Merge branch 'master' into olruwase/no_grad_sync_ctxt

.github/workflows/nv-ds-chat.yml

@@ -12,6 +12,7 @@ on:
         type: string
   pull_request:
     paths:
+      - ".github/workflows/nv-ds-chat.yml"
       - "deepspeed/runtime/zero/stage_1_and_2.py"
       - "deepspeed/runtime/zero/stage3.py"
       - "deepspeed/runtime/hybrid_engine.py"
@@ -42,6 +43,7 @@ jobs:
 
       - name: Install deepspeed
         run: |
+          pip install transformers==4.45.2
           pip install .[dev]
           ds_report
 

.github/workflows/python.yml

@@ -21,10 +21,10 @@ jobs:
   unit-tests:
     strategy:
       matrix:
-        pyVersion: ["3.7", "3.8", "3.9", "3.10"]
+        pyVersion: ["3.8", "3.9", "3.10", "3.11", "3.12"]
       fail-fast: false
 
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-24.04
     container:
       image: deepspeed/gh-builder:py${{ matrix.pyVersion }}
 

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ repos:
     -   id: trailing-whitespace
 
 -   repo: https://github.com/google/yapf
-    rev: v0.32.0
+    rev: v0.40.0
     hooks:
     -   id: yapf
 
@@ -65,7 +65,7 @@ repos:
         ]
 
 -   repo: https://github.com/pycqa/flake8
-    rev: 4.0.1
+    rev: 5.0.4
     hooks:
     -   id: flake8
         args: ['--config=.flake8']

README.md

@@ -142,7 +142,7 @@ DeepSpeed has been integrated with several different popular open-source DL fram
 | PyTorch Nightly | [![nv-torch-nightly-v100](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml) |
 | Integrations | [![nv-transformers-v100](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-transformers-v100.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-transformers-v100.yml) [![nv-lightning-v100](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-lightning-v100.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-lightning-v100.yml) [![nv-accelerate-v100](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-accelerate-v100.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-accelerate-v100.yml) [![nv-mii](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-mii.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-mii.yml) [![nv-ds-chat](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-ds-chat.yml) [![nv-sd](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-sd.yml/badge.svg)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-sd.yml) |
 | Misc | [![Formatting](https://github.com/microsoft/DeepSpeed/actions/workflows/formatting.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/formatting.yml) [![pages-build-deployment](https://github.com/microsoft/DeepSpeed/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/microsoft/DeepSpeed/actions/workflows/pages/pages-build-deployment) [![Documentation Status](https://readthedocs.org/projects/deepspeed/badge/?version=latest)](https://deepspeed.readthedocs.io/en/latest/?badge=latest)[![python](https://github.com/microsoft/DeepSpeed/actions/workflows/python.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/python.yml) |
-| Huawei Ascend NPU | [![Huawei Ascend NPU](https://github.com/cosdt/DeepSpeed/actions/workflows/huawei-ascend-npu.yml/badge.svg?branch=master)](https://github.com/cosdt/DeepSpeed/actions/workflows/huawei-ascend-npu.yml) |
+| Huawei Ascend NPU | [![Huawei Ascend NPU](https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml/badge.svg?branch=main)](https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml) |
 
 # Installation
 

csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu

@@ -101,7 +101,15 @@ __global__ void apply_rotary_pos_half(T* mixed_query,
 
 #if defined(__HIP_PLATFORM_AMD__) and ROCM_WAVEFRONT_SIZE == 64
 #define LAUNCH_FOR_ALIGNMENT(ALIGNMENT)         \
-    if (threads_per_head == 64) {               \
+    if (threads_per_head == 4) {                \
+        LAUNCH_ROT_POS_EMB_HALF(4, ALIGNMENT);  \
+    } else if (threads_per_head == 8) {         \
+        LAUNCH_ROT_POS_EMB_HALF(8, ALIGNMENT);  \
+    } else if (threads_per_head == 16) {        \
+        LAUNCH_ROT_POS_EMB_HALF(16, ALIGNMENT); \
+    } else if (threads_per_head == 32) {        \
+        LAUNCH_ROT_POS_EMB_HALF(32, ALIGNMENT); \
+    } else if (threads_per_head == 64) {        \
         LAUNCH_ROT_POS_EMB_HALF(64, ALIGNMENT); \
     } else {                                    \
         assert(false);                          \

deepspeed/__init__.py

@@ -165,8 +165,8 @@ def initialize(args=None,
     if hasattr(args, "deepscale_config") and args.deepscale_config is not None:
         logger.warning("************ --deepscale_config is deprecated, please use --deepspeed_config ************")
         if hasattr(args, "deepspeed_config"):
-            assert (args.deepspeed_config is
-                    None), "Not sure how to proceed, we were given both a deepscale_config and deepspeed_config"
+            assert (args.deepspeed_config
+                    is None), "Not sure how to proceed, we were given both a deepscale_config and deepspeed_config"
         args.deepspeed_config = args.deepscale_config
         args.deepscale_config = None
 

deepspeed/autotuning/autotuner.py

@@ -248,8 +248,8 @@ def mp_size(self):
         return self.autotuning_config.mp_size
 
     def max_train_micro_batch_size_per_gpu(self):
-        if self.max_train_batch_size(
-        ) and self.max_train_batch_size() > 0:  # if the user specifies a max_train_batch_size
+        if self.max_train_batch_size() and self.max_train_batch_size(
+        ) > 0:  # if the user specifies a max_train_batch_size
             max_train_micro_batch_size = self.max_train_batch_size() * self.mp_size() // (
                 self.exp_num_gpus * self.exp_num_nodes)  # gradient accumulation steps >=1
             return min(self.autotuning_config.max_train_micro_batch_size_per_gpu, max_train_micro_batch_size)
@@ -964,8 +964,8 @@ def get_min_max_micro_batch_size(self, stage, min_micro_batch_size, calculated_m
                     low = mid + 1
                     self.update_records(tuning_space_name, exp, metric_val, 1)
                     used_micro_batch_sizes.append(mid)
-                    if prev_metric_val and (
-                        (metric_val - prev_metric_val) / prev_metric_val) < METRIC_PERCENT_DIFF_CONST:
+                    if prev_metric_val and ((metric_val - prev_metric_val) /
+                                            prev_metric_val) < METRIC_PERCENT_DIFF_CONST:
                         logger.info(f"performance plateaus at mbs = {low}")
                         break
                     prev_metric_val = metric_val
@@ -1026,8 +1026,8 @@ def get_tuning_micro_batch_size_list(self, min_micro_batch_size, max_micro_batch
         # NUM_GPUS=$(( ${NUM_WORKERS} * ${NUM_GPUS_PER_WORKER} ))
         # DP_SIZE=$(( ${NUM_GPUS} / (${PP_SIZE} * ${MP_SIZE}) ))
         # GRAD_ACC_STEPS=$(( ${TARGET_GLOBAL_BATCH_SIZE} / (${BATCH_SIZE} * ${DP_SIZE}) ))
-        if self.max_train_batch_size(
-        ) and self.max_train_batch_size() > 0:  # if the user specifies a max_train_batch_size
+        if self.max_train_batch_size() and self.max_train_batch_size(
+        ) > 0:  # if the user specifies a max_train_batch_size
             max_train_batch_size_per_gpu = self.max_train_batch_size() * self.mp_size() // (self.exp_num_gpus *
                                                                                             self.exp_num_nodes)
         else:

deepspeed/elasticity/elastic_agent.py

@@ -160,8 +160,8 @@ def _invoke_run(self, role: str = "default") -> RunResult:
                          f" Waiting {self._exit_barrier_timeout} seconds for other agents to finish.")
                 self._exit_barrier()
                 return run_result
-            elif state in {WorkerState.UNHEALTHY, WorkerState.FAILED
-                           } or len(participants) > len(rdzv_handler._state_holder.state.participants):
+            elif state in {WorkerState.UNHEALTHY, WorkerState.FAILED} or len(participants) > len(
+                    rdzv_handler._state_holder.state.participants):
                 if self._remaining_restarts > 0:
                     log.info(f"[{role}] Worker group {state.name}. "
                              f"{self._remaining_restarts}/{spec.max_restarts} attempts left;"

deepspeed/module_inject/replace_module.py

@@ -496,9 +496,10 @@ def conv2d_parallel_shard_weights(model, rank, world_size):
         if not dist.is_initialized() or dist.get_rank() == 0:
             print("Saving tp-sharded checkpoints")
             torch.save(
-                OrderedDict({k: v
-                             for k, v in dict(replaced_module.state_dict()).items()
-                             if transformer_name not in k}), f'{config.save_mp_checkpoint_path}/{non_tp_ckpt_name}')
+                OrderedDict({
+                    k: v
+                    for k, v in dict(replaced_module.state_dict()).items() if transformer_name not in k
+                }), f'{config.save_mp_checkpoint_path}/{non_tp_ckpt_name}')
 
             dtype_reprs = {
                 torch.float32: 'float32',

deepspeed/runtime/config.py

@@ -1012,8 +1012,8 @@ def _do_error_check(self):
             self.gradient_accumulation_steps), "DeepSpeedConfig: {} is not defined".format(GRADIENT_ACCUMULATION_STEPS)
 
         if self.zero_enabled:
-            assert (self.zero_optimization_stage <=
-                    ZeroStageEnum.max_stage), "DeepSpeedConfig: Maximum supported ZeRO stage is {}".format(
+            assert (self.zero_optimization_stage
+                    <= ZeroStageEnum.max_stage), "DeepSpeedConfig: Maximum supported ZeRO stage is {}".format(
                         ZeroStageEnum.max_stage)
 
         if self.fp16_master_weights_and_gradients:

deepspeed/runtime/eigenvalue.py

@@ -114,8 +114,8 @@ def compute_eigenvalue(self, module, device=None, scale=1.0):
             eigenvalue_current, eigenvalue_previous = 1., 0.
 
             while (i < self.max_iter) and abs(eigenvalue_current) > 0 and (abs(
-                (eigenvalue_current - eigenvalue_previous) / eigenvalue_current) >=
-                                                                           self.tol):  # test convergence criteria
+                (eigenvalue_current - eigenvalue_previous) / eigenvalue_current)
+                                                                           >= self.tol):  # test convergence criteria
                 eigenvalue_previous = eigenvalue_current
 
                 Hv = torch.autograd.grad(grads, params, grad_outputs=v, only_inputs=True, retain_graph=True)

deepspeed/runtime/pipe/engine.py

@@ -640,9 +640,10 @@ def _aggregate_total_loss(self):
             self.dp_group_loss = losses[0].clone().detach()
             agg_loss = losses[1].clone().detach()
             if additional_losses is not None:
-                self.agg_additional_losses = OrderedDict(
-                    {name: losses[2 + i].clone().detach()
-                     for i, name in enumerate(additional_losses.keys())})
+                self.agg_additional_losses = OrderedDict({
+                    name: losses[2 + i].clone().detach()
+                    for i, name in enumerate(additional_losses.keys())
+                })
         return agg_loss
 
     def set_dataloader(self, loader):

deepspeed/runtime/utils.py

@@ -257,8 +257,8 @@ def has_overflow(self, params, has_moe_params=None):
         elif self.mpu is not None:
             if self.deepspeed is not None:
                 using_pipeline = hasattr(self.deepspeed, 'pipeline_enable_backward_allreduce')
-                if (using_pipeline and self.deepspeed.pipeline_enable_backward_allreduce is False) or (
-                        not using_pipeline and self.deepspeed.enable_backward_allreduce is False):
+                if (using_pipeline and self.deepspeed.pipeline_enable_backward_allreduce
+                        is False) or (not using_pipeline and self.deepspeed.enable_backward_allreduce is False):
                     dist.all_reduce(overflow_gpu, op=dist.ReduceOp.MAX, group=self.mpu.get_data_parallel_group())
             dist.all_reduce(overflow_gpu, op=dist.ReduceOp.MAX, group=self.mpu.get_model_parallel_group())
         elif self.deepspeed is not None and self.deepspeed.enable_backward_allreduce is False:

deepspeed/runtime/zero/parameter_offload.py

@@ -133,20 +133,28 @@ def __init__(
         self.persistent_parameters = self.mark_persistent_parameters(self.param_numel_persistence_threshold,
                                                                      self.model_persistence_threshold)
 
-        self.param_coordinators = {}
         self._prefetch_bucket_sz = int(prefetch_bucket_size)
         self._max_reuse_distance_in_numel = int(max_reuse_distance)
         self._max_available_parameters_in_numel = int(max_live_parameters)
         self.__allgather_stream = None if get_accelerator().is_synchronized_device() else get_accelerator().Stream(
         ) if overlap_comm else get_accelerator().default_stream()
 
         if not hasattr(module, "ds_inflight_param_registry"):
-            module.ds_inflight_param_registry = dict()
-            # we need two registries, one for training and one for eval. They will be used when creating PartitionedParameterCoordinator
-            module.ds_inflight_param_registry[True] = InflightParamRegistry()
-            module.ds_inflight_param_registry[False] = InflightParamRegistry()
+            module.ds_inflight_param_registry = InflightParamRegistry()
         self.__inflight_param_registry = module.ds_inflight_param_registry
 
+        self.param_coordinator = PartitionedParameterCoordinator(
+            prefetch_bucket_sz=self._prefetch_bucket_sz,
+            max_reuse_distance_in_numel=self._max_reuse_distance_in_numel,
+            max_available_parameters_in_numel=self._max_available_parameters_in_numel,
+            allgather_stream=self.__allgather_stream,
+            inflight_param_registry=self.__inflight_param_registry,
+            prefetch_nvme=self.offload_device == OffloadDeviceEnum.nvme,
+            timers=self.timers,
+            zero_quantized_weights=self.zero_quantized_weights,
+            zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights,
+        )
+
         self.forward_hooks = []
         self.backward_hooks = []
         self.setup_zero_stage3_hooks()
@@ -161,26 +169,13 @@ def partition_all_parameters(self):
         """Partitioning Parameters that were not partitioned usually if parameters
         of modules whose input parameters do not require grad computation do not
         trigger post call and will therefore will remain unpartitioned"""
-        self.get_param_coordinator(training=self.module.training).release_and_reset_all(self.module)
+        self.get_param_coordinator().release_and_reset_all(self.module)
         for param in iter_params(self.module, recurse=True):
             if param.ds_status != ZeroParamStatus.NOT_AVAILABLE:
                 raise RuntimeError(f"{param.ds_summary()} expected to be released")
 
-    def get_param_coordinator(self, training):
-        if not training in self.param_coordinators:
-            self.param_coordinators[training] = PartitionedParameterCoordinator(
-                prefetch_bucket_sz=self._prefetch_bucket_sz,
-                max_reuse_distance_in_numel=self._max_reuse_distance_in_numel,
-                max_available_parameters_in_numel=self._max_available_parameters_in_numel,
-                allgather_stream=self.__allgather_stream,
-                inflight_param_registry=self.__inflight_param_registry[training],
-                prefetch_nvme=self.offload_device == OffloadDeviceEnum.nvme,
-                timers=self.timers,
-                zero_quantized_weights=self.zero_quantized_weights,
-                zero_quantized_nontrainable_weights=self.zero_quantized_nontrainable_weights,
-            )
-
-        return self.param_coordinators[training]
+    def get_param_coordinator(self):
+        return self.param_coordinator
 
     def empty_partition_cache(self):
         self.partition_all_parameters()
@@ -228,14 +223,14 @@ def setup_zero_stage3_hooks(self):
 
         #reset step if in inference mode
         @instrument_w_nvtx
-        def _end_of_forward_hook(module, *args):
+        def _start_of_forward_hook(module, *args):
+
+            self.get_param_coordinator().reset_step()
 
-            if not torch._C.is_grad_enabled():
-                self.get_param_coordinator(training=False).reset_step()
+        self.module.register_forward_pre_hook(_start_of_forward_hook)
 
         #likely one of them should be enough but just to be safe
         self._register_hooks_recursively(self.module)
-        self.module.register_forward_hook(_end_of_forward_hook)
 
         # Add top module to stack trace
         global FWD_MODULE_STACK
@@ -447,7 +442,7 @@ def pre_sub_module_forward_function(self, sub_module):
         global FWD_MODULE_STACK
         FWD_MODULE_STACK.append(sub_module)
 
-        param_coordinator = self.get_param_coordinator(training=sub_module.training)
+        param_coordinator = self.get_param_coordinator()
         param_coordinator.trace_prologue(sub_module)
         if param_coordinator.is_record_trace():
             param_coordinator.record_module(sub_module)
@@ -460,29 +455,29 @@ def post_sub_module_forward_function(self, sub_module):
         see_memory_usage(f"After sub module function {sub_module.__class__.__name__} {sub_module.id} before release",
                          force=False)
 
-        param_coordinator = self.get_param_coordinator(training=sub_module.training)
+        param_coordinator = self.get_param_coordinator()
         param_coordinator.release_sub_module(sub_module)
 
         see_memory_usage(f"After sub module function {sub_module.__class__.__name__}  {sub_module.id} after release",
                          force=False)
 
     @torch.no_grad()
     def pre_sub_module_backward_function(self, sub_module):
-        assert sub_module.training, "backward pass is invalid for module in evaluation mode"
-        param_coordinator = self.get_param_coordinator(training=True)
+        # assert sub_module.training, "backward pass is invalid for module in evaluation mode"
+        param_coordinator = self.get_param_coordinator()
         param_coordinator.trace_prologue(sub_module)
         if param_coordinator.is_record_trace():
             param_coordinator.record_module(sub_module)
         param_coordinator.fetch_sub_module(sub_module, forward=False)
 
     @torch.no_grad()
     def post_sub_module_backward_function(self, sub_module):
-        assert sub_module.training, "backward pass is invalid for module in evaluation mode"
+        # assert sub_module.training, "backward pass is invalid for module in evaluation mode"
         see_memory_usage(
             f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} before release",
             force=False)
 
-        self.get_param_coordinator(training=True).release_sub_module(sub_module)
+        self.get_param_coordinator().release_sub_module(sub_module)
 
         see_memory_usage(
             f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} after release",