[pipeline]: support arbitrary batch size in forward_only mode

colossalai/pipeline/schedule/interleaved_pp.py

@@ -1,5 +1,5 @@
 from functools import partial
-from typing import Any, Callable, Iterable, List, Optional, Union
+from typing import Any, Callable, Dict, Iterable, List, Optional, Union
 
 import torch
 import torch.cuda
@@ -22,6 +22,7 @@ def __init__(
         num_model_chunks: int,
         num_microbatch: Optional[int] = None,
         microbatch_size: Optional[int] = None,
+        enable_metadata_cache: bool = True,
     ) -> None:
         super().__init__(stage_manager)
         assert (
@@ -39,6 +40,7 @@ def __init__(
         self.microbatch_offset: List[int]
 
         # P2PMeta cache
+        self.enable_metadata_cache = enable_metadata_cache
         self.send_metadata_forward = True
         self.send_metadata_backward = True
         self.metadata_recv_forward = None
@@ -54,30 +56,33 @@ def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None)
         batch = next(data_iter)
         if device is not None:
             batch = tree_map(partial(to_device, device=device), batch)
+
+        self.microbatch_offset = [0 for _ in range(self.num_model_chunks)]
         self.batch = batch
         self.batch_size = get_batch_size(batch)
-        if self.last_batch_size is None:
-            self.last_batch_size = self.batch_size
-        else:
-            assert self.forward_only or self.last_batch_size == self.batch_size
-            # TODO: support arbitrary batch size when forward_only=True
-        self.microbatch_offset = [0 for _ in range(self.num_model_chunks)]
-        if self.num_microbatch is not None:
+
+        if self.microbatch_size is None:
             assert self.batch_size % self.num_microbatch == 0, "Batch size should divided by the number of microbatch"
             self.microbatch_size = self.batch_size // self.num_microbatch
-        elif self.microbatch_size is not None:
+        if self.num_microbatch is None:
             assert self.batch_size % self.microbatch_size == 0, "Batch size should divided by the microbatch size"
             self.num_microbatch = self.batch_size // self.microbatch_size
-        else:
-            raise ValueError("Either num_microbatch or microbatch_size should be provided")
 
-        assert (
-            self.num_microbatch % self.num_model_chunks == 0
-        ), "Number of microbatch should be an integer multiple of number of model chunks"
+        if not self.forward_only:
+            assert self.last_batch_size is None or self.last_batch_size == self.batch_size
+            assert self.batch_size == self.microbatch_size * self.num_microbatch
 
-        assert (
-            self.num_microbatch % self.stage_manager.num_stages == 0
-        ), "Number of microbatch should be an integer multiple of number of pipeline parallel devices"
+        if self.forward_only:
+            self.num_microbatch = (self.batch_size - 1) // self.microbatch_size + 1
+            # NOTE: disable metadata cache when batch size changes (not valid anymore)
+            if self.batch_size != self.last_batch_size:
+                self.enable_metadata_cache = False
+                self.send_metadata_forward = True
+                self.send_metadata_backward = True
+                self.metadata_recv_forward = None
+                self.metadata_recv_backward = None
+
+        self.last_batch_size = self.batch_size
 
     def load_micro_batch(self, model_chunk_id: int) -> Any:
         """Load a micro batch from the current batch.
@@ -88,6 +93,7 @@ def load_micro_batch(self, model_chunk_id: int) -> Any:
         Returns:
             Any: Micro batch.
         """
+        assert self.microbatch_offset[model_chunk_id] <= self.batch_size, "Microbatches exhausted"
         micro_batch = get_micro_batch(self.batch, self.microbatch_offset[model_chunk_id], self.microbatch_size)
         self.microbatch_offset[model_chunk_id] += self.microbatch_size
         return tree_map(partial(to_device, device=get_current_device()), micro_batch)
@@ -122,7 +128,7 @@ def recv_forward(self, model_chunk_id: int, prev_rank: int = None) -> Any:
         with self.stage_manager.switch_model_chunk_id(model_chunk_id):
             if not self.stage_manager.is_first_stage():
                 input_tensor = self.comm.recv_forward(prev_rank, metadata_recv=self.metadata_recv_forward)
-                if self.metadata_recv_forward is None:
+                if self.enable_metadata_cache and self.metadata_recv_forward is None:
                     self.metadata_recv_forward = create_fast_send_metadata(input_tensor)
 
                 return input_tensor
@@ -141,7 +147,7 @@ def recv_backward(self, model_chunk_id: int, next_rank: int = None) -> Any:
         with self.stage_manager.switch_model_chunk_id(model_chunk_id):
             if not self.stage_manager.is_last_stage():
                 output_tensor_grad = self.comm.recv_backward(next_rank, metadata_recv=self.metadata_recv_backward)
-                if self.metadata_recv_backward is None:
+                if self.enable_metadata_cache and self.metadata_recv_backward is None:
                     self.metadata_recv_backward = create_fast_send_metadata(output_tensor_grad)
 
                 return output_tensor_grad
@@ -158,7 +164,7 @@ def send_forward(self, model_chunk_id: int, output_object: Any, next_rank: int =
         with self.stage_manager.switch_model_chunk_id(model_chunk_id):
             if not self.stage_manager.is_last_stage():
                 self.comm.send_forward(output_object, next_rank, send_metadata=self.send_metadata_forward)
-                self.send_metadata_forward = False
+                self.send_metadata_forward = not self.enable_metadata_cache
 
     def send_backward(self, model_chunk_id: int, input_object: Any, prev_rank: int = None) -> None:
         """Sends the gradient tensor to the previous stage in pipeline.
@@ -172,7 +178,7 @@ def send_backward(self, model_chunk_id: int, input_object: Any, prev_rank: int =
         with self.stage_manager.switch_model_chunk_id(model_chunk_id):
             if not self.stage_manager.is_first_stage():
                 self.comm.send_backward(input_object, prev_rank, send_metadata=self.send_metadata_backward)
-                self.send_metadata_backward = False
+                self.send_metadata_backward = not self.enable_metadata_cache
 
     def send_forward_recv_backward(
         self, model_chunk_id: int, output_object: Any, next_rank: Optional[int] = None
@@ -185,8 +191,8 @@ def send_forward_recv_backward(
                     send_metadata=self.send_metadata_forward,
                     metadata_recv=self.metadata_recv_backward,
                 )
-                self.send_metadata_forward = False
-                if self.metadata_recv_backward is None:
+                self.send_metadata_forward = not self.enable_metadata_cache
+                if self.enable_metadata_cache and self.metadata_recv_backward is None:
                     self.metadata_recv_backward = create_fast_send_metadata(output_tensor_grad)
 
                 return output_tensor_grad
@@ -202,8 +208,8 @@ def send_backward_recv_forward(
                     send_metadata=self.send_metadata_backward,
                     metadata_recv=self.metadata_recv_forward,
                 )
-                self.send_metadata_backward = False
-                if self.metadata_recv_forward is None:
+                self.send_metadata_backward = not self.enable_metadata_cache
+                if self.enable_metadata_cache and self.metadata_recv_forward is None:
                     self.metadata_recv_forward = create_fast_send_metadata(input_tensor)
 
                 return input_tensor
@@ -297,66 +303,74 @@ def backward_step(
                     input_obj_grad[k] = v.grad
         return input_obj_grad
 
-    def forward_backward_step(
+    def run_forward_only(
         self,
         model_chunk: Union[ModuleList, Module],
         data_iter: Iterable,
         criterion: Callable[..., Any],
-        optimizer: Optional[OptimizerWrapper] = None,
         return_loss: bool = False,
         return_outputs: bool = False,
-    ) -> dict:
-        """Runs interleaved schedule, with communication between pipeline stages.
+    ) -> Dict:
+        assert self.forward_only
 
-        Args:
-            model_chunk (ModuleList or Module): Model Chunk to be trained. Original interleaved uses a module list whereas shardformer uses entire model + layer specification
-            data_iter (Iterable): Data iterator.
-            criterion (Callable[[Any, Any], Tensor]): Criterion to be used. It should take two arguments: model outputs and inputs, and returns loss tensor.
-            optimizer (OptimizerWrapper, optional): Optimizer to be used. Can be None when only forward is executed. Defaults to None.
-            return_loss (bool, optional): Whether to return loss. Defaults to False. Whether to return loss.
-            return_outputs (bool, optional): Whether to return model outputs. Defaults to False. Whether to return model outputs.
+        self.load_batch(data_iter)
 
-        Returns:
-            dict: A dict with keys: 'loss' and 'outputs'.
+        outputs = [] if return_outputs and self.stage_manager.is_last_stage(ignore_chunk=True) else None
+
+        accum_loss = None
+        if return_loss and self.stage_manager.is_last_stage(ignore_chunk=True):
+            accum_loss = torch.scalar_tensor(0, device=get_current_device())
+
+        # Run warmup forward passes.
+        for i in range(self.num_microbatch * self.num_model_chunks):
+            model_chunk_id = self.get_model_chunk_id(i, is_forward=True)
+            input_obj = self.recv_forward(model_chunk_id)
+            output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs)
+            self.send_forward(model_chunk_id, output_obj)
+
+        if outputs is not None:
+            outputs = merge_batch(outputs)
+        return {"loss": accum_loss, "outputs": outputs}
+
+    def run_forward_backward(
+        self,
+        model_chunk: Union[ModuleList, Module],
+        data_iter: Iterable,
+        criterion: Callable[..., Any],
+        optimizer: Optional[OptimizerWrapper] = None,
+        return_loss: bool = False,
+        return_outputs: bool = False,
+    ) -> Dict:
         """
-        self.forward_only = not torch.is_grad_enabled()
-        if optimizer is None:
-            assert self.forward_only, "Optimizer should be passed when doing backward."
+        Runs interleaved schedule, with communication between pipeline stages.
+        """
+        assert not self.forward_only
 
         self.load_batch(data_iter)
 
         num_microbatch = self.num_microbatch * self.num_model_chunks
-        if self.forward_only:
-            num_warmup_microbatch = num_microbatch
-        else:
-            num_warmup_microbatch = (self.stage_manager.num_stages - self.stage_manager.stage - 1) * 2
-            num_warmup_microbatch += (self.num_model_chunks - 1) * self.stage_manager.num_stages
-            num_warmup_microbatch = min(num_warmup_microbatch, num_microbatch)
-
+        num_warmup_microbatch = (self.stage_manager.num_stages - self.stage_manager.stage - 1) * 2
+        num_warmup_microbatch += (self.num_model_chunks - 1) * self.stage_manager.num_stages
+        num_warmup_microbatch = min(num_warmup_microbatch, num_microbatch)
         num_microbatch_remaining = num_microbatch - num_warmup_microbatch
 
         # Input, output tensors only need to be saved when doing backward passes
-        input_objs = None
-        output_objs = None
-
-        if not self.forward_only:
-            input_objs = [[] for _ in range(self.num_model_chunks)]
-            output_objs = [[] for _ in range(self.num_model_chunks)]
+        input_objs = [[] for _ in range(self.num_model_chunks)]
+        output_objs = [[] for _ in range(self.num_model_chunks)]
 
         outputs = [] if return_outputs and self.stage_manager.is_last_stage(ignore_chunk=True) else None
 
         accum_loss = None
         if return_loss and self.stage_manager.is_last_stage(ignore_chunk=True):
-            accum_loss = torch.zeros(1, device=get_current_device())
+            accum_loss = torch.scalar_tensor(0, device=get_current_device())
 
         # Run warmup forward passes.
         for i in range(num_warmup_microbatch):
             model_chunk_id = self.get_model_chunk_id(i, is_forward=True)
             input_obj = self.recv_forward(model_chunk_id)
             output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs)
-            if not self.forward_only:
-                input_objs[model_chunk_id].append(input_obj)
-                output_objs[model_chunk_id].append(output_obj)
+            input_objs[model_chunk_id].append(input_obj)
+            output_objs[model_chunk_id].append(output_obj)
             self.send_forward(model_chunk_id, output_obj)
 
         if num_microbatch_remaining > 0:
@@ -369,47 +383,72 @@ def forward_backward_step(
             last_iteration = i == num_microbatch_remaining - 1
 
             output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs)
-            if self.forward_only:
-                if not last_iteration:
-                    input_obj = self.send_forward_recv_backward(model_chunk_id, output_obj)
-                else:
-                    self.send_forward(model_chunk_id, output_obj)
-
-            else:
-                self.send_forward(model_chunk_id, output_obj)
-                # Add input_obj and output_obj to end of list.
-                input_objs[model_chunk_id].append(input_obj)
-                output_objs[model_chunk_id].append(output_obj)
+            self.send_forward(model_chunk_id, output_obj)
+            # Add input_obj and output_obj to end of list.
+            input_objs[model_chunk_id].append(input_obj)
+            output_objs[model_chunk_id].append(output_obj)
 
-                model_chunk_id = self.get_model_chunk_id(i, is_forward=False)
-                output_obj_grad = self.recv_backward(model_chunk_id)
+            model_chunk_id = self.get_model_chunk_id(i, is_forward=False)
+            output_obj_grad = self.recv_backward(model_chunk_id)
 
-                # Pop output_obj and output_obj from the start of the list for
-                # the backward pass.
-                input_obj = input_objs[model_chunk_id].pop(0)
-                output_obj = output_objs[model_chunk_id].pop(0)
+            # Pop output_obj and output_obj from the start of the list for
+            # the backward pass.
+            input_obj = input_objs[model_chunk_id].pop(0)
+            output_obj = output_objs[model_chunk_id].pop(0)
 
-                # backward
-                input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad)
-                self.send_backward(model_chunk_id, input_obj_grad)
+            # backward
+            input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad)
+            self.send_backward(model_chunk_id, input_obj_grad)
 
-                if not last_iteration:
-                    model_chunk_id = self.get_model_chunk_id(i + num_warmup_microbatch + 1, is_forward=True)
-                    input_obj = self.recv_forward(model_chunk_id)
+            if not last_iteration:
+                model_chunk_id = self.get_model_chunk_id(i + num_warmup_microbatch + 1, is_forward=True)
+                input_obj = self.recv_forward(model_chunk_id)
 
         # Run cooldown backward passes.
-        if not self.forward_only:
-            for i in range(num_microbatch_remaining, num_microbatch):
-                model_chunk_id = self.get_model_chunk_id(i, is_forward=False)
-                input_obj = input_objs[model_chunk_id].pop(0)
-                output_obj = output_objs[model_chunk_id].pop(0)
-                output_obj_grad = self.recv_backward(model_chunk_id)
-                input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad)
-                self.send_backward(model_chunk_id, input_obj_grad)
+        for i in range(num_microbatch_remaining, num_microbatch):
+            model_chunk_id = self.get_model_chunk_id(i, is_forward=False)
+            input_obj = input_objs[model_chunk_id].pop(0)
+            output_obj = output_objs[model_chunk_id].pop(0)
+            output_obj_grad = self.recv_backward(model_chunk_id)
+            input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad)
+            self.send_backward(model_chunk_id, input_obj_grad)
 
-        if not self.forward_only:
-            assert all(len(v) == 0 for v in input_objs) and all(len(v) == 0 for v in output_objs)
+        assert all(len(v) == 0 for v in input_objs) and all(len(v) == 0 for v in output_objs)
 
         if outputs is not None:
             outputs = merge_batch(outputs)
         return {"loss": accum_loss, "outputs": outputs}
+
+    def forward_backward_step(
+        self,
+        model_chunk: Union[ModuleList, Module],
+        data_iter: Iterable,
+        criterion: Callable[..., Any],
+        optimizer: Optional[OptimizerWrapper] = None,
+        return_loss: bool = False,
+        return_outputs: bool = False,
+    ) -> dict:
+        """
+        Args:
+            model_chunk (ModuleList or Module): Model Chunk to be trained. Original interleaved uses a module list whereas shardformer uses entire model + layer specification
+            data_iter (Iterable): Data iterator.
+            criterion (Callable[[Any, Any], Tensor]): Criterion to be used. It should take two arguments: model outputs and inputs, and returns loss tensor.
+            optimizer (OptimizerWrapper, optional): Optimizer to be used. Can be None when only forward is executed. Defaults to None.
+            return_loss (bool, optional): Whether to return loss. Defaults to False. Whether to return loss.
+            return_outputs (bool, optional): Whether to return model outputs. Defaults to False. Whether to return model outputs.
+
+        Returns:
+            dict: A dict with keys: 'loss' and 'outputs'.
+        """
+        self.forward_only = not torch.is_grad_enabled()
+        if optimizer is None:
+            assert self.forward_only, "Optimizer should be passed when doing backward."
+
+        if self.forward_only:
+            result = self.run_forward_only(model_chunk, data_iter, criterion, return_loss, return_outputs)
+        else:
+            result = self.run_forward_backward(
+                model_chunk, data_iter, criterion, optimizer, return_loss, return_outputs
+            )
+
+        return result