[Pipeline Middleware] Reduce comm redundancy by getting accurate output (#2232)

* move to cpu to avoid dead lock

* get output by offsets

Co-authored-by: Ziyue Jiang <ziyue.jiang@gmail.com>

colossalai/pipeline/rpc/_pipeline_base.py

@@ -185,18 +185,7 @@ class WorkerBase(ABC):
             self.module_partition: nn.Module = partition_fn(*partition_args).to(device)
             self.partition_condition_lock.notify_all()
 
-    def sync_global_worker_rrefs(self, pp_rank_to_worker_rref: Dict[int, PyRRef]) -> None:
-        assert self.pp_rank_to_worker_rref is None, f"in rank {self.pp_rank}, worker has sync global workers rrefs"
-        assert pp_rank_to_worker_rref is not None, "stage_to_workers must be a dict instead of None"
-        self.pp_rank_to_worker_rref = pp_rank_to_worker_rref
-
-        # for some schedule need the other worker's info to initialise partition (like Chimera)
-        # construction of partition is executed after the registion of pp_rank_to_worker_rref
-        self._initialize_partition()
-
-    # res_use works for lifecycle counter,
-    # if ref_use is True, lifecycle won't add.
-    def get_output_by_key(self, key: UniqueKey, ref_use=False) -> Any:
+    def _get_output_all(self, key: UniqueKey, ref_use=False, rank=None):
         with self.output_list_condition_lock:
             self.output_list_condition_lock.wait_for(lambda: key in self.output_list)
             output_work_item = self.output_list[key]
@@ -214,7 +203,8 @@ class WorkerBase(ABC):
                     lifecycle += 1
             elif output_work_item.phase == Phase.BACKWARD:
                 lifecycle = len(self.get_producer_stage_ids())
-                if self._is_last_step(output_work_item):    # an extra reference for ensure_backward
+                if self.is_model_input() and self._is_last_step(
+                        output_work_item):    # an extra reference for ensure_backward
                     lifecycle += 1
             else:
                 lifecycle = 0
@@ -230,6 +220,26 @@ class WorkerBase(ABC):
 
         return output
 
+    def sync_global_worker_rrefs(self, pp_rank_to_worker_rref: Dict[int, PyRRef]) -> None:
+        assert self.pp_rank_to_worker_rref is None, f"in rank {self.pp_rank}, worker has sync global workers rrefs"
+        assert pp_rank_to_worker_rref is not None, "stage_to_workers must be a dict instead of None"
+        self.pp_rank_to_worker_rref = pp_rank_to_worker_rref
+
+        # for some schedule need the other worker's info to initialise partition (like Chimera)
+        # construction of partition is executed after the registion of pp_rank_to_worker_rref
+        self._initialize_partition()
+
+    # res_use works for lifecycle counter,
+    # if ref_use is True, lifecycle won't add.
+    # offset supports get partial output to reduce comm costs.
+    def get_output_by_key(self, key: UniqueKey, ref_use=False, rank=None, offsets=None) -> Any:
+        output = self._get_output_all(key, ref_use, rank)
+        if offsets is None:    # get all for non iterable output
+            return output
+        else:    # get part for iterable output
+            output = [output[i] for i in offsets]
+        return output
+
     def get_parameters(self) -> List[torch.Tensor]:
         return [p for p in self.module_partition.parameters()]
 
@@ -361,22 +371,35 @@ class WorkerBase(ABC):
                 producer_stage_id = 0
                 producer_output_key = UniqueKey(microbatch_id, Phase.INPUT)
                 producer_worker_rref = self.pp_rank_to_worker_rref[producer_stage_id]
-                subscribe_forward_futures[0] = producer_worker_rref.rpc_async().get_output_by_key(producer_output_key)
+                offsets = self._get_input_offsets_by_index(target_index=0)
+                subscribe_forward_futures[0] = producer_worker_rref.rpc_async().get_output_by_key(producer_output_key,
+                                                                                                  rank=self.pp_rank,
+                                                                                                  offsets=offsets)
 
                 for i in range(0, producer_num - 1):
                     producer_stage_id = producer_stage_ids[i]
                     producer_output_key = UniqueKey(microbatch_id, Phase.FORWARD)
                     producer_worker_rref = self.pp_rank_to_worker_rref[producer_stage_id]
-                    subscribe_forward_futures[i + 1] = producer_worker_rref.rpc_async().get_output_by_key(
-                        producer_output_key)
+                    target_index = i + 1
+                    offsets = self._get_input_offsets_by_index(target_index=target_index)
+                    if offsets is not None and len(offsets) == 0:    # no need to do rpc
+                        subscribe_forward_futures[target_index] = []
+                    else:
+                        subscribe_forward_futures[target_index] = producer_worker_rref.rpc_async().get_output_by_key(
+                            producer_output_key, rank=self.pp_rank)
 
             else:
                 for i in range(producer_num):
                     producer_stage_id = producer_stage_ids[i]
                     producer_output_key = UniqueKey(microbatch_id, Phase.FORWARD)
                     producer_worker_rref = self.pp_rank_to_worker_rref[producer_stage_id]
-                    subscribe_forward_futures[i] = producer_worker_rref.rpc_async().get_output_by_key(
-                        producer_output_key)
+                    target_index = i
+                    offsets = self._get_input_offsets_by_index(target_index=target_index)
+                    if offsets is not None and len(offsets) == 0:    # no need to do rpc
+                        subscribe_forward_futures[target_index] = []
+                    else:
+                        subscribe_forward_futures[target_index] = producer_worker_rref.rpc_async().get_output_by_key(
+                            producer_output_key, rank=self.pp_rank, offsets=offsets)
 
         work_item_from_producer = WorkItem(stage_id, Phase.FORWARD, subscribe_forward_futures, {}, output,
                                            microbatch_id, None, self.num_microbatches, forward_only)
@@ -412,7 +435,13 @@ class WorkerBase(ABC):
             consumer_stage_id = consumer_stage_ids[i]
             consumer_output_key = UniqueKey(microbatch_id, Phase.BACKWARD)
             consumer_worker_rref = self.pp_rank_to_worker_rref[consumer_stage_id]
-            subscribe_backward_futures[i] = consumer_worker_rref.rpc_async().get_output_by_key(consumer_output_key)
+            target_index = i
+            offsets = self._get_output_offsets_by_index(target_index=target_index)
+            if offsets is not None and len(offsets) == 0:    # no need to do rpc
+                subscribe_backward_futures[target_index] = []
+            else:
+                subscribe_backward_futures[target_index] = consumer_worker_rref.rpc_async().get_output_by_key(
+                    consumer_output_key, rank=self.pp_rank, offsets=offsets)
 
         # flatten args
         work_item_from_consumer = WorkItem(stage_id, Phase.BACKWARD, subscribe_backward_futures, {}, output,
@@ -501,6 +530,75 @@ class WorkerBase(ABC):
         topo = self.get_topo()
         return topo is not None
 
+    def _get_input_offsets_by_index(self, target_index):
+        res = []
+        topo: Topo = self.get_topo()
+        self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
+        self_partition: Partition = topo.get_partition_by_id(self_partition_id)
+        model_input_partition_id = topo.get_input_partition_id()
+        input_vals = self_partition.get_input_vals()
+        producer_stage_ids = self.get_producer_stage_ids()
+        if self.need_model_input():
+            # 0 for data from input batch
+            # >= 1 for data from prev stages
+            base = 1
+        else:
+            # data from prev stages
+            base = 0
+        for val in input_vals:
+            val_pos = val.get()
+            src_partition_id = val_pos.partition_id
+            src_offset = val_pos.offset
+            src_index = base
+            src_partition = topo.get_partition_by_id(src_partition_id)
+            output_len = len(src_partition.get_output_vals())
+            # data from not-input partition
+            if src_partition_id != model_input_partition_id:
+                src_stage_id = self.partition_id_to_pp_rank(src_partition_id, topo)
+                src_index = base
+                for i, stage_id in enumerate(producer_stage_ids):
+                    if stage_id == src_stage_id:
+                        src_index += i
+                        break
+            else:    # data from input partition
+                src_index = 0
+            # when output_len = 1, not iterable
+            if target_index == src_index:
+                if output_len == 1:
+                    res = None    # offset = None to get all outputs
+                    return res
+                else:
+                    res.append(src_offset)
+        return res
+
+    def _get_output_offsets_by_index(self, target_index):
+        res = []
+        topo: Topo = self.get_topo()
+        self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
+        self_partition: Partition = topo.get_partition_by_id(self_partition_id)
+        output_vals = self_partition.get_output_vals()
+        consumer_stage_ids = self.get_consumer_stage_ids()
+        for val_list in output_vals:
+            # An output may be passed to many down stages.
+            target = None
+            for val_pos in val_list.get():
+                dst_partition_id = val_pos.partition_id
+                dst_offset = val_pos.offset
+                dst_partition = topo.get_partition_by_id(dst_partition_id)
+                input_len = len(dst_partition.get_input_vals())
+                dst_stage_id = self.partition_id_to_pp_rank(dst_partition_id, topo)
+                for i, stage_id in enumerate(consumer_stage_ids):
+                    if stage_id == dst_stage_id:
+                        dst_index = i
+                        break
+                if target_index == dst_index:
+                    if input_len == 1:
+                        res = None    # offset = None to get all outputs
+                        return res
+                    else:
+                        res.append(dst_offset)
+        return res
+
     # TODO(jiangziyue) get single value instead of the whole output
     def _get_real_args_kwargs_fwd(self, args_or_kwargs):
         if not self.use_middleware():
@@ -521,8 +619,7 @@ class WorkerBase(ABC):
                     flatten_args = []
                     if self.is_first_stage():
                         pytree_map(args_or_kwargs, fn=lambda x: flatten_args.append(x), map_all=True)
-                    # TODO get by offset
-                    else:
+                    else:    # get by offset
                         topo: Topo = self.get_topo()
                         self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
                         self_partition: Partition = topo.get_partition_by_id(self_partition_id)
@@ -557,7 +654,9 @@ class WorkerBase(ABC):
                             if output_len == 1:
                                 target = args_or_kwargs[src_index]
                             else:
-                                target = args_or_kwargs[src_index][src_offset]
+                                offsets = self._get_input_offsets_by_index(src_index)
+                                real_offset = offsets.index(src_offset)
+                                target = args_or_kwargs[src_index][real_offset]
                             flatten_args.append(target)
                     args_or_kwargs = flatten_args
         return args_or_kwargs
@@ -574,10 +673,10 @@ class WorkerBase(ABC):
                     pytree_map(args_or_kwargs, fn=lambda x: flatten_args.append(x), map_all=True)
                     args_or_kwargs = flatten_args
         else:
-            args_or_kwargs = pytree_map(args_or_kwargs, fn=lambda x: x.wait(), process_types=Future)
-            if args_or_kwargs is not None:
+            for i, arg in enumerate(args_or_kwargs):
+                args_or_kwargs[i] = arg.wait()
+            if args_or_kwargs is not None:    # get by offset
                 flatten_args = []
-                # TODO get by offset
                 topo: Topo = self.get_topo()
                 self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
                 self_partition: Partition = topo.get_partition_by_id(self_partition_id)
@@ -599,7 +698,9 @@ class WorkerBase(ABC):
                         if input_len == 1:
                             part_grad = args_or_kwargs[dst_index]
                         else:
-                            part_grad = args_or_kwargs[dst_index][dst_offset]
+                            offsets = self._get_output_offsets_by_index(dst_index)
+                            real_offsets = offsets.index(dst_offset)
+                            part_grad = args_or_kwargs[dst_index][real_offsets]
 
                         if target is None:
                             target = part_grad
@@ -682,10 +783,6 @@ class WorkerBase(ABC):
             else:
                 args_kwargs = self._get_real_args_kwargs_fwd(args)
 
-            # if not forward_only:
-            #     pytree_map(args_kwargs,
-            #                lambda x: x.requires_grad_(True) if torch.is_floating_point(x) else x.requires_grad_(False),
-            #                process_types=torch.Tensor)
             args_kwargs = pyobj_map(args_kwargs, fn=lambda x: x.to(self.device).detach(),
                                     process_types=torch.Tensor)    # torch rpc doesn't support args or rets in GPU
 
@@ -752,14 +849,14 @@ class WorkerBase(ABC):
                                                                                     stage_input_kwargs,
                                                                                     stage_outputs,
                                                                                     checkpoint=use_checkpoint)
+            consume_result = pyobj_map(consume_result, fn=lambda x: x.to('cpu'),
+                                       process_types=torch.Tensor)    # torch rpc doesn't support args or rets in
+
             # if not forward_only, do the backward
             if not forward_only:
                 if is_last_stage:    # if it is the last stage, trigger backward automatic
                     self._begin_backward(microbatch_id)
 
-            consume_result = pyobj_map(consume_result, fn=lambda x: x.to('cpu'),
-                                       process_types=torch.Tensor)    # torch rpc doesn't support args or rets in GPU
-
         elif phase == Phase.BACKWARD:
             # remind its producer to get data before backward
             if not is_first_stage:
@@ -803,10 +900,8 @@ class WorkerBase(ABC):
                         filtered_grads.append(grad)
 
                 stage_outputs = filtered_outputs
-                grad_tensors = filtered_grads
-
-            grad_tensors = pyobj_map(grad_tensors, fn=lambda x: x.to(self.device),
-                                     process_types=torch.Tensor)    # torch rpc doesn't support args or rets in GPU
+                grad_tensors = pyobj_map(filtered_grads, fn=lambda x: x.to(self.device),
+                                         process_types=torch.Tensor)    # torch rpc doesn't support args or rets in GPU
             autograd.backward(stage_outputs, grad_tensors=grad_tensors)
 
             # collect grad of input tensor
@@ -941,8 +1036,6 @@ class PipelineEngineBase(ABC, nn.Module):
 
         self.pp_rank_to_worker_rref: Dict[int, PyRRef] = dict()
 
-        self.step_futs: List[Future] = []
-
         self._check_argument()
         self._create_pp_rank_to_rpc_worker_id()
         self._create_pp_rank_to_module_partition_id()
@@ -1058,9 +1151,14 @@ class PipelineEngineBase(ABC, nn.Module):
                     ret_future[pp_rank][microbatch_id - actual_stage_num].wait()
             else:
                 key = UniqueKey(microbatch_id - actual_stage_num, Phase.BACKWARD)
+                futs = []
                 for pp_rank in input_pp_ranks:
                     worker_rref = self.pp_rank_to_worker_rref[pp_rank]
-                    worker_rref.rpc_sync().get_output_by_key(key, ref_use=True)
+                    fut = worker_rref.rpc_async().get_output_by_key(key, ref_use=True, offsets=[])
+                    futs.append(fut)
+
+                for fut in futs:
+                    fut.wait()
 
     def _create_ret_future(self, output_pp_ranks: List[int]) -> Dict[int, List[Future]]:
         num_microbatches = self.num_microbatches
@@ -1087,10 +1185,16 @@ class PipelineEngineBase(ABC, nn.Module):
 
     def _ensure_backward(self, forward_only: bool, input_pp_ranks: List[int]):
         if not forward_only:
+            backward_result = []
             for pp_rank in input_pp_ranks:
                 worker_rref = self.pp_rank_to_worker_rref[pp_rank]
                 key = UniqueKey(self.num_microbatches - 1, Phase.BACKWARD)
-                worker_rref.rpc_sync().get_output_by_key(key)
+                fut = worker_rref.rpc_async().get_output_by_key(
+                    key, offsets=[])    # only ensure the res exists, no need for real data.
+                backward_result.append(fut)
+
+            for fut in backward_result:
+                fut.wait()
 
     def _collect_forward_result(self, output_pp_ranks: List[int], ret_future: Dict[int, List[Future]]):
         forward_result = []
@@ -1109,12 +1213,13 @@ class PipelineEngineBase(ABC, nn.Module):
 
     def _reset_worker(self):
         actual_stage_num = self._get_actual_stage_num()
+        reset_futs: List[Future] = []
         for pp_rank in range(actual_stage_num):
             worker_rref = self.pp_rank_to_worker_rref[pp_rank]
             fut = worker_rref.rpc_async().reset_context()
-            self.step_futs.append(fut)
+            reset_futs.append(fut)
 
-        for fut in self.step_futs:
+        for fut in reset_futs:
             fut.wait()
 
     def forward_backward(self, batch: torch.Tensor, labels: torch.Tensor = None, forward_only: bool = False):
@@ -1141,7 +1246,7 @@ class PipelineEngineBase(ABC, nn.Module):
         for microbatch_id in range(num_microbatches):
             # control data input  speed
             # to prevent exceed of wait limitations
-            self._consume_constraint(microbatch_id, forward_only, input_pp_ranks, output_pp_ranks, ret_future)
+            # self._consume_constraint(microbatch_id, forward_only, input_pp_ranks, output_pp_ranks, ret_future)
             batch_start = microbatch_size * microbatch_id
             batch_end = min(batch_start + microbatch_size, batch_length)
 
@@ -1178,10 +1283,11 @@ class PipelineEngineBase(ABC, nn.Module):
 
     def step(self):
         actual_stage_num = self._get_actual_stage_num()
+        step_futs: List[Future] = []
         for pp_rank in range(actual_stage_num):
             worker_rref = self.pp_rank_to_worker_rref[pp_rank]
             fut = worker_rref.rpc_async().step()
-            self.step_futs.append(fut)
+            step_futs.append(fut)
 
-        for fut in self.step_futs:
+        for fut in step_futs:
             fut.wait()