} or len(participants) > len( + rdzv_handler._state_holder.state.participants): if self._remaining_restarts > 0: log.info(f"[{role}] Worker group {state.name}. " f"{self._remaining_restarts}/{spec.max_restarts} attempts left;" diff --git a/deepspeed/module_inject/replace_module.py b/deepspeed/module_inject/replace_module.py index 8b1455f20c69..1c5745dcf168 100644 --- a/deepspeed/module_inject/replace_module.py +++ b/deepspeed/module_inject/replace_module.py @@ -496,9 +496,10 @@ def conv2d_parallel_shard_weights(model, rank, world_size): if not dist.is_initialized() or dist.get_rank() == 0: print("Saving tp-sharded checkpoints") torch.save( - OrderedDict({k: v - for k, v in dict(replaced_module.state_dict()).items() - if transformer_name not in k}), f'{config.save_mp_checkpoint_path}/{non_tp_ckpt_name}') + OrderedDict({ + k: v + for k, v in dict(replaced_module.state_dict()).items() if transformer_name not in k + }), f'{config.save_mp_checkpoint_path}/{non_tp_ckpt_name}') dtype_reprs = { torch.float32: 'float32', diff --git a/deepspeed/runtime/config.py b/deepspeed/runtime/config.py index 8be2f7ac4055..fb786f29722d 100755 --- a/deepspeed/runtime/config.py +++ b/deepspeed/runtime/config.py @@ -1012,8 +1012,8 @@ def _do_error_check(self): self.gradient_accumulation_steps), "DeepSpeedConfig: {} is not defined".format(GRADIENT_ACCUMULATION_STEPS) if self.zero_enabled: - assert (self.zero_optimization_stage <= - ZeroStageEnum.max_stage), "DeepSpeedConfig: Maximum supported ZeRO stage is {}".format( + assert (self.zero_optimization_stage + <= ZeroStageEnum.max_stage), "DeepSpeedConfig: Maximum supported ZeRO stage is {}".format( ZeroStageEnum.max_stage) if self.fp16_master_weights_and_gradients: diff --git a/deepspeed/runtime/eigenvalue.py b/deepspeed/runtime/eigenvalue.py index 36300eb904dd..a82d8b1d5c7a 100755 --- a/deepspeed/runtime/eigenvalue.py +++ b/deepspeed/runtime/eigenvalue.py @@ -114,8 +114,8 @@ def compute_eigenvalue(self, module, device=None, scale=1.0): eigenvalue_current, eigenvalue_previous = 1., 0. while (i < self.max_iter) and abs(eigenvalue_current) > 0 and (abs( - (eigenvalue_current - eigenvalue_previous) / eigenvalue_current) >= - self.tol): # test convergence criteria + (eigenvalue_current - eigenvalue_previous) / eigenvalue_current) + >= self.tol): # test convergence criteria eigenvalue_previous = eigenvalue_current Hv = torch.autograd.grad(grads, params, grad_outputs=v, only_inputs=True, retain_graph=True) diff --git a/deepspeed/runtime/pipe/engine.py b/deepspeed/runtime/pipe/engine.py index b75270cbd306..deb44c2e71eb 100644 --- a/deepspeed/runtime/pipe/engine.py +++ b/deepspeed/runtime/pipe/engine.py @@ -640,9 +640,10 @@ def _aggregate_total_loss(self): self.dp_group_loss = losses[0].clone().detach() agg_loss = losses[1].clone().detach() if additional_losses is not None: - self.agg_additional_losses = OrderedDict( - {name: losses[2 + i].clone().detach() - for i, name in enumerate(additional_losses.keys())}) + self.agg_additional_losses = OrderedDict({ + name: losses[2 + i].clone().detach() + for i, name in enumerate(additional_losses.keys()) + }) return agg_loss def set_dataloader(self, loader): diff --git a/deepspeed/runtime/utils.py b/deepspeed/runtime/utils.py index b9617d3e632f..f48adb58c9bf 100755 --- a/deepspeed/runtime/utils.py +++ b/deepspeed/runtime/utils.py @@ -257,8 +257,8 @@ def has_overflow(self, params, has_moe_params=None): elif self.mpu is not None: if self.deepspeed is not None: using_pipeline = hasattr(self.deepspeed, 'pipeline_enable_backward_allreduce') - if (using_pipeline and self.deepspeed.pipeline_enable_backward_allreduce is False) or ( - not using_pipeline and self.deepspeed.enable_backward_allreduce is False): + if (using_pipeline and self.deepspeed.pipeline_enable_backward_allreduce + is False) or (not using_pipeline and self.deepspeed.enable_backward_allreduce is False): dist.all_reduce(overflow_gpu, op=dist.ReduceOp.MAX, group=self.mpu.get_data_parallel_group()) dist.all_reduce(overflow_gpu, op=dist.ReduceOp.MAX, group=self.mpu.get_model_parallel_group()) elif self.deepspeed is not None and self.deepspeed.enable_backward_allreduce is False: diff --git a/tests/unit/runtime/zero/test_zero_context.py b/tests/unit/runtime/zero/test_zero_context.py index ec9e9e94aeaf..1d4fcd60022c 100644 --- a/tests/unit/runtime/zero/test_zero_context.py +++ b/tests/unit/runtime/zero/test_zero_context.py @@ -218,9 +218,9 @@ def test_throughput_calculation(self): engine.tput_timer.stop(global_step=global_step) duration = engine.tput_timer.end_time - engine.tput_timer.start_time # step elapsed time is reset after gradient accumulation steps - assert engine.tput_timer.step_elapsed_time == ( - 0 if engine.tput_timer.global_step_count != engine.tput_timer.start_step else current_duration + - duration) + assert engine.tput_timer.step_elapsed_time == (0 if engine.tput_timer.global_step_count + != engine.tput_timer.start_step else current_duration + + duration) assert engine.tput_timer.total_elapsed_time == total_duration + duration def test_ext_param_getattr(self): From 3beda32e94854e61a138c985e99bde2b3288b1d7 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 6 Nov 2024 15:17:48 -0800 Subject: [PATCH 009/112] Update flake8 version (#6722) This PR is useful for updating the flake8 checks we run, but is mostly needed to update flake8 so that it can run on newer versions of python which are included in newer ubuntu-latest versions from GitHub that we update to in #6717 --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e249411e4868..b5d8afa8e0b4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -65,7 +65,7 @@ repos: ] - repo: https://github.com/pycqa/flake8 - rev: 4.0.1 + rev: 5.0.4 hooks: - id: flake8 args: ['--config=.flake8'] From a1b0c35a1def4bfc20fc3eeb89d6f5831fbc4ae8 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 6 Nov 2024 20:37:52 -0800 Subject: [PATCH 010/112] Switch what versions of python are supported (#5676) Add support for testing compilation with python 3.11/3.12. Also add the dockerfiles used to build those images. --------- Co-authored-by: Michael Wyatt --- .github/workflows/python.yml | 4 +-- docker/gh-builder/Dockerfile.py311 | 35 +++++++++++++++++++++ docker/gh-builder/Dockerfile.py312 | 35 +++++++++++++++++++++ docker/{Dockerfile.rocm => rocm/Dockerfile} | 0 4 files changed, 72 insertions(+), 2 deletions(-) create mode 100644 docker/gh-builder/Dockerfile.py311 create mode 100644 docker/gh-builder/Dockerfile.py312 rename docker/{Dockerfile.rocm => rocm/Dockerfile} (100%) diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml index 3103e3f36e84..37b68f1dbe80 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/python.yml @@ -21,10 +21,10 @@ jobs: unit-tests: strategy: matrix: - pyVersion: ["3.7", "3.8", "3.9", "3.10"] + pyVersion: ["3.8", "3.9", "3.10", "3.11", "3.12"] fail-fast: false - runs-on: ubuntu-22.04 + runs-on: ubuntu-24.04 container: image: deepspeed/gh-builder:py${{ matrix.pyVersion }} diff --git a/docker/gh-builder/Dockerfile.py311 b/docker/gh-builder/Dockerfile.py311 new file mode 100644 index 000000000000..603fb614314f --- /dev/null +++ b/docker/gh-builder/Dockerfile.py311 @@ -0,0 +1,35 @@ +# Start with NGC container +FROM nvcr.io/nvidia/pytorch:24.03-py3 + +# Set noninteractive mode for apt-get +ARG DEBIAN_FRONTEND=noninteractive + +# Install necessary dependencies for building Python +RUN apt-get update && apt-get install -y \ + wget \ + build-essential \ + libssl-dev \ + zlib1g-dev \ + libbz2-dev \ + libreadline-dev \ + libsqlite3-dev \ + curl \ + libncursesw5-dev \ + libgdbm-dev \ + libc6-dev \ + libffi-dev \ + tk-dev \ + && rm -rf /var/lib/apt/lists/* + +# Download and install Python 3.11 +RUN wget https://www.python.org/ftp/python/3.11.9/Python-3.11.9.tgz \ + && tar xzf Python-3.11.9.tgz \ + && cd Python-3.11.9 \ + && ./configure --enable-optimizations \ + && make altinstall \ + && cd .. \ + && rm -rf Python-3.11.9 Python-3.11.9.tgz + +# Set Python 3.11 as the default Python version +RUN update-alternatives --install /usr/bin/python python /usr/local/bin/python3.11 1 \ + && update-alternatives --install /usr/bin/python3 python3 /usr/local/bin/python3.11 1 diff --git a/docker/gh-builder/Dockerfile.py312 b/docker/gh-builder/Dockerfile.py312 new file mode 100644 index 000000000000..a0a7193201d4 --- /dev/null +++ b/docker/gh-builder/Dockerfile.py312 @@ -0,0 +1,35 @@ +# Start with NGC container +FROM nvcr.io/nvidia/pytorch:24.03-py3 + +# Set noninteractive mode for apt-get +ARG DEBIAN_FRONTEND=noninteractive + +# Install necessary dependencies for building Python +RUN apt-get update && apt-get install -y \ + wget \ + build-essential \ + libssl-dev \ + zlib1g-dev \ + libbz2-dev \ + libreadline-dev \ + libsqlite3-dev \ + curl \ + libncursesw5-dev \ + libgdbm-dev \ + libc6-dev \ + libffi-dev \ + tk-dev \ + && rm -rf /var/lib/apt/lists/* + +# Download and install Python 3.12 +RUN wget https://www.python.org/ftp/python/3.12.5/Python-3.12.5.tgz \ + && tar xzf Python-3.12.5.tgz \ + && cd Python-3.12.5 \ + && ./configure --enable-optimizations \ + && make altinstall \ + && cd .. \ + && rm -rf Python-3.12.5 Python-3.12.5.tgz + +# Set Python 3.12 as the default Python version +RUN update-alternatives --install /usr/bin/python python /usr/local/bin/python3.12 1 \ + && update-alternatives --install /usr/bin/python3 python3 /usr/local/bin/python3.12 1 diff --git a/docker/Dockerfile.rocm b/docker/rocm/Dockerfile similarity index 100% rename from docker/Dockerfile.rocm rename to docker/rocm/Dockerfile From 057d25be6775105f4b9e1d41e6c21981a157c849 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Fri, 8 Nov 2024 08:34:20 -0800 Subject: [PATCH 011/112] Update version.txt after 0.15.4 release (#6731) **Auto-generated PR to update version.txt after a DeepSpeed release** Released version - 0.15.4 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 7ffdfa1cad65..1282fff53bfa 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.15.4 +0.15.5 From 08555662282f624f1258d45617aefef1577a4dd3 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 11 Nov 2024 06:22:08 -0800 Subject: [PATCH 012/112] Update GH hosted workflows to 24.04 (#6717) `ubuntu-latset` is moving to be 24.04, so we should test updating as well to ensure it doesn't break any of our workflows. --- .github/workflows/cpu-torch-latest.yml | 2 +- .github/workflows/no-torch.yml | 3 ++- .github/workflows/nv-pre-compile-ops.yml | 2 +- .github/workflows/release.yml | 2 +- 4 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.github/workflows/cpu-torch-latest.yml b/.github/workflows/cpu-torch-latest.yml index 0de6832b37c1..0125fa50bc14 100644 --- a/.github/workflows/cpu-torch-latest.yml +++ b/.github/workflows/cpu-torch-latest.yml @@ -19,7 +19,7 @@ concurrency: jobs: unit-tests: - runs-on: ubuntu-22.04 + runs-on: ubuntu-24.04 steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/no-torch.yml b/.github/workflows/no-torch.yml index eb3ac9b03161..1a13c0f3f4f1 100644 --- a/.github/workflows/no-torch.yml +++ b/.github/workflows/no-torch.yml @@ -19,7 +19,7 @@ permissions: jobs: unit-tests: - runs-on: ubuntu-22.04 + runs-on: ubuntu-24.04 steps: - uses: actions/checkout@v4 @@ -30,6 +30,7 @@ jobs: - name: Python environment run: | pip uninstall torch --yes + pip install setuptools pip list - name: Build deepspeed diff --git a/.github/workflows/nv-pre-compile-ops.yml b/.github/workflows/nv-pre-compile-ops.yml index 72ba8abbd95d..fc810bc190d0 100644 --- a/.github/workflows/nv-pre-compile-ops.yml +++ b/.github/workflows/nv-pre-compile-ops.yml @@ -21,7 +21,7 @@ concurrency: jobs: unit-tests: - runs-on: ubuntu-22.04 + runs-on: ubuntu-24.04 container: image: deepspeed/gh-builder:ubuntu1804-py38-torch1131-cu116 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 02881ef12f39..eb763792f0c4 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -7,7 +7,7 @@ on: jobs: deploy: - runs-on: ubuntu-22.04 + runs-on: ubuntu-24.04 environment: release-env steps: From b7e2ff50807975934c3b181457a95df424d115c3 Mon Sep 17 00:00:00 2001 From: Olatunji Ruwase Date: Mon, 11 Nov 2024 14:51:10 -0500 Subject: [PATCH 013/112] Add COMMITTER file (#6741) Add COMMITTER file --- COMMITTERS.md | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 COMMITTERS.md diff --git a/COMMITTERS.md b/COMMITTERS.md new file mode 100644 index 000000000000..bcb8579bf1f7 --- /dev/null +++ b/COMMITTERS.md @@ -0,0 +1,9 @@ +# DeepSpeed TSC Committers # + +| Name | GitHub ID | Affiliation +|--- | ---- | --- | +| Olatunji Ruwase | [tjruwase](https://github.com/tjruwase) | Microsoft | +| Logan Adams | [loadams](https://github.com/loadams) | Microsoft | +| Masahiro Tanaka | [tohtana](https://github.com/tohtana) | Microsoft | +| Jeff Rasley | [jeffra](https://github.com/jeffra) | SnowFlake | +| Minjia Zhang | [minjiazhang](https://github.com/minjiazhang) | UIUC | From b45ca2635495997bb294f4b9b9dbcb23db0dcac6 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 11 Nov 2024 13:26:41 -0800 Subject: [PATCH 014/112] Update AMD apex version (#6739) --- .github/workflows/amd-mi200.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/amd-mi200.yml b/.github/workflows/amd-mi200.yml index ea8d2f5f806f..6e8d5847835d 100644 --- a/.github/workflows/amd-mi200.yml +++ b/.github/workflows/amd-mi200.yml @@ -48,8 +48,6 @@ jobs: - name: Install (ROCm) apex run: | git clone https://github.com/ROCmSoftwarePlatform/apex.git - cd apex - git checkout torch_2.1_higher CURRENT_VER=$(git rev-parse HEAD) INSTALLED_VER=$(cat /blob/amd-apex/.venv_installed_version) if [[ "$CURRENT_VER" != "$INSTALLED_VER" ]]; then From 99e9cbed1663b13bbc240d79946913bfe430ffb5 Mon Sep 17 00:00:00 2001 From: Xinyu Lian Date: Mon, 11 Nov 2024 17:31:45 -0600 Subject: [PATCH 015/112] Fix Type Name Inconsistency & Typo in cpu_adam (#6732) There is a typing error & inconsistency in cpu-adam code, while not affecting functionality, impacts code readability. Specifically, the type name `ds_params_percision_t` contains a typo ('percision'), whereas the related type name `ds_state_precision_t` is spelled correctly. I think it is beneficial to fix this typo&inconsistency to improve code readability, maintainability and further development. I have tested the corrected version of cpu_adam, and it compiles and runs successfully. Compilation Log: image Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase --- csrc/adagrad/cpu_adagrad.cpp | 30 +++++++++++++++--------------- csrc/adam/cpu_adam_impl.cpp | 30 +++++++++++++++--------------- csrc/includes/cpu_adagrad.h | 20 ++++++++++---------- csrc/includes/cpu_adam.h | 20 ++++++++++---------- csrc/includes/cpu_lion.h | 20 ++++++++++---------- csrc/lion/cpu_lion_impl.cpp | 30 +++++++++++++++--------------- 6 files changed, 75 insertions(+), 75 deletions(-) diff --git a/csrc/adagrad/cpu_adagrad.cpp b/csrc/adagrad/cpu_adagrad.cpp index 5790e79e2bc2..e276ad0856dd 100644 --- a/csrc/adagrad/cpu_adagrad.cpp +++ b/csrc/adagrad/cpu_adagrad.cpp @@ -17,9 +17,9 @@ static std::unordered_map> s_optimizers; // C++ interface -template -void Adagrad_Optimizer::Step_1(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Adagrad_Optimizer::Step_1(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg_sq, size_t _param_size) { @@ -56,9 +56,9 @@ void Adagrad_Optimizer::Step_1(ds_params_percision_t* _params, } } -template -void Adagrad_Optimizer::Step_4(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Adagrad_Optimizer::Step_4(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg_sq, size_t _param_size) { @@ -104,9 +104,9 @@ int create_adagrad_optimizer(int optimizer_id, return 0; } -template -void Adagrad_Optimizer::Step_8(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Adagrad_Optimizer::Step_8(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg_sq, size_t _param_size) { @@ -121,15 +121,15 @@ void Adagrad_Optimizer::Step_8(ds_params_percision_t* _params, (_param_size - rounded_size)); } -template +template void step_invoker(std::shared_ptr opt, void* _params, void* grads, void* _exp_avg_sq, size_t _param_size) { - opt->Step_8((ds_params_percision_t*)(_params), - (ds_params_percision_t*)(grads), + opt->Step_8((ds_params_precision_t*)(_params), + (ds_params_precision_t*)(grads), (ds_state_precision_t*)(_exp_avg_sq), _param_size); } @@ -139,12 +139,12 @@ std::map, invokers; // Fill map with template functions for each type -template +template void create_invoker() { - invokers[std::tuple(c10::CppTypeToScalarType(), + invokers[std::tuple(c10::CppTypeToScalarType(), c10::CppTypeToScalarType())] = - step_invoker; + step_invoker; } struct InvokerInitializer { InvokerInitializer() diff --git a/csrc/adam/cpu_adam_impl.cpp b/csrc/adam/cpu_adam_impl.cpp index 15d4e74d69d5..465aae7b9a34 100644 --- a/csrc/adam/cpu_adam_impl.cpp +++ b/csrc/adam/cpu_adam_impl.cpp @@ -18,9 +18,9 @@ static std::unordered_map> s_optimizers; // C++ interface -template -void Adam_Optimizer::Step_1(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Adam_Optimizer::Step_1(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, ds_state_precision_t* _exp_avg_sq, size_t _param_size) @@ -67,9 +67,9 @@ void Adam_Optimizer::Step_1(ds_params_percision_t* _params, } } -template -void Adam_Optimizer::Step_4(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Adam_Optimizer::Step_4(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, ds_state_precision_t* _exp_avg_sq, size_t _param_size) @@ -126,9 +126,9 @@ int create_adam_optimizer(int optimizer_id, return 0; } -template -void Adam_Optimizer::Step_8(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Adam_Optimizer::Step_8(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, ds_state_precision_t* _exp_avg_sq, size_t _param_size) @@ -145,7 +145,7 @@ void Adam_Optimizer::Step_8(ds_params_percision_t* _params, (_param_size - rounded_size)); } -template +template void step_invoker(std::shared_ptr opt, void* _params, void* grads, @@ -153,8 +153,8 @@ void step_invoker(std::shared_ptr opt, void* _exp_avg_sq, size_t _param_size) { - opt->Step_8((ds_params_percision_t*)(_params), - (ds_params_percision_t*)(grads), + opt->Step_8((ds_params_precision_t*)(_params), + (ds_params_precision_t*)(grads), (ds_state_precision_t*)(_exp_avg), (ds_state_precision_t*)(_exp_avg_sq), _param_size); @@ -165,12 +165,12 @@ std::map, invokers; // Fill map with template functions for each type -template +template void create_invoker() { - invokers[std::tuple(c10::CppTypeToScalarType(), + invokers[std::tuple(c10::CppTypeToScalarType(), c10::CppTypeToScalarType())] = - step_invoker; + step_invoker; } struct InvokerInitializer { InvokerInitializer() diff --git a/csrc/includes/cpu_adagrad.h b/csrc/includes/cpu_adagrad.h index c06d3a6b35e9..6f500250f033 100644 --- a/csrc/includes/cpu_adagrad.h +++ b/csrc/includes/cpu_adagrad.h @@ -14,9 +14,9 @@ #include "simd.h" #define STEP(SPAN) \ - template \ - void Step_##SPAN(ds_params_percision_t* _params, \ - ds_params_percision_t* grads, \ + template \ + void Step_##SPAN(ds_params_precision_t* _params, \ + ds_params_precision_t* grads, \ ds_state_precision_t* _exp_avg_sq, \ size_t _param_size); @@ -28,10 +28,10 @@ class Adagrad_Optimizer { } ~Adagrad_Optimizer() {} #if defined(__AVX512__) or defined(__AVX256__) - template + template void Step_AVX(size_t* rounded_size, - ds_params_percision_t* _params, - ds_params_percision_t* grads, + ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg_sq, size_t param_size); #endif @@ -61,15 +61,15 @@ class Adagrad_Optimizer { }; #if defined(__AVX512__) or defined(__AVX256__) -template +template void Adagrad_Optimizer::Step_AVX(size_t* rounded_size, - ds_params_percision_t* _params, - ds_params_percision_t* grads, + ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg_sq, size_t _param_size) { #if !defined(__AVX512__) - if (std::is_same_v || + if (std::is_same_v || std::is_same_v) { return; } diff --git a/csrc/includes/cpu_adam.h b/csrc/includes/cpu_adam.h index faf99020aee5..a7db6fda3705 100644 --- a/csrc/includes/cpu_adam.h +++ b/csrc/includes/cpu_adam.h @@ -14,9 +14,9 @@ #include "simd.h" #define STEP(SPAN) \ - template \ - void Step_##SPAN(ds_params_percision_t* _params, \ - ds_params_percision_t* grads, \ + template \ + void Step_##SPAN(ds_params_precision_t* _params, \ + ds_params_precision_t* grads, \ ds_state_precision_t* _exp_avg, \ ds_state_precision_t* _exp_avg_sq, \ size_t _param_size); @@ -43,10 +43,10 @@ class Adam_Optimizer { ~Adam_Optimizer() {} #if defined(__AVX512__) or defined(__AVX256__) - template + template void Step_AVX(size_t* rounded_size, - ds_params_percision_t* _params, - ds_params_percision_t* grads, + ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, ds_state_precision_t* _exp_avg_sq, size_t param_size); @@ -106,16 +106,16 @@ class Adam_Optimizer { }; #if defined(__AVX512__) or defined(__AVX256__) -template +template void Adam_Optimizer::Step_AVX(size_t* rounded_size, - ds_params_percision_t* _params, - ds_params_percision_t* grads, + ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, ds_state_precision_t* _exp_avg_sq, size_t _param_size) { #if !defined(__AVX512__) - if (std::is_same_v || + if (std::is_same_v || std::is_same_v) { return; } diff --git a/csrc/includes/cpu_lion.h b/csrc/includes/cpu_lion.h index 62b304923222..beaf357a3211 100644 --- a/csrc/includes/cpu_lion.h +++ b/csrc/includes/cpu_lion.h @@ -14,9 +14,9 @@ #include "simd.h" #define STEP(SPAN) \ - template \ - void Step_##SPAN(ds_params_percision_t* _params, \ - ds_params_percision_t* grads, \ + template \ + void Step_##SPAN(ds_params_precision_t* _params, \ + ds_params_precision_t* grads, \ ds_state_precision_t* _exp_avg, \ size_t _param_size); @@ -32,10 +32,10 @@ class Lion_Optimizer { ~Lion_Optimizer() {} #if defined(__AVX512__) or defined(__AVX256__) - template + template void Step_AVX(size_t* rounded_size, - ds_params_percision_t* _params, - ds_params_percision_t* grads, + ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, size_t param_size); #endif @@ -67,15 +67,15 @@ class Lion_Optimizer { }; #if defined(__AVX512__) or defined(__AVX256__) -template +template void Lion_Optimizer::Step_AVX(size_t* rounded_size, - ds_params_percision_t* _params, - ds_params_percision_t* grads, + ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, size_t _param_size) { #if !defined(__AVX512__) - if (std::is_same_v || + if (std::is_same_v || std::is_same_v) { return; } diff --git a/csrc/lion/cpu_lion_impl.cpp b/csrc/lion/cpu_lion_impl.cpp index 85896ba86e19..6a98162314f9 100644 --- a/csrc/lion/cpu_lion_impl.cpp +++ b/csrc/lion/cpu_lion_impl.cpp @@ -19,9 +19,9 @@ static std::unordered_map> s_optimizers; // C++ interface -template -void Lion_Optimizer::Step_1(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Lion_Optimizer::Step_1(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, size_t _param_size) { @@ -64,9 +64,9 @@ void Lion_Optimizer::Step_1(ds_params_percision_t* _params, } } -template -void Lion_Optimizer::Step_4(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Lion_Optimizer::Step_4(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, size_t _param_size) { @@ -117,9 +117,9 @@ int create_lion_optimizer(int optimizer_id, return 0; } -template -void Lion_Optimizer::Step_8(ds_params_percision_t* _params, - ds_params_percision_t* grads, +template +void Lion_Optimizer::Step_8(ds_params_precision_t* _params, + ds_params_precision_t* grads, ds_state_precision_t* _exp_avg, size_t _param_size) { @@ -134,15 +134,15 @@ void Lion_Optimizer::Step_8(ds_params_percision_t* _params, (_param_size - rounded_size)); } -template +template void step_invoker(std::shared_ptr opt, void* _params, void* grads, void* _exp_avg, size_t _param_size) { - opt->Step_8((ds_params_percision_t*)(_params), - (ds_params_percision_t*)(grads), + opt->Step_8((ds_params_precision_t*)(_params), + (ds_params_precision_t*)(grads), (ds_state_precision_t*)(_exp_avg), _param_size); } @@ -152,12 +152,12 @@ std::map, invokers; // Fill map with template functions for each type -template +template void create_invoker() { - invokers[std::tuple(c10::CppTypeToScalarType(), + invokers[std::tuple(c10::CppTypeToScalarType(), c10::CppTypeToScalarType())] = - step_invoker; + step_invoker; } struct InvokerInitializer { InvokerInitializer() From fabab197f747a4ab3ac9c2a7bdd97b6aaa1db698 Mon Sep 17 00:00:00 2001 From: Chengming Zhang Date: Mon, 11 Nov 2024 17:55:09 -0600 Subject: [PATCH 016/112] Add Domino code (#6733) add domino code Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/__init__.py | 2 +- deepspeed/runtime/domino/__init__.py | 4 + deepspeed/runtime/domino/transformer.py | 518 ++++++++++++++++++++++++ 3 files changed, 523 insertions(+), 1 deletion(-) create mode 100644 deepspeed/runtime/domino/__init__.py create mode 100644 deepspeed/runtime/domino/transformer.py diff --git a/deepspeed/__init__.py b/deepspeed/__init__.py index de405dc40edb..a8d15cd5332b 100755 --- a/deepspeed/__init__.py +++ b/deepspeed/__init__.py @@ -42,7 +42,7 @@ from .utils import log_dist, OnDevice, logger from .comm.comm import init_distributed -from .runtime import zero +from .runtime import zero, domino from .runtime.compiler import is_compile_supported from .pipe import PipelineModule diff --git a/deepspeed/runtime/domino/__init__.py b/deepspeed/runtime/domino/__init__.py new file mode 100644 index 000000000000..208299fb8c50 --- /dev/null +++ b/deepspeed/runtime/domino/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team diff --git a/deepspeed/runtime/domino/transformer.py b/deepspeed/runtime/domino/transformer.py new file mode 100644 index 000000000000..8eb95e49c29d --- /dev/null +++ b/deepspeed/runtime/domino/transformer.py @@ -0,0 +1,518 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import torch +import torch.nn.functional as F +from torch.nn.parameter import Parameter +import deepspeed +from deepspeed import comm as dist +from deepspeed.accelerator import get_accelerator + + +def is_rank_0(): + if dist.get_rank() == 0: + return True + + +class DominoModule(torch.nn.Module): + """extensions of torch Module.""" + + def __init__(self, ): + super(DominoModule, self).__init__() + + +import enum + + +class LayerType(enum.Enum): + encoder = 1 + decoder = 2 + + +class AttnType(enum.Enum): + self_attn = 1 + cross_attn = 2 + + +class AttnMaskType(enum.Enum): + padding = 1 + causal = 2 + + +class ModelType(enum.Enum): + encoder_or_decoder = 1 + encoder_and_decoder = 2 + + +handle_dic = {} + + +def no_oper(input_, dic_, h_id): + return NoOper.apply(input_, dic_, h_id) + + +class NoOper(torch.autograd.Function): + + @staticmethod + def symbolic(graph, input_, handle_dic, h_id): + return input_ + + @staticmethod + def forward(ctx, input_, handle_dic, h_id): + ctx.handle_dic = handle_dic + ctx.h_id = h_id + return input_ + + @staticmethod + def backward(ctx, grad_output): + handle = ctx.handle_dic[ctx.h_id] + handle.wait() + return grad_output, None, None + + +def copy_to_tensor_model_parallel_region_a(mpu, input_, dic_, h_id): + return _CopyToModelParallelRegionA.apply(mpu, input_, dic_, h_id) + + +class _CopyToModelParallelRegionA(torch.autograd.Function): + """Pass the input to the model parallel region.""" + + @staticmethod + def symbolic(graph, mpu, input_, handle_dic, h_id): + return input_ + + @staticmethod + def forward(ctx, mpu, input_, handle_dic, h_id): + ctx.mpu = mpu + ctx.handle_dic = handle_dic + ctx.h_id = h_id + return input_ + + @staticmethod + def backward(ctx, grad_output): + # Bypass the function if we are using only 1 GPU. + if ctx.mpu.get_tensor_model_parallel_world_size() == 1: + return grad_output + + # Async All-reduce. + handle = deepspeed.comm.all_reduce(grad_output, group=ctx.mpu.get_tensor_model_parallel_group(), async_op=True) + ctx.handle_dic[ctx.h_id] = handle + return None, grad_output, None, None + + +class CoreAttention(DominoModule): + + def __init__(self, config, layer_number, mpu, attn_mask_type=AttnMaskType.causal): + super(CoreAttention, self).__init__() + + self.layer_number = max(1, layer_number) + self.att_dropout_p = config.attention_dropout + self.is_causal = True + projection_size = config.kv_channels * config.num_attention_heads + world_size = mpu.get_tensor_model_parallel_world_size() + self.hidden_size_per_partition = projection_size // world_size + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + # attn_mask is None when is_causal=True + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, + key_layer, + value_layer, + attn_mask=None, + dropout_p=self.att_dropout_p, + is_causal=True, + scale=None) + + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + \ + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class ShardedAttention(DominoModule): + """Sharded self-attention layer class. + Only support self attention and causal attention mask + """ + + def __init__(self, + config, + layer_number, + mpu, + ColumnParallelLinear, + RowParallelLinearNoComm, + apply_rotary_pos_emb, + attention_type=AttnType.self_attn, + attn_mask_type=AttnMaskType.causal): + super(ShardedAttention, self).__init__() + self.layer_number = max(1, layer_number) + self.attention_type = attention_type + self.attn_mask_type = attn_mask_type + self.params_dtype = config.params_dtype + self.apply_rotary_pos_emb = apply_rotary_pos_emb + + query_projection_size = config.kv_channels * config.num_attention_heads + kv_projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + world_size = mpu.get_tensor_model_parallel_world_size() + self.hidden_size_per_attention_head = query_projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads // world_size + + self.query_key_value = ColumnParallelLinear(config.hidden_size, + query_projection_size + 2 * kv_projection_size, + config=config, + init_method=config.init_method, + bias=config.add_bias_linear, + gather_output=False) + + self.core_attention = CoreAttention(config, self.layer_number, mpu, self.attn_mask_type) + + self.dense = RowParallelLinearNoComm(query_projection_size, + config.hidden_size, + config=config, + init_method=config.output_layer_init_method, + bias=config.add_bias_linear, + input_is_parallel=True, + skip_bias_add=True) + + def forward(self, hidden_states, attention_mask, rotary_pos_emb=None): + # hidden_states: [s, b, h] + + # Query, Key, and Value + # Attention heads [s, b, h] --> [s, b, np * 3 * hn)] + mixed_x_layer, _ = self.query_key_value(hidden_states) + + # [s, b, np * 3 * hn] --> [s, b, np, 3 * hn] + new_tensor_shape = mixed_x_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head, + ) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [s, b, np, 3 * hn] -> [b, np, s, 3*hn] + mixed_x_layer = mixed_x_layer.permute(1, 2, 0, 3).contiguous() + + # [s, b, np, 3 * hn] --> [s, b, np, hn], [s, b, np, hn], [s, b, np, hn] + (query_layer, key_layer, value_layer) = torch.split(mixed_x_layer, [ + self.hidden_size_per_attention_head, self.hidden_size_per_attention_head, + self.hidden_size_per_attention_head + ], + dim=3) + # [s, b, np, np * hn] -> [s, b, np, hn] + query_layer = query_layer.view(query_layer.size(0), query_layer.size(1), -1, + self.hidden_size_per_attention_head) + + # apply rotary embedding + if rotary_pos_emb is not None: + if isinstance(rotary_pos_emb, tuple): + rotary_pos_emb = rotary_pos_emb + else: + rotary_pos_emb = ((rotary_pos_emb, ) * 2) + q_pos_emb, k_pos_emb = rotary_pos_emb + query_layer = self.apply_rotary_pos_emb(query_layer, q_pos_emb) + key_layer = self.apply_rotary_pos_emb(key_layer, k_pos_emb) + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # Output. [s, b, h] + output, bias = self.dense(context_layer) + + return output, bias + + +class DominoTransformerLayer(DominoModule): + """A domino single transformer layer. + [s, b, h] -> [s, b, h] + """ + + def __init__(self, + config, + layer_number, + mpu, + fused_layer_norm, + _initialize_affine_weight_gpu, + ColumnParallelLinear, + RowParallelLinearNoComm, + apply_rotary_pos_emb, + bias_dropout_add_fused_train, + bias_dropout_add_fused_inference, + skip_bias_add=True, + layer_type=LayerType.encoder, + self_attn_mask_type=AttnMaskType.causal, + drop_path_rate=0., + output_bias=None): + super(DominoTransformerLayer, self).__init__() + + self.llama_model = config.llama_model + self.layer_number = layer_number + self.layer_type = layer_type + self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + self.bias_dropout_add_fused_train = bias_dropout_add_fused_train + self.bias_dropout_add_fused_inference = bias_dropout_add_fused_inference + self.mpu = mpu + self.output_bias = output_bias + + # Layernorm on the input data. + self.input_layernorm = fused_layer_norm(config.hidden_size, + eps=config.layernorm_epsilon, + no_persist_layer_norm=config.no_persist_layer_norm) + + # Self attention. + self.self_attention = ShardedAttention(config, + layer_number, + mpu, + ColumnParallelLinear, + RowParallelLinearNoComm, + apply_rotary_pos_emb, + attention_type=AttnType.self_attn, + attn_mask_type=self_attn_mask_type) + + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = fused_layer_norm(config.hidden_size, + eps=config.layernorm_epsilon, + no_persist_layer_norm=config.no_persist_layer_norm) + + # ------------ init mlp start ------------ + init_method = config.init_method + output_layer_init_method = config.output_layer_init_method + self.add_bias = config.add_bias_linear + self.skip_bias_add = skip_bias_add + + ffn_hidden_size = config.ffn_hidden_size + if config.gated_linear_unit: + ffn_hidden_size *= 2 + self.output_size_c = config.ffn_hidden_size + self.input_size_c = config.hidden_size + self.input_size_r = config.ffn_hidden_size + self.output_size_r = self.input_size_c + + world_size = mpu.get_tensor_model_parallel_world_size() + self.output_size_per_partition = self.output_size_c // world_size + self.input_size_per_partition = self.input_size_r // world_size + + # Initialize weight. + self.weight_c = Parameter( + torch.empty(self.output_size_per_partition, + self.input_size_c, + device=get_accelerator().current_device_name(), + dtype=config.params_dtype)) + self.weight_r = Parameter( + torch.empty(self.output_size_r, + self.input_size_per_partition, + device=get_accelerator().current_device_name(), + dtype=config.params_dtype)) + + if config.perform_initialization: + _initialize_affine_weight_gpu(self.weight_c, init_method, partition_dim=0, stride=1) + + _initialize_affine_weight_gpu(self.weight_r, output_layer_init_method, partition_dim=1, stride=1) + + if self.add_bias: + self.bias_c = Parameter( + torch.empty(self.output_size_per_partition, + device=get_accelerator().current_device_name(), + dtype=config.params_dtype)) + self.bias_r = Parameter( + torch.empty(self.output_size_r, + device=get_accelerator().current_device_name(), + dtype=config.params_dtype)) + if config.perform_initialization: + with torch.no_grad(): + self.bias_c.zero_() + self.bias_r.zero_() + else: + self.register_parameter('bias_c', None) + self.register_parameter('bias_r', None) + + if config.swiglu: + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.mlp_activation_func = swiglu + else: + self.mlp_activation_func = F.gelu + # ------------ init mlp end ------------ + + def forward(self, hidden_states, attention_mask, rotary_pos_emb=None): + # hidden_states: [s, b, h] + hidden_states0, hidden_states1 = hidden_states + + layernorm_output0 = self.input_layernorm(hidden_states0) + layernorm_output1 = self.input_layernorm(hidden_states1) + + if not self.llama_model: + rotary_pos_emb = None + + attention_output0, attention_bias0 = \ + self.self_attention( + layernorm_output0, + attention_mask, + rotary_pos_emb=rotary_pos_emb) + handle0 = deepspeed.comm.all_reduce(attention_output0, + group=self.mpu.get_tensor_model_parallel_group(), + async_op=True) + + attention_output1, attention_bias1 = \ + self.self_attention( + layernorm_output1, + attention_mask, + rotary_pos_emb=rotary_pos_emb) + handle1 = deepspeed.comm.all_reduce(attention_output1, + group=self.mpu.get_tensor_model_parallel_group(), + async_op=True) + handle0.wait() + + # Residual0 connection. + if self.apply_residual_connection_post_layernorm: + residual0 = layernorm_output0 + else: + residual0 = hidden_states0 + + if self.training: + bias_dropout_add_func = self.bias_dropout_add_fused_train + else: + bias_dropout_add_func = self.bias_dropout_add_fused_inference + if attention_bias0 is not None: + attention_bias0 = attention_bias0.expand_as(residual0) + layernorm_input0 = bias_dropout_add_func(attention_output0, attention_bias0, residual0, self.hidden_dropout) + + layernorm_output0 = self.post_attention_layernorm(layernorm_input0) + layernorm_output0 = no_oper(layernorm_output0, handle_dic, f'{self.layer_number}_0') + + # Residual1 connection. + if self.apply_residual_connection_post_layernorm: + residual1 = layernorm_output1 + else: + residual1 = hidden_states1 + + if attention_bias1 is not None: + attention_bias1 = attention_bias1.expand_as(residual1) + layernorm_input1 = bias_dropout_add_func(attention_output1, attention_bias1, residual1, self.hidden_dropout) + + layernorm_output1 = self.post_attention_layernorm(layernorm_input1) + layernorm_output1 = no_oper(layernorm_output1, handle_dic, f'{self.layer_number}_1') + + # ------------ explicit mlp start ------------ + bias_c = self.bias_c if not self.skip_bias_add else None + + input0 = copy_to_tensor_model_parallel_region_a(self.mpu, layernorm_output0, handle_dic, + f'{self.layer_number}_0') + # Batch0 Matrix multiply. + output0 = torch.matmul(input0, self.weight_c.t()) + if bias_c is not None: + output0 = output0 + bias_c + output0 = self.mlp_activation_func(output0) + output0 = torch.matmul(output0, self.weight_r.t()) + handle2 = deepspeed.comm.all_reduce(output0, group=self.mpu.get_tensor_model_parallel_group(), async_op=True) + + handle1.wait() + + input1 = copy_to_tensor_model_parallel_region_a(self.mpu, layernorm_output1, handle_dic, + f'{self.layer_number}_1') + # Batch1 Matrix multiply. + output1 = torch.matmul(input1, self.weight_c.t()) + output1 = self.mlp_activation_func(output1) + if bias_c is not None: + output1 = output1 + bias_c + output1 = torch.matmul(output1, self.weight_r.t()) + deepspeed.comm.all_reduce(output1, group=self.mpu.get_tensor_model_parallel_group()) + + handle2.wait() + + output0 = output0 + self.bias_r if self.bias_r is not None else output0 + output1 = output1 + self.bias_r if self.bias_r is not None else output1 + output_bias = self.output_bias + mlp_output0, mlp_output1, mlp_bias0, mlp_bias1 = output0, output1, output_bias, output_bias + # ------------ explicit mlp end ------------ + + if self.apply_residual_connection_post_layernorm: + residual0 = layernorm_output0 + residual1 = layernorm_output1 + else: + residual0 = layernorm_input0 + residual1 = layernorm_input1 + + if mlp_bias0 is not None: + mlp_bias0 = mlp_bias0.expand_as(residual0) + mlp_bias1 = mlp_bias1.expand_as(residual1) + output0 = bias_dropout_add_func(mlp_output0, mlp_bias0, residual0, self.hidden_dropout) + output1 = bias_dropout_add_func(mlp_output1, mlp_bias1, residual1, self.hidden_dropout) + + return output0, output1 + + +class DominoTransformer(DominoModule): + """Transformer class.""" + + def __init__(self, + config, + model_type, + mpu, + fused_layer_norm, + _initialize_affine_weight_gpu, + ColumnParallelLinear, + RowParallelLinearNoComm, + apply_rotary_pos_emb, + bias_dropout_add_fused_train, + bias_dropout_add_fused_inference, + layer_type=LayerType.encoder, + self_attn_mask_type=AttnMaskType.causal, + pre_process=True, + post_process=True, + post_layer_norm=True, + drop_path_rate=0.0): + super(DominoTransformer, self).__init__() + + self.layer_type = layer_type + self.model_type = model_type + self.post_process = post_process + self.post_layer_norm = post_layer_norm + self.num_layers = config.num_layers + self.drop_path_rate = drop_path_rate + self.drop_path_rates = [rate.item() for rate in torch.linspace(0, self.drop_path_rate, config.num_layers)] + + def build_layer(layer_number): + return DominoTransformerLayer(config, + layer_number, + mpu, + fused_layer_norm, + _initialize_affine_weight_gpu, + ColumnParallelLinear, + RowParallelLinearNoComm, + apply_rotary_pos_emb, + bias_dropout_add_fused_train, + bias_dropout_add_fused_inference, + layer_type=layer_type, + self_attn_mask_type=self_attn_mask_type, + drop_path_rate=self.drop_path_rates[layer_number - 1]) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_process and self.post_layer_norm: + self.final_layernorm = fused_layer_norm(config.hidden_size, + eps=config.layernorm_epsilon, + no_persist_layer_norm=config.no_persist_layer_norm) + + def forward(self, hidden_states, attention_mask, rotary_pos_emb=None): + # hidden_states: [s, b, h] + + for index in range(self.num_layers): + layer = self.layers[index] + hidden_states = layer(hidden_states, attention_mask, rotary_pos_emb) + + hidden_states0, hidden_states1 = hidden_states + if self.post_process and self.post_layer_norm: + hidden_states0 = self.final_layernorm(hidden_states0) + hidden_states1 = self.final_layernorm(hidden_states1) + + return hidden_states0, hidden_states1 From 73d974ee640a95a594be95bb68af00fd77e44409 Mon Sep 17 00:00:00 2001 From: Hongwei Chen <33092912+hwchen2017@users.noreply.github.com> Date: Tue, 12 Nov 2024 05:01:31 -0800 Subject: [PATCH 017/112] Add data type check for bf16 (#6742) Add data type check for bf16 to fix #6723 --- deepspeed/runtime/engine.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 05bb23e8ddd9..b639ef6a1456 100755 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -1077,7 +1077,10 @@ def _supported_optims(self): # Validate configuration based on command line arguments def _do_sanity_check(self): if self.fp16_enabled() and not get_accelerator().is_fp16_supported(): - raise ValueError("Type fp16 is not supported.") + raise ValueError("Type fp16 is not supported on your device.") + + if self.bfloat16_enabled() and not get_accelerator().is_bf16_supported(): + raise ValueError("Type bf16 is not supported on your device.") expected_optim_types = self._supported_optims() expected_optim_types += [type(None), Callable] From 7af3a4beb5bf99517bb2d51b450861ca54bed8d3 Mon Sep 17 00:00:00 2001 From: inkcherry Date: Tue, 12 Nov 2024 22:25:33 +0800 Subject: [PATCH 018/112] add zero3 ```module_granularity_threshold ``` to zero optimization. (#6649) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This PR adds Z3 coalesced fetch to zero optimization. Currently, some logic can be reused, but it's difficult to realize that as optimization choice(I only discovered these logic when trying to implement it). The benefit of this approach is reducing host overhead（reduce many hooks) and during the process of recursive fetching parameters (especially in fine-grained models, such as those with a large number of moe experts). This is particularly helpful for host-sensitive devices (such as hpu), where it achieved a 40% performance improvement in our customer workloads. FYI @delock @deepcharm --------- Co-authored-by: Ma, Guokai Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase --- deepspeed/runtime/engine.py | 5 + deepspeed/runtime/zero/config.py | 9 ++ deepspeed/runtime/zero/parameter_offload.py | 91 ++++++++++++- deepspeed/runtime/zero/stage3.py | 8 +- deepspeed/utils/__init__.py | 2 +- deepspeed/utils/z3_leaf_module.py | 27 ++-- docs/_pages/config-json.md | 5 + .../runtime/zero/test_zero_leaf_module.py | 121 +++++++++++++++++- 8 files changed, 252 insertions(+), 16 deletions(-) diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index b639ef6a1456..e1e745d2b112 100755 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -811,6 +811,9 @@ def zero_max_reuse_distance(self): def zero_prefetch_bucket_size(self): return self._config.zero_config.prefetch_bucket_size + def zero_module_granularity_threshold(self): + return self._config.zero_config.module_granularity_threshold + def zero_param_persistence_threshold(self): return self._config.zero_config.param_persistence_threshold @@ -1614,6 +1617,7 @@ def _configure_zero_optimizer(self, optimizer): zero_param_parallel_group=zero_param_parallel_group, zero_quantized_weights=self.zero_quantized_weights(), zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights(), + zero_module_granularity_threshold=self.zero_module_granularity_threshold(), ) else: log_dist( @@ -1660,6 +1664,7 @@ def _configure_zero_optimizer(self, optimizer): zero_hpz_partition_size=self.zero_hpz_partition_size(), zero_quantized_weights=self.zero_quantized_weights(), zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights(), + zero_module_granularity_threshold=self.zero_module_granularity_threshold(), ) else: diff --git a/deepspeed/runtime/zero/config.py b/deepspeed/runtime/zero/config.py index 1cfcd784e2ce..7cac7e3c1ce7 100644 --- a/deepspeed/runtime/zero/config.py +++ b/deepspeed/runtime/zero/config.py @@ -21,6 +21,7 @@ "stage3_max_live_parameters" : 1000000000, "stage3_max_reuse_distance" : 1000000000, "stage3_use_all_reduce_for_fetch_params": [true|false], + "stage3_module_granularity_threshold": 0, "allgather_partitions": [true|false], "use_multi_rank_bucket_allreduce": [true|false], "allgather_bucket_size": 500000000, @@ -245,6 +246,14 @@ class DeepSpeedZeroConfig(DeepSpeedConfigModel): this option is enabled and then saves the fp16 model weights. """ + module_granularity_threshold: int = Field(pp_int(0), alias="stage3_module_granularity_threshold") + """ + The granularity of a module is determined by the ratio of "parameter_count / (1 + descendant count)". + ZeRO3 classifies modules with a granularity below the threshold as fine-grained, + which are treated as integral units during parameter fetching. This reduces host overhead + and the separate allgather overhead introduced by hooks for fine-grained layers when fetching parameters. + """ + use_all_reduce_for_fetch_params: bool = Field(False, alias="stage3_use_all_reduce_for_fetch_params") """ Use all_reduce op when fetching module parameters at stage3. This improves performance by reducing diff --git a/deepspeed/runtime/zero/parameter_offload.py b/deepspeed/runtime/zero/parameter_offload.py index 4b0ddb7679a9..082d7e874e4d 100644 --- a/deepspeed/runtime/zero/parameter_offload.py +++ b/deepspeed/runtime/zero/parameter_offload.py @@ -6,7 +6,7 @@ import sys import torch from collections import OrderedDict -from deepspeed.utils import z3_leaf_module +from deepspeed.utils import z3_leaf_module, set_z3_leaf_module from deepspeed.runtime.utils import see_memory_usage from deepspeed.runtime.zero.utils import apply_to_tensors_only, is_zero_param from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum @@ -14,6 +14,7 @@ from deepspeed.runtime.zero.partition_parameters import * from deepspeed.runtime.zero.partitioned_param_coordinator import PartitionedParameterCoordinator, InflightParamRegistry, iter_params from deepspeed.accelerator import get_accelerator +from deepspeed import utils FWD_MODULE_STACK = list() @@ -101,6 +102,7 @@ def __init__( zero_param_parallel_group=None, zero_quantized_weights=False, zero_quantized_nontrainable_weights=False, + zero_module_granularity_threshold=0, ): see_memory_usage("DeepSpeedZeRoOffload initialize [begin]", force=True) @@ -155,8 +157,16 @@ def __init__( zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights, ) + if zero_module_granularity_threshold > 0: + self.min_granularity_value = sys.maxsize + self.min_granularity_layer = None + self.granularity_info = set() + self.z3_leaf_layers = [] + self._set_z3_leaf_modules_by_threshold(module, zero_module_granularity_threshold) + self.forward_hooks = [] self.backward_hooks = [] + self.setup_zero_stage3_hooks() print_rank_0( f'Created module hooks: forward = {len(self.forward_hooks)}, backward = {len(self.backward_hooks)}', @@ -482,3 +492,82 @@ def post_sub_module_backward_function(self, sub_module): see_memory_usage( f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} after release", force=False) + + def _set_z3_leaf_modules_by_threshold(self, module, zero_module_granularity_threshold): + + self._get_granularity_recursively(module) + print_rank_0(f"{'MODULE NAME'.ljust(30)}|{'GRANULARITY VALUE'.rjust(20)}", force=True) + for granularity in self.granularity_info: + print_rank_0(granularity, force=True) + + if self.min_granularity_value <= zero_module_granularity_threshold: + self._set_leaf_by_threshold_preorder(module, zero_module_granularity_threshold) + utils.logger.info( + f"z3_leaf_module was set by stage3_module_granularity_threshold:{zero_module_granularity_threshold}") + for layer in self.z3_leaf_layers: + print_rank_0(f"{layer.__class__.__name__}:{layer.ds_model_granularity}", force=True) + else: + utils.logger.warning( + f"The smallest module granularity is [{self.min_granularity_layer}:{self.min_granularity_value}]. "\ + f"To make stage3_module_granularity_threshold effective, you need to set stage3_module_granularity_threshold >= {self.min_granularity_value}. "\ + f"Current Value:{zero_module_granularity_threshold}" + ) + + def _get_granularity_recursively(self, module): + """This function is used to recursively obtain the granularity of each module.""" + + # avoid setting as leaf for particularly large models, even if the granularity is very small + # an oversized leaf module increases the number of live parameters, introducing memory overhead + Z3_MAX_LEAF_SIZE = 1e9 + + if not list(module.parameters()): + # skip Modules without parameters, such as GELU, etc. + module.ds_model_granularity = sys.maxsize + return 0, 0 + + num_layers = 0 + num_params = 0 + num_params += sum(p.ds_numel for p in module.parameters(recurse=False)) + if not any(module.children()): + # torch leaf module + module.ds_model_granularity = sys.maxsize + return 1, num_params + + for child in module.children(): + layers_in_child, params_in_child = self._get_granularity_recursively(child) + num_layers += layers_in_child + num_params += params_in_child + + if module.__class__.__name__ in torch.nn.modules.container.__all__: + # Do not set container modules like ModuleList as leaf modules + # as this will prevent hooks from being set on their children + # and they may do not invoke the forward method + module.ds_model_granularity = sys.maxsize + return num_layers, num_params + + num_layers += 1 + ds_model_granularity = (num_params // num_layers) if num_params <= Z3_MAX_LEAF_SIZE else sys.maxsize + module.ds_model_granularity = ds_model_granularity + # module.ds_model_num_layers = num_layers + # module.ds_model_num_params = num_params + if self.min_granularity_value > ds_model_granularity: + self.min_granularity_value = ds_model_granularity + self.min_granularity_layer = module.__class__.__name__ + self.granularity_info.add(f"{module.__class__.__name__.ljust(30)}|{str(ds_model_granularity).rjust(20)}") + + return num_layers, num_params + + def _set_leaf_by_threshold_preorder(self, module, granularity_treshhold): + '''Set modules as leaf modules based on the threshold, prioritizing parent nodes.''' + + num_params = sum(p.ds_numel for p in module.parameters()) + if num_params == 0: + # skip Modules without parameters, such as GELU, etc. + return + if module.ds_model_granularity <= granularity_treshhold: + set_z3_leaf_module(module, True) + self.z3_leaf_layers.append(module) + return + + for sub_module in module.children(): + self._set_leaf_by_threshold_preorder(sub_module, granularity_treshhold) diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index 2c0c9d498d13..04d52319ae8c 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -157,6 +157,7 @@ def __init__( zero_hpz_partition_size=1, zero_quantized_weights=False, zero_quantized_nontrainable_weights=False, + zero_module_granularity_threshold=0, ): see_memory_usage("Stage 3 initialize beginning", force=True) @@ -227,7 +228,8 @@ def __init__( mpu=mpu, zero_param_parallel_group=zero_param_parallel_group, zero_quantized_weights=zero_quantized_weights, - zero_quantized_nontrainable_weights=zero_quantized_nontrainable_weights) + zero_quantized_nontrainable_weights=zero_quantized_nontrainable_weights, + zero_module_granularity_threshold=zero_module_granularity_threshold) self.persistent_parameters = self.parameter_offload.persistent_parameters self._configure_offloading(offload_optimizer_config, offload_param_config) @@ -458,6 +460,7 @@ def initialize_ds_offload( zero_param_parallel_group, zero_quantized_weights, zero_quantized_nontrainable_weights, + zero_module_granularity_threshold, ): return DeepSpeedZeRoOffload(module=module, timers=timers, @@ -473,7 +476,8 @@ def initialize_ds_offload( mpu=mpu, zero_param_parallel_group=zero_param_parallel_group, zero_quantized_weights=zero_quantized_weights, - zero_quantized_nontrainable_weights=zero_quantized_nontrainable_weights) + zero_quantized_nontrainable_weights=zero_quantized_nontrainable_weights, + zero_module_granularity_threshold=zero_module_granularity_threshold) def _get_trainable_parameter_groups(self): param_groups = [] diff --git a/deepspeed/utils/__init__.py b/deepspeed/utils/__init__.py index c6a202d485eb..983e64642c69 100644 --- a/deepspeed/utils/__init__.py +++ b/deepspeed/utils/__init__.py @@ -16,7 +16,7 @@ from .tensor_fragment import safe_set_full_fp32_param, safe_set_full_optimizer_state, safe_set_full_grad from .tensor_fragment import safe_get_local_fp32_param, safe_get_local_grad, safe_get_local_optimizer_state from .tensor_fragment import safe_set_local_fp32_param, safe_set_local_grad, safe_set_local_optimizer_state -from .z3_leaf_module import set_z3_leaf_modules, unset_z3_leaf_modules, get_z3_leaf_modules, z3_leaf_module, z3_leaf_parameter +from .z3_leaf_module import set_z3_leaf_modules, unset_z3_leaf_modules, get_z3_leaf_modules, z3_leaf_module, z3_leaf_parameter, set_z3_leaf_module from .mixed_precision_linkage import link_hp_params, lazy_init_hp_params_optimizer_state from deepspeed.runtime.dataloader import RepeatingLoader from .numa import get_numactl_cmd diff --git a/deepspeed/utils/z3_leaf_module.py b/deepspeed/utils/z3_leaf_module.py index 47d9ff698f1f..14e8ae2d2823 100644 --- a/deepspeed/utils/z3_leaf_module.py +++ b/deepspeed/utils/z3_leaf_module.py @@ -4,7 +4,7 @@ # DeepSpeed Team import torch -from typing import List, Type +from typing import List, Type, Union def z3_leaf_module(model: torch.nn.Module) -> bool: @@ -40,18 +40,24 @@ def get_z3_leaf_modules(model: torch.nn.Module) -> List[torch.nn.Module]: return [module for module in model.modules() if z3_leaf_module(module)] -def _do_set_z3_leaf_modules(model: torch.nn.Module, leaf_module_classes: List[Type], +def set_z3_leaf_module(model: torch.nn.Module, flag: bool): + model._z3_leaf = flag + + +def _do_set_z3_leaf_modules(model: torch.nn.Module, leaf_module_classes: Union[List[Type], List[str]], flag: bool) -> List[torch.nn.Module]: - assert all(isinstance(module_class, type) for module_class in leaf_module_classes), \ - f'leaf_module_classes must be a list of types, got {leaf_module_classes}' + assert all(isinstance(module_class, (type, str) ) for module_class in leaf_module_classes), \ + f'leaf_module_classes must be a list of types or names, got {leaf_module_classes}' leaf_modules = [] def _set_z3_leaf_flag(model: torch.nn.Module): nonlocal leaf_modules - if model.__class__ in leaf_module_classes: - model._z3_leaf = flag - leaf_modules.append(model) + for module in leaf_module_classes: + if (isinstance(module, type) and model.__class__ == module) or \ + (isinstance(module, str) and model.__class__.__name__ == module): + model._z3_leaf = flag + leaf_modules.append(model) model.apply(_set_z3_leaf_flag) @@ -61,13 +67,14 @@ def _set_z3_leaf_flag(model: torch.nn.Module): return leaf_modules -def set_z3_leaf_modules(model: torch.nn.Module, leaf_module_classes: List[Type]) -> List[torch.nn.Module]: +def set_z3_leaf_modules(model: torch.nn.Module, leaf_module_classes: Union[List[Type], + List[str]]) -> List[torch.nn.Module]: """Sets a flag within a module in `model` to instruct ZeRO3 to stop setting hooks recursively when it encounters a module class listed in `leaf_module_classes`. This is particularly useful in the context of Mixture of Experts (MoE) models. In MoE models, the computation order of experts varies across forward passes. This variability can disrupt ZeRO3's functionality, as ZeRO3 relies on tracking the computation order of modules to prefetch parameters efficiently. By designating a module as a 'leaf' node, ZeRO3 will prefetch parameters for all child modules upon entering the module. Another scenario where this functionality is beneficial is in models with excessively fine-grained nested modules, where it helps to avoid the overhead associated with hooks. Args: model (torch.nn.Module): The model to which the leaf module flag will be applied. - leaf_module_classes (List[Type]): A list of module classes that should be flagged as 'leaf' modules. + leaf_module_classes (Union[List[Type], List[str]]): A list of module classes that should be flagged as 'leaf' modules. Returns: List[torch.nn.Module]: A list of modules that match the module classes in `leaf_module_classes`. """ @@ -79,7 +86,7 @@ def unset_z3_leaf_modules(model: torch.nn.Module, leaf_module_classes: List[Type See `set_z3_leaf_modules` for more details. Args: model (torch.nn.Module): The model to which the leaf module flag will be applied. - leaf_module_classes (List[Type]): A list of module classes that should be flagged as 'leaf' modules. + leaf_module_classes (Union[List[Type], List[str]]): A list of module classes that should be flagged as 'leaf' modules. Returns: List[torch.nn.Module]: A list of modules that match the module classes in `leaf_module_classes`. """ diff --git a/docs/_pages/config-json.md b/docs/_pages/config-json.md index adb2f1679ea0..51e3bbd6eaaa 100755 --- a/docs/_pages/config-json.md +++ b/docs/_pages/config-json.md @@ -489,6 +489,11 @@ Enabling and configuring ZeRO memory optimizations |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ------- | | Consolidate the weights before saving the model by `save_16bit_model()`. Since the weights are partitioned across GPUs, they aren't part of `state_dict`, so this function automatically gathers the weights when this option is enabled and then saves the fp16 model weights. | `False` | +***stage3_module_granularity_threshold***: [integer] +| Description | Default | +|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ------- | +| The granularity of a module is determined by the ratio of `parameter_count` / `(1 + descendant_count)`. ZeRO3 classifies modules with a granularity below the threshold as fine-grained, treating them as integral units during parameter fetching. This reduces host and communication overhead from separate hooks. | `0` | + ***zero_hpz_partition_size***: [integer] | Description | Default | diff --git a/tests/unit/runtime/zero/test_zero_leaf_module.py b/tests/unit/runtime/zero/test_zero_leaf_module.py index 1d3b88a04a4e..74c709883645 100644 --- a/tests/unit/runtime/zero/test_zero_leaf_module.py +++ b/tests/unit/runtime/zero/test_zero_leaf_module.py @@ -3,6 +3,7 @@ # DeepSpeed Team +import pytest import deepspeed.comm as dist import torch @@ -12,6 +13,8 @@ import deepspeed from deepspeed.utils import set_z3_leaf_modules, unset_z3_leaf_modules, get_z3_leaf_modules, z3_leaf_module from deepspeed.accelerator import get_accelerator +from torch import nn +import time class ChooseModuleByCounter(torch.nn.Module): @@ -53,6 +56,49 @@ def forward(self, x, y): return x, loss +class MLPBlock(nn.Module): + + def __init__(self, hidden_dim): + super(MLPBlock, self).__init__() + self.gate_proj = nn.Linear(hidden_dim, hidden_dim, bias=False) + self.up_proj = nn.Linear(hidden_dim, hidden_dim, bias=False) + self.down_proj = nn.Linear(hidden_dim, hidden_dim, bias=False) + self.act_fn = nn.GELU() + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +class FineGrainedBlock(nn.Module): + + def __init__(self, hidden_dim, num_block): + super(FineGrainedBlock, self).__init__() + self.num_block = num_block + self.mlp_layers = torch.nn.ModuleList([MLPBlock(hidden_dim=hidden_dim) for _ in range(self.num_block)]) + + def forward(self, x): + for i in range(self.num_block): + x = self.mlp_layers[i](x) + return x + + +class modelWithFineGrainedBlock(nn.Module): + + def __init__(self, hidden_dim, num_block): + super(modelWithFineGrainedBlock, self).__init__() + self.coarse_grained_layer1 = nn.Linear(hidden_dim, 8 * hidden_dim) + self.coarse_grained_layer2 = nn.Linear(8 * hidden_dim, hidden_dim) + self.fine_grained_layer = FineGrainedBlock(hidden_dim, num_block) + self.cel = torch.nn.CrossEntropyLoss() + + def forward(self, x, y): + x = self.coarse_grained_layer1(x) + x = self.coarse_grained_layer2(x) + x = self.fine_grained_layer(x) + loss = self.cel(x, y) + return x, loss + + def run_model(model, config_dict, hidden_dim, dtype, requires_grad): model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) data_loader = random_dataloader(model=model, @@ -97,9 +143,9 @@ def _test_set_z3_leaf_modules(self, cls, requires_grad): "stage3_max_reuse_distance": 0, } } - if get_accelerator().is_fp16_supported(): + if preferred_dtype() is torch.float16: config_dict["fp16"] = {"enabled": True} - elif get_accelerator().is_bf16_supported(): + elif preferred_dtype() is torch.bfloat16: config_dict["bf16"] = {"enabled": True} model = cls(hidden_dim) @@ -143,3 +189,74 @@ def test_set_no_match_class(self): raise AssertionError("Expected error that no module is set as a leaf module") except ValueError as e: pass + + +@pytest.mark.parametrize("module_granularity_threshold", [0, 100, 12100, 10000000]) +class TestZ3LeafOptimization(DistributedTest): + world_size = 2 + reuse_dist_env = True + + def test_finegrained_optimization(self, module_granularity_threshold: int): + hidden_dim = 128 + num_block = 16 + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "steps_per_print": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-6 + } + }, + "zero_optimization": { + "stage": 3, + "stage3_prefetch_bucket_size": hidden_dim**2, + "stage3_param_persistence_threshold": 0, + "stage3_max_reuse_distance": 0, + } + } + if preferred_dtype() is torch.float16: + config_dict["fp16"] = {"enabled": True} + elif preferred_dtype() is torch.bfloat16: + config_dict["bf16"] = {"enabled": True} + + def bench_loss_and_time(config): + warm_up_step = 10 + model = modelWithFineGrainedBlock(hidden_dim, num_block) + model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config) + data_loader = random_dataloader(model=model, + total_samples=20, + hidden_dim=hidden_dim, + device=model.device, + dtype=preferred_dtype()) + dist.barrier() + loss_list = [] + + for i, batch in enumerate(data_loader): + if i == warm_up_step: + dist.barrier() + get_accelerator().synchronize() + start_time = time.time() + batch[0].requires_grad = True + loss = model(batch[0], batch[1]) + loss = loss[1] + loss_list.append(loss) + model.backward(loss) + model.step() + get_accelerator().synchronize() + end_time = time.time() + duration = end_time - start_time + model.destroy() + return loss_list, duration + + baseline_loss_list, baseline_exec_time = bench_loss_and_time(config_dict) + + config_dict["zero_optimization"]["stage3_module_granularity_threshold"] = module_granularity_threshold + loss, duration = bench_loss_and_time(config_dict) + + if dist.get_rank() == 0: + print(f"baseline exec time:", baseline_exec_time) + print( + f"finegrained optimziation exec time: {duration},granularity threshold:{module_granularity_threshold} " + ) + assert baseline_loss_list == loss, f"incorrect loss value with threshold:{module_granularity_threshold}" From b692cdea479fba8201584054d654f639e925a265 Mon Sep 17 00:00:00 2001 From: Joe Mayer <114769929+jomayeri@users.noreply.github.com> Date: Tue, 12 Nov 2024 08:34:17 -0800 Subject: [PATCH 019/112] AIO File Offsets (#6641) Adding the option for a file offset to the read/write functions of AIO & GDS ops. --------- Co-authored-by: jomayeri Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- accelerator/cpu_accelerator.py | 2 + csrc/aio/common/deepspeed_aio_utils.cpp | 18 ++-- csrc/aio/common/deepspeed_aio_utils.h | 4 +- csrc/aio/py_lib/deepspeed_aio_op_desc.cpp | 6 +- csrc/aio/py_lib/deepspeed_aio_op_desc.h | 4 +- csrc/aio/py_lib/deepspeed_cpu_op.cpp | 19 ++-- csrc/aio/py_lib/deepspeed_cpu_op.h | 3 +- csrc/aio/py_lib/deepspeed_py_aio.cpp | 7 +- csrc/aio/py_lib/deepspeed_py_io_handle.cpp | 65 +++++++++----- csrc/aio/py_lib/deepspeed_py_io_handle.h | 27 ++++-- csrc/aio/py_lib/py_ds_aio.cpp | 24 +++-- csrc/aio/py_test/ds_aio_handle.py | 2 +- csrc/gds/py_lib/deepspeed_gds_op.cpp | 24 +++-- csrc/gds/py_lib/deepspeed_gds_op.h | 3 +- csrc/gds/py_lib/deepspeed_py_gds_handle.cpp | 15 +++- csrc/gds/py_lib/deepspeed_py_gds_handle.h | 3 +- csrc/gds/py_lib/py_ds_gds.cpp | 24 +++-- deepspeed/runtime/swap_tensor/utils.py | 4 +- deepspeed/utils/numa.py | 5 +- tests/unit/ops/aio/test_aio.py | 97 +++++++++++++++++++-- tests/unit/ops/aio/test_gds.py | 87 ++++++++++++++++-- 21 files changed, 342 insertions(+), 101 deletions(-) diff --git a/accelerator/cpu_accelerator.py b/accelerator/cpu_accelerator.py index 1e4335b19292..0e49bd9f6458 100644 --- a/accelerator/cpu_accelerator.py +++ b/accelerator/cpu_accelerator.py @@ -71,6 +71,8 @@ def device_count(self): # In flat mode, HBM is in separate NUMA node with no cores on this node. # Ignore these NUMA nodes with no cores. numa_core_lists = get_numa_cores() + if not numa_core_lists: + return 1 numa_count = 0 prev_core_list = [] for core_list in numa_core_lists: diff --git a/csrc/aio/common/deepspeed_aio_utils.cpp b/csrc/aio/common/deepspeed_aio_utils.cpp index 0536ff6a362e..fb269b58315f 100644 --- a/csrc/aio/common/deepspeed_aio_utils.cpp +++ b/csrc/aio/common/deepspeed_aio_utils.cpp @@ -19,9 +19,14 @@ const int c_io_queue_depth = 8; io_xfer_ctxt::io_xfer_ctxt(const int fd, const int64_t file_offset, + const int64_t buffer_offset, const int64_t num_bytes, const void* buffer) - : _fd(fd), _base_offset(file_offset), _mem_buffer(buffer), _num_bytes(num_bytes) + : _fd(fd), + _file_base_offset(file_offset), + _buffer_base_offset(buffer_offset), + _mem_buffer(buffer), + _num_bytes(num_bytes) { } @@ -41,9 +46,10 @@ void io_prep_context::prep_iocbs(const int n_iocbs, assert(static_cast(n_iocbs) <= _iocbs->size()); for (auto i = 0; i < n_iocbs; ++i) { const auto shift = i * _block_size; - const auto xfer_buffer = (char*)start_buffer + _xfer_ctxt->_base_offset + shift; - const auto xfer_offset = _xfer_ctxt->_base_offset + start_offset + shift; + const auto xfer_buffer = (char*)start_buffer + _xfer_ctxt->_buffer_base_offset + shift; + const auto xfer_offset = _xfer_ctxt->_file_base_offset + start_offset + shift; auto byte_count = _block_size; + if ((shift + _block_size) > num_bytes) { byte_count = num_bytes - shift; } if (_read_op) { @@ -79,10 +85,10 @@ int io_prep_generator::prep_iocbs(const int n_iocbs, std::vector* auto actual_n_iocbs = min(static_cast(n_iocbs), _remaining_io_blocks); for (auto i = 0; i < actual_n_iocbs; ++i, ++_next_iocb_index) { - const auto xfer_offset = _xfer_ctxt->_base_offset + (_next_iocb_index * _block_size); - const auto xfer_buffer = (char*)_xfer_ctxt->_mem_buffer + xfer_offset; + const auto xfer_buffer = (char*)_xfer_ctxt->_mem_buffer + _xfer_ctxt->_buffer_base_offset + + (_next_iocb_index * _block_size); + const auto xfer_offset = _xfer_ctxt->_file_base_offset + (_next_iocb_index * _block_size); const auto num_bytes = min(static_cast(_block_size), _remaining_bytes); - if (_read_op) { io_prep_pread(iocbs->at(i), _xfer_ctxt->_fd, xfer_buffer, num_bytes, xfer_offset); } else { diff --git a/csrc/aio/common/deepspeed_aio_utils.h b/csrc/aio/common/deepspeed_aio_utils.h index 20e81fe8eebd..6b7599acecb4 100644 --- a/csrc/aio/common/deepspeed_aio_utils.h +++ b/csrc/aio/common/deepspeed_aio_utils.h @@ -30,12 +30,14 @@ Functionality for swapping optimizer tensors to/from (NVMe) storage devices. struct io_xfer_ctxt { const int _fd; - const int64_t _base_offset; + const int64_t _file_base_offset; + const int64_t _buffer_base_offset; const void* _mem_buffer; const int64_t _num_bytes; io_xfer_ctxt(const int fd, const int64_t file_offset, + const int64_t buffer_offset, const int64_t num_bytes, const void* buffer); }; diff --git a/csrc/aio/py_lib/deepspeed_aio_op_desc.cpp b/csrc/aio/py_lib/deepspeed_aio_op_desc.cpp index 6f311c5400c7..945251397225 100644 --- a/csrc/aio/py_lib/deepspeed_aio_op_desc.cpp +++ b/csrc/aio/py_lib/deepspeed_aio_op_desc.cpp @@ -13,14 +13,16 @@ io_op_desc_t::io_op_desc_t(const bool read_op, const char* filename, const int64_t file_num_bytes, const int intra_op_parallelism, - const bool validate) + const bool validate, + const int64_t file_offset) : _read_op(read_op), _buffer(buffer), _fd(fd), _filename(filename), _file_num_bytes(file_num_bytes), + _file_offset(file_offset), _intra_op_parallelism(intra_op_parallelism), - _num_bytes_per_thread(file_num_bytes / intra_op_parallelism), + _num_bytes_per_thread(static_cast(buffer.nbytes()) / intra_op_parallelism), _validate(validate) { } diff --git a/csrc/aio/py_lib/deepspeed_aio_op_desc.h b/csrc/aio/py_lib/deepspeed_aio_op_desc.h index f841b8ce520a..ac1cdf90f78b 100644 --- a/csrc/aio/py_lib/deepspeed_aio_op_desc.h +++ b/csrc/aio/py_lib/deepspeed_aio_op_desc.h @@ -19,6 +19,7 @@ struct io_op_desc_t { const int64_t _num_bytes_per_thread; torch::Tensor _contiguous_buffer; const bool _validate; + const int64_t _file_offset; io_op_desc_t(const bool read_op, const torch::Tensor& buffer, @@ -26,7 +27,8 @@ struct io_op_desc_t { const char* filename, const int64_t file_num_bytes, const int intra_op_parallelism, - const bool validate); + const bool validate, + const int64_t file_offset); virtual void run(const int tid, std::unique_ptr& aio_ctxt, diff --git a/csrc/aio/py_lib/deepspeed_cpu_op.cpp b/csrc/aio/py_lib/deepspeed_cpu_op.cpp index da2ff568d74b..56fb33fb1886 100644 --- a/csrc/aio/py_lib/deepspeed_cpu_op.cpp +++ b/csrc/aio/py_lib/deepspeed_cpu_op.cpp @@ -16,8 +16,16 @@ cpu_op_desc_t::cpu_op_desc_t( const char* filename, const int64_t file_num_bytes, const int intra_op_parallelism, - const bool validate) - : io_op_desc_t(read_op, buffer, fd, filename, file_num_bytes, intra_op_parallelism, validate), + const bool validate, + const int64_t file_offset) + : io_op_desc_t(read_op, + buffer, + fd, + filename, + file_num_bytes, + intra_op_parallelism, + validate, + file_offset), _cpu_buffer(buffer), _pinned_tensor_mgr(pinned_tensor_mgr), _is_managed_bounce_buffer(false) @@ -66,10 +74,11 @@ void cpu_op_desc_t::run(const int tid, deepspeed_aio_config_t* aio_config) { assert(tid < _intra_op_parallelism); - const auto base_offset = _num_bytes_per_thread * tid; + const auto buffer_base_offset = _num_bytes_per_thread * tid; + const auto file_base_offset = _file_offset + (_num_bytes_per_thread * tid); - std::unique_ptr xfer_ctxt( - new io_xfer_ctxt(_fd, base_offset, _num_bytes_per_thread, data_ptr())); + std::unique_ptr xfer_ctxt(new io_xfer_ctxt( + _fd, file_base_offset, buffer_base_offset, _num_bytes_per_thread, data_ptr())); if (aio_config->_overlap_events) { do_aio_operation_overlap(_read_op, aio_ctxt, xfer_ctxt, aio_config, nullptr); diff --git a/csrc/aio/py_lib/deepspeed_cpu_op.h b/csrc/aio/py_lib/deepspeed_cpu_op.h index 9de2fa254048..debaf4a90731 100644 --- a/csrc/aio/py_lib/deepspeed_cpu_op.h +++ b/csrc/aio/py_lib/deepspeed_cpu_op.h @@ -20,7 +20,8 @@ struct cpu_op_desc_t : io_op_desc_t { const char* filename, const int64_t file_num_bytes, const int intra_op_parallelism, - const bool validate); + const bool validate, + const int64_t file_offset); void run(const int tid, std::unique_ptr& aio_ctxt, diff --git a/csrc/aio/py_lib/deepspeed_py_aio.cpp b/csrc/aio/py_lib/deepspeed_py_aio.cpp index 02b04057d1ac..1ff0397043fa 100644 --- a/csrc/aio/py_lib/deepspeed_py_aio.cpp +++ b/csrc/aio/py_lib/deepspeed_py_aio.cpp @@ -52,7 +52,9 @@ int deepspeed_py_aio_write(const torch::Tensor& buffer, auto write_buffer = (char*)buffer.data_ptr(); const auto num_write_bytes = static_cast(buffer.nbytes()); - std::unique_ptr xfer_ctxt(new io_xfer_ctxt(fd, 0, num_write_bytes, write_buffer)); + + std::unique_ptr xfer_ctxt( + new io_xfer_ctxt(fd, 0, 0, num_write_bytes, write_buffer)); std::unique_ptr aio_ctxt(new aio_context(config._block_size, config._queue_depth)); if (config._overlap_events) { @@ -97,7 +99,8 @@ int deepspeed_py_aio_read(torch::Tensor& buffer, auto read_buffer = (char*)buffer.data_ptr(); assert(static_cast(buffer.nbytes()) == num_file_bytes); - std::unique_ptr xfer_ctxt(new io_xfer_ctxt(fd, 0, num_file_bytes, read_buffer)); + std::unique_ptr xfer_ctxt( + new io_xfer_ctxt(fd, 0, 0, num_file_bytes, read_buffer)); std::unique_ptr aio_ctxt(new aio_context(config._block_size, config._queue_depth)); if (config._overlap_events) { diff --git a/csrc/aio/py_lib/deepspeed_py_io_handle.cpp b/csrc/aio/py_lib/deepspeed_py_io_handle.cpp index 48ea8a1339d4..64d7c2e0541e 100644 --- a/csrc/aio/py_lib/deepspeed_py_io_handle.cpp +++ b/csrc/aio/py_lib/deepspeed_py_io_handle.cpp @@ -58,7 +58,10 @@ const bool deepspeed_io_handle_t::get_overlap_events() const { return _overlap_e const int deepspeed_io_handle_t::get_intra_op_parallelism() const { return _intra_op_parallelism; } -int deepspeed_io_handle_t::read(torch::Tensor& buffer, const char* filename, const bool validate) +int deepspeed_io_handle_t::read(torch::Tensor& buffer, + const char* filename, + const bool validate, + const int64_t file_offset) { const auto start_time = std::chrono::high_resolution_clock::now(); @@ -76,7 +79,8 @@ int deepspeed_io_handle_t::read(torch::Tensor& buffer, const char* filename, con if (fd == -1) { return -1; } auto read_buffer = (char*)buffer.data_ptr(); - std::unique_ptr xfer_ctxt(new io_xfer_ctxt(fd, 0, num_file_bytes, read_buffer)); + std::unique_ptr xfer_ctxt( + new io_xfer_ctxt(fd, file_offset, 0, num_file_bytes, read_buffer)); if (_aio_config._overlap_events) { do_aio_operation_overlap(true, _aio_ctxt, xfer_ctxt, &_aio_config, nullptr); @@ -98,7 +102,8 @@ int deepspeed_io_handle_t::read(torch::Tensor& buffer, const char* filename, con int deepspeed_io_handle_t::write(const torch::Tensor& buffer, const char* filename, - const bool validate) + const bool validate, + const int64_t file_offset) { assert(_aio_ctxt); @@ -109,7 +114,8 @@ int deepspeed_io_handle_t::write(const torch::Tensor& buffer, auto write_buffer = (char*)buffer.data_ptr(); const auto num_write_bytes = static_cast(buffer.nbytes()); - std::unique_ptr xfer_ctxt(new io_xfer_ctxt(fd, 0, num_write_bytes, write_buffer)); + std::unique_ptr xfer_ctxt( + new io_xfer_ctxt(fd, file_offset, 0, num_write_bytes, write_buffer)); if (_aio_config._overlap_events) { do_aio_operation_overlap(false, _aio_ctxt, xfer_ctxt, &_aio_config, nullptr); @@ -206,7 +212,8 @@ std::shared_ptr deepspeed_io_handle_t::_create_io_op_desc( const int fd, const char* filename, const int64_t file_num_bytes, - const bool validate) + const bool validate, + const int64_t file_offset) { return std::make_shared(read_op, buffer, @@ -215,13 +222,15 @@ std::shared_ptr deepspeed_io_handle_t::_create_io_op_desc( filename, file_num_bytes, _intra_op_parallelism, - validate); + validate, + file_offset); } int deepspeed_io_handle_t::pread(const torch::Tensor& buffer, const char* filename, const bool validate, - const bool async) + const bool async, + const int64_t file_offset) { int64_t num_file_bytes; if (-1 == get_file_size(filename, num_file_bytes)) { @@ -229,20 +238,18 @@ int deepspeed_io_handle_t::pread(const torch::Tensor& buffer, report_file_error(filename, " fstat for read", error_code); return -1; } + + // buffer can exceed file size to enable 4k alignment const auto buffer_bytes = static_cast(buffer.nbytes()); - if (buffer_bytes != num_file_bytes) { - std::cout << filename << ": buffer nbytes != file bytes " << buffer_bytes - << " != " << num_file_bytes << std::endl; - } - assert(buffer_bytes == num_file_bytes); assert((num_file_bytes % _intra_op_parallelism) == 0); - if (!_is_valid_parallel_aio_op(true, num_file_bytes)) { return -1; } + if (!_is_valid_parallel_aio_op(true, buffer_bytes)) { return -1; } const auto fd = open_file(filename, true); if (fd == -1) { return -1; } - auto scheduled_op = _create_io_op_desc(true, buffer, fd, filename, num_file_bytes, validate); + auto scheduled_op = + _create_io_op_desc(true, buffer, fd, filename, num_file_bytes, validate, file_offset); _schedule_aio_work(scheduled_op); @@ -254,7 +261,8 @@ int deepspeed_io_handle_t::pread(const torch::Tensor& buffer, int deepspeed_io_handle_t::pwrite(const torch::Tensor& buffer, const char* filename, const bool validate, - const bool async) + const bool async, + const int64_t file_offset) { const auto num_write_bytes = static_cast(buffer.nbytes()); assert((num_write_bytes % _intra_op_parallelism) == 0); @@ -264,7 +272,8 @@ int deepspeed_io_handle_t::pwrite(const torch::Tensor& buffer, const auto fd = open_file(filename, false); if (fd == -1) { return -1; } - auto scheduled_op = _create_io_op_desc(false, buffer, fd, filename, num_write_bytes, validate); + auto scheduled_op = + _create_io_op_desc(false, buffer, fd, filename, num_write_bytes, validate, file_offset); _schedule_aio_work(scheduled_op); @@ -273,24 +282,32 @@ int deepspeed_io_handle_t::pwrite(const torch::Tensor& buffer, return wait(); } -int deepspeed_io_handle_t::sync_pread(torch::Tensor& buffer, const char* filename) +int deepspeed_io_handle_t::sync_pread(torch::Tensor& buffer, + const char* filename, + const int64_t file_offset) { - return pread(buffer, filename, false, false); + return pread(buffer, filename, false, false, file_offset); } -int deepspeed_io_handle_t::sync_pwrite(const torch::Tensor& buffer, const char* filename) +int deepspeed_io_handle_t::sync_pwrite(const torch::Tensor& buffer, + const char* filename, + const int64_t file_offset) { - return pwrite(buffer, filename, false, false); + return pwrite(buffer, filename, false, false, file_offset); } -int deepspeed_io_handle_t::async_pread(torch::Tensor& buffer, const char* filename) +int deepspeed_io_handle_t::async_pread(torch::Tensor& buffer, + const char* filename, + const int64_t file_offset) { - return pread(buffer, filename, false, true); + return pread(buffer, filename, false, true, file_offset); } -int deepspeed_io_handle_t::async_pwrite(const torch::Tensor& buffer, const char* filename) +int deepspeed_io_handle_t::async_pwrite(const torch::Tensor& buffer, + const char* filename, + const int64_t file_offset) { - return pwrite(buffer, filename, false, true); + return pwrite(buffer, filename, false, true, file_offset); } at::Tensor deepspeed_io_handle_t::new_cpu_locked_tensor(const int64_t num_elem, diff --git a/csrc/aio/py_lib/deepspeed_py_io_handle.h b/csrc/aio/py_lib/deepspeed_py_io_handle.h index 4fedf8080818..dfcb4125ab9a 100644 --- a/csrc/aio/py_lib/deepspeed_py_io_handle.h +++ b/csrc/aio/py_lib/deepspeed_py_io_handle.h @@ -38,27 +38,35 @@ struct deepspeed_io_handle_t { const bool get_overlap_events() const; const int get_intra_op_parallelism() const; - int read(torch::Tensor& buffer, const char* filename, const bool validate); + int read(torch::Tensor& buffer, + const char* filename, + const bool validate, + const int64_t file_offset); - int write(const torch::Tensor& buffer, const char* filename, const bool validate); + int write(const torch::Tensor& buffer, + const char* filename, + const bool validate, + const int64_t file_offset); int pread(const torch::Tensor& buffer, const char* filename, const bool validate, - const bool async); + const bool async, + const int64_t file_offset); int pwrite(const torch::Tensor& buffer, const char* filename, const bool validate, - const bool async); + const bool async, + const int64_t file_offset); - int sync_pread(torch::Tensor& buffer, const char* filename); + int sync_pread(torch::Tensor& buffer, const char* filename, const int64_t file_offset); - int sync_pwrite(const torch::Tensor& buffer, const char* filename); + int sync_pwrite(const torch::Tensor& buffer, const char* filename, const int64_t file_offset); - int async_pread(torch::Tensor& buffer, const char* filename); + int async_pread(torch::Tensor& buffer, const char* filename, const int64_t file_offset); - int async_pwrite(const torch::Tensor& buffer, const char* filename); + int async_pwrite(const torch::Tensor& buffer, const char* filename, const int64_t file_offset); // TODO: Make API's args to be shape and dtype. torch::Tensor new_cpu_locked_tensor(const int64_t num_elem, @@ -81,5 +89,6 @@ struct deepspeed_io_handle_t { const int fd, const char* filename, const int64_t file_num_bytes, - const bool validate); + const bool validate, + const int64_t file_offset); }; diff --git a/csrc/aio/py_lib/py_ds_aio.cpp b/csrc/aio/py_lib/py_ds_aio.cpp index b80fa2d6c8e6..bf298b691b81 100644 --- a/csrc/aio/py_lib/py_ds_aio.cpp +++ b/csrc/aio/py_lib/py_ds_aio.cpp @@ -40,14 +40,16 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) "Synchronous and non-parallel file read. Returns count of completed read ops", "buffer"_a, "filename"_a, - "validate"_a) + "validate"_a, + "file_offset"_a = 0) .def("write", &deepspeed_aio_handle_t::write, "Synchronous and non-parallel file write. Returns count of completed write ops", "buffer"_a, "filename"_a, - "validate"_a) + "validate"_a, + "file_offset"_a = 0) .def("pread", &deepspeed_aio_handle_t::pread, @@ -55,7 +57,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) "buffer"_a, "filename"_a, "validate"_a, - "async"_a) + "async"_a, + "file_offset"_a = 0) .def("pwrite", &deepspeed_aio_handle_t::pwrite, @@ -63,33 +66,38 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) "buffer"_a, "filename"_a, "validate"_a, - "async"_a) + "async"_a, + "file_offset"_a = 0) .def("sync_pread", &deepspeed_aio_handle_t::sync_pread, "Synchrononous parallel file read. Returns count of completed read ops", "buffer"_a, - "filename"_a) + "filename"_a, + "file_offset"_a = 0) .def("sync_pwrite", &deepspeed_aio_handle_t::sync_pwrite, "Synchronous parallel file write. Returns count of completed write ops", "buffer"_a, - "filename"_a) + "filename"_a, + "file_offset"_a = 0) .def("async_pread", &deepspeed_aio_handle_t::async_pread, "Asynchronous parallel file read. Returns 0 on success. Returns 0 on success, and " "following wait() returns count of completed ops.", "buffer"_a, - "filename"_a) + "filename"_a, + "file_offset"_a = 0) .def("async_pwrite", &deepspeed_aio_handle_t::async_pwrite, "Asynchronous parallel file write. Returns 0 on success, and following wait() returns " "count of completed ops.", "buffer"_a, - "filename"_a) + "filename"_a, + "file_offset"_a = 0) .def("new_cpu_locked_tensor", &deepspeed_aio_handle_t::new_cpu_locked_tensor, diff --git a/csrc/aio/py_test/ds_aio_handle.py b/csrc/aio/py_test/ds_aio_handle.py index f4a179deb9ec..6913e9090bf5 100755 --- a/csrc/aio/py_test/ds_aio_handle.py +++ b/csrc/aio/py_test/ds_aio_handle.py @@ -92,7 +92,7 @@ def main_parallel_read(pool_params): start_time = time.time() dest_buffer = BOUNCE_BUFFER if ctxt[BOUNCE_BUFFER] is not None else BUFFER - ret = handle.pread(ctxt[dest_buffer], ctxt['file'], args.validate, True) + ret = handle.pread(ctxt[dest_buffer], ctxt['file'], args.validate, 0, True) assert ret != -1 handle.wait() if dest_buffer == BOUNCE_BUFFER: diff --git a/csrc/gds/py_lib/deepspeed_gds_op.cpp b/csrc/gds/py_lib/deepspeed_gds_op.cpp index f49f74394374..b7055c8cc72b 100644 --- a/csrc/gds/py_lib/deepspeed_gds_op.cpp +++ b/csrc/gds/py_lib/deepspeed_gds_op.cpp @@ -95,8 +95,16 @@ gds_op_desc_t::gds_op_desc_t(const bool read_op, const char* filename, const int64_t file_num_bytes, const int intra_op_parallelism, - const bool validate) - : io_op_desc_t(read_op, buffer, fd, filename, file_num_bytes, intra_op_parallelism, validate) + const bool validate, + const int64_t file_offset) + : io_op_desc_t(read_op, + buffer, + fd, + filename, + file_num_bytes, + intra_op_parallelism, + validate, + file_offset) { _contiguous_buffer = _buffer.contiguous(); const int64_t device = _buffer.get_device(); @@ -124,17 +132,17 @@ void gds_op_desc_t::run(const int tid, { assert(tid < _intra_op_parallelism); check_cudaruntimecall(cudaSetDevice(_buffer.get_device())); - int64_t buf_offset = data_ptr() + (_num_bytes_per_thread * tid) - (char*)_base_ptr; - const auto file_offset = _num_bytes_per_thread * tid; + const auto buf_offset = data_ptr() + (_num_bytes_per_thread * tid) - (char*)_base_ptr; + const auto tid_file_offset = _file_offset + (_num_bytes_per_thread * tid); if (_read_op) { auto ret = - cuFileRead(_cf_handle, _base_ptr, _num_bytes_per_thread, file_offset, buf_offset); - if (ret < 0) { _report_error(ret, errno, buf_offset); } + cuFileRead(_cf_handle, _base_ptr, _num_bytes_per_thread, tid_file_offset, buf_offset); + if (ret < 0) { _report_error(ret, errno, tid_file_offset); } } else { auto ret = - cuFileWrite(_cf_handle, _base_ptr, _num_bytes_per_thread, file_offset, buf_offset); - if (ret < 0) { _report_error(ret, errno, buf_offset); } + cuFileWrite(_cf_handle, _base_ptr, _num_bytes_per_thread, tid_file_offset, buf_offset); + if (ret < 0) { _report_error(ret, errno, tid_file_offset); } } } diff --git a/csrc/gds/py_lib/deepspeed_gds_op.h b/csrc/gds/py_lib/deepspeed_gds_op.h index 380bb0b9b6ae..d955527b1ba3 100644 --- a/csrc/gds/py_lib/deepspeed_gds_op.h +++ b/csrc/gds/py_lib/deepspeed_gds_op.h @@ -24,7 +24,8 @@ struct gds_op_desc_t : io_op_desc_t { const char* filename, const int64_t file_num_bytes, const int intra_op_parallelism, - const bool validate); + const bool validate, + const int64_t file_offset); void run(const int tid, std::unique_ptr& aio_ctxt, diff --git a/csrc/gds/py_lib/deepspeed_py_gds_handle.cpp b/csrc/gds/py_lib/deepspeed_py_gds_handle.cpp index c052144a0190..f11245c75a5e 100644 --- a/csrc/gds/py_lib/deepspeed_py_gds_handle.cpp +++ b/csrc/gds/py_lib/deepspeed_py_gds_handle.cpp @@ -107,12 +107,19 @@ std::shared_ptr deepspeed_gds_handle_t::_create_io_op_desc( const int fd, const char* filename, const int64_t file_num_bytes, - const bool validate) + const bool validate, + const int64_t file_offset) { if (buffer.is_cuda()) { - return std::make_shared( - read_op, buffer, fd, filename, file_num_bytes, _intra_op_parallelism, validate); + return std::make_shared(read_op, + buffer, + fd, + filename, + file_num_bytes, + _intra_op_parallelism, + validate, + file_offset); } return deepspeed_io_handle_t::_create_io_op_desc( - read_op, buffer, fd, filename, file_num_bytes, validate); + read_op, buffer, fd, filename, file_num_bytes, validate, file_offset); } diff --git a/csrc/gds/py_lib/deepspeed_py_gds_handle.h b/csrc/gds/py_lib/deepspeed_py_gds_handle.h index 131e83e7b838..25f68e177b2c 100644 --- a/csrc/gds/py_lib/deepspeed_py_gds_handle.h +++ b/csrc/gds/py_lib/deepspeed_py_gds_handle.h @@ -42,7 +42,8 @@ struct deepspeed_gds_handle_t : deepspeed_io_handle_t { const int fd, const char* filename, const int64_t file_num_bytes, - const bool validate); + const bool validate, + const int64_t file_offset); static int s_cuFile_init; }; diff --git a/csrc/gds/py_lib/py_ds_gds.cpp b/csrc/gds/py_lib/py_ds_gds.cpp index 57bf8d2207c4..2f165ee2c32a 100644 --- a/csrc/gds/py_lib/py_ds_gds.cpp +++ b/csrc/gds/py_lib/py_ds_gds.cpp @@ -33,14 +33,16 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) "Synchronous and non-parallel file read. Returns count of completed read ops", "buffer"_a, "filename"_a, - "validate"_a) + "validate"_a, + "file_offset"_a = 0) .def("write", &deepspeed_gds_handle_t::write, "Synchronous and non-parallel file write. Returns count of completed write ops", "buffer"_a, "filename"_a, - "validate"_a) + "validate"_a, + "file_offset"_a = 0) .def("pread", &deepspeed_gds_handle_t::pread, @@ -48,7 +50,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) "buffer"_a, "filename"_a, "validate"_a, - "async"_a) + "async"_a, + "file_offset"_a = 0) .def("pwrite", &deepspeed_gds_handle_t::pwrite, @@ -56,33 +59,38 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) "buffer"_a, "filename"_a, "validate"_a, - "async"_a) + "async"_a, + "file_offset"_a = 0) .def("sync_pread", &deepspeed_gds_handle_t::sync_pread, "Synchrononous parallel file read. Returns count of completed read ops", "buffer"_a, - "filename"_a) + "filename"_a, + "file_offset"_a = 0) .def("sync_pwrite", &deepspeed_gds_handle_t::sync_pwrite, "Synchronous parallel file write. Returns count of completed write ops", "buffer"_a, - "filename"_a) + "filename"_a, + "file_offset"_a = 0) .def("async_pread", &deepspeed_gds_handle_t::async_pread, "Asynchronous parallel file read. Returns 0 on success. Returns 0 on success, and " "following wait() returns count of completed ops.", "buffer"_a, - "filename"_a) + "filename"_a, + "file_offset"_a = 0) .def("async_pwrite", &deepspeed_gds_handle_t::async_pwrite, "Asynchronous parallel file write. Returns 0 on success, and following wait() returns " "count of completed ops.", "buffer"_a, - "filename"_a) + "filename"_a, + "file_offset"_a = 0) .def("new_cpu_locked_tensor", &deepspeed_gds_handle_t::new_cpu_locked_tensor, diff --git a/deepspeed/runtime/swap_tensor/utils.py b/deepspeed/runtime/swap_tensor/utils.py index 90b2d9b8bd31..1f9825c34638 100644 --- a/deepspeed/runtime/swap_tensor/utils.py +++ b/deepspeed/runtime/swap_tensor/utils.py @@ -18,12 +18,12 @@ def swap_in_tensors(swap_handle, tensor_buffers, swap_paths): for buffer, path in zip(tensor_buffers, swap_paths): - assert (swap_handle.async_pread(buffer, path) == 0) + assert (swap_handle.async_pread(buffer, path, 0) == 0) def swap_out_tensors(swap_handle, tensor_buffers, swap_paths): for buffer, path in zip(tensor_buffers, swap_paths): - assert (swap_handle.async_pwrite(buffer, path) == 0) + assert (swap_handle.async_pwrite(buffer, path, 0) == 0) def print_object(obj, name, exclude_list=[]): diff --git a/deepspeed/utils/numa.py b/deepspeed/utils/numa.py index 4fe7cbba90ae..aba3b5179d41 100644 --- a/deepspeed/utils/numa.py +++ b/deepspeed/utils/numa.py @@ -23,7 +23,10 @@ # ] def get_numa_cores(): ret = [] - output = subprocess.check_output(['numactl', '--hardware']).decode("utf-8") + try: + output = subprocess.check_output(['numactl', '--hardware']).decode("utf-8") + except: + return [] lines = output.split('\n') for line in lines: if line.startswith('available:'): diff --git a/tests/unit/ops/aio/test_aio.py b/tests/unit/ops/aio/test_aio.py index a074cfca317f..1aa5f647a8aa 100644 --- a/tests/unit/ops/aio/test_aio.py +++ b/tests/unit/ops/aio/test_aio.py @@ -35,16 +35,21 @@ def _get_local_rank(): return 0 -def _do_ref_write(tmpdir, index=0): +def _do_ref_write(tmpdir, index=0, file_size=IO_SIZE): file_suffix = f'{_get_local_rank()}_{index}' ref_file = os.path.join(tmpdir, f'_py_random_{file_suffix}.pt') - ref_buffer = os.urandom(IO_SIZE) + ref_buffer = os.urandom(file_size) with open(ref_file, 'wb') as f: f.write(ref_buffer) return ref_file, ref_buffer +def _get_file_path(tmpdir, file_prefix, index=0): + file_suffix = f'{_get_local_rank()}_{index}' + return os.path.join(tmpdir, f'{file_prefix}_{file_suffix}.pt') + + def _get_test_write_file(tmpdir, index): file_suffix = f'{_get_local_rank()}_{index}' return os.path.join(tmpdir, f'_aio_write_random_{file_suffix}.pt') @@ -103,7 +108,7 @@ def test_parallel_read(self, tmpdir, use_cuda_pinned_tensor, single_submit, over _validate_handle_state(h, single_submit, overlap_events) ref_file, _ = _do_ref_write(tmpdir) - read_status = h.sync_pread(aio_buffer, ref_file) + read_status = h.sync_pread(aio_buffer, ref_file, 0) assert read_status == 1 with open(ref_file, 'rb') as f: @@ -131,7 +136,7 @@ def test_async_read(self, tmpdir, use_cuda_pinned_tensor, single_submit, overlap _validate_handle_state(h, single_submit, overlap_events) ref_file, _ = _do_ref_write(tmpdir) - read_status = h.async_pread(aio_buffer, ref_file) + read_status = h.async_pread(aio_buffer, ref_file, 0) assert read_status == 0 wait_status = h.wait() @@ -172,7 +177,7 @@ def test_parallel_write(self, tmpdir, use_cuda_pinned_tensor, single_submit, ove _validate_handle_state(h, single_submit, overlap_events) - write_status = h.sync_pwrite(aio_buffer, aio_file) + write_status = h.sync_pwrite(aio_buffer, aio_file, 0) assert write_status == 1 if not use_cuda_pinned_tensor: @@ -201,7 +206,7 @@ def test_async_write(self, tmpdir, use_cuda_pinned_tensor, single_submit, overla _validate_handle_state(h, single_submit, overlap_events) - write_status = h.async_pwrite(aio_buffer, aio_file) + write_status = h.async_pwrite(aio_buffer, aio_file, 0) assert write_status == 0 wait_status = h.wait() @@ -258,7 +263,7 @@ def test_read(self, tmpdir, async_queue, use_cuda_pinned_tensor, use_unpinned_te _validate_handle_state(h, single_submit, overlap_events) for i in range(async_queue): - read_status = h.async_pread(aio_buffers[i], ref_files[i]) + read_status = h.async_pread(aio_buffers[i], ref_files[i], 0) assert read_status == 0 wait_status = h.wait() @@ -305,7 +310,7 @@ def test_write(self, tmpdir, use_cuda_pinned_tensor, async_queue, use_unpinned_t _validate_handle_state(h, single_submit, overlap_events) for i in range(async_queue): - read_status = h.async_pwrite(aio_buffers[i], aio_files[i]) + read_status = h.async_pwrite(aio_buffers[i], aio_files[i], 0) assert read_status == 0 wait_status = h.wait() @@ -320,3 +325,79 @@ def test_write(self, tmpdir, use_cuda_pinned_tensor, async_queue, use_unpinned_t filecmp.clear_cache() assert filecmp.cmp(ref_files[i], aio_files[i], shallow=False) + + +@pytest.mark.parametrize("use_cuda_pinned_tensor", [True, False]) +@pytest.mark.parametrize('file_partitions', [[1, 1, 1], [1, 1, 2], [1, 2, 1], [2, 1, 1]]) +class TestAsyncFileOffset(DistributedTest): + world_size = 1 + + def test_offset_write(self, tmpdir, file_partitions, use_cuda_pinned_tensor): + + _skip_for_invalid_environment(use_cuda_pinned_tensor=use_cuda_pinned_tensor) + ref_file = _get_file_path(tmpdir, '_py_random') + aio_file = _get_file_path(tmpdir, '_aio_random') + partition_unit_size = BLOCK_SIZE + file_size = sum(file_partitions) * partition_unit_size + + h = AsyncIOBuilder().load().aio_handle(BLOCK_SIZE, QUEUE_DEPTH, True, True, IO_PARALLEL) + + if use_cuda_pinned_tensor: + data_buffer = torch.ByteTensor(list(os.urandom(file_size))).pin_memory() + else: + data_buffer = h.new_cpu_locked_tensor(file_size, torch.empty(0, dtype=torch.uint8)) + + file_offsets = [] + next_offset = 0 + for i in range(len(file_partitions)): + file_offsets.append(next_offset) + next_offset += file_partitions[i] * partition_unit_size + + ref_fd = open(ref_file, 'wb') + for i in range(len(file_partitions)): + src_buffer = torch.narrow(data_buffer, 0, file_offsets[i], file_partitions[i] * partition_unit_size) + + ref_fd.write(src_buffer.numpy().tobytes()) + ref_fd.flush() + + assert 1 == h.sync_pwrite(buffer=src_buffer, filename=aio_file, file_offset=file_offsets[i]) + + filecmp.clear_cache() + assert filecmp.cmp(ref_file, aio_file, shallow=False) + + ref_fd.close() + + if not use_cuda_pinned_tensor: + h.free_cpu_locked_tensor(data_buffer) + + def test_offset_read(self, tmpdir, file_partitions, use_cuda_pinned_tensor): + + _skip_for_invalid_environment(use_cuda_pinned_tensor=use_cuda_pinned_tensor) + partition_unit_size = BLOCK_SIZE + file_size = sum(file_partitions) * partition_unit_size + ref_file, _ = _do_ref_write(tmpdir, 0, file_size) + h = AsyncIOBuilder().load().aio_handle(BLOCK_SIZE, QUEUE_DEPTH, True, True, IO_PARALLEL) + + if use_cuda_pinned_tensor: + data_buffer = torch.zeros(file_size, dtype=torch.uint8, device='cpu').pin_memory() + else: + data_buffer = h.new_cpu_locked_tensor(file_size, torch.empty(0, dtype=torch.uint8)) + + file_offsets = [] + next_offset = 0 + for i in range(len(file_partitions)): + file_offsets.append(next_offset) + next_offset += file_partitions[i] * partition_unit_size + + with open(ref_file, 'rb') as ref_fd: + for i in range(len(file_partitions)): + ref_fd.seek(file_offsets[i]) + bytes_to_read = file_partitions[i] * partition_unit_size + ref_buf = list(ref_fd.read(bytes_to_read)) + + dst_tensor = torch.narrow(data_buffer, 0, 0, bytes_to_read) + assert 1 == h.sync_pread(dst_tensor, ref_file, file_offsets[i]) + assert dst_tensor.tolist() == ref_buf + + if not use_cuda_pinned_tensor: + h.free_cpu_locked_tensor(data_buffer) diff --git a/tests/unit/ops/aio/test_gds.py b/tests/unit/ops/aio/test_gds.py index e94d42cd22af..d97eff452eb5 100644 --- a/tests/unit/ops/aio/test_gds.py +++ b/tests/unit/ops/aio/test_gds.py @@ -29,16 +29,21 @@ def _get_local_rank(): return 0 -def _do_ref_write(tmpdir, index=0): +def _do_ref_write(tmpdir, index=0, file_size=IO_SIZE): file_suffix = f'{_get_local_rank()}_{index}' ref_file = os.path.join(tmpdir, f'_py_random_{file_suffix}.pt') - ref_buffer = os.urandom(IO_SIZE) + ref_buffer = os.urandom(file_size) with open(ref_file, 'wb') as f: f.write(ref_buffer) return ref_file, ref_buffer +def _get_file_path(tmpdir, file_prefix, index=0): + file_suffix = f'{_get_local_rank()}_{index}' + return os.path.join(tmpdir, f'{file_prefix}_{file_suffix}.pt') + + def _get_test_write_file(tmpdir, index): file_suffix = f'{_get_local_rank()}_{index}' return os.path.join(tmpdir, f'_gds_write_random_{file_suffix}.pt') @@ -78,7 +83,7 @@ def test_parallel_read(self, tmpdir, single_submit, overlap_events): _validate_handle_state(h, single_submit, overlap_events) ref_file, _ = _do_ref_write(tmpdir) - read_status = h.sync_pread(gds_buffer, ref_file) + read_status = h.sync_pread(gds_buffer, ref_file, 0) assert read_status == 1 with open(ref_file, 'rb') as f: @@ -97,7 +102,7 @@ def test_async_read(self, tmpdir, single_submit, overlap_events): _validate_handle_state(h, single_submit, overlap_events) ref_file, _ = _do_ref_write(tmpdir) - read_status = h.async_pread(gds_buffer, ref_file) + read_status = h.async_pread(gds_buffer, ref_file, 0) assert read_status == 0 wait_status = h.wait() @@ -128,7 +133,7 @@ def test_parallel_write(self, tmpdir, single_submit, overlap_events): _validate_handle_state(h, single_submit, overlap_events) - write_status = h.sync_pwrite(gds_buffer, gds_file) + write_status = h.sync_pwrite(gds_buffer, gds_file, 0) assert write_status == 1 h.unpin_device_tensor(gds_buffer) @@ -146,7 +151,7 @@ def test_async_write(self, tmpdir, single_submit, overlap_events): _validate_handle_state(h, single_submit, overlap_events) - write_status = h.async_pwrite(gds_buffer, gds_file) + write_status = h.async_pwrite(gds_buffer, gds_file, 0) assert write_status == 0 wait_status = h.wait() @@ -188,7 +193,7 @@ def test_read(self, tmpdir, async_queue): _validate_handle_state(h, single_submit, overlap_events) for i in range(async_queue): - read_status = h.async_pread(gds_buffers[i], ref_files[i]) + read_status = h.async_pread(gds_buffers[i], ref_files[i], 0) assert read_status == 0 wait_status = h.wait() @@ -225,7 +230,7 @@ def test_write(self, tmpdir, async_queue): _validate_handle_state(h, single_submit, overlap_events) for i in range(async_queue): - read_status = h.async_pwrite(gds_buffers[i], gds_files[i]) + read_status = h.async_pwrite(gds_buffers[i], gds_files[i], 0) assert read_status == 0 wait_status = h.wait() @@ -268,3 +273,69 @@ def test_pin_device_tensor(self, use_new_api): h.free_pinned_device_tensor(pinned_buffer) else: h.unpin_device_tensor(pinned_buffer) + + +@pytest.mark.parametrize('file_partitions', [[1, 1, 1], [1, 1, 2], [1, 2, 1], [2, 1, 1]]) +class TestAsyncFileOffset(DistributedTest): + world_size = 1 + + def test_offset_write(self, tmpdir, file_partitions): + ref_file = _get_file_path(tmpdir, '_py_random') + aio_file = _get_file_path(tmpdir, '_aio_random') + partition_unit_size = IO_SIZE + file_size = sum(file_partitions) * partition_unit_size + + h = GDSBuilder().load().gds_handle(BLOCK_SIZE, QUEUE_DEPTH, True, True, IO_PARALLEL) + + gds_buffer = torch.empty(file_size, dtype=torch.uint8, device=get_accelerator().device_name()) + h.pin_device_tensor(gds_buffer) + + file_offsets = [] + next_offset = 0 + for i in range(len(file_partitions)): + file_offsets.append(next_offset) + next_offset += file_partitions[i] * partition_unit_size + + ref_fd = open(ref_file, 'wb') + for i in range(len(file_partitions)): + src_buffer = torch.narrow(gds_buffer, 0, file_offsets[i], + file_partitions[i] * partition_unit_size).to(device='cpu') + + ref_fd.write(src_buffer.numpy().tobytes()) + ref_fd.flush() + + assert 1 == h.sync_pwrite(buffer=src_buffer, filename=aio_file, file_offset=file_offsets[i]) + + filecmp.clear_cache() + assert filecmp.cmp(ref_file, aio_file, shallow=False) + + ref_fd.close() + + h.unpin_device_tensor(gds_buffer) + + def test_offset_read(self, tmpdir, file_partitions): + partition_unit_size = BLOCK_SIZE + file_size = sum(file_partitions) * partition_unit_size + ref_file, _ = _do_ref_write(tmpdir, 0, file_size) + h = GDSBuilder().load().gds_handle(BLOCK_SIZE, QUEUE_DEPTH, True, True, IO_PARALLEL) + + gds_buffer = torch.empty(file_size, dtype=torch.uint8, device=get_accelerator().device_name()) + h.pin_device_tensor(gds_buffer) + + file_offsets = [] + next_offset = 0 + for i in range(len(file_partitions)): + file_offsets.append(next_offset) + next_offset += file_partitions[i] * partition_unit_size + + with open(ref_file, 'rb') as ref_fd: + for i in range(len(file_partitions)): + ref_fd.seek(file_offsets[i]) + bytes_to_read = file_partitions[i] * partition_unit_size + ref_buf = list(ref_fd.read(bytes_to_read)) + + dst_tensor = torch.narrow(gds_buffer, 0, 0, bytes_to_read) + assert 1 == h.sync_pread(dst_tensor, ref_file, file_offsets[i]) + assert dst_tensor.tolist() == ref_buf + + h.unpin_device_tensor(gds_buffer) From 877aa0dba673c2aa2157029c28363b804d6ee03d Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Tue, 12 Nov 2024 10:50:02 -0800 Subject: [PATCH 020/112] Update path for BingBertSquad from DeepSpeedExamples (#6746) In https://github.com/microsoft/DeepSpeedExamples/pull/245, the DeepSpeedExamples directory structure was refactored, this updates the DeepSpeed examples from those changes. --- docs/_tutorials/bert-finetuning.md | 4 ++-- docs/_tutorials/onebit-adam.md | 4 ++-- tests/model/BingBertSquad/run_BingBertSquad.sh | 2 +- tests/model/BingBertSquad/run_BingBertSquad_sanity.sh | 2 +- tests/model/BingBertSquad/run_tests.sh | 2 +- tests/model/BingBertSquad/test_e2e_squad.py | 4 ++-- 6 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/_tutorials/bert-finetuning.md b/docs/_tutorials/bert-finetuning.md index 3014be18d682..f833acebde9a 100755 --- a/docs/_tutorials/bert-finetuning.md +++ b/docs/_tutorials/bert-finetuning.md @@ -10,14 +10,14 @@ In this tutorial we will be adding DeepSpeed to the BingBert model for the SQuAD If you don't already have a copy of the DeepSpeed repository, please clone in now and checkout the DeepSpeedExamples submodule the contains the BingBertSquad -example (DeepSpeedExamples/BingBertSquad) we will be going over in the rest of +example (DeepSpeedExamples/training/BingBertSquad) we will be going over in the rest of this tutorial. ```shell git clone https://github.com/microsoft/DeepSpeed cd DeepSpeed git submodule update --init --recursive -cd DeepSpeedExamples/BingBertSquad +cd DeepSpeedExamples/training/BingBertSquad ``` ### Pre-requisites diff --git a/docs/_tutorials/onebit-adam.md b/docs/_tutorials/onebit-adam.md index b1a8b5369761..e66bba3f818b 100644 --- a/docs/_tutorials/onebit-adam.md +++ b/docs/_tutorials/onebit-adam.md @@ -136,7 +136,7 @@ You can also use a pre-trained BERT model checkpoint from either DeepSpeed, [Hug ### 2.1 Running BingBertSQuAD with DeepSpeed and 1-bit Adam -We provide example scripts under [DeepSpeedExamples/BingBertSquad/1-bit_adam/](https://github.com/microsoft/DeepSpeedExamples/tree/master/BingBertSquad/1-bit_adam). There are 3 sets of scripts corresponding to NCCL-based implementation, MPI-based implementation on Ethernet systems, and MPI-based implementation on InfiniBand systems. For MPI-based implementation, we provide both example scripts when launching with deepspeed or mpirun. +We provide example scripts under [DeepSpeedExamples/training/BingBertSquad/1-bit_adam/](https://github.com/microsoft/DeepSpeedExamples/tree/master/training/BingBertSquad/1-bit_adam). There are 3 sets of scripts corresponding to NCCL-based implementation, MPI-based implementation on Ethernet systems, and MPI-based implementation on InfiniBand systems. 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z1KdrNr!W8zLQZ7N8AVyAZ)(wDbG)V2m)7f2`nbULxe`DuDb}tS?VCNO@cy4PM{!&f5U)1ZO^w_v z2W_0ywFgey8^(@%(7&m$Y;Tj2O9A4k%RCv%mzFH4o06`-09GM43C=UmC(om#@f#JY zH55O_`J3_~+o#8kP1cf!l2#c?=IKsI8S16#;a)?9g15-4PQU)tzpXIP{z0vL=U*kr z9iKAp_f-O8`>F|NO%Ss2T&G q=l|ah1D?12`Fq-Qoe*$7^oP4bf`oHGev172A1{@)UQ{TV2mdc2liF|q literal 0 HcmV?d00001 From 03845dbc85e222263db4db9dc8e9753a39984301 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 25 Nov 2024 12:06:06 -0800 Subject: [PATCH 040/112] Update version.txt before release (#6784) --- version.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/version.txt b/version.txt index 1282fff53bfa..04a373efe6ba 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.15.5 +0.16.0 From e5570b10eed781c96f266e4dc827d22506c606bc Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 25 Nov 2024 12:10:05 -0800 Subject: [PATCH 041/112] Revert release workflow (#6785) --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index eb763792f0c4..02881ef12f39 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -7,7 +7,7 @@ on: jobs: deploy: - runs-on: ubuntu-24.04 + runs-on: ubuntu-22.04 environment: release-env steps: From f743feca033515fdded50a98093da5a48eb41e74 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 25 Nov 2024 12:12:44 -0800 Subject: [PATCH 042/112] Update version.txt after 0.16.0 release (#6786) **Auto-generated PR to update version.txt after a DeepSpeed release** Released version - 0.16.0 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 04a373efe6ba..2a0970ca757c 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.16.0 +0.16.1 From 0c6c98110947300636937d45d6b3392c84996130 Mon Sep 17 00:00:00 2001 From: Guanhua Wang Date: Tue, 3 Dec 2024 08:12:21 -0800 Subject: [PATCH 043/112] Domino news update on readme.md (#6815) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5f5452ce232d..8bfc344a4edc 100755 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ ## 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). - +* [2024/12] [DeepSpeed-Domino: Communication-Free LLM Training Engine](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-domino/README.md) * [2024/08] [DeepSpeed on Windows](https://github.com/microsoft/DeepSpeed/tree/master/blogs/windows/08-2024/README.md) [[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/windows/08-2024/japanese/README.md)] * [2024/08] [DeepNVMe: Improving DL Applications through I/O Optimizations](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-gds/README.md) [[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-gds/japanese/README.md)] * [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)] From fc230070ef3d12bbacfca5205506e648cc4165bc Mon Sep 17 00:00:00 2001 From: Xu Song Date: Thu, 5 Dec 2024 02:16:56 +0800 Subject: [PATCH 044/112] Fix zero checkpoint (#6792) Fix #6791 --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/utils/zero_to_fp32.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/deepspeed/utils/zero_to_fp32.py b/deepspeed/utils/zero_to_fp32.py index e93cb1c95f15..0e759146cadd 100755 --- a/deepspeed/utils/zero_to_fp32.py +++ b/deepspeed/utils/zero_to_fp32.py @@ -514,19 +514,20 @@ def to_torch_tensor(state_dict, return_empty_tensor=False): """ Convert state_dict of GatheredTensor to torch tensor """ + torch_state_dict = {} converted_tensors = {} for name, tensor in state_dict.items(): tensor_id = id(tensor) - if tensor_id in converted_tensors: - shared_tensor = state_dict[converted_tensors[tensor_id]] - state_dict[name] = shared_tensor + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor else: converted_tensors[tensor_id] = name if return_empty_tensor: - state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) else: - state_dict[name] = tensor.contiguous() - return state_dict + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, @@ -660,8 +661,9 @@ def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, else: torch.save(shard_state_dict, output_path) # release the memory of current shard - for tensor_name in shard_state_dict: + for tensor_name in list(shard_state_dict.keys()): del state_dict[tensor_name] + del shard_state_dict[tensor_name] del shard_state_dict gc.collect() From ed7d183bed94d5b48b270586274a76cf6de8902c Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 4 Dec 2024 12:39:45 -0800 Subject: [PATCH 045/112] Update python version but now we need to include setuptools on our own (#6787) TODO: - [x] determine if this means we should technically add setuptools to the requirements.txt --- .github/workflows/release.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 02881ef12f39..b2ccca69dfda 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -7,7 +7,7 @@ on: jobs: deploy: - runs-on: ubuntu-22.04 + runs-on: ubuntu-24.04 environment: release-env steps: @@ -25,6 +25,7 @@ jobs: python release/check_release_version.py --release_version ${{ env.RELEASE_VERSION }} - name: Build DeepSpeed run: | + pip install setuptools DS_BUILD_STRING=" " python setup.py sdist - name: Publish to PyPI uses: pypa/gh-action-pypi-publish@release/v1 From 60a1b57b98c61c322cc76f1936eaec4f18a77b06 Mon Sep 17 00:00:00 2001 From: Jinghan Yao Date: Wed, 4 Dec 2024 18:29:45 -0500 Subject: [PATCH 046/112] Adding the new feature of FPDT (#6462) [FPDT](https://arxiv.org/abs/2408.16978) can only be used with [this version](https://github.com/microsoft/Megatron-DeepSpeed/pull/441) of Megatron-DeepSpeed. --------- Co-authored-by: Jinghan Yao Co-authored-by: Sam Ade Jacobs Co-authored-by: Jinghan Yao Co-authored-by: Logan Adams Co-authored-by: Olatunji Ruwase Co-authored-by: Jinghan Yao Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Co-authored-by: Masahiro Tanaka --- .github/workflows/nv-flash-attn.yml | 64 + .../activation_checkpointing/checkpointing.py | 4 +- deepspeed/sequence/fpdt_layer.py | 1225 +++++++++++++++++ deepspeed/sequence/layer.py | 42 +- requirements/requirements.txt | 1 + .../unit/sequence_parallelism/test_ulysses.py | 98 ++ 6 files changed, 1432 insertions(+), 2 deletions(-) create mode 100644 .github/workflows/nv-flash-attn.yml create mode 100644 deepspeed/sequence/fpdt_layer.py diff --git a/.github/workflows/nv-flash-attn.yml b/.github/workflows/nv-flash-attn.yml new file mode 100644 index 000000000000..310972323043 --- /dev/null +++ b/.github/workflows/nv-flash-attn.yml @@ -0,0 +1,64 @@ +name: nv-flash-attn + +on: + workflow_dispatch: + pull_request: + paths: + - 'deepspeed/sequence/**' + - 'tests/unit/sequence_parallelism/**' + - '.github/workflows/nv-flash-attn.yml' + schedule: + - cron: "0 0 * * *" + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + unit-tests: + runs-on: [self-hosted, nvidia, a6000] + container: + image: nvcr.io/nvidia/pytorch:24.03-py3 + ports: + - 80 + options: --gpus all --shm-size "8G" + + steps: + - uses: actions/checkout@v4 + + - name: Check container state + run: | + ldd --version + nvcc --version + nvidia-smi + python -c "import torch; print('torch:', torch.__version__, torch)" + python -c "import torch; print('CUDA available:', torch.cuda.is_available())" + - name: Install transformers + run: | + git clone --depth=1 https://github.com/huggingface/transformers + cd transformers + git rev-parse --short HEAD + python -m pip install . + - name: Install deepspeed + run: | + python -m pip install .[dev] + ds_report + - name: Install FlashAttention + run: | + python -m pip install flash-attn + - name: Python environment + run: | + python -m pip list + - name: Unit tests + run: | + unset TORCH_CUDA_ARCH_LIST # only jit compile for current arch + cd tests + python -m pytest --color=yes --durations=0 --verbose -rF unit/sequence_parallelism/test_ulysses.py --torch_ver="2.3" --cuda_ver="12" + - name: Open GitHub issue if nightly CI fails + if: ${{ failure() && (github.event_name == 'schedule') }} + uses: JasonEtco/create-an-issue@v2 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + filename: .github/ISSUE_TEMPLATE/ci_failure_report.md + update_existing: true diff --git a/deepspeed/runtime/activation_checkpointing/checkpointing.py b/deepspeed/runtime/activation_checkpointing/checkpointing.py index d65126999199..08c4b81937f9 100644 --- a/deepspeed/runtime/activation_checkpointing/checkpointing.py +++ b/deepspeed/runtime/activation_checkpointing/checkpointing.py @@ -369,7 +369,9 @@ def is_activation_to_checkpoint(item): Is an activation to be checkpointed """ global mp_size - return torch.is_tensor(item) and item.is_floating_point() and item.numel() >= mp_size + extra_flag = (not hasattr(item, 'no_checkpointing')) or (hasattr(item, 'no_checkpointing') + and item.no_checkpointing == False) + return torch.is_tensor(item) and item.is_floating_point() and item.numel() >= mp_size and extra_flag def partition_activations(args, cpu_checkpoint, contiguous_checkpoint): diff --git a/deepspeed/sequence/fpdt_layer.py b/deepspeed/sequence/fpdt_layer.py new file mode 100644 index 000000000000..4fab768ce63c --- /dev/null +++ b/deepspeed/sequence/fpdt_layer.py @@ -0,0 +1,1225 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import torch + +from typing import Optional, Any, Tuple +from torch import Tensor +from packaging import version +import deepspeed.comm as dist +from deepspeed.accelerator import get_accelerator + +try: + import flash_attn + from flash_attn.flash_attn_interface import _flash_attn_forward, _flash_attn_backward + flash_attn_version = version.parse(flash_attn.__version__) +except ImportError: + _flash_attn_forward = None + _flash_attn_backward = None + +from einops import rearrange +from .layer import single_all_to_all, apply_rotary_pos_emb + + +def _rotate_half_backward(x): + x = rearrange(x, '... (j d) -> ... j d', j=2) + x1, x2 = x.unbind(dim=-2) + return torch.cat((x2, -x1), dim=-1) + + +def apply_rotary_pos_emb_backward(grad_output, freqs_cos, freqs_sin): + rot_dim = freqs_cos.shape[-1] + grad, grad_pass = grad_output[..., :rot_dim], grad_output[..., rot_dim:] + grad_t = (grad * freqs_cos) + (_rotate_half_backward(grad * freqs_sin)) + grad = grad_t if grad_pass.shape[-1] == 0 else torch.cat((grad_t, grad_pass), dim=-1) + return grad + + +def _update_out_and_lse( + out: torch.Tensor, + lse: torch.Tensor, + block_out: torch.Tensor, + block_lse: torch.Tensor, +) -> Tuple[torch.Tensor, torch.Tensor]: + + block_out = block_out.to(torch.float32) + block_lse = block_lse.transpose(-2, -1).unsqueeze(dim=-1) + + new_lse = lse + torch.log(1 + torch.exp(block_lse - lse)) + + out = torch.exp(lse - new_lse) * out + torch.exp(block_lse - new_lse) * block_out + + lse = new_lse + return out, lse + + +def update_out_and_lse( + out: Optional[torch.Tensor], + lse: Optional[torch.Tensor], + block_out: torch.Tensor, + block_lse: torch.Tensor, + slice_=None, +) -> Tuple[torch.Tensor, torch.Tensor]: + if out is None: + if slice_ is not None: + raise RuntimeError("first update_out_and_lse should not pass slice_ args") + out = block_out.to(torch.float32) + lse = block_lse.permute(0, 2, 1).contiguous().unsqueeze(dim=-1).contiguous() + elif slice_ is not None: + slice_out, slice_lse = out[slice_], lse[slice_] + slice_out, slice_lse = _update_out_and_lse(slice_out, slice_lse, block_out, block_lse) + out[slice_], lse[slice_] = slice_out, slice_lse + else: + out, lse = _update_out_and_lse(out, lse, block_out, block_lse) + return out, lse + + +class FPDT_InputConstruct(torch.nn.Module): + + def __init__(self, tokens, labels, loss_mask, attention_mask, position_ids, args, sp_size, sp_rank) -> None: + + super(FPDT_InputConstruct, self).__init__() + self.tokens = tokens + self.labels = labels + self.loss_mask = loss_mask + self.attention_mask = attention_mask + self.position_ids = position_ids + global_seq_len = tokens.shape[1] + batch_size = tokens.shape[0] + assert global_seq_len % sp_size == 0 + assert global_seq_len % args.ds_sequence_parallel_fpdt_chunk_size == 0 + num_chunk_per_gpu = global_seq_len // args.ds_sequence_parallel_fpdt_chunk_size + local_seq_len = global_seq_len // sp_size + assert local_seq_len % num_chunk_per_gpu == 0 + + self.num_chunk_per_gpu = num_chunk_per_gpu + self.chunk_size = local_seq_len // num_chunk_per_gpu + self.sp_size = sp_size + self.sp_rank = sp_rank + self.global_seq_len = global_seq_len + self.local_seq_len = local_seq_len + self.batch_size = batch_size + self.device = tokens.device + + def generate(self): + device = self.device + totalChunks = self.global_seq_len // self.chunk_size + token_chunk_idx = torch.arange(self.global_seq_len, device=device, dtype=torch.int) // self.chunk_size + chunk_to_gpu = torch.arange(totalChunks, device=device, dtype=torch.int) + chunk_to_gpu = chunk_to_gpu.reshape(self.num_chunk_per_gpu, -1).t().contiguous() + + gather_chunk = chunk_to_gpu.flatten().unsqueeze(1).contiguous() + mask = gather_chunk == token_chunk_idx + + indices = mask.nonzero(as_tuple=False) + gather_indices = indices[:, 0] + token_chunk_indices = indices[:, 1] + indices = torch.cat([token_chunk_indices[gather_indices == i] for i in range(gather_chunk.shape[0])]) + load_balanced_loss_mask = self.loss_mask[:, indices] if self.loss_mask is not None else self.loss_mask + + indices = indices.reshape(-1, self.chunk_size)[self.num_chunk_per_gpu * self.sp_rank:self.num_chunk_per_gpu * + (self.sp_rank + 1)].flatten().contiguous() + load_balanced_tokens = self.tokens[:, indices] + load_balanced_labels = self.labels[:, indices] if self.labels is not None else self.labels + + load_balanced_attention_mask = self.attention_mask if self.attention_mask is not None else self.attention_mask + load_balanced_position_ids = self.position_ids[:, + indices] if self.position_ids is not None else self.position_ids + + return load_balanced_tokens, load_balanced_labels, load_balanced_loss_mask, load_balanced_attention_mask, load_balanced_position_ids + + +class _FPDTGPUAttentionImpl_(torch.autograd.Function): + generate_vmap_rule = False + + @staticmethod + def forward(ctx: Any, + layernorm_output, + attention_mask, + inference_params, + rotary_pos_emb, + spg, + scatter_idx, + gather_idx, + hidden_size, + projection_size, + hidden_size_per_attention_head, + kv_projection_size, + qkv_linear_weight, + qkv_linear_bias, + dropout, + num_chunks=8, + cpu_offloading=True): + + do_save = layernorm_output.requires_grad + + if rotary_pos_emb is not None: + pos_emb_cos, pos_emb_sin = rotary_pos_emb[0].permute(1, 0, 2, 3), rotary_pos_emb[1].permute(1, 0, 2, 3) + ctx.pos_emb_cos = pos_emb_cos + ctx.pos_emb_sin = pos_emb_sin + else: + ctx.pos_emb_cos = None + ctx.pos_emb_sin = None + + with torch.no_grad(): + per_gpu_seq_len = layernorm_output.shape[0] + chunk_size = per_gpu_seq_len // num_chunks + assert chunk_size * num_chunks == per_gpu_seq_len + assert attention_mask is None + ctx.num_chunks = num_chunks + ctx.cpu_offloading = cpu_offloading + ctx.spg = spg + ctx.scatter_idx = scatter_idx + ctx.gather_idx = gather_idx + + device = get_accelerator().current_device_name() + ctx.device = device + ctx.dtype = layernorm_output.dtype + ctx.projection_size = projection_size + ctx.kv_projection_size = kv_projection_size + + global_q = [] + global_k = [] + global_v = [] + + ctx.softmax_scale = hidden_size_per_attention_head**(-0.5) + + ctx.dropout_p = dropout + ctx.window_size = (-1, -1) + ctx.alibi_slopes = None + + batch_size = layernorm_output.shape[1] + + global_o = [None for _ in range(num_chunks)] + global_lse = [None for _ in range(num_chunks)] + + for i in range(num_chunks): + + st = chunk_size * i + ed = st + chunk_size + + qkv_chunk = torch.matmul(layernorm_output[st:ed], qkv_linear_weight.t()) + qkv_linear_bias + + q_chunk = qkv_chunk[:, :, :projection_size].contiguous().reshape( + qkv_chunk.shape[0], qkv_chunk.shape[1], -1, + hidden_size_per_attention_head).permute(1, 0, 2, 3).contiguous() # b, l, nh, hd + q_chunk = single_all_to_all(q_chunk, scatter_idx, gather_idx, 0, spg) + global_q_chunk_len = q_chunk.shape[1] + if rotary_pos_emb is not None: + q_chunk = apply_rotary_pos_emb(q_chunk, + pos_emb_cos[:, global_q_chunk_len * i:global_q_chunk_len * (i + 1)], + pos_emb_sin[:, global_q_chunk_len * i:global_q_chunk_len * (i + 1)]) + global_q.append(q_chunk) + + k_chunk = qkv_chunk[:, :, projection_size:projection_size + kv_projection_size].contiguous().reshape( + qkv_chunk.shape[0], qkv_chunk.shape[1], -1, + hidden_size_per_attention_head).permute(1, 0, 2, 3).contiguous() # b, l, nh, hd + k_chunk = single_all_to_all(k_chunk, scatter_idx, gather_idx, 0, spg) + if rotary_pos_emb is not None: + k_chunk = apply_rotary_pos_emb(k_chunk, + pos_emb_cos[:, global_q_chunk_len * i:global_q_chunk_len * (i + 1)], + pos_emb_sin[:, global_q_chunk_len * i:global_q_chunk_len * (i + 1)]) + global_k.append(k_chunk) + + v_chunk = qkv_chunk[:, :, projection_size + kv_projection_size:].contiguous().reshape( + qkv_chunk.shape[0], qkv_chunk.shape[1], -1, + hidden_size_per_attention_head).permute(1, 0, 2, 3).contiguous() # b, l, nh, hd + v_chunk = single_all_to_all(v_chunk, scatter_idx, gather_idx, 0, spg) + global_v.append(v_chunk) + + for k_i in range(len(global_k)): + causal_chunk = i == k_i + if flash_attn_version >= version.parse("2.6.0"): + block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward(global_q[i], + global_k[k_i], + global_v[k_i], + ctx.dropout_p, + ctx.softmax_scale, + causal=causal_chunk, + window_size=ctx.window_size, + softcap=0.0, + alibi_slopes=ctx.alibi_slopes, + return_softmax=False) + else: + block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward(global_q[i], + global_k[k_i], + global_v[k_i], + ctx.dropout_p, + ctx.softmax_scale, + causal=causal_chunk, + window_size=ctx.window_size, + alibi_slopes=ctx.alibi_slopes, + return_softmax=False) + + global_o[i], global_lse[i] = update_out_and_lse(global_o[i], global_lse[i], block_out, block_lse) + + global_o[i] = global_o[i].to(q_chunk.dtype) + + output = [None for i in range(num_chunks)] + + for i in range(num_chunks): + global_lse[i] = global_lse[i][:, :, :, 0].permute(0, 2, 1).contiguous() + output[i] = single_all_to_all(global_o[i].to(ctx.dtype).contiguous(), gather_idx, scatter_idx, 0, spg) + output = torch.cat(output, dim=1) + + head_dim = output.shape[-1] + + if do_save: + ctx.save_for_backward(layernorm_output) + ctx.global_q = global_q + ctx.global_k = global_k + ctx.global_v = global_v + ctx.attn_output = global_o + ctx.attn_lse = global_lse + ctx.head_dim = head_dim + ctx.batch_size = batch_size + + ctx.qkv_linear_weight = qkv_linear_weight + ctx.qkv_linear_bias = qkv_linear_bias + + return output + + @staticmethod + def backward(ctx, grad_output): + + num_chunks = ctx.num_chunks + device = ctx.device + dtype = ctx.dtype + spg = ctx.spg + scatter_idx = ctx.scatter_idx + gather_idx = ctx.gather_idx + softmax_scale = ctx.softmax_scale + dropout_p = ctx.dropout_p + window_size = ctx.window_size + alibi_slopes = ctx.alibi_slopes + + projection_size = ctx.projection_size + kv_projection_size = ctx.kv_projection_size + + layernorm_output = ctx.saved_tensors[0] + + global_q = ctx.global_q + global_k = ctx.global_k + global_v = ctx.global_v + attn_output = ctx.attn_output + lse = ctx.attn_lse + + qkv_linear_weight = ctx.qkv_linear_weight + qkv_linear_bias = ctx.qkv_linear_bias + + input_chunk_size = layernorm_output.shape[0] // num_chunks + grad_layernorm_output = [ + torch.zeros((input_chunk_size, layernorm_output.shape[1], layernorm_output.shape[2]), + device=device, + dtype=dtype) for _ in range(num_chunks) + ] + + grad_global_attn_output = [] + chunk_size = grad_output.shape[1] // num_chunks + + for i in range(num_chunks): + st = chunk_size * i + ed = st + chunk_size + grad_global_attn_output.append( + single_all_to_all(grad_output[:, st:ed].contiguous(), scatter_idx, gather_idx, 0, spg)) + + del grad_output + + dq = [torch.zeros(global_q[0].shape, dtype=torch.float, device=device) for _ in range(num_chunks)] + dk = [torch.zeros(global_q[0].shape, dtype=torch.float, device=device) for _ in range(num_chunks)] + dv = [torch.zeros(global_q[0].shape, dtype=torch.float, device=device) for _ in range(num_chunks)] + + grad_qkv_linear_weight = torch.zeros(qkv_linear_weight.shape, + device=qkv_linear_weight.device, + dtype=torch.float) + grad_qkv_linear_bias = torch.zeros(qkv_linear_bias.shape, device=qkv_linear_weight.device, dtype=torch.float) + + for i in range(num_chunks): + k_chunk = global_k[i] + v_chunk = global_v[i] + + for q_i in range(num_chunks): + no_computation = q_i < i + if no_computation: + continue + + causal_chunk = q_i == i + + q_chunk = global_q[q_i] + attn_output_chunk = attn_output[q_i] + lse_chunk = lse[q_i] + d_out = grad_global_attn_output[q_i] + + dq_this = torch.zeros(global_q[0].shape, dtype=dtype, device=device) + dk_this = torch.zeros(global_k[0].shape, dtype=dtype, device=device) + dv_this = torch.zeros(global_v[0].shape, dtype=dtype, device=device) + + if flash_attn_version >= version.parse("2.6.0"): + _flash_attn_backward(d_out, + q_chunk, + k_chunk, + v_chunk, + attn_output_chunk, + lse_chunk, + dq_this, + dk_this, + dv_this, + dropout_p, + softmax_scale, + causal_chunk, + window_size, + softcap=0.0, + alibi_slopes=alibi_slopes, + deterministic=False, + rng_state=None) + else: + _flash_attn_backward(d_out, + q_chunk, + k_chunk, + v_chunk, + attn_output_chunk, + lse_chunk, + dq_this, + dk_this, + dv_this, + dropout_p, + softmax_scale, + causal_chunk, + window_size, + alibi_slopes=alibi_slopes, + deterministic=False, + rng_state=None) + + dq[q_i].add_(dq_this.to(torch.float)) + dk[i].add_(dk_this.to(torch.float)) + dv[i].add_(dv_this.to(torch.float)) + + dk_seq_len = dk[i].shape[1] + + if ctx.pos_emb_cos is not None: + dk[i] = apply_rotary_pos_emb_backward(dk[i].to(dtype), + ctx.pos_emb_cos[:, dk_seq_len * i:dk_seq_len * (i + 1)], + ctx.pos_emb_sin[:, dk_seq_len * i:dk_seq_len * (i + 1)]) + else: + dk[i] = dk[i].to(dtype) + dv[i] = dv[i].to(dtype) + dk[i] = single_all_to_all(dk[i].contiguous(), gather_idx, scatter_idx, 0, spg) + dv[i] = single_all_to_all(dv[i].contiguous(), gather_idx, scatter_idx, 0, spg) + + input_st = i * input_chunk_size + input_ed = input_st + input_chunk_size + + input_chunk = layernorm_output[input_st:input_ed].reshape(-1, layernorm_output.shape[-1]) + + dk[i] = dk[i].flatten(2).permute(1, 0, 2) + dv[i] = dv[i].flatten(2).permute(1, 0, 2) + l, b = dk[i].shape[0], dk[i].shape[1] + grad_qkv_linear_weight[projection_size:projection_size + kv_projection_size].add_( + torch.matmul(dk[i].reshape(l * b, -1).t(), input_chunk)) + grad_qkv_linear_weight[projection_size + kv_projection_size:].add_( + torch.matmul(dv[i].reshape(l * b, -1).t(), input_chunk)) + grad_qkv_linear_bias[projection_size:projection_size + kv_projection_size].add_(dk[i].sum(0).sum(0)) + grad_qkv_linear_bias[projection_size + kv_projection_size:].add_(dv[i].sum(0).sum(0)) + + grad_layernorm_output[i].add_( + torch.matmul(dk[i], qkv_linear_weight[projection_size:projection_size + kv_projection_size])) + grad_layernorm_output[i].add_(torch.matmul(dv[i], + qkv_linear_weight[projection_size + kv_projection_size:])) + + dk[i] = None + dv[i] = None + + for i in range(num_chunks): + dq_seq_len = dq[i].shape[1] + if ctx.pos_emb_cos is not None: + dq[i] = apply_rotary_pos_emb_backward(dq[i].to(dtype), + ctx.pos_emb_cos[:, dq_seq_len * i:dq_seq_len * (i + 1)], + ctx.pos_emb_sin[:, dq_seq_len * i:dq_seq_len * (i + 1)]) + else: + dq[i] = dq[i].to(dtype) + dq[i] = single_all_to_all(dq[i].to(dtype).contiguous(), gather_idx, scatter_idx, 0, spg) + + input_chunk = layernorm_output[:input_chunk_size].reshape(-1, layernorm_output.shape[-1]) + layernorm_output = layernorm_output[input_chunk_size:] + + dq[i] = dq[i].flatten(2).permute(1, 0, 2) + l, b = dq[i].shape[0], dq[i].shape[1] + grad_qkv_linear_weight[:projection_size].add_(torch.matmul(dq[i].reshape(l * b, -1).t(), input_chunk)) + grad_qkv_linear_bias[:projection_size].add_(dq[i].sum(0).sum(0)) + + grad_layernorm_output[i].add_(torch.matmul(dq[i], qkv_linear_weight[:projection_size])) + + dq[i] = None + + return torch.cat( + grad_layernorm_output, + dim=0).to(dtype), None, None, None, None, None, None, None, None, None, None, grad_qkv_linear_weight.to( + dtype), grad_qkv_linear_bias.to(dtype), None, None, None + + +class SequenceChunk: + + def __init__(self, chunk: torch.Tensor, device=None, is_in_use=False): + + self.chunk_shape = chunk.shape + self.chunk_dtype = chunk.dtype + self.device = chunk.device if device is None else device + + cpu_chunk = torch.empty(chunk.shape, dtype=chunk.dtype, device='cpu', pin_memory=True) + + if get_accelerator().on_accelerator(chunk): + cpu_chunk.copy_(chunk, non_blocking=True) + else: + cpu_chunk = chunk + + self.cpu_chunk = cpu_chunk + + self.gpu_chunk = chunk if is_in_use else None + + def load_to_gpu(self): + assert self.gpu_chunk is None + if self.gpu_chunk is not None: + pass + else: + gpu_chunk = torch.empty(self.chunk_shape, device=self.device, dtype=self.chunk_dtype) + gpu_chunk.copy_(self.cpu_chunk, non_blocking=True) + self.gpu_chunk = gpu_chunk + + def get_gpu_chunk(self): + assert self.gpu_chunk is not None and self.gpu_chunk.device == self.device + return self.gpu_chunk + + def check_gpu_chunk(self, ): + assert (self.gpu_chunk is not None) and ( + self.gpu_chunk.device == self.device + ), f"gpu_chunk {self.gpu_chunk is not None} shound be on {self.device}, but it is now on {self.gpu_chunk.device}" + return True + + def offload(self): + assert self.gpu_chunk is not None and self.gpu_chunk.device == self.device + del self.gpu_chunk + self.gpu_chunk = None + + def overwrite_to_cpu(self): + assert self.gpu_chunk is not None and self.gpu_chunk.device == self.device + self.cpu_chunk.copy_(self.gpu_chunk, non_blocking=True) + + +class _FPDTGPUOffloadingAttentionImpl_(torch.autograd.Function): + generate_vmap_rule = False + + @staticmethod + def forward(ctx: Any, + layernorm_output, + attention_mask, + inference_params, + rotary_pos_emb, + spg, + scatter_idx, + gather_idx, + hidden_size, + projection_size, + hidden_size_per_attention_head, + kv_projection_size, + qkv_linear_weight, + qkv_linear_bias, + dropout, + num_chunks=8, + cpu_offloading=True): + + do_save = layernorm_output.requires_grad + + if rotary_pos_emb is not None: + pos_emb_cos, pos_emb_sin = rotary_pos_emb[0].permute(1, 0, 2, 3), rotary_pos_emb[1].permute(1, 0, 2, 3) + ctx.pos_emb_cos = pos_emb_cos + ctx.pos_emb_sin = pos_emb_sin + else: + ctx.pos_emb_cos = None + ctx.pos_emb_sin = None + with torch.no_grad(): + per_gpu_seq_len = layernorm_output.shape[0] + chunk_size = per_gpu_seq_len // num_chunks + assert chunk_size * num_chunks == per_gpu_seq_len + assert attention_mask is None + ctx.num_chunks = num_chunks + ctx.cpu_offloading = cpu_offloading + ctx.spg = spg + ctx.scatter_idx = scatter_idx + ctx.gather_idx = gather_idx + + ctx.chunk_size = chunk_size + device = get_accelerator().current_device_name() + ctx.device = device + ctx.dtype = layernorm_output.dtype + ctx.projection_size = projection_size + ctx.kv_projection_size = kv_projection_size + + global_q = [] + global_k = [] + global_v = [] + + ctx.softmax_scale = hidden_size_per_attention_head**(-0.5) + + ctx.dropout_p = dropout + ctx.window_size = (-1, -1) + ctx.alibi_slopes = None + + batch_size = layernorm_output.shape[1] + + global_o = [] + global_lse = [] + + layernorm_output_cpu = [] + final_output = [] + + offload_stream = get_accelerator().Stream() + general_offload_stream = get_accelerator().Stream() + compute_stream = get_accelerator().default_stream() + + q_compute_chunk_idx = 0 + kv_compute_chunk_idx = 0 + for i in range(num_chunks): + + qkv_chunk = torch.matmul(layernorm_output[:chunk_size], + qkv_linear_weight.t()) + qkv_linear_bias # torch.Size([18126, 1, 12288]) + + with get_accelerator().stream(general_offload_stream): + layernorm_output_cpu.append(SequenceChunk(layernorm_output[:chunk_size])) + + layernorm_output = layernorm_output[chunk_size:] + + q_chunk = qkv_chunk[:, :, :projection_size].contiguous().reshape( + qkv_chunk.shape[0], qkv_chunk.shape[1], -1, + hidden_size_per_attention_head).permute(1, 0, 2, 3).contiguous() # b, l, nh, hd + q_chunk = single_all_to_all(q_chunk, scatter_idx, gather_idx, 0, spg) + global_q_chunk_len = q_chunk.shape[1] + + k_chunk = qkv_chunk[:, :, projection_size:projection_size + kv_projection_size].contiguous().reshape( + qkv_chunk.shape[0], qkv_chunk.shape[1], -1, + hidden_size_per_attention_head).permute(1, 0, 2, 3).contiguous() # b, l, nh, hd + k_chunk = single_all_to_all(k_chunk, scatter_idx, gather_idx, 0, spg) + + v_chunk = qkv_chunk[:, :, projection_size + kv_projection_size:].contiguous().reshape( + qkv_chunk.shape[0], qkv_chunk.shape[1], -1, + hidden_size_per_attention_head).permute(1, 0, 2, 3).contiguous() # b, l, nh, hd + v_chunk = single_all_to_all(v_chunk, scatter_idx, gather_idx, 0, spg) + + dist.barrier() + + if ctx.pos_emb_cos is not None: + pos_emb_cos_chunk = pos_emb_cos[:, global_q_chunk_len * i:global_q_chunk_len * (i + 1)] + pos_emb_sin_chunk = pos_emb_sin[:, global_q_chunk_len * i:global_q_chunk_len * (i + 1)] + + q_chunk = apply_rotary_pos_emb(q_chunk, pos_emb_cos_chunk, pos_emb_sin_chunk) + k_chunk = apply_rotary_pos_emb(k_chunk, pos_emb_cos_chunk, pos_emb_sin_chunk) + + compute_stream.wait_stream(offload_stream) + compute_stream.synchronize() + with get_accelerator().stream(offload_stream): + global_q.append(SequenceChunk(q_chunk, is_in_use=True)) + global_k.append(SequenceChunk(k_chunk, is_in_use=True)) + global_v.append(SequenceChunk(v_chunk, is_in_use=True)) + + del qkv_chunk + + cur_attn_output = None + cur_attn_lse = None + for k_i in range(len(global_k)): + causal_chunk = i == k_i + with get_accelerator().stream(compute_stream): + if flash_attn_version >= version.parse("2.6.0"): + block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward( + global_q[q_compute_chunk_idx].get_gpu_chunk(), + global_k[kv_compute_chunk_idx].get_gpu_chunk(), + global_v[kv_compute_chunk_idx].get_gpu_chunk(), + ctx.dropout_p, + ctx.softmax_scale, + causal=causal_chunk, + window_size=ctx.window_size, + softcap=0.0, + alibi_slopes=ctx.alibi_slopes, + return_softmax=False) + else: + block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward( + global_q[q_compute_chunk_idx].get_gpu_chunk(), + global_k[kv_compute_chunk_idx].get_gpu_chunk(), + global_v[kv_compute_chunk_idx].get_gpu_chunk(), + ctx.dropout_p, + ctx.softmax_scale, + causal=causal_chunk, + window_size=ctx.window_size, + alibi_slopes=ctx.alibi_slopes, + return_softmax=False) + cur_attn_output, cur_attn_lse = update_out_and_lse(cur_attn_output, cur_attn_lse, block_out, + block_lse) + + can_offload_kv = True + if k_i != (len(global_k) - 1) or i != (num_chunks - 1): + if k_i != (len(global_k) - 1): + next_kv_compute_chunk_idx = k_i + 1 + else: + next_kv_compute_chunk_idx = 0 + + if next_kv_compute_chunk_idx == kv_compute_chunk_idx: + can_offload_kv = False + else: + if next_kv_compute_chunk_idx != (len(global_k) - 1): + with get_accelerator().stream(offload_stream): + global_k[next_kv_compute_chunk_idx].load_to_gpu() + global_v[next_kv_compute_chunk_idx].load_to_gpu() + + if i == num_chunks - 1 and k_i == num_chunks - 1: + with get_accelerator().stream(offload_stream): + global_q[0].load_to_gpu() + global_k[0].load_to_gpu() + global_v[0].load_to_gpu() + global_o[0].load_to_gpu() + global_lse[0].load_to_gpu() + + compute_stream.wait_stream(offload_stream) + compute_stream.synchronize() + + if can_offload_kv: + global_k[kv_compute_chunk_idx].offload() + global_v[kv_compute_chunk_idx].offload() + kv_compute_chunk_idx = next_kv_compute_chunk_idx + + global_q[q_compute_chunk_idx].offload() + q_compute_chunk_idx += 1 + + all2all_output = single_all_to_all( + cur_attn_output.to(ctx.dtype).contiguous(), gather_idx, scatter_idx, 0, spg) + final_output.append(all2all_output) + with get_accelerator().stream(general_offload_stream): + global_o.append(SequenceChunk(cur_attn_output.to(ctx.dtype))) + global_lse.append(SequenceChunk(cur_attn_lse[:, :, :, 0].permute(0, 2, 1).contiguous())) + + compute_stream.wait_stream(general_offload_stream) + compute_stream.synchronize() + + final_output = torch.cat(final_output, dim=1) + + head_dim = final_output.shape[-1] + + if do_save: + ctx.layernorm_output = layernorm_output_cpu + ctx.global_q = global_q + ctx.global_k = global_k + ctx.global_v = global_v + ctx.attn_output = global_o + ctx.attn_lse = global_lse + ctx.head_dim = head_dim + ctx.batch_size = batch_size + + ctx.qkv_linear_weight = qkv_linear_weight + ctx.qkv_linear_bias = qkv_linear_bias + + return final_output + + @staticmethod + def backward(ctx, grad_output): + num_chunks = ctx.num_chunks + device = grad_output.device + dtype = ctx.dtype + spg = ctx.spg + scatter_idx = ctx.scatter_idx + gather_idx = ctx.gather_idx + softmax_scale = ctx.softmax_scale + dropout_p = ctx.dropout_p + window_size = ctx.window_size + alibi_slopes = ctx.alibi_slopes + + projection_size = ctx.projection_size + kv_projection_size = ctx.kv_projection_size + + layernorm_output = ctx.layernorm_output + + global_q = ctx.global_q + global_k = ctx.global_k + global_v = ctx.global_v + attn_output = ctx.attn_output + lse = ctx.attn_lse + + qkv_linear_weight = ctx.qkv_linear_weight + qkv_linear_bias = ctx.qkv_linear_bias + + offload_stream = get_accelerator().Stream() + general_offload_stream = get_accelerator().Stream() + compute_stream = get_accelerator().default_stream() + + chunk_size = grad_output.shape[1] // num_chunks + assert chunk_size == layernorm_output[0].cpu_chunk.shape[0] + + grad_layernorm_output = [ + torch.zeros(layernorm_output[0].chunk_shape, device=device, dtype=dtype) for _ in range(num_chunks) + ] + + grad_global_attn_output = [None for _ in range(num_chunks)] + + q_compute_chunk_idx = 0 + kv_compute_chunk_idx = 0 + last_q_accum_idx = 0 + + with get_accelerator().stream(general_offload_stream): + layernorm_output[0].load_to_gpu() + grad_qkv_linear_weight = torch.zeros(qkv_linear_weight.shape, + device=qkv_linear_weight.device, + dtype=torch.float) + grad_qkv_linear_bias = torch.zeros(qkv_linear_bias.shape, + device=qkv_linear_weight.device, + dtype=torch.float) + + grad_global_attn_output_chunk = single_all_to_all(grad_output[:, :chunk_size].contiguous(), scatter_idx, + gather_idx, 0, spg) + get_accelerator().synchronize() + grad_output = grad_output[:, chunk_size:] + + with get_accelerator().stream(offload_stream): + grad_global_attn_output[0] = SequenceChunk(grad_global_attn_output_chunk, is_in_use=True) + dq = [ + SequenceChunk(torch.zeros(global_q[0].chunk_shape, dtype=torch.float, device=device), is_in_use=True) + ] + [ + SequenceChunk(torch.zeros(global_q[0].chunk_shape, dtype=torch.float, device='cpu', pin_memory=True), + device) for _ in range(num_chunks - 1) + ] + dk_accum = torch.zeros(global_k[0].chunk_shape, dtype=torch.float, device=device) + dv_accum = torch.zeros(global_v[0].chunk_shape, dtype=torch.float, device=device) + + for i in range(num_chunks): + for q_i in range(num_chunks): + no_computation = q_i < i + if no_computation: + continue + + causal_chunk = q_i == i + + dq_this = torch.zeros(global_q[0].chunk_shape, dtype=dtype, device=device) + dk_this = torch.zeros(global_k[0].chunk_shape, dtype=dtype, device=device) + dv_this = torch.zeros(global_v[0].chunk_shape, dtype=dtype, device=device) + + with get_accelerator().stream(compute_stream): + if flash_attn_version >= version.parse("2.6.0"): + _flash_attn_backward(grad_global_attn_output[q_compute_chunk_idx].get_gpu_chunk(), + global_q[q_compute_chunk_idx].get_gpu_chunk(), + global_k[kv_compute_chunk_idx].get_gpu_chunk(), + global_v[kv_compute_chunk_idx].get_gpu_chunk(), + attn_output[q_compute_chunk_idx].get_gpu_chunk(), + lse[q_compute_chunk_idx].get_gpu_chunk(), + dq_this, + dk_this, + dv_this, + dropout_p, + softmax_scale, + causal_chunk, + window_size, + softcap=0.0, + alibi_slopes=alibi_slopes, + deterministic=False, + rng_state=None) + else: + _flash_attn_backward(grad_global_attn_output[q_compute_chunk_idx].get_gpu_chunk(), + global_q[q_compute_chunk_idx].get_gpu_chunk(), + global_k[kv_compute_chunk_idx].get_gpu_chunk(), + global_v[kv_compute_chunk_idx].get_gpu_chunk(), + attn_output[q_compute_chunk_idx].get_gpu_chunk(), + lse[q_compute_chunk_idx].get_gpu_chunk(), + dq_this, + dk_this, + dv_this, + dropout_p, + softmax_scale, + causal_chunk, + window_size, + alibi_slopes=alibi_slopes, + deterministic=False, + rng_state=None) + + if i != (len(global_k) - 1): + if q_i != (len(global_q) - 1): + next_q_compute_chunk_idx = q_i + 1 + else: + next_q_compute_chunk_idx = i + 1 + + can_offload_q = True + + if next_q_compute_chunk_idx == q_compute_chunk_idx: + can_offload_q = False + else: + with get_accelerator().stream(offload_stream): + if i > 0 or q_i > 0: + if can_offload_q and last_q_accum_idx != i: # the first q chunk calculate in the loop will be sent out, therefore we do not offload it + dq[last_q_accum_idx].offload() + dq[next_q_compute_chunk_idx].load_to_gpu() + global_q[next_q_compute_chunk_idx].load_to_gpu() + attn_output[next_q_compute_chunk_idx].load_to_gpu() + lse[next_q_compute_chunk_idx].load_to_gpu() + if grad_global_attn_output[next_q_compute_chunk_idx] is not None: + grad_global_attn_output[next_q_compute_chunk_idx].load_to_gpu() + + if grad_global_attn_output[next_q_compute_chunk_idx] is None: + grad_global_attn_output_chunk = single_all_to_all(grad_output[:, :chunk_size].contiguous(), + scatter_idx, gather_idx, 0, spg) + dist.barrier() + grad_output = grad_output[:, chunk_size:] + grad_global_attn_output[next_q_compute_chunk_idx] = SequenceChunk( + grad_global_attn_output_chunk, is_in_use=True) + + compute_stream.wait_stream(offload_stream) + compute_stream.synchronize() + + with get_accelerator().stream(compute_stream): + dq[q_compute_chunk_idx].check_gpu_chunk() + dq[q_compute_chunk_idx].gpu_chunk.add_(dq_this) + dk_accum.add_(dk_this) + dv_accum.add_(dv_this) + + offload_stream.wait_stream(compute_stream) + with get_accelerator().stream(offload_stream): + dq[q_compute_chunk_idx].overwrite_to_cpu() + + if can_offload_q: + global_q[q_compute_chunk_idx].offload() + attn_output[q_compute_chunk_idx].offload() + lse[q_compute_chunk_idx].offload() + grad_global_attn_output[q_compute_chunk_idx].offload() + + last_q_accum_idx = q_compute_chunk_idx + q_compute_chunk_idx = next_q_compute_chunk_idx + + compute_stream.wait_stream(offload_stream) + compute_stream.synchronize() + + dk_seq_len = dk_accum.shape[1] + + if ctx.pos_emb_cos is not None: + dq_accum = apply_rotary_pos_emb_backward(dq[kv_compute_chunk_idx].get_gpu_chunk().to(dtype), + ctx.pos_emb_cos[:, dk_seq_len * i:dk_seq_len * (i + 1)], + ctx.pos_emb_sin[:, dk_seq_len * i:dk_seq_len * (i + 1)]) + dk_accum = apply_rotary_pos_emb_backward(dk_accum.to(dtype), + ctx.pos_emb_cos[:, dk_seq_len * i:dk_seq_len * (i + 1)], + ctx.pos_emb_sin[:, dk_seq_len * i:dk_seq_len * (i + 1)]) + else: + dq_accum = dq[kv_compute_chunk_idx].get_gpu_chunk().to(dtype) + dk_accum = dk_accum.to(dtype) + dv_accum = dv_accum.to(dtype) + + dq_accum = single_all_to_all(dq_accum.contiguous(), gather_idx, scatter_idx, 0, spg) + dk_accum = single_all_to_all(dk_accum.contiguous(), gather_idx, scatter_idx, 0, spg) + dv_accum = single_all_to_all(dv_accum.contiguous(), gather_idx, scatter_idx, 0, spg) + + general_offload_stream.synchronize() + compute_stream.wait_stream(general_offload_stream) + dist.barrier() + + with get_accelerator().stream(compute_stream): + input_chunk = layernorm_output[i].get_gpu_chunk().reshape(-1, layernorm_output[i].chunk_shape[-1]) + + dq_accum = dq_accum.flatten(2).permute(1, 0, 2) + dk_accum = dk_accum.flatten(2).permute(1, 0, 2) + dv_accum = dv_accum.flatten(2).permute(1, 0, 2) + + l, b = dk_accum.shape[0], dk_accum.shape[1] + + grad_qkv_linear_weight[:projection_size].add_( + torch.matmul(dq_accum.reshape(l * b, -1).t(), input_chunk)) + grad_qkv_linear_weight[projection_size:projection_size + kv_projection_size].add_( + torch.matmul(dk_accum.reshape(l * b, -1).t(), input_chunk)) + grad_qkv_linear_weight[projection_size + kv_projection_size:].add_( + torch.matmul(dv_accum.reshape(l * b, -1).t(), input_chunk)) + + grad_qkv_linear_bias[:projection_size].add_(dq_accum.sum(0).sum(0)) + grad_qkv_linear_bias[projection_size:projection_size + kv_projection_size].add_(dk_accum.sum(0).sum(0)) + grad_qkv_linear_bias[projection_size + kv_projection_size:].add_(dv_accum.sum(0).sum(0)) + + grad_layernorm_output[i].add_(torch.matmul(dq_accum, qkv_linear_weight[:projection_size])) + grad_layernorm_output[i].add_( + torch.matmul(dk_accum, qkv_linear_weight[projection_size:projection_size + kv_projection_size])) + grad_layernorm_output[i].add_( + torch.matmul(dv_accum, qkv_linear_weight[projection_size + kv_projection_size:])) + + del dq_accum, dk_accum, dv_accum + dk_accum = torch.zeros(global_k[i].chunk_shape, dtype=torch.float, device=device) + dv_accum = torch.zeros(global_v[i].chunk_shape, dtype=torch.float, device=device) + dq[kv_compute_chunk_idx].offload() + dq[kv_compute_chunk_idx] = None + + if i != (len(global_k) - 1): + next_kv_compute_chunk_idx = kv_compute_chunk_idx + 1 + with get_accelerator().stream(offload_stream): + global_k[next_kv_compute_chunk_idx].load_to_gpu() + global_v[next_kv_compute_chunk_idx].load_to_gpu() + + with get_accelerator().stream(general_offload_stream): + layernorm_output[next_kv_compute_chunk_idx].load_to_gpu() + + compute_stream.wait_stream(offload_stream) + compute_stream.synchronize() + + layernorm_output[kv_compute_chunk_idx].offload() + global_k[kv_compute_chunk_idx].offload() + global_v[kv_compute_chunk_idx].offload() + kv_compute_chunk_idx = next_kv_compute_chunk_idx + + return torch.cat( + grad_layernorm_output, + dim=0).to(dtype), None, None, None, None, None, None, None, None, None, None, grad_qkv_linear_weight.to( + dtype), grad_qkv_linear_bias.to(dtype), None, None, None + + +class FPDT_Attention(torch.nn.Module): + + def __init__(self, + config, + first_weight, + first_bias, + second_weight, + second_bias, + sequence_process_group, + gather_idx: int = 0, + scatter_idx: int = 2, + return_bias=True, + chunk_size=65536, + enable_offloading=True) -> None: + + super(FPDT_Attention, self).__init__() + if _flash_attn_forward is None or _flash_attn_backward is None: + raise ImportError( + "DeepSpeed FPDT requires flash-attn 2.6.3. Please install it with `pip install flash-attn --no-build-isolation`." + ) + + self.spg = sequence_process_group + self.scatter_idx = scatter_idx + self.gather_idx = gather_idx + self.config = config + + self.projection_size = config.kv_channels * config.num_attention_heads + self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.kv_projection_size = config.kv_channels * config.num_key_value_heads + self.hidden_size = config.hidden_size + + self.qkv_linear_weight = first_weight + self.qkv_linear_bias = first_bias + self.qkv_dense_weight = second_weight + self.qkv_dense_bias = second_bias + + self.reture_bias = return_bias + self.dropout = config.attention_dropout + + self.chunk_size = chunk_size + self.double_buffer = enable_offloading + + def forward(self, + layernorm_output, + attention_mask, + inference_params, + rotary_pos_emb, + cpu_offloading=True) -> Tensor: + self.num_chunks_attn = layernorm_output.shape[0] * dist.get_world_size(self.spg) // self.chunk_size + + if not cpu_offloading or self.num_chunks_attn == 1: + output = _FPDTGPUAttentionImpl_.apply(layernorm_output, attention_mask, inference_params, rotary_pos_emb, + self.spg, self.scatter_idx, self.gather_idx, self.hidden_size, + self.projection_size, self.hidden_size_per_attention_head, + self.kv_projection_size, self.qkv_linear_weight, + self.qkv_linear_bias, self.dropout, self.num_chunks_attn, + cpu_offloading) + else: + output = _FPDTGPUOffloadingAttentionImpl_.apply( + layernorm_output, attention_mask, inference_params, rotary_pos_emb, self.spg, self.scatter_idx, + self.gather_idx, self.hidden_size, self.projection_size, self.hidden_size_per_attention_head, + self.kv_projection_size, self.qkv_linear_weight, self.qkv_linear_bias, self.dropout, + self.num_chunks_attn, cpu_offloading) + + output = output.flatten(2).permute(1, 0, 2).contiguous() + + output = torch.matmul(output, self.qkv_dense_weight.t()) + if not self.reture_bias: + output += self.qkv_dense_bias + return output, self.qkv_dense_bias if self.reture_bias else None + + +@torch.jit.script +def bias_gelu(x): + return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))) + + +@torch.jit.script +def bias_gelu_back(g, x): + tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)) + # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243 + ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out) + return ff * g + + +class FPDT_FFN(torch.autograd.Function): + generate_vmap_rule = False + + @staticmethod + def forward(ctx: Any, x, w1, b1, w2, b2, add_bias, chunk_size): + do_save = x.requires_grad + ctx.add_bias = add_bias + device = x.device + + with torch.no_grad(): + num_chunk = x.shape[0] // chunk_size + ctx.num_chunk = num_chunk + result = torch.empty(x.shape, device=device, dtype=x.dtype) + assert chunk_size * num_chunk == x.shape[0] + for i in range(num_chunk): + st = i * chunk_size + ed = st + chunk_size + x_ = torch.matmul(x[st:ed], w1.t()) + b1 + x_ = bias_gelu(x_) + if add_bias: + result[st:ed] = torch.matmul(x_, w2.t()) + b2 + else: + result[st:ed] = torch.matmul(x_, w2.t()) + + del x_ + + if do_save: + ctx.device = device + ctx.dtype = x.dtype + ctx.save_for_backward(x, w1, b1, w2, b2) + ctx.grad_x_shape = x.shape + return result.to(x.dtype), b2 if not add_bias else None + + @staticmethod + def backward(ctx, grad_output, grad_bias): + x, w1, b1, w2, b2 = ctx.saved_tensors + device = ctx.device + dtype = ctx.dtype + add_bias = ctx.add_bias + + num_chunk = ctx.num_chunk + chunk_size = x.shape[0] // num_chunk + assert chunk_size * num_chunk == grad_output.shape[0] + + grad_w2 = torch.zeros(w2.shape, device=device, dtype=torch.float) + grad_b2 = torch.zeros(b2.shape, device=device, dtype=torch.float) + grad_w1 = torch.zeros(w1.shape, device=device, dtype=torch.float) + grad_b1 = torch.zeros(b1.shape, device=device, dtype=torch.float) + + for i in range(num_chunk): + st = i * chunk_size + ed = st + chunk_size + x_chunk = x[st:ed] + + before_act = (torch.matmul(x_chunk, w1.t()) + b1) + before_act_2 = before_act**2 + tanh_out = torch.tanh(0.79788456 * before_act * (1 + 0.044715 * before_act_2)) + ff = 0.5 * before_act * ((1 - tanh_out * tanh_out) * + (0.79788456 + 0.1070322243 * before_act_2)) + 0.5 * (1 + tanh_out) + grad_w2.add_( + torch.matmul(grad_output[st:ed].reshape(-1, grad_output.shape[2]).t(), + (before_act * 0.5 * (1 + tanh_out)).reshape(-1, before_act.shape[2]))) + del before_act, before_act_2, tanh_out + + grad_inter = torch.matmul(grad_output[st:ed], w2) * ff + del ff + + grad_w1.add_(torch.matmul( + grad_inter.reshape(-1, grad_inter.shape[2]).t(), x_chunk.reshape(-1, x.shape[2]))) + grad_b1.add_(grad_inter.sum(0).sum(0)) + + x[st:ed].copy_(torch.matmul(grad_inter, w1)) + + del grad_inter + + if add_bias: + grad_b2.add_(grad_output[st:ed].sum(0).sum(0)) + + return x, grad_w1.to(dtype), grad_b1.to(dtype), grad_w2.to(dtype), grad_b2.to(dtype), None, None + + +class FPDT_LogitsLoss(torch.autograd.Function): + generate_vmap_rule = False + + @staticmethod + def forward(ctx: Any, lm_output, labels, logit_weights, rank, spg_size, spg, num_chunk): + labels = labels.t() + chunk_size = lm_output.shape[0] // num_chunk + assert chunk_size * num_chunk == lm_output.shape[0] + batch_size, local_seq_len = lm_output.shape[1], lm_output.shape[0] + loss = torch.empty((batch_size, local_seq_len), dtype=torch.float, device=lm_output.device) + + ctx.num_chunk = num_chunk + ctx.chunk_size = chunk_size + ctx.device = lm_output.device + ctx.dtype = lm_output.dtype + + ctx.rank = rank + ctx.local_seq_len = local_seq_len + with torch.no_grad(): + for i in range(num_chunk): + st = i * chunk_size + ed = st + chunk_size + logits_chunk = torch.matmul(lm_output[st:ed], logit_weights.t()).float() + + vocab_size = logits_chunk.size(2) + # nll + softmax = torch.nn.functional.softmax(logits_chunk, dim=-1) + loss_chunk = torch.nn.functional.nll_loss(softmax.log().reshape(-1, vocab_size).contiguous(), + labels[st:ed, :].reshape(-1).contiguous(), + reduction='none') + loss[:, st:ed] = loss_chunk.reshape(chunk_size, batch_size).t() + + del logits_chunk + ctx.save_for_backward(lm_output.to('cpu'), labels) + ctx.logit_weights = logit_weights + + seqlen = local_seq_len * spg_size + batch_size = loss.size(0) + loss = loss.t().contiguous() + loss_all = torch.empty(seqlen, batch_size, dtype=loss.dtype, device=loss.device).contiguous() + + dist.allgather_fn(loss_all, loss, group=spg) + + return loss_all + + @staticmethod + def backward(ctx, grad_output): + lm_output, labels = ctx.saved_tensors + logit_weights = ctx.logit_weights + device = ctx.device + dtype = ctx.dtype + num_chunk = ctx.num_chunk + chunk_size = ctx.chunk_size + + rank = ctx.rank + local_seq_len = ctx.local_seq_len + + grad_output = grad_output[rank * local_seq_len:(rank + 1) * local_seq_len] + grad_lm_output = [None for _ in range(num_chunk)] + grad_logit_weights = torch.zeros(logit_weights.shape, device=grad_output.device, dtype=torch.float) + for i in range(num_chunk): + st = i * chunk_size + ed = st + chunk_size + lm_output_chunk = lm_output[st:ed].to(device) + logits_chunk = torch.matmul(lm_output_chunk, logit_weights.t()).float() + + # nll + softmax = torch.nn.functional.softmax(logits_chunk, dim=-1) + vocab_size = logits_chunk.size(2) + + grad_input = softmax + grad_2d = grad_input.reshape(-1, vocab_size).contiguous() + arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=device) + + grad_2d[arange_1d, labels[st:ed, :].reshape(-1).contiguous()] -= 1 + grad_input.mul_(grad_output[:chunk_size, :].unsqueeze(dim=-1)) + grad_input = grad_input.to(dtype) + + grad_output = grad_output[chunk_size:].contiguous() + + grad_lm_output_chunk = torch.matmul(grad_input, logit_weights) + grad_lm_output[i] = grad_lm_output_chunk + + grad_logit_weights.add_( + torch.matmul( + grad_input.reshape(-1, grad_input.shape[2]).t(), + lm_output_chunk.reshape(-1, lm_output_chunk.shape[2]))) + + return torch.cat(grad_lm_output, dim=0).to(dtype), None, grad_logit_weights.to(dtype), None, None, None, None diff --git a/deepspeed/sequence/layer.py b/deepspeed/sequence/layer.py index 8fd3ca918433..3dfe149057e9 100644 --- a/deepspeed/sequence/layer.py +++ b/deepspeed/sequence/layer.py @@ -8,12 +8,41 @@ from torch import Tensor from torch.nn import Module +from einops import rearrange + import deepspeed.comm as dist from deepspeed.accelerator import get_accelerator from deepspeed.module_inject.tp_shard import get_shard_size_list, set_num_kv_heads, get_num_kv_heads from deepspeed.utils import groups +def _rotate_half(x): + """ + change sign so the last dimension becomes [-odd, +even] + """ + x = rearrange(x, '... (j d) -> ... j d', j=2) + x1, x2 = x.unbind(dim=-2) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(t, freqs_cos, freqs_sin): + """ + input tensor t is of shape [seq_length, ..., dim] + rotary positional embeding tensor freqs is of shape [seq_length, ..., dim] + check https://kexue.fm/archives/8265 for detailed formulas + """ + rot_dim = freqs_cos.shape[-1] + # ideally t_pass is empty so rotary pos embedding is applied to all tensor t + t, t_pass = t[..., :rot_dim], t[..., rot_dim:] + + # first part is cosine component + # second part is sine component, need to change signs with _rotate_half method + t = (t * freqs_cos) + (_rotate_half(t) * freqs_sin) + + res = t if t_pass.shape[-1] == 0 else torch.cat((t, t_pass), dim=-1) + return res + + def post_all2all(scatter_idx, batch_dim_idx, seq_world_size, bs, seq_len, num_head, head_dim): def post_func(input): @@ -304,7 +333,14 @@ def layer_sync(self, layer): if self.sp_overlap_comm and hasattr(layer, 'done_event'): self.dafult_stream.wait_event(layer.done_event) - def forward(self, query: Tensor, key: Tensor, value: Tensor, batch_dim_idx: int, *args: Any, **kwargs) -> Tensor: + def forward(self, + query: Tensor, + key: Tensor, + value: Tensor, + batch_dim_idx: int, + rotary_pos_emb=None, + *args: Any, + **kwargs) -> Tensor: """ forward Arguments: @@ -359,6 +395,10 @@ def pre_hook_fun(grad): grad_fn_k.register_prehook(bwd_hook(layer_type='k')) #out shape : e.g., [s:h/p:] + if rotary_pos_emb is not None: + pos_emb_cos, pos_emb_sin = rotary_pos_emb[0].permute(1, 0, 2, 3), rotary_pos_emb[1].permute(1, 0, 2, 3) + query_layer = apply_rotary_pos_emb(query_layer, pos_emb_cos, pos_emb_sin) + key_layer = apply_rotary_pos_emb(key_layer, pos_emb_cos, pos_emb_sin) context_layer = self.local_attn(query_layer, key_layer, value_layer, *args, **kwargs) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 296398f680cc..1af4c69c5807 100755 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,3 +1,4 @@ +einops hjson msgpack ninja diff --git a/tests/unit/sequence_parallelism/test_ulysses.py b/tests/unit/sequence_parallelism/test_ulysses.py index d9ed54322d5c..821847c44265 100644 --- a/tests/unit/sequence_parallelism/test_ulysses.py +++ b/tests/unit/sequence_parallelism/test_ulysses.py @@ -5,11 +5,13 @@ import pytest import torch +import torch.nn.functional as F import deepspeed.comm as dist from deepspeed import initialize from transformers import AutoModel from unit.common import DistributedTest from deepspeed.sequence.layer import _SeqAllToAll +from deepspeed.sequence.fpdt_layer import _FPDTGPUOffloadingAttentionImpl_, FPDT_InputConstruct from unit.util import skip_on_arch from unit.simple_model import * from deepspeed.utils import groups @@ -157,3 +159,99 @@ def seq_batch_heads_hash(d0, d1, h, offset_d0=0, offset_d1=0, offset_h=0): for i in range(0, len(outputs)): assert torch.allclose(inputs[i], outputs[i]), f"[{dist.get_rank()}]Outputs differ for sequence dim {seq_dims[i]}" + + +@pytest.mark.parametrize("d0", [4, 1]) #batch dimension +@pytest.mark.parametrize("d1", [2048, 8192]) #sequence dimension +@pytest.mark.parametrize("chunk_size", [128, 256]) #size of chunk +@pytest.mark.parametrize("num_heads", [8, 4]) +@pytest.mark.parametrize("head_dim", [32]) +class TestFPDTAttention(DistributedTest): + + def test_FPDT_attention_offloading_output_consistency(self, d0: int, d1: int, chunk_size: int, head_dim: int, + num_heads: int) -> None: + skip_on_arch(min_arch=8) + world_size = 2 + + try: + from flash_attn.flash_attn_interface import _flash_attn_forward, _flash_attn_backward + except ImportError: + _flash_attn_forward = None + _flash_attn_backward = None + + if _flash_attn_forward is None or _flash_attn_backward is None: + pytest.skip("Flash Attention is not available.") + + model = AutoModel.from_pretrained('bert-base-uncased') + ds_engine, _, _, _ = initialize( + model=model, + config_params={ + "train_batch_size": 8, + "data_parallel_size": 1, + "sequence_parallel_size": world_size + }, + ) + #3D tensor : l, b, d + dim = head_dim * num_heads + + seed = 42 + torch.manual_seed(seed) + get_accelerator().manual_seed_all(seed) + + input_tensor = torch.randn(d1, d0, dim, device=ds_engine.device, dtype=torch.half) # l, b, d + spg = ds_engine.seq_parallel_group + + dist.broadcast(input_tensor, src=0, group=spg) + + class args: + + def __init__(self): + self.ds_sequence_parallel_fpdt_chunk_size = chunk_size + + fpdt_input_tensor = FPDT_InputConstruct(input_tensor.permute(1, 0, 2), None, None, None, None, args(), + world_size, dist.get_rank()).generate()[0].permute(1, 0, 2) + + if dist.get_rank() == 0: + qkv_linear_weight = torch.nn.Parameter( + torch.empty(dim + 2 * dim, dim, device=dist.get_rank(), dtype=torch.half)) + torch.nn.init.normal_(qkv_linear_weight, mean=0.0, std=0.02) + + qkv_linear_bias = torch.nn.Parameter(torch.empty(dim + 2 * dim, device=dist.get_rank(), dtype=torch.half)) + torch.nn.init.normal_(qkv_linear_bias, mean=0.0, std=0.02) + else: + qkv_linear_weight = torch.nn.Parameter( + torch.empty(dim + 2 * dim, dim, device=dist.get_rank(), dtype=torch.half)) + qkv_linear_bias = torch.nn.Parameter(torch.empty(dim + 2 * dim, device=dist.get_rank(), dtype=torch.half)) + + dist.broadcast(qkv_linear_weight, src=0, group=spg) + dist.broadcast(qkv_linear_bias, src=0, group=spg) + + num_chunks_attn = fpdt_input_tensor.shape[0] * dist.get_world_size(spg) // chunk_size + fpdt_output = _FPDTGPUOffloadingAttentionImpl_.apply(fpdt_input_tensor, None, None, None, spg, 2, 0, dim, dim, + head_dim, dim, qkv_linear_weight, qkv_linear_bias, 0, + num_chunks_attn, True) + + # baseline + qkv = torch.matmul(input_tensor, qkv_linear_weight.t()) + qkv_linear_bias + q = qkv[:, :, :dim].contiguous().reshape(qkv.shape[0], qkv.shape[1], -1, head_dim).permute(1, 2, 0, + 3).contiguous() + k = qkv[:, :, dim:dim * 2].contiguous().reshape(qkv.shape[0], qkv.shape[1], -1, + head_dim).permute(1, 2, 0, 3).contiguous() + v = qkv[:, :, dim * 2:dim * 3].contiguous().reshape(qkv.shape[0], qkv.shape[1], -1, + head_dim).permute(1, 2, 0, + 3).contiguous() # b, nhead, l, d + + scores = torch.matmul(q, k.transpose(-2, -1)) / torch.sqrt(torch.tensor(dim, dtype=torch.half)) + + causal_mask = torch.triu(torch.ones(d1, d1, device=ds_engine.device), diagonal=1).bool() + causal_mask = causal_mask.unsqueeze(0).unsqueeze(0) + scores = scores.masked_fill(causal_mask, float('-inf')) + attn_weights = F.softmax(scores, dim=-1) + output = torch.matmul(attn_weights, v).permute(0, 2, 1, 3) + + baseline_output_shuffled = FPDT_InputConstruct(output, None, None, None, None, args(), world_size, + dist.get_rank()).generate()[0] # b, l, n, d + + assert torch.allclose( + fpdt_output, baseline_output_shuffled, rtol=0.01, atol=0.1 + ), f"rank {dist.get_rank()}, sp size: {dist.get_world_size(spg)}, input_tensor: {input_tensor.shape}, fpdt_input_tensor: {fpdt_input_tensor.shape}, fpdt_output: {fpdt_output.shape}, baseline_output_shuffled: {baseline_output_shuffled.shape},{torch.max(torch.abs(fpdt_output - baseline_output_shuffled))}" From b966e1f97f537a46474582143c2a4e3398adc66d Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Thu, 5 Dec 2024 08:38:01 -0800 Subject: [PATCH 047/112] Pin transformers to avoid errors with latest version (#6820) --- .github/workflows/nv-torch-latest-v100.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/nv-torch-latest-v100.yml b/.github/workflows/nv-torch-latest-v100.yml index 0b8f504d8b5a..2d69d0b94cb5 100644 --- a/.github/workflows/nv-torch-latest-v100.yml +++ b/.github/workflows/nv-torch-latest-v100.yml @@ -38,7 +38,7 @@ jobs: git clone https://github.com/huggingface/transformers cd transformers # if needed switch to the last known good SHA until transformers@master is fixed - # git checkout 1cc453d33 + git checkout 6c3f168b3 git rev-parse --short HEAD pip install . From 0b0fef3d41feeb550845ef4349b36c7f2dd7e840 Mon Sep 17 00:00:00 2001 From: Sam Ade Jacobs Date: Thu, 5 Dec 2024 08:39:44 -0800 Subject: [PATCH 048/112] Ulyssess offload blog (#6814) Ulysses-Offload (FPDT) blog, please see corresponding tutorial page at [link](https://github.com/microsoft/DeepSpeed/pull/6813). --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Logan Adams --- blogs/ulysses-offload/README.md | 263 +++++++++++++++++++++++++ blogs/ulysses-offload/media/image1.png | Bin 0 -> 1314185 bytes blogs/ulysses-offload/media/image2.png | Bin 0 -> 134983 bytes blogs/ulysses-offload/media/image3.png | Bin 0 -> 187041 bytes blogs/ulysses-offload/media/image4.png | Bin 0 -> 319690 bytes blogs/ulysses-offload/media/image5.png | Bin 0 -> 91541 bytes 6 files changed, 263 insertions(+) create mode 100644 blogs/ulysses-offload/README.md create mode 100644 blogs/ulysses-offload/media/image1.png create mode 100644 blogs/ulysses-offload/media/image2.png create mode 100644 blogs/ulysses-offload/media/image3.png create mode 100644 blogs/ulysses-offload/media/image4.png create mode 100644 blogs/ulysses-offload/media/image5.png diff --git a/blogs/ulysses-offload/README.md b/blogs/ulysses-offload/README.md new file mode 100644 index 000000000000..740c07229d9f --- /dev/null +++ b/blogs/ulysses-offload/README.md @@ -0,0 +1,263 @@ +# Ulysses-Offload: Democratizing Long Context LLM Training + + + +Figure 1: Ulysses-Offload supports 16x longer sequence lengths at 55% +Model FLOPs Utilization (MFU) than NVIDIA Megatron-SP and DeepSpeed Ulysses. + + +To cite and for more technical in depth of this release, please see +our [arxiv report](https://arxiv.org/abs/2408.16978): + +@article{yao2024ulysses, + +title={ Training Ultra Long Context Language Model with Fully Pipelined +Distributed Transformer}, + +author={Jinghan Yao and Sam Ade Jacobs and Masahiro Tanaka and Olatunji +Ruwase and Aamir Shafi and Hari Subramoni and Dhabaleswar K. (DK) Panda +}, + +journal={https://arxiv.org/abs/2408.16978}, + +year={2024} + +} + +## Introduction + +In the rapidly evolving field of generative AI and scientific ML, the +ability to train large (language) models with ultra-long context +capabilities is becoming increasingly important. These models are +essential for a variety of complex tasks, such as understanding +lengthy documents, generating images and videos, and processing extensive +sequences in computational biology. However, training such models +efficiently poses significant challenges due to the enormous GPU +memory required. + +Building DeepSpeed Ulysses, our previous project, which developed +system optimizations for training extremely long sequence transformer +models, we are excited to present Ulysses-Offload, in this release. Ulysses-Offload +is an innovative, resource-efficient technique that offers comparable +benefits to DeepSpeed Ulysses and other previous long-context +optimization methods, but with a lower hardware budget. Ulysses-Offload makes +ultra long-context large language models (LLM) training and finetuning +accessible to everyone, including those with limited GPU resources. Ulysses-Offload enables +training with context lengths of up to 2 million tokens using just 4 +NVIDIA A100-40GB GPUs. Ulysses-Offload supports 16x longer sequence lengths at 55% +Model FLOPs Utilization (MFU) than NVIDIA Megatron-SP and DeepSpeed Ulysses +(see Figure 1). The next section highlights the key innovations of Ulysses-Offload, +and subsequent sections provide additional details on the design and +usability of Ulysses-Offload, followed by experimental results. + +## Key Innovations + +### 1. Fully Pipelined Distributed Transformer (FPDT) + +The core innovation of our work is the Fully Pipelined Distributed +Transformer (FPDT). This approach leverages a pipelined sequence +chunking, which allows for the training of LLMs with sequence lengths up +to 2 million tokens on just 4 A100-40GB GPUs. By breaking down the +sequence into manageable chunks and processing them in a pipelined +manner, Ulysses-Offload significantly reduces the memory footprint while +maintaining high computational efficiency. This method ensures that the +GPUs are utilized effectively, even when dealing with extremely long +sequences. + +### 2. Memory Optimization + +One of the critical aspects of our approach is the comprehensive +analysis and optimization of the memory footprint during LLM training. +We target the reduction of redundant intermediate buffers in both the +forward and backward passes of the training process. By optimizing the +use of GPU and host CPU memory, we can train larger models with longer +sequences without running into GPU memory limitations. This optimization +is crucial for enabling the training of ultra-long context models on a +limited number of GPUs. It is worth noting that Ulysses-Offload memory optimization +is orthogonal and complementary to model- parameter-focused memory +optimization techniques used by DeepSpeed ZeRO and PyTorch FSDP. Ulysses-Offload optimizes memory footprint of activations associated with long sequences while ZeRO and FSDP optimize memory footprint of model parameters. + +### 3. Compatibility and Flexibility + +Ulysses-Offload is designed to be agnostic to existing training techniques and +works efficiently across different LLM models, including popular +architecture like GPT and Llama. This flexibility ensures that our +approach can be easily integrated into various training workflows. +Additionally, Ulysses-Offload is compatible with advanced memory optimization +techniques such as DeepSpeed ZeRO and PyTorch FSDP, further enhancing +its usability and performance. + +## Core Design of Ulysses-Offload + +Figure 2 illustrates the core structure of Ulysses-Offload. Ulysses-Offload leverages multiple +memory hierarchies in modern GPU clusters, thus boosting hardware +efficiency and cost-effectiveness while achieving very high model FLOP +utilization (MFU). The design of Ulysses-Offload centers around pipelining, +scheduling, and memory management. These well-known optimization +techniques are essential for scaling LLM context length to a million +scale with a few GPUs and will be discussed in the subsequent +subsections. + + + +Figure 2: Core design + +### + +### Pipelining and Scheduling + +Ulysses-Offload employs sequence chunking and pipelined computation design to manage the memory +and computational load efficiently. In traditional Transformer model, +input (hidden state) tensor is projected to q, k, v tensors. Each of these tensors can be denoted *\[B, S, H, D\]*, where *B* is batch +size, *S* is sequence length, *H* is number of heads and *D* is hidden +dimension per head. With sequence parallelism such as DeepSpeed Ulysses, +input tensor is partitioned along sequence dimension across sequence +parallel group P, that is *\[B, S/P, H, D\]* prior to alltoall collective +communication. The alltoall collective communication gathers partitioned tensors +along sequence dimension and scatter them along head dimension essentially +transforming tensor from *\[B, S/P, H, D\]* to *\[B, S, H/P, D\]*. Post attention computation, a second alltoall communication transforms *\[B, S, H/P, D\]* back to *\[B, S/P, H, D\]* + +In our Ulysses-Offload design, input sequence are partitioned at a much finer granularity than DeepSpeed Ulysses. In other words, we made changes to sequence partitioning such that we further subdivide per GPU *S/P* sequence into smaller *u* +chunks. Thus, the input tensors are now represented as \[*B, S/uP, H, +D*\]. We denote these chunks as *Ti*, +where$\ i\ \in \ 0,1,\ldots,\ u - 1.$ As shown in Figure 1, +*Ti* is projected to query *qi*, key +*ki*, and value *vi*. Then, similar to DeepSpeed Ulysses, an alltoall collective communication gathers partitioned tensor +along sequence dimension and scatter them along head dimension. In our chunk +design, the sequence length for each chunk is reduced by a factor of *u* +compared to Ulysses. Please note that our Ulysses-Offload chunking procedure is generally applicable to other sequence parallelism techniques. + + + +Figure 3: Core design with offload description + +Figure 3 gives an example of how to perform the computation of chunk +*Tm*. After the alltoall collective communication, +*GPUj* receives +$\widehat{q}m,\ \widehat{k}m,\ and\ \widehat{v}m$*.* We then fetch the +previous sequence chunk by chunk from the host memory to +GPUj, and perform online attention with the current +$\widehat{q}m$ and update the output chunk accordingly. Note that, in a +strict manner, at any given time, only one set of chunks +$\widehat{k}i,\ and\ \widehat{v}i$ is placed on GPU's HBM, reducing the +memory footprint to $\frac{1}{u}$ compared to the non-offloading version +without double buffering. With double buffering, memory footprint is +reduced by *2/u*. + +### Memory Management + +Ulysses-Offload optimizes memory usage by carefully managing the allocation and +deallocation of buffers during training. This involves: + +1. Double Buffering: + + - Two sets of buffers are maintained to overlap computation with + data transfer. + + - While one set of buffers is used for computation, the other set is + preloaded with the next chunk of data. + +2. Hierarchical Memory Utilization: + + - GPU High Bandwidth Memory (HBM) is used for active computation. + + - Host memory is used to store intermediate results that are not + immediately needed, reducing the pressure on GPU memory. + +## Integration with Existing Frameworks + +Ulysses-Offload is designed to integrate seamlessly with popular deep learning +frameworks such as PyTorch. Ulysses-Offload provides user-friendly APIs that +abstract the complexities of pipelined training and memory management. +Users can adopt Ulysses-Offload with minimal changes to existing codebases. + +## Experimental Results + + + +Figure 4: Supported sequence lengths and corresponding Model FLOPs +Utilization (MFU) using Megatron-SP, Ulysses, and our proposed Ulysses-Offload (FPDT). OOM +denotes the point where increasing sequence length will cause memory +issues. We show Ulysses-Offload's performance when the sequence length is larger +than 128K, as shorter sequences can be properly handled by existing +strategies. + +### Extended Sequence Lengths + +In our experimental setup, we compare Ulysses-Offload with two existing methods: +Microsoft DeepSpeed Ulysses and NVIDIA Megatron-SP. Both DeepSpeed +Ulysses and Megatron-SP employ similar approaches to sequence +parallelism but differ in the collective communication used for +gathering sequences before the attention block. The former utilizes +alltoall communication, whereas the latter employs allgather. Ulysses-Offload +builds upon the DeepSpeed Ulysses approach. The primary advantage of +Ulysses-Offload is its capability to support the training of large language models +(LLMs) with ultra-long sequence lengths using fewer GPUs. As shown in +Figure 4, our method enables the training of 8B parameter models with +sequence lengths of 2 million tokens using only 4 GPUs. For even larger +models, such as GPT-30B and Llama-70B parameter models, Ulysses-Offload supports +sequence lengths up to 3 million and 4 million tokens using 16 GPUs and +32 GPUs respectively. This represents a 16x increase in sequence length +compared to current state-of-the-art solutions (see Figure 5), making +Ulysses-Offload a game-changer for tasks that require processing long sequences. + +### High Hardware Efficiency + +As shown in Figure 4 with different model sizes ranging from GPT-2.7B to +Llama-80B parameters, Ulysses-Offload achieves over 55% Model FLOPs Utilization +(MFU), ensuring that the hardware resources are utilized effectively. +This high level of efficiency is maintained even when dealing with +extremely long sequences (up to 4 million context length), making Ulysses-Offload +an ideal solution for training large-scale LLMs. By maximizing the use +of available hardware, Ulysses-Offload reduces the overall cost and complexity of +training long-context models. Our [technical report](https://arxiv.org/abs/2408.16978) offers +further insights into optimizing sequence chunks to balance the +trade-off between memory usage and MFU. + + + +Figure 5: A comprehensive analysis on long-context LLM training with +different training techniques: tensor parallelism (TP), activation +checkpoint (AC), activation checkpoint with CPU offloading (OC), Ulysses +(UL), and our approach Ulysses-Offload (FPDT). + +## Implementation and Usability + +Ulysses-Offload is designed to be easily integrated with popular deep learning +frameworks such as DeepSpeed, Megatron-DeepSpeed and PyTorch. Users can +adopt our approach with minimal changes to their existing training +pipeline, making it accessible to a broad audience. The integration +process involves setting up the sequence chunk pipeline and configuring +the memory optimization techniques, both of which are straightforward +and well-documented (see tutorial). + +Our pipeline design and memory optimization techniques are +straightforward to implement, making Ulysses-Offload accessible to researchers and +practitioners aiming to train long-context LLMs efficiently. We provide +detailed [technical report](https://arxiv.org/abs/2408.16978), +documentation and examples to guide users through the setup process, +ensuring a smooth transition to using Ulysses-Offload. Additionally, Ulysses-Offload, in the +tradition of DeepSpeed provides user-friendly API which abstracts the +complexities of mixed precision training and memory optimization, +allowing users to focus on their research and development tasks. + +## General Availability of DeepSpeed Ulysses-Offload + +We are excited to release Ulysses-Offload. Ulysses-Offload has been +fully integrated with Megatron-DeepSpeed and accessible through both +DeepSpeed and Megatron-DeepSpeed GitHub repos. Click here for detailed +[tutorial](https://www.deepspeed.ai/tutorials/fpdt/) on usage. + +We invite the community to explore our implementation, contribute to +further advancements, and join us in pushing the boundaries of what is +possible in LLM and AI. This release is part of the bigger DeepSpeed +ecosystem of large-scale AI training, finetuning 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). diff --git a/blogs/ulysses-offload/media/image1.png b/blogs/ulysses-offload/media/image1.png new file mode 100644 index 0000000000000000000000000000000000000000..df63ef8197633bc1b1055af3b905793538a7be7a GIT binary patch literal 1314185 zcmeFa$+Dy97A|%?adRW?eE^52Cwx6P74*%-5g-HtL?aN5M4mz7zrtjt|o2+{oi`q%o_x4wn`tEo%>@jv{}|KT71_{V>g zSAzAAfBdKa>mUF4AO6e#_}{~p|M!3YU;j_|-+yo@&_y6<1{I`GnPyfe%>z96C zPRF!}|M5?J@$%b0F(j$a{XcQxpBNT2%}-GpoVuVq|5U|2Y=ZZLQ(QfE(CS-S6v00+ zk^BjPxBj!2R#82k;a7bNKTrfV<2?KZZ&1X`uM{IVH;*>b46DDGZ1!98IA#Fzg>IM{Xmi!UDc4=<_!&V3&j6Ncbl z=*!2`=w+L2t0sC&C+4Ei$;2W@Ca*s}+Vhy@By~ZPXmu19aNuZqIRr`~H1TrO==!n= 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zb27bl^aGu-tUpfB$F8~)J*IUxt+!v51L~mOh*#vY>vrYfxz*L_Zy9}ZW?kjoC&!d)%wYE=fg?pg3iJ#{a5vd-W#-hv zg9j2!vgVOkM82n7`*)sT{PPF?@gLboL76v6Vom?+_x^D=@`DlVjNe3Bb^31q<4OPJ zK`E?|04Y%~GURXl%iZuSG}%F!Q>4mQGYEgbkiR^*LqGtI)nTRo`8$lrOAHB6I49)K z_HR$f_=%a{-aZX<;m2mYyu1sIYYtfIls^v-xwW*k=I7>a3#?`$(E~RGEbR_j?E)AfEFM z>g($p@Kz*&W65tG{MojDFJMNa5pyb~nEc=0@bFzshy6Q8%ou<3uKI9rj)dhynjpl{6KL<;_F>7ekbLA^-pY literal 0 HcmV?d00001 From 7b9fc8c74dd989ab2910dc26538623237f267b47 Mon Sep 17 00:00:00 2001 From: Sam Ade Jacobs Date: Thu, 5 Dec 2024 08:44:00 -0800 Subject: [PATCH 049/112] add FPDT tutorial (#6813) Tutorial page for Ulysses-Offload (FPDT), blog page to follow. --------- Co-authored-by: Jinghan Yao Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Logan Adams --- docs/_tutorials/ulysses-offload.md | 68 ++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 docs/_tutorials/ulysses-offload.md diff --git a/docs/_tutorials/ulysses-offload.md b/docs/_tutorials/ulysses-offload.md new file mode 100644 index 000000000000..311d523788ac --- /dev/null +++ b/docs/_tutorials/ulysses-offload.md @@ -0,0 +1,68 @@ +--- +title: "DeepSpeed Ulysses-Offload" +tags: training ultra long context language model with fully pipelined distributed transformer +--- + +DeepSpeed Ulysses-Offload is a system of chunking and offloading long-context transformer model training scheme built on top of [ZeRO](/tutorials/zero/) and [DeepSpeed Ulysses](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/README.md). It adopts Fully Pipeliend Distributed Transformer (FPDT) which enables 2M context size training on 8B models with only 4 GPUs, and 4M context size training on 70B models with 32 GPUs. Read our [Ulysses-Offload blog](https://github.com/microsoft/DeepSpeed/blob/master/blogs/ulysses-offload/README.md) and [paper](https://arxiv.org/pdf/2408.16978) to learn more! + +We recommend that you read the tutorials on [Getting Started](/getting-started/), [ZeRO](/tutorials/zero/) and [Megatron-DeepSpeed](/tutorials/megatron/) before stepping through this tutorial. + + +## Design of Ulysses-Offload +Ulysses-Offload is a chunking and offloading-based transformer implementation, which retain the full precision of the vanilla transformer, while significantly reduce the activation memory required during long-context model training. FPDT breaks long sequence input into smaller chunks, moving them among host and GPU memory to achieve the superior memory efficiency while reaching over 50% of MFU. FPDT adopts a double-buffer design, which overlaps the fetching/offloading with the attention computation. FPDT also allows uUsers to configure the chunk size to match the expected memory budget. + +Ulysses-Offload supports ZeRO, which shards the model and tensors among GPU memory, further pushing the limit of long-context model training with state-of-the-art hardware efficiency. + + +## Training Environment + +For this tutorial, Flash Attention (CUDA) is required. We will configure a 8 billion parameter LLaMA model using the DeepSpeed [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/tree/master/) code. We will use 1 nodes of 4x [NVIDIA Tesla A100-SXM4 Tensor Core GPU](https://www.nvidia.com/en-us/data-center/a100/). + + +## Training a 6.7B parameter GPT with Ulysses-Offload +Users can set the context size at the beginning of the script, for this exercise, we will use 256K context and mini batch of one. +``` +### Main configs +seq_len=262144 # need to be power of 2 +``` + +For 6.7B model, we will enable ZeRO-3, Ulysses, activation checkpointing with CPU offloading first reach a decent GPU memory efficiency, then users can configure the following arguments: + + - ds_sequence_parallel_fpdt: Boolean indicating whether to use FPDT, default is false. + - ds_sequence_parallel_fpdt_chunk_size: Integer indicating the chunk size in FPDT, default is 65536, meaning no matter how long the sequence is, FPDT will always process chunks of 65536 tokens until the entire sequence is all processed. + - ds_sequence_parallel_fpdt_offloading: Boolean indicating whether to use host memory to offload chunks, default is false. + + +### Megatron-DeepSpeed Configuration Changes + +1. An example snippet of megatron-deepspeed configurations with all Ulysses-Offload features enable is shown below: + ``` + megatron_options="\ + --ds-sequence-parallel-fpdt \ + --ds-sequence-parallel-fpdt-chunk-size 65536 \ + --ds-sequence-parallel-fpdt-offloading \ + --ds-sequence-parallel-size 4" + ``` + +2. FPDT requires Flash Attention, and also supports Rotary Position Embedding (RoPE): + ``` + --use-flash-attn-v2 \ + --use-rotary-position-embeddings \ + --rotary-percent 0.25 \ + --rotary-position-embeddings-theta 100000000 \ + ``` + +3. We also enable CPU checkpointing to reduce activation memory footprints: + ``` + if [ "${activation_checkpoint}" = "true" ]; then + deepspeed_options="${deepspeed_options} \ + --deepspeed-activation-checkpointing \ + --checkpoint-in-cpu" + fi + ``` + +You can find the full script [here](https://github.com/microsoft/Megatron-DeepSpeed/tree/main/examples_deepspeed/sequence_parallel/ds_pretrain_gpt_6.7B_fpdt_32k.sh). + +See more details on Megatron-DeepSpeed [tutorial](/tutorials/megatron/) examples on how to launch a Megatron-DeepSpeed job. + +Congratulations! You have completed the Ulysses-Offload tutorial. From 0e92f9b41f864c034aea0a7eec7267f48bb70f80 Mon Sep 17 00:00:00 2001 From: Sam Ade Jacobs Date: Thu, 5 Dec 2024 11:31:52 -0800 Subject: [PATCH 050/112] Update README.md (#6824) Fix broken tutorial link --- blogs/ulysses-offload/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blogs/ulysses-offload/README.md b/blogs/ulysses-offload/README.md index 740c07229d9f..283b5142e274 100644 --- a/blogs/ulysses-offload/README.md +++ b/blogs/ulysses-offload/README.md @@ -250,7 +250,7 @@ allowing users to focus on their research and development tasks. We are excited to release Ulysses-Offload. Ulysses-Offload has been fully integrated with Megatron-DeepSpeed and accessible through both DeepSpeed and Megatron-DeepSpeed GitHub repos. Click here for detailed -[tutorial](https://www.deepspeed.ai/tutorials/fpdt/) on usage. +[tutorial](https://www.deepspeed.ai/tutorials/ulysses-offload/) on usage. We invite the community to explore our implementation, contribute to further advancements, and join us in pushing the boundaries of what is From 2ea181f0c33206bd8362aadcbd732d5b1314adc7 Mon Sep 17 00:00:00 2001 From: Sam Ade Jacobs Date: Thu, 5 Dec 2024 11:32:36 -0800 Subject: [PATCH 051/112] Update README.md (#6825) Add Ulysses-offload to News page Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 8bfc344a4edc..2334fd366ae4 100755 --- a/README.md +++ b/README.md @@ -16,6 +16,7 @@ ## 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). +* [2024/12] [Ulysses-Offload: Democratizing Long Context LLM Training ](https://github.com/microsoft/DeepSpeed/blob/master/blogs/ulysses-offload/README.md) * [2024/12] [DeepSpeed-Domino: Communication-Free LLM Training Engine](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-domino/README.md) * [2024/08] [DeepSpeed on Windows](https://github.com/microsoft/DeepSpeed/tree/master/blogs/windows/08-2024/README.md) [[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/windows/08-2024/japanese/README.md)] * [2024/08] [DeepNVMe: Improving DL Applications through I/O Optimizations](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-gds/README.md) [[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-gds/japanese/README.md)] From 95ead2a055afe0387b18eb7af1c1b973304b7553 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Thu, 5 Dec 2024 12:16:46 -0800 Subject: [PATCH 052/112] Pin transformers version in cpu-torch-latest due to multiprocessing error. (#6823) This is a copy of https://github.com/microsoft/DeepSpeed/pull/6820 for the cpu-torch-latest tests. This PR will revert/fix these: https://github.com/microsoft/DeepSpeed/pull/6822 --- .github/workflows/cpu-torch-latest.yml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/.github/workflows/cpu-torch-latest.yml b/.github/workflows/cpu-torch-latest.yml index 0125fa50bc14..51bc60c2c2ae 100644 --- a/.github/workflows/cpu-torch-latest.yml +++ b/.github/workflows/cpu-torch-latest.yml @@ -37,6 +37,15 @@ jobs: python -c "import torch; print('torch:', torch.__version__, torch)" python -c "import torch; print('CUDA available:', torch.cuda.is_available())" + - name: Install transformers + run: | + git clone https://github.com/huggingface/transformers + cd transformers + # if needed switch to the last known good SHA until transformers@master is fixed + git checkout 6c3f168b3 + git rev-parse --short HEAD + pip install . + - name: Install deepspeed run: | pip install .[dev,autotuning] From 177832ed45c0174673cfeef978afb3ebfce1c2bc Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Thu, 5 Dec 2024 13:51:05 -0800 Subject: [PATCH 053/112] Update pre-commit version (#6821) --- .pre-commit-config.yaml | 2 +- requirements/requirements-dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b5d8afa8e0b4..9a7bb1c9b371 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,7 +5,7 @@ repos: - id: check-useless-excludes - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.0.1 + rev: v5.0.0 hooks: - id: check-case-conflict - id: check-json diff --git a/requirements/requirements-dev.txt b/requirements/requirements-dev.txt index 2e2c880fbeb0..71ad3f0b262b 100644 --- a/requirements/requirements-dev.txt +++ b/requirements/requirements-dev.txt @@ -6,7 +6,7 @@ docutils<0.18 future importlib-metadata>=4 mup -pre-commit>=2.20.0 +pre-commit>=3.2.0 pytest>=7.2.0 pytest-forked pytest-randomly From a4499668fe98d609534e5b629b69e8891f885e42 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Thu, 5 Dec 2024 14:21:53 -0800 Subject: [PATCH 054/112] Update version.txt after 0.16.1 release (#6826) **Auto-generated PR to update version.txt after a DeepSpeed release** Released version - 0.16.1 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 2a0970ca757c..201a22c8fa5c 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.16.1 +0.16.2 From 9ca60160179aacc1efe87121e1ec259ce8d0a476 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Fri, 6 Dec 2024 14:29:00 -0800 Subject: [PATCH 055/112] Pin HPU tests (#6831) HPU tests are impacted by the same issue as other tests that use transformers latest. This PR pins to a version of transformers before the fix. --- .github/workflows/hpu-gaudi2.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/hpu-gaudi2.yml b/.github/workflows/hpu-gaudi2.yml index 9f1a9d973ca2..a06f871b7c56 100644 --- a/.github/workflows/hpu-gaudi2.yml +++ b/.github/workflows/hpu-gaudi2.yml @@ -111,6 +111,8 @@ jobs: run: | git clone https://github.com/huggingface/transformers cd transformers + # if needed switch to the last known good SHA until transformers@master is fixed + git checkout 6c3f168b3 git rev-parse --short HEAD pip install . From 9a41ccaf446cc2026a30040bc55a21df1256cb64 Mon Sep 17 00:00:00 2001 From: Hoa La <87745938+lvhoaa@users.noreply.github.com> Date: Mon, 9 Dec 2024 12:56:54 -0500 Subject: [PATCH 056/112] Flops profiler support einops.einsum (#6755) - Added support for FlopsProfiler to include einops.einsum operation - Added _patch_miscellaneous_operations() and _reload_miscellaneous_operations() to include this operation and potentially include other miscellaneous operations in the future - Added _einops_einsum_flops_compute() that mimic already-existed _einsum_flops_compute() --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- .../profiling/flops_profiler/profiler.py | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/deepspeed/profiling/flops_profiler/profiler.py b/deepspeed/profiling/flops_profiler/profiler.py index f87f1beb7e4e..c6b607aad813 100644 --- a/deepspeed/profiling/flops_profiler/profiler.py +++ b/deepspeed/profiling/flops_profiler/profiler.py @@ -16,6 +16,7 @@ from deepspeed.moe.layer import MoE from deepspeed.utils.timer import FORWARD_GLOBAL_TIMER, BACKWARD_GLOBAL_TIMER, STEP_GLOBAL_TIMER from deepspeed.utils.torch import required_torch_version +import einops Tensor = torch.Tensor @@ -82,6 +83,7 @@ def start_profile(self, ignore_list=None): self.reset_profile() _patch_functionals() _patch_tensor_methods() + _patch_miscellaneous_operations() def register_module_hooks(module, ignore_list): if ignore_list and type(module) in ignore_list: @@ -137,6 +139,7 @@ def stop_profile(self): if self.started and self.func_patched: _reload_functionals() _reload_tensor_methods() + _reload_miscellaneous_operations() self.func_patched = False def remove_profile_attrs(module): @@ -787,6 +790,29 @@ def _einsum_flops_compute(equation, *operands): raise NotImplementedError("Unsupported einsum operation.") +def _einops_einsum_flops_compute(*args): + """ + Count flops for the einops.einsum operation. + """ + *operands, equation = args + input_shapes = [o.shape for o in operands] + + # Re-map equation so that same equation with different alphabet + # representations will look the same. + letter_order = OrderedDict((k, 0) for k in equation if k.isalpha()).keys() + mapping = {ord(x): 97 + i for i, x in enumerate(letter_order)} + equation = equation.translate(mapping) + + np_arrs = [np.zeros(s) for s in input_shapes] + optim = np.einsum_path(equation, *np_arrs, optimize="optimal")[1] + for line in optim.split("\n"): + if "optimized flop" in line.lower(): + flop = int(float(line.split(":")[-1])) + return flop, 0 + + raise NotImplementedError("Unsupported einops.einsum operation.") + + def _tensor_addmm_flops_compute(self, mat1, mat2, *, beta=1, alpha=1, out=None): """ Count flops for the tensor addmm operation. @@ -937,6 +963,10 @@ def _patch_tensor_methods(): torch.baddbmm = wrapFunc(torch.baddbmm, _tensor_addmm_flops_compute) +def _patch_miscellaneous_operations(): + einops.einsum = wrapFunc(einops.einsum, _einops_einsum_flops_compute) + + def _reload_functionals(): # torch.nn.functional does not support importlib.reload() F.linear = old_functions[F.linear.__str__] @@ -995,6 +1025,10 @@ def _reload_tensor_methods(): torch.baddbmm = old_functions[torch.baddbmm.__str__] +def _reload_miscellaneous_operations(): + einops.einsum = old_functions[einops.einsum.__str__] + + def _rnn_flops(flops, rnn_module, w_ih, w_hh, input_size): gates_size = w_ih.shape[0] # matrix matrix mult ih state and internal state From 08b907a226c64fbadace5dba71ed45bbe22bc0ef Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 9 Dec 2024 12:24:33 -0800 Subject: [PATCH 057/112] Pin pytest-subtests version for accelerate tests (#6842) --- .github/workflows/nv-accelerate-v100.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/nv-accelerate-v100.yml b/.github/workflows/nv-accelerate-v100.yml index 346055e2685f..56b1b1ab12d2 100644 --- a/.github/workflows/nv-accelerate-v100.yml +++ b/.github/workflows/nv-accelerate-v100.yml @@ -52,7 +52,7 @@ jobs: pip install .[testing] # force protobuf version due to issues pip install "protobuf<4.21.0" - # tmp fix: force newer datasets version - #pip install "datasets>=2.0.0" + # force pytest-subtests version due to issues + pip install pytest-subtests==0.13.1 pip list pytest $PYTEST_OPTS --color=yes --durations=0 --verbose tests/deepspeed From 0c92c39dd0a870abffb61e3dc219f12539917c14 Mon Sep 17 00:00:00 2001 From: Raza Sikander Date: Tue, 10 Dec 2024 05:45:42 +0530 Subject: [PATCH 058/112] Inference UTs check for trition support from accelerator (#6782) Instead of checking if installed or not check for support. Skip if not supported. Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- .../unit/ops/transformer/inference/test_attention.py | 4 ++-- tests/unit/ops/transformer/inference/test_gelu.py | 4 ++-- .../ops/transformer/inference/test_layer_norm.py | 12 ++++++------ tests/unit/ops/transformer/inference/test_matmul.py | 4 ++-- .../ops/transformer/inference/test_residual_add.py | 4 ++-- tests/unit/ops/transformer/inference/test_softmax.py | 4 ++-- 6 files changed, 16 insertions(+), 16 deletions(-) diff --git a/tests/unit/ops/transformer/inference/test_attention.py b/tests/unit/ops/transformer/inference/test_attention.py index 0521245bcdf3..ecf681542ff6 100644 --- a/tests/unit/ops/transformer/inference/test_attention.py +++ b/tests/unit/ops/transformer/inference/test_attention.py @@ -27,8 +27,8 @@ def ref_torch_attention(q, k, v, mask, sm_scale): @pytest.mark.parametrize("causal", [True, False]) @pytest.mark.parametrize("use_flash", [True, False]) def test_attention(BATCH, H, N_CTX, D_HEAD, causal, use_flash, dtype=torch.float16): - if not deepspeed.HAS_TRITON: - pytest.skip("triton has to be installed for the test") + if not deepspeed.get_accelerator().is_triton_supported(): + pytest.skip("triton is not supported on this system") minus_inf = -65504.0 dev = deepspeed.accelerator.get_accelerator().device_name() diff --git a/tests/unit/ops/transformer/inference/test_gelu.py b/tests/unit/ops/transformer/inference/test_gelu.py index beb74d09ab30..5f820ef3b579 100644 --- a/tests/unit/ops/transformer/inference/test_gelu.py +++ b/tests/unit/ops/transformer/inference/test_gelu.py @@ -61,8 +61,8 @@ def test_gelu(batch, sequence, channels, dtype, use_triton_ops): activations_ds = torch.randn((batch, sequence, channels), dtype=dtype, device=device) activations_ref = activations_ds.clone().detach() - if not deepspeed.HAS_TRITON and use_triton_ops: - pytest.skip("triton has to be installed for the test") + if not deepspeed.get_accelerator().is_triton_supported(): + pytest.skip("triton is not supported on this system") ds_out = run_gelu_ds(activations_ds, use_triton_ops) ref_out = run_gelu_reference(activations_ref) assert (allclose(ds_out, ref_out)) diff --git a/tests/unit/ops/transformer/inference/test_layer_norm.py b/tests/unit/ops/transformer/inference/test_layer_norm.py index 2912807e9f43..7711daf0d887 100644 --- a/tests/unit/ops/transformer/inference/test_layer_norm.py +++ b/tests/unit/ops/transformer/inference/test_layer_norm.py @@ -45,8 +45,8 @@ def ds_triton_implementation(vals, gamma, beta, epsilon): @pytest.mark.parametrize("dtype", get_dtypes()) @pytest.mark.parametrize("use_triton_ops", [False, True]) def test_layer_norm(batch, seq_len, channels, dtype, use_triton_ops): - if not deepspeed.HAS_TRITON and use_triton_ops: - pytest.skip("triton has to be installed for the test") + if not deepspeed.get_accelerator().is_triton_supported(): + pytest.skip("triton is not supported on this system") vals = torch.randn((batch, seq_len, channels), dtype=dtype, device=get_accelerator().current_device_name()) gamma = torch.randn((channels), dtype=dtype, device=get_accelerator().current_device_name()) @@ -93,8 +93,8 @@ def residual_ds_triton_implementation(vals, bias, res, gamma, beta, epsilon): @pytest.mark.parametrize("dtype", get_dtypes()) @pytest.mark.parametrize("use_triton_ops", [False, True]) def test_layer_norm_residual(batch, seq_len, channels, dtype, use_triton_ops): - if not deepspeed.HAS_TRITON and use_triton_ops: - pytest.skip("triton has to be installed for the test") + if not deepspeed.get_accelerator().is_triton_supported(): + pytest.skip("triton is not supported on this system") vals = torch.randn((batch, seq_len, channels), dtype=dtype, device=get_accelerator().current_device_name()) residual = torch.randn((batch, seq_len, channels), dtype=dtype, device=get_accelerator().current_device_name()) @@ -163,8 +163,8 @@ def test_layer_norm_residual_store_pre_ln_res(batch, seq_len, channels, dtype): @pytest.mark.parametrize("residual", [True, False]) @pytest.mark.parametrize("input_bias", [True, False]) def test_triton_layer_norm(M, N, dtype, residual, input_bias, eps=1e-5, device='cuda'): - if not deepspeed.HAS_TRITON: - pytest.skip("triton has to be installed for the test") + if not deepspeed.get_accelerator().is_triton_supported(): + pytest.skip("triton is not supported on this system") dev = get_accelerator().device_name() torch.manual_seed(0) # create data diff --git a/tests/unit/ops/transformer/inference/test_matmul.py b/tests/unit/ops/transformer/inference/test_matmul.py index 804a85750a3a..559aa2c60afe 100644 --- a/tests/unit/ops/transformer/inference/test_matmul.py +++ b/tests/unit/ops/transformer/inference/test_matmul.py @@ -42,8 +42,8 @@ def run_matmul_ds(a, b, use_triton_ops=False): @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("use_triton_ops", [True]) def test_matmul_4d(B, H, M, K, N, dtype, use_triton_ops): - if not deepspeed.HAS_TRITON and use_triton_ops: - pytest.skip("triton has to be installed for the test") + if not deepspeed.get_accelerator().is_triton_supported(): + pytest.skip("triton is not supported on this system") # skip autotune in testing from deepspeed.ops.transformer.inference.triton.matmul_ext import fp16_matmul diff --git a/tests/unit/ops/transformer/inference/test_residual_add.py b/tests/unit/ops/transformer/inference/test_residual_add.py index 807da4904341..cab64d1d0555 100644 --- a/tests/unit/ops/transformer/inference/test_residual_add.py +++ b/tests/unit/ops/transformer/inference/test_residual_add.py @@ -74,8 +74,8 @@ def run_residual_add_reference(hidden_state, residual, attn_output, attn_bias, f @pytest.mark.parametrize("use_triton_ops", [True, False]) def test_residual_add(batch, sequence, hidden_dim, dtype, mlp_after_attn, add_bias, mp_size, pre_attn_norm, use_triton_ops): - if not deepspeed.HAS_TRITON and use_triton_ops: - pytest.skip("triton has to be installed for the test") + if not deepspeed.get_accelerator().is_triton_supported(): + pytest.skip("triton is not supported on this system") ds_out = torch.randn((batch, sequence, hidden_dim), dtype=dtype, device=get_accelerator().device_name()) residual = torch.randn((batch, sequence, hidden_dim), dtype=dtype, device=get_accelerator().device_name()) attn_output = torch.randn((batch, sequence, hidden_dim), dtype=dtype, device=get_accelerator().device_name()) diff --git a/tests/unit/ops/transformer/inference/test_softmax.py b/tests/unit/ops/transformer/inference/test_softmax.py index 4f6d69160aa7..e582be1b926a 100644 --- a/tests/unit/ops/transformer/inference/test_softmax.py +++ b/tests/unit/ops/transformer/inference/test_softmax.py @@ -40,8 +40,8 @@ def run_softmax_ds(input, use_triton_ops=False): @pytest.mark.parametrize("dtype", [torch.float16, torch.float32]) @pytest.mark.parametrize("use_triton_ops", [True]) def test_softmax(batch, sequence, channels, dtype, use_triton_ops): - if not deepspeed.HAS_TRITON and use_triton_ops: - pytest.skip("triton has to be installed for the test") + if not deepspeed.get_accelerator().is_triton_supported(): + pytest.skip("triton is not supported on this system") device = deepspeed.accelerator.get_accelerator().device_name() input_ds = torch.randn((batch, sequence, channels), dtype=dtype, device=device) From 06f1d3609e46420cbcee606a64c60af1a6fae111 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 9 Dec 2024 22:14:59 -0800 Subject: [PATCH 059/112] Unpin pytest-subtests now that 0.14.1 is released (#6844) The issue we encountered was covered here: https://github.com/pytest-dev/pytest-subtests/issues/173 And is resolved with the latest changes from this PR: https://github.com/pytest-dev/pytest-subtests/issues/174, and is published in the latest version 0.14.1. --- .github/workflows/nv-accelerate-v100.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/nv-accelerate-v100.yml b/.github/workflows/nv-accelerate-v100.yml index 56b1b1ab12d2..0792e4e09cb2 100644 --- a/.github/workflows/nv-accelerate-v100.yml +++ b/.github/workflows/nv-accelerate-v100.yml @@ -52,7 +52,5 @@ jobs: pip install .[testing] # force protobuf version due to issues pip install "protobuf<4.21.0" - # force pytest-subtests version due to issues - pip install pytest-subtests==0.13.1 pip list pytest $PYTEST_OPTS --color=yes --durations=0 --verbose tests/deepspeed From 1b58ba5ec04493a112fae10d9cc9c824dfbd40ca Mon Sep 17 00:00:00 2001 From: xyxie Date: Wed, 11 Dec 2024 02:31:11 +0800 Subject: [PATCH 060/112] Merge LoCo with Zero++ (#6730) ### Integration of LoCo Method into ZeRO++ #### Overview This PR introduces the integration of the **LoCo** method, as outlined in [this paper](https://arxiv.org/abs/2407.04480), into the ZeRO++ framework of DeepSpeed. The key enhancement involves applying error feedback compensation to 4-bit gradients before communication. This approach ***improves pre-training loss outcomes without additional time overhead***, though it requires extra GPU memory. The extent of this memory increase depends on model size and training configuration. #### Experimental Results We conducted pre-training experiments using the Llama2 architecture, adjusting the number of layers and hidden size. The experiments included: - **A smaller-scale model with 0.8B parameters trained on 30B tokens**. - **A larger-scale model with 8B parameters trained on 5B tokens**. The training data was sampled from **Redpajama-V2**.

    **Findings**: - **Smaller Models (0.8B parameters)**: Significant gains were observed when applying the LoCo method. - **Larger Models (8B parameters)**: The gains were present but less pronounced. This could be due to: 1. Relatively smaller data volume. 2. Lower pre-training loss for larger models, making significant improvements harder to achieve. However, even a smaller pre-training loss gap in larger models can translate to meaningful gains in downstream tasks. #### Example Script For reference, the [run.sh](https://github.com/user-attachments/files/17679552/zeroplus-7b3.zip) script used for the 8B parameter, 5B tokens experiment is attached. The experiment was conducted using the **DeepSpeed-Megatron** platform. #### Acknowledgments Special thanks to cc @GuanhuaWang for ongoing communication and guidance throughout this work. --- We appreciate your consideration of this PR and welcome any feedback or questions! --------- Co-authored-by: ChuanxinTang Co-authored-by: root Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Logan Adams Co-authored-by: Hongwei Chen <33092912+hwchen2017@users.noreply.github.com> --- csrc/includes/quantization.h | 30 ++ csrc/includes/quantization_utils.h | 1 + csrc/quantization/pt_binding.cpp | 106 +++++++ csrc/quantization/quant_reduce.cu | 294 ++++++++++++++++++ csrc/quantization/swizzled_quantize.cu | 231 ++++++++++++++ .../runtime/comm/coalesced_collectives.py | 79 ++++- deepspeed/runtime/engine.py | 7 +- deepspeed/runtime/zero/config.py | 13 +- deepspeed/runtime/zero/stage3.py | 32 +- .../comm/test_coalesced_collectives.py | 63 ++++ 10 files changed, 851 insertions(+), 5 deletions(-) diff --git a/csrc/includes/quantization.h b/csrc/includes/quantization.h index 45828832d8d2..5bdc96061a31 100644 --- a/csrc/includes/quantization.h +++ b/csrc/includes/quantization.h @@ -52,6 +52,36 @@ void launch_swizzled_quant(int8_t* q_data, int devices_per_node, cudaStream_t stream); +void launch_loco_swizzled_quant(int8_t* quantized_data, + float* quantized_scales, + const __half* uncompressed_data, + __half* error_feedback, + const float err_beta, + int num_bits, + quantize::Type quant_type, + int groups, + int elems_per_group, + int pipelining, + int nodes, + int devices_per_node, + cudaStream_t stream); + +void launch_loco_dequant_reduce(int8_t* reduced_data, + float* reduced_scales, + const int8_t* input_data, + const float* input_scales, + int num_gpus, + int num_bits, + quantize::Type quant_type, + int out_groups, + int elems_per_out_group, + int elems_per_in_tensor, + int groups_per_in_tensor, + int elems_per_in_group, + __half2* error_feedback, + const float err_beta, + cudaStream_t stream); + void launch_dequant_reduce(int8_t* reduced_data, float* reduced_scales, const int8_t* input_data, diff --git a/csrc/includes/quantization_utils.h b/csrc/includes/quantization_utils.h index 26db86ec1e0b..61630d0aae57 100644 --- a/csrc/includes/quantization_utils.h +++ b/csrc/includes/quantization_utils.h @@ -24,6 +24,7 @@ constexpr int max_threads = 1024; Class to hold the quantization parameters for a given tensor. Holds the implementation of the quantization operation. */ + template class Params { public: diff --git a/csrc/quantization/pt_binding.cpp b/csrc/quantization/pt_binding.cpp index e74c541699d7..b48eaacd0881 100644 --- a/csrc/quantization/pt_binding.cpp +++ b/csrc/quantization/pt_binding.cpp @@ -176,6 +176,53 @@ at::Tensor dequantize_int8_to_half_experimental(at::Tensor& data_in, return output; } +std::vector ds_loco_swizzle_quant(at::Tensor& input_vals, + at::Tensor& error_feedback, + float err_beta, + int groups, + int num_bits, + quantize::Type quant_type, + int pipeline_size, + int nodes, + int devices_per_node) +{ + auto scales_options = at::TensorOptions() + .dtype(at::kFloat) + .layout(at::kStrided) + .device(at::kCUDA) + .requires_grad(false); + const int scales_elems = (quantize::requires_offset(quant_type)) ? 2 : 1; + auto scales = torch::empty({groups, scales_elems}, scales_options); + + auto output_options = at::TensorOptions() + .dtype(at::kChar) + .layout(at::kStrided) + .device(at::kCUDA) + .requires_grad(false); + + const int quantization_scalar = 8 / num_bits; + const int compressed_vals = at::numel(input_vals) / quantization_scalar; + + auto output = torch::empty({compressed_vals}, output_options); + const int elems_per_group = at::numel(input_vals) / groups; + + launch_loco_swizzled_quant(reinterpret_cast(output.data_ptr()), + reinterpret_cast(scales.data_ptr()), + reinterpret_cast(input_vals.data_ptr()), + reinterpret_cast<__half*>(error_feedback.data_ptr()), + err_beta, + num_bits, + quant_type, + groups, + elems_per_group, + pipeline_size, + nodes, + devices_per_node, + at::cuda::getCurrentCUDAStream()); + + return {output, scales}; +} + std::vector ds_swizzle_quant(at::Tensor& input_vals, int groups, int num_bits, @@ -265,6 +312,61 @@ std::vector quantized_reduction(at::Tensor& input_vals, return {output, scales}; } +std::vector loco_quantized_reduction(at::Tensor& input_vals, + at::Tensor& input_scales, + at::Tensor& error_feedback, + float err_beta, + int in_groups, + int out_groups, + int num_bits, + quantize::Type quant_type, + int devices_per_node) +{ + auto scales_options = at::TensorOptions() + .dtype(at::kFloat) + .layout(at::kStrided) + .device(at::kCUDA) + .requires_grad(false); + + const int scales_elems = (quantize::requires_offset(quant_type)) ? 2 : 1; + + auto scales = torch::empty({out_groups, scales_elems}, scales_options); + + auto output_options = at::TensorOptions() + .dtype(at::kChar) + .layout(at::kStrided) + .device(at::kCUDA) + .requires_grad(false); + + std::vector sz(input_vals.sizes().begin(), input_vals.sizes().end()); + sz[sz.size() - 1] = sz.back() / devices_per_node; + + const int elems_per_in_tensor = at::numel(input_vals) / devices_per_node; + + auto output = torch::empty(sz, output_options); + + const int elems_per_in_group = elems_per_in_tensor / (in_groups / devices_per_node); + const int elems_per_out_group = elems_per_in_tensor / out_groups; + + launch_loco_dequant_reduce((int8_t*)output.data_ptr(), + (float*)scales.data_ptr(), + (const int8_t*)input_vals.data_ptr(), + (const float*)input_scales.data_ptr(), + devices_per_node, + num_bits, + quant_type, + out_groups, + elems_per_out_group, + elems_per_in_tensor, + in_groups / devices_per_node, + elems_per_in_group, + (__half2*)error_feedback.data_ptr(), + err_beta, + at::cuda::getCurrentCUDAStream()); + + return {output, scales}; +} + PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("ds_quantize_fp32", &ds_quantize, "DeepSpeed Quantize with fp32 (CUDA)"); @@ -295,4 +397,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) "Dequantize int8 to half (experimental)"); m.def("swizzle_quant", &ds_swizzle_quant); m.def("quantized_reduction", &quantized_reduction); + m.def("loco_swizzle_quant", &ds_loco_swizzle_quant, "LoCo Swizzled Quantization Kernel"); + m.def("loco_quantized_reduction", + &loco_quantized_reduction, + "LoCo Quantization and Reduction Kernel"); } diff --git a/csrc/quantization/quant_reduce.cu b/csrc/quantization/quant_reduce.cu index 26db1118c831..4100c5174b80 100644 --- a/csrc/quantization/quant_reduce.cu +++ b/csrc/quantization/quant_reduce.cu @@ -261,3 +261,297 @@ void launch_dequant_reduce(int8_t* reduced_data, } } } + +/* +Modified loco_dequant_reduce function that performs dequantization and reduction, +and incorporates error-feedback by updating the error_feedback tensor in-place. +*/ + +template +__global__ void __launch_bounds__(1024) loco_dequant_reduce(int8_t* reduced_data, + float* reduced_scales, + const int8_t* input_data, + const float* input_scales, + int elems_per_out_group, + int elems_per_in_tensor, + int groups_per_in_tensor, + int elems_per_in_group, + int num_tensors, + __half2* error_feedback, + const float err_beta) +{ + cg::thread_block tb = cg::this_thread_block(); + cg::thread_block_tile warp = cg::tiled_partition(tb); + + constexpr int mem_granularity = (numBits == 8) ? 8 : 4; + constexpr int elems_per_load = mem_granularity / sizeof(int8_t); + constexpr int storage_values = 16 / sizeof(__half2); + + const int block_offset = tb.group_index().x * elems_per_out_group; + const int elem_offset = tb.thread_index().x * elems_per_load; + const int base_offset = block_offset + elem_offset; + const int stride = tb.group_dim().x * elems_per_load; + + constexpr int scaling_factor = elems_per_load / storage_values; + const int block_offset_err = block_offset / scaling_factor; + const int elem_offset_err = tb.thread_index().x * storage_values; + const int base_offset_err = block_offset_err + elem_offset_err; + const int stride_err = tb.group_dim().x * storage_values; + + __half2 local_buffer[totalChunks * storage_values]; + __half2 err_buffer[totalChunks * storage_values]; + + quantize::GroupStats stats; + +#pragma unroll + for (int i = 0; i < totalChunks; i++) { + __half2* iteration_buffer = local_buffer + i * storage_values; + __half2* iter_err_buffer = err_buffer + i * storage_values; + +#pragma unroll + for (int j = 0; j < storage_values; j++) { + iteration_buffer[j] = reduce::init(); + } + + const int iter_offset = i * stride + base_offset; + const int iter_offset_err = i * stride_err + base_offset_err; + const int iter_scale_idx = iter_offset / elems_per_in_group; + bool do_loads = i * stride + elem_offset < elems_per_out_group; + + if (numTensors > 0) { +#pragma unroll + for (int j = 0; j < numTensors; j++) { + if (do_loads) { + int8_t load_buffer[elems_per_load]; + + mem_access::load_global( + load_buffer, input_data + j * elems_per_in_tensor + iter_offset); + + quantize::Params params( + input_scales + j * groups_per_in_tensor, iter_scale_idx); + + __half2 dequant_buffer[storage_values]; + dequantize::chunk(dequant_buffer, load_buffer, params); + +#pragma unroll + for (int k = 0; k < storage_values; k++) { + iteration_buffer[k] = + reduce::element(iteration_buffer[k], dequant_buffer[k]); + } + } + } + } else { +#pragma unroll 4 + for (int j = 0; j < num_tensors; j++) { + if (do_loads) { + int8_t load_buffer[elems_per_load]; + + mem_access::load_global( + load_buffer, input_data + j * elems_per_in_tensor + iter_offset); + + quantize::Params params( + input_scales + j * groups_per_in_tensor, iter_scale_idx); + + __half2 dequant_buffer[storage_values]; + dequantize::chunk(dequant_buffer, load_buffer, params); + +#pragma unroll + for (int k = 0; k < storage_values; k++) { + iteration_buffer[k] = + reduce::element(iteration_buffer[k], dequant_buffer[k]); + } + } + } + } + mem_access::load_global( + iter_err_buffer, error_feedback + iter_offset_err, do_loads); +#pragma unroll + for (int k = 0; k < storage_values; k++) { + iteration_buffer[k] = __hadd2(iteration_buffer[k], iter_err_buffer[k]); + stats.update(iteration_buffer[k]); + } + } + + auto params = stats.template get_params(tb, warp); + + // Initialize dequantization parameters based on params + auto de_params = params; + de_params.scale = 1.0f / params.scale; + if constexpr (quantType == quantize::Type::Asymmetric) { de_params.offset = params.offset; } + + if (tb.thread_index().x == 0) { params.store(reduced_scales, tb.group_index().x); } + +#pragma unroll + for (int i = 0; i < totalChunks; i++) { + const int iter_offset = i * stride + base_offset; + const int iter_offset_err = i * stride_err + base_offset_err; + __half2* iteration_buffer = local_buffer + i * storage_values; + __half2* iter_err_buffer = err_buffer + i * storage_values; + + if (i * stride + elem_offset < elems_per_out_group) { + // ----------- Begin Error-Feedback Modification ----------- + int8_t local_output[elems_per_load]; + quantize::_chunk(local_output, iteration_buffer, params); + mem_access::store_global(reduced_data + iter_offset, local_output); + + // Dequantize the quantized output to compute the dequantized value + __half2 dequant_buffer[storage_values]; + dequantize::chunk(dequant_buffer, local_output, de_params); + +#pragma unroll + for (int k = 0; k < storage_values; k++) { + // __half2 to float2 + float2 iter_buf_f = __half22float2(iteration_buffer[k]); + float2 dequant_buf_f = __half22float2(dequant_buffer[k]); + + // Update within float precision + float2 new_error_f; + new_error_f.x = iter_buf_f.x - dequant_buf_f.x; + new_error_f.y = iter_buf_f.y - dequant_buf_f.y; + + float2 iter_err_buf_f = __half22float2(iter_err_buffer[k]); + + iter_err_buf_f.x = err_beta * iter_err_buf_f.x + (1.0f - err_beta) * new_error_f.x; + iter_err_buf_f.y = err_beta * iter_err_buf_f.y + (1.0f - err_beta) * new_error_f.y; + + // float2 back to __half2 + iter_err_buffer[k] = __float22half2_rn(iter_err_buf_f); + } + mem_access::store_global(error_feedback + iter_offset_err, + iter_err_buffer); + } + } +} + +#define LAUNCH_LOCO_DEQUANT_REDUCE(num_chunks) \ + loco_dequant_reduce \ + <<>>(reduced_data, \ + reduced_scales, \ + input_data, \ + input_scales, \ + elems_per_out_group, \ + elems_per_in_tensor, \ + groups_per_in_tensor, \ + elems_per_in_group, \ + num_tensors, \ + error_feedback, \ + err_beta); + +template +void launch_loco_dequant_reduce_impl(int8_t* reduced_data, + float* reduced_scales, + const int8_t* input_data, + const float* input_scales, + int out_groups, + int elems_per_out_group, + int elems_per_in_tensor, + int groups_per_in_tensor, + int elems_per_in_group, + int num_tensors, + __half2* error_feedback, + const float err_beta, + cudaStream_t stream) +{ + constexpr int elems_per_thread = numBits; + const int one_step_threads = + next_pow2((elems_per_out_group + elems_per_thread - 1) / (elems_per_thread)); + const int threads = (one_step_threads < 1024) ? one_step_threads : 1024; + + dim3 block(threads); + dim3 grid(out_groups); + + const int elems_per_step = threads * elems_per_thread; + const int unroll_raw = (elems_per_out_group + elems_per_step - 1) / elems_per_step; + + const int unroll = (unroll_raw >= 4) ? pow2_round<1>(unroll_raw) : unroll_raw; + + if (unroll == 1) { + LAUNCH_LOCO_DEQUANT_REDUCE(1); + } else if (unroll == 2) { + LAUNCH_LOCO_DEQUANT_REDUCE(2); + } else if (unroll == 3) { + LAUNCH_LOCO_DEQUANT_REDUCE(3); + } else if (unroll == 4) { + LAUNCH_LOCO_DEQUANT_REDUCE(4); + } else if (unroll == 6) { + LAUNCH_LOCO_DEQUANT_REDUCE(6); + } else if (unroll == 8) { + LAUNCH_LOCO_DEQUANT_REDUCE(8); + } else if (unroll == 10) { + LAUNCH_LOCO_DEQUANT_REDUCE(10); + } else if (unroll == 12) { + LAUNCH_LOCO_DEQUANT_REDUCE(12); + } else { + assert(false); + } +} + +#define LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(NUM_BITS, NUM_GPUS, QUANT_TYPE) \ + launch_loco_dequant_reduce_impl(reduced_data, \ + reduced_scales, \ + input_data, \ + input_scales, \ + out_groups, \ + elems_per_out_group, \ + elems_per_in_tensor, \ + groups_per_in_tensor, \ + elems_per_in_group, \ + num_gpus, \ + error_feedback, \ + err_beta, \ + stream); + +void launch_loco_dequant_reduce(int8_t* reduced_data, + float* reduced_scales, + const int8_t* input_data, + const float* input_scales, + int num_gpus, + int num_bits, + quantize::Type quant_type, + int out_groups, + int elems_per_out_group, + int elems_per_in_tensor, + int groups_per_in_tensor, + int elems_per_in_group, + __half2* error_feedback, + const float err_beta, + cudaStream_t stream) +{ + if (quant_type == quantize::Type::Symmetric) { + if (num_bits == 4) { + if (num_gpus == 8) { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(4, 8, quantize::Type::Symmetric); + } else if (num_gpus == 16) { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(4, 16, quantize::Type::Symmetric); + } else { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(4, -1, quantize::Type::Symmetric); + } + } else if (num_bits == 8) { + if (num_gpus == 8) { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(8, 8, quantize::Type::Symmetric); + } else if (num_gpus == 16) { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(8, 16, quantize::Type::Symmetric); + } else { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(8, -1, quantize::Type::Symmetric); + } + } + } else if (quant_type == quantize::Type::Asymmetric) { + if (num_bits == 4) { + if (num_gpus == 8) { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(4, 8, quantize::Type::Asymmetric); + } else if (num_gpus == 16) { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(4, 16, quantize::Type::Asymmetric); + } else { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(4, -1, quantize::Type::Asymmetric); + } + } else if (num_bits == 8) { + if (num_gpus == 8) { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(8, 8, quantize::Type::Asymmetric); + } else if (num_gpus == 16) { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(8, 16, quantize::Type::Asymmetric); + } else { + LAUNCH_LOCO_DEQUANT_REDUCE_IMPL(8, -1, quantize::Type::Asymmetric); + } + } + } +} diff --git a/csrc/quantization/swizzled_quantize.cu b/csrc/quantization/swizzled_quantize.cu index 5a02a0ae8120..a4b6096c81af 100644 --- a/csrc/quantization/swizzled_quantize.cu +++ b/csrc/quantization/swizzled_quantize.cu @@ -3,6 +3,7 @@ // DeepSpeed Team +#include "dequantization_utils.h" #include "memory_access_utils.h" #include "quantization_utils.h" #include "reduction_utils.h" @@ -194,3 +195,233 @@ void launch_swizzled_quant(int8_t* q_data, } } } + +template +__global__ void loco_swizzled_quant_kernel(int8_t* quantized_data, + float* quantized_scales, + const __half* uncompressed_data, + __half* error_feedback, + const float err_beta, + int groups, + int elems_per_group, + int pipelining, + int nodes, + int devices_per_node) +{ + cg::thread_block tb = cg::this_thread_block(); + cg::thread_block_tile warp = cg::tiled_partition(tb); + + // Indexing offsets, same as normal quantization for in-case + const int block_rank_data = + blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y; + const int block_offset_data = block_rank_data * elems_per_group; + const int elem_offset = tb.thread_index().x * quantize::h_per_load; + const int base_offset_data = block_offset_data + elem_offset; + const int stride = tb.size() * quantize::h_per_load; + const __half* uncompressed_data_base = uncompressed_data + base_offset_data; + + const int partition_id = blockIdx.z; + const int partition_offset = partition_id / devices_per_node; + const int partition_base = (partition_id % devices_per_node) * nodes; + const int pipelining_offset = blockIdx.y * (devices_per_node * nodes); + const int output_partition = (pipelining_offset + partition_base + partition_offset); + const int block_rank_err = output_partition * gridDim.x + blockIdx.x; + + const int block_offset_err = block_rank_err * elems_per_group; + const int base_offset_err = block_offset_err + elem_offset; + __half* error_feedback_base = error_feedback + base_offset_err; + + __half2 local_buffer[totalChunks * quantize::h2_per_load]; + __half2 err_buffer[totalChunks * quantize::h2_per_load]; + + quantize::GroupStats stats; + +#pragma unroll + for (int i = 0; i < totalChunks; i++) { + __half2* iteration_buffer = local_buffer + i * quantize::h2_per_load; + __half2* iter_err_buffer = err_buffer + i * quantize::h2_per_load; + const int i_stride = i * stride; + bool do_loads = (elem_offset + i_stride) < elems_per_group; + + mem_access::load_global( + iteration_buffer, uncompressed_data_base + i_stride, do_loads); + + mem_access::load_global( + iter_err_buffer, error_feedback_base + i_stride, do_loads); + +#pragma unroll + for (int j = 0; j < quantize::h2_per_load; j++) { + iteration_buffer[j] = __hadd2(iteration_buffer[j], iter_err_buffer[j]); + stats.update(iteration_buffer[j]); + } + } + + auto params = stats.template get_params(tb, warp); + + // Initialize dequantization parameters based on params + auto de_params = params; + de_params.scale = 1.0f / params.scale; + if constexpr (quantType == quantize::Type::Asymmetric) { de_params.offset = params.offset; } + + if (threadIdx.x == 0) { params.store(quantized_scales, block_rank_err); } + + constexpr int out_scalar_effect = 8 / numBits; + const int out_block_offset = block_rank_err * elems_per_group / out_scalar_effect; + const int out_base_offset = out_block_offset + elem_offset / out_scalar_effect; + int8_t* out_base = quantized_data + out_base_offset; + + const int out_stride = stride / out_scalar_effect; + constexpr int num_int8_out = quantize::h_per_load / out_scalar_effect; + +#pragma unroll + for (int i = 0; i < totalChunks; i++) { + const int i_stride = i * stride; + __half2* iteration_buffer = local_buffer + i * quantize::h2_per_load; + __half2* iter_err_buffer = err_buffer + i * quantize::h2_per_load; + + if (i_stride + elem_offset < elems_per_group) { + int8_t local_output[quantize::h_per_load / out_scalar_effect]; + quantize::_chunk(local_output, iteration_buffer, params); + mem_access::store_global(out_base + i * out_stride, local_output); + + // Dequantize the quantized output to compute the dequantized value + __half2 dequant_buffer[quantize::h2_per_load]; + dequantize::chunk(dequant_buffer, local_output, de_params); + +// Compute new error: sum - dequant_buffer +#pragma unroll + for (int k = 0; k < quantize::h2_per_load; k++) { + // __half2 to float2 + float2 iter_buf_f = __half22float2(iteration_buffer[k]); + float2 dequant_buf_f = __half22float2(dequant_buffer[k]); + + // Update within float precision + float2 new_error_f; + new_error_f.x = iter_buf_f.x - dequant_buf_f.x; + new_error_f.y = iter_buf_f.y - dequant_buf_f.y; + + float2 iter_err_buf_f = __half22float2(iter_err_buffer[k]); + + iter_err_buf_f.x = err_beta * iter_err_buf_f.x + (1.0f - err_beta) * new_error_f.x; + iter_err_buf_f.y = err_beta * iter_err_buf_f.y + (1.0f - err_beta) * new_error_f.y; + + // float2 back to __half2 + iter_err_buffer[k] = __float22half2_rn(iter_err_buf_f); + } + __half2* error_feedback_base_h2 = reinterpret_cast<__half2*>(error_feedback_base); + mem_access::store_global(error_feedback_base_h2 + i_stride / 2, + iter_err_buffer); + } + } +} + +#define LAUNCH_LOCO_SWIZZLE_QUANT(total_chunks, threads) \ + loco_swizzled_quant_kernel \ + <<>>(output_data, \ + params, \ + input_data, \ + error_feedback, \ + err_beta, \ + groups, \ + elems_per_group, \ + pipelining, \ + nodes, \ + devices_per_node); + +template +void launch_loco_swizzled_quant_impl(int8_t* output_data, + float* params, + const __half* input_data, + __half* error_feedback, + const float err_beta, + int groups, + int elems_per_group, + int pipelining, + int nodes, + int devices_per_node, + cudaStream_t stream) +{ + const int one_step_threads = + next_pow2((elems_per_group + swiz_quant::h_per_step - 1) / swiz_quant::h_per_step); + const int max_threads = (one_step_threads < swiz_quant::max_threads) ? one_step_threads + : swiz_quant::max_threads; + const int threads = (max_threads < swiz_quant::min_threads) ? swiz_quant::min_threads + : max_threads; + + dim3 block(threads); + const int groups_per_partition = groups / (nodes * devices_per_node); + assert(groups_per_partition % pipelining == 0); + const int contiguous_groups = groups_per_partition / pipelining; + const int partitions = nodes * devices_per_node; + dim3 grid(contiguous_groups, pipelining, partitions); + + const int elems_per_step = threads * swiz_quant::h_per_step; + const int external_unroll = ((elems_per_group + elems_per_step - 1) / elems_per_step); + const int total_unroll = external_unroll * swiz_quant::step_granularity; + + assert(total_unroll % 2 == 0); + + if (threads == 32) { + LAUNCH_LOCO_SWIZZLE_QUANT(2, 32); + } else if (threads == 64) { + LAUNCH_LOCO_SWIZZLE_QUANT(2, 64); + } else if (threads == 128) { + LAUNCH_LOCO_SWIZZLE_QUANT(2, 128); + } else if (threads == 256) { + LAUNCH_LOCO_SWIZZLE_QUANT(2, 256); + } else if (threads == 512) { + if (total_unroll == 2) { + LAUNCH_LOCO_SWIZZLE_QUANT(2, 512); + } else if (total_unroll == 4) { + LAUNCH_LOCO_SWIZZLE_QUANT(4, 512); + } else if (total_unroll == 6) { + LAUNCH_LOCO_SWIZZLE_QUANT(6, 512); + } else if (total_unroll == 8) { + LAUNCH_LOCO_SWIZZLE_QUANT(8, 512); + } else if (total_unroll == 10) { + LAUNCH_LOCO_SWIZZLE_QUANT(10, 512); + } + } +} + +#define DISPATCH_LOCO_SWIZZLE_QUANT(num_bits, qtype) \ + launch_loco_swizzled_quant_impl(output_data, \ + params, \ + input_data, \ + error_feedback, \ + err_beta, \ + groups, \ + elems_per_group, \ + pipelining, \ + nodes, \ + devices_per_node, \ + stream); + +void launch_loco_swizzled_quant(int8_t* output_data, + float* params, + const __half* input_data, + __half* error_feedback, + const float err_beta, + int num_bits, + quantize::Type q_type, + int groups, + int elems_per_group, + int pipelining, + int nodes, + int devices_per_node, + cudaStream_t stream) +{ + if (num_bits == 4) { + if (q_type == quantize::Type::Asymmetric) { + DISPATCH_LOCO_SWIZZLE_QUANT(4, quantize::Type::Asymmetric); + } else if (q_type == quantize::Type::Symmetric) { + DISPATCH_LOCO_SWIZZLE_QUANT(4, quantize::Type::Symmetric); + } + } else if (num_bits == 8) { + if (q_type == quantize::Type::Asymmetric) { + DISPATCH_LOCO_SWIZZLE_QUANT(8, quantize::Type::Asymmetric); + } else if (q_type == quantize::Type::Symmetric) { + DISPATCH_LOCO_SWIZZLE_QUANT(8, quantize::Type::Symmetric); + } + } +} diff --git a/deepspeed/runtime/comm/coalesced_collectives.py b/deepspeed/runtime/comm/coalesced_collectives.py index 543795126fab..c2fa907d7dbb 100644 --- a/deepspeed/runtime/comm/coalesced_collectives.py +++ b/deepspeed/runtime/comm/coalesced_collectives.py @@ -8,7 +8,7 @@ """ import math -from typing import List +from typing import List, Any import torch from torch import Tensor from deepspeed import comm as dist @@ -76,6 +76,83 @@ def all_to_all_quant_reduce(tensors: List[Tensor], groups: {}) -> List[Tensor]: return output_lst +@instrument_w_nvtx +@torch.no_grad() +def all_to_all_loco_quant_reduce( + params: List[Tensor], + groups: {}, + loco_param: Any = None, +) -> List[Tensor]: + global quantizer_module + global loco_idx + if quantizer_module is None: + quantizer_module = op_builder.QuantizerBuilder().load() + local_world_size = get_accelerator().device_count() + global_world_size = dist.get_world_size() + num_nodes = global_world_size // local_world_size + this_rank = dist.get_rank() + intra_idx = int(this_rank / local_world_size) + inter_idx = this_rank % local_world_size + output_lst: List[Tensor] = [None] * len(params) + for idx, p in enumerate(params): + tensor = p.grad + if tensor.dim() == 1: + output_lst[idx] = reduce_scatter_coalesced([tensor])[0] + elif tensor.numel() % (2 * global_world_size) != 0: + # Due to the constraint of 2-stage all-to-all, the input tensor must be divisible by 2 * global_world_size + # Otherwise, all-to-all cannot be performed because of shape mismatch. + # See more at https://github.com/microsoft/DeepSpeed/pull/5056 + logger.warning( + f"qgZ falls back to reduce_scatter because tensor size = {tensor.numel()} is not divisible by (2 * global_world_size) = {2 * global_world_size}. Please consider allocating a new world to enable qgZ" + ) + output_lst[idx] = reduce_scatter_coalesced([tensor])[0] + else: + err_beta = loco_param['err_beta'] + reset_T = loco_param['reset_T'] + if not hasattr(p, 'intra_ef_buf') or loco_idx > reset_T: + loco_idx = 0 + intra_err = torch.zeros_like(p.grad) + inter_err = torch.zeros(tensor.numel() // local_world_size, device=tensor.device, dtype=tensor.dtype) + else: + intra_err = quantizer_module.dequantize(p.intra_ef_buf[0], p.intra_ef_buf[1], + p.intra_ef_buf[1].numel(), 8, quantizer_module.Symmetric) + inter_err = quantizer_module.dequantize(p.inter_ef_buf[0], p.inter_ef_buf[1], + p.inter_ef_buf[1].numel(), 8, quantizer_module.Symmetric) + + intra_quant_group = max(tensor.shape[0], tensor.shape[1], global_world_size) + inter_quant_group = intra_quant_group // local_world_size + intra_quant_int4, intra_q_scales = quantizer_module.loco_swizzle_quant(tensor, intra_err, err_beta, + intra_quant_group, 4, + quantizer_module.Symmetric, 1, + num_nodes, local_world_size) + local_output = torch.empty_like(intra_quant_int4) + scale_output = torch.empty_like(intra_q_scales) + all_to_all_single(local_output, intra_quant_int4, group=groups[f'local_{intra_idx}']) + all_to_all_single(scale_output, intra_q_scales, group=groups[f'local_{intra_idx}']) + + p.intra_ef_buf = quantizer_module.quantize(intra_err, intra_quant_group, 8, quantizer_module.Symmetric) + + global_input_tensor, global_scales = quantizer_module.loco_quantized_reduction( + local_output, scale_output, inter_err, err_beta, intra_quant_group, inter_quant_group, 4, + quantizer_module.Symmetric, local_world_size) + + global_output = torch.empty_like(global_input_tensor) + global_scale_output = torch.empty_like(global_scales) + all_to_all_single(global_output, global_input_tensor, group=groups[f'global_{inter_idx}']) + all_to_all_single(global_scale_output, global_scales, group=groups[f'global_{inter_idx}']) + + p.inter_ef_buf = quantizer_module.quantize(inter_err, inter_quant_group, 8, quantizer_module.Symmetric) + + final_output = quantizer_module.dequantize(global_output, global_scale_output, global_scale_output.numel(), + 4, quantizer_module.Symmetric) + assert final_output.numel( + ) % num_nodes == 0, f"final_output.numel()={final_output.numel()} is not divisible by num_nodes={num_nodes}" + output_lst[idx] = (sum(list(final_output.chunk(num_nodes))) / num_nodes).view(-1) + loco_idx += 1 + + return output_lst + + @instrument_w_nvtx @torch.no_grad() def reduce_scatter_coalesced( diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 8c5da36e5a78..0aad018528d3 100755 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -912,6 +912,9 @@ def zero_quantized_nontrainable_weights(self): def zero_quantized_gradients(self): return self._config.zero_config.zero_quantized_gradients + def zeropp_loco_param(self): + return self._config.zero_config.zeropp_loco_param + def dump_state(self): return self._config.dump_state @@ -1191,7 +1194,8 @@ def _configure_distributed_model(self, model): # Query the groups module to get information about various parallel groups self.local_all_to_all_group = None if self.zero_quantized_gradients(): - log_dist("Using quantized gradients", ranks=[0]) + message = "Using LoCo quantized gradients" if self.zeropp_loco_param() else "Using quantized gradients" + log_dist(message, ranks=[0]) self.local_all_to_all_group = groups._get_local_all_to_all_group() self.data_parallel_group = groups._get_data_parallel_group() self.dp_world_size = groups._get_data_parallel_world_size() @@ -1667,6 +1671,7 @@ def _configure_zero_optimizer(self, optimizer): zero_quantized_weights=self.zero_quantized_weights(), zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights(), zero_module_granularity_threshold=self.zero_module_granularity_threshold(), + zeropp_loco_param=self.zeropp_loco_param(), ) else: diff --git a/deepspeed/runtime/zero/config.py b/deepspeed/runtime/zero/config.py index 19b272ce9e92..cbc6a15c2057 100644 --- a/deepspeed/runtime/zero/config.py +++ b/deepspeed/runtime/zero/config.py @@ -4,7 +4,7 @@ # DeepSpeed Team import sys -from typing import Optional +from typing import Optional, Dict, Any from enum import Enum from pydantic import Field, model_validator from deepspeed.runtime.config_utils import get_scalar_param, pp_int, DeepSpeedConfigModel @@ -44,6 +44,7 @@ "zero_quantized_gradients": [true|false], "memory_efficient_linear": [true|false], "override_module_apply": [true|false], + "zeropp_loco_param": {...}, } } """ @@ -310,6 +311,16 @@ class DeepSpeedZeroConfig(DeepSpeedConfigModel): Boolean indicating whether to use quantized zero gradients for efficient all_2_all_reduce comm """ + zeropp_loco_param: Optional[Dict[str, Any]] = None + """ + This dictionary contains parameters for using LoCo-Zero++, with two key parameters: + - `err_beta`: A coefficient for the moving average of quantization errors before and after gradient computation. + It ranges between 0 and 1, with a default value of 0.8. + - `reset_T`: The number of steps after which the moving-average error buffer is cleared. The default value is 1024. + These parameters can be adjusted based on performance needs. Example configuration in ds config: + "zeropp_loco_param": { "err_beta": 0.8, "reset_T": 1024 }. + See LoCo paper for more details: (https://arxiv.org/abs/2407.04480). + """ mics_shard_size: int = Field(-1, json_schema_extra={"new_param": "mics_shard_size"}) diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index 99a5ecf41a2f..459cffce52c8 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -17,7 +17,7 @@ from deepspeed.runtime.base_optimizer import ZeROOptimizer from deepspeed.utils import logger from deepspeed.runtime.fp16.loss_scaler import CreateLossScaler -from deepspeed.runtime.comm.coalesced_collectives import reduce_scatter_coalesced, all_to_all_quant_reduce +from deepspeed.runtime.comm.coalesced_collectives import reduce_scatter_coalesced, all_to_all_quant_reduce, all_to_all_loco_quant_reduce from deepspeed.runtime.utils import inf, is_model_parallel_parameter, get_only_unique_item from deepspeed.runtime.zero.partition_parameters import * from deepspeed.runtime.zero.config import ZeroStageEnum @@ -158,6 +158,7 @@ def __init__( zero_quantized_weights=False, zero_quantized_nontrainable_weights=False, zero_module_granularity_threshold=0, + zeropp_loco_param=None, ): see_memory_usage("Stage 3 initialize beginning", force=True) @@ -284,6 +285,8 @@ def __init__( self.partition_count = dist.get_world_size(group=self.dp_process_group) + self.zeropp_loco_param = zeropp_loco_param + if mpu is None: self.model_parallel_group = None self.model_parallel_rank = 0 @@ -1383,7 +1386,10 @@ def __avg_scatter_grads(self, params_to_reduce: List[Parameter]) -> List[Tensor] global_world_size = dist.get_world_size() num_nodes = global_world_size // local_world_size if self.all2all_process_group is not None and num_nodes > 1: - grad_partitions_for_rank = all_to_all_quant_reduce(full_grads_for_rank, self.all2all_process_group) + grad_partitions_for_rank = (all_to_all_loco_quant_reduce(params_to_reduce, self.all2all_process_group, + self.zeropp_loco_param) + if self.zeropp_loco_param is not None else all_to_all_quant_reduce( + full_grads_for_rank, self.all2all_process_group)) else: grad_partitions_for_rank = reduce_scatter_coalesced(full_grads_for_rank, self.dp_process_group) @@ -2009,6 +2015,25 @@ def _overflow_clean_up(self, prev_scale): see_memory_usage('After overflow after clearing gradients', force=False) + def _loco_err_buf_update(self, overflow: bool, scale=1.0): + """ + Loco Error Buffer update. + """ + if not overflow and scale == 1.0: return + if dist.get_rank() == 0: + logger.info(f"update loco-zero++ error buffer with overflow: {overflow}") + # FP32 grad should never exist. + # For speed, set model fp16 grad to None by default + for group in self.fp16_groups: + for p in group: + if hasattr(p, 'intra_ef_buf'): + if overflow: + del p.intra_ef_buf + del p.inter_ef_buf + else: + p.intra_ef_buf[1] *= scale + p.inter_ef_buf[1] *= scale + @instrument_w_nvtx def _overflow_check_and_loss_scale_update(self): @@ -2023,6 +2048,9 @@ def _overflow_check_and_loss_scale_update(self): if self.overflow: self._overflow_clean_up(prev_scale) + #update loco error buf + self._loco_err_buf_update(self.overflow, self.loss_scale / prev_scale) + return self.overflow @instrument_w_nvtx diff --git a/tests/unit/runtime/comm/test_coalesced_collectives.py b/tests/unit/runtime/comm/test_coalesced_collectives.py index 17b2ffbb9d29..2d5db192f2ca 100644 --- a/tests/unit/runtime/comm/test_coalesced_collectives.py +++ b/tests/unit/runtime/comm/test_coalesced_collectives.py @@ -96,3 +96,66 @@ def test_non_divisible(self): elif dist.get_rank() == 1: assert output.shape == (24, ) assert torch.allclose(output, torch.zeros_like(output)) + + +class TestLocoQuantized(DistributedTest): + + world_size = 1 + + @pytest.mark.parametrize("num_bits", [4, 8]) + @pytest.mark.parametrize("tensor_size", [(16, 16), (64, 64)]) + @pytest.mark.parametrize("devices_per_node", [4, 8]) + def test_loco_quantized_reduction(self, num_bits, tensor_size, devices_per_node): + from deepspeed.ops.op_builder import QuantizerBuilder + if not deepspeed.ops.__compatible_ops__[QuantizerBuilder.NAME]: + pytest.skip("QuantizerBuilder is not implemented") + + quantizer_module = QuantizerBuilder().load() + + tensor = torch.randn(tensor_size, device='cuda', dtype=torch.half) + + num_nodes = 2 # Fake world size + total_elements = tensor.numel() + total_devices = devices_per_node * num_nodes + num_groups = max(tensor.shape[0], tensor.shape[1], total_devices) + + # Initialize error_feedback tensor + error_feedback = torch.randn(tensor_size, device=tensor.device, dtype=tensor.dtype) + error_feedback_ori = error_feedback.clone() + # Swizzle the original tensor + tensor_reshaped = tensor.reshape(num_nodes, devices_per_node, total_elements // total_devices) + swizzled_tensor = tensor_reshaped.permute(1, 0, 2).reshape(tensor.size()) + + # Perform loco_swizzle_quant + output, scales = quantizer_module.loco_swizzle_quant(tensor, error_feedback, 0.0, num_groups, num_bits, + quantizer_module.Symmetric, 1, num_nodes, + devices_per_node) + + # Compare swizzled_tensor with the output of loco_swizzle_quant + dequantized = quantizer_module.dequantize(output, scales, scales.numel(), num_bits, + quantizer_module.Symmetric).view(tensor.size()) + + assert torch.allclose(swizzled_tensor + error_feedback_ori, dequantized + error_feedback) + + # Calculate elements per group and groups per partition + elements_per_group = total_elements // num_groups + groups_per_partition = num_groups // devices_per_node + + # Reshape dequantized data to match the grouping in loco_quantized_reduction + dequantized_reshaped = dequantized.view(devices_per_node, groups_per_partition, elements_per_group) + + # Perform reduction across devices_per_node dimension + reduced_dequantized = dequantized_reshaped.cumsum(dim=0)[-1] + # Initialize error_feedback tensor + error_feedback = torch.randn(reduced_dequantized.shape, device=tensor.device, dtype=dequantized.dtype) + error_feedback_ori = error_feedback.clone() + + # perform loco_quantized_reduction + output, scales = quantizer_module.loco_quantized_reduction(output, scales, error_feedback, 0.0, num_groups, + num_groups // devices_per_node, num_bits, + quantizer_module.Symmetric, devices_per_node) + + dequantized_reduced = quantizer_module.dequantize(output, scales, scales.numel(), num_bits, + quantizer_module.Symmetric).view(error_feedback.size()) + + assert torch.allclose(reduced_dequantized + error_feedback_ori, dequantized_reduced + error_feedback) From 9e3125281f992a15486ca083f1acfa5f61931497 Mon Sep 17 00:00:00 2001 From: Yichen Yan Date: Wed, 11 Dec 2024 04:12:42 +0800 Subject: [PATCH 061/112] Fix type error in `ZeROOrderedDict` (#6794) As @keskival pointed in https://github.com/microsoft/DeepSpeed/commit/3d5cf739ead7c78f518a518ccaa15a323bd5c8da#r149582004, I've confirmed there's a type error, which this PR fixes. I didn't run into this because our internal version still use `*r2`. Co-authored-by: Tero Keski-Valkama Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/zero/parameter_offload.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepspeed/runtime/zero/parameter_offload.py b/deepspeed/runtime/zero/parameter_offload.py index f945f5166190..0be88a1e1ba6 100644 --- a/deepspeed/runtime/zero/parameter_offload.py +++ b/deepspeed/runtime/zero/parameter_offload.py @@ -52,7 +52,7 @@ def __init__(self, parent_module, *args, **kwargs): def __reduce__(self): r0, _, *r2 = super().__reduce__() - return (r0, (self._parent_module, )) + r2 + return (r0, (self._parent_module, )) + tuple(r2) def __getitem__(self, key): param = super().__getitem__(key) From ecb4bf32614fa8d6d4433ea579ca867008d34140 Mon Sep 17 00:00:00 2001 From: Eugene29 <52263376+Eugene29@users.noreply.github.com> Date: Tue, 10 Dec 2024 17:58:25 -0600 Subject: [PATCH 062/112] Fix uneven head sequence parallelism bug (#6774) (#6797) Here `gather_idx < 2` represents `is_first_all2all`. During the first all2all, `uneven_head_all2all` will be called if either `num_heads % seq_world_size != 0` or `get_num_kv_heads() is None`. During the second all2all, it'll return return `uneven_head_all2all` if and only if `get_num_kv_heads() is None` which is always set during the first uneven all2all. This means that there will no longer be issue where `uneven_head_all2all ` is returned for the second all2all because of `num_heads % seq_world_size != 0`. Fixes: #6774 --------- 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 3dfe149057e9..ccc43fdf7164 100644 --- a/deepspeed/sequence/layer.py +++ b/deepspeed/sequence/layer.py @@ -184,7 +184,7 @@ def single_all_to_all(input, scatter_idx, gather_idx, batch_dim_idx, group, asyn # we only need num_heads once num_heads = input.shape[2] - if get_num_kv_heads() is not None or num_heads % seq_world_size != 0: + if get_num_kv_heads() is not None or (num_heads % seq_world_size != 0 and not scatter_idx < 2): # Assuming here that the number of heads for q is consistent with kv # If not, additional logic is required for cases like GQA if get_num_kv_heads() is None: From 074d5c69c3eeffdf34b406153b9ece971ae7f115 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 11 Dec 2024 10:34:31 -0800 Subject: [PATCH 063/112] Fix nv-torch-nightly test by pinning transformers (#6849) --- .github/workflows/nv-torch-nightly-v100.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/nv-torch-nightly-v100.yml b/.github/workflows/nv-torch-nightly-v100.yml index 74495812add5..c2d10a454f94 100644 --- a/.github/workflows/nv-torch-nightly-v100.yml +++ b/.github/workflows/nv-torch-nightly-v100.yml @@ -2,6 +2,9 @@ name: nv-torch-nightly-v100 on: workflow_dispatch: + pull_request: + paths: + - '.github/workflows/nv-torch-nightly-v100.yml' schedule: - cron: "0 0 * * *" @@ -34,7 +37,7 @@ jobs: git clone https://github.com/huggingface/transformers cd transformers # if needed switch to the last known good SHA until transformers@master is fixed - # git checkout 1cc453d33 + git checkout 6c3f168b3 git rev-parse --short HEAD pip install . From bd6fd50e9f49a81d71284a49b8449c334bf11074 Mon Sep 17 00:00:00 2001 From: kaiksi-bb <122809228+kaiksi-bb@users.noreply.github.com> Date: Thu, 12 Dec 2024 01:48:18 +0100 Subject: [PATCH 064/112] Remove broken links to non-active site (#6854) The site referenced in various places on the README is no longer active: https://deepspeed4science.ai ![image](https://github.com/user-attachments/assets/8ec47313-2abf-40d6-b1f8-9a9234c15e2f) Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 2334fd366ae4..9f06af022853 100755 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ * [2023/11] [DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-offloadpp) * [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen)] [[中文](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/japanese/README.md)] * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)] -* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] +* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] @@ -78,7 +78,7 @@ To further increase the inference efficiency, DeepSpeed offers easy-to-use and f ## DeepSpeed4Science -In line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called *DeepSpeed4Science*, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. Learn more: [DeepSpeed4Science website](https://deepspeed4science.ai/) and [tutorials](https://www.deepspeed.ai/deepspeed4science/) +In line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called *DeepSpeed4Science*, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. Learn more: [tutorials](https://www.deepspeed.ai/deepspeed4science/) --- From 91829476a8fd4d0d9268c03c1d56795d20a51c12 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=84=8D=F0=9D=95=A0=F0=9D=95=9D=F0=9D=95=9D=F0=9D=95=A0?= =?UTF-8?q?=F0=9D=95=A8=20=F0=9D=95=84=F0=9D=95=92=F0=9D=95=9F?= Date: Thu, 12 Dec 2024 22:48:32 +0200 Subject: [PATCH 065/112] Avoid poisoning process with CUDA calls as soon as importing (#6810) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Call `torch.cuda.device_count() > 0` before `torch.cuda.is_available()`, to give priority to nvml based availability, so that we can try not to poison process with CUDA calls as soon as we execute `import deepspeed`. https://github.com/pytorch/pytorch/blob/v2.5.1/torch/cuda/__init__.py#L120-L124 There are 2 reasons to make this change: Firstly, if we accidentally import deepspeed, since the CUDA runtime initializes when the first CUDA API call is made and caches the device list, changing the CUDA_VISIBLE_DEVICES within the same process after initialization won't have any effect on the visible devices. The specific case: https://github.com/OpenRLHF/OpenRLHF/pull/524#issuecomment-2501505023 A demo for reproduction before the fix is applied: ```python import torch import os os.environ["CUDA_VISIBLE_DEVICES"] = "" import deepspeed os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" torch.cuda.set_device('cuda:0') ``` Secondly, https://pytorch.org/docs/stable/notes/cuda.html When assessing the availability of CUDA in a given environment (is_available()), PyTorch’s default behavior is to call the CUDA Runtime API method cudaGetDeviceCount. Because this call in turn initializes the CUDA Driver API (via cuInit) if it is not already initialized, subsequent forks of a process that has run is_available() will fail with a CUDA initialization error. Signed-off-by: Hollow Man Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- accelerator/real_accelerator.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/accelerator/real_accelerator.py b/accelerator/real_accelerator.py index 69e96d285bb8..a6173ac70abd 100644 --- a/accelerator/real_accelerator.py +++ b/accelerator/real_accelerator.py @@ -167,7 +167,12 @@ def get_accelerator(): import torch # Determine if we are on a GPU or x86 CPU with torch. - if torch.cuda.is_available(): #ignore-cuda + # "torch.cuda.is_available()" provides a stronger guarantee, #ignore-cuda + # ensuring that we are free from CUDA initialization errors. + # While "torch.cuda.device_count() > 0" check ensures that #ignore-cuda + # we won't try to do any CUDA calls when no device is available + # For reference: https://github.com/microsoft/DeepSpeed/pull/6810 + if torch.cuda.device_count() > 0 and torch.cuda.is_available(): #ignore-cuda accelerator_name = "cuda" else: if accel_logger is not None: From 853a97648b9ba3acbb990018eab1dd928a08c390 Mon Sep 17 00:00:00 2001 From: Liangliang Ma Date: Sat, 14 Dec 2024 03:29:48 +0800 Subject: [PATCH 066/112] Fix xpu tests workflow failure by changing pip index url (#6864) Update xpu-max1100.yml and xpu-compile.yml --- .github/workflows/xpu-compile.yml | 8 ++++---- .github/workflows/xpu-max1100.yml | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/workflows/xpu-compile.yml b/.github/workflows/xpu-compile.yml index e095e089fc30..9e8bd9d792fb 100644 --- a/.github/workflows/xpu-compile.yml +++ b/.github/workflows/xpu-compile.yml @@ -31,10 +31,10 @@ jobs: run: | apt-get update apt-get install clinfo libaio-dev python3-pip -y - pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torch/ - pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/intel-extension-for-pytorch/ - pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/oneccl-bind-pt/ - pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torchvision/ + pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/torch/ + pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/intel-extension-for-pytorch/ + pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/oneccl-bind-pt/ + pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/torchvision/ pip install https://github.com/intel/intel-xpu-backend-for-triton/releases/download/v3.0.0b2/triton_xpu-3.0.0b2-cp310-cp310-linux_x86_64.whl pip install py-cpuinfo numpy pip install .[dev,autotuning] diff --git a/.github/workflows/xpu-max1100.yml b/.github/workflows/xpu-max1100.yml index d19e73aeef1c..56bff4a88ba9 100644 --- a/.github/workflows/xpu-max1100.yml +++ b/.github/workflows/xpu-max1100.yml @@ -47,10 +47,10 @@ jobs: run: | apt-get update apt-get install clinfo libaio-dev python3-pip -y - pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torch/ - pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/intel-extension-for-pytorch/ - pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/oneccl-bind-pt/ - pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torchvision/ + pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/torch/ + pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/intel-extension-for-pytorch/ + pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/oneccl-bind-pt/ + pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/torchvision/ pip install py-cpuinfo numpy pip install .[dev,autotuning] From d7750c34291b9dcd892de4a795ecd0e35b28f6ee Mon Sep 17 00:00:00 2001 From: Guanhua Wang Date: Fri, 13 Dec 2024 11:40:41 -0800 Subject: [PATCH 067/112] Domino updates (#6861) Updating our website for Domino --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- docs/_tutorials/domino.md | 6 ++++++ docs/index.md | 6 +++--- 2 files changed, 9 insertions(+), 3 deletions(-) create mode 100644 docs/_tutorials/domino.md diff --git a/docs/_tutorials/domino.md b/docs/_tutorials/domino.md new file mode 100644 index 000000000000..6b116cb87463 --- /dev/null +++ b/docs/_tutorials/domino.md @@ -0,0 +1,6 @@ +--- +title: "Domino" +tags: training +--- + +Domino achieves near-complete communication hiding behind computation for tensor parallel training. Please find our [Domino-tutorial](https://github.com/microsoft/DeepSpeedExamples/blob/master/training/DeepSpeed-Domino/README.md) in DeepSpeedExample repo. diff --git a/docs/index.md b/docs/index.md index 3279682b42d4..3d5f290f2bde 100755 --- a/docs/index.md +++ b/docs/index.md @@ -7,25 +7,25 @@ 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). +* [2024/12] [DeepSpeed Domino: Communication-Free LLM Training Engine](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-domino/README.md) + * [2024/08] [DeepSpeed on Windows](https://github.com/microsoft/DeepSpeed/blob/master/blogs/windows/08-2024/README.md)[[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/windows/08-2024/japanese/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/windows/08-2024/chinese/README.md)] * [2024/08] [DeepNVMe: Improving DL Applications through I/O Optimizations](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-gds/README.md)[[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-gds/japanese/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-gds/chinese/README.md)] * [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) From b5e3fac6a599f80a7db1899e47ea6993131bb51b Mon Sep 17 00:00:00 2001 From: Guanhua Wang Date: Fri, 13 Dec 2024 12:59:08 -0800 Subject: [PATCH 068/112] add domino navigation (#6866) add domino item into navigation list --- docs/_data/navigation.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/_data/navigation.yml b/docs/_data/navigation.yml index b17685b8dc4d..c3ebad4f86af 100755 --- a/docs/_data/navigation.yml +++ b/docs/_data/navigation.yml @@ -75,6 +75,8 @@ lnav: url: /tutorials/data-efficiency/ - title: 'DeepNVMe' url: /tutorials/deepnvme/ + - title: 'Domino' + url: /tutorials/domino/ - title: 'DS4Sci_EvoformerAttention' url: /tutorials/ds4sci_evoformerattention/ - title: 'Flops Profiler' From 8efbcc495c3c7c072d10bfd672932807fb9eb8e5 Mon Sep 17 00:00:00 2001 From: Olatunji Ruwase Date: Fri, 13 Dec 2024 16:49:08 -0500 Subject: [PATCH 069/112] Update TSC (#6867) --- COMMITTERS.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/COMMITTERS.md b/COMMITTERS.md index bcb8579bf1f7..8418bdf8629d 100644 --- a/COMMITTERS.md +++ b/COMMITTERS.md @@ -5,5 +5,7 @@ | Olatunji Ruwase | [tjruwase](https://github.com/tjruwase) | Microsoft | | Logan Adams | [loadams](https://github.com/loadams) | Microsoft | | Masahiro Tanaka | [tohtana](https://github.com/tohtana) | Microsoft | -| Jeff Rasley | [jeffra](https://github.com/jeffra) | SnowFlake | -| Minjia Zhang | [minjiazhang](https://github.com/minjiazhang) | UIUC | +| Jeff Rasley | [jeffra](https://github.com/jeffra) | SnowFlake | +| Minjia Zhang | [minjiazhang](https://github.com/minjiazhang) | UIUC | +| Ashwin Aji | [ashwinma](https://github.com/ashwinma) | AMD | +| Sam Foreman | [saforem2](https://github.com/saforem2) | Argonne National Laboratory | From 6e3e13cb280b684ebedb5c2aecb36efb545ebfce Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Fri, 13 Dec 2024 15:35:12 -0800 Subject: [PATCH 070/112] Remove warnings from autodoc and sphinx (#6788) Co-authored-by: Olatunji Ruwase --- deepspeed/runtime/fp16/onebit/zoadam.py | 4 +++- deepspeed/runtime/lr_schedules.py | 4 ++-- docs/code-docs/source/monitor.rst | 18 +++++++++--------- 3 files changed, 14 insertions(+), 12 deletions(-) diff --git a/deepspeed/runtime/fp16/onebit/zoadam.py b/deepspeed/runtime/fp16/onebit/zoadam.py index 803bd929742d..70282ec41714 100644 --- a/deepspeed/runtime/fp16/onebit/zoadam.py +++ b/deepspeed/runtime/fp16/onebit/zoadam.py @@ -12,9 +12,11 @@ class ZeroOneAdam(torch.optim.Optimizer): - """Implements the 0/1 Adam algorithm. Currently GPU-only. + """ + Implements the 0/1 Adam algorithm. Currently GPU-only. For usage example please see https://www.deepspeed.ai/tutorials/zero-one-adam/ For technical details please read https://arxiv.org/abs/2202.06009 + Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups. diff --git a/deepspeed/runtime/lr_schedules.py b/deepspeed/runtime/lr_schedules.py index f25a19e8e499..899358e2c5ef 100755 --- a/deepspeed/runtime/lr_schedules.py +++ b/deepspeed/runtime/lr_schedules.py @@ -274,7 +274,7 @@ class LRRangeTest(object): """Sets the learning rate of each parameter group according to learning rate range test (LRRT) policy. The policy increases learning rate starting from a base value with a constant frequency, as detailed in - the paper `A disciplined approach to neural network hyper-parameters: Part1`_. + the paper `A disciplined approach to neural network hyper-parameters: Part 1 `_ LRRT policy is used for finding maximum LR that trains a model without divergence, and can be used to configure the LR boundaries for Cyclic LR schedules. @@ -379,7 +379,7 @@ class OneCycle(object): 1CLR policy changes the learning rate after every batch. `step` should be called after a batch has been used for training. - This implementation was adapted from the github repo: `pytorch/pytorch`_ + This implementation was adapted from the github repo: `PyTorch `_. Args: optimizer (Optimizer): Wrapped optimizer. diff --git a/docs/code-docs/source/monitor.rst b/docs/code-docs/source/monitor.rst index 694c72b9b870..b185ed433c1c 100644 --- a/docs/code-docs/source/monitor.rst +++ b/docs/code-docs/source/monitor.rst @@ -9,15 +9,15 @@ overview of what DeepSpeed will log automatically. :header: "Field", "Description", "Condition" :widths: 20, 20, 10 - `Train/Samples/train_loss`,The training loss.,None - `Train/Samples/lr`,The learning rate during training.,None - `Train/Samples/loss_scale`,The loss scale when training using `fp16`.,`fp16` must be enabled. - `Train/Eigenvalues/ModelBlockParam_{i}`,Eigen values per param block.,`eigenvalue` must be enabled. - `Train/Samples/elapsed_time_ms_forward`,The global duration of the forward pass.,`flops_profiler.enabled` or `wall_clock_breakdown`. - `Train/Samples/elapsed_time_ms_backward`,The global duration of the forward pass.,`flops_profiler.enabled` or `wall_clock_breakdown`. - `Train/Samples/elapsed_time_ms_backward_inner`,The backward time that does not include the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time.,`flops_profiler.enabled` or `wall_clock_breakdown`. - `Train/Samples/elapsed_time_ms_backward_allreduce`,The global duration of the allreduce operation.,`flops_profiler.enabled` or `wall_clock_breakdown`. - `Train/Samples/elapsed_time_ms_step`,The optimizer step time,`flops_profiler.enabled` or `wall_clock_breakdown`. + `Train/Samples/train_loss`,"The training loss.",None + `Train/Samples/lr`,"The learning rate during training.",None + `Train/Samples/loss_scale`,"The loss scale when training using `fp16`.",`fp16` must be enabled. + `Train/Eigenvalues/ModelBlockParam_{i}`,"Eigen values per param block.",`eigenvalue` must be enabled. + `Train/Samples/elapsed_time_ms_forward`,"The global duration of the forward pass.",`flops_profiler.enabled` or `wall_clock_breakdown`. + `Train/Samples/elapsed_time_ms_backward`,"The global duration of the forward pass.",`flops_profiler.enabled` or `wall_clock_breakdown`. + `Train/Samples/elapsed_time_ms_backward_inner`,"The backward time that does not include the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time.",`flops_profiler.enabled` or `wall_clock_breakdown`. + `Train/Samples/elapsed_time_ms_backward_allreduce`,"The global duration of the allreduce operation.",`flops_profiler.enabled` or `wall_clock_breakdown`. + `Train/Samples/elapsed_time_ms_step`,"The optimizer step time.",`flops_profiler.enabled` or `wall_clock_breakdown`. TensorBoard ----------- From fc7c07007fe341bf6d78a9126d0cb5a914ce28fd Mon Sep 17 00:00:00 2001 From: keiwoo Date: Sat, 14 Dec 2024 08:41:43 +0800 Subject: [PATCH 071/112] Update real_accelerator.py (#6845) ### Comment out or delete `accelerate_name="cpu"` when `xpu` is not detected. When `xpu `is not detected it just pass at lines from 68 to 74 if `DS_ACCELERATOR` is set. However, `cpu` is assigned to `accelerate_name` if it cannot import `intel_extension_for_pytorch` or find` xpu`, namely, at line from 125 to 133 when`DS_ACCELERATOR` is not set. I found this problem yesterday and spent whole afternoon figuring it out. I got `intel_extension_for_pytorch `installed with other package which I do not use actually and have no idea about this. Then I found that it `cpu` is assigned to accelerate_name directly if it cannot find `xpu` and it affects `cuda` detection. In fact, `cpu` will be assigned finally if `cuda` is even not detected at line from 170 to 177. --------- Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- accelerator/real_accelerator.py | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/accelerator/real_accelerator.py b/accelerator/real_accelerator.py index a6173ac70abd..ced9218d7aca 100644 --- a/accelerator/real_accelerator.py +++ b/accelerator/real_accelerator.py @@ -125,10 +125,9 @@ def get_accelerator(): if accelerator_name is None: try: import intel_extension_for_pytorch as ipex + if ipex._C._has_xpu(): accelerator_name = "xpu" - else: - accelerator_name = "cpu" except ImportError as e: pass if accelerator_name is None: @@ -162,7 +161,6 @@ def get_accelerator(): except ImportError as e: pass if accelerator_name is None: - # borrow this log from PR#5084 try: import torch @@ -174,16 +172,16 @@ def get_accelerator(): # For reference: https://github.com/microsoft/DeepSpeed/pull/6810 if torch.cuda.device_count() > 0 and torch.cuda.is_available(): #ignore-cuda accelerator_name = "cuda" - else: - if accel_logger is not None: - accel_logger.warn( - "Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it." - ) - accelerator_name = "cpu" except (RuntimeError, ImportError) as e: # TODO need a more decent way to detect which accelerator to use, consider using nvidia-smi command for detection - accelerator_name = "cuda" pass + if accelerator_name is None: + # borrow this log from PR#5084 + if accel_logger is not None: + accel_logger.warn( + "Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it.") + # cpu added as catch-all when accelerator detection fails + accelerator_name = "cpu" ds_set_method = "auto detect" From db98cc3ad1e0a20807e0c2513f0eee40f626860e Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Mon, 16 Dec 2024 11:05:55 -0800 Subject: [PATCH 072/112] Fix assertion for offloading states (#6855) This PR fixes the assertions in `offload_states` method mentioned in #6833. Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/engine.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 0aad018528d3..5f023d87f375 100755 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -3738,6 +3738,11 @@ def offload_states(self, assert self.zero_optimization_stage( ) == ZeroStageEnum.weights, "Moving buffers across devices is supported only for ZeRO stage 3." + opt_offload_config = self.zero_offload_optimizer() + assert opt_offload_config is None or opt_offload_config.device == OffloadDeviceEnum.none, "Moving states across devices is not supported for offloaded optimizer states." + param_offload_config = self.zero_offload_param() + assert param_offload_config is None or param_offload_config.device == OffloadDeviceEnum.none, "Moving states across devices is not supported for offloaded parameters." + assert not self.zero_offload_param(), "Moving states across devices is not supported for offloaded parameters." if device == OffloadDeviceEnum.none: From 87c650681eb285ab34a69a011b520f756f42d4b9 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 16 Dec 2024 11:21:51 -0800 Subject: [PATCH 073/112] Remove pin from transformers version and fix Processing/Threading issues in tests (#6822) Changes from https://github.com/huggingface/transformers/pull/34966 caused the `nv-torch-latest-v100` tests to fail with the following error: ``` File "/tmp/azureml/cr/j/e4bfd57a509846d6bbc4914639ad248d/exe/wd/actions-runner/_work/DeepSpeed/DeepSpeed/unit-test-venv/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3941, in from_pretrained raise EnvironmentError( OSError: Can't load the model for 'hf-internal-testing/tiny-random-VisionEncoderDecoderModel-vit-gpt2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'hf-internal-testing/tiny-random-VisionEncoderDecoderModel-vit-gpt2' is the correct path to a directory containing a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack. ``` Sample failure here: https://github.com/microsoft/DeepSpeed/actions/runs/12169422174/job/33942348835?pr=6794#step:8:3506 This was resolved on the Transformers side here: https://github.com/huggingface/transformers/pull/35236 --- .github/workflows/cpu-torch-latest.yml | 2 +- .github/workflows/nv-torch-latest-v100.yml | 2 +- .github/workflows/nv-torch-nightly-v100.yml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/cpu-torch-latest.yml b/.github/workflows/cpu-torch-latest.yml index 51bc60c2c2ae..78a51905834b 100644 --- a/.github/workflows/cpu-torch-latest.yml +++ b/.github/workflows/cpu-torch-latest.yml @@ -42,7 +42,7 @@ jobs: git clone https://github.com/huggingface/transformers cd transformers # if needed switch to the last known good SHA until transformers@master is fixed - git checkout 6c3f168b3 + # git checkout 6c3f168b3 git rev-parse --short HEAD pip install . diff --git a/.github/workflows/nv-torch-latest-v100.yml b/.github/workflows/nv-torch-latest-v100.yml index 2d69d0b94cb5..a1ba4937d164 100644 --- a/.github/workflows/nv-torch-latest-v100.yml +++ b/.github/workflows/nv-torch-latest-v100.yml @@ -38,7 +38,7 @@ jobs: git clone https://github.com/huggingface/transformers cd transformers # if needed switch to the last known good SHA until transformers@master is fixed - git checkout 6c3f168b3 + # git checkout 6c3f168b3 git rev-parse --short HEAD pip install . diff --git a/.github/workflows/nv-torch-nightly-v100.yml b/.github/workflows/nv-torch-nightly-v100.yml index c2d10a454f94..0a9570a1ceaa 100644 --- a/.github/workflows/nv-torch-nightly-v100.yml +++ b/.github/workflows/nv-torch-nightly-v100.yml @@ -37,7 +37,7 @@ jobs: git clone https://github.com/huggingface/transformers cd transformers # if needed switch to the last known good SHA until transformers@master is fixed - git checkout 6c3f168b3 + # git checkout 6c3f168b3 git rev-parse --short HEAD pip install . From da771ed42e41a44d5047813ca4672f1cfe9d1731 Mon Sep 17 00:00:00 2001 From: Yejing-Lai Date: Tue, 17 Dec 2024 06:14:53 +0800 Subject: [PATCH 074/112] Add MLP/lm_head tp grain size setting. (#6828) This PR aims to add MLP/lm_head tp size granularity setting to deepspeed.init_inference() API. It will be more flexible to set the MLP/lm_head sharding grain size. DNN library favors tensor size in granularity of power of 2, we pick 64 as a default size. We aim to be able to set the MLP/lm_head tp grain size flexibly. This is a preliminary solution. If there is a better solution, we can discuss it together. Thanks~ --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase --- deepspeed/inference/config.py | 3 +++ deepspeed/module_inject/replace_module.py | 5 ++++- deepspeed/module_inject/tp_shard.py | 11 ++++++++--- 3 files changed, 15 insertions(+), 4 deletions(-) diff --git a/deepspeed/inference/config.py b/deepspeed/inference/config.py index c7c7684fff79..42ffebbc4386 100644 --- a/deepspeed/inference/config.py +++ b/deepspeed/inference/config.py @@ -40,6 +40,9 @@ class DeepSpeedTPConfig(DeepSpeedConfigModel): tp_size: int = 1 """ Number of devices to split the model across using tensor parallelism. """ + tp_grain_size: int = 64 + "Desired MLP/lm_head tp size granularity. DNN library favors tensor size in granularity of power of 2, we pick 64 as a default size." + mpu: object = None """ A model parallelism unit object that implements diff --git a/deepspeed/module_inject/replace_module.py b/deepspeed/module_inject/replace_module.py index 7afe6ca903fb..e59f84bc8453 100644 --- a/deepspeed/module_inject/replace_module.py +++ b/deepspeed/module_inject/replace_module.py @@ -17,7 +17,7 @@ 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 +from deepspeed.module_inject.tp_shard import set_num_kv_heads, set_n_embd, set_num_attention_heads, set_tp_grain_size from .load_checkpoint import load_model_with_checkpoint import time @@ -303,6 +303,9 @@ def replace_wo_policy(module, all_reduce_linears, prefix="", state_dict=None): if hasattr(model_config, 'num_attention_heads'): set_num_attention_heads(getattr(model_config, 'num_attention_heads')) + # 4.4 set tp_grain_size + set_tp_grain_size(config.tensor_parallel.tp_grain_size) + # 5. Set linear policies _autotp.update_linear_policies() diff --git a/deepspeed/module_inject/tp_shard.py b/deepspeed/module_inject/tp_shard.py index 57be0c793856..3e6fc2b63ef1 100644 --- a/deepspeed/module_inject/tp_shard.py +++ b/deepspeed/module_inject/tp_shard.py @@ -22,6 +22,11 @@ def set_n_embd(num): n_embd = num +def set_tp_grain_size(num): + global tp_grain_size + tp_grain_size = num + + def get_num_kv_heads(): global num_kv_heads if 'num_kv_heads' in globals(): @@ -45,9 +50,9 @@ def get_shard_size(total_size, mp_size, name=None, rank=None): my_slices = (num_kv_heads // mp_size) + (1 if rank < (num_kv_heads % mp_size) else 0) return total_size * my_slices // num_kv_heads else: - if total_size >= 64: - grain_size = total_size // 64 - return (grain_size // mp_size + (1 if rank < (grain_size % mp_size) else 0)) * 64 + if total_size >= tp_grain_size: + grain_size = total_size // tp_grain_size + return (grain_size // mp_size + (1 if rank < (grain_size % mp_size) else 0)) * tp_grain_size else: return total_size // mp_size + (1 if rank < (total_size % mp_size) else 0) From a964e435532699908e5750abdb027ae583ff793d Mon Sep 17 00:00:00 2001 From: Aviv Keshet Date: Tue, 17 Dec 2024 09:33:09 -0800 Subject: [PATCH 075/112] Fix --enable_each_rank_log when used with PDSH multi-node runner (#6863) This PR addresses fixes https://github.com/microsoft/DeepSpeed/issues/6859 by threading this argument into the deepspeed launcher command build by PDSHRunner. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/launcher/multinode_runner.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deepspeed/launcher/multinode_runner.py b/deepspeed/launcher/multinode_runner.py index 74d20a6d53e5..fe2fa1b476be 100644 --- a/deepspeed/launcher/multinode_runner.py +++ b/deepspeed/launcher/multinode_runner.py @@ -104,6 +104,8 @@ def get_cmd(self, environment, active_resources): deepspeed_launch.append("--no_local_rank") if self.args.save_pid: deepspeed_launch += ["--save_pid", f"{os.getpid()}"] + if self.args.enable_each_rank_log: + deepspeed_launch.append(f"--enable_each_rank_log={self.args.enable_each_rank_log}") if self.args.elastic_training: deepspeed_launch.append("--enable_elastic_training") deepspeed_launch.append(f"--max_elastic_nodes={self.args.max_elastic_nodes}") From 2f32966b1cd874aa4373177c8f8c4214ad57d020 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Tue, 17 Dec 2024 11:53:47 -0800 Subject: [PATCH 076/112] Update transformers ops unit tests to use `requried_torch_version` (#6884) --- .../ops/transformer/inference/test_bias_geglu.py | 2 -- .../ops/transformer/inference/test_bias_gelu.py | 2 -- .../ops/transformer/inference/test_bias_relu.py | 2 -- tests/unit/ops/transformer/inference/test_gelu.py | 14 +++++--------- .../unit/ops/transformer/inference/test_matmul.py | 1 - .../unit/ops/transformer/inference/test_softmax.py | 2 -- 6 files changed, 5 insertions(+), 18 deletions(-) diff --git a/tests/unit/ops/transformer/inference/test_bias_geglu.py b/tests/unit/ops/transformer/inference/test_bias_geglu.py index 05de4fbb4cf8..c995d2a8c46d 100644 --- a/tests/unit/ops/transformer/inference/test_bias_geglu.py +++ b/tests/unit/ops/transformer/inference/test_bias_geglu.py @@ -15,8 +15,6 @@ if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: pytest.skip("Inference ops are not available on this system", allow_module_level=True) -torch_minor_version = None - def run_bias_geglu_reference(activations, bias): # Expected behavior is that of casting to float32 internally diff --git a/tests/unit/ops/transformer/inference/test_bias_gelu.py b/tests/unit/ops/transformer/inference/test_bias_gelu.py index b69030e87ace..e3a3bad63961 100644 --- a/tests/unit/ops/transformer/inference/test_bias_gelu.py +++ b/tests/unit/ops/transformer/inference/test_bias_gelu.py @@ -16,8 +16,6 @@ if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: pytest.skip("Inference ops are not available on this system", allow_module_level=True) -torch_minor_version = None - def run_bias_gelu_reference(activations, bias): # Expected behavior is that of casting to float32 internally and using the tanh approximation diff --git a/tests/unit/ops/transformer/inference/test_bias_relu.py b/tests/unit/ops/transformer/inference/test_bias_relu.py index 57134665b241..69078f9f7646 100644 --- a/tests/unit/ops/transformer/inference/test_bias_relu.py +++ b/tests/unit/ops/transformer/inference/test_bias_relu.py @@ -15,8 +15,6 @@ if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: pytest.skip("Inference ops are not available on this system", allow_module_level=True) -torch_minor_version = None - def run_bias_relu_reference(activations, bias): # Expected behavior is that of casting to float32 internally diff --git a/tests/unit/ops/transformer/inference/test_gelu.py b/tests/unit/ops/transformer/inference/test_gelu.py index 5f820ef3b579..a58abfdb100c 100644 --- a/tests/unit/ops/transformer/inference/test_gelu.py +++ b/tests/unit/ops/transformer/inference/test_gelu.py @@ -9,12 +9,11 @@ from deepspeed.ops.op_builder import InferenceBuilder from deepspeed.ops.transformer import DeepSpeedInferenceConfig from deepspeed.ops.transformer.inference.op_binding.bias_gelu import BiasGeluOp +from deepspeed.utils.torch import required_torch_version if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: pytest.skip("Inference ops are not available on this system", allow_module_level=True) -torch_minor_version = None - def allclose(x, y): assert x.dtype == y.dtype @@ -23,14 +22,11 @@ def allclose(x, y): def version_appropriate_gelu(activations): - global torch_minor_version - if torch_minor_version is None: - torch_minor_version = int(torch.__version__.split('.')[1]) - # If torch version = 1.12 - if torch_minor_version < 12: - return torch.nn.functional.gelu(activations) - else: + # gelu behavior changes (correctly) in torch 1.12 + if required_torch_version(min_version=1.12): return torch.nn.functional.gelu(activations, approximate='tanh') + else: + return torch.nn.functional.gelu(activations) def run_gelu_reference(activations): diff --git a/tests/unit/ops/transformer/inference/test_matmul.py b/tests/unit/ops/transformer/inference/test_matmul.py index 559aa2c60afe..2ab195ee0115 100644 --- a/tests/unit/ops/transformer/inference/test_matmul.py +++ b/tests/unit/ops/transformer/inference/test_matmul.py @@ -12,7 +12,6 @@ pytest.skip("Inference ops are not available on this system", allow_module_level=True) inference_module = None -torch_minor_version = None def allclose(x, y): diff --git a/tests/unit/ops/transformer/inference/test_softmax.py b/tests/unit/ops/transformer/inference/test_softmax.py index e582be1b926a..83785ac38ebb 100644 --- a/tests/unit/ops/transformer/inference/test_softmax.py +++ b/tests/unit/ops/transformer/inference/test_softmax.py @@ -11,8 +11,6 @@ if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: pytest.skip("Inference ops are not available on this system", allow_module_level=True) -torch_minor_version = None - def allclose(x, y): assert x.dtype == y.dtype From 4cd1d97460b677563d57f07a293724bdc02e0ef5 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Tue, 17 Dec 2024 17:30:52 -0800 Subject: [PATCH 077/112] Don't error out when cpu accelerator doesn't have torch (as default for whl building) (#6886) This fixes a bug introduced in #6845, which breaks the `no-torch` workflow that we require in order to do releases where we do not require torch to be in the environment when building an sdist. This adds the same logic to the cpuaccelerator that the cudaaccelerator had where we don't require torch to be installed to build the whl. --- .github/workflows/no-torch.yml | 1 + accelerator/cpu_accelerator.py | 8 +++++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/.github/workflows/no-torch.yml b/.github/workflows/no-torch.yml index 1a13c0f3f4f1..5b89a6f36787 100644 --- a/.github/workflows/no-torch.yml +++ b/.github/workflows/no-torch.yml @@ -4,6 +4,7 @@ on: workflow_dispatch: pull_request: paths: + - 'accelerator/**' - '.github/workflows/no-torch.yml' - 'op_builder/**' schedule: diff --git a/accelerator/cpu_accelerator.py b/accelerator/cpu_accelerator.py index 0e49bd9f6458..de711f73144e 100644 --- a/accelerator/cpu_accelerator.py +++ b/accelerator/cpu_accelerator.py @@ -3,9 +3,15 @@ # DeepSpeed Team -import torch from .abstract_accelerator import DeepSpeedAccelerator +# During setup stage torch may not be installed, pass on no torch will +# allow op builder related API to be executed. +try: + import torch +except ImportError as e: + pass + try: import oneccl_bindings_for_pytorch # noqa: F401 # type: ignore oneccl_imported_p = True From 0b25630abe8f7dd4e64c277ff92f5f7e36a27284 Mon Sep 17 00:00:00 2001 From: Daniel Huang Date: Wed, 18 Dec 2024 08:09:31 -0800 Subject: [PATCH 078/112] Add arctic model support by adding w2 to all_reduce (#6856) As title says. Default behavior of arctic model produces shape issues with AutoTP due to the MLP layer performing `w2 * act(w1*w3)`. However, method provided to fix Mixtral-7x8b in #5257 does not work since the MLP for Arctic is also used within a ModuleList for the MoE. This results in MLP weights hiding behind individual experts as layers `#.w#`, which is not caught by the fix in #5257. This adds the check directly within replace, where it can check for actual layer names for the `w2` key in the model to patch with `all_reduce`. --------- Signed-off-by: Daniel Huang Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/module_inject/auto_tp.py | 6 +++++- docs/_tutorials/automatic-tensor-parallelism.md | 1 + 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/deepspeed/module_inject/auto_tp.py b/deepspeed/module_inject/auto_tp.py index 221d490a37d2..5441000e581d 100755 --- a/deepspeed/module_inject/auto_tp.py +++ b/deepspeed/module_inject/auto_tp.py @@ -346,11 +346,15 @@ def _replace(self, child, name, conv_linear_layer): 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 Arctic model, bypass to all_reduce replacement for w2 weights + arctic_w2_all_reduce_linear = False + if 'Arctic' in str(self.module) and 'w2' in name: + arctic_w2_all_reduce_linear = True # For MLP including chunk layer. if 'gate_up_proj' in name or ('dense_h_to_4h' in name and 'GLM' in str(self.module)): weight, bias = shard_chunk_mlp(child.weight.data, child.bias, dist.get_rank(), dist.get_world_size()) return LinearLayer(weight=weight, bias=bias) - if name in self.all_reduce_linears: + if name in self.all_reduce_linears or arctic_w2_all_reduce_linear: # if conv_linear_layer [weight_shape[1], weight_shape[0] // mp_size] # else [weight_shape[0], weight_shape[1] // mp_size] diff --git a/docs/_tutorials/automatic-tensor-parallelism.md b/docs/_tutorials/automatic-tensor-parallelism.md index d5a08b27bf4d..6488f9b718fe 100755 --- a/docs/_tutorials/automatic-tensor-parallelism.md +++ b/docs/_tutorials/automatic-tensor-parallelism.md @@ -121,6 +121,7 @@ The following results were collected using V100 SXM2 32GB GPUs. The following model families have been successfully tested with automatic tensor parallelism. Other models may work but have not been tested yet. - albert +- arctic - baichuan - bert - bigbird_pegasus From b344c04df0fdf058617004924a7aaa15055dccce Mon Sep 17 00:00:00 2001 From: Olatunji Ruwase Date: Wed, 18 Dec 2024 11:49:28 -0500 Subject: [PATCH 079/112] Update code owners (#6890) Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- CODEOWNERS | 45 ++++++++++++++++++++++++--------------------- 1 file changed, 24 insertions(+), 21 deletions(-) diff --git a/CODEOWNERS b/CODEOWNERS index c0fc85cb8b89..b0d3b8b0d77b 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -8,49 +8,52 @@ # top-level repo folders /.github/ @loadams -/azure/ @awan-10 -/benchmarks/ @awan-10 @tjruwase +/azure/ @loadams +/benchmarks/ @guanhuawang @tjruwase /bin/ @loadams -/csrc/ @awan-10 +/csrc/ @tjruwase /deepspeed/ @loadams @tjruwase -/docker/ @awan-10 +/docker/ @loadams @guanhuawang /docs/ @loadams @tjruwase -/examples/ @awan-10 @tohtana +/examples/ @jomayeri @tohtana /op_builder/ @loadams @tjruwase @jomayeri -/release/ @loadams +/release/ @loadams @jomayeri /requirements/ @loadams -/scripts/ @awan-10 +/scripts/ @loadams @tjruwase /tests/ @tjruwase @loadams @tohtana # deepspeed /deepspeed/autotuning/ @loadams /deepspeed/checkpoint/ @tjruwase -/deepspeed/comm/ @awan-10 +/deepspeed/comm/ @guanhuawang /deepspeed/compression/ @tjruwase -/deepspeed/elasticity/ @awan-10 +/deepspeed/elasticity/ @tjruwase /deepspeed/launcher/ @loadams -/deepspeed/module_inject/ @awan-10 +/deepspeed/module_inject/ @hwchen2017 @loadams /deepspeed/moe/ @tohtana -/deepspeed/monitor/ @awan-10 +/deepspeed/monitor/ @tjruwase /deepspeed/nebula/ @tjruwase +/deepspeed/nvme/ @tjruwase @jomayeri /deepspeed/ops/ @tohtana /deepspeed/pipe/ @tohtana @loadams /deepspeed/profiling/ @loadams -/deepspeed/utils/ @tjruwase @awan-10 +/deepspeed/sequence/ @tohtana +/deepspeed/utils/ @tjruwase @tohtana # inference -/deepspeed/inference/ @awan-10 -/deepspeed/model_implementations/ @awan-10 +/deepspeed/inference/ @hwchen2017 @tohtana +/deepspeed/model_implementations/@tohtana @loadams # training /deepspeed/runtime/ @tjruwase @tohtana /deepspeed/runtime/activation_checkpointing/ @tjruwase /deepspeed/runtime/checkpoint_engine/ @tjruwase -/deepspeed/runtime/comm/ @awan-10 -/deepspeed/runtime/compression/ @awan-10 +/deepspeed/runtime/comm/ @guanhuawang +/deepspeed/runtime/compression/ @tjruwase /deepspeed/runtime/data_pipeline/ @tjruwase -/deepspeed/runtime/fp16/ @tjruwase -/deepspeed/runtime/fp16/onebit/ @awan-10 -/deepspeed/runtime/pipe/ @loadams -/deepspeed/runtime/swap_tensor/ @tjruwase -/deepspeed/runtime/zero/ @tjruwase +/deepspeed/runtime/domino/ @guanhuawang @hwchen2017 +/deepspeed/runtime/fp16/ @tjruwase @tohtana +/deepspeed/runtime/fp16/onebit/ @tjruwase +/deepspeed/runtime/pipe/ @loadams @tohtana +/deepspeed/runtime/swap_tensor/ @tjruwase @jomayeri +/deepspeed/runtime/zero/ @tjruwase @tohtana From f9e158a0f5cfa08b475cc1f086accffd8a77b92f Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 18 Dec 2024 09:53:17 -0800 Subject: [PATCH 080/112] Update version.txt after 0.16.2 release (#6893) **Auto-generated PR to update version.txt after a DeepSpeed release** Released version - 0.16.2 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 201a22c8fa5c..7eb3095a3295 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.16.2 +0.16.3 From 4fd79205c6b85b47e00810143c69d342ce874ede Mon Sep 17 00:00:00 2001 From: Nir Sonnenschein Date: Thu, 19 Dec 2024 19:26:50 +0200 Subject: [PATCH 081/112] Allow to compile collective for PT>2.3 (#6899) Allow to compile collective for PT>2.3 commit re-uploaded due to github CI issue originally uploaded by @nelyahu --- deepspeed/comm/torch.py | 50 +++++++++++++++++++++++------------------ 1 file changed, 28 insertions(+), 22 deletions(-) diff --git a/deepspeed/comm/torch.py b/deepspeed/comm/torch.py index 988b74232bb9..5461ae18d1f0 100755 --- a/deepspeed/comm/torch.py +++ b/deepspeed/comm/torch.py @@ -20,6 +20,12 @@ DS_COMM_REDUCE_OFF = False +def disable_compiler_collective(func): + if required_torch_version(min_version=2.3): + return func + return compiler.disable(func) + + def build_shm_op(): builder = get_accelerator().create_op_builder("ShareMemCommBuilder") if builder is None or not deepspeed.ops.__compatible_ops__[builder.NAME]: @@ -114,7 +120,7 @@ def __init__(self, backend, timeout, init_method, rank=-1, world_size=-1, name=' self.shm_comm_op.initialize(self.get_world_size(), self.get_rank()) @classmethod - @compiler.disable + @disable_compiler_collective def get_all_gather_function(self): if hasattr(torch.distributed, "all_gather_into_tensor"): return torch.distributed.all_gather_into_tensor @@ -123,7 +129,7 @@ def get_all_gather_function(self): return None @classmethod - @compiler.disable + @disable_compiler_collective def get_reduce_scatter_function(self): if hasattr(torch.distributed, "reduce_scatter_tensor"): return torch.distributed.reduce_scatter_tensor @@ -146,7 +152,7 @@ def init_process_group(self, backend, timeout, init_method, rank, world_size): world_size=world_size) self.using_mpi = torch.distributed.get_backend() == 'mpi' - @compiler.disable + @disable_compiler_collective def all_reduce(self, tensor, op=torch.distributed.ReduceOp.SUM, group=None, async_op=False): op = self._reduce_op(op) return torch.distributed.all_reduce(tensor=tensor, op=op, group=group, async_op=async_op) @@ -158,7 +164,7 @@ def inference_all_reduce(self, tensor, op, group=None): else: return torch.ops.deepspeed.inference_all_reduce_(tensor) - @compiler.disable + @disable_compiler_collective def all_reduce_coalesced(self, tensors, op=torch.distributed.ReduceOp.SUM, group=None, async_op=False): """ proxy func to torch.distributed.all_reduce_coalesced, which is included in PyTorch 1.13 and above @@ -169,7 +175,7 @@ def all_reduce_coalesced(self, tensors, op=torch.distributed.ReduceOp.SUM, group op = self._reduce_op(op) return torch.distributed.all_reduce_coalesced(tensors=tensors, op=op, group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def reduce(self, tensor, dst, op=ReduceOp.SUM, group=None, async_op=False): if DS_COMM_REDUCE_OFF: if int(os.getenv('RANK', '0')) == 0: @@ -177,7 +183,7 @@ def reduce(self, tensor, dst, op=ReduceOp.SUM, group=None, async_op=False): return Noop() return torch.distributed.reduce(tensor=tensor, dst=dst, op=self._reduce_op(op), group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def reduce_scatter(self, output, input_list, op=ReduceOp.SUM, group=None, async_op=False): if DS_COMM_REDUCE_SCATTER_OFF: if int(os.getenv('RANK', '0')) == 0: @@ -190,7 +196,7 @@ def reduce_scatter(self, output, input_list, op=ReduceOp.SUM, group=None, async_ group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def broadcast(self, tensor, src, group=None, async_op=False): if DS_COMM_BROADCAST_OFF: if int(os.getenv('RANK', '0')) == 0: @@ -199,7 +205,7 @@ def broadcast(self, tensor, src, group=None, async_op=False): else: return torch.distributed.broadcast(tensor=tensor, src=src, group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def all_gather(self, tensor_list, tensor, group=None, async_op=False): if DS_COMM_ALL_GATHER_OFF: if int(os.getenv('RANK', '0')) == 0: @@ -208,7 +214,7 @@ def all_gather(self, tensor_list, tensor, group=None, async_op=False): else: return torch.distributed.all_gather(tensor_list=tensor_list, tensor=tensor, group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def all_gather_into_tensor(self, output_tensor, input_tensor, group=None, async_op=False): if self.has_all_gather_into_tensor(): return self.all_gather_function(output_tensor=output_tensor, @@ -216,7 +222,7 @@ def all_gather_into_tensor(self, output_tensor, input_tensor, group=None, async_ group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def all_gather_base(self, output_tensor, input_tensor, group=None, async_op=False): if DS_COMM_ALL_GATHER_OFF: if int(os.getenv('RANK', '0')) == 0: @@ -234,7 +240,7 @@ def all_gather_base(self, output_tensor, input_tensor, group=None, async_op=Fals "please consider upgrading your pytorch installation.") pass - @compiler.disable + @disable_compiler_collective def all_gather_coalesced(self, output_tensors, input_tensors, group=None, async_op=False): """""" assert len(output_tensors) == len(input_tensors), "" @@ -258,7 +264,7 @@ def all_gather_coalesced(self, output_tensors, input_tensors, group=None, async_ else: reqs[-1].wait() - @compiler.disable + @disable_compiler_collective def reduce_scatter_tensor(self, output_tensor, input_tensor, op=ReduceOp.SUM, group=None, async_op=False): if self.has_reduce_scatter_tensor(): return self.reduce_scatter_function(output_tensor, @@ -272,7 +278,7 @@ def reduce_scatter_tensor(self, output_tensor, input_tensor, op=ReduceOp.SUM, gr "please consider upgrading your pytorch installation.") pass - @compiler.disable + @disable_compiler_collective def all_to_all_single(self, output, input, @@ -287,27 +293,27 @@ def all_to_all_single(self, group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def all_to_all(self, output_tensor_list, input_tensor_list, group=None, async_op=False): return torch.distributed.all_to_all(output_tensor_list, input_tensor_list, group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def send(self, tensor, dst, group=None, tag=0): return torch.distributed.send(tensor=tensor, dst=dst, group=group, tag=tag) - @compiler.disable + @disable_compiler_collective def recv(self, tensor, src=None, group=None, tag=0): return torch.distributed.recv(tensor=tensor, src=src, group=group, tag=tag) - @compiler.disable + @disable_compiler_collective def isend(self, tensor, dst, group=None, tag=0): return torch.distributed.isend(tensor=tensor, dst=dst, group=group, tag=tag) - @compiler.disable + @disable_compiler_collective def irecv(self, tensor, src=None, group=None, tag=0): return torch.distributed.irecv(tensor=tensor, src=src, group=group, tag=tag) - @compiler.disable + @disable_compiler_collective def gather(self, tensor, gather_list=None, dst=0, group=None, async_op=False): return torch.distributed.gather(tensor=tensor, gather_list=gather_list, @@ -315,7 +321,7 @@ def gather(self, tensor, gather_list=None, dst=0, group=None, async_op=False): group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def scatter(self, tensor, scatter_list=None, src=0, group=None, async_op=False): return torch.distributed.scatter(tensor=tensor, scatter_list=scatter_list, @@ -323,13 +329,13 @@ def scatter(self, tensor, scatter_list=None, src=0, group=None, async_op=False): group=group, async_op=async_op) - @compiler.disable + @disable_compiler_collective def barrier(self, group=torch.distributed.GroupMember.WORLD, async_op=False, device_ids=None): if group is None: group = torch.distributed.GroupMember.WORLD return torch.distributed.barrier(group=group, async_op=async_op, device_ids=device_ids) - @compiler.disable + @disable_compiler_collective def monitored_barrier(self, group=torch.distributed.GroupMember.WORLD, timeout=None, wait_all_ranks=False): if group is None: group = torch.distributed.GroupMember.WORLD From 00ea0c46c2296db158d10497602f9832c4445d84 Mon Sep 17 00:00:00 2001 From: Nadav Elyahu <88962733+nelyahu@users.noreply.github.com> Date: Fri, 20 Dec 2024 02:54:45 +0200 Subject: [PATCH 082/112] Zero2: avoid graph breaks in torch.compile by using param_idx (#6803) inside reduce_independent_p_g_buckets_and_remove_grads and in reduce_ipg_grads which are being executed during the BWD hook in zero2, the model param is being stored inside params_in_ipg_bucket. torch.compile has hard time tracing parameters. By using the param's static index inside the group the same logic can be maintain with less complexity. --------- Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Logan Adams --- deepspeed/runtime/zero/stage_1_and_2.py | 9 ++++++--- tests/unit/moe/test_moe.py | 3 ++- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index 7ac89a233808..ecb2a527f870 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -310,6 +310,7 @@ def __init__(self, for param in param_group['params']: if param.requires_grad: param.grad_accum = None + param.param_idx_in_group = len(trainable_parameters) trainable_parameters.append(param) self.bit16_groups.append(trainable_parameters) @@ -961,7 +962,7 @@ def reduce_independent_p_g_buckets_and_remove_grads(self, param, i): assert grad_reduc is not None, f"rank {dist.get_rank()} - Invalid to reduce Param {param_id} with None gradient" self.grads_in_ipg_bucket.append(grad_reduc) - self.params_in_ipg_bucket.append((i, param, param_id)) + self.params_in_ipg_bucket.append((i, param.param_idx_in_group, param_id)) #make sure the average tensor function knows how to average the gradients if is_moe_param(param): @@ -1067,7 +1068,8 @@ def average_tensor(self, tensor): process_group = self.dp_process_group # count = 0 - for i, param, param_id in self.params_in_ipg_bucket: + for i, param_idx_in_group, param_id in self.params_in_ipg_bucket: + param = self.bit16_groups[i][param_idx_in_group] process_group = self.dp_process_group @@ -1383,7 +1385,8 @@ def reduce_ipg_grads(self): stream = get_accelerator().current_stream() with get_accelerator().stream(stream): - for _, param, param_id in self.params_in_ipg_bucket: + for group_idx, param_idx_in_group, param_id in self.params_in_ipg_bucket: + param = self.bit16_groups[group_idx][param_idx_in_group] assert self.params_already_reduced[param_id] == False, \ f"The parameter {param_id} has already been reduced. \ diff --git a/tests/unit/moe/test_moe.py b/tests/unit/moe/test_moe.py index 9ee546437f6c..c67a907c6785 100644 --- a/tests/unit/moe/test_moe.py +++ b/tests/unit/moe/test_moe.py @@ -93,7 +93,8 @@ def strict_average_tensor(tensor): process_group = optimizer.dp_process_group curr_size = 0 pg_offsets = [] - for i, param, param_id in optimizer.params_in_ipg_bucket: + for i, param_idx, param_id in optimizer.params_in_ipg_bucket: + param = optimizer.bit16_groups[i][param_idx] process_group = optimizer.dp_process_group if optimizer.ipg_bucket_has_moe_params: process_group = optimizer.expert_dp_process_group[param.group_name] if is_moe_param( From eea5304807c6a04d0f2c55cb935ec295235d9b54 Mon Sep 17 00:00:00 2001 From: Nadav Elyahu <88962733+nelyahu@users.noreply.github.com> Date: Fri, 20 Dec 2024 07:13:46 +0200 Subject: [PATCH 083/112] hpu_accelerator: use torch.use_deterministic_algorithms (#6897) formal API instead of hpu.setDeterministic --- accelerator/hpu_accelerator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/accelerator/hpu_accelerator.py b/accelerator/hpu_accelerator.py index 723a66e4c6fb..b46351f8ca43 100644 --- a/accelerator/hpu_accelerator.py +++ b/accelerator/hpu_accelerator.py @@ -21,8 +21,8 @@ def __init__(self): self.apply_hpu_workarounds() try: import habana_frameworks.torch.hpu as hpu - hpu.setDeterministic(True) self.hpu = hpu + torch.use_deterministic_algorithms(True) except ImportError as e: raise ValueError( f"HPU_Accelerator requires habana_frameworks.torch.hpu, which is not installed on this system.") From 85cc5f9bb3f0175a2d13ea1ed65bf7d202b7f0d9 Mon Sep 17 00:00:00 2001 From: Hongwei Chen <33092912+hwchen2017@users.noreply.github.com> Date: Thu, 26 Dec 2024 09:12:04 -0800 Subject: [PATCH 084/112] Fix error caused by all_reduce call in domino (#6880) Fix #6851 Initialize communication backend to fix error caused by all_reduce call in the Domino transformer layer. Verified correctness in local test. --------- Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/domino/transformer.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/deepspeed/runtime/domino/transformer.py b/deepspeed/runtime/domino/transformer.py index 8eb95e49c29d..88c5494c8147 100644 --- a/deepspeed/runtime/domino/transformer.py +++ b/deepspeed/runtime/domino/transformer.py @@ -6,8 +6,7 @@ import torch import torch.nn.functional as F from torch.nn.parameter import Parameter -import deepspeed -from deepspeed import comm as dist +import deepspeed.comm as dist from deepspeed.accelerator import get_accelerator @@ -97,7 +96,7 @@ def backward(ctx, grad_output): return grad_output # Async All-reduce. - handle = deepspeed.comm.all_reduce(grad_output, group=ctx.mpu.get_tensor_model_parallel_group(), async_op=True) + handle = dist.all_reduce(grad_output, group=ctx.mpu.get_tensor_model_parallel_group(), async_op=True) ctx.handle_dic[ctx.h_id] = handle return None, grad_output, None, None @@ -249,6 +248,10 @@ def __init__(self, output_bias=None): super(DominoTransformerLayer, self).__init__() + if not dist.is_initialized(): + dist.init_distributed() + assert dist.is_initialized(), "deepspeed.comm is not initialized!" + self.llama_model = config.llama_model self.layer_number = layer_number self.layer_type = layer_type @@ -358,18 +361,14 @@ def forward(self, hidden_states, attention_mask, rotary_pos_emb=None): layernorm_output0, attention_mask, rotary_pos_emb=rotary_pos_emb) - handle0 = deepspeed.comm.all_reduce(attention_output0, - group=self.mpu.get_tensor_model_parallel_group(), - async_op=True) + handle0 = dist.all_reduce(attention_output0, group=self.mpu.get_tensor_model_parallel_group(), async_op=True) attention_output1, attention_bias1 = \ self.self_attention( layernorm_output1, attention_mask, rotary_pos_emb=rotary_pos_emb) - handle1 = deepspeed.comm.all_reduce(attention_output1, - group=self.mpu.get_tensor_model_parallel_group(), - async_op=True) + handle1 = dist.all_reduce(attention_output1, group=self.mpu.get_tensor_model_parallel_group(), async_op=True) handle0.wait() # Residual0 connection. @@ -413,7 +412,7 @@ def forward(self, hidden_states, attention_mask, rotary_pos_emb=None): output0 = output0 + bias_c output0 = self.mlp_activation_func(output0) output0 = torch.matmul(output0, self.weight_r.t()) - handle2 = deepspeed.comm.all_reduce(output0, group=self.mpu.get_tensor_model_parallel_group(), async_op=True) + handle2 = dist.all_reduce(output0, group=self.mpu.get_tensor_model_parallel_group(), async_op=True) handle1.wait() @@ -425,7 +424,7 @@ def forward(self, hidden_states, attention_mask, rotary_pos_emb=None): if bias_c is not None: output1 = output1 + bias_c output1 = torch.matmul(output1, self.weight_r.t()) - deepspeed.comm.all_reduce(output1, group=self.mpu.get_tensor_model_parallel_group()) + dist.all_reduce(output1, group=self.mpu.get_tensor_model_parallel_group()) handle2.wait() From cc03c76d57f41752d8cfb84c2e45b8e0da8083da Mon Sep 17 00:00:00 2001 From: Raza Sikander Date: Fri, 27 Dec 2024 01:37:28 +0530 Subject: [PATCH 085/112] Update Gaudi2 jobs to latest 1.19 build (#6905) Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- .github/workflows/hpu-gaudi2-nightly.yml | 2 +- .github/workflows/hpu-gaudi2.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/hpu-gaudi2-nightly.yml b/.github/workflows/hpu-gaudi2-nightly.yml index 5c5caff1ebb0..c0576360cd61 100644 --- a/.github/workflows/hpu-gaudi2-nightly.yml +++ b/.github/workflows/hpu-gaudi2-nightly.yml @@ -21,7 +21,7 @@ jobs: # The type of runner that the job will run on runs-on: [self-hosted, intel, gaudi2] container: - image: vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest + image: vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.5.1:latest ports: - 80 options: --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice diff --git a/.github/workflows/hpu-gaudi2.yml b/.github/workflows/hpu-gaudi2.yml index a06f871b7c56..b8b6f3cb5502 100644 --- a/.github/workflows/hpu-gaudi2.yml +++ b/.github/workflows/hpu-gaudi2.yml @@ -39,7 +39,7 @@ jobs: # The type of runner that the job will run on runs-on: [self-hosted, intel, gaudi2] container: - image: vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest + image: vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.5.1:latest ports: - 80 options: --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice From 3573858e7ce2c723b8c43231c6c6b0cf97dca2fc Mon Sep 17 00:00:00 2001 From: Nir Sonnenschein Date: Mon, 30 Dec 2024 20:53:41 +0200 Subject: [PATCH 086/112] Change compile for pipeline module torch.compile (#6478) We have encountered and issue with torch.compile and the pipeline module. modifying a member of the module (micro_offset) during the forward function will cause torch compile to restart the analysis and treat the module as dynamic. In order to bypass this issue without significantly changing the way the pipeline module works we propose to compile only the layers in the pipeline module instead of the forward function of pipeline module. this will bypass the issue and should still give most of the benefit of torch compiling the pipeline module while avoiding the issue. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/pipe/module.py | 8 ++++++++ tests/unit/pipe/test_pipe_module.py | 8 ++++++-- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/deepspeed/runtime/pipe/module.py b/deepspeed/runtime/pipe/module.py index 31fec30be788..9fbd91f750a9 100644 --- a/deepspeed/runtime/pipe/module.py +++ b/deepspeed/runtime/pipe/module.py @@ -662,3 +662,11 @@ def get_additional_losses(self): Return a dictionary of {"loss name": loss_value} or None if no additional losses. """ return None + + def compile(self, *args, **kwargs): + for idx, layer in enumerate(self.forward_funcs): + if isinstance(layer, nn.Module): + layer.compile(*args, **kwargs) + else: + new_layer = torch.compile(layer, *args, **kwargs) + self.forward_funcs[idx] = new_layer diff --git a/tests/unit/pipe/test_pipe_module.py b/tests/unit/pipe/test_pipe_module.py index 05c6a82ef55a..2a8a4b9b7d82 100644 --- a/tests/unit/pipe/test_pipe_module.py +++ b/tests/unit/pipe/test_pipe_module.py @@ -60,9 +60,12 @@ def batch_input(): class TestPipeModuleSequential(DistributedTest): world_size = 2 + # needs to be set for torch.compile: running torch.compile with daemonic process causes an error + non_daemonic_procs = True @pytest.mark.parametrize("activation_checkpoints", [False, True]) - def test(self, sequential_model, simple_config, batch_input, activation_checkpoints): + @pytest.mark.parametrize("use_compile", [False, True]) + def test(self, sequential_model, simple_config, batch_input, activation_checkpoints, use_compile): base_model = copy.deepcopy(sequential_model) base_input = batch_input.clone().detach() base_output = base_model(base_input) @@ -71,7 +74,8 @@ def test(self, sequential_model, simple_config, batch_input, activation_checkpoi pipe_model = copy.deepcopy(sequential_model) pipe_model = PipelineModule(layers=pipe_model, num_stages=2) - + if (use_compile): + pipe_model.compile() # Ensure all parameters are accounted for. my_params = sum(p.numel() for p in pipe_model.parameters()) total_pipe_params = torch.LongTensor([my_params]).to(get_accelerator().device_name()) From 456c9ac67975da698e44dfd4f90c4f7b867d08bd Mon Sep 17 00:00:00 2001 From: Max Kovalenko Date: Fri, 3 Jan 2025 17:48:24 +0200 Subject: [PATCH 087/112] Stage3: Use new torch grad accumulation hooks API (#6773) * This commit addresses a Deepspeed issue [#6718](https://github.com/microsoft/DeepSpeed/issues/6718) * The existing code has been using the grad_acc node hook to reduce params grads. The constructs such as `param.data = replicated_tensor.data` used in `allgather_params(..)` are compiled into `param.set()` causing the hook assigned to the grad_acc node not being called. * Starting from PyTorch 2.1 there is a new and robust hook API on a param itself: `param.register_post_accumulate_grad_hook(..)` * This commit will make use of the proper API depending on the PyTorch version * It will also disable compile for PyTorch versions < 2.1 --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> --- deepspeed/runtime/compiler.py | 3 ++- deepspeed/runtime/zero/stage3.py | 7 ++----- deepspeed/utils/torch.py | 9 +++++++++ 3 files changed, 13 insertions(+), 6 deletions(-) diff --git a/deepspeed/runtime/compiler.py b/deepspeed/runtime/compiler.py index fa9220f4fcd0..be778b83f8bb 100644 --- a/deepspeed/runtime/compiler.py +++ b/deepspeed/runtime/compiler.py @@ -4,6 +4,7 @@ # DeepSpeed Team import torch +from deepspeed.utils.torch import required_torch_version try: from torch.compiler import is_compiling as torch_is_compiling @@ -16,7 +17,7 @@ def is_compile_supported(): - return hasattr(torch, "compiler") and hasattr(torch.nn.Module, "compile") + return required_torch_version(min_version=2.1) def disable(func): diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index 459cffce52c8..28f91cb9b3ab 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -16,6 +16,7 @@ from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors from deepspeed.runtime.base_optimizer import ZeROOptimizer from deepspeed.utils import logger +from deepspeed.utils.torch import register_grad_hook from deepspeed.runtime.fp16.loss_scaler import CreateLossScaler from deepspeed.runtime.comm.coalesced_collectives import reduce_scatter_coalesced, all_to_all_quant_reduce, all_to_all_loco_quant_reduce from deepspeed.runtime.utils import inf, is_model_parallel_parameter, get_only_unique_item @@ -1159,7 +1160,6 @@ def overlapping_partition_gradients_reduce_epilogue(self): def create_reduce_and_remove_grad_hooks(self): print_rank_0(f'[Begin] Create gradient reduction hooks') - self.grad_accs = [] self.leaf_parameters = defaultdict(list) for i, param_group in enumerate(self.fp16_groups): for param in param_group: @@ -1172,15 +1172,12 @@ def create_reduce_and_remove_grad_hooks(self): #print(f"After all gather {param.device}, {param.shape}") def wrapper(param): - param_tmp = param.expand_as(param) - grad_acc = param_tmp.grad_fn.next_functions[0][0] @instrument_w_nvtx def reduce_partition_and_remove_grads(*notneeded): self.reduce_ready_partitions_and_remove_grads(param) - self._grad_acc_hooks.append(grad_acc.register_hook(reduce_partition_and_remove_grads)) - self.grad_accs.append(grad_acc) + self._grad_acc_hooks.append(register_grad_hook(param, reduce_partition_and_remove_grads)) #print(f"param grad fn {param.expand_as(param).grad_fn}") if z3_leaf_parameter(param): diff --git a/deepspeed/utils/torch.py b/deepspeed/utils/torch.py index eb22d3561035..1d32775fe64a 100644 --- a/deepspeed/utils/torch.py +++ b/deepspeed/utils/torch.py @@ -20,3 +20,12 @@ def required_torch_version(min_version=None, max_version=None): return False return True + + +def register_grad_hook(param, hook): + if required_torch_version(min_version=2.1): + return param.register_post_accumulate_grad_hook(hook) + else: + param_tmp = param.expand_as(param) + grad_acc = param_tmp.grad_fn.next_functions[0][0] + return grad_acc.register_hook(hook) From a8ede3a9df556a5d6beb22a4bf38fa9852b3bada Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Fri, 3 Jan 2025 08:25:50 -0800 Subject: [PATCH 088/112] Cleanup ops/transformer/inference tests (#6830) --- tests/unit/ops/transformer/inference/test_bias_add.py | 2 -- tests/unit/ops/transformer/inference/test_bias_gelu.py | 4 ++-- tests/unit/ops/transformer/inference/test_matmul.py | 2 -- 3 files changed, 2 insertions(+), 6 deletions(-) diff --git a/tests/unit/ops/transformer/inference/test_bias_add.py b/tests/unit/ops/transformer/inference/test_bias_add.py index f25bbc1be692..eb283924f73c 100644 --- a/tests/unit/ops/transformer/inference/test_bias_add.py +++ b/tests/unit/ops/transformer/inference/test_bias_add.py @@ -15,8 +15,6 @@ if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: pytest.skip("Inference ops are not available on this system", allow_module_level=True) -torch_minor_version = None - def run_bias_add_reference(activations, bias): return activations + bias diff --git a/tests/unit/ops/transformer/inference/test_bias_gelu.py b/tests/unit/ops/transformer/inference/test_bias_gelu.py index e3a3bad63961..f0a09245e890 100644 --- a/tests/unit/ops/transformer/inference/test_bias_gelu.py +++ b/tests/unit/ops/transformer/inference/test_bias_gelu.py @@ -10,8 +10,8 @@ from deepspeed.ops.op_builder import InferenceBuilder from deepspeed.ops.transformer import DeepSpeedInferenceConfig from deepspeed.ops.transformer.inference.op_binding.bias_gelu import BiasGeluOp +from deepspeed.utils.torch import required_torch_version from .inference_test_utils import allclose, get_dtypes -from packaging import version as pkg_version if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: pytest.skip("Inference ops are not available on this system", allow_module_level=True) @@ -34,7 +34,7 @@ def run_bias_gelu_ds(activations, bias): @pytest.mark.parametrize("channels", [512, 1232, 4096]) @pytest.mark.parametrize("dtype", get_dtypes()) def test_bias_gelu(batch, sequence, channels, dtype): - if pkg_version.parse(torch.__version__) < pkg_version.parse("1.12"): + if not required_torch_version(min_version=1.12): pytest.skip("gelu implementation matches only after torch 1.12") activations_ds = torch.randn((batch, sequence, channels), dtype=dtype, device=get_accelerator().device_name()) diff --git a/tests/unit/ops/transformer/inference/test_matmul.py b/tests/unit/ops/transformer/inference/test_matmul.py index 2ab195ee0115..6f5173bbc827 100644 --- a/tests/unit/ops/transformer/inference/test_matmul.py +++ b/tests/unit/ops/transformer/inference/test_matmul.py @@ -11,8 +11,6 @@ if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]: pytest.skip("Inference ops are not available on this system", allow_module_level=True) -inference_module = None - def allclose(x, y): assert x.dtype == y.dtype From 0dbbb70b99f7f251996128115a53c1c8397efa8f Mon Sep 17 00:00:00 2001 From: Quentin Anthony Date: Fri, 3 Jan 2025 21:57:49 -0800 Subject: [PATCH 089/112] Fix `checkpointable_layers` Logic (#6881) **Problem** There's an edge-case in DeepSpeed, where if all three of the following are true: 1. Deepspeed activation checkpointing is applied 2. The user passes `checkpointable_layers` (e.g. https://github.com/EleutherAI/gpt-neox/blob/f5325805678c2b9e35aae4528283e0132c5f5bbc/megatron/model/gpt2_model.py#L175) 3. The user's model class contains `GPT2ModelPipe` or GPTModelPipe` Then the `checkpointable_layers` will not be activation checkpointed. **Reason** This is because in the current logic, `_is_checkpointable` will short-circuit to just return layers matching `ParallelTransformerLayerPipe` in the case of `self.__class__.__name__ in ('GPTModelPipe', 'GPT2ModelPipe')`. See https://github.com/microsoft/DeepSpeed/blob/da771ed42e41a44d5047813ca4672f1cfe9d1731/deepspeed/runtime/pipe/module.py#L653 **Proposed Fixes** I think that `checkpointable_layers` should always be checked for, and added logic to this effect. I also found the documentation for `checkpointable_layers` confusing and contradictory, so I updated the docstring. Lastly, I added a unit test for `checkpointable_layers`. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/pipe/module.py | 14 +++++- .../test_activation_checkpointing.py | 50 +++++++++++++++++++ 2 files changed, 62 insertions(+), 2 deletions(-) diff --git a/deepspeed/runtime/pipe/module.py b/deepspeed/runtime/pipe/module.py index 9fbd91f750a9..49fa2807c355 100644 --- a/deepspeed/runtime/pipe/module.py +++ b/deepspeed/runtime/pipe/module.py @@ -116,7 +116,9 @@ def forward(self, inputs): partition_method (str, optional): The method upon which the layers are partitioned. Defaults to 'parameters'. activation_checkpoint_interval (int, optional): The granularity activation checkpointing in terms of number of layers. 0 disables activation checkpointing. activation_checkpoint_func (callable, optional): The function to use for activation checkpointing. Defaults to ``deepspeed.checkpointing.checkpoint``. - checkpointable_layers(list, optional): Checkpointable layers may not be checkpointed. Defaults to None which does not additional filtering. + checkpointable_layers (list[str], optional): List of layer class names that are eligible for checkpointing. For GPT models, + ParallelTransformerLayerPipe is always checkpointed regardless of this list. If None, all layers with parameters are + considered checkpointable. Defaults to None. dynamic_shape: Allows dynamic shapes of inputs. This might have a performance impact. """ @@ -650,9 +652,17 @@ def _is_checkpointable(self, funcs): # because only non_reentrant_checkpoint can accept inputs with requires_grad=False # otherwise, the backward of the embedding layer won't receive gradients. if self.__class__.__name__ in ('GPTModelPipe', 'GPT2ModelPipe'): - return all('ParallelTransformerLayerPipe' in f.__class__.__name__ for f in funcs) + # For GPT models, checkpoint both transformer layers and any additional + # layers specified in checkpointable_layers (if provided) + return all('ParallelTransformerLayerPipe' in f.__class__.__name__ or ( + self.checkpointable_layers is not None and f.__class__.__name__ in self.checkpointable_layers) + for f in funcs) + if self.checkpointable_layers is not None: + # For non-GPT models, only checkpoint layers specified in checkpointable_layers return all(f.__class__.__name__ in self.checkpointable_layers for f in funcs) + + # Default behavior: checkpoint any layer that has parameters params = [f.parameters() for f in funcs if isinstance(f, torch.nn.Module)] return any(len(list(p)) > 0 for p in params) diff --git a/tests/unit/runtime/activation_checkpointing/test_activation_checkpointing.py b/tests/unit/runtime/activation_checkpointing/test_activation_checkpointing.py index 22a61003b31e..dd3bcd7fb6bd 100644 --- a/tests/unit/runtime/activation_checkpointing/test_activation_checkpointing.py +++ b/tests/unit/runtime/activation_checkpointing/test_activation_checkpointing.py @@ -8,6 +8,7 @@ import pytest import torch import deepspeed +from deepspeed.pipe import PipelineModule, LayerSpec from deepspeed.accelerator import get_accelerator from copy import deepcopy from unit.common import DistributedTest @@ -259,3 +260,52 @@ def test_ckpt_non_tensor_output_ordering(self, non_tensor_output): else: ordering += [torch.is_tensor(non_tensor_output)] _test_activation_checkpoint_ordering(module, ordering, inputs) + + +class TestCheckpointableLayersConfig(DistributedTest): + world_size = 1 + + def test_gpt2_checkpointable_layers(self): + if get_accelerator().device_name() == "cpu": + pytest.skip("CPU accelerator does not support this test yet") + + # Create a simple topology for testing + from deepspeed.runtime.pipe.topology import PipeModelDataParallelTopology + topo = PipeModelDataParallelTopology(num_pp=1, num_mp=1, num_dp=1) + + # Create test classes that we want to checkpoint + class TestTransformerLayer(torch.nn.Module): + + def forward(self, x): + return x + + class ParallelTransformerLayerPipe(TestTransformerLayer): + pass + + class GMLPBlock(TestTransformerLayer): + pass + + # Create a mock GPT2 model with different layer types + class TestGPT2ModelPipe(PipelineModule): + + def __init__(self): + self.layers_spec = [ + LayerSpec(ParallelTransformerLayerPipe), + LayerSpec(GMLPBlock), + LayerSpec(torch.nn.Linear, 10, 10), # Should not be checkpointed + ] + + super().__init__(layers=self.layers_spec, + topology=topo, + checkpointable_layers=["GMLPBlock", "ParallelTransformerLayerPipe"]) + + model = TestGPT2ModelPipe() + model.to(get_accelerator().device_name()) + + # Build layers manually for testing + layers = [spec.build() for spec in model.layers_spec] + + # Test that _is_checkpointable returns correct values + assert model._is_checkpointable([layers[0]]) == True # ParallelTransformerLayerPipe + assert model._is_checkpointable([layers[1]]) == True # GMLPBlock + assert model._is_checkpointable([layers[2]]) == False # Linear layer From f8c9f314ffe7eddfbd3645a94143301e610f68de Mon Sep 17 00:00:00 2001 From: hj-wei Date: Tue, 7 Jan 2025 01:38:19 +0800 Subject: [PATCH 090/112] [BUG FIX]:fix get torch.version.cuda error when cuda is None in rocm (#6909) HI, I found some error when using deepspeed with rocm-torch ``` torch_cuda_version = ".".join(torch.version.cuda.split('.')[:2]) ``` will raise an AttributeError when torch.version.cuda is None. This occurs because the CUDA version in rocm-torch/version.py is set to always be None, leading to potential runtime errors in environments where ROCm is being used. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- op_builder/builder.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/op_builder/builder.py b/op_builder/builder.py index 461281d4a569..ab26054bda7d 100644 --- a/op_builder/builder.py +++ b/op_builder/builder.py @@ -415,10 +415,11 @@ def cpu_arch(self): return '-mcpu=native' return '-march=native' - def is_cuda_enable(self): + def get_cuda_compile_flag(self): try: - assert_no_cuda_mismatch(self.name) - return '-D__ENABLE_CUDA__' + if not self.is_rocm_pytorch(): + assert_no_cuda_mismatch(self.name) + return "-D__ENABLE_CUDA__" except MissingCUDAException: print(f"{WARNING} {self.name} cuda is missing or is incompatible with installed torch, " "only cpu ops can be compiled!") @@ -839,7 +840,7 @@ def cxx_args(self): CPU_ARCH = self.cpu_arch() SIMD_WIDTH = self.simd_width() - CUDA_ENABLE = self.is_cuda_enable() + CUDA_ENABLE = self.get_cuda_compile_flag() args += [ CPU_ARCH, '-fopenmp', From c5e48f49d8368216b4f99ef4023d2855f1ce3983 Mon Sep 17 00:00:00 2001 From: Omar Elayan <142979319+oelayan7@users.noreply.github.com> Date: Mon, 6 Jan 2025 20:54:57 +0200 Subject: [PATCH 091/112] Add fp8_gemm fallback for non-triton systems (#6916) - Removed try/except from __init__ file in fp_quantizer and added a single entry point instead - Renamed file fp8_gemm to fp8_gemm_triton, and the function matmul_fp8 to matmul_fp8_triton - Added a new entry point fp8_gemm with matmul_fp8 inside, and if the system supports triton it calls the triton implementation and if not it calls the fallback Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/ops/fp_quantizer/__init__.py | 7 +- deepspeed/ops/fp_quantizer/fp8_gemm.py | 163 +---------------- deepspeed/ops/fp_quantizer/fp8_gemm_triton.py | 171 ++++++++++++++++++ tests/unit/ops/fp_quantizer/test_fp8_gemm.py | 16 +- 4 files changed, 189 insertions(+), 168 deletions(-) create mode 100644 deepspeed/ops/fp_quantizer/fp8_gemm_triton.py diff --git a/deepspeed/ops/fp_quantizer/__init__.py b/deepspeed/ops/fp_quantizer/__init__.py index 51377bc6092c..f9cf23373c26 100644 --- a/deepspeed/ops/fp_quantizer/__init__.py +++ b/deepspeed/ops/fp_quantizer/__init__.py @@ -4,9 +4,4 @@ # DeepSpeed Team from .quantize import FP_Quantize, Quantizer - -try: - import triton - from .fp8_gemm import matmul_fp8 -except ImportError: - pass +from .fp8_gemm import matmul_fp8 diff --git a/deepspeed/ops/fp_quantizer/fp8_gemm.py b/deepspeed/ops/fp_quantizer/fp8_gemm.py index 55504e3af8c9..db4fa5ae2c92 100644 --- a/deepspeed/ops/fp_quantizer/fp8_gemm.py +++ b/deepspeed/ops/fp_quantizer/fp8_gemm.py @@ -11,161 +11,18 @@ ################################### import torch -import triton -import triton.language as tl -@triton.jit -def matmul_kernel_fp8_bf16(inp_ptr, weight_ptr, out_ptr, scale_ptr, M, N, K, stride_am, stride_ak, stride_bk, - stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, - BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, - quantization_group_size: tl.constexpr): - pid = tl.program_id(axis=0) - num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) - num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) - num_pid_in_group = GROUP_SIZE_M * num_pid_n - group_id = pid // num_pid_in_group - first_pid_m = group_id * GROUP_SIZE_M - group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) - pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m) - pid_n = (pid % num_pid_in_group) // group_size_m +def matmul_fp8(inp, weight, scale, quantization_group_size, quantizer): + from deepspeed import get_accelerator - offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M - offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N - offs_k = tl.arange(0, BLOCK_SIZE_K) + if not get_accelerator().is_triton_supported(): + return matmul_fp8_fallback(inp, weight, scale, quantization_group_size, quantizer) + else: + # Import dynamically to prevent failures on systems without triton. + from .fp8_gemm_triton import matmul_fp8_triton + return matmul_fp8_triton(inp, weight, scale, quantization_group_size) - inp_data = inp_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) - weight_data = weight_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) - weight_ptrs_offset = offs_k[:, None] * (stride_bk // quantization_group_size) + ( - (pid_n * BLOCK_SIZE_N) // quantization_group_size) - weight = tl.load(weight_data, mask=offs_k[:, None] < K, other=0.0) - scale = tl.load(scale_ptr + weight_ptrs_offset) - - accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) - for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): - inp = tl.load(inp_data, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) - # Dequantize weight (fp8 -> bf16) - w = (((weight & 0x80) << 8) | ((weight & 0x7f) << 4)).to(tl.uint16) - w = (w + 0x3C00).to(tl.uint16) - w = (w.to(tl.bfloat16, bitcast=True) * scale).to(tl.bfloat16) - - inp_data += BLOCK_SIZE_K * stride_ak - weight_data += BLOCK_SIZE_K * stride_bk - weight_mask = offs_k[:, None] < K - (k + 1) * BLOCK_SIZE_K - weight = tl.load(weight_data, mask=weight_mask, other=0.0) - scale = tl.load(scale_ptr + (weight_ptrs_offset + - (((k + 1) * BLOCK_SIZE_K * stride_bk) // quantization_group_size)), - mask=weight_mask, - other=0.0) - - accumulator += tl.dot(inp, w) - - out = accumulator.to(tl.bfloat16) - - offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - out_data = out_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] - tl.store(out_data, out, mask=(offs_cm[:, None] < M) & (offs_cn[None, :] < N)) - - -@triton.jit -def matmul_kernel_fp8_fp16(inp_ptr, weight_ptr, out_ptr, scale_ptr, M, N, K, stride_am, stride_ak, stride_bk, - stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, - BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, - quantization_group_size: tl.constexpr): - pid = tl.program_id(axis=0) - num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) - num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) - num_pid_in_group = GROUP_SIZE_M * num_pid_n - group_id = pid // num_pid_in_group - first_pid_m = group_id * GROUP_SIZE_M - group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) - pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m) - pid_n = (pid % num_pid_in_group) // group_size_m - - offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M - offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N - offs_k = tl.arange(0, BLOCK_SIZE_K) - - inp_data = inp_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) - weight_data = weight_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) - weight_ptrs_offset = offs_k[:, None] * (stride_bk // quantization_group_size) + ( - (pid_n * BLOCK_SIZE_N) // quantization_group_size) - - weight = tl.load(weight_data, mask=offs_k[:, None] < K, other=0.0) - scale = tl.load(scale_ptr + weight_ptrs_offset) - - accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) - for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): - inp = tl.load(inp_data, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) - # Dequantize weight (fp8 -> fp16) - w = (((weight & 0x80) << 8) | ((weight & 0x7f) << 7)).to(tl.uint16) - w = (w + 0x2000).to(tl.uint16) - w = (w.to(tl.float16, bitcast=True) * scale).to(tl.float16) - - inp_data += BLOCK_SIZE_K * stride_ak - weight_data += BLOCK_SIZE_K * stride_bk - - weight = tl.load(weight_data, mask=offs_k[:, None] < K - (k + 1) * BLOCK_SIZE_K, other=0.0) - scale = tl.load(scale_ptr + (weight_ptrs_offset + - (((k + 1) * BLOCK_SIZE_K * stride_bk) // quantization_group_size))) - - accumulator += tl.dot(inp, w) - - out = accumulator.to(tl.float16) - - offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - out_data = out_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] - tl.store(out_data, out, mask=(offs_cm[:, None] < M) & (offs_cn[None, :] < N)) - - -def matmul_fp8(inp, weight, scale, quantization_group_size): - - assert inp.shape[1] == weight.shape[0], \ - f"Incompatible dimensions (input: {inp.shape}, weight: {weight.shape})" - - M, K = inp.shape - K, N = weight.shape - - out = torch.empty((M, N), device=inp.device, dtype=inp.dtype) - - # GEMM tuning parameters! - # TODO: Add a more configurable tuning for selecting the best GeMM - BLOCK_SIZE_M = 16 if M <= 16 else 32 if M <= 32 else 64 if M <= 64 else 128 - BLOCK_SIZE_N = 64 - BLOCK_SIZE_K = max(64, quantization_group_size) - GROUP_SIZE_M = 8 - num_stages = 4 - num_warps = 4 - if M >= 256: - BLOCK_SIZE_M = 256 - BLOCK_SIZE_N = 128 - BLOCK_SIZE_K = max(128, quantization_group_size) - num_stages = 3 - num_warps = 8 - - grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), ) - kernel = matmul_kernel_fp8_bf16 if inp.dtype == torch.bfloat16 else matmul_kernel_fp8_fp16 - kernel[grid](inp, - weight, - out, - scale, - M, - N, - K, - inp.stride(0), - inp.stride(1), - weight.stride(0), - weight.stride(1), - out.stride(0), - out.stride(1), - quantization_group_size=quantization_group_size, - BLOCK_SIZE_M=BLOCK_SIZE_M, - BLOCK_SIZE_N=BLOCK_SIZE_N, - BLOCK_SIZE_K=BLOCK_SIZE_K, - GROUP_SIZE_M=GROUP_SIZE_M, - num_stages=num_stages, - num_warps=num_warps) - return out +def matmul_fp8_fallback(inp, weight, scale, quantization_group_size, quantizer): + return torch.matmul(inp, quantizer.dequantize(weight, scale=scale)) diff --git a/deepspeed/ops/fp_quantizer/fp8_gemm_triton.py b/deepspeed/ops/fp_quantizer/fp8_gemm_triton.py new file mode 100644 index 000000000000..746e217d4194 --- /dev/null +++ b/deepspeed/ops/fp_quantizer/fp8_gemm_triton.py @@ -0,0 +1,171 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +######## Fused MoE kernel ######### +# These kernels are implemented for +# fusing GeMM with dequantization of +# fp8 weight data when using bit-16 +# activation. +################################### + +import torch +import triton +import triton.language as tl + + +@triton.jit +def matmul_kernel_fp8_bf16(inp_ptr, weight_ptr, out_ptr, scale_ptr, M, N, K, stride_am, stride_ak, stride_bk, + stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, + quantization_group_size: tl.constexpr): + pid = tl.program_id(axis=0) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m) + pid_n = (pid % num_pid_in_group) // group_size_m + + offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + offs_k = tl.arange(0, BLOCK_SIZE_K) + + inp_data = inp_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) + weight_data = weight_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) + weight_ptrs_offset = offs_k[:, None] * (stride_bk // quantization_group_size) + ( + (pid_n * BLOCK_SIZE_N) // quantization_group_size) + + weight = tl.load(weight_data, mask=offs_k[:, None] < K, other=0.0) + scale = tl.load(scale_ptr + weight_ptrs_offset) + + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + inp = tl.load(inp_data, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) + # Dequantize weight (fp8 -> bf16) + w = (((weight & 0x80) << 8) | ((weight & 0x7f) << 4)).to(tl.uint16) + w = (w + 0x3C00).to(tl.uint16) + w = (w.to(tl.bfloat16, bitcast=True) * scale).to(tl.bfloat16) + + inp_data += BLOCK_SIZE_K * stride_ak + weight_data += BLOCK_SIZE_K * stride_bk + weight_mask = offs_k[:, None] < K - (k + 1) * BLOCK_SIZE_K + weight = tl.load(weight_data, mask=weight_mask, other=0.0) + scale = tl.load(scale_ptr + (weight_ptrs_offset + + (((k + 1) * BLOCK_SIZE_K * stride_bk) // quantization_group_size)), + mask=weight_mask, + other=0.0) + + accumulator += tl.dot(inp, w) + + out = accumulator.to(tl.bfloat16) + + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + out_data = out_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + tl.store(out_data, out, mask=(offs_cm[:, None] < M) & (offs_cn[None, :] < N)) + + +@triton.jit +def matmul_kernel_fp8_fp16(inp_ptr, weight_ptr, out_ptr, scale_ptr, M, N, K, stride_am, stride_ak, stride_bk, + stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, + quantization_group_size: tl.constexpr): + pid = tl.program_id(axis=0) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m) + pid_n = (pid % num_pid_in_group) // group_size_m + + offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + offs_k = tl.arange(0, BLOCK_SIZE_K) + + inp_data = inp_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) + weight_data = weight_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) + weight_ptrs_offset = offs_k[:, None] * (stride_bk // quantization_group_size) + ( + (pid_n * BLOCK_SIZE_N) // quantization_group_size) + + weight = tl.load(weight_data, mask=offs_k[:, None] < K, other=0.0) + scale = tl.load(scale_ptr + weight_ptrs_offset) + + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + inp = tl.load(inp_data, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) + # Dequantize weight (fp8 -> fp16) + w = (((weight & 0x80) << 8) | ((weight & 0x7f) << 7)).to(tl.uint16) + w = (w + 0x2000).to(tl.uint16) + w = (w.to(tl.float16, bitcast=True) * scale).to(tl.float16) + + inp_data += BLOCK_SIZE_K * stride_ak + weight_data += BLOCK_SIZE_K * stride_bk + + weight = tl.load(weight_data, mask=offs_k[:, None] < K - (k + 1) * BLOCK_SIZE_K, other=0.0) + scale = tl.load(scale_ptr + (weight_ptrs_offset + + (((k + 1) * BLOCK_SIZE_K * stride_bk) // quantization_group_size))) + + accumulator += tl.dot(inp, w) + + out = accumulator.to(tl.float16) + + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + out_data = out_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + tl.store(out_data, out, mask=(offs_cm[:, None] < M) & (offs_cn[None, :] < N)) + + +def matmul_fp8_triton(inp, weight, scale, quantization_group_size): + + assert inp.shape[1] == weight.shape[0], \ + f"Incompatible dimensions (input: {inp.shape}, weight: {weight.shape})" + + M, K = inp.shape + K, N = weight.shape + + out = torch.empty((M, N), device=inp.device, dtype=inp.dtype) + + # GEMM tuning parameters! + # TODO: Add a more configurable tuning for selecting the best GeMM + BLOCK_SIZE_M = 16 if M <= 16 else 32 if M <= 32 else 64 if M <= 64 else 128 + BLOCK_SIZE_N = 64 + BLOCK_SIZE_K = max(64, quantization_group_size) + GROUP_SIZE_M = 8 + num_stages = 4 + num_warps = 4 + if M >= 256: + BLOCK_SIZE_M = 256 + BLOCK_SIZE_N = 128 + BLOCK_SIZE_K = max(128, quantization_group_size) + num_stages = 3 + num_warps = 8 + + grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), ) + kernel = matmul_kernel_fp8_bf16 if inp.dtype == torch.bfloat16 else matmul_kernel_fp8_fp16 + kernel[grid](inp, + weight, + out, + scale, + M, + N, + K, + inp.stride(0), + inp.stride(1), + weight.stride(0), + weight.stride(1), + out.stride(0), + out.stride(1), + quantization_group_size=quantization_group_size, + BLOCK_SIZE_M=BLOCK_SIZE_M, + BLOCK_SIZE_N=BLOCK_SIZE_N, + BLOCK_SIZE_K=BLOCK_SIZE_K, + GROUP_SIZE_M=GROUP_SIZE_M, + num_stages=num_stages, + num_warps=num_warps) + return out diff --git a/tests/unit/ops/fp_quantizer/test_fp8_gemm.py b/tests/unit/ops/fp_quantizer/test_fp8_gemm.py index d66f7c8cb4cc..a4cf579f5943 100644 --- a/tests/unit/ops/fp_quantizer/test_fp8_gemm.py +++ b/tests/unit/ops/fp_quantizer/test_fp8_gemm.py @@ -14,6 +14,8 @@ from deepspeed.ops.fp_quantizer import FP_Quantize, matmul_fp8 +from deepspeed import get_accelerator + @pytest.mark.parametrize("dtype", [torch.bfloat16], ids=["bf16"]) @pytest.mark.parametrize("q_bits", [8], ids=[ @@ -21,23 +23,19 @@ ]) @pytest.mark.parametrize("M", [1, 2, 4, 8, 32, 64, 128, 256, 512, 1024, 2048]) def test_fp_quant(dtype, q_bits, M): + device_name = get_accelerator().device_name() quantization_group_size = 128 fpq = FP_Quantize(group_size=quantization_group_size) N = 8192 H = 4096 - x = torch.randn(M, H, dtype=dtype, device='cuda') - weight_bf16 = torch.randn(H, N, dtype=dtype, device='cuda') + x = torch.randn(M, H, dtype=dtype, device=device_name) + weight_bf16 = torch.randn(H, N, dtype=dtype, device=device_name) - weight, _ = fpq.quantize(weight_bf16.data, q_bits=8, return_meta_tensor=True) + weight, _ = fpq.quantize(weight_bf16.data, q_bits=q_bits, return_meta_tensor=True) scale = fpq.get_scales() - out = matmul_fp8( - x, - weight, - scale, - quantization_group_size, - ) + out = matmul_fp8(x, weight, scale, quantization_group_size, fpq) out_q = torch.matmul(x, fpq.dequantize(weight, scale=fpq.scale)) From b0040b6ca4799c34ebb7543e4edf6658505d9dc6 Mon Sep 17 00:00:00 2001 From: inkcherry Date: Tue, 7 Jan 2025 04:06:06 +0800 Subject: [PATCH 092/112] Reduce the device bubble introduced by heavy loop synchronization in coalesced fetch/release(z3_leaf_module) (#6694) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit depend on https://github.com/microsoft/DeepSpeed/pull/6649 When performing fetch/release operations on Z3 leaf modules, the loop time is excessively long in fine-grained module. Compared to non-leaf modules, Z3 leaf modules may include a larger number of parameters. Although each loop unit does not consume much time, the overall loop length can be significant. ![image](https://github.com/user-attachments/assets/9891835a-2620-47f3-aba6-ea22b8905d1c) **The fetch time is impacted by:** Post-allgather operations (narrow， slice ，cat, difficult to avoid) Memory pressure（record_stream/fetch event create&sync） **The release time is impacted by:** slice Free parameter record_stream Considering the fine-grained leaf modules, where each parameter is relatively small, we can treat the parameters within each leaf module as a unified entity to handle memory pressure. This approach can approximately halve the CPU time required for fetch/release operations. --------- Co-authored-by: Ma, Guokai Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase --- deepspeed/runtime/zero/mics.py | 2 +- deepspeed/runtime/zero/parameter_offload.py | 19 ++++--- .../runtime/zero/partition_parameters.py | 39 +++++++++------ .../zero/partitioned_param_coordinator.py | 50 ++++++++++++------- 4 files changed, 69 insertions(+), 41 deletions(-) diff --git a/deepspeed/runtime/zero/mics.py b/deepspeed/runtime/zero/mics.py index c9ae58a121de..628bf86a61da 100755 --- a/deepspeed/runtime/zero/mics.py +++ b/deepspeed/runtime/zero/mics.py @@ -38,7 +38,7 @@ class MiCS_AllGatherCoalescedHandle(AllGatherCoalescedHandle): def __init__(self, allgather_handle, params: List[Parameter], partitions: List[Tensor], world_size: int) -> None: super().__init__(allgather_handle, params, partitions, world_size) - def wait(self) -> None: + def wait(self, **kwargs) -> None: """ """ # let the current stream to op diff --git a/deepspeed/runtime/zero/parameter_offload.py b/deepspeed/runtime/zero/parameter_offload.py index 0be88a1e1ba6..d5b7bac55146 100644 --- a/deepspeed/runtime/zero/parameter_offload.py +++ b/deepspeed/runtime/zero/parameter_offload.py @@ -145,6 +145,16 @@ def __init__( module.ds_inflight_param_registry = InflightParamRegistry() self.__inflight_param_registry = module.ds_inflight_param_registry + self.fast_sharding_for_leaf_module = False + + if zero_module_granularity_threshold > 0: + self.min_granularity_value = sys.maxsize + self.min_granularity_layer = None + self.granularity_info = set() + self.z3_leaf_layers = [] + self._set_z3_leaf_modules_by_threshold(module, zero_module_granularity_threshold) + self.fast_sharding_for_leaf_module = True + self.param_coordinator = PartitionedParameterCoordinator( prefetch_bucket_sz=self._prefetch_bucket_sz, max_reuse_distance_in_numel=self._max_reuse_distance_in_numel, @@ -155,14 +165,7 @@ def __init__( timers=self.timers, zero_quantized_weights=self.zero_quantized_weights, zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights, - ) - - if zero_module_granularity_threshold > 0: - self.min_granularity_value = sys.maxsize - self.min_granularity_layer = None - self.granularity_info = set() - self.z3_leaf_layers = [] - self._set_z3_leaf_modules_by_threshold(module, zero_module_granularity_threshold) + fast_sharding_for_leaf_module=self.fast_sharding_for_leaf_module) self.forward_hooks = [] self.backward_hooks = [] diff --git a/deepspeed/runtime/zero/partition_parameters.py b/deepspeed/runtime/zero/partition_parameters.py index cb0cd7c8017d..e8cb797b8a5b 100755 --- a/deepspeed/runtime/zero/partition_parameters.py +++ b/deepspeed/runtime/zero/partition_parameters.py @@ -55,7 +55,7 @@ def __init__(self, param: Parameter) -> None: non_blocking=True).view(param.ds_shape) self.__param = param - def wait(self) -> None: + def wait(self, **kwargs) -> None: if not get_accelerator().resolves_data_dependency(): get_accelerator().current_stream().synchronize() self.__param.ds_status = ZeroParamStatus.AVAILABLE @@ -78,7 +78,7 @@ def __init__(self, params: List[Parameter]) -> None: non_blocking=True).view(param.ds_shape) @instrument_w_nvtx - def wait(self) -> None: + def wait(self, **kwargs) -> None: if self.__complete: return @@ -639,7 +639,7 @@ def __init__(self, handle, param: Parameter, quantization=None) -> None: self.__param = param self.__quantization = quantization - def wait(self) -> None: + def wait(self, handle_dependency=True) -> None: instrument_w_nvtx(self.__handle.wait)() if self.__quantization: instrument_w_nvtx(self.__quantization.quant_handle.wait)() @@ -650,6 +650,8 @@ def wait(self) -> None: class AllGatherCoalescedHandle: + data_buffer = [] + def __init__( self, allgather_handle, @@ -672,7 +674,7 @@ def __init__( raise RuntimeError(f"expected param {param.ds_summary()} to not be available") @instrument_w_nvtx - def wait(self) -> None: + def wait(self, handle_dependency=True) -> None: if self.complete: return @@ -704,14 +706,20 @@ def wait(self) -> None: partitions.append(part_to_copy) param.data = instrument_w_nvtx(torch.cat)(partitions).view(param.ds_shape) param.ds_status = ZeroParamStatus.AVAILABLE - - for part_to_copy in partitions: - if not get_accelerator().is_synchronized_device(): + if not get_accelerator().is_synchronized_device() and handle_dependency: + for part_to_copy in partitions: part_to_copy.record_stream(get_accelerator().current_stream()) param_offset += ds_tensor_numel self.complete = True + if not get_accelerator().is_synchronized_device() and not handle_dependency: + # if the device needs to handle dependencies and opts for explicit processing outside the function. + AllGatherCoalescedHandle.data_buffer.append(partitions) + + @staticmethod + def free_buffer(): + AllGatherCoalescedHandle.data_buffer = [] class MultipleAllGatherHandles: @@ -719,9 +727,9 @@ class MultipleAllGatherHandles: def __init__(self, handles: List[AllGatherCoalescedHandle]): self.handles = handles - def wait(self) -> None: + def wait(self, handle_dependency=True) -> None: for handle in self.handles: - handle.wait() + handle.wait(handle_dependency) class AllReduceCoalescedHandle: @@ -1377,13 +1385,13 @@ def all_gather_coalesced(params: Iterable[Parameter], quantization=quant_info, ) - def partition(param_list=None, hierarchy=0, has_been_updated=False): + def partition(param_list=None, hierarchy=0, has_been_updated=False, free_data=True): cls = param print_rank_0(f"{'--'*hierarchy}----Partitioning param {debug_param2name_id_shape_device(cls)}", force=False) if param_list is None: param_list = [cls] - self._partition(param_list, has_been_updated=has_been_updated) + self._partition(param_list, has_been_updated=has_been_updated, free_data=True) def reduce_gradients_at_owner(param_list=None, hierarchy=0): cls = param @@ -1527,12 +1535,12 @@ def _all_gather(self, param_list, async_op=False, hierarchy=None): return handles - def _partition(self, param_list, force=False, has_been_updated=False): + def _partition(self, param_list, force=False, has_been_updated=False, free_data=True): for param in param_list: print_rank_0(f"Before Partitioning Param {param.ds_id}", force=False) if self.zero_param_process_group is not None: self._partition_param_sec(param) - self._partition_param(param, has_been_updated=has_been_updated) + self._partition_param(param, has_been_updated=has_been_updated, free_data=True) param.ds_status = ZeroParamStatus.NOT_AVAILABLE # if param.ds_tensor is not None: @@ -1540,7 +1548,7 @@ def _partition(self, param_list, force=False, has_been_updated=False): # "After the parameters are initially partitioned, make sure we are not recreating the partition." #print_rank_0(f"After Partitioning Param {param.ds_id} {param.ds_tensor.size()} {param.ds_tensor}",force=False) @instrument_w_nvtx - def _partition_param(self, param, buffer=None, has_been_updated=False): + def _partition_param(self, param, buffer=None, has_been_updated=False, free_data=True): assert param.ds_status is not ZeroParamStatus.INFLIGHT, f" {param} Cannot partition a param in flight" global reuse_buffers print_rank_0(f"Param id {param.ds_id} status is {param.ds_status}", force=False) @@ -1565,7 +1573,8 @@ def _partition_param(self, param, buffer=None, has_been_updated=False): see_memory_usage(f'Before partitioning param {param.ds_id} {param.shape}', force=False) # param.data does not store anything meaningful in partitioned state - free_param(param) + if free_data: + free_param(param) see_memory_usage(f'After partitioning param {param.ds_id} {param.shape}', force=False) if param.ds_tensor.final_location == OffloadDeviceEnum.nvme: diff --git a/deepspeed/runtime/zero/partitioned_param_coordinator.py b/deepspeed/runtime/zero/partitioned_param_coordinator.py index 596d0e9c20f9..08cb6c0de54f 100644 --- a/deepspeed/runtime/zero/partitioned_param_coordinator.py +++ b/deepspeed/runtime/zero/partitioned_param_coordinator.py @@ -76,18 +76,17 @@ class __ParamInTrace: param: Parameter step_id_last_used_at: int - def __init__( - self, - prefetch_bucket_sz: int, - max_reuse_distance_in_numel: int, - max_available_parameters_in_numel: int, - allgather_stream: get_accelerator().Stream, - inflight_param_registry: InflightParamRegistry, - prefetch_nvme: bool = False, - timers=None, - zero_quantized_weights=False, - zero_quantized_nontrainable_weights=False, - ) -> None: + def __init__(self, + prefetch_bucket_sz: int, + max_reuse_distance_in_numel: int, + max_available_parameters_in_numel: int, + allgather_stream: get_accelerator().Stream, + inflight_param_registry: InflightParamRegistry, + prefetch_nvme: bool = False, + timers=None, + zero_quantized_weights=False, + zero_quantized_nontrainable_weights=False, + fast_sharding_for_leaf_module=False) -> None: # mapping of param -> handle for each param that is currently in flight self.__inflight_param_registry = inflight_param_registry # keeps track of the number of submodules invoked so far. @@ -130,6 +129,10 @@ def __init__( self.__max_ongoing_fetch_events: int = 2 self.__profiler = PartitionedParameterProfiler(timers if ENABLE_PROFILER else None) + # whether to enable fast fetch for the z3 leaf module. + # this will improve fetch speed but will not break down leaf module parameters to alleviate memory pressure. + self.fast_sharding_for_leaf_module = fast_sharding_for_leaf_module + """Tracing and Tracking TODO. consider performing trace before initializing PartitionedParameterCoordinator and passing trace results into constructor. This way all the code in here can @@ -308,6 +311,7 @@ def fetch_sub_module(self, current_submodule: Module, forward: bool) -> None: wait_numel = 0 wait_event_name = __class__.FORWARD_FETCH_WAIT if forward else __class__.BACKWARD_FETCH_WAIT self.__profiler.start_event(wait_event_name) + fast_fetch = self.fast_sharding_for_leaf_module and z3_leaf_module(current_submodule) # wait for parameters in the immediately needed submodule to become available for param in params_to_fetch: param.ds_active_sub_modules.add(current_submodule.id) @@ -321,9 +325,9 @@ def fetch_sub_module(self, current_submodule: Module, forward: bool) -> None: if len(self.__ongoing_fetch_events) > self.__max_ongoing_fetch_events: self.__ongoing_fetch_events.popleft().synchronize() - self.__inflight_param_registry.pop(param).wait() + self.__inflight_param_registry.pop(param).wait(handle_dependency=not fast_fetch) - if not get_accelerator().handles_memory_backpressure(): + if not get_accelerator().handles_memory_backpressure() and not fast_fetch: event = get_accelerator().Event() event.record() self.__ongoing_fetch_events.append(event) @@ -331,6 +335,8 @@ def fetch_sub_module(self, current_submodule: Module, forward: bool) -> None: assert param.ds_status == ZeroParamStatus.AVAILABLE, param.ds_summary() if not get_accelerator().resolves_data_dependency(): get_accelerator().current_stream().wait_stream(self.__allgather_stream) + if fast_fetch: + AllGatherCoalescedHandle.free_buffer() self.__profiler.stop_event(wait_event_name, wait_numel) # kick off parameter prefetches for upcoming modules @@ -412,10 +418,20 @@ def release_sub_module(self, submodule: Module) -> None: be released.""" params_to_release = (self.__params_to_release(submodule, self.__step_id) if self.is_complete_trace() else set( p.ds_id for p in iter_params(submodule, recurse=z3_leaf_module(submodule)))) + + free_data = not z3_leaf_module(submodule) or not self.fast_sharding_for_leaf_module + if not free_data: + # wait for the computation to finish and launch as early as possible. + empty_buffer = torch.empty(1, device=get_accelerator().current_device()) + for param in iter_params(submodule, recurse=z3_leaf_module(submodule)): param.ds_active_sub_modules.discard(submodule.id) if param.ds_id in params_to_release and not param.is_external_param: - self.__release_param(param) + self.__release_param(param, free_data) + if not free_data: + if param.ds_id in params_to_release and not param.is_external_param: + # empty buffer ensures that all computations are complete + param.data = empty_buffer @instrument_w_nvtx @torch.no_grad() @@ -490,11 +506,11 @@ def __all_gather_params_(self, params: Set[Parameter], forward: bool, quantize: @compiler.disable @instrument_w_nvtx - def __release_param(self, param: Parameter) -> None: + def __release_param(self, param: Parameter, free_data: bool = True) -> None: if param.ds_status == ZeroParamStatus.AVAILABLE and not param.ds_active_sub_modules: if logger.isEnabledFor(logging.DEBUG): debug_rank0(f"-release: {param.ds_summary()}") - param.partition() + param.partition(free_data=free_data) self.__n_available_params -= param.ds_numel @instrument_w_nvtx From c348c5b11a4fd3f70a53f5c9445f260472de47a8 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 6 Jan 2025 14:35:50 -0800 Subject: [PATCH 093/112] Cleanup ops/transformer/inference tests (#6925) --- .../inference/inference_test_utils.py | 42 +++++++------------ .../transformer/inference/test_attention.py | 4 +- .../transformer/inference/test_layer_norm.py | 4 +- 3 files changed, 18 insertions(+), 32 deletions(-) diff --git a/tests/unit/ops/transformer/inference/inference_test_utils.py b/tests/unit/ops/transformer/inference/inference_test_utils.py index 9cfcae809f09..d63c51267e51 100644 --- a/tests/unit/ops/transformer/inference/inference_test_utils.py +++ b/tests/unit/ops/transformer/inference/inference_test_utils.py @@ -3,6 +3,8 @@ # DeepSpeed Team +from typing import Tuple + import torch from deepspeed.accelerator import get_accelerator @@ -23,38 +25,22 @@ def get_tolerances(): DTYPES = None -def get_dtypes(): +def get_dtypes(include_float=True): global DTYPES if DTYPES is None: - DTYPES = get_accelerator().supported_dtypes() + DTYPES = [torch.float16, torch.float32] if include_float else [torch.float16] + try: + if get_accelerator().is_bf16_supported(): + DTYPES.append(torch.bfloat16) + except (AssertionError, AttributeError): + pass return DTYPES -def allclose(x, y): +def allclose(x, y, tolerances: Tuple[int, int] = None): assert x.dtype == y.dtype - rtol, atol = get_tolerances()[x.dtype] + if tolerances is None: + rtol, atol = get_tolerances()[x.dtype] + else: + rtol, atol = tolerances return torch.allclose(x, y, rtol=rtol, atol=atol) - - -def assert_almost_equal(x, y, decimal=2, err_msg=''): - import numpy.testing as npt - if isinstance(x, torch.Tensor): - if x.dtype == torch.bfloat16: - x = x.float() - x = x.cpu().detach().numpy() - if isinstance(y, torch.Tensor): - if y.dtype == torch.bfloat16: - y = y.float() - y = y.cpu().detach().numpy() - npt.assert_array_almost_equal(x, y, err_msg=err_msg, decimal=decimal) - - -def max_diff(a, b): - a = a.to(torch.float32).flatten() - b = b.to(torch.float32).flatten() - diff = torch.abs(a - b) - max_diff_indices = torch.argsort(diff)[-1] - print("Max difference indices:", max_diff_indices) - print("Max difference values:", diff[max_diff_indices]) - print(f"{a[max_diff_indices]} vs {b[max_diff_indices]}") - return max_diff_indices diff --git a/tests/unit/ops/transformer/inference/test_attention.py b/tests/unit/ops/transformer/inference/test_attention.py index ecf681542ff6..cae201d747a3 100644 --- a/tests/unit/ops/transformer/inference/test_attention.py +++ b/tests/unit/ops/transformer/inference/test_attention.py @@ -7,7 +7,7 @@ import torch import deepspeed from deepspeed.accelerator import get_accelerator -from .inference_test_utils import assert_almost_equal +from .inference_test_utils import allclose # reference timplementation @@ -88,4 +88,4 @@ def test_attention(BATCH, H, N_CTX, D_HEAD, causal, use_flash, dtype=torch.float use_triton_flash=False, use_ds_attention=False) tri_out = tri_out.reshape((BATCH, N_CTX, H, D_HEAD)).permute(0, 2, 1, 3) - assert_almost_equal(ref_out, tri_out) + assert (allclose(ref_out, tri_out)) diff --git a/tests/unit/ops/transformer/inference/test_layer_norm.py b/tests/unit/ops/transformer/inference/test_layer_norm.py index 7711daf0d887..4a84add16046 100644 --- a/tests/unit/ops/transformer/inference/test_layer_norm.py +++ b/tests/unit/ops/transformer/inference/test_layer_norm.py @@ -9,7 +9,7 @@ from deepspeed.accelerator import get_accelerator from deepspeed.ops.op_builder import InferenceBuilder from deepspeed.ops.transformer.inference.op_binding.layer_norm import LayerNormOp -from .inference_test_utils import allclose, get_dtypes, assert_almost_equal +from .inference_test_utils import allclose, get_dtypes try: import triton # noqa: F401 # type: ignore from deepspeed.ops.transformer.inference.triton import ( @@ -188,4 +188,4 @@ def test_triton_layer_norm(M, N, dtype, residual, input_bias, eps=1e-5, device=' y_ref = torch.nn.functional.layer_norm(x + res + (x_bias if input_bias else 0), w_shape, weight, bias, eps).to(dtype) # compare - assert_almost_equal(y_tri, y_ref) + assert (allclose(y_tri, y_ref)) From f2cc80909b8bdd399a029acf3ed0c894279974b6 Mon Sep 17 00:00:00 2001 From: Lev Kurilenko <113481193+lekurile@users.noreply.github.com> Date: Mon, 6 Jan 2025 17:13:35 -0800 Subject: [PATCH 094/112] Check transformers version in BLOOM for inference v1 (#6766) This PR checks that the `transformers` version is `<= 4.43.4` in the BLOOM container for inference v1, due to breaking changes in `transformers > 4.43.4`. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/module_inject/containers/bloom.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/deepspeed/module_inject/containers/bloom.py b/deepspeed/module_inject/containers/bloom.py index a78ac8120346..7a9b9ca2065b 100644 --- a/deepspeed/module_inject/containers/bloom.py +++ b/deepspeed/module_inject/containers/bloom.py @@ -19,6 +19,18 @@ class DS_BloomContainer(MetaTensorContainer, HybridEngineContainer, BaseTransformerContainer): def __init__(self, **kwargs): + # Check transformers version, error if > 4.43.4 (breaks at 4.44.0) + from importlib.metadata import version + v_transformers = version('transformers') + vers = v_transformers.split('.') + major = int(vers[0]) + minor = int(vers[1]) + if major > 4 or (major == 4 and minor > 43): + import sys + sys.exit( + f"Transformers version {v_transformers} exceeds version 4.43.4! After transformers version 4.43.4, BLOOM inference with DeepSpeed is no longer supported." + ) + super().__init__(**kwargs) # All model specific things should be defined here instead of the base class. From c7f30322fd7db165307ffd3a4f322fa18ab9bae0 Mon Sep 17 00:00:00 2001 From: Nadav Elyahu <88962733+nelyahu@users.noreply.github.com> Date: Tue, 7 Jan 2025 20:10:24 +0200 Subject: [PATCH 095/112] inference: remove unused _validate_args function (#5505) Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/inference/engine.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/deepspeed/inference/engine.py b/deepspeed/inference/engine.py index cfca1ff4fe4c..131dce07d22d 100755 --- a/deepspeed/inference/engine.py +++ b/deepspeed/inference/engine.py @@ -80,7 +80,6 @@ def __init__(self, model, config): self.mp_group = config.tensor_parallel.tp_group self.mpu = config.tensor_parallel.mpu - #self._validate_args(self.mpu, config.replace_with_kernel_inject) self.quantize_merge_count = 1 self.quantization_scales = None @@ -300,29 +299,6 @@ def _init_quantization_setting(self, quantization_setting): f"mlp_extra_grouping = {self.mlp_extra_grouping}, " f"quantize_groups = {self.quantize_groups}", [0]) - # TODO: remove this function and add this functionality to pydantic config checking - def _validate_args(self, mpu, replace_with_kernel_inject): - # TODO: to support SD pipeline we need to avoid this check for now - if replace_with_kernel_inject and not isinstance(self.module, Module): - raise ValueError(f"model must be a torch.nn.Module, got {type(self.module)}") - if not isinstance(self._config.tensor_parallel.tp_size, int) or self._config.tensor_parallel.tp_size < 1: - raise ValueError(f"mp_size must be an int >= 1, got {self._config.tensor_parallel.tp_size}") - - if mpu: - methods = ["get_model_parallel_group", "get_data_parallel_group"] - for method in methods: - if not hasattr(mpu, method): - raise ValueError(f"mpu is missing {method}") - if self._config.checkpoint is not None and not isinstance(self._config.checkpoint, (str, dict)): - raise ValueError(f"checkpoint must be None, str or dict, got {type(self._config.checkpoint)}") - - supported_dtypes = [None, torch.half, torch.int8, torch.float, torch.bfloat16] - if self._config.dtype not in supported_dtypes: - raise ValueError(f"{self._config.dtype} not supported, valid dtype: {supported_dtypes}") - - if self.injection_dict is not None and not isinstance(self.injection_dict, dict): - raise ValueError(f"injection_dict must be None or a dict, got: {self.injection_dict}") - def load_model_with_checkpoint(self, r_module): self.mp_replace = ReplaceWithTensorSlicing( mp_group=self.mp_group, mp_size=self._config.tensor_parallel.tp_size) #, out_dim=0, in_dim=1) From c41b0c2855b24618870399cbbf22a63551b3fc52 Mon Sep 17 00:00:00 2001 From: Sergii Dymchenko Date: Tue, 7 Jan 2025 17:27:30 -0800 Subject: [PATCH 096/112] Use `torch.log1p` (#6930) This function provides greater precision than `log(1 + x)` for small values of `x`. Found with TorchFix https://github.com/pytorch-labs/torchfix/ --- deepspeed/sequence/fpdt_layer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepspeed/sequence/fpdt_layer.py b/deepspeed/sequence/fpdt_layer.py index 4fab768ce63c..4fa2cc988a19 100644 --- a/deepspeed/sequence/fpdt_layer.py +++ b/deepspeed/sequence/fpdt_layer.py @@ -47,7 +47,7 @@ def _update_out_and_lse( block_out = block_out.to(torch.float32) block_lse = block_lse.transpose(-2, -1).unsqueeze(dim=-1) - new_lse = lse + torch.log(1 + torch.exp(block_lse - lse)) + new_lse = lse + torch.log1p(torch.exp(block_lse - lse)) out = torch.exp(lse - new_lse) * out + torch.exp(block_lse - new_lse) * block_out From 6628127a379e3c3fd70e48534c4b5952c5c23a72 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 8 Jan 2025 10:43:06 -0800 Subject: [PATCH 097/112] Update python version classifiers (#6933) Update python version classifiers in setup.py to reflect python versions currently supported. --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index c0452f867b31..cc5eb4a3500c 100755 --- a/setup.py +++ b/setup.py @@ -321,9 +321,9 @@ def op_enabled(op_name): include_package_data=True, scripts=scripts, classifiers=[ - 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', - 'Programming Language :: Python :: 3.10' + 'Programming Language :: Python :: 3.10', 'Programming Language :: Python :: 3.11', + 'Programming Language :: Python :: 3.12' ], license='Apache Software License 2.0', ext_modules=ext_modules, From b62c84d88db26f6ce8c8f9caeef132638599a74d Mon Sep 17 00:00:00 2001 From: woctordho Date: Thu, 9 Jan 2025 02:59:41 +0800 Subject: [PATCH 098/112] Fix building on Windows with presence of Triton (#6749) This fixes some errors when installing DeepSpeed on Windows with the presence of Triton. I guess we can assume we don't need the warning about NFS on Windows for now. I did not try how to detect NFS path on Windows, but we can detect UNC path starting with `\\` if needed. `os.rename` does not allow overwriting the file on Windows, and `os.replace` is more cross-platform. Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/ops/transformer/inference/triton/matmul_ext.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/deepspeed/ops/transformer/inference/triton/matmul_ext.py b/deepspeed/ops/transformer/inference/triton/matmul_ext.py index 412c8740a216..9be4b0098c37 100644 --- a/deepspeed/ops/transformer/inference/triton/matmul_ext.py +++ b/deepspeed/ops/transformer/inference/triton/matmul_ext.py @@ -19,6 +19,9 @@ # ----------------------------------------------------------------------------- # util class/functions for triton def is_nfs_path(path): + if os.name == 'nt': + return False + # Normalize the path to get the absolute path path = os.path.abspath(path) @@ -99,7 +102,7 @@ def put(self, table): with FileLock(self.lock_path): with open(self.file_path + ".tmp", 'wb') as handle: pickle.dump(table, handle) - os.rename(self.file_path + ".tmp", self.file_path) + os.replace(self.file_path + ".tmp", self.file_path) def load(self): if os.path.exists(self.file_path): From 53fb5795a10ed25a824f134dd44cb625d8ad23ac Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Wed, 8 Jan 2025 12:54:19 -0800 Subject: [PATCH 099/112] Fix windows blog examples (#6934) --- blogs/windows/08-2024/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blogs/windows/08-2024/README.md b/blogs/windows/08-2024/README.md index 34e11bd47792..8a23372a1d64 100644 --- a/blogs/windows/08-2024/README.md +++ b/blogs/windows/08-2024/README.md @@ -48,7 +48,7 @@ Regardless of the installation choice, you can check that the installation was s We use an image classification model, CIFAR10, and a language model, BERT, to demonstrate pretraining on Windows with DeepSpeed. ## Pretraining CIFAR10 -The scripts and codes required for CIFAR10 pretraining example are available in the following path: DeepSpeedExamples\training\cifar. You can launch the CIFAR10 pretraining experiment using the following command: `deepspeed cifar10_deepspeed.py –deepspeed`. The final output should look something like this: +The scripts and codes required for CIFAR10 pretraining example are available in the following path: DeepSpeedExamples\training\cifar. You can launch the CIFAR10 pretraining experiment using the following command: `deepspeed cifar10_deepspeed.py --deepspeed`. The final output should look something like this:
    From 45fce45c95c78c3adf72b7332fe6d0051be553b0 Mon Sep 17 00:00:00 2001 From: Yejing-Lai Date: Fri, 10 Jan 2025 02:11:32 +0800 Subject: [PATCH 100/112] Add deepseek autotp (#6937) Deepseek including Multi-Head Latent Attention(MLA) and MoE. For MLA TP, we need to skip two low-rank layers("q_a_proj" and "kv_a_proj_with_mqa) For Deepseek MoE, tp_parse gets this moe layer name is layer_idx.down_proj, it is hard to add the policy, so we set the down_proj layer to all_reduce_linears default. --- deepspeed/module_inject/auto_tp.py | 15 ++++++++++----- deepspeed/module_inject/tp_shard.py | 7 ++++++- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/deepspeed/module_inject/auto_tp.py b/deepspeed/module_inject/auto_tp.py index 5441000e581d..66d7c2659359 100755 --- a/deepspeed/module_inject/auto_tp.py +++ b/deepspeed/module_inject/auto_tp.py @@ -134,7 +134,8 @@ def is_load_module(module): load_layer_names = [ "LPLayerNorm", "SharedEmbedding", "OPTLearnedPositionalEmbedding", "LlamaRMSNorm", "FalconLinear", "MistralRMSNorm", "T5LayerNorm", "MixtralRMSNorm", "Phi3RotaryEmbedding", "Phi3SuScaledRotaryEmbedding", - "Phi3RMSNorm", "YuanRMSNorm", "YuanRotaryEmbedding", "Phi3LongRoPEScaledRotaryEmbedding", "Qwen2RMSNorm" + "Phi3RMSNorm", "YuanRMSNorm", "YuanRotaryEmbedding", "Phi3LongRoPEScaledRotaryEmbedding", "Qwen2RMSNorm", + "DeepseekV2RMSNorm", "DeepseekV2YarnRotaryEmbedding", "MoEGate" ] return module.__class__ in load_layers or module._get_name() in load_layer_names @@ -332,9 +333,9 @@ def _replace(self, child, name, conv_linear_layer): return weight_shape = child.weight.shape mp_replace = ReplaceWithTensorSlicing(mp_group=self.mp_group) - # For mixtral-7x8b, need to skip MoE gate linear replace. - if name == "block_sparse_moe.gate" or (('mlp.shared_expert_gate' == name or 'mlp.gate' == name) - and 'qwen2_moe' in str(type(self.module))): + # For TP layer skip, e.g., MoE gate, deepseek low rank layer skip + if "q_a_proj" in name or "kv_a_proj_with_mqa" in name or name == "block_sparse_moe.gate" or ( + ('mlp.shared_expert_gate' == name or 'mlp.gate' == name) and 'qwen2_moe' in str(type(self.module))): return child # For Yuan model if 'Yuan' in str(self.module): @@ -350,11 +351,15 @@ def _replace(self, child, name, conv_linear_layer): arctic_w2_all_reduce_linear = False if 'Arctic' in str(self.module) and 'w2' in name: arctic_w2_all_reduce_linear = True + # For MoE MLP model, e.g., deepseek and jamba + down_proj = False + if 'down_proj' in name: + down_proj = True # For MLP including chunk layer. if 'gate_up_proj' in name or ('dense_h_to_4h' in name and 'GLM' in str(self.module)): weight, bias = shard_chunk_mlp(child.weight.data, child.bias, dist.get_rank(), dist.get_world_size()) return LinearLayer(weight=weight, bias=bias) - if name in self.all_reduce_linears or arctic_w2_all_reduce_linear: + if name in self.all_reduce_linears or arctic_w2_all_reduce_linear or down_proj: # if conv_linear_layer [weight_shape[1], weight_shape[0] // mp_size] # else [weight_shape[0], weight_shape[1] // mp_size] diff --git a/deepspeed/module_inject/tp_shard.py b/deepspeed/module_inject/tp_shard.py index 3e6fc2b63ef1..ded262edcf61 100644 --- a/deepspeed/module_inject/tp_shard.py +++ b/deepspeed/module_inject/tp_shard.py @@ -42,11 +42,16 @@ def get_num_attention_heads(): def get_shard_size(total_size, mp_size, name=None, rank=None): global num_kv_heads last_linear = ["lm_head", "embed_out"] + # MoE MLP layer use near even division will get better perf. + moe_mlp_layer = ["gate_proj", "up_proj", "down_proj", "w1", "w2", "w3"] + not_moe_mlp_layer = True + if name != None and any(s in str(name) for s in moe_mlp_layer): + not_moe_mlp_layer = False # When we have num_kv_heads defined, uneven division is possible, otherwise enforce near even division if rank == None: rank = dist.get_rank() if num_kv_heads != None and total_size % num_kv_heads == 0 and "mlp" not in str(name) and str( - name) not in last_linear: + name) not in last_linear and not_moe_mlp_layer: my_slices = (num_kv_heads // mp_size) + (1 if rank < (num_kv_heads % mp_size) else 0) return total_size * my_slices // num_kv_heads else: From 0fc3daade7b982884c60a6ac08c7a71f19cd0a5d Mon Sep 17 00:00:00 2001 From: Lev Kurilenko <113481193+lekurile@users.noreply.github.com> Date: Thu, 9 Jan 2025 12:11:35 -0800 Subject: [PATCH 101/112] Add position_ids arg to OPTEmbedding forward function (#6939) This PR updates the DeepSpeed `OPTEmbedding` forward function to include a new `positions_ids` argument. --------- Co-authored-by: Logan Adams --- .github/workflows/nv-ds-chat.yml | 2 +- deepspeed/module_inject/layers.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/nv-ds-chat.yml b/.github/workflows/nv-ds-chat.yml index 329a1060f5eb..7e209cbe4397 100644 --- a/.github/workflows/nv-ds-chat.yml +++ b/.github/workflows/nv-ds-chat.yml @@ -43,7 +43,7 @@ jobs: - name: Install deepspeed run: | - pip install transformers==4.45.2 + pip install transformers pip install .[dev] ds_report diff --git a/deepspeed/module_inject/layers.py b/deepspeed/module_inject/layers.py index 22d24820d404..722ba413a671 100644 --- a/deepspeed/module_inject/layers.py +++ b/deepspeed/module_inject/layers.py @@ -191,7 +191,7 @@ def __init__(self, weight_shape=None, weight=None, bias=None): self.offset = 2 super().__init__(weight_shape, weight=weight) - def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0): + def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0, position_ids: int = 0): """`input_ids_shape` is expected to be [bsz x seqlen].""" attention_mask = attention_mask.long() From 1d15ef0acfcbbd0efb90458fe08c234a21328e77 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Thu, 9 Jan 2025 15:56:54 -0800 Subject: [PATCH 102/112] Add information on security expectations with this software (#6941) Inspired by the link vllm [includes](https://github.com/vllm-project/vllm/blob/main/SECURITY.md), this starts to give users insight into the security expectations they should have from using DeepSpeed. --- SECURITY.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/SECURITY.md b/SECURITY.md index 9e9391ee0bac..3061748e610b 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -39,3 +39,7 @@ We prefer all communications to be in English. Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd). + +--- + +Please see [PyTorch's Security Policy](https://github.com/pytorch/pytorch/blob/main/SECURITY.md) for more information and recommendations on how to securely interact with models. From fa8db5cf2f9cf724fd2703353d40e3b37a8e7310 Mon Sep 17 00:00:00 2001 From: Yejing-Lai Date: Sat, 11 Jan 2025 06:18:01 +0800 Subject: [PATCH 103/112] Support pure meta model lm_head tp (#6812) Add lm_head tp support when checkpoint not provided to deepspeed.init_inference(). --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Ma, Guokai --- deepspeed/module_inject/replace_module.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/deepspeed/module_inject/replace_module.py b/deepspeed/module_inject/replace_module.py index e59f84bc8453..00b22aac81d8 100644 --- a/deepspeed/module_inject/replace_module.py +++ b/deepspeed/module_inject/replace_module.py @@ -342,13 +342,11 @@ def set_lm_head(module): module.lm_head, "weight") and module.lm_head.weight.is_meta: module.lm_head.weight = embedding_weight # enable tensor parallel for the last linear - if hasattr(module, "lm_head") and hasattr(module.lm_head, - "weight") and not module.lm_head.weight.is_meta and isinstance( - module.lm_head, torch.nn.Linear): + if hasattr(module, "lm_head") and hasattr(module.lm_head, "weight") and isinstance( + module.lm_head, torch.nn.Linear): module = replace_wo_policy(module, ("lm_head", ), 0, "lm_head") - elif hasattr(module, "embed_out") and hasattr(module.embed_out, - "weight") and not module.embed_out.weight.is_meta and isinstance( - module.embed_out, torch.nn.Linear): + elif hasattr(module, "embed_out") and hasattr(module.embed_out, "weight") and isinstance( + module.embed_out, torch.nn.Linear): module = replace_wo_policy(module, ("embed_out", ), 0, "embed_out") elif hasattr(module, "language_model") and hasattr(module.language_model, "lm_head"): module = replace_wo_policy(module.language_model, ("lm_head", ), 0, "lm_head") @@ -389,7 +387,6 @@ def conv2d_parallel_shard_weights(model, rank, world_size): checkpoint=checkpoint_file) 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): @@ -399,6 +396,9 @@ def conv2d_parallel_shard_weights(model, rank, world_size): orig_class=orig_layer_impl, replace_fn=replace_fn, _replace_policy=config.injection_policy_tuple) + # AutoTP default set lm_head tp + if not config.replace_with_kernel_inject: + replaced_module = set_lm_head(replaced_module) quantizer = GroupQuantizer(q_int8=quantize) world_size = dist.get_world_size() if dist.is_initialized() else 1 From 396f8db793b37db9b11847df8245f85bc57eeaa3 Mon Sep 17 00:00:00 2001 From: Nir Sonnenschein Date: Mon, 13 Jan 2025 18:50:22 +0200 Subject: [PATCH 104/112] Remove op compilation flags due to perf issue (#6944) in some scenarios some of the optimization flags for the ops compiler for HPU can cause a significant performance degradation. remove the flags until the issue is resolved --- op_builder/hpu/builder.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/op_builder/hpu/builder.py b/op_builder/hpu/builder.py index c176a586ba49..11e710a8ee48 100644 --- a/op_builder/hpu/builder.py +++ b/op_builder/hpu/builder.py @@ -32,9 +32,6 @@ def builder(self): def cxx_args(self): args = ['-O3', '-g', '-Wno-reorder'] - CPU_ARCH = self.cpu_arch() - SIMD_WIDTH = self.simd_width() - args += [CPU_ARCH, '-fopenmp', SIMD_WIDTH] return args def libraries_args(self): From 66d3d3e94dbdfbbf6535cab66256c238983fc7c3 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Mon, 13 Jan 2025 10:34:15 -0800 Subject: [PATCH 105/112] Pin nv-a6000 workflow (#6938) Breaking change in transformers is https://github.com/huggingface/transformers/pull/35235. Need to make changes to unpin nv-a6000 workflow. --- .github/workflows/nv-a6000.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/nv-a6000.yml b/.github/workflows/nv-a6000.yml index 639f27498dd9..0547431e3099 100644 --- a/.github/workflows/nv-a6000.yml +++ b/.github/workflows/nv-a6000.yml @@ -40,8 +40,9 @@ jobs: python -c "import torch; print('CUDA available:', torch.cuda.is_available())" - name: Install transformers run: | - git clone --depth=1 https://github.com/huggingface/transformers + git clone https://github.com/huggingface/transformers cd transformers + git checkout v4.47.1 git rev-parse --short HEAD python -m pip install . - name: Install deepspeed From fae714d6bdf45ab0f6b98554d542a0f4e04a0eb9 Mon Sep 17 00:00:00 2001 From: Omar Elayan <142979319+oelayan7@users.noreply.github.com> Date: Wed, 15 Jan 2025 21:25:29 +0200 Subject: [PATCH 106/112] [inf] Add config var to enable keeping module on host (#6846) Using keep_module_on_host config var will let us control if the loaded checkpoints to model parameters will be moved to the device or stay on host --------- Co-authored-by: Olatunji Ruwase Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/inference/config.py | 9 +++++++ deepspeed/inference/engine.py | 2 +- deepspeed/module_inject/auto_tp.py | 33 +++++++++++++++-------- deepspeed/module_inject/replace_module.py | 3 ++- tests/unit/inference/test_inference.py | 21 +++++++++++++-- 5 files changed, 53 insertions(+), 15 deletions(-) diff --git a/deepspeed/inference/config.py b/deepspeed/inference/config.py index 42ffebbc4386..6df61f7c8841 100644 --- a/deepspeed/inference/config.py +++ b/deepspeed/inference/config.py @@ -174,6 +174,15 @@ class DeepSpeedInferenceConfig(DeepSpeedConfigModel): values for :any:`DeepSpeedMoEConfig`. """ + keep_module_on_host: bool = False + """ + When loading checkpoints to model parameters, they are moved to the device. In very large models + this might fill the device and cause OOM. Setting this flag to true, will keep checkpoints on + host and not move them directly to the device (giving an option to quantize checkpoint data before + moving it to the device for example). + Set only for models with injection policies and auto TP. + """ + quant: QuantizationConfig = {} """ NOTE: only works for int8 dtype. diff --git a/deepspeed/inference/engine.py b/deepspeed/inference/engine.py index 131dce07d22d..be6336d02a19 100755 --- a/deepspeed/inference/engine.py +++ b/deepspeed/inference/engine.py @@ -169,7 +169,7 @@ def __init__(self, model, config): is_meta_device = hasattr(self.module, "device") and self.module.device.type == 'meta' if is_meta_device: self.module.to_empty(device=device) - else: + elif not config.keep_module_on_host: self.module.to(device) if config.tensor_parallel.tp_size > 1: diff --git a/deepspeed/module_inject/auto_tp.py b/deepspeed/module_inject/auto_tp.py index 66d7c2659359..8bdcf6faa053 100755 --- a/deepspeed/module_inject/auto_tp.py +++ b/deepspeed/module_inject/auto_tp.py @@ -17,14 +17,14 @@ from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list -def move(tensor, device): +def move(tensor, device, copy=True): if tensor.is_meta: return torch.empty_like(tensor, device=device) else: # Using new tensors help in freeing memory (after split for example) was done before by calling clone(). # Using copy=True instead of clone() will help in case of cpu --> cpu. # Otherwise to() will not create a new copy for the view of the full tensor, and it will not be de-referenced. - return tensor.to(device, copy=True) + return tensor.to(device, copy=copy) class ReplaceWithTensorSlicing: @@ -189,7 +189,14 @@ def load(module, state_dict, prefix, mp_group=None): class AutoTP(): - def __init__(self, module, all_reduce_linears, prefix, state_dict, linear_layer_setting, orig_layer_impl): + def __init__(self, + module, + all_reduce_linears, + prefix, + state_dict, + linear_layer_setting, + orig_layer_impl, + keep_module_on_host=False): self.module = module self.all_reduce_linears = all_reduce_linears self.prefix = prefix @@ -201,6 +208,7 @@ def __init__(self, module, all_reduce_linears, prefix, state_dict, linear_layer_ self.orig_layer_impl = orig_layer_impl self.linear_policies = None self.conv_linear_layer = False + self.keep_module_on_host = keep_module_on_host def in_module_list(module, module_list): for item in module_list: @@ -331,6 +339,10 @@ def set_tensor_parallel_config(self, mp_size, mp_group): def _replace(self, child, name, conv_linear_layer): if getattr(child, "replaced", False) == True: return + device_name = 'cpu' if self.keep_module_on_host else get_accelerator().current_device_name() + # keep_module_on_host is used to keep the module on the host. Checkpoints are loaded to the host first (in some + # cases it can be done from the disk even to prevent filling host's memory), thus no need to create a new copy. + return_new_copy = not self.keep_module_on_host weight_shape = child.weight.shape mp_replace = ReplaceWithTensorSlicing(mp_group=self.mp_group) # For TP layer skip, e.g., MoE gate, deepseek low rank layer skip @@ -368,7 +380,7 @@ def _replace(self, child, name, conv_linear_layer): data = child.weight.data.split(get_shard_size_list( weight_shape[0] if self.conv_linear_layer else weight_shape[1], self.mp_size, name), dim=1) - data_dc = move(data[mp_replace.gpu_index], get_accelerator().current_device_name()).detach() + data_dc = move(data[mp_replace.gpu_index], device_name, return_new_copy).detach() del data setattr(child, "replaced", True) @@ -376,10 +388,9 @@ def _replace(self, child, name, conv_linear_layer): return LmHeadLinearAllreduce( torch.nn.parameter.Parameter(data_dc, requires_grad=False), dist.get_rank(), dist.get_world_size(), child.bias if child.bias is None else torch.nn.parameter.Parameter( - move(child.bias, - get_accelerator().current_device_name())), self.mp_group) + move(child.bias, device_name, return_new_copy)), self.mp_group) return LinearAllreduce(torch.nn.parameter.Parameter(data_dc, requires_grad=False), child.bias if child.bias is None else \ - torch.nn.parameter.Parameter(move(child.bias, get_accelerator().current_device_name())), self.mp_group) + torch.nn.parameter.Parameter(move(child.bias, device_name, return_new_copy)), self.mp_group) else: # if conv_linear_layer [weight_shape[1], weight_shape[0] // mp_size] @@ -392,22 +403,22 @@ def _replace(self, child, name, conv_linear_layer): #The copy is a regular copy, The shape of dst and src is the same data_dc = move( prepare_tp_fused_qkvw(self.module, child.weight.data, self.mp_size, mp_replace.gpu_index), - get_accelerator().current_device_name()) + device_name, return_new_copy) bias_data_dc = None if child.bias is None else move( prepare_tp_fused_qkvw(self.module, child.bias.data, self.mp_size, mp_replace.gpu_index), - get_accelerator().current_device_name()) + device_name, return_new_copy) else: data = child.weight.data.split(get_shard_size_list(weight_shape[0], self.mp_size, name), dim=1 if self.conv_linear_layer else 0) - data_dc = move(data[mp_replace.gpu_index], get_accelerator().current_device_name()).detach() + data_dc = move(data[mp_replace.gpu_index], device_name, return_new_copy).detach() del data if child.bias is not None: bias_data = child.bias.data.split(get_shard_size_list( weight_shape[1] if self.conv_linear_layer else weight_shape[0], self.mp_size, name), dim=0) - bias_data = move(bias_data[mp_replace.gpu_index], get_accelerator().current_device_name()) + bias_data = move(bias_data[mp_replace.gpu_index], device_name, return_new_copy) bias_data_dc = torch.nn.parameter.Parameter(bias_data, requires_grad=False) del bias_data else: diff --git a/deepspeed/module_inject/replace_module.py b/deepspeed/module_inject/replace_module.py index 00b22aac81d8..32c88549c821 100644 --- a/deepspeed/module_inject/replace_module.py +++ b/deepspeed/module_inject/replace_module.py @@ -268,7 +268,8 @@ def replace_wo_policy(module, all_reduce_linears, prefix="", state_dict=None): #mp_replace = ReplaceWithTensorSlicing(mp_group=config.tensor_parallel.tp_group) # 1. Create AutoTP object - _autotp = AutoTP(module, all_reduce_linears, prefix, state_dict, linear_layer_setting, orig_layer_impl) + _autotp = AutoTP(module, all_reduce_linears, prefix, state_dict, linear_layer_setting, orig_layer_impl, + config.keep_module_on_host) # 2. Set the tensor parallelism config _autotp.set_tensor_parallel_config(config.tensor_parallel.tp_size, config.tensor_parallel.tp_group) diff --git a/tests/unit/inference/test_inference.py b/tests/unit/inference/test_inference.py index 9b563523dbeb..df85ed232a2e 100644 --- a/tests/unit/inference/test_inference.py +++ b/tests/unit/inference/test_inference.py @@ -554,6 +554,7 @@ def test(self, model_w_task, injection_policy, query, inf_kwargs, assert_fn, dty @pytest.mark.seq_inference +@pytest.mark.parametrize('keep_module_on_host', [True, False]) @pytest.mark.parametrize( "model_w_task", [("Helsinki-NLP/opus-mt-en-de", "translation"), ("Salesforce/codegen-350M-mono", "text-generation")], @@ -570,6 +571,7 @@ def test( inf_kwargs, assert_fn, dtype, + keep_module_on_host, ): invalid_test_msg = validate_test(model_w_task, dtype, enable_cuda_graph=False, enable_triton=False) if invalid_test_msg: @@ -592,13 +594,20 @@ def test( framework="pt") bs_output = pipe(query, **inf_kwargs) - pipe.model = deepspeed.init_inference(pipe.model, mp_size=world_size, dtype=dtype) + pipe.model = deepspeed.init_inference(pipe.model, + mp_size=world_size, + dtype=dtype, + keep_module_on_host=keep_module_on_host) ds_output = pipe(query, **inf_kwargs) print(local_rank, "baseline", bs_output) print(local_rank, "deepspeed", ds_output) assert assert_fn(bs_output, ds_output) + if keep_module_on_host: + for name, param in model.named_parameters(): + assert param.device == torch.device('cpu'), f"keep_module_on_host is on but param {name} is not on cpu" + @pytest.mark.world_size(3) def test_odd_world_size( self, @@ -607,6 +616,7 @@ def test_odd_world_size( inf_kwargs, assert_fn, dtype, + keep_module_on_host, ): invalid_test_msg = validate_test(model_w_task, dtype, enable_cuda_graph=False, enable_triton=False) if invalid_test_msg: @@ -624,13 +634,20 @@ def test_odd_world_size( framework="pt") bs_output = pipe(query, **inf_kwargs) - pipe.model = deepspeed.init_inference(pipe.model, mp_size=world_size, dtype=dtype) + pipe.model = deepspeed.init_inference(pipe.model, + mp_size=world_size, + dtype=dtype, + keep_module_on_host=keep_module_on_host) ds_output = pipe(query, **inf_kwargs) print(local_rank, "baseline", bs_output) print(local_rank, "deepspeed", ds_output) assert assert_fn(bs_output, ds_output) + if keep_module_on_host: + for name, param in model.named_parameters(): + assert param.device == torch.device('cpu'), f"keep_module_on_host is on but param {name} is not on cpu" + @pytest.mark.nightly @pytest.mark.parametrize( From 05eaf3d1cab0f42f130a153802c7b94d86ecc872 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Quentin=20Gallou=C3=A9dec?= <45557362+qgallouedec@users.noreply.github.com> Date: Wed, 15 Jan 2025 23:08:56 +0100 Subject: [PATCH 107/112] `warn` to `warning` (#6952) `warn` is deprecated, see https://docs.python.org/3/library/logging.html#logging.Logger.warning ```DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead``` --- accelerator/real_accelerator.py | 2 +- deepspeed/runtime/base_optimizer.py | 2 +- deepspeed/runtime/engine.py | 4 ++-- deepspeed/runtime/lr_schedules.py | 2 +- deepspeed/runtime/zero/stage_1_and_2.py | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/accelerator/real_accelerator.py b/accelerator/real_accelerator.py index ced9218d7aca..eb4e17850882 100644 --- a/accelerator/real_accelerator.py +++ b/accelerator/real_accelerator.py @@ -178,7 +178,7 @@ def get_accelerator(): if accelerator_name is None: # borrow this log from PR#5084 if accel_logger is not None: - accel_logger.warn( + accel_logger.warning( "Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it.") # cpu added as catch-all when accelerator detection fails accelerator_name = "cpu" diff --git a/deepspeed/runtime/base_optimizer.py b/deepspeed/runtime/base_optimizer.py index b8df7499450d..d2c54155da89 100644 --- a/deepspeed/runtime/base_optimizer.py +++ b/deepspeed/runtime/base_optimizer.py @@ -28,7 +28,7 @@ def load_hp_checkpoint_state_from_checkpoint_dir(self, lp_groups_name: str, chec tp_rank = bwc_tensor_model_parallel_rank(mpu=self.mpu) if self.mpu is None: - logger.warn("MPU is not provided, setting tp size to 1 in checkpoint loading.") + logger.warning("MPU is not provided, setting tp size to 1 in checkpoint loading.") tp_world_size = 1 else: tp_world_size = self.mpu.get_slice_parallel_world_size() if hasattr(self.mpu, "get_slice_parallel_world_size") \ diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 5f023d87f375..9b9a2e509d61 100755 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -3120,7 +3120,7 @@ def _get_all_zero_checkpoints(self, load_dir, tag): if bf16_mode is not self.bfloat16_enabled(): checkpoint_bit16 = BFLOAT16 if bf16_mode else FP16 engine_bit16 = BFLOAT16 if self.bfloat16_enabled() else FP16 - logger.warn(f'Loading {checkpoint_bit16} zero checkpoints into {engine_bit16} training engine') + logger.warning(f'Loading {checkpoint_bit16} zero checkpoints into {engine_bit16} training engine') return self._get_all_zero_checkpoint_state_dicts(zero_ckpt_names) return None @@ -3276,7 +3276,7 @@ def _save_moe_checkpoint(self, save_dir, tag, client_state={}, exclude_frozen_pa local_expert_id = None if not m: - logger.warn(f'No expert found in key {key}.') + logger.warning(f'No expert found in key {key}.') else: local_expert_id = m.group(1) diff --git a/deepspeed/runtime/lr_schedules.py b/deepspeed/runtime/lr_schedules.py index 899358e2c5ef..2ffd0bf9f036 100755 --- a/deepspeed/runtime/lr_schedules.py +++ b/deepspeed/runtime/lr_schedules.py @@ -508,7 +508,7 @@ def _initialize_lr(self, optimizer, cycle_min_lr, cycle_max_lr, decay_lr_rate, l def _initialize_momentum(self, optimizer, cycle_min_mom, cycle_max_mom, decay_mom_rate, last_batch_iteration): if 'betas' not in optimizer.defaults: optimizer_name = type(optimizer).__name__ - logger.warn( + logger.warning( f"cycle_momentum is disabled because optimizer {optimizer_name} does not support momentum, no betas attribute in defaults" ) self.cycle_momentum = False diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index ecb2a527f870..0508766f8896 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -614,7 +614,7 @@ def _configure_moe_settings(self): assert self.contiguous_gradients, "Contiguous Gradients in ZeRO Stage 2 must be set to True for MoE. Other code paths are not tested with MoE" # NOTE: To run ZeRO stage 1 with MoE, we need to set self.contiguous_gradients to True or ignore the assertion if not self.partition_gradients and not self.contiguous_gradients: - logger.warn( + logger.warning( "ZeRO Stage 1 has not been thoroughly tested with MoE. This configuration is still experimental.") assert self.reduce_scatter, "Reduce Scatter in ZeRO Stage 2 must be set to True for MoE. Other code paths are not tested with MoE" From 018ece5af2d89a11a4a235f81f94496c78b4f990 Mon Sep 17 00:00:00 2001 From: Xia Weiwen Date: Thu, 16 Jan 2025 10:11:07 -0800 Subject: [PATCH 108/112] Add extra_repr to Linear classes for debugging purpose (#6954) **Summary** This PR adds `extra_repr` method to some Linear classes so that additional info is printed when printing such modules. It is useful for debugging. Affected modules: - LinearLayer - LinearAllreduce - LmHeadLinearAllreduce The `extra_repr` method gives the following info: - in_features - out_features - bias (true or false) - dtype **Example** Print llama-2-7b model on rank 0 after `init_inference` with world size = 2. Previously we only got class names of these modules: ``` InferenceEngine( (module): LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 4096) (layers): ModuleList( (0-31): 32 x LlamaDecoderLayer( (self_attn): LlamaSdpaAttention( (q_proj): LinearLayer() (k_proj): LinearLayer() (v_proj): LinearLayer() (o_proj): LinearAllreduce() (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): LinearLayer() (up_proj): LinearLayer() (down_proj): LinearAllreduce() (act_fn): SiLU() ) (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05) (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05) ) ) (norm): LlamaRMSNorm((4096,), eps=1e-05) (rotary_emb): LlamaRotaryEmbedding() ) (lm_head): LmHeadLinearAllreduce() ) ) ``` Now we get more useful info: ``` InferenceEngine( (module): LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 4096) (layers): ModuleList( (0-31): 32 x LlamaDecoderLayer( (self_attn): LlamaSdpaAttention( (q_proj): LinearLayer(in_features=4096, out_features=2048, bias=False, dtype=torch.bfloat16) (k_proj): LinearLayer(in_features=4096, out_features=2048, bias=False, dtype=torch.bfloat16) (v_proj): LinearLayer(in_features=4096, out_features=2048, bias=False, dtype=torch.bfloat16) (o_proj): LinearAllreduce(in_features=2048, out_features=4096, bias=False, dtype=torch.bfloat16) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): LinearLayer(in_features=4096, out_features=5504, bias=False, dtype=torch.bfloat16) (up_proj): LinearLayer(in_features=4096, out_features=5504, bias=False, dtype=torch.bfloat16) (down_proj): LinearAllreduce(in_features=5504, out_features=4096, bias=False, dtype=torch.bfloat16) (act_fn): SiLU() ) (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05) (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05) ) ) (norm): LlamaRMSNorm((4096,), eps=1e-05) (rotary_emb): LlamaRotaryEmbedding() ) (lm_head): LmHeadLinearAllreduce(in_features=2048, out_features=32000, bias=False, dtype=torch.bfloat16) ) ) ``` --- deepspeed/module_inject/layers.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/deepspeed/module_inject/layers.py b/deepspeed/module_inject/layers.py index 722ba413a671..2f884ba4fb09 100644 --- a/deepspeed/module_inject/layers.py +++ b/deepspeed/module_inject/layers.py @@ -91,6 +91,13 @@ def forward(self, input): output += self.bias return output + def extra_repr(self): + out_features, in_features = self.weight.shape if self.weight is not None else (None, None) + dtype = self.weight.dtype if self.weight is not None else None + extra_repr_str = "in_features={}, out_features={}, bias={}, dtype={}".format( + in_features, out_features, self.bias is not None, dtype) + return extra_repr_str + class LmHeadLinearAllreduce(nn.Module): @@ -120,6 +127,13 @@ def forward(self, input): output += self.bias return output + def extra_repr(self): + out_features, in_features = self.weight.shape if self.weight is not None else (None, None) + dtype = self.weight.dtype if self.weight is not None else None + extra_repr_str = "in_features={}, out_features={}, bias={}, dtype={}".format( + in_features, out_features, self.bias is not None, dtype) + return extra_repr_str + class LinearLayer(nn.Module): @@ -144,6 +158,13 @@ def forward(self, input): output += self.bias return output + def extra_repr(self): + out_features, in_features = self.weight.shape + dtype = self.weight.dtype + extra_repr_str = "in_features={}, out_features={}, bias={}, dtype={}".format( + in_features, out_features, self.bias is not None, dtype) + return extra_repr_str + class Normalize(nn.Module): From f97f0885cf942aab1559d0f6a060d4801bff3a61 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Fri, 17 Jan 2025 09:43:51 -0800 Subject: [PATCH 109/112] Update import for torchvision.transformers (#6958) Fixes import - found via [torchfix](https://github.com/pytorch-labs/torchfix). --- tests/unit/alexnet_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/alexnet_model.py b/tests/unit/alexnet_model.py index 51e80e7f9e62..6fe84edf4eda 100644 --- a/tests/unit/alexnet_model.py +++ b/tests/unit/alexnet_model.py @@ -84,7 +84,7 @@ def cast_to_half(x): def cifar_trainset(fp16=False): torchvision = pytest.importorskip("torchvision", minversion="0.5.0") - import torchvision.transforms as transforms + from torchvision import transforms transform_list = [ transforms.ToTensor(), From 7f3d669b40f8d29010efd9578d4a2cdd0f16b20e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Hyogeun=20Oh=20=28=EC=98=A4=ED=9A=A8=EA=B7=BC=29?= Date: Sat, 18 Jan 2025 02:44:49 +0900 Subject: [PATCH 110/112] Remove Duplicate Declaration of pandas in `Dockerfile` (#6959) ### Description This pull request removes the redundant installation of `pandas` from the `Dockerfile`. It was previously declared twice, and this update eliminates the duplicate entry, improving the clarity and maintainability of the `Dockerfile`. https://github.com/microsoft/DeepSpeed/blob/018ece5af2d89a11a4a235f81f94496c78b4f990/docker/Dockerfile#L124 https://github.com/microsoft/DeepSpeed/blob/018ece5af2d89a11a4a235f81f94496c78b4f990/docker/Dockerfile#L135 ### Changes Removed the duplicate pandas installation line from the `RUN pip install` command. --- docker/Dockerfile | 1 - 1 file changed, 1 deletion(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 035a094d0051..5a62a5a01aba 100755 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -132,7 +132,6 @@ RUN pip install psutil \ sentencepiece \ msgpack \ requests \ - pandas \ sphinx \ sphinx_rtd_theme \ scipy \ From bc76b04e2800d7761c4e1b99dd9a4049bb94f569 Mon Sep 17 00:00:00 2001 From: inkcherry Date: Wed, 22 Jan 2025 00:49:06 +0800 Subject: [PATCH 111/112] Add the missing view operations from sequence parallel(async). (#6750) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit FYI @loadams a view operation was missing in some updates compared to the original version https://github.com/microsoft/DeepSpeed/blob/17ed7c77c58611a923a6c8d2a3d21d359cd046e8/deepspeed/sequence/layer.py#L56 add missing view operation. The shape required for the view cannot be easily obtained in the current function， so refactor layout params code. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> --- deepspeed/sequence/layer.py | 129 +++++++++++++++++++----------------- 1 file changed, 70 insertions(+), 59 deletions(-) diff --git a/deepspeed/sequence/layer.py b/deepspeed/sequence/layer.py index ccc43fdf7164..ba5e596e0d6d 100644 --- a/deepspeed/sequence/layer.py +++ b/deepspeed/sequence/layer.py @@ -16,6 +16,71 @@ from deepspeed.utils import groups +def _generate_layout_params(scatter_idx, batch_dim_idx, seq_world_size, input): + """ + This function generates the parameters required for `permute` and `reshape` operations, + which are used to process data before and after `all2all` communication. + """ + if batch_dim_idx == 0: + if scatter_idx < 2: + bs, global_seq_len, num_local_head, head_dim = input.shape + pre_all2all_inp_shape = [bs, seq_world_size, global_seq_len // seq_world_size, num_local_head, head_dim] + pre_all2all_permute_idx = (1, 0, 2, 3, 4) + + post_all2all_permute_idx = (1, 2, 0, 3, 4) + post_all2all_res_shape = [bs, global_seq_len // seq_world_size, seq_world_size * num_local_head, head_dim] + else: + bs, local_seq_len, num_total_head, head_dim = input.shape + assert num_total_head % seq_world_size == 0, f"Number of heads ({num_total_head}) must be divisible by the sequence parallel size ({seq_world_size})!" + pre_all2all_inp_shape = [bs, local_seq_len, seq_world_size, num_total_head // seq_world_size, head_dim] + pre_all2all_permute_idx = (2, 0, 1, 3, 4) + + post_all2all_permute_idx = (1, 0, 2, 3, 4) + post_all2all_res_shape = [bs, seq_world_size * local_seq_len, num_total_head // seq_world_size, head_dim] + else: + if scatter_idx < 2: + global_seq_len, bs, num_local_head, head_dim = input.shape + pre_all2all_inp_shape = [seq_world_size, global_seq_len // seq_world_size, bs, num_local_head, head_dim] + pre_all2all_permute_idx = None + + post_all2all_permute_idx = (1, 2, 0, 3, 4) + post_all2all_res_shape = [bs, seq_world_size * global_seq_len, num_local_head // seq_world_size, head_dim] + else: + local_seq_len, bs, num_total_head, head_dim = input.shape + assert num_total_head % seq_world_size == 0, f"Number of heads ({num_total_head}) must be divisible by the sequence parallel size ({seq_world_size})!" + pre_all2all_inp_shape = [local_seq_len, bs, seq_world_size, num_total_head // seq_world_size, head_dim] + pre_all2all_permute_idx = (2, 0, 1, 3, 4) + post_all2all_permute_idx = None + post_all2all_res_shape = [local_seq_len * seq_world_size, bs, num_total_head // seq_world_size, head_dim] + + return pre_all2all_permute_idx, pre_all2all_inp_shape, post_all2all_permute_idx, post_all2all_res_shape + + +def post_all2all(permute_idx, res_shape): + """ + Post-processing function for `all2all` communication. + """ + + def post_func(input): + if permute_idx is not None: + input = input.permute(permute_idx).contiguous() + output = input.reshape(res_shape).contiguous() + + return output + + return post_func + + +def pre_all2all_fun(permute_idx, inp_shape, input): + """ + Pre-processing function for `all2all` communication. + """ + input_t = input.reshape(inp_shape).contiguous() + if permute_idx is not None: + input_t = input_t.permute(permute_idx).contiguous() + return input_t + + def _rotate_half(x): """ change sign so the last dimension becomes [-odd, +even] @@ -43,32 +108,6 @@ def apply_rotary_pos_emb(t, freqs_cos, freqs_sin): return res -def post_all2all(scatter_idx, batch_dim_idx, seq_world_size, bs, seq_len, num_head, head_dim): - - def post_func(input): - if batch_dim_idx == 0: - # b, s, n, h - if scatter_idx < 2: - output = input.permute(1, 2, 0, 3, 4).contiguous() - output = output.reshape(bs, seq_len // seq_world_size, seq_world_size * num_head, - head_dim).contiguous() - else: - output = input.permute(1, 0, 2, 3, 4).contiguous() - output = output.reshape(bs, seq_world_size * seq_len, num_head // seq_world_size, - head_dim).contiguous() - else: - # s, b, n, h - if scatter_idx < 2: - output = input.permute(1, 2, 0, 3, 4).contiguous() - output = output.reshape(seq_len // seq_world_size, bs, seq_world_size * num_head, - head_dim).contiguous() - else: - output = input.reshape(seq_len * seq_world_size, bs, num_head // seq_world_size, head_dim).contiguous() - return output - - return post_func - - def uneven_heads_all2all(input, scatter_idx, gather_idx, batch_dim_idx, group): seq_world_size = dist.get_world_size(group) inp_shape = list(input.shape) @@ -195,39 +234,12 @@ def single_all_to_all(input, scatter_idx, gather_idx, batch_dim_idx, group, asyn assert async_op == False, "uneven head sp does not support async op" return uneven_heads_all2all(input, scatter_idx, gather_idx, batch_dim_idx, group) - if batch_dim_idx == 0: - # b, s, n, h - if scatter_idx < 2: - bs, global_seq_len, num_local_head, head_dim = input.shape - input_t = input.reshape([bs, seq_world_size, global_seq_len // seq_world_size, num_local_head, - head_dim]).contiguous() - input_t = input_t.permute(1, 0, 2, 3, 4).contiguous() - else: - bs, local_seq_len, num_total_head, head_dim = input.shape - assert num_total_head % seq_world_size == 0, f"Number of heads ({num_total_head}) must be divisible by the sequence parallel size ({seq_world_size})!" - input_t = input.reshape([bs, local_seq_len, seq_world_size, num_total_head // seq_world_size, - head_dim]).contiguous() - input_t = input_t.permute(2, 0, 1, 3, 4).contiguous() - else: - # s, b, n, h - if scatter_idx < 2: - global_seq_len, bs, num_local_head, head_dim = input.shape - input_t = input.reshape([seq_world_size, global_seq_len // seq_world_size, bs, num_local_head, - head_dim]).contiguous() - else: - local_seq_len, bs, num_total_head, head_dim = input.shape - assert num_total_head % seq_world_size == 0, f"Number of heads ({num_total_head}) must be divisible by the sequence parallel size ({seq_world_size})!" - input_t = input.reshape([local_seq_len, bs, seq_world_size, num_total_head // seq_world_size, - head_dim]).contiguous() - input_t = input_t.permute(2, 0, 1, 3, 4).contiguous() + pre_all2all_permute_idx, pre_all2all_inp_shape, post_all2all_permute_idx, post_all2all_res_shape = _generate_layout_params( + scatter_idx, batch_dim_idx, seq_world_size, input) - if scatter_idx < 2: - post_all2all_fun = post_all2all(scatter_idx, batch_dim_idx, seq_world_size, bs, global_seq_len, num_local_head, - head_dim) - else: - post_all2all_fun = post_all2all(scatter_idx, batch_dim_idx, seq_world_size, bs, local_seq_len, num_total_head, - head_dim) + input_t = pre_all2all_fun(pre_all2all_permute_idx, pre_all2all_inp_shape, input) + post_all2all_fun = post_all2all(post_all2all_permute_idx, post_all2all_res_shape) output = torch.empty_like(input_t) work = dist.all_to_all_single(output, input_t, group=group, async_op=async_op) @@ -236,7 +248,7 @@ def single_all_to_all(input, scatter_idx, gather_idx, batch_dim_idx, group, asyn handle[type + '_work'] = work handle[type + '_grad'] = output handle[type + '_post_all2all_func'] = post_all2all_fun - return output + return output.view(post_all2all_res_shape) res = post_all2all_fun(output) return res @@ -271,7 +283,6 @@ def forward(ctx: Any, assert ctx.stream != None res = single_all_to_all(input, scatter_idx, gather_idx, batch_dim_idx, group, False) get_accelerator().current_stream().wait_stream(ctx.stream) - del ctx.stream.activation_buffer_list # The computation of d o_weight can overlap with the communication of d o_input elif not is_fwd and type in ('q', 'k'): From 8d1bc0a042cfa2a739719f88f91ffc6f86c8711a Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Tue, 21 Jan 2025 08:49:58 -0800 Subject: [PATCH 112/112] Update `torch.norm` to `torch.linalg.norm` and `torch.linalg.vector_norm` (#6931) - [x] Update PR since `torch.norm` and `torch.linalg.norm` have [different function signatures](https://pytorch.org/docs/stable/generated/torch.linalg.norm.html#torch.linalg.norm). - [x] Check if there are any numeric differences between the functions. - [x] Determine why there appear to be performance differences from others [here](https://github.com/pytorch/pytorch/issues/136360). - [x] Update to `torch.linalg.vectornorm` Follow up PR handles these in the comm folder: #6960 --- deepspeed/runtime/comm/compressed.py | 2 +- deepspeed/runtime/comm/hccl.py | 2 +- deepspeed/runtime/fp16/onebit/lamb.py | 2 +- deepspeed/runtime/zero/stage3.py | 2 +- deepspeed/runtime/zero/stage_1_and_2.py | 5 +++-- 5 files changed, 7 insertions(+), 6 deletions(-) diff --git a/deepspeed/runtime/comm/compressed.py b/deepspeed/runtime/comm/compressed.py index 7f8c7395451d..2c5482eb1ad7 100644 --- a/deepspeed/runtime/comm/compressed.py +++ b/deepspeed/runtime/comm/compressed.py @@ -96,7 +96,7 @@ def compressed_allreduce(self, buffer_m: torch.tensor, worker_error, server_erro compensated_server_m.add_(server_error) - server_scale = torch.norm(compensated_server_m) / np.sqrt(compensated_server_m.numel()) + server_scale = torch.linalg.norm(compensated_server_m) / np.sqrt(compensated_server_m.numel()) server_error.set_(compensated_server_m - server_scale * compensated_server_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0)) diff --git a/deepspeed/runtime/comm/hccl.py b/deepspeed/runtime/comm/hccl.py index 09fb11a731b8..b8639c7da4c9 100644 --- a/deepspeed/runtime/comm/hccl.py +++ b/deepspeed/runtime/comm/hccl.py @@ -83,7 +83,7 @@ def compressed_allreduce(self, buffer_m: torch.tensor, worker_error, server_erro compensated_server_m.add_(server_error) - server_scale = torch.norm(compensated_server_m) / np.sqrt(compensated_server_m.numel()) + server_scale = torch.linalg.norm(compensated_server_m) / np.sqrt(compensated_server_m.numel()) server_error.set_(compensated_server_m - server_scale * compensated_server_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0)) diff --git a/deepspeed/runtime/fp16/onebit/lamb.py b/deepspeed/runtime/fp16/onebit/lamb.py index 89b6f40a308c..9e7bae816ecd 100644 --- a/deepspeed/runtime/fp16/onebit/lamb.py +++ b/deepspeed/runtime/fp16/onebit/lamb.py @@ -177,7 +177,7 @@ def step(self, closure=None, grads=None): # This is used to reduce compression error during compression stage. momentum_scales = [] for group in self.param_groups: - momentum_scales.append([(torch.linalg.norm(self.state[p]['exp_avg']) / + momentum_scales.append([(torch.linalg.vector_norm(self.state[p]['exp_avg']) / np.sqrt(torch.numel(self.state[p]['exp_avg']))).item() for p in group['params']]) united_scale = sum([sum(x) for x in momentum_scales]) / sum([len(x) for x in momentum_scales]) diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index 28f91cb9b3ab..9c06567ed100 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -2101,7 +2101,7 @@ def step(self, closure=None): return norm_groups = self._get_norm_groups() - scaled_global_grad_norm = torch.linalg.norm(torch.stack(norm_groups)) + scaled_global_grad_norm = torch.linalg.vector_norm(torch.stack(norm_groups)) # Stash unscaled gradient norm self._global_grad_norm = scaled_global_grad_norm / self.loss_scale diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index 0508766f8896..ed3425167944 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -1691,7 +1691,8 @@ def get_grad_norm_direct(self, gradients, params, norm_type=2): continue if is_model_parallel_parameter(p) or (self.model_parallel_rank == 0): all_norms.append( - torch.norm(g.data.double().detach(), norm_type).to(get_accelerator().current_device_name())) + torch.linalg.vector_norm(g.data.double().detach(), + ord=norm_type).to(get_accelerator().current_device_name())) if len(all_norms) > 0: total_norm = torch.stack(all_norms).square().sum().float() else: @@ -1795,7 +1796,7 @@ def scaled_global_norm(self, norm_type=2): self._average_expert_grad_norms(norm_groups) # calculating L2 norm - return torch.norm(torch.stack(norm_groups), p=norm_type) + return torch.linalg.vector_norm(torch.stack(norm_groups), ord=norm_type) def get_bit16_param_group(self, group_no): bit16_partitions = self.parallel_partitioned_bit16_groups[group_no]
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