[DSV3] Add PP support for DSV3

H-Huang · H-Huang · commit df8288b95ed7 · 2025-07-10T08:56:58.000-07:00
diff --git a/torchtitan/models/deepseek_v3/README.md b/torchtitan/models/deepseek_v3/README.md
@@ -33,14 +33,14 @@ CONFIG_FILE="./torchtitan/models/deepseek_v3/train_configs/deepseek_v3_16b.toml"
 - Activation checkpointing
 - Tensor Parallel (TP)
 - Expert Parallel (EP)
+- Pipeline Parallel (PP)
 
 
 ## To be added
 - Modeling
     - Merge DeepSeek-V3 and Llama4 MoE common components
 - Parallelism
     - Context Parallel support for DeepSeek-V3
-    - PP support for DeepSeek-V3
 - torch.compile
 - Quantization
 - Testing
diff --git a/torchtitan/models/deepseek_v3/__init__.py b/torchtitan/models/deepseek_v3/__init__.py
@@ -15,6 +15,7 @@
 from torchtitan.protocols.train_spec import register_train_spec, TrainSpec
 
 from .infra.parallelize import parallelize_deepseekv3
+from .infra.pipeline import pipeline_deepseekv3
 from .model.args import DeepSeekV3ModelArgs
 from .model.model import DeepSeekV3Model
 
@@ -116,7 +117,7 @@
         cls=DeepSeekV3Model,
         config=deepseekv3_configs,
         parallelize_fn=parallelize_deepseekv3,
-        pipelining_fn=None,
+        pipelining_fn=pipeline_deepseekv3,
         build_optimizers_fn=build_llama4_optimizers,  # use optimizer hooks to update expert weights
         build_lr_schedulers_fn=build_lr_schedulers,
         build_dataloader_fn=build_hf_dataloader,
diff --git a/torchtitan/models/deepseek_v3/infra/pipeline.py b/torchtitan/models/deepseek_v3/infra/pipeline.py
@@ -0,0 +1,311 @@
+# 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.
+
+# This file applies the PT-D pipeline parallelism to the Llama model.
+
+import copy
+
+import torch
+
+import torch.nn as nn
+from torch.distributed import DeviceMesh
+from torch.distributed.pipelining import PipelineStage
+from torch.distributed.pipelining.schedules import (
+    _PipelineSchedule,
+    get_schedule_class,
+    PipelineScheduleSingle,
+    ScheduleZBVZeroBubble,
+)
+
+from torchtitan.components.loss import LossFunction
+from torchtitan.config_manager import JobConfig
+from torchtitan.distributed import ParallelDims
+from torchtitan.distributed.pipeline import build_pipeline_schedule, stage_ids_this_rank
+from torchtitan.protocols.train_spec import DeviceType, ParallelizeFunction
+from torchtitan.tools.logging import logger
+
+from ..model.args import DeepSeekV3ModelArgs
+
+def generate_module_names_per_stage(
+    num_stages: int,
+    num_layers: int,
+    input_weight: int = 1,
+    output_weight: int = 1,
+) -> list[list[str]]:
+    """
+    Programmatically generates module names per stage for pipeline parallelism with weighting.
+
+    Args:
+        num_stages: Number of pipeline stages
+        num_layers: Total number of transformer layers in the model
+        input_weight: Weight for input modules (tok_embeddings) in layer calculation
+        output_weight: Weight for output modules (norm + output) in layer calculation
+
+    Returns:
+        List of lists containing module names for each stage
+
+    Example:
+        generate_module_names_per_stage(2, 3, input_weight=2, output_weight=2)
+        treats embeddings as 2 layers and norm+output as 2 layers for distribution
+    """
+    if num_stages < 1:
+        raise ValueError("Number of stages must be at least 1")
+
+    if num_stages == 1:
+        # Single stage gets everything
+        layer_names = [f"layers.{i}" for i in range(num_layers)]
+        return [["tok_embeddings"] + layer_names + ["norm", "output"]]
+
+    # Calculate effective layers including weights
+    num_effective_layers = num_layers + input_weight + output_weight
+
+    if num_stages > num_effective_layers:
+        raise ValueError(
+            f"Number of stages ({num_stages}) cannot be greater than effective layers ({num_effective_layers})"
+        )
+
+    # Calculate layers per stage (distribute evenly)
+    layers_per_stage = num_effective_layers // num_stages
+    extra_layers = num_effective_layers % num_stages
+
+    # Ensure each stage gets at least the weight of input/output modules
+    if layers_per_stage < max(input_weight, output_weight):
+        raise ValueError(
+            f"Layers per stage ({layers_per_stage}) must be >= max(input_weight={input_weight}, output_weight={output_weight})"
+        )
+
+    module_names_per_stage = []
+    current_layer = 0
+
+    for stage_idx in range(num_stages):
+        stage_modules = []
+
+        # Calculate effective layers for this stage
+        effective_layers_for_stage = layers_per_stage
+        if stage_idx < extra_layers:
+            effective_layers_for_stage += 1
+
+        # First stage: handle input modules with weighting
+        if stage_idx == 0:
+            stage_modules.append("tok_embeddings")
+            # Account for input weight in layer distribution
+            remaining_layers_for_stage = effective_layers_for_stage - input_weight
+
+            # Add transformer layers
+            for _ in range(remaining_layers_for_stage):
+                if current_layer < num_layers:
+                    stage_modules.append(f"layers.{current_layer}")
+                    current_layer += 1
+
+        # Last stage: handle output modules with weighting
+        elif stage_idx == num_stages - 1:
+            # Account for output weight in layer distribution
+            remaining_layers_for_stage = effective_layers_for_stage - output_weight
+
+            # Add transformer layers
+            for _ in range(remaining_layers_for_stage):
+                if current_layer < num_layers:
+                    stage_modules.append(f"layers.{current_layer}")
+                    current_layer += 1
+
+            # Add output modules
+            stage_modules.extend(["norm", "output"])
+
+        # Middle stages: only transformer layers
+        else:
+            for _ in range(effective_layers_for_stage):
+                if current_layer < num_layers:
+                    stage_modules.append(f"layers.{current_layer}")
+                    current_layer += 1
+
+        module_names_per_stage.append(stage_modules)
+
+    return module_names_per_stage
+
+
+def pipeline_deepseekv3(
+    model: nn.Module,
+    world_mesh: DeviceMesh,
+    parallel_dims: ParallelDims,
+    job_config: JobConfig,
+    device: DeviceType,
+    model_config: DeepSeekV3ModelArgs,
+    parallelize_fn: ParallelizeFunction,
+    loss_fn: LossFunction,
+) -> tuple[_PipelineSchedule, list[nn.Module], bool, bool]:
+    pp_mesh = world_mesh["pp"]
+
+    # Determine the number of virtual stages based on schedule type
+    schedule_class = get_schedule_class(
+        job_config.parallelism.pipeline_parallel_schedule
+    )
+    is_single_stage_schedule = issubclass(schedule_class, PipelineScheduleSingle)
+
+    # For multi-stage schedules, default is 2 virtual stages per rank
+    # For single-stage schedules, default is 1 virtual stage per rank
+    stages_per_rank = 1 if is_single_stage_schedule else 2
+    num_virtual_stages = parallel_dims.pp * stages_per_rank
+
+    # Generate module names per stage programmatically with weighting
+    num_layers = model_config.n_layers
+
+    # You can adjust these weights based on the computational cost of embeddings and output layers
+    # Higher weights mean these modules are treated as "heavier" in the distribution
+    input_weight = 1  # Weight for tok_embeddings
+    output_weight = 1  # Weight for norm + output layers
+
+    module_names_per_stage = generate_module_names_per_stage(
+        num_virtual_stages, num_layers, input_weight, output_weight
+    )
+    for i, stage_ms in enumerate(module_names_per_stage):
+        logger.info(f"Stage {i}: {stage_ms}")
+
+    stages, model_parts = pipeline_module_split(
+        model,
+        pp_mesh,
+        job_config.parallelism.pipeline_parallel_schedule,
+        device,
+        module_names_per_stage,
+    )
+
+    # For PP with looped schedules, each item in model_parts is one stage-model-chunk.
+    # We need to iterate through model_parts to apply SPMD parallelisms, compilation,
+    # optimizer, and checkpointing
+    for i, m in enumerate(model_parts):
+        # apply SPMD-style PT-D techniques
+        m = parallelize_fn(m, world_mesh, parallel_dims, job_config)
+        model_parts[i] = m
+        # NOTE: this is to update the model in the stage
+        #       in case the model is modified e.g. by torch.compile
+        stages[i].submod = m
+
+    pp_schedule = build_pipeline_schedule(job_config, stages, loss_fn)
+
+    # This is used in the train loop to determine whether to pass in the input_ids and labels
+    has_first_stage = False
+    has_last_stage = False
+    for stage in stages:
+        if stage.is_first:
+            has_first_stage = True
+        if stage.is_last:
+            has_last_stage = True
+
+    return pp_schedule, model_parts, has_first_stage, has_last_stage
+
+
+def pipeline_module_split(
+    whole_model: nn.Module,
+    pp_mesh: DeviceMesh,
+    pp_schedule: str,
+    device: DeviceType,
+    module_names_per_stage: list[list[str]],
+) -> tuple[list[PipelineStage], list[nn.Module]]:
+    """
+    This API creates pipeline stages based on specified module names for each stage.
+
+    Args:
+        whole_model: The complete model to be split
+        pp_mesh: Pipeline parallel device mesh
+        pp_schedule: Name of pipeline parallelism schedule
+        device: Device type
+        module_names_per_stage: List of lists, where each inner list contains the module names
+                               that should be included in that stage. Module names should be
+                               dot-separated paths. Examples:
+                               - "tok_embeddings" for token embeddings
+                               - "layers.0", "layers.1" for specific transformer layers
+                               - "norm" for the final normalization layer
+                               - "output" for the output projection layer
+
+    Returns:
+        Tuple of (stages, models) where stages are PipelineStage objects and models are the
+        corresponding model chunks
+
+    Example usage:
+        module_names_per_stage = [
+            ["tok_embeddings", "layers.0"],     # Stage 0: embeddings + first layer
+            ["layers.1", "layers.2"],           # Stage 1: middle layers
+            ["norm", "output"]                  # Stage 2: final norm + output
+        ]
+    """
+    pp_rank = pp_mesh.get_local_rank()
+    pp_size = pp_mesh.size()
+
+    def _build_stage_from_modules(
+        stage_idx: int, module_names: list[str], num_stages: int
+    ) -> tuple[PipelineStage, nn.Module]:
+        model = copy.deepcopy(whole_model)
+
+        # Create a set of modules to keep for faster lookup
+        modules_to_keep = set(module_names)
+        print(f"Stage {stage_idx}: Modules to keep: {modules_to_keep}")
+        for module_name, module_value in model.named_children():
+            # Handle layer-like structures (e.g., "layers.0", "layers.1")
+            if isinstance(module_value, (nn.ModuleDict, nn.ModuleList)):
+                layers_to_keep = {
+                    name.split(".", 1)[1]
+                    for name in modules_to_keep
+                    if name.startswith(f"{module_name}.")
+                }
+                if layers_to_keep:
+                    # Keep only specified layers
+                    if isinstance(module_value, nn.ModuleDict):
+                        for layer_name in list(module_value.keys()):
+                            if layer_name not in layers_to_keep:
+                                del module_value[layer_name]
+                    elif isinstance(module_value, nn.ModuleList):
+                        indices_to_keep = {
+                            int(idx) for idx in layers_to_keep if idx.isdigit()
+                        }
+                        new_layers = nn.ModuleList(
+                            [
+                                layer
+                                for i, layer in enumerate(module_value)
+                                if i in indices_to_keep
+                            ]
+                        )
+                        setattr(model, module_name, new_layers)
+                else:
+                    # No layers from this structure needed, set to empty structure
+                    if isinstance(module_value, nn.ModuleDict):
+                        setattr(model, module_name, nn.ModuleDict())
+                    elif isinstance(module_value, nn.ModuleList):
+                        setattr(model, module_name, nn.ModuleList())
+            # Handle simple module attributes (e.g., "linear", "norm")
+            elif module_name not in modules_to_keep:
+                # Replace with identity module instead of None
+                setattr(model, module_name, nn.Identity())
+
+        stage = PipelineStage(
+            model,
+            stage_idx,
+            num_stages,
+            device,
+            group=pp_mesh.get_group("pp"),
+        )
+        return stage, model
+
+    num_stages = len(module_names_per_stage)
+    stages = []
+    models = []
+
+    schedule_class = get_schedule_class(pp_schedule)
+    style = "v" if schedule_class == ScheduleZBVZeroBubble else "loop"
+
+    for stage_idx in stage_ids_this_rank(pp_rank, pp_size, num_stages, style=style):
+        module_names = module_names_per_stage[stage_idx]
+        stage, model_chunk = _build_stage_from_modules(
+            stage_idx,
+            module_names,
+            num_stages,
+        )
+        logger.info(
+            f"PP rank {pp_rank} is building stage_idx {stage_idx} "
+            f"with modules {module_names}"
+        )
+        stages.append(stage)
+        models.append(model_chunk)
+
+    return stages, models
diff --git a/torchtitan/models/deepseek_v3/model/args.py b/torchtitan/models/deepseek_v3/model/args.py
@@ -75,7 +75,7 @@ class DeepSeekV3ModelArgs(BaseModelArgs):
     n_limited_groups: int = 1
     score_func: Literal["softmax", "sigmoid"] = "softmax"
     route_scale: float = 1.0
-    use_grouped_mm: bool = True
+    use_grouped_mm: bool = False
     load_balance_coeff: float = 1e-3
     # Multi-Head Latent Attention (MLA)
     q_lora_rank: int = 0
diff --git a/torchtitan/models/deepseek_v3/model/model.py b/torchtitan/models/deepseek_v3/model/model.py
diff --git a/torchtitan/models/deepseek_v3/train_configs/deepseek_v3_16b.toml b/torchtitan/models/deepseek_v3/train_configs/deepseek_v3_16b.toml
