support fp8 communication in pipeline parallelism

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -992,6 +992,7 @@ class HybridParallelPlugin(PipelinePluginBase):
         make_vocab_size_divisible_by: int = 64,
         dp_outside: bool = True,
         overlap_p2p: bool = True,
+        fp8_communication: bool = False,
     ) -> None:
         super().__init__()
         assert (
@@ -1082,6 +1083,7 @@ class HybridParallelPlugin(PipelinePluginBase):
                     microbatch_size=microbatch_size,
                     enable_metadata_cache=enable_metadata_cache,
                     overlap_p2p=overlap_p2p,
+                    fp8_communication=fp8_communication,
                 )
             elif pp_style == "1f1b":
                 self.schedule = OneForwardOneBackwardSchedule(
@@ -1089,6 +1091,7 @@ class HybridParallelPlugin(PipelinePluginBase):
                     num_microbatches=num_microbatches,
                     microbatch_size=microbatch_size,
                     enable_metadata_cache=enable_metadata_cache,
+                    fp8_communication=fp8_communication,
                 )
             else:
                 raise NotImplementedError()

colossalai/pipeline/schedule/interleaved_pp.py

@@ -12,6 +12,7 @@ from colossalai.interface import OptimizerWrapper
 from colossalai.pipeline.p2p import PipelineP2PCommunication, create_send_metadata
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.utils import get_current_device
+from colossalai.quantization.fp8 import cast_to_fp8_pipeline, cast_from_fp8_pipeline
 
 from ._utils import detach, get_batch_size, get_micro_batch, merge_batch, model_forward, retain_grad, to_device
 from .base import PipelineSchedule
@@ -32,6 +33,7 @@ class InterleavedSchedule(PipelineSchedule):
         microbatch_size: Optional[int] = None,
         enable_metadata_cache: bool = True,
         overlap_p2p: bool = True,
+        fp8_communication: bool = False,
     ) -> None:
         super().__init__(stage_manager)
         assert (
@@ -56,6 +58,7 @@ class InterleavedSchedule(PipelineSchedule):
         self.tensor_metadata_recv = None
         self.grad_metadata_recv = None
 
+        self.fp8_communication = fp8_communication
     def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None) -> None:
         """Load a batch from data iterator.
 
@@ -191,8 +194,12 @@ class InterleavedSchedule(PipelineSchedule):
         """
         with self.stage_manager.switch_model_chunk_id(model_chunk_id):
             if not self.stage_manager.is_last_stage():
+                if self.fp8_communication:
+                    cast_to_fp8_pipeline(output_tensor)
                 send_handles = self.comm.send_forward(output_tensor, next_rank, send_metadata=self.send_tensor_metadata)
                 self.send_tensor_metadata = not self.enable_metadata_cache
+                if self.fp8_communication:
+                    cast_from_fp8_pipeline(output_tensor)
                 return send_handles
         return []
 
@@ -210,10 +217,14 @@ class InterleavedSchedule(PipelineSchedule):
         """
         with self.stage_manager.switch_model_chunk_id(model_chunk_id):
             if not self.stage_manager.is_first_stage():
+                if self.fp8_communication:
+                    cast_to_fp8_pipeline(input_tensor_grad)
                 send_handles = self.comm.send_backward(
                     input_tensor_grad, prev_rank, send_metadata=self.send_grad_metadata
                 )
                 self.send_grad_metadata = not self.enable_metadata_cache
+                if self.fp8_communication:
+                    cast_from_fp8_pipeline(input_tensor_grad)
                 return send_handles
         return []
 
@@ -224,6 +235,8 @@ class InterleavedSchedule(PipelineSchedule):
             is_send = not self.stage_manager.is_last_stage()
         with self.stage_manager.switch_model_chunk_id(model_chunk_id_recv):
             is_recv = not self.stage_manager.is_first_stage()
+        if self.fp8_communication:
+            cast_to_fp8_pipeline(output_tensor)
         input_tensor, wait_handles = self.comm.send_forward_recv_forward(
             output_tensor,
             is_send,
@@ -237,6 +250,8 @@ class InterleavedSchedule(PipelineSchedule):
         if is_recv and self.enable_metadata_cache and self.tensor_metadata_recv is None:
             self.tensor_metadata_recv = create_send_metadata(input_tensor)
 
+        if self.fp8_communication:
+            cast_from_fp8_pipeline(output_tensor)
         return input_tensor, wait_handles
 
     def send_backward_recv_backward(
@@ -246,6 +261,8 @@ class InterleavedSchedule(PipelineSchedule):
             is_send = not self.stage_manager.is_first_stage()
         with self.stage_manager.switch_model_chunk_id(model_chunk_id_recv):
             is_recv = not self.stage_manager.is_last_stage()
+        if self.fp8_communication:
+            cast_to_fp8_pipeline(input_tensor_grad)
         output_tensor_grad, wait_handles = self.comm.send_backward_recv_backward(
             input_tensor_grad,
             is_send,
@@ -258,6 +275,8 @@ class InterleavedSchedule(PipelineSchedule):
         self.send_grad_metadata = not self.enable_metadata_cache and is_send
         if is_recv and self.enable_metadata_cache and self.grad_metadata_recv is None:
             self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
+        if self.fp8_communication:
+            cast_from_fp8_pipeline(input_tensor_grad)
         return output_tensor_grad, wait_handles
 
     def forward_step(
@@ -379,6 +398,8 @@ class InterleavedSchedule(PipelineSchedule):
 
             # Wait until current input is received
             _wait_p2p(fwd_wait_handles)
+            if self.fp8_communication and input_obj is not None:
+                cast_from_fp8_pipeline(input_obj)
             output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs)
 
             if not last_batch:
@@ -441,6 +462,8 @@ class InterleavedSchedule(PipelineSchedule):
 
             # Wait for input
             _wait_p2p(fwd_wait_handles)
+            if self.fp8_communication and input_obj is not None:
+                cast_from_fp8_pipeline(input_obj)
             output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs)
             input_objs[model_chunk_id].append(input_obj)
             output_objs[model_chunk_id].append(output_obj)
@@ -467,6 +490,8 @@ class InterleavedSchedule(PipelineSchedule):
 
             # Wait for input.
             _wait_p2p(fwd_wait_handles)
+            if self.fp8_communication and input_obj is not None:
+                cast_from_fp8_pipeline(input_obj)
             output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs)
             # Add input_obj and output_obj to end of list.
             input_objs[model_chunk_id].append(input_obj)
@@ -511,6 +536,8 @@ class InterleavedSchedule(PipelineSchedule):
             input_obj, fwd_wait_handles = send_forward_recv_forward()
             # Wait for upstream grad
             _wait_p2p(bwd_wait_handles)
+            if self.fp8_communication and output_obj_grad is not None:
+                cast_from_fp8_pipeline(output_obj_grad)
             input_obj_grad = self.backward_step(optimizer, _input_obj, _output_obj, output_obj_grad)
             # NOTE: It's documented by NCCL that running two concurrent communicators (batch_isend_irecv)
             # risks deadlock (https://docs.nvidia.com/deeplearning/nccl/archives/nccl_2134/user-guide/docs/usage/communicators.html)
@@ -532,6 +559,8 @@ class InterleavedSchedule(PipelineSchedule):
 
             # Wait for upstream grad
             _wait_p2p(bwd_wait_handles)
+            if self.fp8_communication and output_obj_grad is not None:
+                cast_from_fp8_pipeline(output_obj_grad)
             # backward local grads
             input_obj_grad = self.backward_step(optimizer, _input_obj, _output_obj, output_obj_grad)
             if not last_batch:

colossalai/pipeline/schedule/one_f_one_b.py

@@ -11,6 +11,7 @@ from colossalai.interface import ModelWrapper, OptimizerWrapper
 from colossalai.pipeline.p2p import PipelineP2PCommunication, create_send_metadata
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.utils import get_current_device
+from colossalai.quantization.fp8 import cast_to_fp8_pipeline, cast_from_fp8_pipeline
 
 from ._utils import (
     detach,
@@ -32,6 +33,7 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
         num_microbatches: Optional[int] = None,
         microbatch_size: Optional[int] = None,
         enable_metadata_cache: bool = True,
+        fp8_communication: bool = False,
     ) -> None:
         """1F1B pipeline schedule.
 
@@ -61,6 +63,8 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
         self.tensor_metadata_recv = None
         self.grad_metadata_recv = None
 
+        self.fp8_communication = fp8_communication
+
     def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None) -> None:
         """Load a batch from data iterator.
 
@@ -129,6 +133,8 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
             if self.enable_metadata_cache and self.tensor_metadata_recv is None:
                 self.tensor_metadata_recv = create_send_metadata(input_tensor)
 
+            if self.fp8_communication:
+                cast_from_fp8_pipeline(input_tensor)
             return input_tensor
 
     def recv_backward(self, next_rank: int = None) -> Any:
@@ -143,6 +149,8 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
         """
         if not self.stage_manager.is_last_stage():
             output_tensor_grad, _ = self.comm.recv_backward(next_rank, metadata_recv=self.grad_metadata_recv)
+            if self.fp8_communication:
+                cast_from_fp8_pipeline(output_tensor_grad)
             if self.enable_metadata_cache and self.grad_metadata_recv is None:
                 self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
 
@@ -157,9 +165,13 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
             next_rank (int, optional): The rank of the recipient of the tensor.
         """
         if not self.stage_manager.is_last_stage():
+            if self.fp8_communication:
+                cast_to_fp8_pipeline(output_tensor)
             self.comm.send_forward(output_tensor, next_rank, send_metadata=self.send_tensor_metadata)
             self.send_tensor_metadata = not self.enable_metadata_cache
 
+            if self.fp8_communication:
+                cast_from_fp8_pipeline(output_tensor, del_metadata=False)
     def send_backward(self, input_tensor_grad: Any, prev_rank: int = None) -> None:
         """Sends the gradient tensor to the previous stage in pipeline.
            For 1F1B.
@@ -169,8 +181,12 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
             prev_rank (int, optional): The rank of the recipient of the tensor
         """
         if not self.stage_manager.is_first_stage():
+            if self.fp8_communication:
+                cast_to_fp8_pipeline(input_tensor_grad)
             self.comm.send_backward(input_tensor_grad, prev_rank, send_metadata=self.send_grad_metadata)
             self.send_grad_metadata = not self.enable_metadata_cache
+            if self.fp8_communication:
+                cast_from_fp8_pipeline(input_tensor_grad, del_metadata=False)
 
     def send_forward_recv_backward(self, output_tensor: Any, send_first: Optional[bool] = None) -> Any:
         """Sends the input tensor to the next stage and copy the gradient tensor from the next stage in pipeline.
@@ -183,6 +199,8 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
         if not self.stage_manager.is_last_stage():
             if not self.send_tensor_metadata and self.grad_metadata_recv is not None:
                 send_first = None
+            if self.fp8_communication:
+                cast_to_fp8_pipeline(output_tensor)
             output_tensor_grad, _ = self.comm.send_forward_recv_backward(
                 output_tensor,
                 send_metadata=self.send_tensor_metadata,
@@ -192,6 +210,9 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
             self.send_tensor_metadata = not self.enable_metadata_cache
             if self.enable_metadata_cache and self.grad_metadata_recv is None:
                 self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
+            if self.fp8_communication:
+                cast_from_fp8_pipeline(output_tensor, del_metadata=False)
+                cast_from_fp8_pipeline(output_tensor_grad)
 
             return output_tensor_grad
 
@@ -206,6 +227,8 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
         if not self.stage_manager.is_first_stage():
             if not self.send_grad_metadata and self.tensor_metadata_recv is not None:
                 send_first = None  # must not fallback
+            if self.fp8_communication:
+                cast_to_fp8_pipeline(input_tensor_grad)
             input_tensor, _ = self.comm.send_backward_recv_forward(
                 input_tensor_grad,
                 send_metadata=self.send_grad_metadata,
@@ -215,6 +238,9 @@ class OneForwardOneBackwardSchedule(PipelineSchedule):
             self.send_grad_metadata = not self.enable_metadata_cache
             if self.enable_metadata_cache and self.tensor_metadata_recv is None:
                 self.tensor_metadata_recv = create_send_metadata(input_tensor)
+            if self.fp8_communication:
+                cast_from_fp8_pipeline(input_tensor)
+                cast_from_fp8_pipeline(input_tensor_grad, del_metadata=False)
 
             return input_tensor
 

colossalai/quantization/fp8.py

@@ -105,3 +105,70 @@ def all_reduce_fp8(tensor: torch.Tensor, fp8_format="e4m3") -> None:
         tensor_list[i] = tensor_list[i].view(fp8_type).to(input_type) * scale_list[i]
     tensor_out = torch.cat(tensor_list, dim=0)
     tensor.data = tensor_out.view(input_shape).to(input_type)
+
+
+
+def cast_to_fp8_pipeline(inp: Any) -> None:
+    """
+    Cast the hidden_states tensor of inp object to fp8 format before p2p communication in pipeline.
+    The activations tensor is indexed by 'hidden_states' in the inp dict.
+    After FP8 casting, the resulting tensor is saved as float16 or bfloat16 format but the size becomes halved.
+    Metadata such as fp8_scale is saved into inp dict for communication.
+    """
+    if inp is None:
+        return
+    # In pipeline parallelism, when inp is torch.Tensor, it only contains one element, thus can be omitted.
+    if type(inp) == torch.Tensor:
+        return
+
+    assert 'hidden_states' in inp, 'required by pipeline parallelism.'
+    inp_tensor = inp["hidden_states"]
+
+    min_val, max_val = inp_tensor.aminmax()
+    amax = torch.maximum(min_val.abs(), max_val.abs())
+
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    if amax > finfo.max:
+        fp8_type = torch.float8_e5m2
+        fp8_view_type = torch.float16
+    else:
+        fp8_type = torch.float8_e4m3fn
+        fp8_view_type = torch.bfloat16
+
+    finfo = torch.finfo(fp8_type)
+    scale = torch.tensor(1.0).to(inp_tensor.device) if amax == 0.0 else finfo.max / amax.float()
+    q_tensor = (inp_tensor.data.float() * scale)
+    # Todo: Currently we use fp8_view_type <float16, bfloat16> to indicate which fp8 format is used. This is a temporary workaround due to 'Only support tensor for fast send'.
+    #  inp_tensor needs to be a float datatype to avoid error during gradient placement.
+    inp_tensor.data = q_tensor.to(fp8_type).view(fp8_view_type)
+
+    inp["fp8_scale"] = scale.float().reciprocal()
+
+
+
+def cast_from_fp8_pipeline(inp: Any, del_metadata=True) -> None:
+    """
+    Cast the FP8 encoded hidden_states tensor back to original dtype after p2p communication in pipeline.
+    del_metadata = False is useful when this function is called before p2p communication.
+    """
+    if inp is None:
+        return
+    if type(inp) == torch.Tensor:
+        return
+
+    assert 'hidden_states' in inp, 'required by pipeline parallelism.'
+    inp_tensor = inp["hidden_states"]
+    scale = inp["fp8_scale"]
+
+    fp8_view_type = inp_tensor.dtype
+    if fp8_view_type == torch.float16:
+        fp8_type = torch.float8_e5m2
+    elif fp8_view_type == torch.bfloat16:
+        fp8_type = torch.float8_e4m3fn
+    else:
+        raise TypeError("Only float16, bfloat16 are implemented.")
+
+    inp_tensor.data = inp_tensor.data.view(fp8_type).to(torch.float16) * scale
+
+    if del_metadata:
+        del inp["fp8_scale"]