zero3 performance optimizations

deepspeed/runtime/zero/partition_parameters.py

@@ -11,6 +11,7 @@
 import functools
 import itertools
 from typing import List
+import logging
 import torch
 from torch import Tensor
 from deepspeed import comm as dist
@@ -898,7 +899,8 @@ def all_gather_coalesced(params: Iterable[Parameter], safe_mode: bool = False) -
             # to debug correctness issues.
             params = sorted(params, key=lambda p: p.ds_id)
 
-            debug_rank0(f"-allgather_coalesced: {[p.ds_id for p in params]}")
+            if logger.isEnabledFor(logging.DEBUG):
+                debug_rank0(f"-allgather_coalesced: {[p.ds_id for p in params]}")
 
             if safe_mode:
                 # ensure that same list (with same ordering) of parameters are

deepspeed/runtime/zero/partitioned_param_coordinator.py

@@ -15,6 +15,7 @@
 from deepspeed.runtime.swap_tensor.partitioned_param_swapper import PartitionedParamStatus
 from deepspeed.utils.debug import debug_module2name_id, debug_param2name_id
 from deepspeed.accelerator import get_accelerator
+import logging
 
 
 def debug_rank0(message: str) -> None:
@@ -235,25 +236,28 @@ def fetch_sub_module(self, current_submodule: Module) -> None:
         2. kick off fetch for next few parameters we will need later (prefetch)
         3. block on parameters in immediately required sub module
         """
-        debug_rank0(
-            f"{self.__step_id}: M{current_submodule.id}({type(current_submodule).__name__}) P{[p.ds_id for p in iter_params(current_submodule)]} "
-            + 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],
-            }))
+        if logger.isEnabledFor(logging.DEBUG):
+            debug_rank0(
+                f"{self.__step_id}: M{current_submodule.id}({type(current_submodule).__name__}) P{[p.ds_id for p in iter_params(current_submodule)]} "
+                + 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 = frozenset(iter_params(current_submodule))
 
         # kick off all gather for params in the immediately required submodule
-        for param in params_to_fetch:
-            debug_rank0(f"-fetch: {param.ds_summary()}")
+        if logger.isEnabledFor(logging.DEBUG):
+            for param in params_to_fetch:
+                debug_rank0(f"-fetch: {param.ds_summary()}")
         self.__all_gather_params(params_to_fetch)
 
         # wait for parameters in the immediately needed submodule to become available
         for param in params_to_fetch:
             param.ds_active_sub_modules.add(current_submodule.id)
-            debug_rank0(f"-wait: {param.ds_summary()}")
+            if logger.isEnabledFor(logging.DEBUG):
+                debug_rank0(f"-wait: {param.ds_summary()}")
             if param in self.__inflight_param_registry:
                 with get_accelerator().stream(self.__allgather_stream):
                     while self.__ongoing_fetch_events and self.__ongoing_fetch_events[0].query():
@@ -328,8 +332,9 @@ def _is_currently_on_nvme(param):
                         params_to_prefetch.add(param_in_trace.param)
                         numel_prefetching += param_in_trace.param.ds_numel
 
-                for param in params_to_prefetch:
-                    debug_rank0(f"-prefetch: {param.ds_summary()}")
+                if logger.isEnabledFor(logging.DEBUG):
+                    for param in params_to_prefetch:
+                        debug_rank0(f"-prefetch: {param.ds_summary()}")
                 self.__all_gather_params(params_to_prefetch)
 
                 if self.__prefetch_nvme:
@@ -394,7 +399,8 @@ def __all_gather_params(self, params: Set[Parameter]) -> None:
     @instrument_w_nvtx
     def __release_param(self, param: Parameter) -> None:
         if param.ds_status == ZeroParamStatus.AVAILABLE and not param.ds_active_sub_modules:
-            debug_rank0(f"-release: {param.ds_summary()}")
+            if logger.isEnabledFor(logging.DEBUG):
+                debug_rank0(f"-release: {param.ds_summary()}")
             param.partition()
             self.__n_available_params -= param.ds_numel
 

deepspeed/runtime/zero/stage3.py

@@ -261,7 +261,6 @@ def __init__(self,
         if self.swap_optimizer:
             self._configure_tensor_swapping(offload_optimizer_config, aio_config)
 
-        self.params_in_ipg_bucket = []
         self.is_gradient_accumulation_boundary: bool = True
 
         self.param_reduce_events: Deque[get_accelerator().Event] = collections.deque()
@@ -277,7 +276,6 @@ def __init__(self,
         self.grads_in_ipg_bucket = []
         self.params_in_ipg_bucket = []
 
-        self.params_already_reduced = []
         self.is_gradient_accumulation_boundary = True
         self._release_ipg_buffers()
         self.previous_reduced_grads = None
@@ -291,7 +289,6 @@ def __init__(self,
                 unique_id = id(param)
                 self.param_id[unique_id] = count
                 self.param_dict[count] = param
-                self.params_already_reduced.append(False)
                 count = count + 1
 
         #Largest partitioned param
@@ -307,7 +304,6 @@ def __init__(self,
 
         if self.offload_optimizer:
             self.norm_for_param_grads = {}
-            self.local_overflow = False
 
         # stores if a partition has been reduced in this step
         self.is_partition_reduced = {}
@@ -397,20 +393,20 @@ def _setup_for_real_optimizer(self):
                                                                 dtype=self.dtype,
                                                                 device=get_accelerator().current_device_name())
 
-        grad_partitions_flat_buffer = None
+        self.grad_partitions_flat_buffer = None
         self.__param_id_to_grad_partition: Dict[int, Tensor] = {}
 
         all_params = list(itertools.chain.from_iterable(self.fp16_groups))
 
-        grad_partitions_flat_buffer: Tensor = torch.zeros(sum(p.partition_numel() for p in all_params),
-                                                          dtype=self.dtype,
-                                                          device=self.device)
+        self.grad_partitions_flat_buffer: Tensor = torch.zeros(sum(p.partition_numel() for p in all_params),
+                                                               dtype=self.dtype,
+                                                               device=self.device)
         if self.offload_optimizer_pin_memory:
-            grad_partitions_flat_buffer = get_accelerator().pin_memory(grad_partitions_flat_buffer)
+            self.grad_partitions_flat_buffer = get_accelerator().pin_memory(self.grad_partitions_flat_buffer)
 
         offset = 0
         for param in all_params:
-            self.__param_id_to_grad_partition[param.ds_id] = grad_partitions_flat_buffer.narrow(
+            self.__param_id_to_grad_partition[param.ds_id] = self.grad_partitions_flat_buffer.narrow(
                 0, offset, param.partition_numel())
             offset += param.partition_numel()
 
@@ -966,11 +962,6 @@ def independent_gradient_partition_epilogue(self):
 
         self.reduce_and_partition_stream.synchronize()
 
-        # if dist.get_rank() == 0:
-        #    logger.info("Params already reduced %s", self.params_already_reduced)
-        for i in range(len(self.params_already_reduced)):
-            self.params_already_reduced[i] = False
-
         #in case of cpu offload, averaged gradients are already in fp32_partitioned_groups_flat.grad
         #TODO: use a similar code path for both cpu_offload and non-cpu offload
         if not self.offload_optimizer:
@@ -1045,18 +1036,11 @@ def reduce_independent_p_g_buckets_and_remove_grads(self, param, i):
         # 0). Otherwise if the incoming param.ds_numel is large, this branch may get triggered on a
         # garbage data and `self.average_tensor()` will crash because its params_to_reduce will be
         # empty, while reduction_list will have that garbage data.
-        if self.elements_in_ipg_bucket > 0 and self.elements_in_ipg_bucket + param.ds_numel > self.reduce_bucket_size:
+        if self.elements_in_ipg_bucket + param.ds_numel > self.reduce_bucket_size and self.elements_in_ipg_bucket > 0:
             self.report_ipg_memory_usage("In ipg_remove_grads before reduce_ipg_grads", param.ds_numel)
 
             self.__reduce_and_partition_ipg_grads()
 
-        param_id = self.get_param_id(param)
-
-        assert self.params_already_reduced[param_id] == False, \
-            f"The parameter {param_id} has already been reduced. \
-            Gradient computed twice for this partition. \
-            Multiple gradient reduction is currently not supported"
-
         self.__add_grad_to_ipg_bucket(param)
 
     @instrument_w_nvtx
@@ -1087,8 +1071,6 @@ def __reduce_and_partition_ipg_grads(self, safe_mode: bool = False) -> None:
                 raise RuntimeError(f"{param.grad.numel()} != {param.ds_numel} Cannot reduce scatter "
                                    f"gradients whose size is not same as the params")
 
-        self.params_in_ipg_bucket.sort(key=lambda p: p.ds_id)
-
         assert len(set(p.ds_id for p in self.params_in_ipg_bucket)) == len(self.params_in_ipg_bucket)
 
         while self.param_reduce_events and self.param_reduce_events[0].query():
@@ -1100,7 +1082,13 @@ def __reduce_and_partition_ipg_grads(self, safe_mode: bool = False) -> None:
             if safe_mode:
                 assert_ints_same_as_other_ranks([p.ds_id for p in self.params_in_ipg_bucket])
 
-            grad_partitions = self.__avg_scatter_grads(self.params_in_ipg_bucket)
+            if self.contiguous_gradients and not self.reduce_scatter:
+                grad_bucket = self.__ipg_bucket_flat_buffer.narrow(0, 0, self.elements_in_ipg_bucket)
+                grad_partitions = self.__avg_scatter_contiguous_grads(grad_bucket)
+            else:
+                self.params_in_ipg_bucket.sort(key=lambda p: p.ds_id)
+                grad_partitions = self.__avg_scatter_grads(self.params_in_ipg_bucket)
+
             self.partition_grads(self.params_in_ipg_bucket, grad_partitions)
 
             self.params_in_ipg_bucket.clear()
@@ -1109,6 +1097,47 @@ def __reduce_and_partition_ipg_grads(self, safe_mode: bool = False) -> None:
             event.record()
             self.param_reduce_events.append(event)
 
+    @instrument_w_nvtx
+    def __avg_scatter_contiguous_grads(self, buffer_to_reduce: Tensor) -> List[Tensor]:
+        dtype = buffer_to_reduce.dtype
+        if self.communication_data_type == self.dtype:
+            buffer_to_reduce = buffer_to_reduce.to(self.communication_data_type)
+        if self.postscale_gradients and self.gradient_predivide_factor != 1.0:
+            buffer_to_reduce = buffer_to_reduce.div_(self.gradient_predivide_factor)
+
+        world_sz = dist.get_world_size(self.dp_process_group)
+        rank = dist.get_rank(self.dp_process_group)
+        buffer_to_reduce.div_(world_sz)
+
+        dist.all_reduce(buffer_to_reduce, group=self.dp_process_group)
+
+        if self.postscale_gradients and self.gradient_predivide_factor != world_sz:
+            buffer_to_reduce = buffer_to_reduce.mul(self.gradient_predivide_factor)
+
+        if self.communication_data_type != self.dtype:
+            buffer_to_reduce = buffer_to_reduce.to(self.dtype)
+
+        grad_partitions = []
+        grad_offset_in_buffer = 0
+        for param in self.params_in_ipg_bucket:
+            grad = param.grad
+            chunk_sz = math.ceil(grad.numel() / world_sz)
+
+            start_offset = grad_offset_in_buffer + min(rank * chunk_sz, grad.numel())
+            end_offset = grad_offset_in_buffer + min(rank * chunk_sz + chunk_sz, grad.numel())
+
+            partition = buffer_to_reduce[start_offset:end_offset]
+            if param.partition_numel() != partition.numel():
+                padded_partition = torch.empty(param.partition_numel(), device=grad.device, dtype=grad.dtype)
+                if partition.numel() > 0:
+                    padded_partition[:partition.numel()] = partition
+                grad_partitions.append(padded_partition)
+            else:
+                grad_partitions.append(partition)
+            grad_offset_in_buffer += grad.numel()
+
+        return grad_partitions
+
     @instrument_w_nvtx
     def __avg_scatter_grads(self, params_to_reduce: List[Parameter]) -> List[Tensor]:
         """average gradients and scatter partitions across ranks"""
@@ -1222,15 +1251,6 @@ def partition_grads(self, params_to_release: List[Parameter], grad_partitions: L
                 # operations and so it can be used asynchronously
                 grad_buffer = cuda_grad_buffer
 
-            if hasattr(self.inf_or_nan_tracker, "logical_or_"):
-                self.inf_or_nan_tracker.logical_or_(torch.isinf(grad_buffer).any())
-                self.inf_or_nan_tracker.logical_or_(torch.isnan(grad_buffer).any())
-            else:
-                # logical_or_ not available in older versions of pytorch
-                self.inf_or_nan_tracker += torch.isinf(grad_buffer).any()
-                self.inf_or_nan_tracker += torch.isnan(grad_buffer).any()
-                self.inf_or_nan_tracker = self.inf_or_nan_tracker > 0
-
             # offload the gradient partition if applicable
             if self.offload_optimizer:
                 i, dest_offset, _ = self.grad_position[self.get_param_id(param)]
@@ -1561,7 +1581,6 @@ def free_grad_in_param_list(self, param_list):
 
     def reset_cpu_buffers(self):
         self.norm_for_param_grads = {}
-        self.local_overflow = False
 
     def log_timers(self, timer_names):
         if self.timers is None:
@@ -1895,12 +1914,19 @@ def has_overflow_partitioned_grads_serial(self):
     def has_overflow(self, partition_gradients=True):
         if partition_gradients:
             with get_accelerator().stream(self.reduce_and_partition_stream):
-                self.local_overflow = bool(self.inf_or_nan_tracker.item())
+                if hasattr(self.inf_or_nan_tracker, "logical_or_"):
+                    self.inf_or_nan_tracker.logical_or_(torch.isinf(self.grad_partitions_flat_buffer).any())
+                    self.inf_or_nan_tracker.logical_or_(torch.isnan(self.grad_partitions_flat_buffer).any())
+                else:
+                    # logical_or_ not available in older versions of pytorch
+                    self.inf_or_nan_tracker += torch.isinf(self.grad_partitions_flat_buffer).any()
+                    self.inf_or_nan_tracker += torch.isnan(self.grad_partitions_flat_buffer).any()
+                    self.inf_or_nan_tracker = self.inf_or_nan_tracker > 0
+
+                overflow_gpu = self.inf_or_nan_tracker.clone().to(torch.uint8)
                 self.inf_or_nan_tracker.zero_()
 
-            overflow = self.local_overflow
-            #overflow = self.has_overflow_partitioned_grads_serial()
-            overflow_gpu = get_accelerator().ByteTensor([overflow])
+            get_accelerator().default_stream().wait_stream(self.reduce_and_partition_stream)
             dist.all_reduce(overflow_gpu, op=dist.ReduceOp.MAX, group=self.dp_process_group)
 
         else:

tests/unit/runtime/zero/test_zero.py

@@ -736,10 +736,11 @@ def create_tensor(vals, dtype: torch.dtype = None) -> Tensor:
 
 
 @pytest.mark.parametrize("init_context_manager", [True, False])
+@pytest.mark.parametrize("reduce_scatter", [True, False])
 class TestZero3ParamPartitioningLargeParam(DistributedTest):
     world_size = 4
 
-    def test(self, init_context_manager: bool, param_sz: int = 8100) -> None:
+    def test(self, init_context_manager: bool, reduce_scatter: bool, param_sz: int = 8100) -> None:
 
         class LargeParamModel(Module):
 
@@ -767,6 +768,7 @@ def forward(self, x: Tensor) -> Tensor:
                 "stage3_max_reuse_distance": 0,
                 "contiguous_gradients": True,
                 "overlap_comm": True,
+                "reduce_scatter": reduce_scatter,
             },
             "optimizer": {
                 "type": "Adam",