[MoE][PoC] Expert Parallel: tp and tp2ep

[ghstack-poisoned]

torchtitan/config_manager.py

@@ -364,6 +364,16 @@ def __init__(self):
             default=1,
             help="Context parallelism degree. 1 means disabled.",
         )
+        self.parser.add_argument(
+            "--experimental.expert_parallel_mode",
+            type=str,
+            default="none",
+            choices=["none", "tp", "tp2ep"],
+            help="""
+                Expert Parallel mode.
+                'tp2ep' would use the entire TP mesh to shard non-shared experts on the num_experts dimension.
+            """,
+        )
         self.parser.add_argument(
             "--training.mixed_precision_param",
             type=str,

torchtitan/parallelisms/expert_parallel.py

@@ -0,0 +1,327 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from functools import partial
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torch.distributed.tensor import (
+    DeviceMesh,
+    distribute_module,
+    distribute_tensor,
+    DTensor,
+    Partial,
+    Replicate,
+    Shard,
+)
+from torch.distributed.tensor.parallel import ParallelStyle
+from torch.distributed.tensor.placement_types import Placement
+
+
+# This is similar to PrepareModuleInput and PrepareModuleOutput,
+# but applies them simultaneously.
+class PrepareModuleInputOutput(ParallelStyle):
+    def __init__(
+        self,
+        *,
+        input_layouts: Optional[Union[Placement, Tuple[Optional[Placement]]]] = None,
+        desired_input_layouts: Optional[
+            Union[Placement, Tuple[Optional[Placement]]]
+        ] = None,
+        input_kwarg_layouts: Optional[Dict[str, Placement]] = None,
+        desired_input_kwarg_layouts: Optional[Dict[str, Placement]] = None,
+        use_local_input: bool = True,
+        output_layouts: Union[Placement, Tuple[Placement]],
+        desired_output_layouts: Union[Placement, Tuple[Placement]],
+        use_local_output: bool = True,
+    ):
+        # for input
+        self.input_layouts = (
+            (input_layouts,) if isinstance(input_layouts, Placement) else input_layouts
+        )
+        self.desired_input_layouts = (
+            (desired_input_layouts,)
+            if isinstance(desired_input_layouts, Placement)
+            else desired_input_layouts
+        )
+        self.use_local_input = use_local_input
+        if self.input_layouts is not None:
+            assert (
+                self.desired_input_layouts is not None
+            ), "desired module inputs should not be None!"
+            assert len(self.input_layouts) == len(
+                self.desired_input_layouts
+            ), "input_layouts and desired_input_layouts should have same length!"
+        self.with_kwargs = input_kwarg_layouts is not None
+        self.input_kwarg_layouts = input_kwarg_layouts or {}
+        self.desired_input_kwarg_layouts = desired_input_kwarg_layouts or {}
+        if self.with_kwargs:
+            assert len(self.input_kwarg_layouts) == len(
+                self.desired_input_kwarg_layouts
+            ), "input_kwarg_layouts and desired_input_kwarg_layouts should have same length!"
+
+        # for output
+        self.output_layouts = (
+            (output_layouts,)
+            if isinstance(output_layouts, Placement)
+            else output_layouts
+        )
+        self.desired_output_layouts = (
+            (desired_output_layouts,)
+            if isinstance(desired_output_layouts, Placement)
+            else desired_output_layouts
+        )
+        self.use_local_output = use_local_output
+        assert len(self.output_layouts) == len(
+            self.desired_output_layouts
+        ), "output_layouts and desired_output_layouts should have same length!"
+
+    def _prepare_input_arg(
+        self,
+        input: Any,
+        mesh: DeviceMesh,
+        input_layout: Optional[Placement],
+        desired_layout: Optional[Placement],
+    ):
+        if input_layout is not None:
+            if isinstance(input, DTensor):
+                # TODO: re-enable the check once we fix the compile path
+                # assert inp.placements[0] == input_layout
+                dt_inp = input
+            else:
+                assert isinstance(
+                    input, torch.Tensor
+                ), "expecting input to be a torch.Tensor!"
+                dt_inp = DTensor.from_local(
+                    input, mesh, (input_layout,), run_check=False
+                )
+
+            if desired_layout is not None and input_layout != desired_layout:
+                dt_inp = dt_inp.redistribute(placements=(desired_layout,))
+
+            return dt_inp.to_local() if self.use_local_input else dt_inp
+        else:
+            return input
+
+    def _prepare_input_fn(self, inputs, device_mesh):
+        if self.input_layouts is None:
+            return inputs
+        prepared_inputs = []
+        if not isinstance(inputs, tuple):
+            inputs = (inputs,)
+        if len(inputs) != len(self.input_layouts):
+            raise ValueError("module inputs and input_layouts should have same length!")
+
+        assert (
+            self.desired_input_layouts is not None
+        ), "desired module inputs should not be None!"
+        for inp, input_layout, desired_layout in zip(
+            inputs, self.input_layouts, self.desired_input_layouts
+        ):
+            prepared_inputs.append(
+                self._prepare_input_arg(inp, device_mesh, input_layout, desired_layout)
+            )
+        return tuple(prepared_inputs)
+
+    def _prepare_input_kwarg_fn(self, inputs, kwarg_inputs, device_mesh):
+        prepared_arg_inputs = self._prepare_input_fn(inputs, device_mesh)
+        prepared_kwarg_inputs = {}
+        for kwarg_key in kwarg_inputs.keys():
+            kwarg_val = kwarg_inputs[kwarg_key]
+            input_layout = self.input_kwarg_layouts.get(kwarg_key)
+            desired_input_layout = self.desired_input_kwarg_layouts.get(kwarg_key)
+
+            prepared_kwarg_inputs[kwarg_key] = self._prepare_input_arg(
+                kwarg_val, device_mesh, input_layout, desired_input_layout
+            )
+
+        return (prepared_arg_inputs, prepared_kwarg_inputs)
+
+    def _prepare_out_fn(self, outputs, device_mesh):
+        prepared_outputs = []
+        if not isinstance(outputs, tuple):
+            outputs = (outputs,)
+        if len(outputs) != len(self.output_layouts):
+            raise ValueError(
+                "module outputs and output_layouts should have same length!"
+            )
+        for out, out_layout, desired_out_layout in zip(
+            outputs, self.output_layouts, self.desired_output_layouts
+        ):
+            if out_layout is not None:
+                if isinstance(out, DTensor):
+                    # TODO: re-enable the check once we fix the compile path
+                    # assert out.placements[0] == out_layout
+                    dt_out = out
+                else:
+                    dt_out = DTensor.from_local(
+                        out, device_mesh, (out_layout,), run_check=False
+                    )
+
+                if out_layout != desired_out_layout:
+                    dt_out = dt_out.redistribute(placements=(desired_out_layout,))
+                prepared_outputs.append(
+                    dt_out.to_local() if self.use_local_output else dt_out
+                )
+            else:
+                prepared_outputs.append(out)
+        if len(prepared_outputs) == 1:
+            return prepared_outputs[0]
+        else:
+            return tuple(prepared_outputs)
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        # for input
+        if self.with_kwargs:
+            module.register_forward_pre_hook(
+                lambda _, inputs, kwargs: self._prepare_input_kwarg_fn(
+                    inputs, kwargs, device_mesh
+                ),
+                with_kwargs=True,
+            )  # type: ignore[misc]
+        else:
+            module.register_forward_pre_hook(
+                lambda _, inputs: self._prepare_input_fn(inputs, device_mesh)
+            )  # type: ignore[misc, call-arg]
+
+        # for output
+        module.register_forward_hook(
+            lambda _, inputs, outputs: self._prepare_out_fn(outputs, device_mesh)
+        )  # type: ignore[misc, call-arg]
+
+        return module
+
+
+class TensorParallel(ParallelStyle):
+    def __init__(
+        self,
+        *,
+        input_layouts: Optional[Placement] = None,
+        output_layouts: Optional[Placement] = None,
+        use_local_output: bool = True,
+    ):
+        super().__init__()
+        self.input_layouts = (input_layouts or Replicate(),)
+        self.output_layouts = (output_layouts or Partial(),)
+        self.desired_input_layouts = (Replicate(),)
+        self.use_local_output = use_local_output
+
+    @staticmethod
+    def _prepare_input_fn(
+        input_layouts, desired_input_layouts, mod, inputs, device_mesh
+    ):
+        # TODO: figure out dynamo support for instance method and switch this to instance method
+
+        # annotate module input placements/sharding with input_layouts
+        input_tensor = inputs[0]
+        if not isinstance(input_tensor, DTensor):
+            input_tensor = DTensor.from_local(
+                input_tensor, device_mesh, input_layouts, run_check=False
+            )
+
+        if input_layouts != desired_input_layouts:
+            input_tensor = input_tensor.redistribute(
+                placements=desired_input_layouts, async_op=True
+            )
+        return input_tensor
+
+    def _partition_fn(self, name, module, device_mesh):
+        module.register_parameter(
+            "gate_proj",
+            nn.Parameter(distribute_tensor(module.gate_proj, device_mesh, [Shard(2)])),
+        )  # Column-wise sharding
+        module.register_parameter(
+            "down_proj",
+            nn.Parameter(distribute_tensor(module.down_proj, device_mesh, [Shard(1)])),
+        )  # Row-wise sharding
+        module.register_parameter(
+            "up_proj",
+            nn.Parameter(distribute_tensor(module.up_proj, device_mesh, [Shard(2)])),
+        )  # Column-wise sharding
+
+    @staticmethod
+    def _prepare_output_fn(output_layouts, use_local_output, mod, outputs, device_mesh):
+        if outputs.placements != output_layouts:
+            outputs = outputs.redistribute(placements=output_layouts, async_op=True)
+        # back to local tensor
+        return outputs.to_local() if use_local_output else outputs
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        return distribute_module(
+            module,
+            device_mesh,
+            self._partition_fn,
+            partial(
+                self._prepare_input_fn, self.input_layouts, self.desired_input_layouts
+            ),
+            partial(
+                self._prepare_output_fn, self.output_layouts, self.use_local_output
+            ),
+        )
+
+
+class ExpertParallel(ParallelStyle):
+    def __init__(
+        self,
+        *,
+        input_layouts: Optional[Placement] = None,
+        output_layouts: Optional[Placement] = None,
+        use_local_output: bool = True,
+    ):
+        super().__init__()
+        self.input_layouts = (input_layouts or Shard(0),)
+        self.output_layouts = (output_layouts or Shard(0),)
+        self.desired_input_layouts = (Shard(0),)
+        self.use_local_output = use_local_output
+
+    @staticmethod
+    def _prepare_input_fn(
+        input_layouts, desired_input_layouts, mod, inputs, device_mesh
+    ):
+        # TODO: figure out dynamo support for instance method and switch this to instance method
+
+        # annotate module input placements/sharding with input_layouts
+        input_tensor = inputs[0]
+        if not isinstance(input_tensor, DTensor):
+            input_tensor = DTensor.from_local(
+                input_tensor, device_mesh, input_layouts, run_check=False
+            )
+
+        if input_layouts != desired_input_layouts:
+            input_tensor = input_tensor.redistribute(
+                placements=desired_input_layouts, async_op=True
+            )
+        return input_tensor
+
+    def _partition_fn(self, name, module, device_mesh):
+        # shard on the expert dimension
+        for name, param in module.named_parameters(recurse=False):
+            dist_param = nn.Parameter(distribute_tensor(param, device_mesh, [Shard(0)]))
+            module.register_parameter(name, dist_param)
+
+    @staticmethod
+    def _prepare_output_fn(output_layouts, use_local_output, mod, outputs, device_mesh):
+        # outputs is a shard on last dimension DTensor, i.e. Shard(-1)
+        if outputs.placements != output_layouts:
+            outputs = outputs.redistribute(placements=output_layouts, async_op=True)
+        # back to local tensor
+        return outputs.to_local() if use_local_output else outputs
+
+    def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
+        return distribute_module(
+            module,
+            device_mesh,
+            self._partition_fn,
+            partial(
+                self._prepare_input_fn, self.input_layouts, self.desired_input_layouts
+            ),
+            partial(
+                self._prepare_output_fn, self.output_layouts, self.use_local_output
+            ),
+        )