From 500bd7f9360744f80c9c09b2276442b131062da8 Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Sun, 25 Jan 2026 08:20:01 -0800 Subject: [PATCH 01/20] Improve engine's cleanup (#7813) Fix #7812: This PR makes DeepSpeedEngine cleanup safe for partial initialization. This prevents destructor-time tracebacks by guarding access to unitialized attributes of DeepSpeed engine. Signed-off-by: Masahiro Tanaka Signed-off-by: Kento Sugama --- deepspeed/runtime/engine.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 272fe832cb2f..6123c0359a03 100755 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -565,17 +565,23 @@ def broadcast_and_check(args, bcast_rank, bcast_group): with_kwargs=True) def __del__(self): - self.destroy() + try: + self.destroy() + except Exception as exc: + # Avoid destructor-time exceptions for partially initialized engines. + logger.debug("DeepSpeedEngine.__del__ cleanup skipped: %s", exc, exc_info=True) def destroy(self): - if self.optimizer is not None and hasattr(self.optimizer, 'destroy'): - self.optimizer.destroy() + optimizer = getattr(self, "optimizer", None) + if optimizer is not None and hasattr(optimizer, 'destroy'): + optimizer.destroy() if self.is_deepcompile_active(): get_deepcompile_handle().cleanup() debug_clear_module_and_param_names() - if self.checkpoint_engine is not None and self.checkpoint_engine.is_decoupled(): - self.checkpoint_engine.cleanup() + checkpoint_engine = getattr(self, "checkpoint_engine", None) + if checkpoint_engine is not None and checkpoint_engine.is_decoupled(): + checkpoint_engine.cleanup() def _get_model_parameters(self): if self.autotuning_profile_model_info(): @@ -4376,7 +4382,7 @@ def is_deepcompile_enabled(self) -> bool: return self._config.compile_config.deepcompile def is_deepcompile_active(self) -> bool: - return self._deepcompile_active + return getattr(self, "_deepcompile_active", False) @property def is_compiled(self) -> bool: From 792d52ca73ff7281a2a45c55422289a8bacfd6ed Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Sun, 25 Jan 2026 22:18:40 -0800 Subject: [PATCH 02/20] Ignore evoformer test (#7815) Evoformer tests fail with this error. We ignore this in the full test for now. ``` RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method ``` Signed-off-by: Masahiro Tanaka Signed-off-by: Kento Sugama --- .github/workflows/aws-torch-latest-full.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/aws-torch-latest-full.yml b/.github/workflows/aws-torch-latest-full.yml index 892d87ada368..9d11249d5455 100644 --- a/.github/workflows/aws-torch-latest-full.yml +++ b/.github/workflows/aws-torch-latest-full.yml @@ -133,4 +133,5 @@ jobs: --ignore=unit/launcher/test_user_args.py \ --ignore=unit/runtime/zenflow \ --ignore=unit/ops/adam/test_zf_torch_adam.py \ + --ignore=unit/ops/deepspeed4science/test_DS4Sci_EvoformerAttention.py \ --torch_ver=${{ env.TORCH_VER }} --cuda_ver=${{ env.CUDA_VER }} From 374abb83600525a2484e40d86890a4f2155fad42 Mon Sep 17 00:00:00 2001 From: nathon Date: Wed, 28 Jan 2026 07:50:54 +0800 Subject: [PATCH 03/20] Fix typos in accelerator setup guide (#7818) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two spelling errors in docs/_tutorials/accelerator-setup-guide.md: Line 50: comma-seperated-dash-range → comma-separated-dash-range Line 97: optimzied → optimized Both typos are in the Intel Architecture CPU section of the accelerator setup guide. Signed-off-by: leejianwoo-collab Signed-off-by: Kento Sugama --- docs/_tutorials/accelerator-setup-guide.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/_tutorials/accelerator-setup-guide.md b/docs/_tutorials/accelerator-setup-guide.md index ecb77f475375..8a21fcaedbff 100644 --- a/docs/_tutorials/accelerator-setup-guide.md +++ b/docs/_tutorials/accelerator-setup-guide.md @@ -47,7 +47,7 @@ This switch would automatically detect the number of CPU NUMA node on the host, If a user wishes to have more control on the number of workers and specific cores that can be used by the workload, user can use the following command line switches. ``` -deepspeed --num_accelerators --bind_cores_to_rank --bind_core_list +deepspeed --num_accelerators --bind_cores_to_rank --bind_core_list ``` For example: ``` @@ -94,7 +94,7 @@ Intel Extension for PyTorch compatible with DeepSpeed AutoTP tensor parallel inf ``` ipex_model = ipex.llm.optimize(deepspeed_model) ``` -to get model optimzied by Intel Extension for PyTorch. +to get model optimized by Intel Extension for PyTorch. ## More examples for using DeepSpeed on Intel CPU Refer to [LLM examples](https://github.com/intel/intel-extension-for-pytorch/tree/main/examples/cpu/llm) for more code samples of running inference with DeepSpeed on Intel CPU. From c272de48bc97765da9d8cda1fd9836e6390bd6fd Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Tue, 27 Jan 2026 16:29:13 -0800 Subject: [PATCH 04/20] Raise clear error on in-place GatheredParameters edits without modifier_rank (#7817) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fix #7811 Issue: ZeRO-3 crashes when `zero.GatheredParameters` is used with `modifier_rank=None` and a parameter is modified in-place. The failure surfaced as an internal invariant assert in free_param, which is not actionable for users. Solution: `GatheredParameters` now detects in-place mutation with `modifier_rank=None` and raises a clear, user-facing error early. The mutation check is synchronized across ranks to avoid divergence and hangs. This PR also raises a clearer error from free_param when parameters are still active in submodules.” --------- Signed-off-by: Masahiro Tanaka Signed-off-by: Kento Sugama --- .../runtime/zero/partition_parameters.py | 20 +++++++++++++++- tests/unit/runtime/zero/test_zero_context.py | 23 +++++++++++++++++++ 2 files changed, 42 insertions(+), 1 deletion(-) diff --git a/deepspeed/runtime/zero/partition_parameters.py b/deepspeed/runtime/zero/partition_parameters.py index 47aa4f41bcbc..de7b2c11aed8 100755 --- a/deepspeed/runtime/zero/partition_parameters.py +++ b/deepspeed/runtime/zero/partition_parameters.py @@ -299,7 +299,13 @@ def recurse(cl): @instrument_w_nvtx def free_param(param: Parameter) -> None: """Free underlying storage of a parameter.""" - assert not param.ds_active_sub_modules, param.ds_summary() + if param.ds_active_sub_modules: + raise RuntimeError("Cannot free a ZeRO-3 parameter while it is still active in submodules. " + "This can happen if: (1) submodules have not released the parameter, or " + "(2) you modified parameters inside a `GatheredParameters` context with " + "`modifier_rank=None`. For case (2), use `modifier_rank=` to broadcast " + "updates consistently across ranks. " + f"param={param.ds_summary()}") if get_accelerator().on_accelerator(param.data): # need to make sure that we don't free the parameter while it is still # being used for computation @@ -2324,6 +2330,18 @@ def __exit__(self, *exc): self.params[0].partition(param_list=self.params, has_been_updated=False) return + # Broadcast parameters from modifier_rank to all other ranks. + # NCCL backend requires tensors to be on GPU. If parameters have been moved to a different + # device (e.g., CPU) inside the context, broadcasting will fail. Users should use + # modifier_rank=None if they don't need to broadcast updates across ranks. + expected_device = torch.device(get_accelerator().current_device_name()) + for p in self.params: + if p.data.device != expected_device: + raise RuntimeError( + f"Parameter {p.ds_id} is on {p.data.device} but broadcast requires it to be on {expected_device}. " + f"When using GatheredParameters with modifier_rank set, parameters must remain on " + f"the accelerator device. If you don't need to broadcast updates, use modifier_rank=None.") + handles = [dist.broadcast(p.data, self.src_rank, group=p.ds_process_group, async_op=True) for p in self.params] for h in handles: h.wait() diff --git a/tests/unit/runtime/zero/test_zero_context.py b/tests/unit/runtime/zero/test_zero_context.py index 1c065bb791f1..71e762e5819f 100644 --- a/tests/unit/runtime/zero/test_zero_context.py +++ b/tests/unit/runtime/zero/test_zero_context.py @@ -107,6 +107,29 @@ def __init__(self, hidden_dim): assert model.l1.weight.numel() == 0, "outside of GatheredParameters the param should go back to be 0-sized" +class TestZeroFreeParamActiveSubmodule(DistributedTest): + world_size = 2 + + def test(self): + config_dict = {"train_micro_batch_size_per_gpu": 1, "zero_optimization": {"stage": 3}} + hidden_dim = 10 + + class MyModel(torch.nn.Module): + + def __init__(self, hidden_dim): + super(MyModel, self).__init__() + self.l1 = torch.nn.Linear(hidden_dim, hidden_dim) + self.l2 = torch.nn.Linear(hidden_dim, hidden_dim) + + with deepspeed.zero.Init(config_dict_or_path=config_dict): + model = MyModel(hidden_dim) + + with pytest.raises(RuntimeError, match="in-place modification"): + with deepspeed.zero.GatheredParameters([model.l1.weight, model.l2.weight], modifier_rank=None): + with torch.no_grad(): + model.l1.weight.add_(0.0) + + class TestSerialContext(DistributedTest): world_size = 1 init_distributed = False From d2aaf0f9e6244f3d94b12ee11b60576bfa873656 Mon Sep 17 00:00:00 2001 From: Vensen Date: Thu, 29 Jan 2026 01:00:17 +0800 Subject: [PATCH 05/20] [Bugfix] Resolve Rank index out of range during BWD when sp_size < world_size in Ulysses (#7809) ### Description This PR addresses Issue #7672. When sequence_parallel_size is smaller than world_size (e.g., sp_size=2 on 4 GPUs) with PyTorch < 2.3, using torch.distributed.nn.functional.all_gather for loss aggregation triggers an IndexError: tuple index out of range during the backward pass. This is due to a known PyTorch issue where the backward hook accesses the global rank instead of the group rank. ### Solution 1. Regression Test & Workaround: Updated the regression test TestUlyssesLossBackward to implement a Weighted All-Reduce pattern. - Before: all_gather -> manual sum (Vulnerable to rank indexing mismatch on older PyTorch). - After: all_reduce(weighted_loss) / all_reduce(total_weight) (Robust and supports weighted averaging). 2. Runtime Warning: Added a version check (required_torch_version) in DeepSpeedEngine. It now logs a warning if Sequence Parallelism is enabled on PyTorch < 2.3, providing a link to the workaround test case. 3. Documentation: Updated ulysses-alst-sequence-parallelism.md with a note regarding legacy PyTorch versions and the recommended workaround. ### Verification Added and verified the regression test tests/unit/sequence_parallelism/test_ulysses.py which now validates the weighted averaging logic. **1. Reproduction (Before Fix)** Confirmed IndexError crash on Rank 2/3 with sp_size=2 on a 4-GPU setup. Screenshot 2026-01-23 at 23 53 42 **2. Verification (After Fix)** Verified the fix using the regression test logic on 4x RTX A6000. The backward pass now completes successfully on all ranks without error. Screenshot 2026-01-23 at 23 52 54 --------- Signed-off-by: vensen Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Signed-off-by: Kento Sugama --- deepspeed/runtime/engine.py | 8 +++ .../ulysses-alst-sequence-parallelism.md | 6 ++ .../unit/sequence_parallelism/test_ulysses.py | 55 +++++++++++++++++++ 3 files changed, 69 insertions(+) diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 6123c0359a03..5f8585d65d76 100755 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -1416,6 +1416,14 @@ def _configure_distributed_model(self, model): self.expert_data_parallel_group = groups._get_expert_data_parallel_group_dict() self.sequence_parallel_size = groups._get_sequence_parallel_world_size() if self.sequence_parallel_size > 1: + # Inserted Warning for PyTorch < 2.3 + if not required_torch_version(min_version=2.3): + logger.warning( + "DeepSpeed Sequence Parallelism (Ulysses) with PyTorch < 2.3 may encounter " + "rank indexing errors during backward pass when sp_size < world_size. " + "Please use the weighted all-reduce workaround shown in the regression test " + "(https://github.com/deepspeedai/DeepSpeed/blob/master/tests/unit/sequence_parallelism/test_ulysses.py) " + "or upgrade to PyTorch 2.3+.") self.communication_data_type = self._config.seq_parallel_communication_data_type self.seq_parallel_group = groups._get_sequence_parallel_group() diff --git a/docs/_tutorials/ulysses-alst-sequence-parallelism.md b/docs/_tutorials/ulysses-alst-sequence-parallelism.md index d03535ca3992..4d426913108a 100644 --- a/docs/_tutorials/ulysses-alst-sequence-parallelism.md +++ b/docs/_tutorials/ulysses-alst-sequence-parallelism.md @@ -222,6 +222,12 @@ In theory you could just average `losses_per_rank`, but the system supports vari ## Nuances +### Note on PyTorch Versions < 2.3 + +If you are using Sequence Parallelism with **PyTorch version < 2.3**, you may encounter an `IndexError: tuple index out of range` during the backward pass when `sequence_parallel_size < world_size`. This is due to a known issue in the `torch.distributed.all_gather` backward implementation in older versions. + +**Workaround:** We recommend using a **weighted `all_reduce` pattern** instead of `all_gather` for loss averaging. You can refer to our [regression test case](https://github.com/deepspeedai/DeepSpeed/blob/master/tests/unit/sequence_parallelism/test_ulysses.py) for a code example of this workaround. + ### Why do labels need to be pre-shifted? When using batch sharding one can't let the upstream `loss` function do the labels shifting. Here is why: diff --git a/tests/unit/sequence_parallelism/test_ulysses.py b/tests/unit/sequence_parallelism/test_ulysses.py index bd20900a3d1e..7c1371864073 100644 --- a/tests/unit/sequence_parallelism/test_ulysses.py +++ b/tests/unit/sequence_parallelism/test_ulysses.py @@ -255,3 +255,58 @@ def __init__(self): 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))}" + + +@pytest.mark.parametrize("sp_size", [2]) +class TestUlyssesLossBackward(DistributedTest): + world_size = 4 + + def test_sp_loss_backward_stability(self, sp_size: int) -> None: + """ + Regression test for Issue #7672. + Verifies that using all_reduce for loss aggregation is stable + when sequence_parallel_size < world_size, preventing IndexError. + """ + skip_on_arch(min_arch=8) + + # Setup + dp_size = self.world_size // sp_size + model = SimpleModel(4) + ds_engine, _, _, _ = initialize( + model=model, + config_params={ + "train_batch_size": 8, + "data_parallel_size": dp_size, + "sequence_parallel_size": sp_size + }, + ) + + sp_group = ds_engine.seq_parallel_group + + # Simulate Loss on each rank + rank = dist.get_rank() + local_loss = torch.tensor(float(rank + 1), device=ds_engine.device, requires_grad=True) + local_weight = torch.tensor(1.0, device=ds_engine.device) + + # Numerator: Weighted Loss summation + weighted_loss = local_loss * local_weight + dist.all_reduce(weighted_loss, op=dist.ReduceOp.SUM, group=sp_group) + + # B. Denominator: Sum of total weights + total_weight = local_weight.clone() + dist.all_reduce(total_weight, op=dist.ReduceOp.SUM, group=sp_group) + + # C. Calculate the final loss + dist_loss = weighted_loss / total_weight + + # Backward Pass verification + try: + dist_loss.backward() + except IndexError as e: + pytest.fail(f"Backward crashed with IndexError: {e}") + + # Verify Gradients + # Loss = (L1*1 + L2*1) / 2 = 0.5*L1 + 0.5*L2 + expected_grad = 0.5 + assert torch.allclose(local_loss.grad, torch.tensor(expected_grad, device=ds_engine.device)), \ + f"Gradient mismatch! Expected {expected_grad}, got {local_loss.grad}" From 0bc2dd2640ad9f2dacd6310647904f583b049bf7 Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Thu, 29 Jan 2026 13:24:45 -0800 Subject: [PATCH 06/20] Update PyTorch to v2.9 for modal tests (#7816) Update PyTorch to v2.9 for modal tests --------- Signed-off-by: Masahiro Tanaka Signed-off-by: Kento Sugama --- ci/accelerate.py | 2 +- ci/torch_latest.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ci/accelerate.py b/ci/accelerate.py index cbf396a991f1..21570a85f40b 100644 --- a/ci/accelerate.py +++ b/ci/accelerate.py @@ -11,7 +11,7 @@ # yapf: disable image = (modal.Image - .from_registry("pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel", add_python="3.10") + .from_registry("pytorch/pytorch:2.9.1-cuda12.8-cudnn9-devel", add_python="3.10") .apt_install("git") .pip_install("uv") # uv_pip_install already includes --compile-bytecode diff --git a/ci/torch_latest.py b/ci/torch_latest.py index 796ef9f3a031..98d26b95101a 100644 --- a/ci/torch_latest.py +++ b/ci/torch_latest.py @@ -11,7 +11,7 @@ # yapf: disable image = (modal.Image - .from_registry("pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel", add_python="3.10") + .from_registry("pytorch/pytorch:2.9.1-cuda12.8-cudnn9-devel", add_python="3.10") .run_commands("apt update && apt install -y libaio-dev") .pip_install_from_requirements(ROOT_PATH / "requirements/requirements.txt", gpu="any") .pip_install_from_requirements(ROOT_PATH / "requirements/requirements-dev.txt", gpu="any") @@ -34,7 +34,7 @@ def pytest(): import subprocess subprocess.run( - "pytest -n 4 --verbose tests/unit/v1/ --torch_ver=2.6 --cuda_ver=12.4".split(), + "pytest -n 4 --verbose tests/unit/v1/ --torch_ver=2.9 --cuda_ver=12.8".split(), check=True, cwd=ROOT_PATH / ".", ) From 4cb023c5340fb40340e3e1e720c6914a003a8734 Mon Sep 17 00:00:00 2001 From: Logan Adams <114770087+loadams@users.noreply.github.com> Date: Fri, 30 Jan 2026 10:46:55 -0800 Subject: [PATCH 07/20] Update version.txt to 0.18.6 after latest release (#7826) Signed-off-by: Kento Sugama --- version.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/version.txt b/version.txt index 543466e4da59..a1b6a99c6f00 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.18.5 +0.18.6 From 6064c2aeeeb710444a343d10786308728ec5e1cb Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Fri, 30 Jan 2026 14:35:34 -0800 Subject: [PATCH 08/20] Fix leaf module race condition (#7825) Fix #7824 For [leaf modules](https://deepspeed.readthedocs.io/en/latest/training.html#configuring-zero-leaf-modules), ZeRO3 manages all parameters within the module uniformly. When a module returns multiple output tensors, PyTorch's autograd can trigger backward hooks from multiple threads concurrently. This causes race conditions when multiple threads simultaneously modify __inflight_param_registry and parameter states. This PR ensures that for leaf modules, only one thread performs the actual parameter fetching work while other concurrent threads wait and return early, preventing the race condition. --------- Signed-off-by: Masahiro Tanaka Signed-off-by: Kento Sugama --- .../zero/partitioned_param_coordinator.py | 51 ++++++++- .../runtime/zero/test_zero_leaf_module.py | 106 ++++++++++++++++++ 2 files changed, 154 insertions(+), 3 deletions(-) diff --git a/deepspeed/runtime/zero/partitioned_param_coordinator.py b/deepspeed/runtime/zero/partitioned_param_coordinator.py index a754bee633fa..4877b44c8934 100644 --- a/deepspeed/runtime/zero/partitioned_param_coordinator.py +++ b/deepspeed/runtime/zero/partitioned_param_coordinator.py @@ -6,6 +6,7 @@ from dataclasses import dataclass import collections from collections import UserDict +import threading from typing import Deque, Set from deepspeed import comm as dist @@ -139,6 +140,13 @@ def __init__( # 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 + # Thread synchronization for leaf module fetches during backward pass. + # When autograd executes hooks in multiple threads (e.g., for modules returning multiple tensors), + # we need to ensure only one thread fetches parameters for a given leaf module at a time. + # This is only needed during backward pass; forward pass is single-threaded. + self.__ongoing_fetch_leaf_module_events = collections.defaultdict(threading.Event) + self.__leaf_module_lock = threading.Lock() + """Tracing and Tracking TODO. consider performing trace before initializing PartitionedParameterCoordinator and passing trace results into constructor. This way all the code in here can @@ -263,6 +271,8 @@ def reset_step(self) -> None: self.__step_id = 0 self.__n_available_params = 0 self.__profiler.reset_events() + # Clear leaf module fetch events for clean state + self.__ongoing_fetch_leaf_module_events.clear() def _dump_params(self, tag, sub_module, params, step_id=None): if step_id is None: @@ -288,16 +298,51 @@ def fetch_sub_module(self, current_submodule: Module, forward: bool) -> None: 2. kick off fetch for next few parameters we will need later (prefetch) 3. block on parameters in immediately required sub module """ + # For leaf modules during backward pass, autograd may trigger hooks from multiple + # threads concurrently (e.g., when a module returns multiple tensors). We need to + # serialize access to prevent race conditions in parameter state management. + # Forward pass is single-threaded, so no synchronization is needed there. + is_leaf = z3_leaf_module(current_submodule) + needs_sync = is_leaf and not forward + if needs_sync: + event_to_wait = None + with self.__leaf_module_lock: + event = self.__ongoing_fetch_leaf_module_events.get(current_submodule.ds_id) + if event is not None: + # Another thread is already fetching this leaf module, wait for it + event_to_wait = event + else: + # Mark that we're starting a fetch for this leaf module + new_event = threading.Event() + self.__ongoing_fetch_leaf_module_events[current_submodule.ds_id] = new_event + + if event_to_wait is not None: + # Wait outside the lock to avoid deadlock + event_to_wait.wait() + return + + try: + self._fetch_sub_module_impl(current_submodule, forward, is_leaf) + finally: + if needs_sync: + # Signal that we're done fetching this leaf module and remove the event + with self.__leaf_module_lock: + event = self.__ongoing_fetch_leaf_module_events.pop(current_submodule.ds_id, None) + if event is not None: + event.set() + + def _fetch_sub_module_impl(self, current_submodule: Module, forward: bool, is_leaf: bool) -> None: + """Implementation of fetch_sub_module, separated for thread synchronization.""" if logger.isEnabledFor(logging.DEBUG): debug_rank0( - f"{self.__step_id}: M{current_submodule.ds_id}({type(current_submodule).__name__}) P{[p.ds_id for p in iter_params(current_submodule, recurse=z3_leaf_module(current_submodule))]} " + f"{self.__step_id}: M{current_submodule.ds_id}({type(current_submodule).__name__}) P{[p.ds_id for p in iter_params(current_submodule, recurse=is_leaf)]} " + str({ "avail": f"{self.__n_available_params:.1e}", "queue_sz": f"{len(self.__param_queue or [])}", "inflight": [p.ds_id for p in self.__inflight_param_registry], })) - params_to_fetch = set(iter_params(current_submodule, recurse=z3_leaf_module(current_submodule))) + params_to_fetch = set(iter_params(current_submodule, recurse=is_leaf)) fetch_numel = sum( [p.partition_numel() for p in params_to_fetch if p.ds_status == ZeroParamStatus.NOT_AVAILABLE]) @@ -320,7 +365,7 @@ 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) + fast_fetch = self.fast_sharding_for_leaf_module and is_leaf # 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.ds_id) diff --git a/tests/unit/runtime/zero/test_zero_leaf_module.py b/tests/unit/runtime/zero/test_zero_leaf_module.py index b878ac89c522..88898403ec43 100644 --- a/tests/unit/runtime/zero/test_zero_leaf_module.py +++ b/tests/unit/runtime/zero/test_zero_leaf_module.py @@ -60,6 +60,40 @@ def forward(self, x, y): return x, loss +class MultiOutputMoEBlock(nn.Module): + """A simplified MoE block that returns multiple tensors. + + This model mimics Qwen3 MoE which returns (hidden_states, router_logits). + When used with ZeRO3 leaf modules and autograd multithreading enabled, + this pattern previously caused race conditions in fetch_sub_module + because backward hooks could be triggered concurrently from multiple threads. + + See: https://github.com/deepspeedai/DeepSpeed/issues/7824 + """ + + def __init__(self, hidden_dim, num_experts=4): + super(MultiOutputMoEBlock, self).__init__() + self.num_experts = num_experts + self.gate = nn.Linear(hidden_dim, num_experts, bias=False) + self.experts = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim, bias=False) for _ in range(num_experts)]) + self.act = nn.ReLU() + self.cel = nn.CrossEntropyLoss() + + def forward(self, x, y): + # Compute router logits - this tensor will have gradients flowing through it + router_logits = self.gate(x) + + # Process through experts + for expert in self.experts: + x = expert(x) + x = self.act(x) + loss = self.cel(x, y) + + # Return multiple tensors - this triggers concurrent backward hooks + # when autograd multithreading is enabled + return x, loss, router_logits + + class MLPBlock(nn.Module): def __init__(self, hidden_dim): @@ -308,6 +342,78 @@ def test_choose_module_by_counter(self): def test_choose_module_by_rank(self): self._test_set_z3_leaf_modules(ChooseModuleByRankModel, True) + def test_multi_output_leaf_module_thread_safety(self): + """Test that leaf modules returning multiple tensors work correctly with autograd multithreading. + + This tests the fix for https://github.com/deepspeedai/DeepSpeed/issues/7824 + where MoE models (like Qwen3) returning multiple tensors caused race conditions + in fetch_sub_module when autograd executed backward hooks from multiple threads. + """ + # Ensure autograd multithreading is enabled (this is the default, but be explicit) + torch.autograd.set_multithreading_enabled(True) + + hidden_dim = 128 + config_dict = self._create_zero_config(hidden_dim) + + model = MultiOutputMoEBlock(hidden_dim, num_experts=4) + + assert not z3_leaf_module(model) + set_z3_leaf_modules(model, [MultiOutputMoEBlock]) + assert z3_leaf_module(model) + + model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + + data_loader = random_dataloader(model=model, + total_samples=10, + hidden_dim=hidden_dim, + device=model.device, + dtype=preferred_dtype()) + dist.barrier() + + # Run multiple iterations to increase chance of hitting race conditions + for batch in data_loader: + batch[0].requires_grad = True + # Model returns (output, loss, router_logits) + output, loss, router_logits = model(batch[0], batch[1]) + # Include router_logits in the loss to ensure multiple backward paths + total_loss = loss + 0.01 * router_logits.mean() + model.backward(total_loss) + model.step() + + model.destroy() + + def test_multi_output_non_leaf_module_thread_safety(self): + """Ensure non-leaf modules returning multiple tensors remain thread-safe. + + This covers the multi-output autograd multithreading case without marking the + module as a ZeRO leaf module. + """ + torch.autograd.set_multithreading_enabled(True) + + hidden_dim = 128 + config_dict = self._create_zero_config(hidden_dim) + + model = MultiOutputMoEBlock(hidden_dim, num_experts=4) + assert not z3_leaf_module(model) + + model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + + data_loader = random_dataloader(model=model, + total_samples=10, + hidden_dim=hidden_dim, + device=model.device, + dtype=preferred_dtype()) + dist.barrier() + + for batch in data_loader: + batch[0].requires_grad = True + output, loss, router_logits = model(batch[0], batch[1]) + total_loss = loss + 0.01 * router_logits.mean() + model.backward(total_loss) + model.step() + + model.destroy() + def test_no_grad_input_error(self): try: self._test_set_z3_leaf_modules(ChooseModuleByCounter, False) From e86db608f78c3cf4afda9ca9aeb3f70c91f5bfb6 Mon Sep 17 00:00:00 2001 From: jp Date: Sat, 31 Jan 2026 07:36:26 +0900 Subject: [PATCH 09/20] Skip sequence parallel operations during eval (#7821) hi deepspeed! [Make it possible to evaluate when using sequence parallel in HF Trainer #43517](https://github.com/huggingface/transformers/pull/43517) I initially opened a PR in transformers to prevent errors that occur when running eval with deepspeed sequence parallel applied, but there was feedback that this should be handled in deepspeed rather than in transformers or accelerate, so I'm opening this PR here. If you have any questions, feel free to ask. Signed-off-by: Kento Sugama --- .../runtime/sequence_parallel/ulysses_sp.py | 16 +++++ tests/unit/ulysses_alst/test_ulysses_sp_hf.py | 69 +++++++++++++++++++ 2 files changed, 85 insertions(+) diff --git a/deepspeed/runtime/sequence_parallel/ulysses_sp.py b/deepspeed/runtime/sequence_parallel/ulysses_sp.py index eb74cbbf639a..846cb2431547 100644 --- a/deepspeed/runtime/sequence_parallel/ulysses_sp.py +++ b/deepspeed/runtime/sequence_parallel/ulysses_sp.py @@ -78,6 +78,9 @@ class UlyssesSPAttentionHF(torch.nn.Module): kv_head_count (int): total number of kv heads num_hidden_layers (int): total number of layers process_group (dist.ProcessGroup): Ulysses process group + disable_in_eval (bool): whether to disable sequence parallelism during evaluation (default: False). + When True, SP operations are bypassed during eval to avoid potential issues with frameworks + like HF Trainer that may run eval with different data distribution. Extras: @@ -96,6 +99,7 @@ def __init__( seq_length_is_variable: bool = False, local_seq_length: int = None, global_seq_length: int = None, + disable_in_eval: bool = False, ) -> None: super().__init__() self.attn = attn @@ -107,6 +111,7 @@ def __init__( self.seq_length_is_variable = seq_length_is_variable self.local_seq_length = local_seq_length self.global_seq_length = global_seq_length + self.disable_in_eval = disable_in_eval self.attn_head_size = attn_head_size self.attn_head_count = attn_head_count @@ -246,6 +251,12 @@ def forward( # print_rank0(f"{key.shape=}") # print_rank0(f"{value.shape=}") # print_rank0(f"{self.required_input_shape=}") + + # Skip SP operations during eval if disable_in_eval is True + # This avoids issues with frameworks like HF Trainer that may run eval with different data distribution + if not module.training and self.disable_in_eval: + return self.attn(module, query, key, value, attention_mask, *args, **kwargs) + if self.seq_length_is_variable: current_local_seq_length = query.shape[2] self.local_seq_length = current_local_seq_length @@ -350,6 +361,7 @@ def register_with_transformers( micro_batch_size, seq_length=None, seq_length_is_variable=True, + disable_in_eval=False, # deprecated max_length=None, ): @@ -364,6 +376,9 @@ def register_with_transformers( - micro_batch_size (int): micro batch size - seq_length (int): set this argument if the sequence length is fixed in all batches - seq_length_is_variable (bool): whether global seqlen may change between batches an optimization flag - the default is `True` + - disable_in_eval (bool): whether to disable sequence parallelism during evaluation (default: False). + When True, SP operations are bypassed during eval to avoid issues with frameworks + like HF Trainer that may run eval with different data distribution. - max_length (int): actual global sequence length - this argument is deprecated - use `seq_length` instead """ @@ -431,6 +446,7 @@ def register_with_transformers( seq_length_is_variable=seq_length_is_variable, local_seq_length=local_seq_length, global_seq_length=global_seq_length, + disable_in_eval=disable_in_eval, ) def uattn_wrapper( diff --git a/tests/unit/ulysses_alst/test_ulysses_sp_hf.py b/tests/unit/ulysses_alst/test_ulysses_sp_hf.py index 4056ee1bb25e..9122d044c07b 100644 --- a/tests/unit/ulysses_alst/test_ulysses_sp_hf.py +++ b/tests/unit/ulysses_alst/test_ulysses_sp_hf.py @@ -232,3 +232,72 @@ def __init__(self, config): assert sp_group is not None sp_world_size = mpu.get_sequence_parallel_world_size() assert sp_world_size == sequence_parallel_size + + +class TestUlyssesSPHFDisableInEval(DistributedTest): + world_size = 2 + + def test_disable_in_eval(self): + """Test that disable_in_eval parameter controls SP behavior during evaluation. + + When disable_in_eval=True, SP operations should be bypassed during eval mode, + allowing the user to pass full (non-sharded) sequences directly. + This should produce the same output as a model without SP registered. + """ + from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS + + model_name_or_path = 'hf-internal-testing/tiny-random-LlamaForCausalLM' + seq_length = 64 + sequence_parallel_size = self.world_size + micro_batch_size = 1 + + dtype = preferred_dtype() + rank = dist.get_rank() + + # Full sequence input (not sharded) - this is what users would pass during eval + # when they want to bypass SP and process sequences independently per rank + input_ids = tensor([[1, 10, 10, 10, 2, 2]], device=f"cuda:{rank}") + position_ids = tensor([[0, 1, 2, 3, 4, 5]], device=f"cuda:{rank}") + + # 1. Baseline: model without SP, processing full sequence + model_baseline = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=dtype) + model_baseline = model_baseline.to(f"cuda:{rank}") + model_baseline.eval() + + # Save original attention function for comparison + original_sdpa = ALL_ATTENTION_FUNCTIONS["sdpa"] + + with torch.no_grad(): + outputs_baseline = model_baseline(input_ids=input_ids, position_ids=position_ids) + logits_baseline = outputs_baseline.logits.clone() + + del model_baseline + + # 2. Model with SP registered but disable_in_eval=True + # In eval mode, SP is bypassed so full sequence can be passed directly + mpu = UlyssesSPAttentionHF.register_with_transformers( + model_name_or_path=model_name_or_path, + core_attn_implementation="sdpa", + sequence_parallel_size=sequence_parallel_size, + micro_batch_size=micro_batch_size, + seq_length=seq_length, + seq_length_is_variable=True, + disable_in_eval=True, + ) + + # Verify that register_with_transformers actually registered the wrapper + assert mpu is not None, "mpu should not be None when sequence_parallel_size > 1" + assert ALL_ATTENTION_FUNCTIONS["sdpa"] is not original_sdpa, \ + "register_with_transformers should have replaced the attention function" + + model_sp = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=dtype) + model_sp = model_sp.to(f"cuda:{rank}") + model_sp.eval() + + with torch.no_grad(): + outputs_sp = model_sp(input_ids=input_ids, position_ids=position_ids) + logits_sp = outputs_sp.logits.clone() + + # Verify: with disable_in_eval=True, full sequence input should produce + # the same output as baseline (SP is bypassed) + torch_assert_equal(logits_baseline, logits_sp) From 129b42c84b5ea4f4857e6b79c41b2879704f655c Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Sat, 31 Jan 2026 01:52:44 -0800 Subject: [PATCH 10/20] Support custom partitioning patterns for AutoTP (#7806) This PR introduces a flexible, configuration-driven API for AutoTP (Automatic Tensor Parallelism) that allows users to define custom layer partitioning patterns for training. @inkcherry @delock ## Motivation Previously, AutoTP relied on hardcoded layer detection logic that was difficult to customize for new model architectures. This PR enables: 1. **Custom models**: Users can define exact regex patterns to match their model's parameter names 2. **Fused layers**: Support for fused QKV, gate_up_proj, and other packed weight matrices with unequal sub-parameter sizes (e.g., GQA with different Q/K/V dimensions) 3. **Extensibility**: Easy to add new model presets or customize existing ones Here is an example of a config including custom partitioning patterns: ```json { "tensor_parallel": { "autotp_size": 4, "partition_config": { "use_default_specs": false, "layer_specs": [ { "patterns": [".*\\.o_proj\\.weight$", ".*\\.down_proj\\.weight$"], "partition_type": "row" }, { "patterns": [".*\\.[qkv]_proj\\.weight$"], "partition_type": "column" }, { "patterns": [".*\\.gate_up_proj\\.weight$"], "partition_type": "column", "shape": [2, -1], "partition_dim": 0 } ] } } } ``` Refer to the [document](https://github.com/tohtana/DeepSpeed/blob/tohtana/autotp_custom_patterns/docs/code-docs/source/training.rst) for more details (including preset models and how to define partitioning for fused models). We also opened a new [PR](https://github.com/deepspeedai/DeepSpeedExamples/pull/998) to show the usage. ## Simplified initialization step AutoTP previously required calling ``set_autotp_mode(training=True)`` and ``deepspeed.tp_model_init`` before ``deepspeed.initialize``. Now we can include all the necessary configurations in the DeepSpeed config. We still support the traditional initialization path for backward compatibility. When you use both (i.e. calling ``set_autotp_mode(training=True)`` and ``deepspeed.tp_model_init`` and passing the config to ``deepspeed.initialize``), we will merge the settings at initialization. When we have conflicting settings, we will error out. --------- Signed-off-by: Masahiro Tanaka Signed-off-by: Kento Sugama --- blogs/huggingface-tp/README.md | 6 +- deepspeed/__init__.py | 97 ++- deepspeed/module_inject/__init__.py | 3 +- deepspeed/module_inject/auto_tp.py | 91 ++- deepspeed/module_inject/autotp_config.py | 569 ++++++++++++++++++ deepspeed/module_inject/layers.py | 353 ++++++++++- deepspeed/module_inject/replace_module.py | 15 +- deepspeed/module_inject/tp_shard.py | 7 +- deepspeed/runtime/engine.py | 50 ++ deepspeed/runtime/tensor_parallel/config.py | 64 +- .../runtime/tensor_parallel/init_utils.py | 144 +++++ docs/_pages/config-json.md | 90 +++ .../automatic-tensor-parallelism.md | 2 + docs/_tutorials/autotp-training.md | 188 ++++++ .../_static/autotp-subparams-gate-up.png | Bin 0 -> 692874 bytes .../source/_static/autotp-subparams-gqa.png | Bin 0 -> 240876 bytes docs/code-docs/source/training.rst | 219 +++++++ .../test_autotp_custom_patterns.py | 302 ++++++++++ .../model_parallelism/test_autotp_training.py | 364 +++++++---- 19 files changed, 2412 insertions(+), 152 deletions(-) create mode 100644 deepspeed/module_inject/autotp_config.py create mode 100644 deepspeed/runtime/tensor_parallel/init_utils.py create mode 100644 docs/_tutorials/autotp-training.md create mode 100644 docs/code-docs/source/_static/autotp-subparams-gate-up.png create mode 100644 docs/code-docs/source/_static/autotp-subparams-gqa.png create mode 100644 tests/unit/model_parallelism/test_autotp_custom_patterns.py diff --git a/blogs/huggingface-tp/README.md b/blogs/huggingface-tp/README.md index 44469f1818c3..ceb95417ae5f 100644 --- a/blogs/huggingface-tp/README.md +++ b/blogs/huggingface-tp/README.md @@ -230,13 +230,11 @@ Furthermore, if users are not using transformers library, you can replace the `` # Ongoing Work - **Optimization**: Communication/Activation optimization. -- **Usability**: Support [Transformers TP plan](https://github.com/huggingface/transformers/blob/336dc69d63d56f232a183a3e7f52790429b871ef/src/transformers/models/llama/configuration_llama.py#L145), decouple AutoTP parser and more model testing, - +- **Usability**: Support the [Transformers TP plan](https://github.com/huggingface/transformers/blob/336dc69d63d56f232a183a3e7f52790429b871ef/src/transformers/models/llama/configuration_llama.py#L145), decouple the AutoTP parser, and expand model testing. + - [UPDATE] We now support [custom partitioning](https://deepspeed.readthedocs.io/en/latest/training.html#custom-layer-specs) in the same spirit as HF's partitioning plan, and will build Transformers TP plan support on top of that ([PR](http://github.com/deepspeedai/DeepSpeed/pull/7806)). Theoretically, features supported by ZeRO should also be supported, though extensive testing is pending. - Welcome bug reports, enhancement, and additional model training examples. - # Contributors This work was made possible through a deep collaboration between Intel and Microsoft. The contributors include Mingzhi Liu, Guokai Ma, Kiefer Kuah, Yejing Lai, Kurt Chen, Yejun Guo, Guangxin Xu, Xiaofei Feng, and Yang Wang from Intel; Guanhua Wang and Olatunji Ruwase from Microsoft. diff --git a/deepspeed/__init__.py b/deepspeed/__init__.py index 10b71eef7a5c..0d53a172e64e 100755 --- a/deepspeed/__init__.py +++ b/deepspeed/__init__.py @@ -48,6 +48,8 @@ from .pipe import PipelineModule from .git_version_info import version, git_hash, git_branch +from .runtime.tensor_parallel.init_utils import (load_ds_config, merge_tp_model_init_into_config, + record_tp_model_init_args) def _parse_version(version_str): @@ -159,17 +161,6 @@ def initialize(args=None, if config is None and config_params is not None: config = config_params - mesh_device = None - if mesh_param: - logger.info(f"mesh_param to Initialize mesh device: {mesh_param}") - mesh_device = dist.initialize_mesh_device(mesh_param, ("data_parallel", "sequence_parallel")) - #if config file has sequence parallelize and data parallelize, then use them to initialize mesh device - elif config is not None: - if "sequence_parallel_size" in config and "data_parallel_size" in config: - logger.info(f"config to Initialize mesh device: {config}") - mesh_device = dist.initialize_mesh_device((config["data_parallel_size"], config["sequence_parallel_size"]), \ - ("data_parallel", "sequence_parallel")) - # Check for deepscale_config for backwards compat if hasattr(args, "deepscale_config") and args.deepscale_config is not None: logger.warning("************ --deepscale_config is deprecated, please use --deepspeed_config ************") @@ -184,6 +175,26 @@ def initialize(args=None, assert config is None, "Not sure how to proceed, we were given deepspeed configs in the deepspeed arguments and deepspeed.initialize() function call" config = args.deepspeed_config assert config is not None, "DeepSpeed requires --deepspeed_config to specify configuration file" + + if not isinstance(config, dict): + config = load_ds_config(config) + + mesh_device = None + if mesh_param: + logger.info(f"mesh_param to Initialize mesh device: {mesh_param}") + mesh_device = dist.initialize_mesh_device(mesh_param, ("data_parallel", "sequence_parallel")) + #if config file has sequence parallelize and data parallelize, then use them to initialize mesh device + else: + if "sequence_parallel_size" in config and "data_parallel_size" in config: + logger.info(f"config to Initialize mesh device: {config}") + mesh_device = dist.initialize_mesh_device((config["data_parallel_size"], config["sequence_parallel_size"]), \ + ("data_parallel", "sequence_parallel")) + + merge_tp_model_init_into_config(config, mpu, mesh_param, dist) + + autotp_size = config.get("tensor_parallel", {}).get("autotp_size", 0) + if autotp_size and autotp_size > 0: + set_autotp_mode(training=True) if not isinstance(model, PipelineModule): config_class = DeepSpeedConfig(config, mpu, mesh_device=mesh_device) set_optimizer_flags(config_class, model) @@ -379,7 +390,49 @@ def init_inference(model, config=None, **kwargs): def tp_model_init(model, tp_size, dtype, config=None, **kwargs): """ - Initialize the model for tensor parallelism. + Record tensor-parallel initialization arguments for training. + + Note (compatibility and initialization behavior): + AutoTP sharding is applied during ``deepspeed.initialize(...)``. This + function exists for backward compatibility and only records TP arguments so + they can be validated and merged with the DeepSpeed config at initialization. + When you use both (i.e., calling ``set_autotp_mode(training=True)`` and + ``deepspeed.tp_model_init`` while also passing the config to + ``deepspeed.initialize``), DeepSpeed merges the settings at initialization. + Conflicting settings raise an error. The table below summarizes the behavior + across input combinations. + + Inputs: + - TPI: tp_model_init was called? (Y/N) + - TPG: tp_model_init provided tp_group? (Y/N) + - CFG: tensor_parallel in DeepSpeed config? (Y/N) + - MPU: mpu passed to deepspeed.initialize()? (Y/N) + + | TPI | TPG | CFG | MPU | Outcome | Notes | + |-----|-----|-----|-----|----------------------------------------|-------| + | N | N | N | N | Error | No TP intent; nothing to initialize | + | N | N | N | Y | No AutoTP | mpu may be used for other MP, but TP not enabled | + | N | N | Y | N | Init AutoTP from config | Use config; need TP group via config-driven init | + | N | N | Y | Y | Init AutoTP from config | mpu used to build TP group | + | Y | N | N | N | Error | No TP group source | + | Y | N | N | Y | Init AutoTP from tp_model_init | Use recorded args + mpu for TP group | + | Y | N | Y | N | Init AutoTP from config | Fill missing from TPI; error on mismatches; need TP group source | + | Y | N | Y | Y | Init AutoTP from config | Fill missing from TPI; error on mismatches | + | Y | Y | N | N | Init AutoTP from tp_model_init | Use recorded tp_group; config absent | + | Y | Y | N | Y | Error | tp_group + mpu conflict | + | Y | Y | Y | N | Init AutoTP from config | Error on mismatches; use tp_group from TPI; reject mpu | + | Y | Y | Y | Y | Error | tp_group + mpu conflict | + + Field-level merge rules when both tp_model_init and config exist: + - Canonical source: config + - Allowed: fill missing config fields from tp_model_init + - Error on mismatch: autotp_size, dtype, tp_group size or identity + + Extra checks: + - If tp_group is provided, reject mpu. + - If tp_group is not provided, require mpu (or another TP group source). + - If tensor_parallel is absent and only tp_model_init was called, require + a TP group source (direct tp_group or mpu). Args: model (torch.nn.Module): The model to be initialized. @@ -387,23 +440,15 @@ def tp_model_init(model, tp_size, dtype, config=None, **kwargs): dtype (torch.dtype): The data type to be used for the model. Returns: - torch.nn.Module: The initialized model with tensor parallelism. + torch.nn.Module: The original model (no sharding applied here). """ - # avoid re-entry if hasattr(model, 'ds_autotp_parsed'): - logger.warning("ds_autotp_parsed' attribute already exists in the model, re-entry is not allowed.") - return - - set_autotp_mode(training=True) + logger.warning("ds_autotp_parsed' attribute already exists in the model; tp_model_init is now record-only.") - from deepspeed.runtime.tensor_parallel import TpTrainingManager - # The expected usage here is for it to be invoked by transformers package. + tp_group = kwargs.get("tp_group") + record_tp_model_init_args(tp_size=tp_size, dtype=dtype, tp_group=tp_group, dist_module=dist) - #TODO: We should provide a custom TP mapping solution without using autoTP - #as modifying the autoTP logic may be more difficult for users compared to configuring it - - model = TpTrainingManager(model=model, tp_size=tp_size, dtype=dtype).module - - setattr(model, 'ds_autotp_parsed', True) + # Keep AutoTP training mode active for backward compatibility. + set_autotp_mode(training=True) return model diff --git a/deepspeed/module_inject/__init__.py b/deepspeed/module_inject/__init__.py index 9fc2f979a04b..2299ef6e7c3a 100755 --- a/deepspeed/module_inject/__init__.py +++ b/deepspeed/module_inject/__init__.py @@ -6,5 +6,6 @@ from .replace_module import replace_transformer_layer, revert_transformer_layer, ReplaceWithTensorSlicing, GroupQuantizer, generic_injection from .module_quantize import quantize_transformer_layer from .replace_policy import HFBertLayerPolicy -from .layers import LinearAllreduce, LinearLayer, EmbeddingLayer, Normalize, set_autotp_mode +from .layers import LinearAllreduce, LinearLayer, EmbeddingLayer, Normalize, set_autotp_mode, SubParamLinearLayer, SubParamLinearAllreduce from .policy import DSPolicy +from .autotp_config import TPLayerSpec, AutoTPConfig, PartitionType, AutoTPPresets, merge_autotp_configs diff --git a/deepspeed/module_inject/auto_tp.py b/deepspeed/module_inject/auto_tp.py index 82cd9042071e..121e3938444a 100755 --- a/deepspeed/module_inject/auto_tp.py +++ b/deepspeed/module_inject/auto_tp.py @@ -17,6 +17,7 @@ from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list from deepspeed.utils import groups from deepspeed.module_inject.layers import is_autotp_training_mode +from .autotp_config import TPLayerSpec, AutoTPConfig, PartitionType def move(tensor, device, copy=True): @@ -199,7 +200,8 @@ def __init__(self, state_dict, linear_layer_setting, orig_layer_impl, - keep_module_on_host=False): + keep_module_on_host=False, + partition_config: Optional[AutoTPConfig] = None): self.module = module self.all_reduce_linears = all_reduce_linears self.prefix = prefix @@ -211,6 +213,7 @@ def __init__(self, self.orig_layer_impl = orig_layer_impl self.linear_policies = None self.conv_linear_layer = False + self.partition_config = partition_config TensorParallel_Layer.set_keep_module_on_host(keep_module_on_host) def in_module_list(module, module_list): @@ -353,6 +356,11 @@ def _replace(self, child, name, conv_linear_layer): weight_shape = child.weight.shape mp_replace = ReplaceWithTensorSlicing(mp_group=self.mp_group) + + # If partition_config is provided, use the new configurable API + if self.partition_config is not None: + return self._replace_with_config(child, name) + # For TP layer skip, e.g., MoE gate, deepseek low rank layer skip if "mlp.gate" == name or "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))): @@ -396,6 +404,87 @@ def _replace(self, child, name, conv_linear_layer): return LinearLayer(child, self.mp_group, name=name) + def _replace_with_config(self, child, name): + """ + Replace layer using the new configurable AutoTP API. + + This method uses TPLayerSpec to determine how to partition the layer. + """ + if getattr(child, "replaced", False) == True: + return child + + # Build the full parameter name for pattern matching + param_name = name + ".weight" if not name.endswith(".weight") else name + + # Find matching spec + model_type = self._get_model_type() + spec = self.partition_config.find_matching_spec(param_name, model_type) + + if spec is None: + # No matching spec found + if self.partition_config.strict_mode: + raise ValueError(f"No matching spec for {param_name}") + # Default: column parallel for Linear layers + spec = TPLayerSpec(patterns=[], partition_type=PartitionType.COLUMN) + + setattr(child, "replaced", True) + + if spec.partition_type == PartitionType.SKIP: + return child + + if spec.partition_type == PartitionType.ROW: + return self._create_row_parallel_layer(child, spec, name) + else: + return self._create_column_parallel_layer(child, spec, name) + + def _create_row_parallel_layer(self, module, spec: TPLayerSpec, name: str): + """Create row-parallel layer (AllReduce after forward).""" + # Check for lm_head / embed_out + if name == "lm_head" or name == 'embed_out': + return LmHeadLinearAllreduce(module, self.mp_group) + + if spec.shape is not None: + return SubParamLinearAllreduce( + module, + self.mp_group, + shape=spec.shape, + partition_dim=spec.get_partition_dim(), + name=name, + ) + return LinearAllreduce(module, self.mp_group, name=name) + + def _create_column_parallel_layer(self, module, spec: TPLayerSpec, name: str): + """Create column-parallel layer (AllReduce in backward).""" + if spec.shape is not None: + return SubParamLinearLayer( + module, + self.mp_group, + shape=spec.shape, + partition_dim=spec.get_partition_dim(), + name=name, + ) + return LinearLayer(module, self.mp_group, name=name) + + def _get_model_type(self) -> Optional[str]: + """Extract model type from module config or class name.""" + config = getattr(self.module, "config", None) + if config is not None: + model_type = getattr(config, "model_type", None) + if model_type: + return str(model_type).lower() + module_str = str(type(self.module)) + # Try to extract model type from class name (e.g., "LlamaDecoderLayer" -> "llama") + patterns = [ + r"(\w+)DecoderLayer", + r"(\w+)Block", + r"(\w+)Layer", + ] + for pattern in patterns: + match = re.search(pattern, module_str) + if match: + return match.group(1).lower() + return None + def _slice_embedding(self, child, name, conv_linear_layer): if getattr(child, "replaced", False) == True: return diff --git a/deepspeed/module_inject/autotp_config.py b/deepspeed/module_inject/autotp_config.py new file mode 100644 index 000000000000..4bafea806829 --- /dev/null +++ b/deepspeed/module_inject/autotp_config.py @@ -0,0 +1,569 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team +""" +Configurable AutoTP API + +This module provides a unified specification for tensor parallel layer partitioning. +The design is inspired by Universal Checkpointing's SubparamShape and provides +a single, well-defined format that users can easily understand, customize, and extend. +""" + +import re +from dataclasses import dataclass, field +from typing import List, Tuple, Union, Optional +from enum import Enum +from deepspeed.utils.logging import warning_once + + +class PartitionType(Enum): + """How the layer should be partitioned for tensor parallelism.""" + COLUMN = "column" # Partition output dim, AllReduce in backward + ROW = "row" # Partition input dim, AllReduce in forward + SKIP = "skip" # Do not partition this layer + + +@dataclass +class TPLayerSpec: + """ + Unified specification for tensor parallel layer partitioning. + + This is inspired by Universal Checkpointing's SubparamShape but extended + for AutoTP's needs (forward/backward communication patterns). + + The `shape` parameter supports at most 1-level nesting at the partition dimension: + - (3, -1) -> 3 equal-size sub-params + - ((q, k, v), -1) -> 3 unequal-size sub-params (1-level nesting) + + Examples: + # Simple row-parallel layer (e.g., o_proj, down_proj) + TPLayerSpec( + patterns=[".*\\.o_proj$", ".*\\.down_proj$"], + partition_type=PartitionType.ROW, + ) + + # Simple column-parallel layer (e.g., q_proj, k_proj, v_proj) + TPLayerSpec( + patterns=[".*\\.[qkv]_proj$"], + partition_type=PartitionType.COLUMN, + ) + + # Fused QKV - GLM style [Q, K, V] concatenated on dim 0 + TPLayerSpec( + patterns=[".*\\.query_key_value\\.weight$"], + partition_type=PartitionType.COLUMN, + shape=(3, -1), # 3 equal sub-params, -1 = infer + partition_dim=0, + ) + + # Fused QKV - Bloom style [q1,k1,v1,q2,k2,v2,...] + TPLayerSpec( + patterns=[".*\\.query_key_value\\.weight$"], + partition_type=PartitionType.COLUMN, + # No reshape needed, just split along dim 0 + ) + + # GQA with different Q/K/V sizes (1-level nesting) + TPLayerSpec( + patterns=[".*\\.qkv_proj\\.weight$"], + partition_type=PartitionType.COLUMN, + shape=((q_size, k_size, v_size), -1), # Unequal sub-params + partition_dim=0, + ) + + # Chunked MLP (gate_up_proj) + TPLayerSpec( + patterns=[".*\\.gate_up_proj\\.weight$"], + partition_type=PartitionType.COLUMN, + shape=(2, -1), # [gate, up] packed + partition_dim=0, + ) + + # MoE FFN with expert dimension + TPLayerSpec( + patterns=[".*\\.experts\\..*\\.w1\\.weight$"], + partition_type=PartitionType.COLUMN, + shape=(num_experts, -1, hidden_in), # View as 3D + partition_dim=1, # Partition the hidden_out dimension + ) + + # Skip layer (e.g., MoE gate) + TPLayerSpec( + patterns=[".*\\.gate$", ".*\\.router$"], + partition_type=PartitionType.SKIP, + ) + """ + + # Layer identification - regex patterns to match parameter names + patterns: List[str] + + # Partition type determines communication pattern + partition_type: PartitionType = PartitionType.COLUMN + + # Optional: logical shape for partitioning + # - Use -1 for dimensions that should be inferred + # - Use tuple of ints at partition_dim for unequal sub-params (1-level nesting only) + # Examples: + # (3, -1) -> 3 equal sub-params + # ((4096, 1024, 1024), -1) -> 3 unequal sub-params (GQA) + # (n_experts, -1, hidden) -> MoE reshape + shape: Optional[Tuple[Union[int, Tuple[int, ...]], ...]] = None + + # Which dimension to partition (after optional reshape) + # Default: 0 for COLUMN, 1 for ROW (standard 2D weight matrix) + partition_dim: Optional[int] = None + + # Optional: model type constraint (only apply for specific models) + model_types: Optional[List[str]] = None + + def __post_init__(self): + if isinstance(self.partition_type, str): + self.partition_type = PartitionType(self.partition_type.lower()) + if self.shape is not None: + self.shape = self._normalize_shape(self.shape) + self._validate_shape_format() + + @staticmethod + def _normalize_shape(shape): + if isinstance(shape, list): + return tuple(TPLayerSpec._normalize_shape(item) for item in shape) + if isinstance(shape, tuple): + return tuple(TPLayerSpec._normalize_shape(item) if isinstance(item, list) else item for item in shape) + return shape + + def _validate_shape_format(self): + if not isinstance(self.shape, tuple): + raise ValueError("AutoTP shape must be a tuple of ints or a tuple at partition_dim.") + partition_dim = self.get_partition_dim() + if partition_dim < 0 or partition_dim >= len(self.shape): + raise ValueError( + f"AutoTP partition_dim {partition_dim} is out of range for shape length {len(self.shape)}.") + nested_tuple_seen = False + for idx, dim in enumerate(self.shape): + if isinstance(dim, tuple): + if idx != partition_dim: + raise ValueError( + f"AutoTP shape nested tuple only allowed at partition_dim={partition_dim}, got at {idx}.") + if nested_tuple_seen: + raise ValueError("AutoTP shape supports only 1-level nesting at partition_dim.") + nested_tuple_seen = True + if len(dim) == 0: + raise ValueError("AutoTP shape nested tuple cannot be empty.") + for val in dim: + if isinstance(val, tuple): + raise ValueError("AutoTP shape supports only 1-level nesting at partition_dim.") + if not isinstance(val, int) or val <= 0: + raise ValueError("AutoTP nested sub-parameter sizes must be positive integers.") + elif isinstance(dim, int): + if dim == 0 or dim < -1: + raise ValueError("AutoTP shape dimensions must be positive integers or -1.") + else: + raise ValueError("AutoTP shape must contain only integers or a tuple at partition_dim.") + + def get_partition_dim(self) -> int: + """Get effective partition dimension.""" + if self.partition_dim is not None: + return self.partition_dim + # Default based on partition type for 2D weight matrices + return 0 if self.partition_type == PartitionType.COLUMN else 1 + + def has_unequal_sub_params(self) -> bool: + """Check if this spec has unequal sub-parameters (nested tuple at partition_dim).""" + if self.shape is None: + return False + dim = self.get_partition_dim() + if dim >= len(self.shape): + return False + return isinstance(self.shape[dim], tuple) + + def get_sub_param_sizes(self) -> Optional[Tuple[int, ...]]: + """Get sub-parameter sizes if using unequal sub-params.""" + if not self.has_unequal_sub_params(): + return None + return self.shape[self.get_partition_dim()] + + def get_num_sub_params(self) -> Optional[int]: + """Get the number of sub-parameters.""" + if self.shape is None: + return None + dim = self.get_partition_dim() + if dim >= len(self.shape): + return None + if isinstance(self.shape[dim], tuple): + return len(self.shape[dim]) + elif isinstance(self.shape[dim], int) and self.shape[dim] > 0: + return self.shape[dim] + return None + + def matches(self, param_name: str, model_type: Optional[str] = None) -> bool: + """Check if this spec matches the given parameter.""" + # Check model type constraint + if self.model_types: + if model_type is None: + return False + model_type_norm = str(model_type).lower() + model_types_norm = [str(mt).lower() for mt in self.model_types] + if model_type_norm not in model_types_norm: + return False + # Check pattern match + return any(re.match(pattern, param_name) for pattern in self.patterns) + + +@dataclass +class AutoTPConfig: + """ + Configuration for Automatic Tensor Parallelism. + + Example usage: + config = AutoTPConfig( + tp_size=4, + layer_specs=[ + # Row-parallel layers (AllReduce after forward) + TPLayerSpec( + patterns=[".*\\.o_proj", ".*\\.down_proj"], + partition_type=PartitionType.ROW, + ), + # Column-parallel layers + TPLayerSpec( + patterns=[".*\\.[qkv]_proj", ".*\\.up_proj", ".*\\.gate_proj"], + partition_type=PartitionType.COLUMN, + ), + # Skip MoE gates + TPLayerSpec( + patterns=[".*\\.gate$"], + partition_type=PartitionType.SKIP, + ), + ], + ) + """ + + tp_size: int = 1 + + # Unified layer specifications + layer_specs: List[TPLayerSpec] = field(default_factory=list) + + # Embedding configuration + embedding_partition_dim: int = 1 # Usually partition vocab dim + + # LM head configuration + lm_head_patterns: List[str] = field(default_factory=lambda: ["lm_head", "embed_out"]) + + # Behavior flags + use_default_specs: bool = True # Merge with built-in specs + strict_mode: bool = False # Fail if unmatched Linear layers found + + def find_matching_spec(self, param_name: str, model_type: Optional[str] = None) -> Optional[TPLayerSpec]: + """Find the first matching spec for a parameter.""" + matches = [spec for spec in self.layer_specs if spec.matches(param_name, model_type)] + if not matches: + return None + if len(matches) > 1: + matched_patterns = [spec.patterns for spec in matches] + warning_once(f"AutoTPConfig: parameter {param_name} matched multiple layer_specs {matched_patterns}; " + "using the first match.") + return matches[0] + + @classmethod + def from_dict(cls, config_dict: dict) -> "AutoTPConfig": + """Create config from dictionary (JSON config).""" + layer_specs = [] + for spec_dict in config_dict.get("layer_specs", []): + # Convert partition_type string to enum + partition_type_str = spec_dict.get("partition_type", "column") + if isinstance(partition_type_str, str): + partition_type = PartitionType(partition_type_str.lower()) + else: + partition_type = partition_type_str + + # Convert shape from list to tuple if necessary + shape = spec_dict.get("shape") + if shape is not None: + shape = cls._convert_shape(shape) + + layer_specs.append( + TPLayerSpec( + patterns=spec_dict.get("patterns", []), + partition_type=partition_type, + shape=shape, + partition_dim=spec_dict.get("partition_dim"), + model_types=spec_dict.get("model_types"), + )) + + return cls( + tp_size=config_dict.get("tp_size", 1), + layer_specs=layer_specs, + embedding_partition_dim=config_dict.get("embedding_partition_dim", 1), + lm_head_patterns=config_dict.get("lm_head_patterns", ["lm_head", "embed_out"]), + use_default_specs=config_dict.get("use_default_specs", True), + strict_mode=config_dict.get("strict_mode", False), + ) + + @staticmethod + def _convert_shape(shape): + """Convert shape from list to tuple, handling nested structures.""" + if isinstance(shape, list): + return tuple(AutoTPConfig._convert_shape(item) if isinstance(item, list) else item for item in shape) + return shape + + +class AutoTPPresets: + """Built-in presets for common model architectures.""" + + @staticmethod + def llama() -> AutoTPConfig: + """LLaMA-style models (separate Q, K, V projections).""" + return AutoTPConfig(layer_specs=[ + TPLayerSpec( + patterns=[r".*\.self_attn\.o_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.self_attn\.[qkv]_proj\.weight$"], + partition_type=PartitionType.COLUMN, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.down_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.(up|gate)_proj\.weight$"], + partition_type=PartitionType.COLUMN, + ), + ], ) + + @staticmethod + def llama_gqa(num_heads: int, num_kv_heads: int, head_dim: int) -> AutoTPConfig: + """LLaMA with Grouped Query Attention (fused QKV variant).""" + q_size = num_heads * head_dim + kv_size = num_kv_heads * head_dim + return AutoTPConfig( + layer_specs=[ + TPLayerSpec( + patterns=[r".*\.self_attn\.o_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + # Fused QKV with unequal sizes (GQA) + TPLayerSpec( + patterns=[r".*\.self_attn\.qkv_proj\.weight$"], + partition_type=PartitionType.COLUMN, + shape=((q_size, kv_size, kv_size), -1), # 1-level nesting + partition_dim=0, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.down_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.(up|gate)_proj\.weight$"], + partition_type=PartitionType.COLUMN, + ), + ], ) + + @staticmethod + def bloom() -> AutoTPConfig: + """BLOOM-style models (fused QKV with interleaved heads).""" + return AutoTPConfig( + layer_specs=[ + TPLayerSpec( + patterns=[r".*\.self_attention\.dense\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.self_attention\.query_key_value\.weight$"], + partition_type=PartitionType.COLUMN, + # Bloom style: [q1,k1,v1,q2,k2,v2,...] - no reshape needed + ), + TPLayerSpec( + patterns=[r".*\.mlp\.dense_4h_to_h\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.dense_h_to_4h\.weight$"], + partition_type=PartitionType.COLUMN, + ), + ], ) + + @staticmethod + def chatglm() -> AutoTPConfig: + """ChatGLM-style models (GLM-style fused QKV).""" + return AutoTPConfig( + layer_specs=[ + TPLayerSpec( + patterns=[r".*\.self_attention\.dense\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.self_attention\.query_key_value\.weight$"], + partition_type=PartitionType.COLUMN, + shape=(3, -1), # [Q, K, V] concatenated + partition_dim=0, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.dense_4h_to_h\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.dense_h_to_4h\.weight$"], + partition_type=PartitionType.COLUMN, + shape=(2, -1), # [gate, up] packed + partition_dim=0, + ), + ], ) + + @staticmethod + def mixtral() -> AutoTPConfig: + """Mixtral MoE model.""" + return AutoTPConfig( + layer_specs=[ + TPLayerSpec( + patterns=[r".*\.self_attn\.o_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.self_attn\.[qkv]_proj\.weight$"], + partition_type=PartitionType.COLUMN, + ), + # MoE experts + TPLayerSpec( + patterns=[r".*\.block_sparse_moe\.experts\.\d+\.w2\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.block_sparse_moe\.experts\.\d+\.w[13]\.weight$"], + partition_type=PartitionType.COLUMN, + ), + # Skip MoE gate + TPLayerSpec( + patterns=[r".*\.block_sparse_moe\.gate\.weight$"], + partition_type=PartitionType.SKIP, + ), + ], ) + + @staticmethod + def deepseek_v2() -> AutoTPConfig: + """DeepSeek-V2 with MLA (Multi-head Latent Attention).""" + return AutoTPConfig( + layer_specs=[ + # Standard attention output + TPLayerSpec( + patterns=[r".*\.self_attn\.o_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + # MLA uses compressed KV, skip low-rank projections + TPLayerSpec( + patterns=[r".*\.self_attn\.(q_a_proj|kv_a_proj_with_mqa)\.weight$"], + partition_type=PartitionType.SKIP, + ), + # Q/K/V projections from latent + TPLayerSpec( + patterns=[r".*\.self_attn\.(q_b_proj|kv_b_proj)\.weight$"], + partition_type=PartitionType.COLUMN, + ), + # MoE experts + TPLayerSpec( + patterns=[r".*\.mlp\.experts\.\d+\.down_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.experts\.\d+\.(up|gate)_proj\.weight$"], + partition_type=PartitionType.COLUMN, + ), + # Skip MoE gate + TPLayerSpec( + patterns=[r".*\.mlp\.gate\.weight$"], + partition_type=PartitionType.SKIP, + ), + # Shared expert + TPLayerSpec( + patterns=[r".*\.mlp\.shared_experts\.down_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.shared_experts\.(up|gate)_proj\.weight$"], + partition_type=PartitionType.COLUMN, + ), + ], ) + + @staticmethod + def qwen2() -> AutoTPConfig: + """Qwen2 model.""" + return AutoTPConfig(layer_specs=[ + TPLayerSpec( + patterns=[r".*\.self_attn\.o_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.self_attn\.[qkv]_proj\.weight$"], + partition_type=PartitionType.COLUMN, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.down_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.(up|gate)_proj\.weight$"], + partition_type=PartitionType.COLUMN, + ), + ], ) + + @staticmethod + def phi3() -> AutoTPConfig: + """Phi3 model with fused QKV and chunked MLP.""" + return AutoTPConfig( + layer_specs=[ + TPLayerSpec( + patterns=[r".*\.self_attn\.o_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + # Phi3 has fused qkv_proj + TPLayerSpec( + patterns=[r".*\.self_attn\.qkv_proj\.weight$"], + partition_type=PartitionType.COLUMN, + shape=(3, -1), # [Q, K, V] concatenated + partition_dim=0, + ), + TPLayerSpec( + patterns=[r".*\.mlp\.down_proj\.weight$"], + partition_type=PartitionType.ROW, + ), + # Phi3 has gate_up_proj fused + TPLayerSpec( + patterns=[r".*\.mlp\.gate_up_proj\.weight$"], + partition_type=PartitionType.COLUMN, + shape=(2, -1), # [gate, up] packed + partition_dim=0, + ), + ], ) + + @staticmethod + def get_preset(model_type: str) -> Optional[AutoTPConfig]: + """Get a preset configuration by model type name.""" + presets = { + "llama": AutoTPPresets.llama, + "bloom": AutoTPPresets.bloom, + "chatglm": AutoTPPresets.chatglm, + "mixtral": AutoTPPresets.mixtral, + "deepseek_v2": AutoTPPresets.deepseek_v2, + "qwen2": AutoTPPresets.qwen2, + "phi3": AutoTPPresets.phi3, + } + preset_fn = presets.get(model_type.lower()) + if preset_fn: + return preset_fn() + return None + + +def merge_autotp_configs(base: AutoTPConfig, override: AutoTPConfig) -> AutoTPConfig: + """Merge two AutoTP configs, with override taking precedence.""" + # Combine layer specs - override specs come first (higher priority) + merged_specs = list(override.layer_specs) + list(base.layer_specs) + + return AutoTPConfig( + tp_size=override.tp_size if override.tp_size > 1 else base.tp_size, + layer_specs=merged_specs, + embedding_partition_dim=override.embedding_partition_dim, + lm_head_patterns=override.lm_head_patterns or base.lm_head_patterns, + use_default_specs=override.use_default_specs, + strict_mode=override.strict_mode, + ) diff --git a/deepspeed/module_inject/layers.py b/deepspeed/module_inject/layers.py index a9faac71361a..5f55d2e78e26 100644 --- a/deepspeed/module_inject/layers.py +++ b/deepspeed/module_inject/layers.py @@ -20,7 +20,8 @@ __all__ = [ "TensorParallel_Layer", "LinearAllreduce", "LinearLayer", "LmHeadLinearAllreduce", "Yuan_LinearAllreduce", - "Yuan_LinearLayer", "GateUpPack_LinearLayer", "Conv_LinearALlreduce", "fused_LinearLayer", "conv_LinearLayer" + "Yuan_LinearLayer", "GateUpPack_LinearLayer", "Conv_LinearALlreduce", "fused_LinearLayer", "conv_LinearLayer", + "SubParamLinearLayer", "SubParamLinearAllreduce" ] DEEPSPEED_AUTOTP_MODE = AUTOTP_MODE.INFERENCE @@ -801,6 +802,356 @@ def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int return super().forward(positions + self.offset) +def _shape_prod(values): + result = 1 + for val in values: + result *= val + return result + + +def _normalize_shape_spec(shape): + if isinstance(shape, list): + return tuple(_normalize_shape_spec(item) for item in shape) + if isinstance(shape, tuple): + return tuple(_normalize_shape_spec(item) if isinstance(item, list) else item for item in shape) + return shape + + +def _infer_subparam_logical_shapes(weight_shape, shape, partition_dim, name=None): + shape = _normalize_shape_spec(shape) + if not isinstance(shape, tuple): + raise ValueError("AutoTP shape must be a tuple for sub-parameter partitioning.") + if partition_dim < 0 or partition_dim >= len(shape): + raise ValueError(f"AutoTP partition_dim {partition_dim} is out of range for shape length {len(shape)}.") + + layer_label = f"AutoTP layer '{name}'" if name else "AutoTP layer" + partition_elem = shape[partition_dim] + subparam_sizes = None + num_subparams = None + + if isinstance(partition_elem, tuple): + if len(partition_elem) == 0: + raise ValueError(f"{layer_label} sub-parameter size tuple cannot be empty.") + if any(isinstance(val, tuple) for val in partition_elem): + raise ValueError(f"{layer_label} supports only 1-level nesting at partition_dim.") + if any((not isinstance(val, int)) or val <= 0 for val in partition_elem): + raise ValueError(f"{layer_label} sub-parameter sizes must be positive integers.") + subparam_sizes = tuple(int(val) for val in partition_elem) + partition_dim_size = sum(subparam_sizes) + elif isinstance(partition_elem, int): + if partition_elem == -1: + partition_dim_size = None + elif partition_elem > 0: + num_subparams = partition_elem + partition_dim_size = None + else: + raise ValueError(f"{layer_label} partition_dim spec must be positive integer or -1.") + else: + raise ValueError(f"{layer_label} partition_dim spec must be int or tuple.") + + logical_dims = [] + for idx, dim in enumerate(shape): + if idx == partition_dim: + logical_dims.append(partition_dim_size) + continue + if isinstance(dim, tuple): + raise ValueError(f"{layer_label} nested tuple only allowed at partition_dim={partition_dim}.") + if isinstance(dim, int): + if dim == -1: + logical_dims.append(None) + elif dim > 0: + logical_dims.append(dim) + else: + raise ValueError(f"{layer_label} shape dimensions must be positive integers or -1.") + else: + raise ValueError(f"{layer_label} shape dimensions must be integers.") + + total_numel = _shape_prod(weight_shape) + known_product = _shape_prod([dim for dim in logical_dims if dim is not None]) + unknown_indices = [idx for idx, dim in enumerate(logical_dims) if dim is None] + + if len(unknown_indices) == 0: + if known_product != total_numel: + raise ValueError(f"{layer_label} shape product {known_product} != weight numel {total_numel}.") + elif len(unknown_indices) == 1: + inferred = total_numel // known_product + if inferred * known_product != total_numel: + raise ValueError(f"{layer_label} cannot infer shape for weight with numel {total_numel}.") + logical_dims[unknown_indices[0]] = inferred + else: + if len(shape) == len(weight_shape): + for idx in unknown_indices: + logical_dims[idx] = weight_shape[idx] + if _shape_prod(logical_dims) != total_numel: + raise ValueError( + f"{layer_label} shape product {_shape_prod(logical_dims)} != weight numel {total_numel}.") + else: + raise ValueError(f"{layer_label} shape has multiple inferred dims and is ambiguous for weight.") + + logical_shape = tuple(logical_dims) + if logical_shape[-1] != weight_shape[-1]: + raise ValueError( + f"{layer_label} shape last dim {logical_shape[-1]} must match weight input dim {weight_shape[-1]}.") + + output_shape = logical_shape[:-1] + if len(output_shape) == 0: + raise ValueError(f"{layer_label} shape must include at least one output dimension.") + if _shape_prod(output_shape) != weight_shape[0]: + raise ValueError( + f"{layer_label} output shape product {_shape_prod(output_shape)} != weight output dim {weight_shape[0]}.") + + partition_dim_size = logical_shape[partition_dim] + if partition_dim_size is None or partition_dim_size <= 0: + raise ValueError(f"{layer_label} partition_dim size must be a positive integer.") + + if num_subparams is not None: + if partition_dim_size % num_subparams != 0: + raise ValueError( + f"{layer_label} partition_dim size {partition_dim_size} not divisible by sub-param count {num_subparams}." + ) + subparam_sizes = tuple([partition_dim_size // num_subparams] * num_subparams) + + if subparam_sizes is not None and sum(subparam_sizes) != partition_dim_size: + raise ValueError( + f"{layer_label} sub-parameter sizes sum {sum(subparam_sizes)} != partition_dim size {partition_dim_size}.") + + bias_partition_dim = partition_dim if partition_dim < len(output_shape) else None + return logical_shape, output_shape, subparam_sizes, bias_partition_dim + + +def _partition_logical_tensor(tensor, partition_dim, tp_world_size, tp_index, name=None, subparam_sizes=None): + if tp_world_size == 1: + return tensor + layer_label = f"AutoTP layer '{name}'" if name else "AutoTP layer" + if subparam_sizes: + for size in subparam_sizes: + if size % tp_world_size != 0: + raise ValueError(f"{layer_label} sub-parameter size {size} not divisible by tp_size {tp_world_size}.") + sub_params = torch.split(tensor, subparam_sizes, dim=partition_dim) + partitioned_sub_params = [torch.chunk(sp, tp_world_size, dim=partition_dim)[tp_index] for sp in sub_params] + return torch.cat(partitioned_sub_params, dim=partition_dim) + if tensor.shape[partition_dim] % tp_world_size != 0: + raise ValueError( + f"{layer_label} partition_dim size {tensor.shape[partition_dim]} not divisible by tp_size {tp_world_size}." + ) + return torch.chunk(tensor, tp_world_size, dim=partition_dim)[tp_index] + + +def _all_gather_along_dim(tensor, partition_dim, mp_group, tp_world_size): + if mp_group is None or tp_world_size == 1: + return tensor + perm = [partition_dim] + [idx for idx in range(tensor.dim()) if idx != partition_dim] + inv_perm = [0] * len(perm) + for idx, dim in enumerate(perm): + inv_perm[dim] = idx + tensor_perm = tensor.permute(perm).contiguous() + output = torch.empty((tp_world_size * tensor_perm.shape[0], *tensor_perm.shape[1:]), + dtype=tensor.dtype, + device=tensor.device) + dist.all_gather_into_tensor(output, tensor_perm, group=mp_group) + return output.permute(inv_perm).contiguous() + + +def _gather_logical_tensor(tensor, + logical_shape, + partition_dim, + mp_group, + tp_world_size, + name=None, + subparam_sizes=None): + if mp_group is None or tp_world_size == 1: + return tensor.reshape(logical_shape) + layer_label = f"AutoTP layer '{name}'" if name else "AutoTP layer" + if logical_shape[partition_dim] % tp_world_size != 0: + raise ValueError( + f"{layer_label} partition_dim size {logical_shape[partition_dim]} not divisible by tp_size {tp_world_size}." + ) + partitioned_shape = list(logical_shape) + partitioned_shape[partition_dim] = logical_shape[partition_dim] // tp_world_size + tensor_view = tensor.reshape(partitioned_shape) + + if subparam_sizes: + for size in subparam_sizes: + if size % tp_world_size != 0: + raise ValueError(f"{layer_label} sub-parameter size {size} not divisible by tp_size {tp_world_size}.") + partitioned_sizes = [size // tp_world_size for size in subparam_sizes] + sub_params = torch.split(tensor_view, partitioned_sizes, dim=partition_dim) + gathered_sub_params = [_all_gather_along_dim(sp, partition_dim, mp_group, tp_world_size) for sp in sub_params] + return torch.cat(gathered_sub_params, dim=partition_dim) + return _all_gather_along_dim(tensor_view, partition_dim, mp_group, tp_world_size) + + +class SubParamLinearLayer(TensorParallel_Layer): + """ + Column-parallel linear layer with sub-parameter support. + + Handles cases where weights contain multiple logical sub-parameters + that need to be partitioned separately (e.g., fused QKV, chunked MLP, GQA). + + The `shape` parameter controls how the weight is viewed and partitioned: + - (3, -1) with partition_dim=0: 3 equal sub-params, partition each at dim 0 + - ((q, k, v), -1) with partition_dim=0: 3 unequal sub-params (1-level nesting) + """ + + def __init__(self, module, mp_group, shape, partition_dim=0, **kwargs): + super(SubParamLinearLayer, self).__init__(mp_group, **kwargs) + self.weight = module.weight + self.bias = module.bias + self.shape = shape + self.partition_dim = partition_dim + + self._orig_weight_shape = tuple(module.weight.shape) + self._orig_bias_shape = tuple(module.bias.shape) if self.bias is not None else None + (self._logical_shape, self._output_shape, self._subparam_sizes, + self._bias_partition_dim) = _infer_subparam_logical_shapes(self._orig_weight_shape, self.shape, + self.partition_dim, self.name) + if self.bias is not None and self.bias.numel() != _shape_prod(self._output_shape): + raise ValueError(f"AutoTP layer '{self.name}' bias size {self.bias.numel()} does not match output shape " + f"{self._output_shape}.") + + self._tp_partition([self.weight, self.bias]) + self.support_training = True + self.config_tp_params(self.weight) + if self.bias is not None: + self.config_tp_params(self.bias) + + def forward(self, input): + if getattr(self, 'mp_group', None) is not None: + input = ColumnParallel.apply(self.mp_group, input) + output = torch.matmul(input, self.weight.transpose(-1, -2)) + if self.bias is not None: + output = add_bias(output, self.bias) + return output + + @torch.no_grad() + def gather_params(self, params_list): + """Gather partitioned parameters back to full size.""" + for idx, param in enumerate(params_list): + if param is None: + continue + params_list[idx].data_partition = param.data + if idx == 0: + full_view = _gather_logical_tensor(param, + self._logical_shape, + self.partition_dim, + self.mp_group, + self.tp_world_size, + name=self.name, + subparam_sizes=self._subparam_sizes) + params_list[idx].data = full_view.reshape(self._orig_weight_shape) + else: + if self._bias_partition_dim is None: + params_list[idx].data = param.data + else: + full_bias_view = _gather_logical_tensor(param, + self._output_shape, + self._bias_partition_dim, + self.mp_group, + self.tp_world_size, + name=self.name, + subparam_sizes=self._subparam_sizes) + params_list[idx].data = full_bias_view.reshape(self._orig_bias_shape) + + @torch.no_grad() + def _tp_partition(self, params_list): + weight = params_list[0] + if weight is None: + return + + weight_view = weight.reshape(self._logical_shape) + partitioned_view = _partition_logical_tensor(weight_view, + self.partition_dim, + self.tp_world_size, + self.tp_index, + name=self.name, + subparam_sizes=self._subparam_sizes) + params_list[0].data = self.move(partitioned_view.reshape(-1, partitioned_view.shape[-1])).detach() + + if params_list[1] is not None: + if self._bias_partition_dim is None: + params_list[1].data = self.move(params_list[1]).detach() + else: + bias_view = params_list[1].reshape(self._output_shape) + bias_partitioned = _partition_logical_tensor(bias_view, + self._bias_partition_dim, + self.tp_world_size, + self.tp_index, + name=self.name, + subparam_sizes=self._subparam_sizes) + params_list[1].data = self.move(bias_partitioned.reshape(-1)).detach() + + +class SubParamLinearAllreduce(TensorParallel_Layer): + """ + Row-parallel linear layer with sub-parameter support (AllReduce after forward). + + Handles cases where weights contain multiple logical sub-parameters + that need to be partitioned separately. + """ + + def __init__(self, module, mp_group, shape, partition_dim=1, **kwargs): + super(SubParamLinearAllreduce, self).__init__(mp_group, **kwargs) + self.weight = module.weight + self.bias = module.bias + self.shape = shape + self.partition_dim = partition_dim + + self._orig_weight_shape = tuple(module.weight.shape) + self._orig_bias_shape = tuple(module.bias.shape) if self.bias is not None else None + (self._logical_shape, self._output_shape, self._subparam_sizes, + self._bias_partition_dim) = _infer_subparam_logical_shapes(self._orig_weight_shape, self.shape, + self.partition_dim, self.name) + + self._tp_partition([self.weight, self.bias]) + self.support_training = True + self.config_tp_params(self.weight) + if self.bias is not None: + self.config_requires_grad(self.bias) + + def forward(self, input): + output = torch.matmul(input, self.weight.transpose(-1, -2)) + output = RowParallel.apply(self.mp_group, output, not self.is_training_mode()) + if self.bias is not None: + output = add_bias(output, self.bias) + return output + + @torch.no_grad() + def gather_params(self, params_list): + """Gather partitioned parameters back to full size.""" + for idx, param in enumerate(params_list): + if param is None or idx > 0: + # don't gather bias for row parallel + return + params_list[idx].data_partition = param.data + full_view = _gather_logical_tensor(param, + self._logical_shape, + self.partition_dim, + self.mp_group, + self.tp_world_size, + name=self.name, + subparam_sizes=self._subparam_sizes) + params_list[idx].data = full_view.reshape(self._orig_weight_shape) + + @torch.no_grad() + def _tp_partition(self, params_list): + weight = params_list[0] + if weight is None: + return + + weight_view = weight.reshape(self._logical_shape) + partitioned_view = _partition_logical_tensor(weight_view, + self.partition_dim, + self.tp_world_size, + self.tp_index, + name=self.name, + subparam_sizes=self._subparam_sizes) + params_list[0].data = self.move(partitioned_view.reshape(-1, partitioned_view.shape[-1])).detach() + + # Bias is not partitioned for row parallel (it's applied after all-reduce) + if params_list[1] is not None: + params_list[1].data = self.move(params_list[1]).detach() + + class RMSNormalize(nn.Module): def __init__(self, dim=None, dtype=torch.float, eps=1e-5, weight=None): diff --git a/deepspeed/module_inject/replace_module.py b/deepspeed/module_inject/replace_module.py index 26752cfa4fec..263369fc0484 100644 --- a/deepspeed/module_inject/replace_module.py +++ b/deepspeed/module_inject/replace_module.py @@ -273,9 +273,20 @@ def replace_with_policy(child, policy_cls, triangular_masking, inference=False, def replace_wo_policy(module, all_reduce_linears, prefix="", state_dict=None): #mp_replace = ReplaceWithTensorSlicing(mp_group=config.tensor_parallel.tp_group) + # Get the configurable partition_config if available + partition_config = None + if hasattr(config, 'get_partition_config_object'): + partition_config = config.get_partition_config_object() + # 1. Create AutoTP object - _autotp = AutoTP(module, all_reduce_linears, prefix, state_dict, linear_layer_setting, orig_layer_impl, - config.keep_module_on_host) + _autotp = AutoTP(module, + all_reduce_linears, + prefix, + state_dict, + linear_layer_setting, + orig_layer_impl, + config.keep_module_on_host, + partition_config=partition_config) # 2. Set the tensor parallelism config _autotp.set_tensor_parallel_config(config.tensor_parallel.tp_size, config.tensor_parallel.tp_group) diff --git a/deepspeed/module_inject/tp_shard.py b/deepspeed/module_inject/tp_shard.py index ded262edcf61..f1dbaae43ec9 100644 --- a/deepspeed/module_inject/tp_shard.py +++ b/deepspeed/module_inject/tp_shard.py @@ -4,7 +4,12 @@ # DeepSpeed Team from deepspeed import comm as dist -global num_kv_heads + +# Defaults for optional TP globals. These can be overridden by setters. +num_kv_heads = None +num_attention_heads = None +n_embd = None +tp_grain_size = 1 def set_num_kv_heads(num): diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 5f8585d65d76..410d1ad6c46c 100755 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -499,6 +499,7 @@ def _optimized_linear_offload_setup(self): def _configure_tensor_parallel(self, model, tp_config): self._configure_tensor_parallel_states(model) configure_tensor_parallel_runtime(tp_config) + self._apply_autotp_partitioning(model, tp_config) def _configure_tensor_parallel_states(self, model): """ @@ -564,6 +565,55 @@ def broadcast_and_check(args, bcast_rank, bcast_group): prepend=True, with_kwargs=True) + def _apply_autotp_partitioning(self, model, tp_config): + if getattr(model, "ds_autotp_parsed", False): + return + if get_accelerator().is_available() and self.local_rank >= 0: + get_accelerator().set_device(self.local_rank) + + tp_size = self.autotp_size() + if tp_config.tensor_parallel.tp_size not in (1, tp_size): + raise ValueError(f"tensor_parallel.tp.tp_size ({tp_config.tensor_parallel.tp_size}) " + f"does not match tensor_parallel.autotp_size ({tp_size}).") + tp_config.tensor_parallel.tp_size = tp_size + if tp_config.tensor_parallel.tp_group is None: + tp_config.tensor_parallel.tp_group = groups.get_tensor_model_parallel_group() + + from deepspeed.module_inject.auto_tp import AutoTP + + partition_config = None + if hasattr(tp_config, "get_partition_config_object"): + partition_config = tp_config.get_partition_config_object() + + if partition_config is not None: + autotp = AutoTP(module=model, + all_reduce_linears=(), + prefix="", + state_dict=None, + linear_layer_setting=(torch.nn.Linear, torch.nn.Embedding), + orig_layer_impl=None, + keep_module_on_host=tp_config.keep_module_on_host, + partition_config=partition_config) + autotp.set_tensor_parallel_config(tp_size, tp_config.tensor_parallel.tp_group) + autotp.update_linear_policies() + autotp._replace_module(model) + setattr(model, "ds_autotp_parsed", True) + return + + if tp_size <= 1: + setattr(model, "ds_autotp_parsed", True) + return + + model_config = getattr(model, "config", None) + from deepspeed.module_inject import replace_transformer_layer + + parser_dict = AutoTP.tp_parser(model) + for client_module, injection_policy in parser_dict: + tp_config.injection_policy_tuple = injection_policy + replace_transformer_layer(client_module, model, None, tp_config, model_config) + + setattr(model, "ds_autotp_parsed", True) + def __del__(self): try: self.destroy() diff --git a/deepspeed/runtime/tensor_parallel/config.py b/deepspeed/runtime/tensor_parallel/config.py index 957984e9f8b3..326277f6eb5c 100644 --- a/deepspeed/runtime/tensor_parallel/config.py +++ b/deepspeed/runtime/tensor_parallel/config.py @@ -7,7 +7,7 @@ from deepspeed.runtime.config_utils import DeepSpeedConfigModel import torch from pydantic import Field -from typing import Optional +from typing import Optional, Dict, Any class AUTOTP_MODE(Enum): @@ -57,6 +57,41 @@ class TPTrainingConfig(DeepSpeedConfigModel): """ injection_policy_tuple: Optional[tuple] = None + + # New configurable AutoTP settings + partition_config: Optional[Dict[str, Any]] = None + """ + Configuration for the new configurable AutoTP API. + Allows users to specify custom layer partitioning rules via TPLayerSpec. + + Example: + "partition_config": { + "use_default_specs": false, + "layer_specs": [ + { + "patterns": [".*\\.o_proj\\.weight$", ".*\\.down_proj\\.weight$"], + "partition_type": "row" + }, + { + "patterns": [".*\\.[qkv]_proj\\.weight$"], + "partition_type": "column" + }, + { + "patterns": [".*\\.gate_up_proj\\.weight$"], + "partition_type": "column", + "shape": [2, -1], + "partition_dim": 0 + } + ] + } + """ + + preset_model: Optional[str] = None + """ + Use a built-in preset for common model architectures. + Available presets: "llama", "bloom", "chatglm", "mixtral", "deepseek_v2", "qwen2", "phi3" + """ + #The following parameters are required by autoTP parser. ######################################## keep_module_on_host: bool = False @@ -74,8 +109,35 @@ class TPTrainingConfig(DeepSpeedConfigModel): linear layers as a tuple: `(attention_output projection, transformer output projection)` """ + ######################################## + def get_partition_config_object(self): + """ + Get the AutoTPConfig object from the configuration. + Returns None if no custom config is specified. + """ + from deepspeed.module_inject.autotp_config import AutoTPConfig, AutoTPPresets, merge_autotp_configs + + config = None + + # First check for preset + if self.preset_model: + config = AutoTPPresets.get_preset(self.preset_model) + + # Then check for custom config + if self.partition_config: + custom_config = AutoTPConfig.from_dict(self.partition_config) + if config and custom_config.use_default_specs: + config = merge_autotp_configs(config, custom_config) + else: + config = custom_config + + if config: + config.tp_size = self.autotp_size + + return config + def get_tensor_parallel_config(ds_config): diff --git a/deepspeed/runtime/tensor_parallel/init_utils.py b/deepspeed/runtime/tensor_parallel/init_utils.py new file mode 100644 index 000000000000..95dacb984cbe --- /dev/null +++ b/deepspeed/runtime/tensor_parallel/init_utils.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import base64 +import os +from typing import Optional, Union + +import hjson +import torch + +from deepspeed.runtime.config_utils import dict_raise_error_on_duplicate_keys + +_TP_MODEL_INIT_ARGS = None + + +def load_ds_config(config: Union[str, dict]) -> dict: + if isinstance(config, dict): + return config + if isinstance(config, str): + if os.path.exists(config): + return hjson.load(open(config, "r"), object_pairs_hook=dict_raise_error_on_duplicate_keys) + try: + config_decoded = base64.urlsafe_b64decode(config).decode('utf-8') + return hjson.loads(config_decoded) + except (UnicodeDecodeError, AttributeError, ValueError) as exc: + raise ValueError( + f"Expected a string path to an existing deepspeed config, or a dictionary or a valid base64. " + f"Received: {config}") from exc + raise ValueError(f"Expected a string path to an existing deepspeed config, or a dictionary or a valid base64. " + f"Received: {config}") + + +def record_tp_model_init_args(tp_size, dtype, tp_group, dist_module): + global _TP_MODEL_INIT_ARGS + new_args = { + "tp_size": tp_size, + "dtype": dtype, + "tp_group": tp_group, + } + + if _TP_MODEL_INIT_ARGS is None: + _TP_MODEL_INIT_ARGS = new_args + return + + if _TP_MODEL_INIT_ARGS["tp_size"] != tp_size or _TP_MODEL_INIT_ARGS["dtype"] != dtype: + raise ValueError("Conflicting tp_model_init arguments detected across multiple calls.") + + existing_group = _TP_MODEL_INIT_ARGS.get("tp_group") + if existing_group is None and tp_group is None: + return + if (existing_group is None) != (tp_group is None): + raise ValueError("Conflicting tp_model_init arguments detected across multiple calls.") + + existing_group_size = tp_group_world_size(existing_group, dist_module) + new_group_size = tp_group_world_size(tp_group, dist_module) + if existing_group_size != new_group_size: + raise ValueError("Conflicting tp_model_init arguments detected across multiple calls.") + + +def tp_group_world_size(tp_group, dist_module): + if tp_group is None or dist_module is None: + return None + return dist_module.get_world_size(group=tp_group) + + +def infer_config_dtype(config_dict: dict) -> Optional[torch.dtype]: + bf16_config = config_dict.get("bf16", {}) + if isinstance(bf16_config, dict) and bf16_config.get("enabled", False): + return torch.bfloat16 + fp16_config = config_dict.get("fp16", {}) + if isinstance(fp16_config, dict) and fp16_config.get("enabled", False): + return torch.float16 + return None + + +def merge_tp_model_init_into_config(config_dict: dict, mpu, mesh_param, dist_module): + if _TP_MODEL_INIT_ARGS is None: + return + + tp_size = _TP_MODEL_INIT_ARGS["tp_size"] + dtype = _TP_MODEL_INIT_ARGS["dtype"] + tp_group = _TP_MODEL_INIT_ARGS["tp_group"] + + if tp_group is not None and mpu is not None: + raise ValueError("tp_model_init provided tp_group; deepspeed.initialize must not receive mpu.") + if tp_group is None and mpu is None and mesh_param is None: + raise ValueError("tp_model_init did not provide tp_group; deepspeed.initialize requires mpu or mesh_param.") + + tp_section = config_dict.get("tensor_parallel") + if tp_section is None: + tp_section = {} + config_dict["tensor_parallel"] = tp_section + + config_autotp_size = tp_section.get("autotp_size") + if config_autotp_size is not None and config_autotp_size != tp_size: + raise ValueError( + f"Conflicting tensor_parallel.autotp_size in config ({config_autotp_size}) and tp_model_init ({tp_size}).") + + if config_autotp_size is None: + tp_section["autotp_size"] = tp_size + + tp_config = tp_section.get("tp") or {} + if not isinstance(tp_config, dict): + raise ValueError("tensor_parallel.tp must be a dict when provided.") + + config_tp_size = tp_config.get("tp_size") + if config_tp_size is not None and config_tp_size != tp_size: + raise ValueError( + f"Conflicting tensor_parallel.tp.tp_size in config ({config_tp_size}) and tp_model_init ({tp_size}).") + if config_tp_size is None: + tp_config["tp_size"] = tp_size + + if tp_group is not None: + config_tp_group = tp_config.get("tp_group") + if config_tp_group is not None and config_tp_group is not tp_group: + raise ValueError("Conflicting tensor_parallel.tp.tp_group in config and tp_model_init.") + tp_config["tp_group"] = tp_group + + tp_group_size = tp_group_world_size(tp_group, dist_module) + if tp_group_size is not None and tp_group_size != tp_size: + raise ValueError(f"tp_model_init tp_size ({tp_size}) does not match tp_group size ({tp_group_size}).") + + tp_section["tp"] = tp_config + + config_dtype = infer_config_dtype(config_dict) + if config_dtype is not None and config_dtype != dtype: + raise ValueError(f"Conflicting dtype: config uses {config_dtype} but tp_model_init requested {dtype}.") + + tp_dtype = tp_section.get("dtype") + if tp_dtype is not None: + if isinstance(tp_dtype, str): + tp_dtype_map = { + "fp16": torch.float16, + "bf16": torch.bfloat16, + "fp32": torch.float32, + } + tp_dtype_value = tp_dtype_map.get(tp_dtype.lower()) + else: + tp_dtype_value = tp_dtype + if tp_dtype_value is not None and tp_dtype_value != dtype: + raise ValueError(f"Conflicting tensor_parallel.dtype in config ({tp_dtype}) and tp_model_init ({dtype}).") diff --git a/docs/_pages/config-json.md b/docs/_pages/config-json.md index 5ac33f3e4447..d5344d3b2320 100755 --- a/docs/_pages/config-json.md +++ b/docs/_pages/config-json.md @@ -730,6 +730,96 @@ Configuring the asynchronous I/O module for offloading parameter and optimizer s | -------------------------------------------------------------------------------------------------------------- | ------- | | Submit requests to storage device in an overlapped fashion without waiting for completion of earlier requests. | `true` | +### Tensor Parallel (AutoTP) +Configure AutoTP tensor parallelism for training via the DeepSpeed config and hybrid TP + ZeRO. AutoTP supports ZeRO stages 0, 1, and 2 (stage 3 is not supported). `deepspeed.tp_model_init()` remains supported for backward compatibility but is not required when `tensor_parallel` is set in the config. +```json + "tensor_parallel": { + "autotp_size": 4, + "preset_model": "llama", + "tp_overlap_comm": false, + "partition_config": { + "use_default_specs": false, + "layer_specs": [ + { + "patterns": [".*\\.o_proj\\.weight$", ".*\\.down_proj\\.weight$"], + "partition_type": "row" + } + ] + } + } +``` +**tensor_parallel**: [dictionary] + +| Description | Default | +| ------------------------------------------------------------------------------------------ | ------- | +| Enable AutoTP tensor parallelism and configure preset or custom partitioning rules. | `{}` | + +***autotp_size***: [integer] + +| Description | Default | +| --------------------------------------------------------------------------- | ------- | +| Tensor-parallel degree. Set to `0` to disable AutoTP. | `0` | + +***preset_model***: [string] + +| Description | Default | +| ----------------------------------------------------------------------------------------------------- | ------- | +| Built-in model presets: `llama`, `bloom`, `chatglm`, `mixtral`, `deepseek_v2`, `qwen2`, `phi3`. | `null` | + +***tp_overlap_comm***: [boolean] + +| Description | Default | +| -------------------------------------------------------------------------------------------------------- | ------- | +| Overlap tensor-parallel allreduce communication with computation (training only). | `false` | + +***partition_config***: [dictionary] + +| Description | Default | +| ------------------------------------------------------------------------------------------------------------------------------- | ------- | +| Custom AutoTP layer partitioning rules. Use with or without `preset_model` to customize sharding patterns. | `null` | + +***use_default_specs***: [boolean] + +| Description | Default | +| -------------------------------------------------------------------------------------------------------------------- | ------- | +| Merge custom `layer_specs` with preset defaults when `preset_model` is set; otherwise use only custom specs. | `true` | + +***layer_specs***: [list] + +| Description | Default | +| ---------------------------------------------------------------------------------------------------------------- | ------- | +| Ordered list of pattern rules that define how to partition matching parameters. | `[]` | + +***patterns***: [list of strings] + +| Description | Default | +| ---------------------------------------------------------------------------------------------------------------- | ------- | +| Regex patterns to match parameter names for this partition rule. | `[]` | + +***partition_type***: [string] + +| Description | Default | +| ---------------------------------------------------------------------------- | ------- | +| Partition type for matching parameters: `row`, `column`, or `skip`. | `column` | + +***shape***: [list] + +| Description | Default | +| ---------------------------------------------------------------------------------------------------------------- | ------- | +| Optional sub-parameter shape for fused weights before TP partitioning (e.g., `[2, -1]`). | `null` | + +***partition_dim***: [integer] + +| Description | Default | +| ---------------------------------------------------------------------------------------------------------------- | ------- | +| Dimension to split when `shape` is provided (e.g., `0` for fused QKV or gate/up). | `null` | + +***model_types***: [list of strings] + +| Description | Default | +| ---------------------------------------------------------------------------------------------------------------- | ------- | +| Optional model type filters (from `model.config.model_type`) for shared configs. | `null` | + ***ignore_unused_parameters***: [boolean] | Description | Default | diff --git a/docs/_tutorials/automatic-tensor-parallelism.md b/docs/_tutorials/automatic-tensor-parallelism.md index f18b823e2490..1433df080ee3 100755 --- a/docs/_tutorials/automatic-tensor-parallelism.md +++ b/docs/_tutorials/automatic-tensor-parallelism.md @@ -3,6 +3,8 @@ title: "Automatic Tensor Parallelism for HuggingFace Models" tags: inference --- +> **Note:** This tutorial covers AutoTP for **inference**. For **training** with tensor parallelism and ZeRO optimization, see [AutoTP Training API](autotp-training). + # Contents * [Introduction](#introduction) * [Example Script](#example-script) diff --git a/docs/_tutorials/autotp-training.md b/docs/_tutorials/autotp-training.md new file mode 100644 index 000000000000..9eb51d3e0900 --- /dev/null +++ b/docs/_tutorials/autotp-training.md @@ -0,0 +1,188 @@ +--- +title: "AutoTP Training API" +tags: training tensor-parallelism +--- + +# AutoTP Training API + +This tutorial covers the **AutoTP Training API** for combining tensor parallelism with ZeRO optimization during training. For inference-only tensor parallelism, see [Automatic Tensor Parallelism for HuggingFace Models](automatic-tensor-parallelism). + +## Contents +- [Introduction](#introduction) +- [Quick Start](#quick-start) +- [Custom Layer Specifications](#custom-layer-specifications) +- [Limitations](#limitations) + +## Introduction + +The AutoTP Training API enables hybrid parallelism by combining: +- **Tensor Parallelism (TP)**: Split model weights across GPUs within a node +- **Data Parallelism (DP)**: Replicate model across GPU groups +- **ZeRO Optimization**: Memory-efficient optimizer states (Stage 0, 1, or 2) + +Tensor parallelism (TP) splits the computations and parameters of large layers +across multiple GPUs so each rank holds only a shard of the weight matrix. This +is an efficient way to train large-scale transformer models by reducing per-GPU +memory pressure while keeping the layer math distributed across the TP group. + + +## Quick Start + +### Basic Usage + +AutoTP training can be enabled entirely through the DeepSpeed config. When +`tensor_parallel` is set in the config, `deepspeed.initialize(...)` applies +AutoTP sharding during engine initialization, so the training loop itself does +not change. + +```python +import torch +import deepspeed + +# 1. Create your model +model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B") + +# 2. Define the DeepSpeed config with tensor_parallel settings +ds_config = { + "train_micro_batch_size_per_gpu": 1, + "zero_optimization": {"stage": 2}, + "bf16": {"enabled": True}, + "tensor_parallel": {"autotp_size": 4}, +} + +# 3. Initialize DeepSpeed with AutoTP + ZeRO +engine, optimizer, _, _ = deepspeed.initialize( + model=model, + optimizer=optimizer, + config=ds_config, + mpu=mpu # Model parallel unit (optional if you provide tp_group elsewhere) +) + +# 4. Train as usual +for batch in dataloader: + outputs = engine(input_ids=batch["input_ids"], labels=batch["labels"]) + engine.backward(outputs.loss) + engine.step() +``` + +Compatibility note: For backward compatibility, you can still call +`set_autotp_mode(training=True)` and `deepspeed.tp_model_init(...)`, but they +are not required when the DeepSpeed config provides the necessary +`tensor_parallel` settings. + +### Preset-based Sharding + +If your model matches a built-in preset, set `tensor_parallel.preset_model` in the DeepSpeed config: + +```json +{ + "train_batch_size": 8, + "train_micro_batch_size_per_gpu": 1, + "bf16": { "enabled": true }, + "zero_optimization": { "stage": 2 }, + "tensor_parallel": { + "autotp_size": 4, + "preset_model": "llama" + } +} +``` + +For the list of available presets, see [supported models](../code-docs/training#autotp-supported-models). + + + +## Custom Patterns + +If you are training a custom model, define regex-based patterns and partition rules in `tensor_parallel.partition_config`: + +```json +{ + "tensor_parallel": { + "autotp_size": 4, + "partition_config": { + "use_default_specs": false, + "layer_specs": [ + { + "patterns": [".*\\.o_proj\\.weight$", ".*\\.down_proj\\.weight$"], + "partition_type": "row" + }, + { + "patterns": [".*\\.[qkv]_proj\\.weight$"], + "partition_type": "column" + }, + { + "patterns": [".*\\.gate_up_proj\\.weight$"], + "partition_type": "column", + "shape": [2, -1], + "partition_dim": 0 + } + ] + } + } +} +``` + +## Custom Layer Specifications + +For models not covered by presets, define custom layer specs: + +```json +{ + "tensor_parallel": { + "autotp_size": 4, + "partition_config": { + "use_default_specs": false, + "layer_specs": [ + { + "patterns": [".*\\.o_proj\\.weight$", ".*\\.down_proj\\.weight$"], + "partition_type": "row" + }, + { + "patterns": [".*\\.[qkv]_proj\\.weight$"], + "partition_type": "column" + }, + { + "patterns": [".*\\.gate_up_proj\\.weight$"], + "partition_type": "column", + "shape": [2, -1], + "partition_dim": 0 + } + ] + } + } +} +``` + +### Fused Layers with Unequal Sub-parameters (GQA) + +For Grouped Query Attention with different Q/K/V sizes: + +```json +{ + "tensor_parallel": { + "partition_config": { + "layer_specs": [ + { + "patterns": [".*\\.qkv_proj\\.weight$"], + "partition_type": "column", + "shape": [[q_size, kv_size, kv_size], -1], + "partition_dim": 0 + } + ] + } + } +} +``` + +## Limitations + +1. **ZeRO Stage 3 not supported**: AutoTP currently only works with ZeRO stages 0, 1, and 2. + +2. **TP size must divide model dimensions**: The tensor parallel size must evenly divide the attention head count and hidden dimensions. + + +## See Also + +- [Automatic Tensor Parallelism for Inference](automatic-tensor-parallelism) +- [ZeRO Optimization](zero) +- [DeepSpeed Configuration](config-json) diff --git a/docs/code-docs/source/_static/autotp-subparams-gate-up.png b/docs/code-docs/source/_static/autotp-subparams-gate-up.png new file mode 100644 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top of the preset (when ``preset_model`` is provided). + +For fused or packed weights (for example QKV or gate/up projections), the +``shape`` and ``partition_dim`` options control sub-parameter partitioning. +Sub-parameter partitioning lets AutoTP split a single weight tensor into +logical chunks before applying tensor-parallel sharding. For example, the +``gate_up_proj`` weight can be viewed as two packed matrices (gate and up) by +setting ``shape`` to ``[2, -1]`` and ``partition_dim`` to ``0``; AutoTP then +partitions each chunk consistently across tensor-parallel ranks. + +.. image:: /_static/autotp-subparams-gate-up.png + :alt: AutoTP sub-parameter partitioning + +Another example is GQA-style fused QKV weights. The tensor can contain unequal +Q/K/V segments stacked along the output dimension. For example, set ``shape`` +to the explicit sizes (for example ``[(q_size, kv_size, kv_size), -1]``) and +``partition_dim`` to ``0`` so AutoTP splits the Q, K, and V regions first, then +shards each region across tensor-parallel ranks. + +.. code-block:: json + + { + "patterns": [".*\\.qkv_proj\\.weight$"], + "partition_type": "column", + "shape": [[q_size, kv_size, kv_size], -1], + "partition_dim": 0 + } + +.. image:: /_static/autotp-subparams-gqa.png + :alt: AutoTP sub-parameter partitioning + + +Model-type filtering for shared configs +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Use ``model_types`` when you want a single config to work across multiple model +families but apply different specs. This is useful in shared training scripts +or when patterns overlap across architectures. + +.. code-block:: json + + { + "tensor_parallel": { + "autotp_size": 4, + "partition_config": { + "layer_specs": [ + { + "patterns": [".*\\.qkv_proj\\.weight$"], + "partition_type": "column", + "shape": [[q_size, kv_size, kv_size], -1], + "partition_dim": 0, + "model_types": ["llama"] + }, + { + "patterns": [".*\\.qkv_proj\\.weight$"], + "partition_type": "column", + "shape": [3, -1], + "partition_dim": 0, + "model_types": ["qwen2"] + } + ] + } + } + } + + +.. _autotp-supported-models: + +Supported models +~~~~~~~~~~~~~~~~ +The following model families are supported by built-in AutoTP presets: + +- ``llama`` +- ``bloom`` +- ``chatglm`` +- ``mixtral`` +- ``deepseek_v2`` +- ``qwen2`` +- ``phi3`` + +Preset definitions live in `AutoTPPresets `_. +If you add a new model family, you can easily add a new preset by defining +patterns like the existing presets, and we welcome PRs for those additions. + +These strings are the values accepted by ``preset_model`` and are matched +against the model type in ``model.config.model_type`` (case-insensitive). When +``preset_model`` is not set, AutoTP uses the legacy automatic sharding rules +unless you provide a custom ``partition_config``. +These presets are also useful when you want to extend the default patterns: +set ``use_default_specs`` to ``true`` in ``partition_config`` to merge your custom +specs on top of the selected preset. diff --git a/tests/unit/model_parallelism/test_autotp_custom_patterns.py b/tests/unit/model_parallelism/test_autotp_custom_patterns.py new file mode 100644 index 000000000000..d19d8b595eeb --- /dev/null +++ b/tests/unit/model_parallelism/test_autotp_custom_patterns.py @@ -0,0 +1,302 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import pytest +import torch +import deepspeed.comm as dist +import deepspeed +from copy import deepcopy +from torch import nn + +from unit.common import DistributedTest, preferred_dtype +from deepspeed.accelerator import get_accelerator +from deepspeed.utils import groups +from deepspeed.module_inject.layers import (LinearAllreduce, LinearLayer, SubParamLinearLayer) +from deepspeed.module_inject.autotp_config import AutoTPConfig +from deepspeed.module_inject.auto_tp import AutoTP + + +def skip_on_device(): + if get_accelerator().device_name() == 'xpu': + pytest.skip("XPU requires a higher version for test") + + +class SequentialLinearModel(torch.nn.Module): + + def __init__(self, hidden_dim, nlayers=1): + super(SequentialLinearModel, self).__init__() + self.linears = torch.nn.ModuleList([torch.nn.Linear(hidden_dim, hidden_dim) for _ in range(nlayers)]) + + def forward(self, x): + for layer in self.linears: + x = layer(x) + return x + + +def init_tp_engine(tp_size, partition_config=None): + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-6 + } + }, + "tensor_parallel": { + "autotp_size": tp_size, + }, + "zero_optimization": { + "stage": 0, + } + } + if partition_config is not None: + config_dict["tensor_parallel"]["partition_config"] = partition_config + else: + config_dict["tensor_parallel"]["partition_config"] = { + "use_default_specs": False, + "layer_specs": [{ + "patterns": [".*\\.weight$"], + "partition_type": "skip", + }], + } + if preferred_dtype() is torch.float16: + config_dict["fp16"] = {"enabled": True} + elif preferred_dtype() is torch.bfloat16: + config_dict["bf16"] = {"enabled": True} + + model = SequentialLinearModel(hidden_dim=8) + deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + + +def apply_autotp_with_partition_config(model, tp_size, partition_config): + groups._init_tp_mesh_device(tensor_model_parallel_size=tp_size) + autotp_config = AutoTPConfig.from_dict(partition_config) + autotp = AutoTP(module=model, + all_reduce_linears=[], + prefix="", + state_dict=None, + linear_layer_setting=None, + orig_layer_impl=None, + keep_module_on_host=False, + partition_config=autotp_config) + autotp.set_tensor_parallel_config(tp_size, groups.get_tensor_model_parallel_group()) + autotp.update_linear_policies() + autotp._replace_module(model) + return model + + +def gather_subparam_output(output, subparam_sizes, mp_group): + tp_world_size = dist.get_world_size(group=mp_group) + local_sizes = [size // tp_world_size for size in subparam_sizes] + output_chunks = torch.split(output, local_sizes, dim=-1) + gathered_chunks = [] + for chunk in output_chunks: + chunk = chunk.contiguous() + gathered = [torch.empty_like(chunk) for _ in range(tp_world_size)] + dist.all_gather(gathered, chunk, group=mp_group) + gathered_chunks.append(torch.cat(gathered, dim=-1)) + return torch.cat(gathered_chunks, dim=-1) + + +class TestAutoTPCustomPatterns(DistributedTest): + world_size = 2 + reuse_dist_env = False + + def test_custom_pattern_replacement(self): + skip_on_device() + partition_config = { + "use_default_specs": + False, + "layer_specs": [ + { + "patterns": [".*linears\\.0\\.weight$"], + "partition_type": "row", + }, + { + "patterns": [".*linears\\.1\\.weight$"], + "partition_type": "column", + }, + { + "patterns": [".*linears\\.2\\.weight$"], + "partition_type": "skip", + }, + ], + } + model = SequentialLinearModel(hidden_dim=16, nlayers=3) + model = apply_autotp_with_partition_config(model, tp_size=2, partition_config=partition_config) + + assert isinstance(model.linears[0], LinearAllreduce) + assert isinstance(model.linears[1], LinearLayer) + assert isinstance(model.linears[2], nn.Linear) + + def test_custom_patterns_applied_via_config(self): + skip_on_device() + partition_config = { + "use_default_specs": + False, + "layer_specs": [ + { + "patterns": [".*linears\\.0\\.weight$"], + "partition_type": "row", + }, + { + "patterns": [".*linears\\.1\\.weight$"], + "partition_type": "column", + }, + { + "patterns": [".*linears\\.2\\.weight$"], + "partition_type": "skip", + }, + ], + } + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-6 + } + }, + "tensor_parallel": { + "autotp_size": 2, + "partition_config": partition_config, + }, + "zero_optimization": { + "stage": 0, + } + } + if preferred_dtype() is torch.float16: + config_dict["fp16"] = {"enabled": True} + elif preferred_dtype() is torch.bfloat16: + config_dict["bf16"] = {"enabled": True} + + model = SequentialLinearModel(hidden_dim=16, nlayers=3) + engine, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + assert isinstance(engine.module.linears[0], LinearAllreduce) + assert isinstance(engine.module.linears[1], LinearLayer) + assert isinstance(engine.module.linears[2], nn.Linear) + + def test_first_match_precedence(self): + skip_on_device() + partition_config = { + "use_default_specs": + False, + "layer_specs": [ + { + "patterns": [".*linears\\.0\\.weight$"], + "partition_type": "skip", + }, + { + "patterns": [".*linears\\.0\\.weight$"], + "partition_type": "column", + }, + ], + } + model = SequentialLinearModel(hidden_dim=16, nlayers=1) + model = apply_autotp_with_partition_config(model, tp_size=2, partition_config=partition_config) + + assert isinstance(model.linears[0], nn.Linear) + + +def test_invalid_custom_shape_rejected(): + bad_config = { + "layer_specs": [{ + "patterns": [".*"], + "partition_type": "column", + "shape": [2, [1, 1]], + }] + } + with pytest.raises(ValueError, match="nested tuple only allowed at partition_dim"): + AutoTPConfig.from_dict(bad_config) + + +class TestAutoTPFusedWeights(DistributedTest): + world_size = 2 + reuse_dist_env = False + + def test_gate_up_fused_weight_partition(self): + skip_on_device() + init_tp_engine(tp_size=2) + + hidden_dim = 8 + torch.manual_seed(42) + linear = nn.Linear(hidden_dim, + hidden_dim * 2, + bias=True, + dtype=preferred_dtype(), + device=get_accelerator().current_device()) + full_weight = deepcopy(linear.weight.data) + full_bias = deepcopy(linear.bias.data) + + layer = SubParamLinearLayer(deepcopy(linear), + groups.get_tensor_model_parallel_group(), + shape=(2, -1), + partition_dim=0, + name="mlp.gate_up_proj") + assert layer._subparam_sizes == (hidden_dim, hidden_dim) + assert layer.weight.shape == (hidden_dim, hidden_dim) + + layer.gather_params([layer.weight, layer.bias]) + torch.testing.assert_close(layer.weight.data, full_weight) + torch.testing.assert_close(layer.bias.data, full_bias) + + def test_gqa_uneven_qkv_fused_weight_partition(self): + skip_on_device() + init_tp_engine(tp_size=2) + + hidden_dim = 8 + q_size, k_size, v_size = 8, 4, 4 + torch.manual_seed(123) + linear = nn.Linear(hidden_dim, + q_size + k_size + v_size, + bias=True, + dtype=preferred_dtype(), + device=get_accelerator().current_device()) + full_weight = deepcopy(linear.weight.data) + full_bias = deepcopy(linear.bias.data) + + layer = SubParamLinearLayer(deepcopy(linear), + groups.get_tensor_model_parallel_group(), + shape=((q_size, k_size, v_size), -1), + partition_dim=0, + name="self_attn.qkv_proj") + assert layer._subparam_sizes == (q_size, k_size, v_size) + assert layer.weight.shape == ((q_size + k_size + v_size) // 2, hidden_dim) + + layer.gather_params([layer.weight, layer.bias]) + torch.testing.assert_close(layer.weight.data, full_weight) + torch.testing.assert_close(layer.bias.data, full_bias) + + def test_gqa_uneven_qkv_fused_forward(self): + skip_on_device() + groups._init_tp_mesh_device(tensor_model_parallel_size=2) + + hidden_dim = 8 + q_size, k_size, v_size = 8, 4, 4 + torch.manual_seed(321) + linear = nn.Linear(hidden_dim, + q_size + k_size + v_size, + bias=True, + dtype=preferred_dtype(), + device=get_accelerator().current_device()) + layer = SubParamLinearLayer(deepcopy(linear), + groups.get_tensor_model_parallel_group(), + shape=((q_size, k_size, v_size), -1), + partition_dim=0, + name="self_attn.qkv_proj") + + torch.manual_seed(42) + inputs = torch.randn(2, hidden_dim, dtype=preferred_dtype(), device=get_accelerator().current_device()) + full_output = linear(inputs) + tp_output = layer(inputs) + + gathered_output = gather_subparam_output(tp_output, (q_size, k_size, v_size), + groups.get_tensor_model_parallel_group()) + atol = 1e-3 + rtol = 2e-2 + if preferred_dtype() is torch.float32: + atol = 1e-5 + rtol = 1e-5 + torch.testing.assert_close(gathered_output, full_output, atol=atol, rtol=rtol) diff --git a/tests/unit/model_parallelism/test_autotp_training.py b/tests/unit/model_parallelism/test_autotp_training.py index 9d2b04211520..baaca247c229 100644 --- a/tests/unit/model_parallelism/test_autotp_training.py +++ b/tests/unit/model_parallelism/test_autotp_training.py @@ -16,7 +16,7 @@ from deepspeed.utils import groups from contextlib import contextmanager from torch import nn -from deepspeed.module_inject.layers import LinearAllreduce, LinearLayer, set_autotp_mode +from deepspeed.module_inject.layers import LinearAllreduce, LinearLayer, set_autotp_mode, is_autotp_training_mode from unit.checkpoint.common import compare_lr_scheduler_states, compare_optimizer_states import os from deepspeed.runtime.utils import is_model_parallel_parameter @@ -27,6 +27,39 @@ def skip_on_device(): pytest.skip("XPU requires a higher version for test") +def reset_tp_model_init_state(): + deepspeed._TP_MODEL_INIT_ARGS = None + set_autotp_mode(training=False) + + +class DummyMPU: + + def __init__(self, tp_world_size=1): + self.rank = dist.get_rank() + self.world_size = dist.get_world_size() + self.tp_world_size = tp_world_size + self.dp_group = dist.get_world_group() + self.tp_group = dist.get_world_group() + + def get_model_parallel_rank(self): + return self.rank % self.tp_world_size + + def get_model_parallel_world_size(self): + return self.tp_world_size + + def get_data_parallel_rank(self): + return self.rank // self.tp_world_size + + def get_data_parallel_world_size(self): + return self.world_size // self.tp_world_size + + def get_data_parallel_group(self): + return self.dp_group + + def get_model_parallel_group(self): + return self.tp_group + + class SequentialLinearModel(torch.nn.Module): def __init__(self, hidden_dim, empty_grad=False, nlayers=1): @@ -69,13 +102,19 @@ class TestTpParallelStates(DistributedTest): def test(self, tp_size: int): skip_on_device() - set_autotp_mode(training=True) dp_size = 4 / tp_size hidden_dim = 128 config_dict = { "train_micro_batch_size_per_gpu": 1, "tensor_parallel": { - "autotp_size": tp_size + "autotp_size": tp_size, + "partition_config": { + "use_default_specs": False, + "layer_specs": [{ + "patterns": [".*\\.weight$"], + "partition_type": "skip", + }], + } }, "zero_optimization": { "stage": 0 @@ -87,6 +126,104 @@ def test(self, tp_size: int): assert groups.get_data_parallel_world_size() == dp_size +class TestTpModelInitCompatibility(DistributedTest): + world_size = 4 + reuse_dist_env = False + + def test_tp_model_init_merges_config(self): + skip_on_device() + reset_tp_model_init_state() + model = SimpleModel(hidden_dim=8) + deepspeed.tp_model_init(model, tp_size=1, dtype=preferred_dtype()) + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "zero_optimization": { + "stage": 0, + } + } + engine, _, _, _ = deepspeed.initialize(model=model, + model_parameters=model.parameters(), + config=config_dict, + mpu=DummyMPU()) + assert engine.autotp_size() == 1 + assert is_autotp_training_mode() + + def test_tp_model_init_config_autotp_size_mismatch(self): + skip_on_device() + reset_tp_model_init_state() + model = SimpleModel(hidden_dim=8) + deepspeed.tp_model_init(model, tp_size=1, dtype=preferred_dtype()) + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "tensor_parallel": { + "autotp_size": 2, + }, + "zero_optimization": { + "stage": 0, + } + } + with pytest.raises(ValueError, match="tensor_parallel.autotp_size"): + deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict, mpu=DummyMPU()) + + def test_tp_model_init_requires_mpu_or_mesh_param(self): + skip_on_device() + reset_tp_model_init_state() + model = SimpleModel(hidden_dim=8) + deepspeed.tp_model_init(model, tp_size=1, dtype=preferred_dtype()) + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "zero_optimization": { + "stage": 0, + } + } + with pytest.raises(ValueError, match="requires mpu or mesh_param"): + deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + + def test_tp_model_init_tp_group_rejects_mpu(self): + skip_on_device() + reset_tp_model_init_state() + model = SimpleModel(hidden_dim=8) + tp_group = dist.new_group(ranks=[0]) + deepspeed.tp_model_init(model, tp_size=1, dtype=preferred_dtype(), tp_group=tp_group) + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "zero_optimization": { + "stage": 0, + } + } + with pytest.raises(ValueError, match="tp_model_init provided tp_group"): + deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict, mpu=DummyMPU()) + + def test_tp_model_init_dtype_mismatch(self): + skip_on_device() + reset_tp_model_init_state() + model = SimpleModel(hidden_dim=8) + deepspeed.tp_model_init(model, tp_size=1, dtype=torch.float16) + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "bf16": { + "enabled": True, + }, + "zero_optimization": { + "stage": 0, + } + } + with pytest.raises(ValueError, match="Conflicting dtype"): + deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict, mpu=DummyMPU()) + + @pytest.mark.sequential + @pytest.mark.parametrize("tp_size", [2, 4]) + @pytest.mark.parametrize("tp_overlap_comm", [True, False]) + def test_tp_model_init_row_parallel(self, tp_size: int, tp_overlap_comm: bool): + run_tp_layer_fwd_bwd(tp_size, tp_overlap_comm, column_parallel=False, use_tp_model_init=True) + + @pytest.mark.sequential + @pytest.mark.parametrize("tp_size", [2, 4]) + @pytest.mark.parametrize("tp_overlap_comm", [True, False]) + def test_tp_model_init_column_parallel(self, tp_size: int, tp_overlap_comm: bool): + run_tp_layer_fwd_bwd(tp_size, tp_overlap_comm, column_parallel=True, use_tp_model_init=True) + + @pytest.mark.parametrize("tp_size", [2, 4]) class TestTpDataloaderCorrectness(DistributedTest): world_size = 4 @@ -95,7 +232,6 @@ class TestTpDataloaderCorrectness(DistributedTest): def test(self, tp_size: int): skip_on_device() hidden_dim = 128 - set_autotp_mode(training=True) config_dict = { "train_micro_batch_size_per_gpu": 1, "steps_per_print": 1, @@ -106,7 +242,14 @@ def test(self, tp_size: int): } }, "tensor_parallel": { - "autotp_size": tp_size + "autotp_size": tp_size, + "partition_config": { + "use_default_specs": False, + "layer_specs": [{ + "patterns": [".*\\.weight$"], + "partition_type": "skip", + }], + } }, "zero_optimization": { "stage": 0, @@ -164,137 +307,116 @@ def process_linear_layer(hidden_dim, input): return torch_linear, torch_out -@pytest.mark.sequential -@pytest.mark.parametrize("tp_size", [2, 4]) -@pytest.mark.parametrize("tp_overlap_comm", [True, False]) -class TestTpLayerFwdBwd(DistributedTest): - world_size = 4 - reuse_dist_env = False - - def testRowParallel(self, tp_size: int, tp_overlap_comm: bool): - skip_on_device() - hidden_dim = 128 - batch_size_per_device = 1 - set_autotp_mode(training=True) - config_dict = { - "train_micro_batch_size_per_gpu": 1, - "steps_per_print": 1, - "optimizer": { - "type": "Adam", - "params": { - "lr": 1e-6 - } - }, - "tensor_parallel": { - "autotp_size": tp_size, - "tp_overlap_comm": tp_overlap_comm - }, - "zero_optimization": { - "stage": 0, +def run_tp_layer_fwd_bwd(tp_size, tp_overlap_comm, column_parallel, use_tp_model_init=False): + skip_on_device() + hidden_dim = 128 + batch_size_per_device = 1 + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "steps_per_print": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-6 } + }, + "tensor_parallel": { + "autotp_size": tp_size, + "tp_overlap_comm": tp_overlap_comm + }, + "zero_optimization": { + "stage": 0, } - if preferred_dtype() is torch.float16: - config_dict["fp16"] = {"enabled": True} - elif preferred_dtype() is torch.bfloat16: - config_dict["bf16"] = {"enabled": True} - model = SequentialLinearModel(hidden_dim=hidden_dim) + } + partition_type = "column" if column_parallel else "row" + config_dict["tensor_parallel"]["partition_config"] = { + "use_default_specs": False, + "layer_specs": [{ + "patterns": [".*\\.weight$"], + "partition_type": partition_type, + }], + } + if preferred_dtype() is torch.float16: + config_dict["fp16"] = {"enabled": True} + elif preferred_dtype() is torch.bfloat16: + config_dict["bf16"] = {"enabled": True} + + model = SequentialLinearModel(hidden_dim=hidden_dim) + if use_tp_model_init: + reset_tp_model_init_state() + deepspeed.tp_model_init(model, tp_size=tp_size, dtype=preferred_dtype()) + mpu = DummyMPU(tp_world_size=tp_size) + model, _, _, _ = deepspeed.initialize(model=model, + model_parameters=model.parameters(), + config=config_dict, + mpu=mpu) + else: model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) - input = torch.randn(batch_size_per_device, - hidden_dim, - dtype=preferred_dtype(), - requires_grad=True, - device=get_accelerator().current_device()) - dist.broadcast(input, - groups.get_tensor_model_parallel_src_rank(), - group=groups.get_tensor_model_parallel_group()) - - torch_linear, torch_out = process_linear_layer(hidden_dim, input) - linear = LinearAllreduce(deepcopy(torch_linear), groups.get_tensor_model_parallel_group()) - - input_ = torch.chunk(input, tp_size, dim=-1)[groups.get_tensor_model_parallel_rank()] - out = linear(input_.to(get_accelerator().current_device())) + input = torch.randn(batch_size_per_device, + hidden_dim, + dtype=preferred_dtype(), + requires_grad=True, + device=get_accelerator().current_device()) + dist.broadcast(input, groups.get_tensor_model_parallel_src_rank(), group=groups.get_tensor_model_parallel_group()) + + # Note: correctness checks below use standalone TP wrappers and do not + # rely on the model's AutoTP-partitioned parameters. + torch_linear, torch_out = process_linear_layer(hidden_dim, input) + if column_parallel: + linear = LinearLayer(deepcopy(torch_linear), groups.get_tensor_model_parallel_group()) + out = linear(input.to(get_accelerator().current_device())) loss = out.sum() loss.backward() - torch_grad = torch.chunk(torch_linear.weight.grad, tp_size, dim=1)[groups.get_tensor_model_parallel_rank()] - torch_bias_grad = torch_linear.bias.grad - # Use assert_close with rtol for proper floating-point comparisons + cur_device_out = torch.chunk(torch_out, tp_size, dim=-1)[groups.get_tensor_model_parallel_rank()] + torch_grad = torch.chunk(torch_linear.weight.grad, tp_size, dim=0)[groups.get_tensor_model_parallel_rank()] + torch_bias_grad = torch.chunk(torch_linear.bias.grad, tp_size, dim=0)[groups.get_tensor_model_parallel_rank()] + torch.testing.assert_close(linear.bias.grad, torch_bias_grad.to(get_accelerator().current_device()), atol=1e-3, rtol=1e-3) - # The gradient of the weight is not the same as the torch_linear.weight.grad torch.testing.assert_close(linear.weight.grad, torch_grad.to(get_accelerator().current_device()), atol=1e-3, rtol=1e-3) - torch.testing.assert_close(out, torch_out.to(get_accelerator().current_device()), atol=1e-2, rtol=1e-2) - - def testColumnParallel(self, tp_size: int, tp_overlap_comm: bool): - skip_on_device() - hidden_dim = 128 - batch_size_per_device = 1 - set_autotp_mode(training=True) - config_dict = { - "train_micro_batch_size_per_gpu": 1, - "steps_per_print": 1, - "optimizer": { - "type": "Adam", - "params": { - "lr": 1e-6 - } - }, - "tensor_parallel": { - "autotp_size": tp_size, - "tp_overlap_comm": tp_overlap_comm - }, - "zero_optimization": { - "stage": 0, - } - } - if preferred_dtype() is torch.float16: - config_dict["fp16"] = {"enabled": True} - elif preferred_dtype() is torch.bfloat16: - config_dict["bf16"] = {"enabled": True} - - model = SequentialLinearModel(hidden_dim=hidden_dim) - model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) - input = torch.randn(batch_size_per_device, - hidden_dim, - dtype=preferred_dtype(), - requires_grad=True, - device=get_accelerator().current_device()) - dist.broadcast(input, - groups.get_tensor_model_parallel_src_rank(), - group=groups.get_tensor_model_parallel_group()) - - torch_linear, torch_out = process_linear_layer(hidden_dim, input) - - linear = LinearLayer(deepcopy(torch_linear), groups.get_tensor_model_parallel_group()) - - out = linear(input.to(get_accelerator().current_device())) + torch.testing.assert_close(cur_device_out.to(get_accelerator().current_device()).contiguous(), + out.contiguous(), + atol=1e-2, + rtol=1e-2) + else: + linear = LinearAllreduce(deepcopy(torch_linear), groups.get_tensor_model_parallel_group()) + input_ = torch.chunk(input, tp_size, dim=-1)[groups.get_tensor_model_parallel_rank()] + out = linear(input_.to(get_accelerator().current_device())) loss = out.sum() loss.backward() - cur_device_out = torch.chunk(torch_out, tp_size, dim=-1)[groups.get_tensor_model_parallel_rank()] - torch_grad = torch.chunk(torch_linear.weight.grad, tp_size, dim=0)[groups.get_tensor_model_parallel_rank()] - - torch_bias_grad = torch.chunk(torch_linear.bias.grad, tp_size, dim=0)[groups.get_tensor_model_parallel_rank()] - # Use assert_close with rtol for proper floating-point comparisons + torch_grad = torch.chunk(torch_linear.weight.grad, tp_size, dim=1)[groups.get_tensor_model_parallel_rank()] + torch_bias_grad = torch_linear.bias.grad torch.testing.assert_close(linear.bias.grad, torch_bias_grad.to(get_accelerator().current_device()), atol=1e-3, rtol=1e-3) - torch.testing.assert_close(linear.weight.grad, torch_grad.to(get_accelerator().current_device()), atol=1e-3, rtol=1e-3) - torch.testing.assert_close(cur_device_out.to(get_accelerator().current_device()).contiguous(), - out.contiguous(), - atol=1e-2, - rtol=1e-2) + torch.testing.assert_close(out, torch_out.to(get_accelerator().current_device()), atol=1e-2, rtol=1e-2) + + +@pytest.mark.sequential +@pytest.mark.parametrize("tp_size", [2, 4]) +@pytest.mark.parametrize("tp_overlap_comm", [True, False]) +class TestTpLayerFwdBwd(DistributedTest): + world_size = 4 + reuse_dist_env = False + + def testRowParallel(self, tp_size: int, tp_overlap_comm: bool): + run_tp_layer_fwd_bwd(tp_size, tp_overlap_comm, column_parallel=False) + + def testColumnParallel(self, tp_size: int, tp_overlap_comm: bool): + run_tp_layer_fwd_bwd(tp_size, tp_overlap_comm, column_parallel=True) # @pytest.mark.sequential @@ -307,7 +429,6 @@ def test(self, layer_type): skip_on_device() tp_size = 4 hidden_dim = 128 - set_autotp_mode(training=True) config_dict = { "train_micro_batch_size_per_gpu": 1, "optimizer": { @@ -317,7 +438,14 @@ def test(self, layer_type): } }, "tensor_parallel": { - "autotp_size": tp_size + "autotp_size": tp_size, + "partition_config": { + "use_default_specs": False, + "layer_specs": [{ + "patterns": [".*\\.weight$"], + "partition_type": "skip", + }], + } }, "zero_optimization": { "stage": 0, @@ -382,6 +510,15 @@ def test(self, layer_type): def dummy_init_engine(config): # This is a dummy initialization function for the DeepSpeed engine. # We only need to use the config to initialize the distributed settings for the test. + # Add default partition_config for simple test models if not provided + if "tensor_parallel" in config and "partition_config" not in config["tensor_parallel"]: + config["tensor_parallel"]["partition_config"] = { + "use_default_specs": False, + "layer_specs": [{ + "patterns": [".*\\.weight$"], + "partition_type": "skip", + }], + } model = SequentialLinearModel(hidden_dim=8) model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config) @@ -412,7 +549,6 @@ class TestSave(DistributedTest): def test_save_original_weight(self, tp_size: int, zero_stage: int): skip_on_device() hidden_dim = 64 - set_autotp_mode(training=True) config_dict = { "train_micro_batch_size_per_gpu": 1, "steps_per_print": 1, @@ -471,7 +607,6 @@ def compare_state_dicts(state_dict1, state_dict2): def test_ckpt_save(self, tmpdir, tp_size: int, zero_stage: int): skip_on_device() hidden_dim = 64 - set_autotp_mode(training=True) config_dict = { "train_micro_batch_size_per_gpu": 1, "steps_per_print": 1, @@ -544,7 +679,6 @@ class TestTpGradNorm(DistributedTest): def test(self, tp_size: int, zero_stage: int): skip_on_device() hidden_dim = 64 - set_autotp_mode(training=True) config_dict = { "train_micro_batch_size_per_gpu": 1, "steps_per_print": 1, From d307396d36f90e3640e2a7a42cacaee1c1d4b9e0 Mon Sep 17 00:00:00 2001 From: Kento Sugama Date: Mon, 2 Feb 2026 14:33:22 -0500 Subject: [PATCH 11/20] flatten gpu side Signed-off-by: Kento Sugama --- deepspeed/runtime/zero/stage_1_and_2.py | 76 +++++++++++++++++-------- 1 file changed, 53 insertions(+), 23 deletions(-) diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index 107e47a44042..28e57228292e 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -366,18 +366,36 @@ def _enforce_cpu_offload(): # not sure why apex was cloning the weights before flattening # removing cloning here - see_memory_usage(f"Before moving param group {i} to CPU") - # move all the parameters to cpu to free up GPU space for creating flat buffer - - # Create temp CPU param copies, free accelerator tensors - orig_group_numel = 0 - for param in self.bit16_groups[i]: - orig_group_numel += param.numel() - param.cpu_data = param.data.cpu() - param.data = torch.empty(1).to(param.device) + # Compute group size for VRAM check (need 2x model size on GPU to flatten in place: params + flat copy) + orig_group_numel = sum(param.numel() for param in self.bit16_groups[i]) + alignment = self.nccl_start_alignment_factor * dist.get_world_size( + group=self.real_dp_process_group[i]) + aligned_numel = ((orig_group_numel + alignment - 1) // alignment) * alignment + param_dtype = self.bit16_groups[i][0].dtype + element_size = torch.tensor([], dtype=param_dtype).element_size() + flat_buffer_bytes = aligned_numel * element_size empty_cache() - see_memory_usage(f"After moving param group {i} to CPU", force=False) + accelerator = get_accelerator() + available_vram = accelerator.available_memory() if accelerator.is_available() else 0 + # Flatten on GPU only if we have enough VRAM for the flat buffer (2x = params already there + copy) + flatten_on_gpu = ( + not self.cpu_offload + and accelerator.is_available() + and (available_vram >= flat_buffer_bytes)) + + if flatten_on_gpu: + # Keep params on GPU and flatten on accelerator + see_memory_usage(f"Flattening param group {i} on GPU (sufficient VRAM)") + else: + see_memory_usage(f"Before moving param group {i} to CPU") + # move all the parameters to cpu to free up GPU space for creating flat buffer + for param in self.bit16_groups[i]: + param.cpu_data = param.data.cpu() + param.data = torch.empty(1).to(param.device) + + empty_cache() + see_memory_usage(f"After moving param group {i} to CPU", force=False) # Reorder group parameters for load balancing of gradient partitioning during backward among ranks. # This ensures that gradients are reduced in a fashion such that ownership round robins among the ranks. @@ -396,24 +414,36 @@ def _enforce_cpu_offload(): # Create meta tensors list, ordered according to round_robin_tensors meta_tensors = [] for param in round_robin_tensors: - meta_tensors.append(torch.zeros_like(param.cpu_data, device="meta")) + if flatten_on_gpu: + meta_tensors.append(torch.zeros_like(param.data, device="meta")) + else: + meta_tensors.append(torch.zeros_like(param.cpu_data, device="meta")) self.round_robin_bit16_meta.append(meta_tensors) - # create flat buffer in CPU - flattened_buffer = self.flatten_dense_tensors_aligned( - self.round_robin_bit16_groups[i], - self.nccl_start_alignment_factor * dist.get_world_size(group=self.real_dp_process_group[i]), - use_cpu_data=True) + if flatten_on_gpu: + # create flat buffer on GPU + flattened_buffer = self.flatten_dense_tensors_aligned( + self.round_robin_bit16_groups[i], + alignment, + use_cpu_data=False) + self.bit16_groups_flat.append(flattened_buffer) + see_memory_usage(f"After flattening param group {i} on GPU", force=False) + else: + # create flat buffer in CPU + flattened_buffer = self.flatten_dense_tensors_aligned( + self.round_robin_bit16_groups[i], + alignment, + use_cpu_data=True) - # free temp CPU params - for param in self.bit16_groups[i]: - del param.cpu_data + # free temp CPU params + for param in self.bit16_groups[i]: + del param.cpu_data - # Move CPU flat tensor to the accelerator memory. - self.bit16_groups_flat.append(flattened_buffer.to(get_accelerator().current_device_name())) - del flattened_buffer + # Move CPU flat tensor to the accelerator memory. + self.bit16_groups_flat.append(flattened_buffer.to(get_accelerator().current_device_name())) + del flattened_buffer - see_memory_usage(f"After flattening and moving param group {i} to GPU", force=False) + see_memory_usage(f"After flattening and moving param group {i} to GPU", force=False) if dist.get_rank(group=self.real_dp_process_group[i]) == 0: see_memory_usage(f"After Flattening and after emptying param group {i} cache", force=False) From 6eb35e8eb330973334ec713a6245706a908a317c Mon Sep 17 00:00:00 2001 From: Kento Sugama Date: Mon, 2 Feb 2026 21:58:01 +0000 Subject: [PATCH 12/20] repro script Signed-off-by: Kento Sugama --- repro.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 repro.py diff --git a/repro.py b/repro.py new file mode 100644 index 000000000000..690ca589dffc --- /dev/null +++ b/repro.py @@ -0,0 +1,16 @@ +import sys, deepspeed, torch +from transformers import AutoModel, AutoConfig; +model = "Qwen/Qwen3-30B-A3B-Instruct-2507" +c = AutoConfig.from_pretrained(model) +c.num_hidden_layers=16 # cut the number of layers for faster loading +m = AutoModel.from_pretrained(model, config=c) +dsc = dict( + train_micro_batch_size_per_gpu=1, + train_batch_size=1, + zero_optimization=dict(stage=2), + bf16=dict(enabled=True), + optimizer=dict(type="AdamW"), + gradient_accumulation_steps=1, +) +deepspeed.initialize(model=m, config=dsc) +torch.distributed.destroy_process_group() From 48ecb1d7bdd66fc95101b240c434ea869418e901 Mon Sep 17 00:00:00 2001 From: Kento Sugama Date: Mon, 2 Feb 2026 22:00:06 +0000 Subject: [PATCH 13/20] detect gpu count in repro Signed-off-by: Kento Sugama --- repro.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/repro.py b/repro.py index 690ca589dffc..d00f42fbe059 100644 --- a/repro.py +++ b/repro.py @@ -1,12 +1,16 @@ import sys, deepspeed, torch -from transformers import AutoModel, AutoConfig; +from transformers import AutoModel, AutoConfig + +num_gpus = max(1, torch.cuda.device_count()) +batch_size = num_gpus + model = "Qwen/Qwen3-30B-A3B-Instruct-2507" c = AutoConfig.from_pretrained(model) c.num_hidden_layers=16 # cut the number of layers for faster loading m = AutoModel.from_pretrained(model, config=c) dsc = dict( train_micro_batch_size_per_gpu=1, - train_batch_size=1, + train_batch_size=batch_size, zero_optimization=dict(stage=2), bf16=dict(enabled=True), optimizer=dict(type="AdamW"), From 7f98cc859f5d146b38bb61d20f047fca8636038c Mon Sep 17 00:00:00 2001 From: Kento Sugama Date: Mon, 2 Feb 2026 22:07:25 +0000 Subject: [PATCH 14/20] add .venv to path Signed-off-by: Kento Sugama --- repro.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/repro.py b/repro.py index d00f42fbe059..5c4a5bd99c8b 100644 --- a/repro.py +++ b/repro.py @@ -1,4 +1,14 @@ -import sys, deepspeed, torch +import os +import sys + +# Ensure venv's bin (e.g. ninja) is on PATH when launched via deepspeed +if sys.executable: + path_prepend = os.path.normpath(os.path.dirname(sys.executable)) + if path_prepend not in os.environ.get("PATH", "").split(os.pathsep): + os.environ["PATH"] = path_prepend + os.pathsep + os.environ.get("PATH", "") + +import deepspeed +import torch from transformers import AutoModel, AutoConfig num_gpus = max(1, torch.cuda.device_count()) From b3944b4e6de1771318b848f18176996205b3ba9e Mon Sep 17 00:00:00 2001 From: Kento Sugama Date: Tue, 3 Feb 2026 14:49:44 +0000 Subject: [PATCH 15/20] clean up Signed-off-by: Kento Sugama --- deepspeed/runtime/zero/stage_1_and_2.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index 28e57228292e..450a33534a3a 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -14,6 +14,7 @@ from deepspeed.runtime.zenflow import zenflow_utils import gc +import math from typing import Container from deepspeed.runtime.zero.offload_states import offload_optimizer_states, reload_optimizer_states from deepspeed.runtime.base_optimizer import ZeROOptimizer @@ -370,7 +371,7 @@ def _enforce_cpu_offload(): orig_group_numel = sum(param.numel() for param in self.bit16_groups[i]) alignment = self.nccl_start_alignment_factor * dist.get_world_size( group=self.real_dp_process_group[i]) - aligned_numel = ((orig_group_numel + alignment - 1) // alignment) * alignment + aligned_numel = int(math.ceil(orig_group_numel / alignment)) * alignment param_dtype = self.bit16_groups[i][0].dtype element_size = torch.tensor([], dtype=param_dtype).element_size() flat_buffer_bytes = aligned_numel * element_size @@ -380,14 +381,14 @@ def _enforce_cpu_offload(): available_vram = accelerator.available_memory() if accelerator.is_available() else 0 # Flatten on GPU only if we have enough VRAM for the flat buffer (2x = params already there + copy) flatten_on_gpu = ( - not self.cpu_offload - and accelerator.is_available() + accelerator.is_available() and (available_vram >= flat_buffer_bytes)) - + if flatten_on_gpu: # Keep params on GPU and flatten on accelerator - see_memory_usage(f"Flattening param group {i} on GPU (sufficient VRAM)") + logger.info(f"Flattening param group {i} on GPU (sufficient VRAM)") else: + logger.info(f"Flattening param group {i} on CPU (insufficient VRAM)") see_memory_usage(f"Before moving param group {i} to CPU") # move all the parameters to cpu to free up GPU space for creating flat buffer for param in self.bit16_groups[i]: @@ -421,7 +422,6 @@ def _enforce_cpu_offload(): self.round_robin_bit16_meta.append(meta_tensors) if flatten_on_gpu: - # create flat buffer on GPU flattened_buffer = self.flatten_dense_tensors_aligned( self.round_robin_bit16_groups[i], alignment, @@ -429,7 +429,6 @@ def _enforce_cpu_offload(): self.bit16_groups_flat.append(flattened_buffer) see_memory_usage(f"After flattening param group {i} on GPU", force=False) else: - # create flat buffer in CPU flattened_buffer = self.flatten_dense_tensors_aligned( self.round_robin_bit16_groups[i], alignment, From 78e58fce0168605852da3cd7e0c82ba7300f18a9 Mon Sep 17 00:00:00 2001 From: Kento Sugama Date: Tue, 3 Feb 2026 15:20:39 +0000 Subject: [PATCH 16/20] format and delete repro script Signed-off-by: Kento Sugama --- deepspeed/runtime/zero/stage_1_and_2.py | 23 ++++++++----------- repro.py | 30 ------------------------- 2 files changed, 9 insertions(+), 44 deletions(-) delete mode 100644 repro.py diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index 450a33534a3a..835133af5957 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -369,8 +369,7 @@ def _enforce_cpu_offload(): # Compute group size for VRAM check (need 2x model size on GPU to flatten in place: params + flat copy) orig_group_numel = sum(param.numel() for param in self.bit16_groups[i]) - alignment = self.nccl_start_alignment_factor * dist.get_world_size( - group=self.real_dp_process_group[i]) + alignment = self.nccl_start_alignment_factor * dist.get_world_size(group=self.real_dp_process_group[i]) aligned_numel = int(math.ceil(orig_group_numel / alignment)) * alignment param_dtype = self.bit16_groups[i][0].dtype element_size = torch.tensor([], dtype=param_dtype).element_size() @@ -380,10 +379,8 @@ def _enforce_cpu_offload(): accelerator = get_accelerator() available_vram = accelerator.available_memory() if accelerator.is_available() else 0 # Flatten on GPU only if we have enough VRAM for the flat buffer (2x = params already there + copy) - flatten_on_gpu = ( - accelerator.is_available() - and (available_vram >= flat_buffer_bytes)) - + flatten_on_gpu = (accelerator.is_available() and (available_vram >= flat_buffer_bytes)) + if flatten_on_gpu: # Keep params on GPU and flatten on accelerator logger.info(f"Flattening param group {i} on GPU (sufficient VRAM)") @@ -422,17 +419,15 @@ def _enforce_cpu_offload(): self.round_robin_bit16_meta.append(meta_tensors) if flatten_on_gpu: - flattened_buffer = self.flatten_dense_tensors_aligned( - self.round_robin_bit16_groups[i], - alignment, - use_cpu_data=False) + flattened_buffer = self.flatten_dense_tensors_aligned(self.round_robin_bit16_groups[i], + alignment, + use_cpu_data=False) self.bit16_groups_flat.append(flattened_buffer) see_memory_usage(f"After flattening param group {i} on GPU", force=False) else: - flattened_buffer = self.flatten_dense_tensors_aligned( - self.round_robin_bit16_groups[i], - alignment, - use_cpu_data=True) + flattened_buffer = self.flatten_dense_tensors_aligned(self.round_robin_bit16_groups[i], + alignment, + use_cpu_data=True) # free temp CPU params for param in self.bit16_groups[i]: diff --git a/repro.py b/repro.py deleted file mode 100644 index 5c4a5bd99c8b..000000000000 --- a/repro.py +++ /dev/null @@ -1,30 +0,0 @@ -import os -import sys - -# Ensure venv's bin (e.g. ninja) is on PATH when launched via deepspeed -if sys.executable: - path_prepend = os.path.normpath(os.path.dirname(sys.executable)) - if path_prepend not in os.environ.get("PATH", "").split(os.pathsep): - os.environ["PATH"] = path_prepend + os.pathsep + os.environ.get("PATH", "") - -import deepspeed -import torch -from transformers import AutoModel, AutoConfig - -num_gpus = max(1, torch.cuda.device_count()) -batch_size = num_gpus - -model = "Qwen/Qwen3-30B-A3B-Instruct-2507" -c = AutoConfig.from_pretrained(model) -c.num_hidden_layers=16 # cut the number of layers for faster loading -m = AutoModel.from_pretrained(model, config=c) -dsc = dict( - train_micro_batch_size_per_gpu=1, - train_batch_size=batch_size, - zero_optimization=dict(stage=2), - bf16=dict(enabled=True), - optimizer=dict(type="AdamW"), - gradient_accumulation_steps=1, -) -deepspeed.initialize(model=m, config=dsc) -torch.distributed.destroy_process_group() From 7aa7073a95aa7049b50b259dace5b6f1579322b5 Mon Sep 17 00:00:00 2001 From: Kento Sugama Date: Tue, 3 Feb 2026 17:13:23 +0000 Subject: [PATCH 17/20] add dedicated test Signed-off-by: Kento Sugama --- deepspeed/runtime/zero/stage_1_and_2.py | 9 +- .../v1/zero/test_stage2_flatten_on_gpu.py | 100 ++++++++++++++++++ 2 files changed, 104 insertions(+), 5 deletions(-) create mode 100644 tests/unit/v1/zero/test_stage2_flatten_on_gpu.py diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index 835133af5957..7f29826ca7c5 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -381,11 +381,7 @@ def _enforce_cpu_offload(): # Flatten on GPU only if we have enough VRAM for the flat buffer (2x = params already there + copy) flatten_on_gpu = (accelerator.is_available() and (available_vram >= flat_buffer_bytes)) - if flatten_on_gpu: - # Keep params on GPU and flatten on accelerator - logger.info(f"Flattening param group {i} on GPU (sufficient VRAM)") - else: - logger.info(f"Flattening param group {i} on CPU (insufficient VRAM)") + if not flatten_on_gpu: see_memory_usage(f"Before moving param group {i} to CPU") # move all the parameters to cpu to free up GPU space for creating flat buffer for param in self.bit16_groups[i]: @@ -419,12 +415,15 @@ def _enforce_cpu_offload(): self.round_robin_bit16_meta.append(meta_tensors) if flatten_on_gpu: + logger.info(f"Flattening param group {i} on GPU (sufficient VRAM)") flattened_buffer = self.flatten_dense_tensors_aligned(self.round_robin_bit16_groups[i], alignment, use_cpu_data=False) self.bit16_groups_flat.append(flattened_buffer) see_memory_usage(f"After flattening param group {i} on GPU", force=False) else: + logger.info(f"Flattening param group {i} on CPU (insufficient VRAM)") + flattened_buffer = self.flatten_dense_tensors_aligned(self.round_robin_bit16_groups[i], alignment, use_cpu_data=True) diff --git a/tests/unit/v1/zero/test_stage2_flatten_on_gpu.py b/tests/unit/v1/zero/test_stage2_flatten_on_gpu.py new file mode 100644 index 000000000000..c4abb21d40a0 --- /dev/null +++ b/tests/unit/v1/zero/test_stage2_flatten_on_gpu.py @@ -0,0 +1,100 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team +""" +Test that ZeRO Stage 2 uses the GPU flatten path when VRAM is sufficient. +Runs for fp32, fp16, or bf16 depending on accelerator support. +""" + +import pytest +import deepspeed +from deepspeed.accelerator import get_accelerator +from deepspeed.utils import set_log_level_from_string +from unit.common import DistributedTest +from unit.simple_model import SimpleModel + + +class TestStage2FlattenOnGPU(DistributedTest): + """ZeRO-2 with small model should flatten on GPU (sufficient VRAM).""" + + world_size = 2 # Run on 2 GPUs when available + + def test_flatten_on_gpu_path_taken(self, monkeypatch): + """Assert the GPU flatten path was used (not CPU flatten + move).""" + if not get_accelerator().is_available(): + pytest.skip("Accelerator not available") + config_dict = { + "train_micro_batch_size_per_gpu": 2, + "gradient_accumulation_steps": 1, + "zero_optimization": { + "stage": 2 + }, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-3 + } + }, + } + if get_accelerator().is_bf16_supported(): + config_dict["bf16"] = {"enabled": True} + elif get_accelerator().is_fp16_supported(): + config_dict["fp16"] = {"enabled": True, "initial_scale_power": 8} + + set_log_level_from_string("info") + log_messages = [] + + def mock_logger_info(msg, *args, **kwargs): + log_messages.append(msg if isinstance(msg, str) else str(msg)) + + monkeypatch.setattr("deepspeed.utils.logger.info", mock_logger_info) + + hidden_dim = 64 + model = SimpleModel(hidden_dim=hidden_dim, nlayers=2) + deepspeed.initialize( + config=config_dict, + model=model, + model_parameters=model.parameters(), + ) + + # Small model + no CPU offload => GPU path; that path logs "on GPU" + gpu_path_logs = [m for m in log_messages if "Flattening param group" in m and "on GPU" in m] + assert gpu_path_logs, ( + f"Expected GPU flatten path (logger.info should be called with 'Flattening param group' and 'on GPU'). " + f"Captured messages: {log_messages}") + + def test_flat_buffers_on_accelerator(self): + """Regression: flat buffers must end up on the accelerator (not left on CPU).""" + if not get_accelerator().is_available(): + pytest.skip("Accelerator not available") + config_dict = { + "train_micro_batch_size_per_gpu": 2, + "gradient_accumulation_steps": 1, + "zero_optimization": { + "stage": 2 + }, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-3 + } + }, + } + if get_accelerator().is_bf16_supported(): + config_dict["bf16"] = {"enabled": True} + elif get_accelerator().is_fp16_supported(): + config_dict["fp16"] = {"enabled": True, "initial_scale_power": 8} + + hidden_dim = 64 + model = SimpleModel(hidden_dim=hidden_dim, nlayers=2) + engine, _, _, _ = deepspeed.initialize( + config=config_dict, + model=model, + model_parameters=model.parameters(), + ) + opt = engine.optimizer + assert hasattr(opt, "bit16_groups_flat"), "ZeRO-2 optimizer should have bit16_groups_flat" + device_type = get_accelerator().device_name() + for i, flat in enumerate(opt.bit16_groups_flat): + assert flat.device.type == device_type, (f"Flat buffer {i} must be on {device_type}, got {flat.device}") From 85d670ad8c66458f17d33a0646cee4502a98df0f Mon Sep 17 00:00:00 2001 From: Kento Sugama Date: Tue, 3 Feb 2026 17:30:58 +0000 Subject: [PATCH 18/20] parametrize tests Signed-off-by: Kento Sugama --- .../v1/zero/test_stage2_flatten_on_gpu.py | 41 +++++++++++-------- 1 file changed, 25 insertions(+), 16 deletions(-) diff --git a/tests/unit/v1/zero/test_stage2_flatten_on_gpu.py b/tests/unit/v1/zero/test_stage2_flatten_on_gpu.py index c4abb21d40a0..d3dc6e7c45f8 100644 --- a/tests/unit/v1/zero/test_stage2_flatten_on_gpu.py +++ b/tests/unit/v1/zero/test_stage2_flatten_on_gpu.py @@ -3,8 +3,8 @@ # DeepSpeed Team """ -Test that ZeRO Stage 2 uses the GPU flatten path when VRAM is sufficient. -Runs for fp32, fp16, or bf16 depending on accelerator support. +Test that ZeRO Stage 1 and 2 use the GPU flatten path when VRAM is sufficient. +Parametrized over zero_stage (1, 2) and dtype (fp32, fp16, bf16). """ import pytest @@ -15,12 +15,27 @@ from unit.simple_model import SimpleModel +def _apply_dtype_to_config(config_dict, dtype): + """Set bf16/fp16 in config_dict based on dtype; skip if not supported.""" + if dtype == "bf16": + if not get_accelerator().is_bf16_supported(): + pytest.skip("bf16 is not supported on this accelerator") + config_dict["bf16"] = {"enabled": True} + elif dtype == "fp16": + if not get_accelerator().is_fp16_supported(): + pytest.skip("fp16 is not supported on this accelerator") + config_dict["fp16"] = {"enabled": True, "initial_scale_power": 8} + # fp32: no half-precision block + + +@pytest.mark.parametrize("zero_stage", [1, 2]) +@pytest.mark.parametrize("dtype", ["fp32", "fp16", "bf16"], ids=["fp32", "fp16", "bf16"]) class TestStage2FlattenOnGPU(DistributedTest): - """ZeRO-2 with small model should flatten on GPU (sufficient VRAM).""" + """ZeRO-1 and ZeRO-2 with small model should flatten on GPU (sufficient VRAM).""" world_size = 2 # Run on 2 GPUs when available - def test_flatten_on_gpu_path_taken(self, monkeypatch): + def test_flatten_on_gpu_path_taken(self, monkeypatch, zero_stage, dtype): """Assert the GPU flatten path was used (not CPU flatten + move).""" if not get_accelerator().is_available(): pytest.skip("Accelerator not available") @@ -28,7 +43,7 @@ def test_flatten_on_gpu_path_taken(self, monkeypatch): "train_micro_batch_size_per_gpu": 2, "gradient_accumulation_steps": 1, "zero_optimization": { - "stage": 2 + "stage": zero_stage }, "optimizer": { "type": "Adam", @@ -37,10 +52,7 @@ def test_flatten_on_gpu_path_taken(self, monkeypatch): } }, } - if get_accelerator().is_bf16_supported(): - config_dict["bf16"] = {"enabled": True} - elif get_accelerator().is_fp16_supported(): - config_dict["fp16"] = {"enabled": True, "initial_scale_power": 8} + _apply_dtype_to_config(config_dict, dtype) set_log_level_from_string("info") log_messages = [] @@ -64,7 +76,7 @@ def mock_logger_info(msg, *args, **kwargs): f"Expected GPU flatten path (logger.info should be called with 'Flattening param group' and 'on GPU'). " f"Captured messages: {log_messages}") - def test_flat_buffers_on_accelerator(self): + def test_flat_buffers_on_accelerator(self, zero_stage, dtype): """Regression: flat buffers must end up on the accelerator (not left on CPU).""" if not get_accelerator().is_available(): pytest.skip("Accelerator not available") @@ -72,7 +84,7 @@ def test_flat_buffers_on_accelerator(self): "train_micro_batch_size_per_gpu": 2, "gradient_accumulation_steps": 1, "zero_optimization": { - "stage": 2 + "stage": zero_stage }, "optimizer": { "type": "Adam", @@ -81,10 +93,7 @@ def test_flat_buffers_on_accelerator(self): } }, } - if get_accelerator().is_bf16_supported(): - config_dict["bf16"] = {"enabled": True} - elif get_accelerator().is_fp16_supported(): - config_dict["fp16"] = {"enabled": True, "initial_scale_power": 8} + _apply_dtype_to_config(config_dict, dtype) hidden_dim = 64 model = SimpleModel(hidden_dim=hidden_dim, nlayers=2) @@ -94,7 +103,7 @@ def test_flat_buffers_on_accelerator(self): model_parameters=model.parameters(), ) opt = engine.optimizer - assert hasattr(opt, "bit16_groups_flat"), "ZeRO-2 optimizer should have bit16_groups_flat" + assert hasattr(opt, "bit16_groups_flat"), "ZeRO-1/2 optimizer should have bit16_groups_flat" device_type = get_accelerator().device_name() for i, flat in enumerate(opt.bit16_groups_flat): assert flat.device.type == device_type, (f"Flat buffer {i} must be on {device_type}, got {flat.device}") From 60d5cb9736e872e1941cf1fc161e0105b824fdf7 Mon Sep 17 00:00:00 2001 From: Olatunji Ruwase Date: Tue, 3 Feb 2026 17:26:42 -0500 Subject: [PATCH 19/20] Fix gradient is ready with z2 (#7829) Correctly handle `ds_grad_is_ready` in ZeRO2 --------- Signed-off-by: Olatunji Ruwase Signed-off-by: Kento Sugama --- deepspeed/runtime/zero/stage_1_and_2.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py index 7f29826ca7c5..ce7d51547c06 100755 --- a/deepspeed/runtime/zero/stage_1_and_2.py +++ b/deepspeed/runtime/zero/stage_1_and_2.py @@ -1080,6 +1080,10 @@ def reduce_independent_p_g_buckets_and_remove_grads(self, param, i): bucket.index = 1 - bucket.index self.report_ipg_memory_usage("In ipg_remove_grads after reduce_ipg_grads", param.numel()) + # deal with a use-case of transient grads that will be generated in a loop for the same computation involving some model params - e.g. when performing a tiled memory calculation that shards the normal single sub-module call into a loop over a shards. + if not getattr(param, "ds_grad_is_ready", True): + return + param_id = self.get_param_id(param) assert self.params_already_reduced[param_id] == False, \ f"The parameter {debug_param2name(param)} has already been reduced. \ @@ -1103,10 +1107,8 @@ 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" - # deal with a use-case of transient grads that will be generated in a loop for the same computation involving some model params - e.g. when performing a tiled memory calculation that shards the normal single sub-module call into a loop over a shards. - if getattr(param, "ds_grad_is_ready", True): - bucket.grads.append(grad_reduc) - bucket.params.append((i, param.param_idx_in_group, param_id)) + bucket.grads.append(grad_reduc) + bucket.params.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): From 36106318e9e822942cbe41cb0f8c9a4a8a35f599 Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Sat, 7 Feb 2026 01:36:22 -0800 Subject: [PATCH 20/20] Fix AutoTP custom patterns: respect use_default_specs (#7827) The current code has the following issues: - `use_default_specs: false` doesn't work - Injection by the traditional pattern runs even when custom patterns are set - `mpu` needs to be passed to `deepspeed.initialize` (HF integration doesn't pass mpu) This PR fixes AutoTP setup to respect `use_default_specs: false` and disable the traditional injection path when custom patterns are enabled. Also, when `mpu` is not passed, we create a TP group in the initialization process. With these changes, the [related tests](https://github.com/deepspeedai/DeepSpeed/tree/master/tests/unit/model_parallelism) pass and [all AutoTP examples](https://github.com/tohtana/DeepSpeedExamples/tree/tohtana/custom_auto_tp/training/tensor_parallel) in DeepSpeedExamples work now ([PR](https://github.com/deepspeedai/DeepSpeedExamples/pull/998)). --------- Signed-off-by: Masahiro Tanaka Signed-off-by: Kento Sugama --- deepspeed/module_inject/auto_tp.py | 38 +++- .../runtime/tensor_parallel/init_utils.py | 4 +- .../test_autotp_custom_patterns.py | 206 +++++++++++++++++- .../model_parallelism/test_autotp_training.py | 21 +- 4 files changed, 254 insertions(+), 15 deletions(-) diff --git a/deepspeed/module_inject/auto_tp.py b/deepspeed/module_inject/auto_tp.py index 121e3938444a..fd35f0beef59 100755 --- a/deepspeed/module_inject/auto_tp.py +++ b/deepspeed/module_inject/auto_tp.py @@ -424,8 +424,8 @@ def _replace_with_config(self, child, name): # No matching spec found if self.partition_config.strict_mode: raise ValueError(f"No matching spec for {param_name}") - # Default: column parallel for Linear layers - spec = TPLayerSpec(patterns=[], partition_type=PartitionType.COLUMN) + # With partition_config, rely only on explicit specs and skip unmatched layers. + return child setattr(child, "replaced", True) @@ -439,6 +439,8 @@ def _replace_with_config(self, child, name): def _create_row_parallel_layer(self, module, spec: TPLayerSpec, name: str): """Create row-parallel layer (AllReduce after forward).""" + if self.conv_linear_layer: + return Conv_LinearALlreduce(module, self.mp_group, name=name) # Check for lm_head / embed_out if name == "lm_head" or name == 'embed_out': return LmHeadLinearAllreduce(module, self.mp_group) @@ -455,6 +457,12 @@ def _create_row_parallel_layer(self, module, spec: TPLayerSpec, name: str): def _create_column_parallel_layer(self, module, spec: TPLayerSpec, name: str): """Create column-parallel layer (AllReduce in backward).""" + if self.conv_linear_layer: + return conv_LinearLayer(module, self.mp_group, name=name) + # Only use fused-QKV heuristics when no partition_config is provided. + elif self.partition_config is None and require_tp_fused_qkvw(name, self.mp_size): + # Check and handle fused qkv for TP + return fused_LinearLayer(module, self.mp_group, fused_module=self.module) if spec.shape is not None: return SubParamLinearLayer( module, @@ -488,6 +496,7 @@ def _get_model_type(self) -> Optional[str]: def _slice_embedding(self, child, name, conv_linear_layer): if getattr(child, "replaced", False) == True: return + mp_replace = ReplaceWithTensorSlicing(mp_group=self.mp_group) if hasattr(child.weight, 'ds_tensor'): @@ -551,7 +560,30 @@ def _replace_module(self, r_module, prev_name='', prev_class_name=''): continue if len(child._buffers) != 0 and self.state_dict is not None: Loading.load_buffer(child, self.state_dict, checking_key) - if child.__class__ in self.linear_policies: + + # When using partition_config (custom patterns/presets), use pattern-based routing + # instead of linear_policies. This keeps all pattern logic centralized here. + if self.partition_config is not None: + full_name = prev_name + '.' + name if prev_name else name + if isinstance(child, nn.Embedding): + # Check if embedding matches any pattern + param_name = full_name + ".weight" + model_type = self._get_model_type() + spec = self.partition_config.find_matching_spec(param_name, model_type) + if spec is not None and spec.partition_type != PartitionType.SKIP: + new_child = self._slice_embedding(child, full_name, False) + if new_child is not None: + setattr(r_module, name, new_child) + # If no pattern matched or skip, leave embedding unchanged + elif hasattr(child, "weight") and getattr(child.weight, "dim", lambda: 0)() == 2: + new_child = self._replace_with_config(child, full_name) + if new_child is not None: + setattr(r_module, name, new_child) + else: + self.update_mp_params(child) + self._replace_module(child, name, class_name) + # Traditional path: use linear_policies for type-based routing + elif child.__class__ in self.linear_policies: setattr(r_module, name, self.linear_policies[child.__class__](child, prev_name + '.' + name, self.conv_linear_layer)) elif any(isinstance(child, lp) for lp in self.linear_policies): diff --git a/deepspeed/runtime/tensor_parallel/init_utils.py b/deepspeed/runtime/tensor_parallel/init_utils.py index 95dacb984cbe..453f00af6db9 100644 --- a/deepspeed/runtime/tensor_parallel/init_utils.py +++ b/deepspeed/runtime/tensor_parallel/init_utils.py @@ -87,7 +87,9 @@ def merge_tp_model_init_into_config(config_dict: dict, mpu, mesh_param, dist_mod if tp_group is not None and mpu is not None: raise ValueError("tp_model_init provided tp_group; deepspeed.initialize must not receive mpu.") if tp_group is None and mpu is None and mesh_param is None: - raise ValueError("tp_model_init did not provide tp_group; deepspeed.initialize requires mpu or mesh_param.") + # Auto-create TP groups for compatibility with HF Trainer (mpu is not passed). + from deepspeed.utils import groups + groups._init_tp_mesh_device(tensor_model_parallel_size=tp_size) tp_section = config_dict.get("tensor_parallel") if tp_section is None: diff --git a/tests/unit/model_parallelism/test_autotp_custom_patterns.py b/tests/unit/model_parallelism/test_autotp_custom_patterns.py index d19d8b595eeb..38e7fc7b0b59 100644 --- a/tests/unit/model_parallelism/test_autotp_custom_patterns.py +++ b/tests/unit/model_parallelism/test_autotp_custom_patterns.py @@ -13,7 +13,7 @@ from unit.common import DistributedTest, preferred_dtype from deepspeed.accelerator import get_accelerator from deepspeed.utils import groups -from deepspeed.module_inject.layers import (LinearAllreduce, LinearLayer, SubParamLinearLayer) +from deepspeed.module_inject.layers import (LinearAllreduce, LinearLayer, SubParamLinearLayer, fused_LinearLayer) from deepspeed.module_inject.autotp_config import AutoTPConfig from deepspeed.module_inject.auto_tp import AutoTP @@ -35,6 +35,49 @@ def forward(self, x): return x +class CustomLinearModule(torch.nn.Module): + + def __init__(self, hidden_dim): + super(CustomLinearModule, self).__init__() + self.weight = torch.nn.Parameter(torch.empty(hidden_dim, hidden_dim)) + self.bias = torch.nn.Parameter(torch.empty(hidden_dim)) + torch.nn.init.uniform_(self.weight, -0.02, 0.02) + torch.nn.init.uniform_(self.bias, -0.02, 0.02) + + def forward(self, x): + return torch.matmul(x, self.weight.transpose(-1, -2)) + self.bias + + +class CustomLinearModel(torch.nn.Module): + + def __init__(self, hidden_dim): + super(CustomLinearModel, self).__init__() + self.custom = CustomLinearModule(hidden_dim) + + def forward(self, x): + return self.custom(x) + + +class QKVLinearModule(torch.nn.Module): + + def __init__(self, hidden_dim): + super(QKVLinearModule, self).__init__() + self.qkv_proj = torch.nn.Linear(hidden_dim, hidden_dim * 3) + + def forward(self, x): + return self.qkv_proj(x) + + +class QKVLinearModel(torch.nn.Module): + + def __init__(self, hidden_dim): + super(QKVLinearModel, self).__init__() + self.self_attn = QKVLinearModule(hidden_dim) + + def forward(self, x): + return self.self_attn(x) + + def init_tp_engine(tp_size, partition_config=None): config_dict = { "train_micro_batch_size_per_gpu": 1, @@ -100,6 +143,15 @@ def gather_subparam_output(output, subparam_sizes, mp_group): return torch.cat(gathered_chunks, dim=-1) +def assert_close_for_preferred_dtype(actual, expected): + atol = 1e-3 + rtol = 2e-2 + if preferred_dtype() is torch.float32: + atol = 1e-5 + rtol = 1e-5 + torch.testing.assert_close(actual, expected, atol=atol, rtol=rtol) + + class TestAutoTPCustomPatterns(DistributedTest): world_size = 2 reuse_dist_env = False @@ -178,6 +230,151 @@ def test_custom_patterns_applied_via_config(self): assert isinstance(engine.module.linears[1], LinearLayer) assert isinstance(engine.module.linears[2], nn.Linear) + def test_use_default_specs_false_skips_unmatched_layers(self): + skip_on_device() + # Verify unmatched layers remain unsharded when defaults are disabled. + partition_config = { + "use_default_specs": + False, + "layer_specs": [ + { + "patterns": [".*linears\\.0\\.weight$"], + "partition_type": "row", + }, + { + "patterns": [".*linears\\.1\\.weight$"], + "partition_type": "column", + }, + ], + } + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-6 + } + }, + "tensor_parallel": { + "autotp_size": 2, + "partition_config": partition_config, + }, + "zero_optimization": { + "stage": 0, + } + } + if preferred_dtype() is torch.float16: + config_dict["fp16"] = {"enabled": True} + elif preferred_dtype() is torch.bfloat16: + config_dict["bf16"] = {"enabled": True} + + model = SequentialLinearModel(hidden_dim=16, nlayers=3) + engine, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + assert isinstance(engine.module.linears[0], LinearAllreduce) + assert isinstance(engine.module.linears[1], LinearLayer) + assert isinstance(engine.module.linears[2], nn.Linear) + + def test_custom_module_replacement_with_patterns(self): + skip_on_device() + # Verify custom linear-like modules are partitioned via patterns. + partition_config = { + "use_default_specs": False, + "layer_specs": [ + { + "patterns": [".*custom\\.weight$"], + "partition_type": "column", + }, + ], + } + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-6 + } + }, + "tensor_parallel": { + "autotp_size": 2, + "partition_config": partition_config, + }, + "zero_optimization": { + "stage": 0, + } + } + if preferred_dtype() is torch.float16: + config_dict["fp16"] = {"enabled": True} + elif preferred_dtype() is torch.bfloat16: + config_dict["bf16"] = {"enabled": True} + + model = CustomLinearModel(hidden_dim=16) + engine, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + assert isinstance(engine.module.custom, LinearLayer) + + def test_custom_pattern_disables_fused_qkv_heuristic(self): + skip_on_device() + # Use a qkv_proj name that would trigger the fused-QKV heuristic, then + # verify custom patterns override that path and preserve correctness. + torch.manual_seed(1234) + hidden_dim = 16 + qkv_sizes = (hidden_dim, hidden_dim, hidden_dim) + partition_config = { + "use_default_specs": + False, + "layer_specs": [ + { + "patterns": [".*self_attn\\.qkv_proj\\.weight$"], + "partition_type": "column", + "shape": [list(qkv_sizes), -1], + "partition_dim": 0, + }, + ], + } + config_dict = { + "train_micro_batch_size_per_gpu": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 1e-6 + } + }, + "tensor_parallel": { + "autotp_size": 2, + "partition_config": partition_config, + }, + "zero_optimization": { + "stage": 0, + } + } + if preferred_dtype() is torch.float16: + config_dict["fp16"] = {"enabled": True} + elif preferred_dtype() is torch.bfloat16: + config_dict["bf16"] = {"enabled": True} + + model = QKVLinearModel(hidden_dim=hidden_dim) + baseline = deepcopy(model).to(get_accelerator().current_device(), dtype=preferred_dtype()) + engine, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + qkv_layer = engine.module.self_attn.qkv_proj + # Custom pattern should force SubParamLinearLayer (shape-based path), + # and avoid the legacy fused-QKV heuristic despite the qkv_proj name. + assert isinstance(qkv_layer, SubParamLinearLayer) + assert not isinstance(qkv_layer, fused_LinearLayer) + + assert qkv_layer.partition_dim == 0 + assert qkv_layer._subparam_sizes == qkv_sizes + assert qkv_layer._orig_weight_shape == (hidden_dim * 3, hidden_dim) + + qkv_layer.gather_params([qkv_layer.weight, qkv_layer.bias]) + torch.testing.assert_close(qkv_layer.weight, baseline.self_attn.qkv_proj.weight) + if qkv_layer.bias is not None: + torch.testing.assert_close(qkv_layer.bias, baseline.self_attn.qkv_proj.bias) + + torch.manual_seed(4321) + inputs = torch.randn(2, hidden_dim, dtype=preferred_dtype(), device=get_accelerator().current_device()) + full_output = baseline(inputs) + tp_output = engine.module(inputs) + assert_close_for_preferred_dtype(tp_output, full_output) + def test_first_match_precedence(self): skip_on_device() partition_config = { @@ -294,9 +491,4 @@ def test_gqa_uneven_qkv_fused_forward(self): gathered_output = gather_subparam_output(tp_output, (q_size, k_size, v_size), groups.get_tensor_model_parallel_group()) - atol = 1e-3 - rtol = 2e-2 - if preferred_dtype() is torch.float32: - atol = 1e-5 - rtol = 1e-5 - torch.testing.assert_close(gathered_output, full_output, atol=atol, rtol=rtol) + assert_close_for_preferred_dtype(gathered_output, full_output) diff --git a/tests/unit/model_parallelism/test_autotp_training.py b/tests/unit/model_parallelism/test_autotp_training.py index baaca247c229..64f0b1113b16 100644 --- a/tests/unit/model_parallelism/test_autotp_training.py +++ b/tests/unit/model_parallelism/test_autotp_training.py @@ -165,19 +165,32 @@ def test_tp_model_init_config_autotp_size_mismatch(self): with pytest.raises(ValueError, match="tensor_parallel.autotp_size"): deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict, mpu=DummyMPU()) - def test_tp_model_init_requires_mpu_or_mesh_param(self): + def test_tp_model_init_autocreates_tp_group(self): skip_on_device() reset_tp_model_init_state() + # Verify tp_model_init creates TP groups when no mpu is provided. model = SimpleModel(hidden_dim=8) - deepspeed.tp_model_init(model, tp_size=1, dtype=preferred_dtype()) + tp_size = 2 + deepspeed.tp_model_init(model, tp_size=tp_size, dtype=preferred_dtype()) config_dict = { "train_micro_batch_size_per_gpu": 1, + "tensor_parallel": { + "partition_config": { + "use_default_specs": False, + "layer_specs": [{ + "patterns": [".*\\.weight$"], + "partition_type": "skip", + }], + } + }, "zero_optimization": { "stage": 0, } } - with pytest.raises(ValueError, match="requires mpu or mesh_param"): - deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + engine, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + assert engine.autotp_size() == tp_size + assert groups.get_tensor_model_parallel_world_size() == tp_size + assert groups.get_data_parallel_world_size() == dist.get_world_size() // tp_size def test_tp_model_init_tp_group_rejects_mpu(self): skip_on_device()